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Metformin ( Met ) is an anti-hyperglycemic and potential anti-cancer agent which may exert its anti-proliferative effects via the induction of energetic stress . In this study we investigated the in vitro and in vivo efficacy of Met against the larval stage of Echinococcus granulosus . Metformin showed significant dose- and time-dependent killing effects on in vitro cultured protoscoleces and metacestodes . Notably , the combination of Met together with the minimum effective concentration of ABZSO had a synergistic effect after days 3 and 12 on metacestodes and protoscoleces , respectively . Oral administration of Met ( 50 mg/kg/day ) in E . granulosus-infected mice was highly effective in reducing the weight and number of parasite cysts , yet its combination with the lowest recommended dose of ABZ ( 5 mg/kg/day ) was even more effective . Coincidentally , intracystic Met accumulation was higher in animals treated with both drugs compared to those administered Met alone . Furthermore , the safe plant-derived drug Met exhibited remarkable chemopreventive properties against secondary hydatidosis in mice . In conclusion , based on our experimental data , Met emerges as a promising anti-echinococcal drug as it has proven to efficiently inhibit the development and growth of the E . granulosus larval stage and its combination with ABZ may improve the current anti-parasitic therapy .
Cystic echinococcosis ( CE ) , also called hydatid disease or hydatidosis , is a neglected zoonotic disease caused by the infection with the larval stage of the cestode Echinococcus granulosus [1] . The greatest prevalence of this disease is found in countries of the temperate zones , and it is an endemic disease in some parts of the world , such as South America , Asia , Australia and North Africa [2] . It has been estimated that human CE results in the loss of 1–3 million disability-adjusted life years ( DALYs ) and that up to $2 billion are annually lost in the livestock industry [3] . The mortality rate due to CE is about 2–4% , but it may increase considerably if medical treatment is not suitable [4] . The life cycle of E . granulosus is complex and involves two mammalian hosts . The adult cestode inhabits the small intestine of a carnivorous definitive host ( usually dogs ) and produces eggs , which are released into the environment and may then be ingested by an intermediate host ( ungulates , or humans ) . Metacestodes or hydatid cysts proliferate asexually in the intermediate host and produce protoscoleces from the inner germinal layer [5] . The liver is the most commonly affected organ in patients infected with CE , followed by the lungs and , less frequently by organs such as spleen , kidneys , heart , bones , and central nervous system [2] . The growth of hydatid cysts is usually slow and asymptomatic , and clinical manifestations are related to compression of the involved organs . However , cyst rupture may lead to anaphylactic reactions as well as dissemination and/or recurrence of the infection [6] . The treatment options for CE are PAIR ( Puncture , Aspiration , Injection , Reaspiration ) , surgery and chemotherapy , and the choice is often based on cyst characteristics as well as availability of medical experts and equipment . Surgery is accompanied by pre- and post-operative chemotherapy and , in inoperable cases , chemotherapy is the only alternative [4] . Despite the fact that the benzimidazoles ( BMZs ) albendazole ( ABZ ) and mebendazole ( MBZ ) are currently the only two drugs licensed for the treatment of CE , the cure rate of both drugs in treatment of CE was reported to be only about 30% [7] . The unsatisfactory results of the oral chemotherapy with BMZs are usually attributed to their poor absorption rate , resulting in low drug levels in the plasma and thus in the hydatid cysts [8] . For this reason , and the fact that the adverse effects of BMZ seem to be inevitable under therapeutic doses , it is necessary to find other alternatives for chemotherapy against CE , and to focus on new drugs with higher anthelminthic activity against E . granulosus . Metformin ( Met ) is an anti-hyperglycemic agent widely used for the treatment of type II diabetes which shows good oral bioavailability ( 50–60% ) and a favorable safety profile [9 , 10] . Notably , this drug also has anti-proliferative properties on cancer cells , which it may exert both indirectly , through the systemic reduction of insulin levels in diabetic patients , and directly , via the induction of energetic stress , in non-diabetic and diabetic patients [11–13] . Direct cellular effects of Met involve inhibition of ATP production , activation of AMPK , and consequent inhibition of TORC1 ( Target Of Rapamycin Complex 1 ) , which couples protein synthesis to external growth factors and intracellular energy stores [12 , 14] . Recently , we have reported that rapamycin , an inhibitor of TOR , is an effective in vitro anti-echinococcal agent and autophagy inducer , allowing the identification of TORC1-controlled events in this cestode [15] . In addition , we have demonstrated that , under in vitro conditions , both larval forms of E . granulosus are susceptible to Met and that this drug indirectly activates Eg-AMPK ( AMP activated protein kinase ) , as a consequence of cellular energy charge depletion [16] . The Met pharmacokinetics is regulated by transporters of the major facilitator superfamily ( MFS ) through a balance between uptake and expulsion mechanisms . The first ones are dependent on expression of solute carrier family 22 members -SLC22- ( OCT 1 , 2 , and 3 and OCTN1 ) , while the second ones are dependent on expression of multidrug and toxin extrusion proteins ( MATE1 and MATE 2 ) [12 , 17] . Here we demonstrate that Met alone or in combination with low concentration of ABZ is effective against the larval stage of E . granulosus in the murine CE infection model , and we discuss the Met-intracyst accumulation .
Metformin ( 1 , 1-dimethylbiguanide hydrochloride ) was obtained from Sigma-Aldrich ( USA ) and ABZ and ABZSO were kindly provided by C . Salomon ( National University of Rosario , Argentina ) . For in vitro assays , Met and ABZSO were kept as a 100 mM and a 100 μM stock solution in water and in dimetyl sulfoxide ( DMSO ) , respectively , and added to the medium either separately or in combination . For in vivo experiments , aqueous solution of Met and oil solution of ABZ ( corn oil , Sigma-Aldrich ) were prepared every 2 days from solid drug and maintained under refrigeration ( 3–5°C ) . Animal procedures and management protocols were carried out in accordance with the National Health Service and Food Quality ( SENASA ) guidelines , Argentina , and with the 2011 revised form of The Guide for the Care and Use of Laboratory Animals published by the U . S . National Institutes of Health . Experimental protocols were evaluated and approved by the Animal Experimental Committee at the Faculty of Exact and Natural Sciences , Mar del Plata University ( permit number: 2555-08-15 ) . Protoscoleces were removed aseptically from hydatid cysts of infected cattle slaughtered in the Liminal abattoir ( official number: 3879 ) located in the Southeast of Buenos Aires , Argentina . Viable and morphologically intact protoscoleces ( n = 3 , 000 ) were cultured using medium 199 ( Gibco ) supplemented with glucose ( 4 mg/ml ) and antibiotics ( penicillin , streptomycin and gentamicin 100 μg/ml ) in 24-well culture plates under normal atmospheric conditions as we described in detail previously [15] . Moreover , from10 to 20 E . granulosus murine cysts per replica were incubated in Leighton tubes under the same culture conditions as described for protoscoleces [16] . In vitro protoscolex and metacestode treatments were performed with 1 and 5 mM Met , 2 . 5 μM ABZSO ( equivalent to 0 . 84 μg/ml ) , and the combination of 1 and 5 mM Met plus 2 . 5 μM ABZSO for 7 ( metacestodes ) and 27 ( protoscoleces ) days [16] . Parasites incubated in culture medium containing DMSO were used as controls . In vitro protoscolex cultures were kept at 37°C with medium changes every 3 days . At those time points , the protoscolex viability was determined by the methylene blue exclusion test ( at least 100 protoscoleces per replica were counted each time ) . The metacestode viability was assessed daily by trypan blue staining of detached germinal layers . Each experiment was performed in triplicates and repeated three times . All of the experiments were carried out until that the viability of the control was lower than 90% or all treated parasites were dead . For molecular assays , protoscoleces and metacestodes were cultured in presence or absence of 1 or 5 mM Met for 48 h and stored at −80°C until experimental use . Healthy female CF-1 mice ( 30–35 g of 8 weeks old ) supplied by the SENASA , Mar del Plata , were acclimatized for one week before initiation of the experiment . Mice were infected by intraperitoneal infection with 1 , 000 protoscoleces in 0 . 5 ml of medium 199 to produce experimental secondary hydatid disease [16] . The animals were maintained in standard polyethylene cages ( five mice per cage ) , under controlled laboratory conditions ( temperature 20±2°C , 12 hour light/12 hour dark with lights off at 8 . 00 p . m . , 50±5% humidity ) . Food and water were provided ad libitum . Every 3 days , animals were placed into a clean cage with fresh sawdust . All the pharmacological treatments were performed by intragastric administration of a drug-aqueous suspension ( 0 . 3 ml/animal ) . At the end of experiments , mice were euthanized by cervical dislocation and previous anesthesia with ketamine–xylazine ( 50 mg/kg/mouse– 5 mg/kg/mouse ) . All efforts were made to minimize suffering . Minimum number of animals was used in each experiment . At necropsy , the peritoneal cavity was opened , the hydatid cysts were carefully recorded , and their weights were determined for each animal . The efficacy of treatments was calculated using the following formula: 100 x { ( mean cyst weight of control group ) – ( mean cyst weight of treated group ) }/ ( mean cyst weight of control group ) . In addition , samples were processed for scanning electron microscopy ( SEM ) with a JEOL JSM-6460LV electron microscope and for transmission electron microscopy ( TEM ) with a JEM 1200 EX II ( JEOL Ltd . , Tokio , Japan ) microscopy as previously described [15] . Further , histological examination and evaluation of hepatic fibrosis in mice were carried out . Liver samples were fixed in formalin , embedded in paraffin and 5 μm thick sections were stained with hematoxylin and eosin and Masson’s trichrome [18] . These specimens were photographed and the collagen depositions were quantified from 3–5 images of each sample using Image-J software . At 4 months p . i . , mice were randomly assigned into 4 groups of 10 animals each . Drugs were applied by per oral gavage daily for 60 days as follows: control group ( receiving corn oil as a placebo ) , ABZ at 5 mg/kg/day , Met at 50 mg/kg/day , and a combination of ABZ ( 5 mg/kg/day ) plus Met ( 50 mg/kg/day ) . At the end of treatment period , animals were euthanized , necropsy was carried out immediately thereafter , and Met content was determined from hydatid liquid as described below . At the time point of infection , 20 CF-1 mice were allocated into 2 experimental groups ( 10 animals/group ) as untreated control group ( corn oil ) and Met-treated group ( 50 mg/kg/day ) . The treatment was performed daily by 60 days . Four months after infection , mice were euthanized and necropsied . Two spectrophotometric methods were used for the estimation of intracystic Met concentrations , based on the reaction in alkaline medium of the primary amino group of Met with ninhydrin or hydrogen peroxide , to form a violet ( 570 nm ) or yellow ( 400 nm ) chromogen , respectively [19 , 20] . Standard curves were prepared using a double spectrophotometer ( Shimatzu-UV-100 ) and different concentrations of pure Met solutions ( 10–100 μg of drug ) , which obeyed Beer´s law in the range of 5–20 μg/ml . Hydatidic cyst fluid was extracted immediately after necropsy from samples of untreated or treated mice , the precipitated protein ( at 2000×g for 15 min ) was removed and the supernatants were stored at -20°C until colorimetric analysis . Given that Met is positively charged at physiological pH and its cellular transport depends on cationic transporters , BLASTp search for solute carrier transporter family 22 ( SLC22 ) homologs in the E . granulosus genome database ( http://www . sanger . ac . uk/Projects/Echinococcus , [21] ) was carried out using Mus musculus and Homo sapiens orthologs as queries . Orthologs were selected based on reciprocal best BLAST hits [25 , 26] on an E-value cut-off of 10−25 and on the presence of the characteristic domains in each deduced amino acid sequence . Sequence alignments were generated with the CLUSTALX software program and modeling of secondary structure of the putative transport proteins was obtained from the deduced primary structure using the Gen-THREADER ( http://bioinf . cs . ucl . ac . uk/psipred/ ) . In addition , an expression study of Eg-pgp genes ( Eg-pgp1-a/b , Eg-pgp2 , Eg-pgp3 , Eg-pgp4 and Eg-pgp5 ) was carried out using the specific primers reported by Nicolao et al [22] . Total RNA extractions and RT-PCR from E . granulosus protoscoleces and metacestodes were performed as previously described Cumino et al [23] . To analyze the gene expression in pharmacologically treated and control parasites , cDNA was generated using 10 or 5 μg of total RNA from protoscoleces and metacestodes , respectively ( with Superscript II reverse transcriptase -Invitrogen , Argentina—and Pfu DNA polymerase—Promega , USA ) . RT-PCR assays were carried out under the following conditions: 30 cycle PCRs of 94°C ( 30 s ) , 42°C ( 1 min ) , and 72°C ( 1 min ) plus a single step at 72°C for 10 min , their products were analyzed and confirmed as it was previously described [23] . E . granulosus actin I ( actI , GenBank accession number L07773 ) was used as a loading control [16 , 24] . Data within experiments were compared and significance was determined using the student’s t test and the non-parametric Mann-Whitney test . All data were shown as arithmetic mean ± S . D . and p values are indicated in each assay . The list of accession numbers mentioned in the text is shown below: GenBank , L07773: Echinococcus granulosus actin I ( Eg-actI ) ; GenBank , EUB61931: E . granulosus SLC22 B-6 ( Eg-OCT-A ) ; GenBank , EUB63000: E . granulosus SLC22 5 ( Eg-OCT-B ) ; GenBank , EUB61465: E . granulosus SLC22 5 ( Eg-OCT-C ) ; GenBank , EUB64421: E . granulosus SLC22 ( Eg-OCT-D ) ; GenBank , EUB65032: E . granulosus SLC22 ( Eg-SLC22-like ) ; GenBank , O15245: Homo sapiens S22A1 ( Hs-OCT1 ) ; GenBank , O15244: H . sapiens S22A2 ( Hs-OCT2 ) ; GenBank , Q9H015: H . sapiens S22A4 ( Hs-OCTN1 ) ; GenBank , O08966: Mus musculus S22A1 ( Mm-OCT1 ) ; GenBank , O70577: M . musculus S22A2 ( Mm-OCT2 ) , GenBank , NP_062661: M . musculus S22A4 ( Mm-OCTN1 ) .
We have previously reported that Met exerts a dose-dependent effect on the viability of protoscoleces and metacestodes after 10 and 4 days of incubation , respectively . In addition , its combination with 15μM ABZSO resulted in a greater anti-echinococcal effect than the one observed for Met alone [16] . Here , we extended the study by using the minimum effective concentration of ABZSO ( 2 . 5μM ) and evaluating the viability of protoscoleces and cysts over time . Administration of Met to cultured protoscoleces and metacestodes showed significant dose- and time-dependent killing effects . The mortality rate of both metacestodes and protoscoleces reached 100% during the combined chemotherapy with 1 mM Met and 2 . 5 μM ABZSO at days 7 and 27 , respectively , whereas parasites treated with Met alone remained 90% ( metacestodes ) and 95% ( protoscoleces ) viable ( Fig 1 ) . Protoscolex mortality registered after incubations with 5 mM Met or 2 . 5 μM ABZSO for 27 days was only 50% and 30% , respectively ( Fig 1B ) . As for metacestode viability , it decreased only 40% with 5 mM Met and 20% with 2 . 5 μM ABZSO in culture after 7 days ( Fig 1A ) . These results suggest a statistically significant synergistic effect between Met and ABZSO from day 12 and day 3 against protoscoleces and metacestodes , respectively . Control parasites remained at least 99 ± 1 . 0% viable during the complete experiments . To investigate the in vivo therapeutic effect of Met and ABZ , protoscoleces were intraperitoneally injected in CF1 mice and treated 4 months later by oral administration of vehicle , Met ( 50 mg/kg/day ) , ABZ ( 5 mg/kg/day ) or the combination of Met plus ABZ ( 50 mg/kg/day plus 5 mg/kg/day ) over a period of 60 days . All infected animals in this study developed hydatid cysts in their abdominal cavity . At 6 months p . i . , every treatment from the therapeutic efficacy study ( ABZ , Met and ABZ plus Met ) resulted in a significant reduction ( n = 10 p < 0 . 01 ) of the cyst weights compared to those obtained from untreated mice ( 1 . 450 ± 0 . 310 g ) ( Fig 2A ) . Cysts developed in mice belonging to the combined therapy group ( 0 . 08 ± 0 . 01 g for Met plus ABZ treatment ) weighed significantly less ( p < 0 . 05 ) than those from groups treated with each drug alone ( 0 . 200 ± 0 . 023 g for Met and 0 . 470 ± 0 . 040 g for ABZ treatments ) . Moreover , Met seemed to be more effective than ABZ when acting alone , since Met-treated mice cysts were reduced in weight ( p < 0 . 2 ) in comparison with those recovered from ABZ-treated mice ( Fig 2A ) . Nonetheless , both the number and size of cysts decreased after every treatment in contrast with the control , but particularly after treatment with Met and Met plus ABZ ( 30 ± 5 cysts for control , 20 ± 4 cysts for ABZ- , 12 ± 2 cysts for Met- and 5 ± 2 cysts for ABZ plus Met-treatments ) ( Fig 2D ) . No adverse effects or weight change were observed in mice . In order to analyze the ultrastructural changes of cysts recovered from the different treatments , SEM and TEM studies were performed . Cysts from control mice at six months p . i . appeared turgid , with a massive amount of intact cells in germinal layers , according to SEM ( Fig 2Ba and S1 Fig ) . By TEM analysis it was possible to determine that neither the external acellular laminated layer , the syncitial tegument with microtriches protruding into the laminated layer nor the germinal layer with intact tegumental cells showed any signs of ultrastructural alterations ( Fig 2Bb and 2Bc and S1 Fig ) . Although , metacestodes collected from Met-treated mice displayed no marked reduction in the amount of germinal cells by SEM analysis ( Fig 2Bd and S1 Fig ) , the TEM images showed a distorted tegument and several vesicles and lysosomes in the germinal layer , with reduction in glycogen storage ( Fig 2Be and 2Bf and S1 Fig ) . In contrast , germinal layers of metacestodes obtained from ABZ- and ABZ-Met-treated mice exhibited partial and overall loss of cells , when analyzed by SEM , respectively ( Fig 2Bg and 2Bj and S1 Fig ) . Moreover , TEM analysis of cysts obtained from ABZ-treated animals presented vesicles budding from the tegument with infiltration on the laminated layer and showed lack of microtriches ( Fig 2Bh and 2Bi and S1 Fig ) . Meanwhile , metacestodes from ABZ-Met-treated mice evidenced complete disruption of the tissue containing autophagosomes ( Fig 2Bk and 2Bl and S1 Fig ) . Furthermore , Met concentration was measured in cysts obtained from Met- or Met plus ABZ-treated mice using hydatid liquid from cysts of untreated mice as negative control . ( Fig 2C ) . Drug concentration was 35±7 μg/ g cyst in samples from animals treated with Met alone and 60±5 μg/ g cyst in those receiving both drugs . To assess the potential chemopreventive effect of Met in vivo , the treatment ( 50 mg/kg/day ) was initiated at the time of infection and followed during a period of 60 days . At 4 months p . i . , mice were necropsied in order to remove the hydatid cysts from their abdominal cavities . All of the infected mice from the untreated group ( 10/10 ) developed metacestodes . However , the infection had not progressed in 2 out of the total of 10 mice treated with Met . Significant differences ( p < 0 . 01 ) were registered in the weight of cysts obtained from untreated mice ( 0 . 730 ± 0 . 150 g ) in comparison with those recovered from Met-medicated mice ( 0 . 131 ± 0 . 020 g , Fig 3A ) . Met-treated mice developed less and smaller cysts compared with untreated animals ( 35 ± 10 cysts for control and 4 ± 2 cysts for Met treatment ) ( Fig 3C ) . No adverse effects or weight change were observed in the group of treated mice . Cysts removed from control mice were turgid and no changes in the ultrastructure of their germinal layers were detected by SEM ( Fig 3Ba and 3Bb and S2 Fig ) . Moreover , their laminated and germinal layers showed typical features by TEM ( Fig 3Bc and 3Bd and S2 Fig ) . Conversely , the ultrastructural analysis of metacestodes from Met-treated mice showed alterations in the germinal layer surface detected by SEM ( Fig 3Be and 3Bf and S2 Fig ) and revealed contraction of the distal cytoplasm as well as presence of autophagosomes and autophagolysosomes by TEM analysis ( Fig 3Bg and 3Bh and S2 Fig ) . Interestingly , calcified cysts of 2–3 mm diameter revealed by TEM were found either in the liver or the surrounding abdominal area of 4 out of the total of 10 mice which were treated with Met . Fibrosis was semiquantified using Image-J by histological examination of liver tissues stained with Masson’s trichrome , yet no significant differences were found between control and Met-treated samples ( S3 Fig ) . In order to evaluate the possible Met transporters that could account for intracystic drug accumulation , we investigated the occurrence of SLC22 family members in the E . granulosus larval stage . The SLC22 protein family includes several members which operate as transporters for organic cations ( OCTs ) , organic zwitterions/cation ( OCTNs ) and organic anions ( OATs ) . We previously identified two putative OCT/OCTNs ( EgrG_001058900 and EgrG_000957000 ) in the E . granulosus genome [16] , both of which displayed structural similarities and an identity range of 24–27% with the H . sapiens and M . musculus orthologs ( S4 Fig ) . In this work , we completed the sequence analysis of all members of the SLC22 group found in the Echinococcus genome annotation . All putative SLC22 transporters show a membrane topology in accordance with the prototype carrier [27] , which includes 10–12 α-helical transmembrane domains ( TMDs ) , a large extracellular loop between TMDs 1 and 2 and an intracellular loop between TMDs 6 and 7 ( S4 Fig ) . Due to the high identity of these predicted proteins with OCT/OCTN vertebrate orthologs , their genes were named as Eg-octA , Eg-octB , Eg-octC and Eg-octD ( corresponding to EgrG_001058900 , EgrG_000957000 , EgrG_001099600 and EgrG_000994100 , respectively ) . Only EgrG_000780900 showed an E-value higher than e-25 and thus its encoding gene was referred to as Eg-slc22-like . In the corresponding predicted proteins characteristic motifs of the amphiphilic solute facilitator ( ASF ) family ( S[T/S]IVTE[W/F][D/N]LVC ) were also identified together with the major facilitator superfamily ( -MFS- 13-residue between TMDs 2 and 3: G-[RKPATY]-L-[GAS]-[DN]-[RK]-[FY]-G-R-[RKP]-[LIVGST]-[LIM] ) and signature sequences after TMDs 10 and 11 as well ( ELYPT and LP[D/E]TI , respectively ) ( S4 Fig ) . Since Met and ABZSO have been suggested to be Pgp substrates [28 , 29] and it has been shown that Met can modify Pgp expression in human cancer cell lines [30] , we analyzed the transcriptional levels of the five Pgp isoforms described in both larval forms of E . granulosus [22] . No changes were observed when comparing transcript amounts between Met or ABZSO-treated and non-treated parasites ( S5 Fig ) .
Human CE , though neglected , is a serious life-threatening zoonotic disease that occurs worldwide , which is recognized as a major public health problem [6] . Chemotherapeutic treatment success lies in the ability of the drug to affect the germinal layer and the intracystic protoscoleces at optimal concentrations for sufficient periods of time [31] . Currently , ABZ ( which undergoes hepatic bioconversion into ABZSO ) is the drug of preference to treat CE . However , since it is characterized by a low solubility in water , poor absorption in the intestinal tract and erratic penetration into hydatid cyst , its therapeutic effectiveness against CE is reduced [32] . Therefore , there is a dire need to find new drugs for the treatment of CE . In a previous report , the use of combined albendazole/nitazoxanide chemotherapy has been shown to exhibit anti-parasitic activity in an experimental model of murine alveolar echinococcosis [33] . However , neither nitazoxanide monotherapy nor albendazole-nitazoxanide combination therapies were effective against human alveolar echinococcosis [34] . In addition , drugs such as flubendazole and tamoxifen have been tested in the murine CE model as well as , showing a satisfactory success [35–37] . On the other hand , current studies have reported that Met , a hydrophilic drug which does not undergo hepatic metabolism , has an anti-proliferative effect on different cancer cell lines and several cancers in animal models [38–40] . In this line of evidence , we have previously found that Met by itself or in combination with ABZSO was effective against protoscoleces and metacestodes in in vitro assays [16] . In the present study , we report for the very first time that Met either by itself or together with ABZ has potential therapeutic effects and confers protection against the E . granulosus infection in an in vivo experimental model of secondary hydatidosis . On the basis of our in vitro results , the effects of Met on protoscolex and metacestode viability were both dose- and time-dependent ( [16] and this work ) . Moreover , the combined treatment with ABZSO ( 0 . 84 μg/ml ) had a synergistic effect on the larval stage ( Fig 1 ) . The administration of a single 5 mg/kg/day oral dose of ABZ in mice [41] or 10–15 mg/kg/day in humans [42 , 43] , gives rise to a mean plasma concentration of 0 . 4–1 μg/ml of ABZSO after 2–4 h . Therefore , the here applied concentration of ABZSO is similar to the level reached in plasma on in vivo experimental models and corresponds to the minimum effective concentration of the drug [44–47] . On the other hand , the plasma Met concentration ( 0 . 5–4 μg/ml , about 3–25 μmol/l ) recorded in mice 30 min after oral administration of a single bolus of 50 mg/kg is consistent with the maximum concentration observed in humans after consumption of a dose of about 8–25 mg/kg [9 , 48] . In our in vitro experiments , Met concentrations used were a magnitude of order higher than plasma levels ( Fig 1 ) , but they are in the range used to examine the in vitro effects of the drug on cell metabolism and proliferation [49–51] . Regarding our in vivo assays , treatments with Met and ABZ have shown to have a marked therapeutic effect against murine experimental CE . Under these treatments the amount and weight of the recovered cysts were reduced , and ultrastructural changes were also observed ( Fig 2D , 2A and 2B and S1 Fig ) with Met being slightly more effective than ABZ . Nonetheless , the loss of cells from the germinal layer was more pronounced when using ABZ alone or combined compared to the use of Met alone ( Fig 2Bd-i and S1 Fig ) . This can be attributed to the differences between both drugs in terms of biochemical actions . Given the fact that BZMs act on parasite tubulin and inhibit the assembly of microtubules , the cytological and cytostatic effects of ABZ could be associated to its interference with the correct functioning of the cellular cytoskeleton [52 , 53] . As a consequence , this directly induces alterations in the vesicular transport in the absorptive/secretory tissues of helminths [52 , 54] . In relation to this aspect , BZMs have shown to inhibit glucose uptake and to deplete glycogen stores , leading to starvation and death of the parasite [55 , 56] . In addition , the loss of microtriches produced by ABZ ( Fig 2Bh and 2Bi and S1 Fig ) contributes to the reduction of nutrient uptake and subsequent anthelmintic effects [54] . On the other hand , Met inhibits complex I of the mitochondrial electron-transport chain , uncoupling ATP production and inducing energy stress in protoscoleces and germinal cells of the metacestode [16] . This mechanism of action would be enough to inhibit the growth of hydatid cysts , but not to induce cytotoxicity in germinal cells of metacestodes as ABZ does ( Fig 2Bd-i and S1 Fig ) . The different cellular targets ABZ and Met would lead to synergistic effects , justifying the greater effectiveness of the combined treatment . Moreover , the results of therapeutic efficacy assays in presence of Met showed drug accumulation in the cysts ( 300 μmol / kg of tissue ) ( Fig 2C ) at similar levels to those found in liver tissue ( 200 μmol / kg of tissue ) [9] . Although passive diffusion of Met through cell membranes is rather limited , its accumulation in the liver could generate a concentration gradient towards the hydatid cysts and , due to the high permeability of the cystic membranes to water [57 , 58] , the drug may penetrate by passive diffusion , as well as by facilitated transport through the OCTs [59] . Based on this , detection of higher levels of Met in cysts recovered from mice treated with the drug combination ( Fig 2C ) could also be attributed to the increase in permeability induced by the structural alterations of the metacestode laminar and germinal layers produced by ABZ . On the other hand , the intracystic accumulation of Met , is likely to be due to the presence of transporters involved in the uptake and efflux of Met in either the host [60] or the parasite ( Fig 2C and S4 Fig ) . Met is a hydrophilic base which exists as an organic cation at physiological pH ( pKa = 12 . 4 ) and requires SLC22 family proteins to be transported into the gut , the liver ( OCT1 ) and the kidney ( OCT1 and OCT2 ) of mammals [61] . In this work , we carried out an in silico analysis of sequences identified as members of the SLC22 family of E . granulosus . All five sequences presented the characteristic topology of these transporters , according to informatic predictions ( S4 Fig ) [62] . The occurrence and RNA levels of Eg-octA ( EUB61931 ) and Eg-slc22-like ( EUB65032 ) have been reported in different E . granulosus stages by Zheng et al [63] . Further studies should be performed to evaluate the possible transport of Met in metacestodes via Eg-OCTs . A second transporter family implicated in the drug response involves the ABC ( ATP-binding cassette ) transporters , which include Pgp ( an efflux pump that extrudes xenobiotics from cells ) , previously identified in E . granulosus [22] . Despite prior reports showing that Met is transported by Pgp [29] and that it inhibits Pgp transcriptional expression in human cancer cells [29] , our findings indicated no differences in the transcriptional expression pattern of all Eg-Pgp isoforms in Met-treated protoscoleces and metacestodes ( S5 Fig ) . Likewise , no transcriptional changes of these transporters were detected with ABZSO , a previously reported Eg-PgP substrate [22] . For this reason , Met accumulation in cysts ( Fig 2C ) could not be explained by changes in Pgp expression after treatment with Met or ABZSO . In order to know the potential of Met to prevent a secondary hydatidosis caused by the release of protoscoleces during the surgery or the spontaneous cystic rupture , we carried out chemoprophylaxis assays . Met showed a remarkable inhibitory effect on cyst development ( Fig 3A and 3C ) . In the first place , 20% of Met-treated mice did not develop any cyst , while the infection progressed in all untreated mice . The cystic development in treated mice is considered to occur from the originally injected protoscoleces that survived the therapy . In addition , Met not only reduced the number and weight of the cysts but also affected the integrity of germinal layer ( Fig 3Be and 3Bf and S2 Fig ) . These results coincide with previously reported experimental chemopreventive studies using flubendazole and tamoxifen [37 , 64] . However , it is important to consider the fact that Met has high anti-echinococcal efficacy and low toxicity , compared to those drugs . In this line of evidence , Met has been proposed as a safe and promising candidate for the prevention of colorectal cancer in normoglycemic patients [11 , 13] . The mechanistic rationale for an anti-proliferative effect of Met is convincing , by antagonizing signaling pathways involved in cell division and migration , involving activation of the LKB1/AMPK pathway and subsequent inhibition of TORC1 pathway [65 , 66] . We have previously identified TORC1 in the parasite , suggesting that the effects of Met against E . granulosus could be , at least partly , mediated by the AMPK-TORC1 pathway [16] . Additionally , activating AMPK , Met may oppose to the Warburg effect [38] , a strategy acquired by Echinococcus germinal cells to cope with the high demand of both energy and intermediate metabolites under limited oxygen supply [67] . This metabolic switch from oxidative metabolism to glycolysis could confer to the germinal cells susceptibility to Met , as it has been described for tumor cells [68] . Besides , it has been reported that low doses of Met selectively kill cancer stem cells in different types of breast cancer [69] . This is consistent with the promising results of our chemoprophylaxis assay , since the drug could specifically affect germinal cells of the cyst . Conversely , BZMs have limited efficacy against undifferentiated germinal cells of this parasite [54 , 70 , 71] . Therefore , the combination of Met and ABZ may kill both stem cells and differentiated cells in the experimental echinococcosis model but this would have to be proven in further assays . On the other hand , it has been shown that Met has anti-angiogenic and anti-inflammatory activities , through which it could contribute to the interference in the establishment of the nematode parasite Trichinella spiralis [72] . Further studies must be carried out to evaluate the effects of Met in E . granulosus given that our preliminary study on the degree of fibrosis in the liver of infected mice was not enough to conclude on this point . For clinical practice during long term treatment , Met meets the necessary criteria of a good candidate as chemopreventive and therapeutic agent such as safety , good compliance , cost-effectiveness and a clear mechanism of action [13] . Its most frequent adverse events are mild and transient gastrointestinal symptoms [73] as well as rare incidence of lactic acidosis [74 , 75] . In conclusion , in this report we provide evidence into the potential benefits of Met as a new treatment option for CE , and these observations provide the impetus to evaluate the role of Met in the development of other helminths . These findings enhance the importance of carrying out further studies to determine the significance of the use of Met in relation to hydatidosis in humans .
|
Cystic echinococcosis is a worldwide zoonosis of public health concern and economic significance caused by infection with the larval stage of the cestode Echinococcus granulosus . Chemotherapy treatment for this disease has had limited effectiveness thus far , which is why it is a dire need to find new drugs for its treatment . In order to survive , E . granulosus must compete with the host for the same metabolic resources . However , the cell energy is a commodity that these organisms cannot directly obtain from the host . Therefore , our treatment strategy was to interfere with the energy-generating and cell proliferation mechanisms in the larval stage of this cestode . In this study we focus on metformin , an anti-hyperglycemic and anti-proliferative drug , which exhibits considerable in vitro and in vivo activity against E . granulosus metacestodes . In experimentally infected mice , metformin-chemopreventive effect was evidenced and the combination of this drug with low doses of albendazole improved the anti-parasitic therapy results in late stage of cyst development . Therefore , the effects of these two drugs against E . granulosus warrant further investigations , in comparison to the current monotherapy of choice in humans carried out with albendazole .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"cystic",
"echinococcosis",
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"helminths",
"tropical",
"diseases",
"fibrosis",
"parasitic",
"diseases",
"animals",
"echinococcus",
"developmental",
"biology",
"pharmaceutics",
"microscopy",
"neglected",
"tropical",
"diseases",
"sequence",
"motif",
"analysis",
"research",
"and",
"analysis",
"methods",
"echinococcosis",
"sequence",
"analysis",
"bioinformatics",
"flatworms",
"scanning",
"electron",
"microscopy",
"helminth",
"infections",
"chemotherapy",
"database",
"and",
"informatics",
"methods",
"electron",
"microscopy",
"biology",
"and",
"life",
"sciences",
"drug",
"therapy",
"organisms"
] |
2017
|
Metformin exhibits preventive and therapeutic efficacy against experimental cystic echinococcosis
|
Primates display a remarkable ability to adapt to novel situations . Determining what is most pertinent in these situations is not always possible based only on the current sensory inputs , and often also depends on recent inputs and behavioral outputs that contribute to internal states . Thus , one can ask how cortical dynamics generate representations of these complex situations . It has been observed that mixed selectivity in cortical neurons contributes to represent diverse situations defined by a combination of the current stimuli , and that mixed selectivity is readily obtained in randomly connected recurrent networks . In this context , these reservoir networks reproduce the highly recurrent nature of local cortical connectivity . Recombining present and past inputs , random recurrent networks from the reservoir computing framework generate mixed selectivity which provides pre-coded representations of an essentially universal set of contexts . These representations can then be selectively amplified through learning to solve the task at hand . We thus explored their representational power and dynamical properties after training a reservoir to perform a complex cognitive task initially developed for monkeys . The reservoir model inherently displayed a dynamic form of mixed selectivity , key to the representation of the behavioral context over time . The pre-coded representation of context was amplified by training a feedback neuron to explicitly represent this context , thereby reproducing the effect of learning and allowing the model to perform more robustly . This second version of the model demonstrates how a hybrid dynamical regime combining spatio-temporal processing of reservoirs , and input driven attracting dynamics generated by the feedback neuron , can be used to solve a complex cognitive task . We compared reservoir activity to neural activity of dorsal anterior cingulate cortex of monkeys which revealed similar network dynamics . We argue that reservoir computing is a pertinent framework to model local cortical dynamics and their contribution to higher cognitive function .
One of the properties that sets primates apart in the animal kingdom is their extraordinary adaptation skills which are supported by efficient context-dependent learning mechanisms . The ability to reliably encode unanticipated behavioral contexts appears to be crucial to such adaptive capabilities . Indeed , one of the most influential theories of prefrontal cortex ( PFC ) function states that in order to link sensory information to appropriate actions , the PFC must develop the relevant contextual representations , with a high capacity for multimodality and integration [1] . Although the remarkable representation capabilities in the activity of PFC areas have been explored in a wide variety of tasks , their origin is still unknown , as is the formidable capacity of the PFC to represent such diverse relevant situations . Recently , Rigotti et al . [2] proposed that , rather than prewiring a network for the relevant representations needed for a task , activity in a randomly connected network could represent essentially all possible combinations of the task stimuli . The corresponding recombination of inputs observed in the activity of single units has been termed mixed selectivity , and its non-linear components are believed to support the representation of the conjunction of several stimuli . Although observed in early PFC studies while animals performed tasks involving multiple variables [3–6] , mixed selectivity in the PFC has only recently become a specific research focus [7] . In the latter study , the authors demonstrated that these non-linear combinations of task variables were absent in PFC activity when monkeys made errors , emphasizing the importance of mixed selectivity in encoding behavioral context . This context can be defined not only with the current set of stimuli directly available from the environment , but also with previous stimuli and actions that define the internal state of the agent . Interestingly , such network representations of arbitrary combinations of current and past inputs have been the focus of several research groups studying the recently baptized reservoir computing framework . Reservoirs are recurrent networks with fixed connections that are randomly generated according to certain parameters to obtain rich spatial ( i . e . nature of inputs ) and temporal ( i . e . previous inputs , and their order and timing ) representations composed of combinations of inputs . A simple linear output reads the activity of the recurrent network to extract meaningful representations . The first instantiation of reservoir computing was Dominey et al . 's temporal recurrent network model [8] of cortico-striatal function in sequence processing and production . With the PFC as the reservoir and the striatum as readout , the model provided an explanation of one of the first neurophysiological studies of mixed selectivity in PFC [4] . Barone and Joseph [4] identified neurons in the peri-arcuate oculomotor area whose responses encoded a mixture of spatial location and sequence rank , in an oculomotor sequencing task . In Dominey et al . [8] , the recurrent PFC reservoir modeled a prevalent feature of cortical connectivity which is a strong local recurrence , and generated a mixture of spatial and sequential rank selectivity in the reservoir neurons , as observed in primate prefrontal cortex single units [4] . This reservoir computing paradigm was further developed independently by two teams in computational neuroscience [9] and machine learning [10 , 11] . Maass et al . [9] developed a spiking neuron reservoir called the “liquid state machine” and demonstrated the universal computing power of this type of network , while Jaeger investigated the signal processing capacities of an analog reservoir called the “echo state network” . Remarkably , these spatio-temporal reservoir properties have also been found in primary cortical areas of monkeys and cats [12–15] , as stimuli presented in the past influence the representation of subsequent stimuli . Furthermore , and importantly , in vitro randomly connected recurrent networks of cortical rat neurons display spatio-temporal processing similar to a reservoir [16] . The simplest architecture of the reservoir networks does not include feedback from output neurons to the recurrent network . Hence , the memory of previous inputs is only supported by recurrent connections that create loops in the connectivity of the network , yielding a dynamic system that allows past inputs to reverberate and influence the processing of current inputs . As a consequence , this classic architecture supports a fading memory of inputs . To circumvent this temporal limitation , Pascanu and Jaeger [17] demonstrated that output units , which feed back into the recurrent network and explicitly represent relevant information act as a working memory through an input driven attractor which can indefinitely hold this information in memory . Similarly , Maass and colleagues demonstrated the simultaneity of attracting dynamics and real-time computing in reservoirs with feedback units , thereby expanding the computational power of reservoirs [18 , 19] . This feedback mechanism would allow the representation of task related contexts that are defined by current/recent inputs and contextual information that span longer time periods than the limited fading memory of a classic reservoir . Pascanu and Jaeger [17] thus note the crucial distinction between attractors in autonomous systems , vs . input-driven systems . They introduce a mechanism whereby memory states intuitively correspond to attractors in an input driven system . Memory is implemented by neurons that are trained to lock into an on state when the remembered item appears . This on state is reinjected into the reservoir thus creating modified attractor state . They demonstrate this in scenarios where the memory task is to keep track of bracket nesting in a sequence of characters , and the ongoing task is to predict the next character , which varies depending on the bracketing level . Six WM units allow up to six levels of bracketing to be represented . The activity of these WM units feeds back into the reservoir thus creating different attractors , such that the reservoir behaves differently in the prediction task depending on which attractor it is in . Pascanu and Jaeger [17] note that a similar switchable system was demonstrated by Sussillo and Abbott [20] via learning that operated simultaneously on reservoir and readout to yield robust submodes of reservoir dynamics . In the present study , we present a proof of concept of the reservoir computing framework to model information representation schemes and neural dynamics properties of the PFC . With a reservoir , we modeled a complex cognitive task initially developed for monkeys and explored neural activity representations at both the single unit and population levels . Non-linear mixed selectivity was inherently present in the network as was a dynamic mixed-selectivity that related to temporal information . In the memory version of the model , a feedback neuron explicitly representing behavioral context created a hybrid dynamical regime with two input driven attractor states that were induced by the behavioral context in memory allowing the system to process stimuli and perform the task in a context dependent manner . These experiments demonstrate the spatio-temporal processing capacities of reservoir networks in both situations—with and without explicit context feedback—in the context of a cognitive task originally developed for monkeys . In order to compare reservoir and cortical activity and dynamics , we used similar analyses on dorsal anterior cingulate cortex ( dACC ) activity from monkeys that performed the same cognitive task . We previously demonstrated that the dACC [21] and dorsolateral prefrontal cortex [22] play complimentary roles in this task [23] . dACC plays a greater role than DLPFC in integration of positive and negative feedback and tracking exploration vs . exploitation phases of the task . DLPFC activity was more tightly related to monkeys’ behavior than dACC activity , displaying higher mutual information with animals’ choices than dACC . dACC thus displays rich activity related to behavioral feedback , exploration vs . exploitation and behavior selection . We show that both representational and dynamical features present in the reservoir are observed in this prefrontal area , further validating the reservoir computing framework as a relevant approach to understand information processing and representation in the PFC .
All procedures were carried out according to the 1986 European Community Council Directives ( 86/609/EEC ) , the French Ministère de l’Agriculture et de la Forêt , French Commission of animal experimentation , the Department of Veterinary Services ( DDSV Lyon , France ) . At the time of the experiments authorization was granted under regional rules to the laboratory for a range of experiments , rather than for specific studies . Specific authorization covering this study was delivered by the ‘‘Préfet de la Région Rhône Alpes” and the ‘‘Directeur départemental de la protection des populations” under Permit Number: #A690290402 , including approved protocols in NHPs ( #047 , #048 , #0198 , #0199 , #0200 ) . All procedures complied with guidelines for animal welfare in accordance with the recommendations of the Weatherall report , ‘‘The use of non-human primates in research” . In order to compare the neural activity in the recurrent network model with that of the behaving primate cortex , we tested both systems using a problem solving task that was originally developed by Procyk and Goldman-Rakic [24] to investigate shifts between exploration and exploitation behavior ( see Quilodran et al . , [21] for detailed description ) . Two rhesus monkeys had to find by trial and error which among four targets presented on a touch screen was rewarded by fruit juice ( Fig 1A ) . At the onset of a trial , monkeys fixated a central fixation point and held their hand on a lever displayed on the screen below the fixation point ( Fig 1B ) . After a delay period of 1 . 5 seconds , 4 targets appeared on the screen . The animals made a saccade to one target and fixated it for 0 . 5 seconds until the lever disappeared , giving the GO signal to touch the chosen ( fixated ) target . Feedback was preceded by a 0 . 6-second delay , and followed by a 2-second delay ending at the beginning of next trial . The search phase included the incorrect trials ( INC ) and the first rewarded trial ( COR1 ) during which animals explored the targets . The following 3 correct trials ( COR ) allowed the monkeys to repeat the rewarded choice and constituted the repetition phase . Occasionally ( 10% of cases ) repetition lasted for 7 or 11 trials to prevent the animals from anticipating the end of the repetition phase . A signal to change appeared at the end of the last repetition trial to indicate to the animal that a new target was going to be rewarded . A search phase and its following repeat phase are referred to as a problem . In only 10% of cases the same target was rewarded in two consecutive problems . After training , monkeys performed the task in a nearly optimal fashion . In each search , they avoided previously explored targets that were not rewarded , and correctly repeated the rewarded choice . Likewise , they generally avoided repeating the previously rewarded target in the subsequent problem . The average number of trials in search was 2 . 4 ± 0 . 15 for first monkey and 2 . 65 ± 0 . 23 for second monkey ( knowing that the same target is not rewarded two problems in a row , the expected number of trials of an optimal search is ~2 . 2 ) and in repetition 3 . 14 ± 0 . 7 and 3 . 4 ± 0 . 55 for first and second monkey respectively ( the optimal-repetition trial number is above 3 , as some problems had more than 3 rewarded repetition trials ) . We developed a recurrent neural network model using reservoir computing ( RC ) to perform the cognitive task in order to generate predictions that could then be tested with dACC monkey data . According to the RC principle , a fixed , large , random reservoir ( recurrent neural network RNN ) is excited by input signals , and the desired output is combined from the excited reservoir signals by a trainable readout mechanism ( a simple linear regression in the most simple versions ) . As mentioned , the RC principle has been independently discovered in cognitive neuroscience ( temporal recurrent networks , [8 , 25] ) , in computational neuroscience ( liquid state machine , [9] ) , and in machine learning ( echo state networks , [10] ) . Models have been recently developed along the RC principle to reproduce cognitive functions like working memory [17] and language comprehension and production [26–28] . Two versions of the model were used in order to obtain the results of this paper: the original version and a second version implementing a simple contextual memory . The initial version was used in single neuron analyses in the first part of the results while the contextual memory version was introduced later to show the benefits of context encoding [29] . In both versions , a recurrent network of firing rate neurons received task inputs and were fully connected to a readout layer , the output of the model ( Fig 2A ) . Reservoir recurrent connections provide rich dynamics formed by nonlinear recombinations of inputs that evolve through time . Readout neurons activate to represent model's target choice , and feed back the choice through readout-reservoir connections . Reservoir-readout connections are the only modifiable connections of the model . Several parameters define the reservoir . The principal necessary property for reservoirs is to have rich dynamics . The essential characteristics are to have a sufficient number of non-linear neurons that are sparsely and randomly connected . Fixed network parameters include networks size ( 1000 neurons ) , and standard values for input sparsity ( 10% ) , internal reservoir connection sparsity ( 10% ) , and spectral radios ( 0 . 9 ) . The simulation time step is set at 25ms in order to give a reasonable granularity for comparison with the primate data . Fixed unit level parameters include the choice of the tanh non-linearity and the time constant or leak rate of the reservoir units . The tanh non-linearity is traditionally used in the echo state networks , but others can be used such as the Fermi sigmoid . However , reservoirs with Fermi neurons have been shown to have significantly smaller short-term memory [30] . The reservoir unit leak rate was optimized for performance , and was set at 375ms ( 15 network timesteps ) . Deviations from this value resulted in degraded performance . Neurons were simulated as leaky-integrator firing-rate units . Inputs were integrated over time with the following equation: x ( t+Δt ) = ( 1−Δtτ ) x ( t ) +Δtτ ( Wres⋅r ( t ) +Win⋅u ( t ) +Wfb⋅z ( t ) ) ( 1 ) where x ( t ) denotes the membrane potential vector of reservoir neurons , Δt the time step ( 25 ms ) , τ the time constant of the leaky integration ( 375 ms or 15 time steps ) , Wres the reservoir internal-weight matrix , r ( t ) the firing rate vector of the reservoir neurons , Win is the input weight matrix , u ( t ) the input neuron vector , Wfb the readout to reservoir weight matrix and z ( t ) the readout neuron vector . At each time step the firing rate r ( t ) of reservoir neurons was computed as the hyperbolic tangent of its membrane potential x ( t ) generating a nonlinearity in the dynamics of the neuron: r ( t ) =tanh ( x ( t ) ) The readout unit activity was defined as the weighted sum of the reservoir-neuron firing rate: z ( t ) =Woutr ( t ) where Wout is the readout-weight matrix . Experiments included a version of the model in which noise was added to the model to test its robustness and the effect of noise on performances and mixed selectivity . Noise with the same properties was injected during training and testing . Because a large proportion of noise in cortical populations has been found to be correlated among neurons [31] , noise was simulated as a random Gaussian component added to the activity of input neurons: unoisy ( t ) =u ( t ) +N ( 0 , σ ) where N ( 0 , σ ) is a vector the size of u ( t ) of pseudo randomly generated values following a Gaussian distribution of mean 0 and standard deviation σ . To assess the effect of noise injection into the model , values of σ ranging from 0 to 7 in increments of 0 . 5 were each used in 30 simulation instances . We implemented an RC model where a reservoir of 1000 recurrently connected neurons was fully connected to a readout layer . Learning took place only between the reservoir neurons and the readout units , at the level of the readout weights . Weights between reservoir neurons ( internal weights ) and between input and reservoir neurons ( input weights ) were stochastically generated and fixed . Input weights were generated with a uniform distribution in the interval [–1 , 1] with a 0 . 1 probability of connection . Internal weights followed a Gaussian distribution ( μ = 0 , σ = 1 ) with a 0 . 1 probability of connection between each pair of neurons . These were scaled so that the largest absolute eigen-value of the weight matrix—commonly referred to as the spectral radius—was equal to 0 . 9 . This ensured a dynamical regime allowing for sustained activity in the recurrent network without saturation . Activity in the network thus developed and integrated successive stimuli inputs so that activity at each time point represented the combination of previous and current inputs ( reservoir computing principle ) . Input neurons represented the major external features of the task ( Fig 2A ) . They included 5 inputs , each represented by one neuron: the fixation point , the lever , the targets , the reward and the signal to change . Each of these neurons had a 0 . 1 chance of connecting with each reservoir neuron . Weights were generated following a uniform distribution in the interval [–1 , 1] . The model generated outputs corresponding to oculomotor saccades and arm touches to the spatial targets corresponding to the monkeys' behavioral output and time course of the task events . A first set of 4 readout neurons represented the four possible target choices for eye saccades and a second set of 4 readout neurons represented arm touches . The highest activated neuron for each of the two sets represented the model's choice and both neurons were required to represent the same target in each trial . In the contextual memory version of the model , an additional readout neuron was trained to represent the phase ( search vs repetition ) . In both versions , all reservoir neurons were connected to the readout neurons and constituted the only modifiable connections of the network . The readout neurons were connected back to the reservoir neurons to feed the choice information back to the recurrent neurons with a 0 . 1 chance of connection . These connections were generated prior to learning and remained fixed for the duration of the experiment . Connection weights were drawn from a uniform distribution between -1 and 1 for the choice outputs and for the contextual readout neuron in the contextual memory version of the model . We trained the model to learn a task that reproduces the major features of the actual task performed by monkeys ( Fig 2B ) . Timing of these elements closely matched the actual monkey task in order to compare evolution of activity in dACC and reservoir neurons . Fixation point and lever were each simulated as the activation of their corresponding input neurons . They provided GO signals as they switched off for saccade to and fixation of a target , and for touching this target respectively . The readout neurons corresponding to these choices were trained to activate at their respective GO signal after a reaction time of 250 ms and deactivated after a 250 ms reaction time following touch . Following the fixation point , an input neuron represented the presence of the targets on screen and is deactivated after touch . Activation of the arm touch neuron started before the actual touch event to allow the neuron representing arm choice to reach full activation before switching off the targets input and to simulate the preparation and movement itself . Feedback was simulated with a reward input neuron that activated when a choice was correct . At the end of a problem , a fifth input neuron was activated to represent the signal to change indicating the start of a new problem . In the contextual memory version , the context neuron representing the phase was trained to activate when the signal to change input neuron was being activated , and to remain active for the duration of the search phase , until the first reward . Each trial lasted 5550 ms ( 222 time steps ) , except for the last correct trial ( COR4 ) that ended with the presentation of the signal to change and lasted 8050 ms ( 322 time steps ) . The task was taught to the reservoir with supervised learning using a matched set of <stimuli , desired output> pairs made of 600 problems . Readout neurons were trained to represent choice by activating to value 1 at periods of choice while remaining silent the rest of the time , thus acting like binary neurons . The training procedure employed a slightly modified version of the FORCE learning method developed by Sussillo and Abbott [20] . With the FORCE method , learning of connection weights between reservoir and readout neurons is based on an on-line process of weight adjustment that allows for sampling of the readout error by the system . Weights are corrected so that a small fraction of the readout error is fed back to the reservoir . Readout weights are successively modified to produce the target output while sampling deviations in the reservoir activity that result from readout feedback with a slight discrepancy between actual and desired output . Hence , the system learns to produce a stable readout even in the face of readout errors that are propagated to the reservoir . We used the recursive least-squares algorithm in combination with the FORCE learning principle to modify readout weights , as described in Sussillo and Abbott [20]: Wout ( t ) =Wout ( t−Δt ) −e ( t ) P ( t ) r ( t ) 1+rTP ( t ) r ( t ) Where e ( t ) is the error before weights are modified and is defined as the difference between actual and desired output . The error of readout neuron i is defined as follows: e ( t ) =WoutTi ( t−Δt ) r ( t ) −di ( t ) where Wout is the weight vector between the reservoir neurons and the readout neurons and di ( t ) is the desired output . P ( t ) can be assimilated to the matrix of all learning rates for each pair of reservoir and readout neurons and is modified as follows: P ( t ) =P ( t−Δt ) −P ( t−Δt ) r ( t ) rT ( t ) P ( t−Δt ) 1+rT ( t ) P ( t−Δt ) r ( t ) with P ( 0 ) =I where I is the identity matrix . To allow for better convergence of the weights , we modified the feedback from the readout to the reservoir generated with the original FORCE learning method . We blended the actual output , produced after weight modification according to the FORCE principle , with a clamped feedback i . e . a delayed version of the desired output . The proportion of clamped feedback and actual output varied smoothly and steadily during training , starting with only clamped feedback and ending with actual output . The signal f ( t ) was fed back to the reservoir and replaced z ( t ) in eq ( 1 ) : f ( t ) =tLz ( t ) +L−tLc ( t ) where L is the full duration of training ( entire 600 problem block ) and c ( t ) the clamped feedback that is a 325 ms ( 13 time steps , determined through optimization ) delayed version of the desired output . This delay greatly improved learning in our experiment . With a delayed desired output as clamped feedback , readout neurons had to learn to activate at the onset of fixation and arm choice without the correct and expected readout activity that would have been fed back to the reservoir with FORCE-learning fast adaptation of the weights . Likewise , when readout neurons should deactivate at the end of fixation and arm choice , the reservoir neurons still received the clamped activity resembling a readout that was not deactivated . Similar to the FORCE learning principle , this method allowed the learning algorithm to sample a higher number of time steps with discrepancies between actual and desired readout around the activation and deactivation of the readout neurons . In order to assess the trained model's behavioral performance , a sequence of 200 problems was provided as input to the reservoir and the output choices were evaluated . The maximally activated neurons for saccade and hand choices had to match , and thus represented the model's choice . Trials where saccade and hand choices did not match were counted as errors . Performance was assessed on the basis of three rules: ( 1 ) do not repeat an unrewarded target choice; ( 2 ) repeat rewarded target choice once found; ( 3 ) while searching for the rewarded target , do not choose the target rewarded on the previous problem . Performance of the model was measured according to these rules . Trials that did not respect one of the three rules counted as an error . Error rate was defined as the number of trials that did not respect the rules over the total number of trials . In order to balance the length of the search period , the number of search trials was generated for each problem in advance . Thus , no target was predefined as rewarded , rather , after a predefined number of search trials ( from 1 to 3 ) , the reward was given and the behavioral output of the model was assessed according to the above described rules ( for a similar method used to test human subjects , see Amiez , Sallet , Procyk , & Petrides [32] ) . We are interested in the capacity of the model to perform the problem solving task . Previous detailed analyses of monkey behavior in this task have shown that the animals produced planned and structured search behaviors [23] . Rather than trying to reproduce trial-by-trial behavior of the monkeys , we trained the model on examples that followed the above rules , and then tested its performance and analyzed its activity . We generated training data based on three different search behaviors , among which two were structured . All three search behaviors used to train the model complied with the above mentioned rules . A fourth training set was created from data from one of the monkeys trained on this task [21] . We thus tested four training schedules: First , using a random search where the targets were explored in a different order at each problem . Second , using an ordered search where targets were explored following the same target sequence at each problem while avoiding the previously rewarded target in the sequence . In other words , the search always started with the same target , except if it was rewarded on the previous problem , and followed the same sequence , again , avoiding the target rewarded on the previous problem . Third , using a circular search where targets were explored in infinite repeating circle . As an example , let's define the repeating sequence upper-left ( UL ) , upper-right ( UR ) , lower-right ( LR ) , lower-left ( LL ) , UL , UR , LR and so on . If for a given problem , the rewarded target is UR , the search of the next problem will start with the next target in the sequence , namely , LR and continue with LL and UL until it finds the rewarded target . Fourth , the model was trained with the search behavior from monkey 1 who best solved the task . In order for the model to effectively learn the task , error trials from the monkey were removed from the behavior fed to the network . Khamassi et al . [23] provide a detailed description of the monkey's behavior with reinforcement learning models . Reservoir neuron analyses reported here are based on the activity of networks that learned to explore targets with the circular search . Results did not differ when the model was trained with the ordered search . Fig 2C illustrates the activity of reservoir and readout neurons corresponding to a sequence of inputs once the task has been taught with a circular search . Quilodran et al . recorded 546 neurons in the dorsal bank of the cingulate sulcus of two rhesus monkeys and analyzed them along with local field potential for their correlation with the behavioral shift [21] . The present article reports on a new and separate reanalysis of this dataset to support findings obtained with modeling . All reanalyzes of these data were based on firing rate estimates of the recorded neurons . Subsets of this pool of neurons were selected depending on the requirements of the analyses . The number of neurons per analysis is specified in each case in the related method description . Mixed-selectivity analysis was performed by using the same methods for both reservoir neurons from the model ( neurons from the recurrent network ) and dACC neurons . The analysis focused on specific 500 ms trial epochs . Epochs used were: early fixation ( 0–500 ms from fixation onset ) , late fixation ( -500–0 ms to targets appearance ) , before touch ( -500–0 ms to target touch ) , before feedback ( -500–0 ms to feedback ) and after feedback ( 0–500 ms from feedback ) ( Fig 2B ) . Firing rates of reservoir neurons were averaged within these periods , thus obtaining a single firing rate value for each epoch . Average activity of dACC neurons for each epoch of each trial was estimated as the number of spikes within these epochs . Epoch , along with phase ( search vs . repetition ) and choice ( UL , UR , LR , LL ) constitute the three factors used in single neuron analysis with 5 ( epoch ) , 2 ( phase ) , and 4 ( choice ) possible levels respectively ( 40 conditions total ) . The dACC neuron pool for the mixed selectivity analysis was a subset of 85/546 dACC neurons selected for having at least 15 trials per condition . All reservoir neurons were included in the analysis . A three-way ANOVA was conducted on the activity of each neuron with factors Epoch x Phase x Choice . A neuron was considered significant for a factor or an interaction between factors if its p-value was inferior to 0 . 05 ( corrected for multiple comparisons with false discovery rate across all neurons ) . Interaction effects between phase and choice are considered here as an indicator of mixed selectivity which is defined by the interaction of these variables in their contribution to the firing rate of a single neuron . Thus in this present study we use the term “mixed selectivity” to refer exclusively to its non-linear component . Moreover , we introduce the terminology “dynamic mixed selectivity” to refer to mixed selectivity patterns that interact with epoch and correspond in our experiment to the interaction between epoch , phase and choice variables in the ANOVA analyses . In the monkey , responses specific to the first correct choice ( COR1 ) were considered important as they mark the transition from search to repetition [21] . Thus , reservoir neurons of the model were also analyzed for their response to the first correct choice in a problem to compare with results obtained in Quilodran et al . [21] . For that purpose , firing rate activities of single reservoir neurons were averaged over the time window 300 ms to 800 ms after feedback onset and then pooled in incorrect ( INC ) , first correct ( COR1 ) and correct ( COR ) trials . Pairwise t-test with false discovery rate correction over all tests was used to quantify the number of reservoir neurons that fired significantly more in COR1 trials than in INC and COR trials ( pooling tests of all neurons and all simulations , and with a threshold p-value of 0 . 05 ) . To demonstrate the presence of the COR1 information in the activity of the reservoir layer in each simulation , we trained an additional readout neuron to activate specifically for the first reward ( reward during the COR1 trial ) in a problem with the same method used to train other readout neurons . The successful learning of the COR1 readout neuron was assessed over 30 simulations with different pseudo-randomly generated weights according to the parameter values defined above . Population analyses were performed on neural activity from full trials . Firing rate of each dACC neuron was first estimated with a Gaussian kernel ( standard deviation = 100 ms ) convolved through time every millisecond , eliciting a firing rate estimate at each millisecond . Activities were time normalized to accommodate for trial-time variations with the following method . The average duration of periods between key events of the task was calculated and allowed us to determine the number of time bins of a specified size ( see below ) within each period . The activity of each neuron was then divided in the number of time bins . For each neuron , estimated firing rate within these time bins was averaged to elicit a single average firing rate value per time bin . The events used to normalize time were: lever touch , targets appearance , target touch , feedback , next-trial lever touch . We computed an autocorrelation on population activity of reservoir and dACC neurons to assess the dynamic nature of each of these populations . All reservoir neurons were included in the analysis . For dACC data , a subset of 290/546 neurons was selected for having at least 20 incorrect trials , 20 COR1 trials , 20 correct trials ( excluding COR1 and COR4 trials ) and 20 COR4 trials . The activity of each dACC neuron was time normalized using the above described method with 20 ms time bins . The activity of each neuron was averaged across all trials to derive population activity vectors for each of the time points composing a full trial ( excluding signal to change period in COR4 trials ) . The resulting autocorrelation matrix is composed of all the Pearson correlation coefficient obtained from all possible vector-pair comparisons . A decoding method was used to assess the capability of a linear readout to extract a continuous phase signal from the dynamic activity of the reservoir and dACC neurons . In the absence of a context neuron , a readout unit without feedback to the reservoir was trained to activate similarly to the phase context neuron , i . e . to start firing when the signal to change was given to the network , firing continuously during search phase and deactivate when the first reward was given during COR1 trials . With this method , phase information extraction had to rely only on the activity of the reservoir neurons . The result of this training produces a linear readout of the phase . Similarly , task phase was decoded from dACC population activity with time normalization ( 20ms bins ) over all time bins of a trial after training a ridge regression on full trial activities . For training the decoder , the search trials included all INC trials , and the repeat phase was composed of COR2-3 trials . COR1 and COR4 trials lying at the transition between search and repeat were only used in testing . A subset of 290/546 neurons were selected for having at least 20 trials in each category ( search/repeat ) . A linear readout model was derived from a linear regression between the full trial length ( all time bins considered as an observation ) and the desired output which was 1 for search trials and 0 for repeat trials . The output of the decoder was classified as correct if it was superior to 0 . 5 in search trials , and inferior to 0 . 5 in repeat trials . Ridge regression ( Tikhonov regularization ) was used to avoid overfitting of the linear readout/decoder . The ridge parameter was derived from a 10-fold cross-validation on the INC and COR2-3 trials: 2 test trials in each of the search and repeat categories , and 18 train trials in each category . The ridge parameter value obtained after optimization ( 10−8 ) was used for all the decoding analyses . To assess the separability of the search and repeat activities , the decoder was trained and tested on all INC / COR2-3 trials . For this first analysis , error rates were computed as the number of time bins ( from all test trials combined ) incorrectly classified over the total number of time bins . To demonstrate the generalization capabilities of the linear readout/decoder to new data , it was similarly trained on INC / COR2-3 trials and then tested on all the time bins of 20 COR1 and 20 COR4 trials . Permutations tests were performed to ensure the significance of the decoder: INC / COR2-3 labels were shuffled 10 , 000 times , which allowed to derive a 95% confidence interval for each time step . Every time step with a decoding accuracy equal or superior to the confidence interval was considered as significant . A state space analysis was performed , allowing visualization of population activity trajectories , with a principal component analysis ( PCA ) . Activities of each successive trial in a problem were averaged at the level of single neurons over each trial type: INC1 and INC2 were the first and second unrewarded search trials , COR1 the first rewarded trial , and COR2-4 the repeat trials with the presentation of the signal to change at the end of the COR4 trial . INC3 trials were ignored due to lack of trials in dACC data , but results with few trials show that INC3 trajectory was very close to INC2 trajectory and did not add relevant information to this analysis . A subset of 184 dACC neurons were selected for having at least 10 trials of each type . The activity of each dACC neuron was time normalized using the above described method with 100 ms time bins and then mean normalized over all bins for each trial type . Similarly , reservoir neuron activity was averaged over all trials of a trial type for each time point . PCA was performed on the data matrix where columns correspond to individual neurons and rows represent the concatenated time points of each trial type . Each cell in the matrix was the mean normalized average firing rate of one neuron at one time bin for one trial type . All reservoir neurons were included in this analysis .
The model learned to perform the task almost perfectly with all training protocols , except for the random search . As expected , it was impossible for the model to learn to perform the task with a random search ( 41 . 64% ± 10 . 91% of suboptimal choices over 30 simulations , 1000 reservoir neurons ) ( see Methods for full description of performance calculation ) . In the absence of a pattern in the trained search sequence , the model could not produce a coherent output . In contrast , the model performed the task almost perfectly with the circular search ( 0 . 11% ± 0 . 43% of suboptimal choices over 30 simulations , 1000 neurons ) and the ordered search ( 5 . 92% ± 5 . 40% of suboptimal choices over 30 simulations , 1000 neurons ) . Interestingly , the model also learned successfully to perform the task when trained on a schedule derived from the performance of Monkey 1 after training ( 4 . 71% ± 4 . 35% of suboptimal choices over 30 simulations , 1000 neurons ) . As a comparison , the rate of suboptimal choices over all trials in monkeys assessed with the same method were 0 . 53% and 2 . 21% for each monkey , respectively . The observation that the reservoir model could learn the task , i . e . learning to repeat when it receives reward , and shift when it does not , represents a novel extension of the “cognitive” functions of reservoirs . Because the model successfully learned to perform the task , it is of interest to examine the neural coding of behavior within the recurrent network , and compare it with that in the primate cortex . We examined the coding of two pertinent variables in this task: the target choice , and an internal variable , corresponding to the phase within a problem ( search or repetition ) . To explore the variance in reservoir activity explained by these two variables , we systematically tested each neuron ( total of 1000 neurons ) in 30 independent simulations with the circular search using ANOVA , a commonly used parametric test in single-unit electrophysiology experiments [33 , 34] . Tests were performed at 5 different epochs: early fixation , late fixation , before touch , before feedback and after feedback ( Fig 2B ) . Fig 3A and 3B illustrate two typical examples of reservoir neurons that display activity profiles that code for phase , and choice , respectively . The presence of such neurons was tested by ANOVA . The majority of reservoir single units displayed significant main effects for choice and phase ( three-way ANOVA , Epoch x Phase x Choice; mean ± std: Phase 97 . 5 ± 0 . 6% of a total of 1000 neurons; Choice 99 . 8 ± 0 . 2% , see Fig 3A and 3B for two example units ) . These results did not differ with the ordered search training . Focusing on pre and post feedback epochs , Quilodran et al . [21] showed that some dACC neurons respond differentially depending on the phase of a problem . For the purpose of comparison with the model , we generalized this approach by assessing the difference in activity of the 85 dACC neurons that had at least 15 trials per condition with the same method used for the model: 57 neurons displayed a significant effect for phase ( 67 . 1% ) and 49 a main effect for choice ( 57 . 6% ) ( Fig 4A and 4B ) . Thus , single units within the recurrent reservoir network and the dACC encoded task related variables including target choice , and task phase , as revealed by significant main effects in ANOVA . Neither the target choice , nor the phase could be directly derived from the current inputs , whether they be external inputs ( reward for phase variable ) or feedback of responses ( choice feedback for choice variable ) . Rather , they depended on the history of previous inputs and responses . This selectivity for previous inputs and states in reservoir activity is due to the recurrent dynamics and is part of the defining properties of reservoir computing [8–10] . While such task related activity invites a straightforward explanation of cortical function , as noted above , recent research suggests that cortex does not uniquely rely on such simple coding schemes , and also displays more complex mixtures of selectivity to task related variables [7] . As just seen , analysis of neural activity in terms of single task variables can provide an explanation of neural coding that appears simple . However , the activity of PFC neurons has often been described as complex , reflecting different combinations of task-related variables [3–6] . This phenomenon has been the focus of several recent studies which revealed its importance for cognitive tasks , and may underlie the capacity of the cortex to represent any contingency explained by a combination of task variables [7] . Fig 3C and 3D illustrate these mixed selectivity effects in the reservoir: coding of phase was dependent on choice , and epoch , respectively . The presence of such neurons was tested by ANOVA . The analysis of Choice x Phase interactions can reveal whether reservoir neurons display mixed-selectivity properties ( three-way Epoch x Phase x Choice ANOVA , Choice x Phase interaction , p-value < 0 . 05; see Methods ) . Nearly all reservoir neurons showed mixed selectivity effects ( 99 . 9 ± 0 . 1% out of 1000 neurons , over 30 simulations ) as revealed by the Choice x Phase interaction ( Fig 3C ) . While mixed selectivity has been described in PFC neurons , no study has yet to our knowledge systematically and specifically explored it in the dACC . We thus tested the prediction that this same mixed selectivity , as revealed by the Choice x Phase interaction , should be present in dACC neurons in animals trained to perform the same task as the reservoir model . Our reanalysis of the Quilodran et al . data by ANOVA indeed revealed that 28 out of 85 dACC neurons ( 32 . 9% ) selected for the analysis displayed mixed selectivity ( Fig 4C ) . Rigotti et al . [2] showed that neurons of recurrent networks display complex recombination of current input that are similar to mixed-selectivity activities observed in PFC . These recombinations would allow external units connected to the recurrent network to detect the high dimensional combinations of multiple inputs relevant to the task to be learned . Recurrent networks of the reservoir type have the double property of recombining inputs and maintaining information about inputs across time [8–10 , 25] . This is exemplified in the current experiment as the recombination of phase and choice information expressed in mixed selectivity that cannot be explained by a simple linear combination of the two variables contributions , and is dependent on the history of previous inputs . Modulation of selectivity through time is a well described feature of PFC neuronal activity , whether the neurons show selectivity at a specific time in a trial or shift selectivity within a trial [33–35] . We will refer to this pattern of selectivity that changes over time as dynamic selectivity . Model reservoir neurons displayed clear dynamic selectivity , with nearly all the neurons showing modulation of selectivity across epochs for both phase and choice task variables as revealed by Epoch x Phase and Epoch x Choice interactions ( mean ± std: 99 . 2 ± 0 . 4% for Epoch x Phase interaction and 99 . 4 ± 0 . 4% for Epoch x Choice interaction for 1000 reservoir neurons across 30 simulations , three-way ANOVA , Epoch x Phase x Choice , p-value < 0 . 05 , Fig 3C and 3D ) . Single variables were encoded dynamically which suggests that mixed selectivity could be encoded in a dynamic fashion as well . Pursuing this dynamic aspect , in the following , a pattern of mixed selectivity that changes over time is referred to as dynamic mixed selectivity . Fig 3E illustrate these dynamic mixed selectivity effects in the reservoir: the interaction between phase and choice is itself dependent on the task epoch . We consider dynamic mixed selectivity when there is a significant three-way interaction between task epoch , phase and choice with the ANOVA . Dynamic mixed selectivity was observed in the majority of model neurons ( 99 . 2 ± 0 . 6% of 1000 neurons , three-way ANOVA , interaction between Epoch x Phase x Choice , p-value < 0 . 05 , Fig 3E ) . In order to determine if this property of the reservoir was equally observed in the primate data we performed the three-way ANOVA and examined the two way and three way interactions . Dynamic selectivity was similarly found in the majority of dACC neurons ( 63 out of 85 neurons ( 74 . 1% ) for Phase x Epoch interaction , and 49 out of 85 neurons ( 57 . 6% ) for Choice x Epoch interaction , Fig 4C and 4D ) , as well as dynamic mixed selectivity , as revealed by the Epoch x Phase x Choice interaction ( 14 out of 85 neurons , 16 . 5% , Fig 4E ) . This prevalence of forms of dynamic selectivity suggests that the characterization of neurons as showing mixed selectivity features through the interactions of two variables excluding time can be extended to include the dynamical nature of these mixed-selectivity representations . Most of the neurons in the model were significant for each test , while dACC neurons displayed high percentages of neurons selective for main effects of phase , choice and epoch , and then progressively reduced percentages for two- and three-way interactions ( Table 1 ) . This suggests that these higher order mixed selectivity properties are more fragile , and might be potentially susceptible to the addition of noise to the system . Indeed , the simulations were performed in the ideal noiseless condition . Injecting noise into the model let us explore the robustness of all types of selectivity ( simple , mixed and dynamic mixed selectivity ) . We used progressively increasing Gaussian noise that was added to the input fed to the network during training and testing ( see Methods for details ) . Increasing noise elicited a decrease in the scores of all types of selectivity ( Fig 5 ) . Interestingly , the more complex was the selectivity , the faster the number of unit displaying this selectivity dropped . The single task variable selectivities decreased more progressively than mixed selectivities , and the dynamic mixed selectivity was the most sensitive to noise . Interestingly , the model performance decreased directly with the mixed selectivity . Selectivity ratios similar to that of the dACC population were obtained in the model with varying standard-deviation of noise , the tendency being that the more complex selectivities require less noise to attain the corresponding ratios of the dACC . We calculated the Pearson r correlation between number of significant neurons for each test the dACC , and the model with different levels of noise . As illustrated in Table 2 . With noise from 1 to 4 . 5 SD , the p-value of the correlation is below 0 . 05 ( see Table 1 for a comparison between dACC and the model with a 1 SD Gaussian noise ) . However , in this range of noise the model performed poorly . Interestingly , the performances dropped with slight noise , as was the case with the dynamic mixed selectivity . Although this result cannot be accepted as a causal relation between dynamic mixed selectivity and performance , it is reminiscent of the correlation observed by Rigotti et al . ( 2013 ) between non-linear mixed selectivity and monkey performance . Reservoir networks are by nature dynamic , and representations within single reservoir neurons are themselves highly dynamic . Likewise , it was demonstrated that the presence of mixed selectivity in the activity of single PFC neurons is important for behavior [7] , and that this feature may be necessary to represent conjunction of variables that are not represented in neurons firing only for single variables [2] . Still , it is relatively non-intuitive to understand the role of dynamic mixed selectivity in single units for producing complex but stable behaviors , leading to questions concerning how any pertinent information can be extracted from such unstable coding . The current analyses reveal ( 1 ) task related neural activity , ( 2 ) mixed selectivity , and ( 3 ) dynamic mixed selectivity . The question remains , what is the underlying meaning or content of these representations ? In the past we have seen “meaningful” forms of mixed selectivity , as in the combined retino-topic and sequence rank effects in PFC during sequencing tasks [4] . What about in the current case ? Are there forms of mixed selectivity that can be observed to be meaningful in this task ? Can we extract this information in a meaningful way ? Can we visualize these representations ? These questions will be addressed in the following sections . We have seen that certain neurons encode uninterpretable forms of mixed selectivity ( e . g . Figs 3 and 4C and 4D ) . Are there forms of dynamic mixed selectivity that are more meaningful ? In the problem solving task the first reward is the key transient signal to stop exploring and concentrate on the rewarded target , i . e . to initiate the repeat phase . Quilodran and colleagues found that this transition signal is represented in the activity of a sub-population of dACC neurons . These feedback-related neurons activated only after the feedback of the first rewarded trial ( COR1 , see Fig 6A ) , and thus represented the conjunction of reward and search phase . Their activity was significantly different from incorrect ( INC ) and correct trials in repeat phase ( COR ) ( 12% and 24% , of 146 and 88 feedback related neurons in first and second monkey respectively , see Quilodran et al . , [21] ) . This is an example of mixed selectivity that is highly relevant for the task , as a signal to shift from the search phase to the repetition phase , each of which has specific and distinct behavioral requirements . In order to determine if neurons in the reservoir displayed this task-relevant mixed-selectivity COR1 response , we compared the average firing rate during the post feedback period ( 300ms to 800ms after the feedback ) and found that 1 . 1% ± 1 . 1% ( mean ± std , 30 simulations , example in Fig 6B ) of the neurons had a significantly higher firing rate for the first rewarded trial than for the unrewarded and other rewarded trials ( one-sided pairwise t-test with Bonferroni correction for each neuron , p-value < 0 . 05 ) . Correcting p-values with FDR over all neurons of all simulation elicits 0 . 24% ± 0 . 3% significant COR1 neurons . Following correct statistical procedure , it seems that the number of COR1 neuron in the reservoir is 0 , and all observed COR1 neurons are due to chance only . Furthermore , Fig 6B clearly shows how the difference in activity between correct and COR1 trials is slight . However , each of the 30 simulations without exception produced COR1 neurons ( minimum 2—maximum 65 ) . Indeed , one of the principles of a recurrent network with random connections is that “by chance” some neurons will show a pattern of activity corresponding to a given combination of inputs and/or internal states . While the number of neurons encoding the COR1 information in the reservoir is not statistically significant , their consistent presence should allow for extraction and strengthening of the signal for more explicit processing . It is possible that the COR1 response in the monkey was increased by learning to allow the system to represent a task relevant information more explicitly . To illustrate this point , we trained an additional readout neuron to activate specifically when the first reward of a problem was given to the reservoir . We reasoned that if the information is robustly present in the recurrent network , the training of a COR1 readout neuron should be straightforward . Indeed , each of the 30 instances of the model trained to produce a COR1 neuron were successful ( Fig 6C illustrates the activity of the COR1 readout of an example simulation ) . Therefore , the COR1 information was present , distributed in the activity of the reservoir population . The single unit analyses revealed that mixed selectivity changes in time—that is , it is dynamic . To further demonstrate the dynamic nature of neural activity at the population level , we used an autocorrelation method on the reservoir neural population . By correlating successive population activity vectors , this method allowed us to reveal how fast the population activity is changing . The activity of each neuron at each time point within a full trial was averaged over all trials . Results of the autocorrelation are represented on a heat map in which every point represents a correlation between the population activity vectors at two different time points within a trial . The diagonal represents the correlation between one time point with itself and will necessarily elicit a correlation coefficient of 1 . Slow variations in the activity pattern elicit extended correlation around the diagonal , while dynamic coding in the population elicits narrow bands along the diagonal line . Results show a band of positive correlations largely concentrated around the diagonal , which confirms the globally dynamic character of representations observed in single unit analyses ( Fig 7A ) . As expected from a reservoir network exhibiting the echo state property , the shape of the correlation pattern follows the precise sequence of inputs fed to the network . The narrowest pattern along the diagonal is observed at the beginning of the trial ( lower left corner ) when both the lever and fixation point inputs were activated , triggering a strong change of activity in the population . Similarly , successive events of the task ( indicated by dashed lines in Fig 7A ) are associated with a following narrowing of the correlation pattern , while absence of inputs left the network in a more stable and persisting state . A more stable pattern of activity follows the feedback , corresponding to the inter-trial period when no inputs were active . Here in the absence of input the network activity begins to stabilize , but still retains sufficient coding of the phase so as to continue correctly in the next trial . Applied to the dACC population activity , this method displays a strikingly similar correlation pattern: dynamic activity at trial start , after target appearance and after feedback and more stable activity between these events ( Fig 7B ) . Event related narrowing patterns appeared slightly later in the case of the dACC , probably because the inputs associated with these events reached the dACC through other brain regions , whereas the reservoir network was directly connected to the inputs . A widening of the correlation pattern between touch and task feedback represents the sole main discrepancy between dACC and model results . The similarity between model and dACC may also be explained by a similar time constant in the model and dACC . The time constant τ of the neurons in the model was first obtained through optimization , based on the performances of the model to perform the task . Interestingly , Murray et al . [36] recently found an empirical time constant of ACC neurons ( between 257 ms and 340 ms ) relatively close to our optimized value ( 375 ms ) . Among the prefrontal areas investigated in this study ACC has the highest intrinsic time constant . Overall , on the period of a full trial , these results suggest that population activity patterns are quite dynamic in both dACC and model , suggesting a transient dynamic which is a characteristic of reservoir systems . For the model these transient patterns depend on the inherent network dynamics , and the externally imposed task schedule . The similarity with the dACC results suggest that the dynamic character of this area can be explained by the same mechanism . We have shown that single unit ( Figs 3E and 4E ) and population activities ( Fig 7 ) in the model and the monkey displayed complex and dynamic activities that are not easily interpretable as to their contribution to stable and controlled output . That is , the mapping of this complex time-varying activity onto coherent behavior is not immediately evident . However , both monkeys and the model were quite efficient at performing the task . This implies that in both , there was a mechanism that can extract coherent task-relevant representations from dynamic activity . Recent studies suggest that complex non-linear combinations of inputs create rich activities in recurrent networks from which relevant information is easily extracted using simple linear regression [7 , 37] . In other words , RC networks expand the input space into a rich state space , now composed of spatial and temporal information . The role of learning is to find a linear readout that best separate states representing relevant information . Here , we attempted to determine whether a linear readout could be used to generate meaningful stable outputs across time by separating relevant states of a dynamic and complex activity . For this purpose we trained a readout unit , with the method used for other readout units , to represent the task phase ( i . e . whether the system is in search or repetition ) without feeding it back to the reservoir . Thus , the readout unit had to extract phase from a reservoir population that did not receive this readout as a feedback . The learning procedure converged on weights that activated this special readout neuron throughout the search phase even though phase representation was dynamic . Fig 8A illustrates the activity of target choice readout neurons of the model , along with the new trained readout unit extracting phase after the task has been learned ( results were replicated on 30 simulations ) . The output of the phase readout unit was steady during search phase and activated and deactivated sharply with key events ( signal to change and first reward ) . This demonstrates that a stable , task relevant signal ( here the state of whether the system is in search or repeat phase ) can be read out from the complex dynamic mixed activity generated in a reservoir network . A simple linear readout could extract the task phase steadily from the reservoir population activity that seemed dynamic . Can we demonstrate the same decoding for cortical neurons ? Astrand et al . [38] recently showed that one of the best decoders for task related variables on a population of prefrontal neurons is a simple regularized linear-regression which elicits performance similar to those using complex machine learning methods such as Support Vector Machines , while being simpler and less expensive computationally . We thus set out to reproduce the continuous decoding results observed with reservoir neurons , now with dACC activity , by training a readout through ridge regression . The readout was trained and then tested on the activity of full search ( INC ) and repeat ( COR2-3 ) trials to see if it could continuously decode task phase ( 290/546 neurons having at least 20 trials of each category , ridge parameter obtained through cross-validation , see Methods for details ) . This method allowed us to assess if a single readout , that can be considered as a downstream neuron connected to the dACC population , can continuously extract phase information from a dynamic population activity . On average , over all time bins of every search and repeat trials tested , the decoder made only 3 . 0% ± 2 . 1% of errors ( mean ± std ) . We then tested this decoder , that was trained on all INC and COR2-3 trials , with data not used in training , from COR1 and COR4 trials . This allowed us to explore the transition between search and repetition ( COR1 ) , and between repetition and search ( COR4 ) in decoding phase with the linear readout . For both COR1 and COR4 , decoding accuracy was very high in the period preceding the transition . The representation of the new phase built up gradually after the transition event ( signal to change for COR4 trials , feedback for COR1 trials ) . Considering the time bins with significant decoding , the search to repeat transition occurred considerably faster ( ~300 ms ) than its repeat to search counterpart ( ~1 . 5 s ) . Overall , apart from the transition period , the decoder performed extremely well , and thus demonstrated the success of a simple linear readout to continuously extract phase information from a dACC population whose phase representation was globally dynamic . In addition , this suggests that the states representing phase within the population activity belong to well separated areas of the population activity state space . So far we explored the coding schemes of a classic reservoir , and demonstrated that its recurrent property produced a rich dynamic activity composed of input recombinations , in accordance with the universal spatio-temporal representational power of reservoirs . However , we showed how the influence of past inputs on the activity of the reservoir is limited in time , which consequently limits the time aspect of the reservoir representational power . To circumvent this inherent limitation , several studies in the reservoir computing community introduced units in the reservoir to explicitly represent a contextual information over time that will thereby influence the processing in the rest of the reservoir [17 , 19] . In this context , we observed that the reservoir required a high number of neurons ( on the order of 1000 ) to perform the task optimally . Phase information in the network ( i . e . whether the system is in search or repetition phase ) was the consequence of the reward , yet this short reward input had a limited influence on the reservoir dynamics due to fading memory [39] as shown by the auto-correlation analysis . In the previous section we demonstrated that phase information could be steadily extracted from the activity of the reservoir . To explore the effect of making this phase information explicit to the reservoir , we trained one additional output neuron to represent phase explicitly and fed it back to the reservoir ( explicit context version ) . For the current analysis , this readout neuron was connected to the reservoir neurons by modifiable connections , and fed back its activity to the network as well ( see Fig 2A ) . Like the phase readout , it was trained to activate steadily only during the search phase , hence mimicking the tendency of numerous dACC neurons to display higher activity in search versus repeat phase [21] . We refer to this as a “context” neuron as it encoded the phase , which provided a form of behavioral context . After learning , the context neuron became activated following the signal to change input at the end of a problem , and fired steadily until the first reward . Note that the activation of the phase neuron was learned and was produced solely through learning of the readout connections to this phase neuron , just like the other readout neurons . The only difference with respect to other output neurons is that rather than coding an actual behavioral response , it coded an internal state variable , the phase ( search or repetition ) . Also , a major difference with the phase readout of the previous section is that because the contextual neuron fed its activity back into the reservoir , the contextual neuron operated in a loop in which it both influenced and was influenced by the activity of the reservoir . Interestingly , feeding this explicit contextual information back into the reservoir greatly reduced the number of suboptimal choices made by the model . The explicit context version of the model performed very well with less than half the number of neurons in the reservoir originally required ( Fig 9 ) . The initial version of the model compensated for the lack of explicit contextual memory with a higher number of neurons that provided extended memory of the reward [40–42] . In the contextual memory version of the model , since phase information was explicitly encoded with the context neuron , neurons responding to the combination of phase and reward should be more numerous . Indeed , the percentage of neurons having a significantly higher activity for COR1 trials than for INC and COR trials was much higher with the context neuron feeding back phase information ( 13 . 2% ± 1 . 2% , p-value < 10−15 ) , approaching the proportions seen in the primate . While previous results illustrated the intrinsic capacity of reservoir network to encode complex combinations of previous and current inputs , here we demonstrate how information already present in the network can become explicitly encoded and amplified through learning . As a consequence , this strengthening of the internal representation allowed for combinations of internal and external inputs to be more widely represented in single unit activities , revealed by the increase in COR1 neurons , and for a significant performance increase , revealed by the reduced number of neurons required to solve the task . We also tested the effects of noise on the coding of mixed selectivity in the presence of the phase neuron . Table 3 shows the percentage of neurons with different main effects and interactions , in networks with and without the Phase neuron . Presence of the phase neuron yields an increase in the mixed selectivity ( interactions ) . Training the context neuron revealed that dynamic mixed selectivity can be used to generate stable meaningful output at the population level . The constant activation of the contextual phase neuron during search phase and its complete inactivation during the repeat phase indicates that the phase information is available in the reservoir population activity . Reinjecting the activity of this context neuron back into the reservoir creates a system that can operate in two distinct submodes of dynamics . Pascanu and Jaeger [17] develop a framework in which working memory is implemented by such mechanisms that are characterized as input driven attractors . Indeed , the principle of the learning algorithm used in this experiment ( FORCE learning ) is to converge on weights that shape attractors of the neural dynamics in order to produce stable and robust dynamics [20] . In order to reveal this specific dynamical property of the population activity we employed a PCA analysis to visualize the state trajectories of the reservoir neural population so as to assess whether search and repeat population activities were well separated . Full trial activities were averaged for each neuron of the reservoir population over each trial of a problem: INC1 and INC2 were the first and second unrewarded search trials ( INC3 trials were ignored due to lack of trials ) , COR1 the first rewarded trial , COR2-4 the repeat trials with the presentation of the signal to change at the end of COR4 trial ( see Methods for further details ) . Reservoir neuron population activity ( without the context neuron ) projected onto the first 3 dimensions shows the transient cyclic nature of all the trajectories , representing the common path of successive events in each trial ( 92% of explained variance over the first 3 dimensions , Fig 10A ) . Search and repeat trajectories were separated after feedback but then collapsed slowly and seemed to merge at the beginning of next trial . Since phase information was solely given by the reward in this original version of the model , trajectories were most separated after this event . While the trajectories tend to converge at the middle of the next trial , the phase information is sufficiently present to allow the model to successfully perform the task . We then performed the PCA on reservoir neurons in the explicit context version of the model . In this version of the model , phase information was explicitly encoded in the network dynamics as illustrated in the PCA results that show two well separated sets of cyclic trajectories representing the search and repeat phases ( 89% of explained variance over 3 first dimensions , Fig 10B ) . The signal to change at the end of a problem deviated the end of COR4 trajectory towards the start of search trials as the contextual phase neuron activation was triggered . Likewise , the first reward during COR1 trial acted as a signal for the transition from the search to the repeat trajectories as the context neuron shut off . Previous modeling and machine learning studies have shown how information representations could be maintained through attractors to develop a form working memory and participate in contextual processing of inputs [17 , 43 , 44] . These theories are supported by experimental results that also suggest rule representation and cognitive states through attractors [29 , 45 , 46] . Note that the dynamical regime of the reservoir population is more complicated than a single dimensional ( point attractor ) or limit cycle . As pointed out by Maass and colleagues [19] , we are in the case of high dimensional attractors , whereby a few dimensions were caught in a attractor while the rest of the neural population dynamic was free of the attractor and still contributed to the real-time computing properties that are more generally associated with reservoirs . In our experiment , the attracting dimensions were the ones separating both loops , while the attractor free dimensions still involved in real-time spatio-temporal processing are seen in the loop pattern representing the successive events of the task . Furthermore , the strict definition of an attractor does not apply here , since the reservoir is constantly receiving new inputs and cannot be considered a closed system . This type of attractor , dependent on the inputs , has been termed input-induced attractors by Pascanu and Jaeger [17] . Our analyses support attracting dynamics as a plausible mechanism to implement contextual memory in neural networks to represent contextual information [17 , 29] . We performed the same PCA analysis with dACC data to determine if dACC population activity displayed similar dynamics by comparing the qualitative features of the trajectories with those of the model . In a similar manner , each neuron’s activity was averaged over all the trials of the same type ( a subset of 184/546 selected dACC neurons was used , see Methods ) . Projection of the dACC population activity onto the first two dimensions of PCA ( 53% of variance explained over 2 first dimensions , Fig 10C ) revealed cyclic trajectories representing the common path for all trials , akin to the loop pattern observed in the model . However , these dimensions displayed slight trajectory differences between search and repeat trials , especially around feedback . This separation between search and repeat phase was much stronger with dimension 3 ( 43% of variance explained variance over dimension 1 and 3 , Fig 10D ) . dACC neural population activity differed most after feedback and until the end of the trial which is explained by the presence of a reward only in the repeat trials . Yet , trajectories were still separated between the start of a trial and the feedback . This suggests that the population dynamics actively maintained separate states underlying behavioral context through attractors . As with the model trajectories , COR1 and COR4 trials displayed clear transitions between search and repeat trajectories . These results support the claim that a reservoir with feedback connections is able to create a hybrid dynamical regime within a recurrent network , mixing attracting and transient dynamics , to extend the processing capabilities of a recurrent network beyond the exclusive attracting or transient regimes largely explored in the literature . dACC analyses strongly suggest the presence of an attractor to represent phase , while showing a globally dynamic activity thereby constituting a proof of concept of reservoir computing and its hybrid dynamical regime in a prefrontal area . If we hope to better understand computation in recurrent cortical networks in terms of computation in reservoirs , then we should first better understand what is happening in these reservoirs . The high dimensional projection of inputs and previous states in the reservoir provides a vast repertoire of states that can be selected for the task at hand by training the readouts . In order to understand how the neural dynamics implement system behavior Sussillo et al . [47] developed methods that revealed how trained recurrent neural networks solved tasks by creating attractors ( in this case fixed points ) that represent memory states , and saddle points that transition between the attractors as required for the task . In our case , we demonstrate that in an untrained reservoir the information related to the task phase ( search vs . repeat ) is reliably coded in the network population . When we then create a feedback neuron that is trained to explicitly represent this information , we in effect create an input dependent attractor that allows the network to solve the task with half the number of neurons . Thus , similar to the input driven working memory units in [17] , our phase neuron corresponds to a task dependent input-induced attractor that is created by the network in order to solve the problem . Thus , already within untrained networks , the high dimensional projection creates representations required for solving a class of problems . Adaptation can isolate such representations and make them explicit and thus optimizing the network for the task . The attractor properties of these representations can be illustrated by showing how its activity is stable to perturbations . Fig 11 displays the stability of the phase neuron in response to noise pulses of different intensities .
Rigotti et al . [2] recently postulated that randomly connected networks could account for a form of universal combinations of inputs to the system . One of the principal characteristics of these networks is the mixed selectivity to task-related factors that is observed in single neurons . We first observed such responses as a mix of target location and sequence rank responses in neurons of a reservoir model trained in a motor sequencing task [8] , as had been observed in primate PFC [4] . This same mixed selectivity in single cortical units is also crucial for the successful performance of these tasks by trained primates [7] . However , in many tasks involving executive function , the context provided by previous stimuli will be required for meeting current behavioral demands . Past inputs that influence future behavior create a context that forms a temporal bridge between past events and the current situation [48] . So , context should be internally represented and contribute to the mixed selectivity combinations and universal representation of contingencies that will include past events . Interestingly , random recurrent networks like reservoirs combine current inputs with current states resulting from previous inputs . Consequently , in addition to its inherent ability to generate mixed selectivity , the reservoir computing ( RC ) paradigm provide an hypothesis on how temporal information can be combined with mixed selectivity to generate a dynamic form of mixed selectivity . The present study demonstrates the presence of dynamic mixed selectivity in a complex cognitive task , both in a reservoir and in a prefrontal cortical area . Because of their complexity , dynamic mixed selectivity activities do not lend themselves to straightforward interpretations . However , in the RC paradigm , they are the signature of a high dimensional projection resulting from the combination of current inputs ( spatial information ) and previous inputs and their order ( temporal information ) , which is characteristic of what has been termed spatiotemporal processing in neural networks [37] . Recurrent networks are particularly suited to carry out spatiotemporal processing . Recognizing that the random connectivity in recurrent networks itself provides rich spatiotemporal representations , the RC paradigm eliminates the training of the recurrent connections and demonstrates that computational power is inherently present in this type of network . Given that our RC model reproduced qualitative features of the dynamic mixed selectivity observed in dACC , we support the hypothesis that local recurrent connectivity in the cortex may be at the origin of this representational feature . A corollary of this hypothesis is that mixed selectivity should be present before learning a new task [2 , 9] . Further experiments recording activity in prefrontal areas could demonstrate the presence of mixed selectivity prior learning , as shown in other parts of the cortex [13 , 15] . As in any neural network model , simulating a local cortical network in isolation of external perturbations elicited extremely stable activities in our reservoir compared to the recorded dACC data . Adding noise to the model during training and testing reduced the otherwise high mixed selectivity values . As predicted , performances decreased rapidly with reduced mixed selectivity , and the more complex selectivity ( i . e . the 2 and 3 way ANOVA interactions ) decreased more than simple selectivity ( i . e . ANOVA main effects ) , leaving the model with a proportion of more complex selectivity gradually lower than simpler ones . Interestingly , proportions of selectivity in the dACC data was similarly ordered in decreasing complexity . Because the reservoir networks have a fixed connectivity , their modeling power lies in the inherent capacity of random recurrent networks to produce rich combinations of inputs . Yet learning in monkeys most certainly influences local network connectivity , therefore , learning might have strengthened already present mixed selectivity to elicit robust representation in the face of noise . To illustrate how learning may have influenced representations , we explored the representation of the first reward in a problem ( COR1 reward ) , which indicated the crucial switch from exploration to exploitation behavior . dACC neurons displayed a strong and specific activity at the time of the first reward . Our model with fixed connections seem to present only slight differences in the activity of only a few COR1 neurons which may be attributed to chance . However , training the network to represent this information in a manner similar to the dACC COR1 activities demonstrates that this information , while not robustly present in the activity of single units , can robustly be extracted from the dynamic mixed selectivity of the population , and represented explicitly . Learning may be the mechanism by which inherently but weakly represented information may be extracted to be represented explicitly . Explicit representation has several non-negligible benefits . Locally , it increases the robustness of the represented information , adding an internally generated variable that might be further combined with other internal variables and inputs , thereby expanding the representational power of the local network . In addition , long range communication may benefit from explicitly represented information which relies on more condensed representations and could save bandwidth . COR1 activities are sharp and transient , acting putatively as a detector of a particular contingency . A putative local role of the COR1 activity might be to robustly trigger the transition from the search to repeat attractors while signaling impending change in behavioral demand to more distal areas . This type of universal coding that is then shaped by learning could be a key feature of primate adaptation . A developmental form of this universal coding and subsequent adaptation has been observed for the vowel space in the auditory system that is initially universal and then focused to the native language in early development [49] . Reservoir models also provide insights on the dynamics of the population . Since the recurrent network connectivity in RC is fixed and generated according to only a few parameters suited for the task at hand , it elicits generic dynamics that are of interest to understand the inherent dynamics that might be generated in a local cortical network . The autocorrelation results illustrate this point: dynamics in the model tend to closely follow the sequence of inputs fed to the network and appeared to reproduce the major features of the dynamics in the dACC , leading to two conclusions . First , the population activity in the model as in the dACC was transient ( dynamic ) over the course of full trials . Secondly , as mentioned above , the shape of the autocorrelation pattern tended to follow the course of events in the task , in both model and dACC . The dACC autocorrelation pattern could also be explained by an increase in activity changes resulting from event processing mechanism appearing thanks to learning the task , which would also coincide with task events , but would not be a result of local cortical network connectivity . However , our results demonstrate that these dynamic patterns are inherently present in recurrent networks , which do not support this alternative hypothesis as a unique explanation and , on the contrary , suggest dynamics inherent to the recurrent connectivity , that could possibly be modified with learning . Since the population dynamic as a whole was transient it is unclear how stable information could be robustly encoded throughout complete trials . However , a key principle of RC is computing without stable states , where high dimensional and transient activity is elicited by the recurrent network while a linear readout separate states of interest , in a manner similar to the state of the art SVM [50–52] . Indeed , the reservoir could continuously represent the two states of the task phase ( search vs . repeat ) via a linear readout even though the population activity is globally transient and no single neuron represented this variable . Similarly , phase was steadily decoded from dACC population activity and revealed a sharp transition between the two task phases . Phase information was crucial in this task to adjust behavior , and may explain the numerous neurons sensitive to phase in dACC . Indeed , adding a feedback connection to a readout neuron explicitly representing phase drastically reduced the number of reservoir neurons necessary to perform the task , emphasizing the importance of coding this variable in this task . Indeed , in the absence of explicit representation , the network relied on its fading memory ( which depends on the number of neurons ) of the reward input to access phase information . Explicit representation of task phase in a feedback loop created an attractor that maintained the state of the phase variable . Yet , no single dACC neuron was found to continuously discriminate behavioral phase with an ON or OFF state as was the case with the model , and improvements of our implementation of the phase representation may yield interesting insights into the nature and origin of long-lasting distributed transient contextual representations through attractors . Pascanu and Jaeger [17] addressed this issue , training working-memory ( WM ) neurons to enter on or off states depending on particular inputs . Activation of these WM neurons shifted the network into a new subspace of dynamics , effectively generating input driven attractor states . This is analogous to our situation with the phase neuron . The learned on- or off-state of the phase neuron puts the reservoir in two distinct respective states , corresponding to the behavior appropriate for the search vs . repeat phases , respectively . We can thus consider that our results provide a concrete neurophysiologically grounded example of the input-driven attractor defined by Pascanu and Jaeger . PCA visualization of the population activities illustrated how explicit representation of the phase variable separated trajectories of activities in distinct parts of the state space that were otherwise very similar . The same analysis on dACC data supports the hypothesis that the phase variable was maintained through attractors as well . Though the separation of the distinct phases was not as distinct , trajectories were separated at all times , as confirmed by the linear decoding of phase . Both model and dACC might be considered as dynamical systems switching between two attractors representing internal states . Attractors have been proposed as a candidate mechanism underlying robust information maintenance in working memory [53 , 54] , and also for decision making [55] . While autocorrelation emphasized a transient population dynamic , reservoir feedback mechanisms and PCA support the hypothesis of attracting states . Our results demonstrate how these seemingly incompatible dynamical regimes can actually coexist in neural dynamics . Indeed , Jaeger and Maass have both demonstrated that reservoir networks can create attracting dynamics while transient activities more familiar to this type of networks are still present [17 , 19] . The type of attractors described here has been termed input driven attractors by Jaeger because they are triggered by a combination of internal states and inputs . In our case , it corresponds to the current state of the phase and the presence/absence of the reward input . Maass has described these as high dimensional attractors since they span only a few dimensions of the state space , thereby leaving other dimensions free to carry on spatiotemporal processing [19] . This hybrid dynamical regime provides high computational power , exceeding the capacities of systems relying purely on attractor or transient dynamics . While transient dynamics are inherent to a fixed recurrent network , attracting dynamics adapted to the task at hand may only appear with learning for explicit maintenance of task relevant information . The comparison between reservoir and dACC activity can inform us about the nature of dACC function . The reservoir integrates sensory inputs including reward feedback , and determines appropriate responses based on the behavioral context . The context is determined based on the reward feedback . This is consistent with our understanding of dACC function , as an area that integrates outcomes to generate internal states that determine appropriate behavior . This is in contrast to related prefrontal areas such as DLPFC that receive input from dACC and are closer to the choice selection and behavioral response generation [23] . The use of such network studies to understand cortical function is becoming increasingly pertinent . In an effort to understand prefrontal cortical neurophysiology of working memory , Barak et al . [56] consider three models that define a functional transition between a form of pre-specified network , a recurrent network whose internal structure adapts to the structure of the stimuli , and a recurrent reservoir network . They observe that in accounting for the PFC data from a working memory task , it is the recurrent model whose internal structure adapts to the task that best accounts for the data . Rigotti et al . [57] similarly demonstrate how learning within a recurrent network allows for the creation of new attractors that can become the building blocks for representing task relevant context . Recently , Saez et al . [58] found evidence for the coding of context in a task where behaving primates frequently had to shift between two distinct behavioral contexts . Because of the arbitrary nature of the task , these context encoding neurons could only have emerged through the animals’ adaptation to the task . This is consistent with the proposal that intrinsic capabilities to code task relevant context , implemented in recurrent connections , can be captured and enhanced as needed through learning , as observed by Sussillo and Abbott [20] , and Pascanu and Jaeger [17] , and illustrated here with our phase context neuron . The results reported in this work are proposed as a proof of concept of the reservoir computing ( RC ) paradigm as a model of representational and dynamical properties of local generic prefrontal cortical networks in a cognitive task . Strong local recurrence is a striking property of the cortex , and constitutes the main architectural feature modeled by a reservoir . Here we demonstrated that an RC architecture can perform a complex cognitive task , and that certain characteristic electrophysiological features and dynamical patterns of activity of dACC neurons were replicated . We argue that the similarities in activity patterns between the dACC and RC model are the result of their common recurrent architecture , emphasizing its role in information representation schemes in the PFC . While mere random recurrent connectivity has been shown to provide universal spatio-temporal processing capabilities to RC networks [9] , their distributed mixed-selectivity representations may need to be strengthened and their inherent fading memory may restrict their processing capabilities in time . In this context , the role of task-learning might be to reinforce already present weak yet relevant signals , and to robustly extend the time-limited influence of previous state through time via input driven attractors [17 , 20] .
|
One of the most noteworthy properties of primate behavior is its diversity and adaptability . Human and non-human primates can learn an astonishing variety of novel behaviors that could not have been directly anticipated by evolution . How then can the nervous system be prewired to anticipate the ability to represent such an open class of behaviors ? Recent developments in a branch of recurrent neural networks , referred to as reservoir computing , begins to shed light on this question . The novelty of reservoir computing is that the recurrent connections in the network are fixed , and only the connections from these neurons to the output neurons change with learning . The fixed recurrent connections provide the network with an inherent high dimensional dynamics that creates essentially all possible spatial and temporal combinations of the inputs which can then be selected , by learning , to perform the desired task . This high dimensional mixture of activity inherent to reservoirs has begun to be found in the primate cortex . Here we make direct comparisons between dynamic coding in the cortex and in reservoirs performing the same task , and contribute to the emerging evidence that cortex has significant reservoir properties .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"learning",
"medicine",
"and",
"health",
"sciences",
"decision",
"making",
"neural",
"networks",
"prefrontal",
"cortex",
"brain",
"social",
"sciences",
"vertebrates",
"neuroscience",
"learning",
"and",
"memory",
"animals",
"mammals",
"primates",
"cognitive",
"psychology",
"animal",
"behavior",
"cognition",
"memory",
"zoology",
"computer",
"and",
"information",
"sciences",
"monkeys",
"animal",
"cells",
"behavior",
"cellular",
"neuroscience",
"psychology",
"cell",
"biology",
"anatomy",
"neurons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"cognitive",
"science",
"amniotes",
"organisms"
] |
2016
|
Reservoir Computing Properties of Neural Dynamics in Prefrontal Cortex
|
Toxoplasmosis is a cosmopolitan infection caused by an intracellular obligatory protozoan , Toxoplasma gondii . Infection to this parasite in immunocompetent patients is usually asymptomatic , but today it is believed that the infection can be a risk factor for a variety of diseases , including rheumatoid arthritis ( RA ) . RA is an autoimmune disease and the most common type of inflammatory arthritis that is a major cause of disability . The aim of this systematic review and meta-analysis was to address the association between RA and toxoplasmosis in light of the available research . Based on the keywords , a systematic search of eight databases was conducted to retrieve the relevant English-language articles . Then , the studies were screened based on the inclusion and exclusion criteria . The random effect model was used to calculate the odds ratio ( OR ) using forest plot with 95% confidence interval ( CI ) . Overall , 4168 Individual , extracted from 9 articles were included for systematic review evaluation , with 1369 RA patients ( 46% positive toxoplasmosis ) and 2799 individuals as controls ( 21% positive toxoplasmosis ) . Then , eight articles ( 10 datasets ) were used for meta-analysis ( 1244 rheumatoid arthritis patients and 2799 controls ) . By random effect model , the combined OR was 3 . 30 ( 95% CI: 2 . 05 to 5 . 30 ) with P < 0 . 0001 . Although toxoplasmosis could be considered as a potential risk factor for rheumatoid arthritis , more and better quality studies are needed to determine the effect of T . gondii infection on induction or exacerbation of RA . Our study was registered at the International Prospective Register of Systematic Reviews ( PROSPERO; code: CRD42017069384 ) .
Toxoplasmosis is a parasitic disease with worldwide distribution caused by obligate intracellular coccidian protozoan Toxoplasma gondii ( T . gondii ) [1] . It is estimated that one-third of the world’s population are infected with this parasite in both developed and developing countries [2 , 3] . Humans can be infected with the parasite through different routes , including consumption of raw or undercooked meat containing tissue cysts of the parasite , ingestion of sporulated oocysts from contaminated water and food , and vertical transmission during pregnancy through the placenta to the fetus [4] . T . gondii remains in the infected host tissues perpetually [5] . Most immunocompetent individuals , if infected with this parasite , are asymptomatic or show minor symptoms [6] . The most common symptom of toxoplasmosis in humans is lymphadenopathy that may be associated with fever , sore throat , muscle pain , fatigue , and headache [4] . In congenitally infected and immunocompromised patients , this disease is more likely to bring about severe complications [7] . Myocarditis and polymyositis have been reported in immunocompetent individuals with acute toxoplasmosis [8] . Furthermore , toxoplasmosis may cause polyarthritis in the hand and knee joints [9] . Polytenosynovitis ( inflammation of a tendon sheath ) caused by T . gondii has also been reported [10] . Rheumatoid arthritis ( RA ) is a common autoimmune disease , which is a major cause of inflammation of the joints and the principal cause of disability that affects 0 . 5–1% of the population [11 , 12] . The disease presents with swollen joints , production of autoantibodies ( rheumatoid factor ) , and systemic effects [13] . In recent years , the role of infectious agents , especially bacteria and viruses , has been identified in the pathogenesis of autoimmune diseases , while the role of parasitic infections due to their vague effects on host immunity has not been well-investigated . Experimental evidence may support the protective effect of specific parasitic infections in the susceptibility to autoimmunity [14] . Some geoepidemiological studies showed that host genetic susceptibility interacts with lifestyle and environmental factors , such as socioeconomic status , dietary habits , environmental pollutants , and ultraviolet radiation exposure; further , infections increase the risk of developing autoimmunity [15] . On the other hand , infectious diseases may contribute to the development of autoimmune diseases through molecular mimicry and epitope spreading [16] . Therefore , the aim of this systematic review and meta-analysis was to provide an updated review of data about the relationship between toxoplasmosis and RA .
This study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis ( PRISMA ) and its checklist [17] . Individuals with RA , along with a control group , were surveyed . To begin , we searched scientific databases for all the articles on the association between toxoplasmosis and RA published up to the first of January 2018 . These keywords were used alone or in combination: “Toxoplasma gondii” , “toxoplasmosis” , “seroprevalence” , “prevalence” , “rheumatoid arthritis” , “rheumatoid factor” , “meta-analysis” , and “systematic review” . A literature review was carried out using English databases including “PubMed” , “Google Scholar” , “Science Direct” , “Scopus” , “Web of Science” , “EMBASE” , “CINAHL” , and “ProQuest” . The systematic search of articles was conducted from March 4 to December 31 , 2017 by two researchers independently . Also , for completing the checklist , we investigated all the references lists of the selected articles manually . In this study , only English-language articles were analyzed; furthermore , unpublished studies were not evaluated . After completing the search , the selected articles were reviewed by the two researchers independently . All the duplicate and irrelevant studies were excluded after reviewing the title , abstract , and full text of the articles . Moreover , to prevent reprint bias , the results of the articles were carefully investigated and duplicates were omitted . In order to assess the quality of reporting of the studies , standard Strengthening the Reporting of Observational Studies in Epidemiology checklist ( STROBE ) was used [18] . S1 Checklist represents the quality score of different eligible studies . This checklist included items assessing the study methodology , study type , study population , sample size , sample collection methods , statistical tests , and presentations . In our study , articles were evaluated based on STROBE assessment ( low quality: less than 16 . 5 , moderate quality: 16 . 6–25 . 5 , and high quality: 25 . 6–34 ) . The articles we entered in our meta-analysis had acceptable quality . Abstracts and full texts were assessed independently by the two researchers using a piloted form . The final decisions about the eligibility or exclusion of studies were made separately . Disagreements were resolved with provision for arbitration from a third reviewer . Following the removal of duplicate entries , articles were evaluated according to the following criteria: ( 1 ) cohort or case-control studies about the relationship between toxoplasmosis as an exposure and rheumatoid arthritis as a disease , ( 2 ) the studies conducted only on humans , ( 3 ) the presence of case and control groups , ( 4 ) the studies where toxoplasmosis was diagnosed by detecting IgG and/or IgM antibodies against T . gondii in individuals with definitive diagnosis of RA , and ( 5 ) the studies providing details on the seroprevalence rate of toxoplasmosis and RA . The exclusion criteria comprised: ( 1 ) studies that were only descriptive , ( 2 ) studies that only presented the final result and did not provide the raw data , ( 3 ) articles that were not available in English language , and ( 4 ) the studies conducted on animals . Articles were carefully studied and the following data were extracted: first author , year of publication , the number of patients and controls , the number and percentage of the positive and negative cases of serum IgG and IgM in patients and controls , as well as information about age and gender and laboratory results . In studies where two different populations were studied , data were extracted separately . The meta-analysis was executed with the Stats Direct statistical software ( http://statsdirect . com ) . For displaying the heterogeneity between studies , χ2-based Cochrane test ( Q ) and I2 index were applied [19] . Due to significant heterogeneity between the studies , a random effect model was used to combine the results of the studies . Forest plot was used to indicate the prevalence of toxoplasmosis in each study and to determine pooled estimate prevalence in the studies . Odds ratios ( ORs ) and 95% confidence intervals ( CI ) were used for estimating the risk of T . gondii infection ( the significance of P<0 . 05 ) . OR > 1 indicates the positive effect of Toxoplasma on RA and an OR < 1 shows that toxoplasmosis has a protective effect against RA . Publication bias was examined by funnel plots and the statistical significance was assessed by the Egger test [20] . Also , it was performed a sensitivity analysis to identify probably effect of each article on the overall results by excluding them using Stata version 14 ( Stata Corp , College Station , TX , USA ) . The study protocol ( CRD42017069384 ) was registered on the website of the International Prospective Register of Systematic Reviews ( PROSPERO ) [21] .
Our preliminary search of eight databases yielded 8234 papers . After a primary screening of the titles of the articles based on keywords , 124 studies were extracted . Sixty-five articles were also excluded from the study due to duplication . In the next step , by screening the abstracts of the articles and based on the inclusion/exclusion criteria , 43 other articles were excluded . After reading the full text of the articles , 10 other papers were omitted , and three studies were added to the collection after reviewing the references . After the final review of the articles , nine eligible studies [14 , 16 , 22–28] were identified for systematic review . Another study was excluded due to the absence of a healthy control group [16] . Finally , eight of these nine articles [14 , 22–28] were entered into the meta-analysis with respect to the inclusion/exclusion criteria ( Fig 1 ) . The studied articles were published between 2007 and 2017 . We identified 11 datasets from the nine articles that met the inclusion criteria , eight of which were case-control , two cross-sectional , and one were cohort studies ( Table 1 ) . The surveys were conducted in Latin America [14] , Europe [14 , 16] , Egypt [24 , 25] , Iraq [22 , 23 , 27] , Czech and Slovak [26] , and China [28] . Our meta-analysis was performed among 4168 people including 1369 RA patients and 2799 controls . In all the studies , blood samples were collected from patients and controls . To identify anti-Toxoplasma antibodies ( IgG and IgM ) in those studies , ELISA [22–24 , 26–28] , CFT [26] , EIA [25] , chemiluminescence [16] , and BioPlex 2200 system [14] were used ( Table 1 ) . Except for Fischer et al . [16] , who only evaluated IgG , other authors surveyed both IgG and IgM antibodies . However , only three studies had reported a titer of antibodies [23–25] , and others had described the percentage of positive antibodies in patients and controls . Finally , all the studies analyzed the relationship between toxoplasmosis and RA with respect to the percentage of seropositive and seronegative individuals ( patients and controls ) . As shown in Fig 2 , the prevalence of toxoplasmosis in RA patients in these studies varied from 25% to 77% with an overall seroprevalence of 46% ( 95% CI [37; 56] ) . However , the total prevalence of this disease in the control subjects entered in these studies was 21% ( 95% CI [14; 28] ) , which varied from 0% to 48% in various studies ( Fig 3 ) . According to Fig 4 , the odds of toxoplasmosis in RA patients are 3 . 30 times compared to that of controls with 95% CI: 2 . 05 to 5 . 30 and P < 0 . 0001 . Nonetheless , the heterogeneity analysis of the effect size of arthritis ( Q = 32 . 77 , P = 0 . 0001 , I2 = 72 . 5% ) showed a relatively high heterogeneity in our meta-analysis . Begg and Egger tests were used to evaluate publication bias . Negligible publication bias was observed using both Begg test ( P = 0 . 0286 ) and Egger test ( P = 0 . 0446 ) in the included studies . The results of sensitivity analysis showed that the impact of each study on meta-analysis was not significant on overall estimates ( Fig 5 ) .
One of the most important achievements of our study is that although T . gondii infection affects about one-third of the world’s population and possibly causes and exacerbates the symptoms of RA , only few studies have addressed this subject . These studies were conducted only in Latin America , Europe , and few regions of Asia and Africa . Accordingly , further studies are needed to achieve accurate results from other parts of the world . Also , further studies will be necessary to clarify the pathogenesis of T . gondii in humans to understand whether T . gondii is a cofactor in the development of autoimmune diseases .
|
Toxoplasma gondii is an intracellular obligatory protozoan , which causes toxoplasmosis . T . gondii infection in immunocompetent individuals is mostly asymptomatic , but it may be reactivated as a result of immune disorders inducing serious complications . Rheumatoid arthritis ( RA ) , as a complex autoimmune disease , is a major cause of significant and progressive disability , articular complications , and premature death . Studies confirmed an interaction between infections and environmental factors as the potential risk or protective factors determining the development of autoimmune diseases . In this study , we investigated the association between toxoplasmosis and RA .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"rheumatology",
"medicine",
"and",
"health",
"sciences",
"enzyme-linked",
"immunoassays",
"toxoplasma",
"gondii",
"immunology",
"parasitic",
"diseases",
"parasitic",
"protozoans",
"rheumatoid",
"arthritis",
"clinical",
"medicine",
"protozoans",
"mathematics",
"toxoplasma",
"statistics",
"(mathematics)",
"immunologic",
"techniques",
"research",
"and",
"analysis",
"methods",
"mathematical",
"and",
"statistical",
"techniques",
"arthritis",
"immunoassays",
"protozoan",
"infections",
"research",
"assessment",
"toxoplasmosis",
"eukaryota",
"clinical",
"immunology",
"meta-analysis",
"systematic",
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"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"statistical",
"methods",
"organisms"
] |
2018
|
Toxoplasmosis seroprevalence in rheumatoid arthritis patients: A systematic review and meta-analysis
|
Protein evolution is most commonly studied by analyzing related protein sequences and generating ancestral sequences through Bayesian and Maximum Likelihood methods , and/or by resurrecting ancestral proteins in the lab and performing ligand binding studies to determine function . Structural and dynamic evolution have largely been left out of molecular evolution studies . Here we incorporate both structure and dynamics to elucidate the molecular principles behind the divergence in the evolutionary path of the steroid receptor proteins . We determine the likely structure of three evolutionarily diverged ancestral steroid receptor proteins using the Zipping and Assembly Method with FRODA ( ZAMF ) . Our predictions are within ∼2 . 7 Å all-atom RMSD of the respective crystal structures of the ancestral steroid receptors . Beyond static structure prediction , a particular feature of ZAMF is that it generates protein dynamics information . We investigate the differences in conformational dynamics of diverged proteins by obtaining the most collective motion through essential dynamics . Strikingly , our analysis shows that evolutionarily diverged proteins of the same family do not share the same dynamic subspace , while those sharing the same function are simultaneously clustered together and distant from those , that have functionally diverged . Dynamic analysis also enables those mutations that most affect dynamics to be identified . It correctly predicts all mutations ( functional and permissive ) necessary to evolve new function and ∼60% of permissive mutations necessary to recover ancestral function .
Proteins are effective and efficient machines that carry out a wide range of essential biochemical functions in the cell . Beyond being robust and efficient , the outstanding property of proteins is that they can evolve and they show a remarkable capacity to acquire new functions and structures . In fact , modern proteins have emerged from only a few common ancestors over millions to billions of years [1]–[3] . Moreover , the emergence of drug resistance and enzymes with the capacity to degrade new chemicals indicates the ongoing contemporary evolution of proteins [1]–[7] . Therefore , understanding the mechanism by which mutations lead to functional diversity is critical in many aspects from protein engineering to drug design and personalized medicine . Indeed , computational protein design through analysis of mutations has attained major breakthroughs , with profound biotechnological and biomedical implications: design of a new fold [8] , design of new biocatalysts and biosensors [9]–[11] , design of binding affinity [12] , [13] , and design of proteins to bind non-biological cofactors [14] . Moreover , there are computational bioinformatics-based tools based on evolutionary information aspects to identify mutations leading to functional loss or disease [15]–[17] . From a phylogenetics perspective , horizontal and vertical approaches have been used to analyze the set of mutations that lead to changes in protein function throughout evolution [18] . The horizontal approach compares modern day proteins at the tips of the evolutionary tree . It identifies the amino acid residue differences within the functionally divergent members of a protein family based on primary sequence and structural analyses and then characterizes the functional role of these residues by swapping them between these family members through site-directed mutagenesis in the laboratory to check for loss of function [19]–[21] . Although the horizontal method gives insight into mutations critical to function , it often fails to identify permissive mutations necessary to switch function between family members . Protein function has evolved as mutations throughout history , i . e . “vertically” , in the ancestral protein lineages . Therefore , it is important to incorporate the historical background which contains both neutral and key function-switching mutations when examining function-altering mutations [18] . The vertical approach determines the likely ancestral sequences at nodes along the evolutionary tree and compares modern day proteins to their ancestors . Recent advances in molecular phylogenetic methods make it possible to obtain ancestral sequences by protein sequence alignments in a phylogenetic framework using Bayesian and Maximum Likelihood methods [22] , [23] . DNA molecules are synthesized coding for the most probable ancestral sequences and the protein expressed , allowing for experimental characterization of the ancient protein . The vertical approach has been used to gain insight into the underlying principles of protein function and evolution in several proteins including opsins [24] , [25] , GFP-like protein [26] , [27] , and others [28]–[32] . More recently , a vertical analysis of two ancestral nuclear receptors has been coupled with X-ray structure determination in successfully elucidating the switching of function between divergent members [33] , [34] . Such studies highlight the importance of including ancient protein structures into evolutionary studies . Although coarse-grained and all-atom models have furthered our understanding of sequence/structure relationship in evolution , further study of the inherent structural dynamics is crucial to give a more complete understanding of protein evolution [35] . A small local structural change due to a single mutation can lead to a large difference in conformational dynamics , even at quite distant residues due to structural allostery [36]–[38] . Thus the one sequence-one structure-one function paradigm is being extended to a new view: an ensemble of different conformations in equilibrium that can evolve new function [1] , [39]–[41] . The importance of structural dynamics has been demonstrated by a recent experimental study which shows that mutations distant from a binding site can increase enzyme efficiency by changing the conformational dynamics [42] . The modulation of rigidity/flexibility of residues both near and distant from the active region ( s ) as related to promiscuous and specific binding has also been noted in tRNA synthetase complexes [43] , [44] . Here we have developed a method to predict structural and dynamic evolution of ancestral sequences by using a modified version of our protein structure prediction tool , Zipping and Assembly Method with FRODA ( ZAMF ) [45] . ZAMF combines two crucial features of ZAM [46] , and FRODA [47] , [48] : i ) FRODA is a constraint-based geometric simulation technique that speeds up the search for native like topologies by accounting only for geometric relationships between atoms instead of detailed energetics , ii ) Molecular dynamics identifies the low free energy structures and further refines these structures toward the actual native conformation . Thus , it is a two-step multi-scale computational method that performs fast and extensive conformational sampling . As an outcome , we not only predict protein structures but also obtain detailed conformational dynamics of the predicted structures . With modified ZAMF , we analyze the role of structural dynamics in the evolution of three ancestral steroid receptors ( AncCR , AncGR1 and AncGR2 ) , the ancestors of mineralocorticoid and glucocorticoid receptors ( MR and GR ) . MR and GR arose by duplication of a single ancestor ( AncCR ) deep in the vertebrate lineage and then diverged function . MR is activated by aldosterone to control electrolyte homeostasis , kidney and colon function and other processes [33] . It is also activated by cortisol , albeit to a lesser extent [18] . On the other hand , GR regulates the stress response and is activated only by cortisol [33] . The structural comparison of human MR and GR ( i . e . horizontal approach ) suggested the two mutations ( S106P and L111Q ) to be critical in ligand specificity , however , swapping these residues between human MR and human GR yielded receptors with no binding activity [49] . Conversely , by resurrecting key ancestral proteins ( AncCR , AncGR1 and AncGR2 ) in MR and GR evolution and determining the crystal structures , Thornton et al . were able to shed insight into how function diverges through time by using both functional and permissive ( compensatory ) mutations [33] , [34] . AncCR ( main ancestor ) , ∼470 million years old , is a promiscuous steroid receptor which is activated by aldosterone , cortisol , and deoxycortisol ligands . AncCR branched into the mineralocorticoid steroid receptors . AncGR1 ( ancestor of sharks ) is ∼440 million years old with 25 mutations from AncCR and also promiscuously binds to and functions with aldosterone , cortisol , and deoxycortisol . AncGR1 later evolved into the Elasmobranch glucocorticoid receptor protein . AncGR2 ( ancestor of humans and fish ) is ∼420 million years old with 36 mutations from AncGR1 and preferentially binds to cortisol alone . These two ancestral proteins , AncGR1 and AncGR2 , which diverge functionally , have highly similar experimental structures that have <1 Å RMSD between them . Among 36 mutations between AncGR1 and AncGR2 , two conserved mutations {S106P , L111Q} ( i . e . group X ) when introduced together are sufficient to increase cortisol specificity . However three more functionally critical conserved mutations {L29M , F98I , S212Δ} ( i . e . group Y ) are needed for the loss of aldosterone binding activity when they are introduced together with two other permissive ( i . e . compensatory ) mutations {N26T and Q105L} ( i . e . group Z ) . Thus , making the X , Y , Z mutations in AncGR1 enables AncGR1 to function as AncGR2 ( i . e . forward evolution ) [34] . To make AncGR2 function as AncGR1 ( backward evolution ) the X , Y , Z mutations are insufficient and render the protein inactive . A fourth set of permissive mutations ( W ) is required to reverse function in addition to the X , Y , and Z , sets . The W mutation set is {H84Q , Y91C , A107Y , G114Q , L197M} [33] . A mutation between AncCR and AncGR1 , Y27R , is also a necessary mutation to eventually alter function to cortisol specificity , though it was not experimentally considered as part of the X , Y , Z , or W mutation sets [34] . We ask here whether an analysis of the predicted 3-D structures and corresponding equilibrated dynamics can distinguish the functional divergence and function swapping mutations between AncCR , AncGR1 , and AncGR2 . By applying ZAMF , we obtain the 3-D structures within ∼2 . 7 Å all-atom RMSD of the experimental structures . More importantly , when we analyze their structure-encoded dynamics , we observe that changes in the dynamics indicate functional divergence: that the most collective fluctuation profiles of AncCR and AncGR1 ( i . e . the slowest mode ) are much closer and distinctively separated from the functionally divergent AncGR2 . Moreover , AncCR and AncGR1 have a more flexible binding pocket , suggesting the role of flexibility in their promiscuous binding specificity . On the other hand , the mutations of AncGR2 lead to a rigid binding pocket , which suggests that as the binding becomes cortisol specific , evolution acts to shape the binding pocket toward a specific ligand . Finally , using their mean square fluctuation profiles and cross correlation maps to analyze the change in dynamics at each residue position enables us to distinguish critical mutations needed for swapping the function . Overall , all these findings suggest that conformational epistasis may play an important role where new functions evolve through novel molecular interactions and an analysis of detailed dynamics might provide insight into the mechanisms behind these novel interactions .
Many of the modern day homologs to ancestral proteins in the steroid receptor class of the nuclear receptor superfamily have high sequence similarity ( ∼40–50% ) , and , as prediction accuracy scales with sequence similarity [50]–[52] our secondary structures for the ancestral sequences are sufficiently accurate to provide native-like structures [45] . Indeed , predicted secondary structures are all correct within one residue to the experimentally determined ancestral cortisol receptor protein [34] . Using these secondary structures as input to the assembly and refinement stages of ZAMF , we determine the 3D structure of the AncCR from its experimentally determined structure to 2 . 5 Å all atom RMSD ( 2 . 2 Å backbone ) , AncGR1 from its experimentally determined structure to 2 . 9 Å all atom RMSD ( 2 . 6 Å backbone ) AncGR2 from its experimentally determined structure to 2 . 9 Å all atom RMSD ( 2 . 4 Å backbone ) ( Fig . 1 and Table S1 ) . To test the accuracy of these predictions , we first compare the structural differences between the experimental structures . The experimental structures are very similar , with an RMSD of 1 . 49 Å between AncCR and AncGR1 , 1 . 68 Å between AncCR and AndGR2 , and 1 . 70 Å between AncGR1 and AncGR2 . However alignment excludes the atoms of the mutational residues . We also ran a 4 ns REMD simulation of the experimentally determined AncCR and AncGR2 under the same conditions . The ensembles for AncCR and AncGR2 converges at ∼2 . 5 Å backbone RMSD from their respective experimentally determined structures ( Fig . S1 ) . The 2 . 5 Å RMSD indicates that our predicted structures are as accurate as our force field permits . Closer analysis reveals that helix h9 in the predicted structure of AncGR2 is slightly less stable than in the experimental structure REMD simulations . However , both simulations show a high degree of flexibility in the loop region between helices h9 and h10 and ends of helices h9 and h10 at this loop region . As these three proteins diverged in function and have >10% sequence mutation between each successive protein , we expect to see some differences in structure . Therefore , we first look at a mean square displacement ( MSD ) between the static structures of AncCR , AncGR1 and AncGR2 . The MSD versus residue profile gives an indication of which residues are mutating , as mutated residues pack into stereochemically unique conformations ( Fig . S2 ) . Fig . S2 reveals conformational shifts in helices h7 and h10 and in the β-sheet region , b1 . We attempt to determine which of the 36 mutated residues between AncGR1 and AncGR2 are critical for cortisol binding specificity through distinguishing residues having an MSD cutoff of >6 Å2 between the AncGR1 and AncGR2 predicted structures . The residues identified from X , Y , Z and W sets are Y91C , Q105L , and S212Δ , with no false positives . The S212Δ and Q105L mutations are permissive mutations to shift function to cortisol specificity whereas Y91C is a permissive mutation necessary for “reverse evolution” i . e . to return binding promiscuity to AncGR2 . Experimental work indicates that S212Δ removes a hydrogen bond and imparts greater mobility to the loop before the activation function ( AF ) helix , allowing it to hydrogen bond with helix h3 , while Q105L indirectly restores a hydrogen bond with the activation helix by allowing for tighter packing of helices h3 and h7 [34] . An analysis of hydrogen bonding patterns [53] shows the loss of the S212 hydrogen bond with V217 ( in the loop before the AF helix ) in the AncGR2 structure as compared to the AncCR/AncGR1 structures , agreeing with experimental results . Y91C is one of the W mutations required for reverse evolution of AncGR1 from AncGR2 and we find it forms a hydrogen bond with N86 in AncGR2 but does not in AncCR or AncGR1 . Interestingly , none of these mutations occur in the binding pocket itself . Therefore , an MSD analysis is not sensitive enough to find functionally critical mutations in the binding pocket , and only finds a few of the necessary mutations to diverge function . We investigate the role of structural dynamics in functional divergence observed among the three ancestral steroid proteins . The extensive conformational sampling of our method enables us to capture the dynamics along with the most native-like structure ( Fig . S4 ) . We obtain the most collective modes of these three ancestral structures ( i . e . slowest fluctuation profiles ) through principal component analysis of our restraint-free trajectories ( See Method ) . We then form an Mx3N matrix where the M columns are the eigenvectors weighted by their eigenvalues , with each M column being a 3 column super-element composed from the slowest modes of AncCR , AncGR1 and AncGR2 and N being the number of C-α atoms . We chose to analyze the top 10 slowest modes and therefore there are 30 columns . By performing a singular value decomposition on this matrix , we measure how the most collective motions of these three ancestral proteins are distributed in dynamic space . Interestingly , as shown in Fig . 2A , AncCR and AncGR1 are much closer and distinctively separated in dynamic space from the functionally divergent ancestor of the human glucocorticoid receptor , AncGR2 . Clustering in dynamics space is significant because it shows that these structurally similar but functionally unique proteins differ in functionally governing dynamics , as observed in previous studies [42] , [54]–[56] . Moreover , previous studies indicate that functionally critical mutations alter modes that characterize biologically functional motion , while random sequence variations typically have non-statistically significant impact on those modes [57] . These findings indeed suggest that the governing functional dynamics is encoded within the structure and that only critical mutations lead to a shift in collective motion and therefore in binding selectivity as well [55] , [58] . Fig . 2B presents the color coded ribbon diagrams of these three ancestral proteins with respect to their functionally related collective fluctuation ( obtained by PCA ) profiles within a spectrum of red to blue , where rigid regions are denoted by blue/green and flexible regions are denoted with red/orange . Experimentally determined function altering mutations are highlighted in the sphere representation . Strikingly , residues in and near the functional site ( i . e . binding site ) are much more flexible for the two promiscuous enzymes ( AncGR1 and AncCR ) whereas the human ancestor AncGR2 , which has affinity only to cortisol , has very rigid functional site residues . The new view of proteins states that , rather than a single structure with induced binding , proteins interconvert between bound and unbound conformations in the native ensemble . Thus , promiscuous binding proteins utilize greater flexibility to interconvert between a greater number of conformations in the native ensemble as compared to specific binding proteins . Therefore , our dynamic analysis agrees with the new view that while the promiscuous ancestors are more flexible around the functional site , the functional site rigidifies as Nature biases towards binding only a single ligand with greater affinity [1] . Upon confirmation that dynamics can indeed distinguish functional divergence , the next question is whether dynamics can indicate which residues in the protein are critical to diverging function . We investigate whether we can distinguish the mutations , including function altering and permissive ( i . e . compensatory ) , that cause AncCR/GR1 to shift function to specifically bind cortisol as AncGR2 does , and also those that reverse the function of AncGR2 to promiscuously bind in the same way as AncCR/AncGR1 . To identify the critical residues for swapping function , we analyze how the fluctuation profile changes over these three successive ancestral proteins . Thus , using their most collective fluctuation profile ( i . e the slowest mode obtained by PCA ) , we compute the net change in fluctuation from AncCR to AncGR1 and AncGR1 to AncGR2 and show them in a 2-D plot to distinguish the mutations that have a higher impact on the change in dynamics between AncGR2 and AncGR1 compared to those mutations affecting the change in dynamics between AncGR1 and AncCR ( Fig . 3 ) . The upper left region of the graph in Fig . 3 indicates mutations that most alter dynamics when comparing the function-altering mutation from AncGR1 ( binding promiscuity ) to AncGR2 ( binding specificity to cortisol ) whereas the lower right region of the plot indicates mutations that most alter dynamics when comparing AncCR and AncGR1 , which do not diverge functionally . The central region of the graph ( between the parallel cutoff lines ) contains those mutations that do not alter the dynamics in a significantly different manner between successive homologs . Interestingly , most of the function altering mutation sites such as 106 , 212 ( shown as 211 and 213 due to deletion ) and most of the W mutations ( mutations necessary for backward evolution , e . g . altering AncGR2 to become promiscuous ) are in the upper left region . Permissive mutations 27 , 29 , 105 , and the mutations in the activation function helix are in the lower right region of the plot . 111 , a critical mutation for changing the specificity to cortisol only , is also in the lower right region . However , experimental analysis showed that the 111 mutation alone does not alter function in any appreciable manner . Thus , we propose it is only after permissive mutations alter the dynamics at site 111 can the necessary critical mutation at site 111 have a function altering effect . Additionally , certain mutations such as 214 and 173 both show large dynamic transitions . Mutation 214 is associated with the loop region that contains the critical mutation S212Δ , and it is in at the edge of a loop region . It undergoes transitions between being at the end of the h10 helix to being in the loop . The change in dynamics can be associated with the S212Δ mutation to identify the loop as a critical region . The 173 mutation is in a region that was not able to be crystallized in the experimental AncCR structure . Though the REMD simulations were determined to have converged , there is a possibility of some influence near site 173 due to the loop having to be built into the structure prior to REMD simulation . However , we expect that the shift in dynamics at mutation 173 may be correlated with movement of helix h10 , and is therefore potentially significant . We also obtain the net absolute change in the successive Δr2 fluctuation profiles along the slowest mode using the formulation ∥ΔfluctuationAncCR-AncGR1|–|ΔfluctuationAncGR1-AncGR2∥ for mutated residues based the alignment of AncCR and AncGR2 ( Fig . 4A ) and predict those residues with a net |ΔΔfluctuation|>0 . 002 Å2 to be critical . The forward mutations required to shift function to cortisol specificity are N26T , L29M , F98I , Q105L , S106P , L111Q , and S212Δ , and all of these are captured as critical as they are above the cutoff . The reverse mutations required to shift function from cortisol specific to promiscuous binding are H84Q , Y91C , A107Y , G114Q , and L197M . With the chosen cutoff , the identified permissive mutations are H84Q , A107Y , and G114Q , with Y91C only slightly below the cutoff . Interestingly , A107Y is the only W mutation that by itself partially recovered the promiscuous binding function [33] and it shows a high |ΔΔfluctuation| in our plot . We also find eight other mutated residues above the cutoff . Three of those are false positives I65L , Q117K and M158I . Each of these mutations occurred between AncCR and AncGR1 , prior to a shift in function . Among mutations identified is Y27R , which is not explicitly in the X , Y , or Z set , yet it is highly conserved in the GR family and is an experimentally determined permissive mutation critical for GR function [34] . The three mutations at the activation function helix are also identified as critical . The other mutation above the cutoff is 211 , which is correlated with S212Δ . Overall , our dynamic method identifies all mutations that are necessary for the evolution of GR function . We also distinguish three of the five mutations necessary for reversal of evolution ( e . g . permissive mutations to AncGR2 which are necessary to recover the promiscuous binding of AncCR/AncGR1 ) . Interestingly , many of the identified critical mutations such as N26T , H84Q , Y91C , F98I , Q105L , and S212Δ , are not interacting with the ligand , but rather are distant from the binding pocket ( i . e . >5 Å from any atom in the ligand ) . Additionally , the high |ΔΔfluctuation| at the C-terminus is associated with the activation-function ( AF ) helix , which does not contain critical mutations but its dynamics is critical to function . We also investigate the pairwise cross correlations of AncGR1 and AncGR2 ( Fig . 4B ) . Interestingly , comparing the cross correlations reveals differences along the regions containing critical mutations . The cross-correlations between helix h5 ( containing the critical mutation H84Q ) and helix h7 ( containing the critical mutations: Q105L , S106P , A107Y , L111Q , G114Q ) become highly positively correlated in AncGR2 whereas there is no correlation in AncGR1 . Analysis of hydrogen bonds [53] in predicted structures showed that additional hydrogen bonds are found between the β-sheet b1 and helices h5 and h7 , indicating the observed increased correlation in AncGR2 is likely due to the repacking of helices h5 and h7 after mutation which incorporates/creates these new hydrogen bonds . Moreover , we also observe increased positive correlations between the AF-helix and helices h3 and h10 in AncGR2 . These regions contain multiple permissive mutations ( N26T , L29M , L197M , S212Δ ) and thus , the change in correlations relate to the change in the stability of the AF helix caused by these permissive mutations necessary to alter function [34] . Furthermore , in Fig . 4C we compare the cross correlations of the most critical mutation for swapping the function to GR ( X mutations ) and the permissive mutations necessary to reverse the function to MR ( W mutations ) between AncGR1 and AncGR2 . In AncGR2 these mutations are significantly more correlated than in AncGR1 . This indeed suggests that W mutations play a critical role for GR function from the dynamics-perspective and therefore , they also need to be reversed along with the X , Y , Z mutation to recover the MR function . To test the robustness of our method in other proteins we repeated our method for benign and disease associated mutations [59]–[61] in the human ferritin protein [62] ( Fig . S5 ) . We observe that , indeed , benign and disease associated mutations are individually clustered together while separated from each other in dynamics space . In summary , by comparative dynamics analysis among the three ancestral steroid hormone receptors we identify all functionally critical and permissive mutations necessary to evolve new function from the ancestral MR promiscuous binding proteins to the ancestral GR cortisol-specific binding proteins . We also identify 60% of the permissive mutations necessary to revert to ancestral function along with an additional functionally critical mutation . We observe significant loss of flexibility in key residues both near and distant from the binding pocket in the transition from promiscuous to specific binding . A loss in flexibilty agrees well with the new view of proteins being conformationally dynamic in which bound and unbound conformations are sampled within the native ensemble . Thus , proteins evolve not just through those mutations that alter function in the immediate sense , but also due to those mutations that are permissive and alter the dynamic space in which the protein exists , thereby giving the protein the potential to evolve new function .
We previously used the Zipping and Assembly Method with FRODA [ZAMF] [45]–[48] , [63] on a set of test proteins to predict the 3D structure from their 1D amino acid sequence . Here , we slightly modify ZAMF for the prediction of ancestral protein structures , particularly the three ancestral steroid receptor proteins , the corticoid receptor [AncCR] , the glucocorticoid/corticoid receptor [AncGR1] , and the glucocorticoid receptor [AncGR2] [33] , [34] . Since structure is more conserved than sequence [64]–[66] , we incorporate structural data acquired from modern day homologues into our prediction method . The modified version of ZAMF as outlined in Fig . 5 includes several steps: ( i ) obtaining secondary structural motifs and common contacts based on modern homologs , ( ii ) generation of an unfolded ensemble , ( iii ) generation of compact-native like conformations using FRODA , and ( iv ) refinement by ZAMF . Overall , all these steps lead to an extensive search in conformational space , which comes with several advantages . First , we increased our prediction accuracy for native structures compared to the previous version of ZAMF . Second , we obtain converged dynamics trajectories through the refinement stage of ZAMF , which is used for dynamic evolution analysis of the ancient proteins . We summarize each step in our approach below . Convergence is critical and , as such , a sample window of 1 ns is slid along the trajectory at 0 . 5 ns intervals and Principal Component Analysis is done . The PCA is done by first aligning and centering each snapshot of the trajectory to remove the translations and rotations , generating a matrix Xn for each sampling window ( 1 ) where xn are 3N dimensional position vectors and the < > denote a time average for a specific sampling window . Then , the covariance matrix of that sampling window , Cn , n , is calculated by ( 2 ) From the covariance matrix , the matrix of eigenvectors ( Vn ) and the matrix of eigenvalues ( Λn ) are ( 3 ) The eigenvectors and eigenvalues are sorted in order of decreasing eigenvalue and only the top 30 are kept as , once converged , any higher order ( faster fluctuation/smaller positional deviations ) are not relevant in determining biologically relevant large scale motion of the protein [75] . The reduced set of principal components is then ( 4 ) The fluctuation profile along each mode is simply the Δr of each residue in that mode . By plotting these against each other , we confirm convergence when the Pearson correlation coefficient , Pij , of the trajectory for sampling window i ( Xi ) and sampling window j ( Xj ) is >0 . 8 ( 5 ) σi and σj are the standard deviations of their trajectories . If the run has not converged it is continued until convergence is confirmed over a 3 ns window ( Fig . S3 ) . Using the Saguaro high performance computer at Arizona State University , a 250 residue protein with 40 temperature replicas ( 1 logical core per replica ) finishes just under 300 ps/day . The most native like structures are assumed to be those that dominate the lowest temperature replica , while those in higher temperature replicas are dismissed . After confirming convergence , in order to obtain the dynamics difference between the most collective motions ( i . e . slowest frequency fluctuation profiles ) of these three ancestral structures we apply the Singular Value Decomposition ( SVD ) technique to the matrix of dynamics profiles , G ( i . e . the dynamics profile of each protein will be the column in the matrix , and each super-element , ik corresponds the X , Y , and Z fluctuations of the kth residue in the sequence of protein i ) . ( 6 ) G matrix includes most collective modes of ( i . e . global motion ) individual proteins that we obtained separately from REMD trajectories . With construction of the G matrix our goal is to cluster the proteins with similar global motion . Since global dynamics ( i . e . most spatially extensive collective mode ) is most related to the function , proteins with similar global dynamics should cluster together and execute similar function . In order to do clustering we perform an SVD on G matrix ( 7 ) The first through nth values in each column of W can be plotted against each other to visualize the dynamic space occupied by each protein .
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Proteins are remarkable machines of the living systems that show diverse biochemical functions . Biochemical diversity has grown over time via molecular evolution . In order to understand how diversity arose , it is fundamental to understand how the earliest proteins evolved and served as templates for the present diverse proteome . The one sequence - one structure - one function paradigm is being extended to a new view: an ensemble of different conformations in equilibrium can evolve new function and the analysis of inherent structural dynamics is crucial to give a more complete understanding of protein evolution . Therefore , we aim to bring structural dynamics into protein evolution through our zipping and assembly method with FRODA . ( ZAMF ) . We apply ZAMF to simultaneously obtain structures and structural dynamics of three ancestral sequences of steroid receptor proteins . By comparative dynamics analysis among the three ancestral steroid hormone receptors: ( i ) we show that changes in the structural dynamics indicates functional divergence and ( ii ) we identify all functionally critical and most of the permissive mutations necessary to evolve new function . Overall , all these findings suggest that conformational dynamics may play an important role where new functions evolve through novel molecular interactions .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Methods"
] |
[
"physics",
"biology",
"biophysics"
] |
2012
|
Collective Dynamics Differentiates Functional Divergence in Protein Evolution
|
Absence epilepsy ( AE ) is a common type of genetic generalized epilepsy ( GGE ) , particularly in children . AE and GGE are complex genetic diseases with few causal variants identified to date . Gria4 deficient mice provide a model of AE , one for which the common laboratory inbred strain C3H/HeJ ( HeJ ) harbors a natural IAP retrotransposon insertion in Gria4 that reduces its expression 8-fold . Between C3H and non-seizing strains such as C57BL/6 , genetic modifiers alter disease severity . Even C3H substrains have surprising variation in the duration and incidence of spike-wave discharges ( SWD ) , the characteristic electroencephalographic feature of absence seizures . Here we discovered extensive IAP retrotransposition in the C3H substrain , and identified a HeJ-private IAP in the Pcnxl2 gene , which encodes a putative multi-transmembrane protein of unknown function , resulting in decreased expression . By creating new Pcnxl2 frameshift alleles using TALEN mutagenesis , we show that Pcnxl2 deficiency is responsible for mitigating the seizure phenotype – making Pcnxl2 the first known modifier gene for absence seizures in any species . This finding gave us a handle on genetic complexity between strains , directing us to use another C3H substrain to map additional modifiers including validation of a Chr 15 locus that profoundly affects the severity of SWD episodes . Together these new findings expand our knowledge of how natural variation modulates seizures , and highlights the feasibility of characterizing and validating modifiers in mouse strains and substrains in the post-genome sequence era .
Laboratory mouse strains are well known to vary in their susceptibility to convulsive seizures , including acute experimentally induced seizures [1] , [2] , [3] , [4] , [5] , [6] , and spontaneous seizures induced by genetic mutation [7] , [8] , [9] , [10] , [11] , [12] , [13] . Most of the known strain effects have been for convulsive seizures , where motor manifestations are obvious , including one modifier identified to date ( Kcnv2 as a modifier of Scn2a1Q54 induced convulsive seizures [13] . There has been less attention to such effects in non-convulsive phenotypes , such as absence epilepsy , where the seizures lack a convulsive element . Nevertheless , strain differences were noted for three different genes that cause absence seizures when mutated – Scn8a [14] ) , Gabrg2 [15] and Gria4 [16] , and for at least two the C3H strain generally worsens the absence seizure phenotype , compared with the relatively protective strain C57BL/6J ( B6J ) . For absence seizures caused by Gria4 mutation , the C3H strain has been a paradox . Each of three very closely-related C3H/He substrains has a similar level of spontaneous spike-wave discharges ( SWD ) - the distinctive electroencephalographic hallmark of absence seizures in human and in animal models - but only one substrain , C3H/HeJ ( HeJ ) , carries a Gria4 mutation . This mutation is caused by an intracisternal A-particle retrotransposon ( IAP ) insertion in Gria4 [16] , [17] , [18] . Although genetic and later functional analysis proved that Gria4 is the cause of these seizures in HeJ [19] , two other C3H/He substrains that lack this mutation still have appreciable SWD . At least one of these strains was shown to have a polygenic etiology , with no indication of any effect from proximal Chr 9 where Gria4 resides [18] . The further surprise was when HeJ mice were outcrossed to other strains - even to another C3H substrain , C3HeB/FeJ ( FeJ ) , that does not have frequent SWD - about half of the next generation Gria4 deficient progeny had significantly higher SWD incidence than HeJ itself [16] . Together these results suggested the model whereby the HeJ substrain has both the initial seizure-causing Gria4 mutation , and also a mitigating or protective mutation , without which the seizures would be much more severe . The model also suggests that C3H strains in general have a high baseline susceptibility to absence seizures , compared with strains such as C57BL/6 . IAP insertions like the element in Gria4 are known to cause deleterious mutant phenotypes by reducing RNA expression of the target gene , and the vast majority of spontaneous IAP insertion mutations in mice have arisen in the C3H strain family ( reviewed in [20] ) . While searching for genetic markers to distinguish C3H substrains for analysis of the putative HeJ suppressor , we discovered extensive IAP retrotransposition among C3H substrains . One of the HeJ substrain-private IAP insertions resides in the same chromosomal region as an epistatic modifier of Gria4 absence seizures mapped to Chr 8 [17] . Here we report that this Chr 8 IAP is inserted in the previously unstudied Pcnxl2 ( pecanex-like 2 ) gene , affecting its RNA expression . We further use TALEN mutagenesis to create new Pcnxl2 alleles , confirming that Pcnxl2 loss of expression is responsible for mitigating Gria4 seizures of HeJ mice , accounting for the substrain difference . We also mapped modifiers that differ between C3H and B6J , and show that one – G4swdm1 – has a profound effect on seizure severity . The identification of the first absence seizure modifier Pcnxl2 provides significant traction to the complex genetics of absence seizures in the C3H strain family and possible new mechanisms for mitigating disease .
Prior studies of Gria4-deficient mice showed significant genetic background influence on the incidence and duration of spike-wave discharges ( SWD ) between C57BL/6J ( B6J ) and C3H strains [16] , [17] , whether the natural Gria4spkw1 allele ( abbreviated hereafter as Gria4IAP ) or the engineered knockout Gria4tm1Dgen ( Gria4KO ) . While mapping the main phenotype to Gria4 in a backcross between C3H/HeJ ( HeJ ) and B6J , about half of the progeny were noted to have significantly more SWD than HeJ itself , and an epistatic modifier putatively due to this effect was mapped to distal Chr 8 [16] , [17] . Interestingly , a similar phenomenon from crosses between HeJ and C3HeB/FeJ ( FeJ ) suggested this was due to a substrain difference [16] , [17] . To confirm this , we backcrossed Gria4IAP allele from HeJ to FeJ and examined SWD in this congenic pair and compared to B6J-Gria4KO for reference . FeJ-Gria4IAP had significantly more frequent and longer SWD than HeJ or B6J-Gria4KO ( Table 1 ) . These results indicate that HeJ has a suppressor allele ( s ) not present in FeJ . It also suggest that further modifiers differ between FeJ and B6J strains . The HeJ suppressor is a challenge to pursue by classical recombination mapping because of the paucity of genetic markers between FeJ and HeJ substrains , which split into separate lines around 1950 [21] . However , C3H mice are known to have frequent spontaneous germline IAP retrotransposition , with de novo insertions accounting for a significant fraction of the spontaneous mutation load in C3H ( see review by [20] ) . Moreover , almost all de novo IAP insertions , including Gria4IAP , are of a particular IAP subtype - IAP-1Δ1 - containing a characteristic in-frame 1 . 2 kb deletion of the gag-pol fusio gene [22] . Sequence similarity search of the B6J mouse genome assembly with an oligonucleotide sequence spanning the IAP-1Δ1 deletion detected approximately 200 independent genomic sites , whereas the same region from full-length IAP detected over 700 sites ( data not shown ) , suggesting the feasibility of direct hybridization approaches to identify substrain-specific de novo IAP-1Δ1 insertions . To determine whether C3H substrains vary significantly in IAP-1Δ1 content , we designed an oligonucleotide specific for the IAP-1Δ1 1 . 2 kb common deletion to examine proviral-host DNA junction fragments by direct dried gel hybridization and to subclone them by inverse PCR ( Fig . 1A ) . Although it is unlikely that any visible ‘band’ in the direct hybridization represents a single IAP insertion , from the differential pattern– best observed in the 2 kb range - there are ample variation between four substrains examined; with HeJ appearing to have the highest load including at least 20 HeJ-specific bands evident ( Fig . 1B ) . Inverse-PCR was used to identify and clone select IAP-1Δ1 – host junction fragments from FeJ and HeJ . A representative experiment to identify lower molecular weight junction fragments revealed a banding pattern reminiscent of the gel hybridization , and recapitulates the corresponding paucity of bands in this region in the FeJ strain ( Fig . 1C ) . From this and other experiments , bands were excised , cloned and junction fragments sequenced , and PCR assays were developed ultimately leading to identification of at least 26 insertion-host junction fragments that are present in the HeJ substrain and absent from FeJ ( Table 2 ) . 9 are intergenic , but the majority are in introns of known or unclassified genes ( Table 2 ) . Most HeJ insertions we cloned were ultimately identified in the recent retrotransposon-mining of the whole genome sequence of HeJ and other mouse strains from the Sanger Mouse Genomes Project [23] , although substrain specificity was not ascertained in that study . One HeJ-specific IAP insertion resides at 128 . 3 Mb on Chr 8 , in the same general region of the previously mapped epistatic modifier of Gria4 SWD [17] . This insertion was not seen in any of the 17 inbred mouse strains for which genome sequence is available [23] , and the PCR assays used to identify it ( Table 2 ) determined that it was also absent from C3HeB/FeJ ( FeJ ) , C3H/HeOuJ and C3H/HeSnJ substrains ( data not shown ) . This IAP is integrated in the 5′-LTR-3-′LTR orientation in intron 19 of Pcnxl2 , the gene that encodes pecanex-like 2 . Pcnxl2 is one of three mammalian paralogs of Drosophila melanogaster pecanex , a neurogenic gene about which little is known , but was recently suggested to be part of the notch signaling pathway in the endoplasmic reticulum [24] , [25] . In adult mouse brain , Pcnxl2 is expressed highest in the hippocampal pyramidal cell layer , it is also expressed prominently in the area of cerebral cortex corresponding to layer V , and sparsely in the reticular thalamic nucleus ( Allen Brain Atlas: see http://mouse . brain-map . org/experiment/show/70239051 ) . As the latter two regions are key in abnormal cortico-thalamic oscillations associated with absence seizures generally and specifically in Gria4 mutants [19] , Pcnxl2 is a suitable candidate for the suppressor . To determine whether the IAP insertion affects Pcnxl2 gene expression , we initially examined Pcnxl2 transcript expression in brain from public datasets , comparing HeJ to other inbred strains . From inspecting publically available mouse strain hippocampal microarray ( http://www . genenetwork . org ) and in the Sanger Center's whole-brain RNAseq ( ftp://ftp-mouse . sanger . ac . uk/current_rna_bams ) , we noted an HeJ-specific drop in relative expression or abundance of exons downstream of exon 19 ( data not shown ) . To examine this between HeJ and FeJ substrains , by using quantitative RT-PCR we confirmed that Pcnxl2 expression across intron 19 is indeed lower in HeJ compared to FeJ ( Table 3 , compare sample IDs 1–2 ) . To confirm that Pcnxl2 encodes the Gria4 SWD suppressor , we used TALEN mutagenesis [26] in the FeJ-Gria4IAP congenic strain to create Pcnxl2 frameshift mutations ( Fig . 2A ) . Two Pcnxl2 exons were targeted separately: exon 16 , roughly in the middle of the gene , and exon 29 nearer the 3′ end , the latter containing the so-called “pecanex” domain – a conserved domain of unknown function shared among pecanex paralogues . Three in 106 liveborn mice contained germline mutations and each created a frameshift allele that resulted in a premature translational stop codon ( Figure 2 ) . To test the effect on SWD , EEG was examined in FeJ-Gria4IAP homozygotes carrying each of the three Pcnxl2 mutations ( A−11 , A+1 and B−2 , hereafter referred to as FS1 – frameshift 1 , FS2 and FS3 , respectively ) . Pcnxl2FS1 and Pcnxl2FS2 mutations significantly lowered SWD incidence ( Fig . 2B ) and length ( Fig . 2C ) , showing an additive effect across genotypes . The homozygotes were also slightly more suppressed than HeJ with its natural Pcnxl2IAP allele . An examination of Pcnxl2 transcripts by qPCR showed decreased ( between 2 and 3 . 5-fold; Table 3 , compare sample ID's 3–4 , 5–6 , 7–8 ) but not eliminated expression in each homozygous genotype; this is expected as frameshift mutations in middle exons often cause nonsense-mediated decay . However , the Pcnxl2FS3 allele did not decrease SWD as effectively as the others ( Fig . 2B , C ) . Since commercial antisera to PCNXL2 are not effective in mouse brain ( data not shown ) , we cannot know whether this is because a partial or truncated protein is still made and has some residual function , or whether nonsense-mediated decay was less complete . Regardless , the effect of at least two independent new Pcnxl2 alleles on SWD , combined with transcript reduction shows that Pcnxl2 encodes the suppressor of Gria4 SWD . To determine whether Pcnxl2 deficiency can affect SWD in other absence seizure mouse models , we examined double mutant genotypes between FeJ-Pcnxl2FS1 and either Scn8a8J [14] , or Gabrg2tm1Spet [15] , each of which encodes a dominant , SWD-causing missense mutation in the respective ion channel gene . Pcnxl2 deficiency had no significant effect on SWD on either of these mutants ( Fig . 2C ) , suggesting that Pcnxl2 may be specific for mechanisms that involve Gria4 . However , if Gria4-specific , the mechanism appears not to be on IAP mutagenesis itself , because there is no increase in Gria4 RNA expression in Gria4IAP , Pcnxl2FS1 double mutants ( Table 3 , compare sample IDs 9–10 ) . We also note that the Pcnxl2 paralogue , Pcnx , is expressed widely in adult mouse brain , including likely overlap with Pcnxl2 ( http://mouse . brain-map . org/experiment/show/73818801 ) , suggesting the possibility of compensation . However , the Pcnx transcript is not altered in any of the three new Pcnxl2 frameshift alleles ( Table 3 , compare sample IDs 13–14 , 15–16 , 17–18 ) , suggesting that if there is any compensation by this overlapping pecanex gene , it is not at the transcript level . The fact that Gria4 mutant associated SWD are more pronounced when placed on the C3HeB/FeJ ( FeJ ) strain compared with B6J suggests additional modifiers , i . e . separate from Pcnxl2 whose genotype does not differ between B6J and FeJ ( Table 1 ) . To pursue these , we backcrossed ( FeJ-Gria4IAP×B6J-Gria4KO ) F1 animals to B6J- Gria4KO , creating 89 N2 mice segregating B6J vs . FeJ strain variants on a Gria4 mutant background ( either homozygous Gria4KO or compound heterozygous Gria4KO/IAP ) and scored their EEG for SWD incidence and length . The broad , almost continuous distribution of each raw SWD measure in this population suggests multiple factors ( Fig . 3A , 3B insets ) . A genome-wide scan of the two SWD measures and their principal components revealed two highly significant regions ( Fig . 3A , 3B ) : proximal Chr 8 ( peak LOD , 3 . 4 ) and mid Chr 15 ( peak LOD , 2 . 9 ) . The Chr 15 region was significant for SWD length but negligible for incidence , and vice versa for Chr 8 ( compare Fig . 3A with 3B ) , suggesting each region is primarily responsible for one of the two measures , at least in this cross . A region in mid Chr 3 was significant for SWD length only , and several suggestive peaks were observed for at least one SWD measure on Chrs 1 , 2 and 5 . A pairwise scan was done to look for pairwise epistatic interactions , but no significant interactions were observed ( data not shown ) . Because of the increased SWD length , we focused on validating the Chr 15 locus , now termed G4swdm1 , for Gria4 spike-wave discharges modifier 1 . We made a congenic strain in which the middle of Chr 15 was selectively bred from FeJ into B6J-Gria4KO , and N9F1 and N10F1 intercross mice were generated and tested . Highly significant effects were observed this time for both SWD length and incidence across the introgressed interval ( Figure 4A , 4B ) . While G4swdm1 was initially detected as a dominant allele , the congenic intercross reveals additivity: FeJ homozygotes experience significantly longer and more frequent SWD ( Figure 4C ) . Although SWD length was still the leading phenotype , in the B6J background the effect of G4swdm1 on SWD incidence was stronger than in the N2 population , likely reflecting additional genetic complexity . Indeed , several congenic individuals had SWD 15 s long ( Fig . 4D ) ; further , when the homozygous interval was placed together with the Gria4 mutant genotype on a ( B6J×FeJ ) F1 hybrid background , several very striking SWD , exceeding 40 s were observed ( Figure 4D ) . The estimated 95% confidence interval ( CI ) for G4swdm1 is large , with the narrowed SWD length interval covering 27 . 2 Mb including 191 protein coding and 75 small RNA or unclassified genes ( File S1 ) , although the “bumpy” likelihood curve suggests the possibility of multiple modifiers . Among these genes are 27 that have 56 non-synonymous coding or potential splice altering SNPs or indels , between published C3H/HeJ and B6J genomes; further , comparison of FeJ exome sequence to HeJ revealed no similar coding variants ( File S1 ) . To gain additional evidence for candidacy , we examined gene expression by RNAseq , using somatosensory cortex and thalamus as tissue source , comparing parent strains B6J and FeJ to each other . In the SWD length 95% CI , of 93 mRNAs expressed , 15 had abundance differences ( e . g . q<0 . 1; File S1 ) . Most were modest ( <5% change ) but for Ly6a , one of several members of a cell membrane protein-encoding gene family , FeJ had a 21% transcript reduction in thalamus and 32% in cortex . Although Ly6a is best known for expression in lymphocytes , it is also expressed in brain and at least one Ly6a knockout allele has prenatal lethality [27] . Further refining the G4swdm1 critical interval , and more extensive testing of existing mutants or creation of others by mutagenesis is required before the correct G4swdm1 candidate ( s ) can be identified .
Here we unravel the genetic complexity of Gria4-deficiency absence seizure susceptibility in C3H mice . First , we show genetically that Pcnxl2 deficiency accounts for the unusual substrain difference in spike-wave discharges ( SWD ) in Gria4 mutants on C3H/HeJ ( HeJ ) compared to C3HeB/FeJ ( FeJ ) . We determined that two new frameshift alleles , generated directly in FeJ-Gria4IAP by TALEN mutagenesis , confer SWD mitigation even more than the natural , presumably hypomorphic Pcnxl2 IAP insertion allele of HeJ . This insertion is of the same IAP-1Δ1 subtype that caused the original Gria4IAP mutation . Given increased incidence and severity of SWD in FeJ-Gria4IAP compared to HeJ-Gria4IAP , and the absence of the Pcnxl2 IAP from two other C3H/He substrains , we think it is reasonable to speculate that Gria4IAP conferred a selective disadvantage to the progenitors of HeJ mice , one that was later diminished upon fixation of the Pcnxl2IAP insertion . These findings also suggest that a number of apparent IAP element differences between C3H substrains , nominally 20% of the IAP-1Δ1 pool , remain a potentially powerful source of genome plasticity , which naturally would not be restricted to neurological phenotypes . Although it was suggested previously that most functional IAP insertions in mouse strains were lost because of deleterious effects , [23] , clearly some remain functional – perhaps in the case of Pcnxl2 due to selective advantage such as the one we hypothesize . The further phenotypic difference between FeJ and B6J strains highlights the existence of additional genetic influences on Gria4 associated absence seizures . To begin to dissect these , genome scans revealed several potential modifiers , affecting one or the other SWD measure . By using a congenic strain we validated one modifier locus – G4swdm1 on Chr 15 and observed its striking effect on SWD length; in F1 hybrid mutants , for example , we observed several SWD episodes lasting over 40 seconds . It is difficult to imagine that such frequent and contiguous states of neural hypersynchrony do not have a broader impact on behavior . Despite its clear effect , G4swdm1 accounts for only some of the phenotype difference between strains – perhaps 30% by comparing SWD length of B6J to F1 hybrid ( e . g . as illustrated in Figure 4C to 4D ) . But when multiple interactions are likely , as in complex traits , any such estimates of effect are overly simplistic . Approaches to map such modifiers using conventional quantitative trait locus , especially in small crosses , are limited to loci that show significant main effects despite interactions , or to simple , pairwise interactions when they are quite strong . Novel computational approaches such as CAPE , which incorporates relationships between phenotypic features directly into the gene interaction model [28] , may be required to parse more complex interactions in conventional cross designs . Pcnxl2 is the first absence seizure modifier gene to be identified in any species , and as such it represents the first of what is likely to be many genetic interactions beneath the complexity of absence seizures . The predicted peptide structure of PCNXL2 is similar to that of other pecanex orthologues , with 8 putative transmembrane domains followed by a conserved so-called pecanex domain . But no known primary or predicted secondary structures have been identified that would predict further function . D . melanogaster pecanex ( pcnx ) localizes to the endoplasmic reticulum ( ER ) and functional genetic studies utilizing the protein unfolded response as a readout , suggest that it shows maternal inheritance and has a role in notch signaling [25] . From conservation among pecanex family members we might expect that Pcnxl2 is also expressed in the ER . Although much further discussion of function is merely speculation , if it is an ER protein one tempting possibility is a role in trafficking or in posttranslational modification of synaptic receptors such as Gria4 or other compensating ion channel receptors . The prominent Pcnxl2 expression in layer V of the cerebral cortex opens the door to the possibility that it is involved in mediating excitatory output from layer V pyramidal neurons to the reticular thalamus and thalamus . Whether any such function is the result of an acute Pcnxl2 role , or instead a role in circuit development , will require further studies , for example , the creation of a conditional allele to be induced at different ages . Laboratory mouse strains are well known to vary in susceptibility to experimentally-induced partial or generalized tonic-clonic seizures and there are several well-characterized strain differences modifying the penetrance or severity of so-called monogenic seizure mutations ( as discussed earlier ) although only one such modifier gene has been identified to date , and interestingly this has also be implicated in human epilepsy [13] . The rate of human gene discovery is rapidly accelerating due to efficient high-throughput exome sequencing [29] , but the new progress so far is for syndromic pediatric encephalopathies such as Lennox-Gastaut syndrome . The pace remains much slower for genetic generalized epilepsies , including absence epilepsy . With new mutagenesis technologies such as TALEN and CRISPR to more readily validate candidate genes in large intervals such as those defined in modifier or QTL mapping , the pursuit of modifiers still holds promise for unbiased discovery of new genes , pathways and future novel therapies for idiopathic disease .
All mice were housed and procedures performed with approval of Institutional Animal Care and Use Committee ( IACUC ) . All mice were obtained from The Jackson Laboratory , maintained in a room with a 14 h hour light on/10 h light off cycle , and given free access to LabChow meal and water . C3H/HeJ ( HeJ ) , C3HeB/FeJ ( FeJ ) , C3H/HeOuJ , C3H/HeSnJ and C57BL/6J ( B6J ) inbred mouse strains were obtained from The Jackson Laboratory production colonies and subsequently maintained by sib-matings . C57BL/6J . 129-Gria4KO congenic knockout mice were originally obtained from Deltagen , Inc , as previously described [16] . FeJ . HeJ-Gria4IAP congenic mice were generated by backcrossing the Gria4spkw1 mutation from its original strain , HeJ , to FeJ for 14 generations , bred to homozygosity and maintained by sib-matings . FeJ-Gabrg2tmSpet congenic mice were generated by backcrossing the Gabrg2tmSpet ( also known as Gabrg2R43Q ) knockout mutation from B6J . 129-Gabrg2tmSpet congenic mice ( originally obtained from Bionomics , LTD ) successively for at least 20 generations to FeJ and maintained in the same way . FeJ . B6J-Scn8a8J congenic mice were generated by successive backcrossing of Scn8a8J ( also known as Scn8aV929F ) for at least 15 generations to FeJ and maintained in the same way , as described recently [30] . The 89 backcross mice used for genome-wide mapping were generated by mating ( B6J . 129-Gria4KO/KO×FeJ . HeJ-Gria4IAP/IAP ) F1 hybrids to B6J . 129-Gria4KO/KO congenic mice . The 84 B6J-FeJ-G4swdm1 congenic intercross mice were created by successively backcrossing the FeJ alleles for genetic markers in the critical interval on Chr15 from an N2 to the B6J . 129 strain while also selecting for Gria4KO homozygotes , and then intercrossing at generation N9 or N10 . The generation of Pcnxl2 mutants is described below . We contracted Transposagen Biopharmaceuticals , Inc . to design and construct plasmids for TALENs specific to exon 16 ( TALEN A ) and exon 29 ( TALEN B ) of Pcnxl2 . Target sequences were as follows; TGAGCCGGCAGAGCAGTG and GTGAGTAGCTGTCCTGTA for TALEN A and TATTTGCTGACATGGAC and TTGTTCCAGCCATCCGAA for TALEN B . TALEN plasmids were linearized by PmeI endonuclease digestion . One microgram of linearized plasmid was used as a template for in vitro transcription using AmpliCap-Max T7 High Yield Message Maker Kit ( CELLSCRIPT ) according to the manufactures instruction . A poly ( A ) tail was added to the synthesized RNA with the A-Plus Poly ( A ) Polymerase Tailing Kit ( CELLSCRIPT ) according to the manufactures instruction . The poly ( A ) tailed capped RNA was purified by ammonium acetate precipitation , resuspended in RNase free water and the concentration determined by spectrophotometry . TALEN mRNA was diluted to 10 ng/υl in RNase free 1X TE ( 10 mM Tris-HCl , 1 mM EDTA , pH 7 . 5 ) immediately before microinjection into embryos obtained from superovulated FeJ . HeJ-Gria4IAP/IAP congenic mice . The genomic DNA made from the tail tip of 63 TALENA mutant founders was amplified with primers aFXTN ( 5′-CATCGTGGCTGTCGTAATTC -3′ ) and aRXTN ( 5′-CATAGCGTGGGAGAGAAAGA-3′ ) . The product was purified and sequenced using primer aF2XTN ( 5′-GCACACACCACTCATTCATC-3′ ) . The genomic DNA made from the tail tip of 43TALENB mutant founders was amplified with primers bFXTN ( 5′- GCTTTGTAATGTGGGTTCTG-3′ ) and bRXTN ( 5′- GGTTCTCTACTTCAGCCTATG-3′ ) . The product was purified and sequenced using primer bF2XTN ( 5′-GAACTCGGGATCCATGTTTG -3′ ) . For the genome scan , genomic DNA was prepared from tail tips as previously described and sent to Kbioscience ( currently LGC Genomics , LLC . Beverly , MA ) , using a custom single nucleotide polymorphisms ( SNP ) panel comprised of 187 roughly evenly spaced SNPs . Prior to interval mapping , the raw traits SWD incidence and SWD length were rank-ordered and normal quantile transformed , and from which principal components were derived using JMP software ( SAS Institute ) ; once obtained , both principal components were also rank- and normal-quantile transformed , also using tools in JMP . The computer program J/qtl [31] was then used for genome-wide interval mapping of the initial backcross , and for Chr 15 interval mapping of congenic intercross mice . To control for a modest effect ( p<0 . 04 ) of Gria4KO/KO homozygous null vs Gria4KO/IAP compound heterozygous null/hypomorph genotypes segregating in the N2 cross , a marker linked to Gria4 on Chr 9 was used as a covariate . Sex-averaged genetic map coordinates for SNP markers were obtained from the Mouse Genome Informatics database at The Jackson Laboratory ( http://informatics . jax . org ) . For interval mapping , the multiple imputation model was used and permutation shuffling employed to determine genome-wide significance thresholds . C3H/HeJ-Pcnxl2IAP ( IAP insertion in intron 19 of C3H/HeJ ) was genotyped in standard PCR conditions and agarose gel electrophoresis using one assay for the wild-type allele ( primers c8-128 . 3F 5′-AGCGATGAGGACTGTGGTTT-3′; c8-128 . 3R 5′-CGAGCCCTTCAGCTACTCAC-3′ ) and a second assay for the insertion allele ( primers c8-128 . 3F 5′-AGCGATGAGGACTGTGGTTT-3′; IAPLTR5′R 5′-GGCTCATGCGCAGATTATTT-3′ ) giving a 364 bp product from the insertion allele and 432 bp product from the endogenous allele . The TALEN A+1 or frameshift 1 ( FS2 ) allele was genotyped in standard PCR conditions at an annealing temperature of 64°C and agarose gel electrophoresis using one assay for the wild-type allele ( primers Pcnxl2A1WF2 5′-GCAGAGCAGTGATCCTTCAG -3′; Pcnxl2A1WR2 5′-CCATAGCGTGGGAGAGAAAGAA -3′ ) and a second assay for the mutant allele ( primers Pcnxl2A1MF2 5′-GCAGAGCAGTGATCCTTCAC-3′; Pcnxl2A1WR2 5′-CCATAGCGTGGGAGAGAAAGAA -3′ ) giving a 311 bp product for both alleles . TALEN B-2 or FS3 allele was genotyped in standard PCR conditions with an annealing temperature of 67°C and agarose gel electrophoresis using one assay for the wild-type allele ( primers Pcnxl2BwtF 5′-TAGATGCTGGTAGGAGTGAAGA -3′; Pcnxl2BwtR 5′-GGCTGGAACAACAACTTTGTGT-3′ ) and a second assay for the mutant allele ( primers Pcnxl2BwtF 5′-TAGATGCTGGTAGGAGTGAAGA -3′; Pcnxl2BmutR 5′-GGCTGGAACAACAACTTTGTAG-3′ ) giving a 228 bp product from the mutagenized allele and a 227 bp product from the wildtype allele . TALEN A-11 or FS1 allele was genotyped in standard PCR conditions at an annealing temperature of 55°C and agarose gel electrophoresis using primers Pcnxl2AF2 ( 5′- CACTGTTCTCGGCCTTCTG-3′ ) and Pcnxl2AR ( 5′-AGACATGTGGACATGCGTTTA-3′ ) giving a 123 bp product from the mutagenized allele and a 134 bp product from the endogenous allele . For direct detection of IAP-1Δ1 insertions , dried gel hybridization was done essentially as previously described [32] except using an oligonucleotide probe that spans the 1 . 2 gag-pol common deletion of IAP-1Δ1 elements [22] . Briefly , 8 µg of high quality mouse genomic DNA ( obtained from The Jackson Laboratory DNA Resource ) was digested with restriction enzyme Bgl II , electrophoresed overnight on 0 . 8% Tris-borate EDTA agarose gels , EtBr-stained and imaged , denatured in NaOH , neutralized and dried for several hours on a flat slab gel dryer with minimal vacuum . 5′-32P radiolabeled 31-nt oligonucleotide probe ( IAPd1oligo1-R; 5′ ATACCTCTTATCAGGTTCAGCAGAATAAGCTC-3′ ) was hybridized overnight , the dried gel was washed and imaged on x-ray film . For cloning of IAP-1Δ1-host junction fragments by inverse PCR: 1 µg of genomic DNA was digested with BglII , diluted , then 10 ng was ligated for 2 hrs at room-temperature using T4 DNA ligase , heat-inactivated , then circular product was amplified in a polymerase chain reaction ( PCR ) using an oligonucleotide that spanned the IAP-1Δ1 deletion ( IAPd1oligo2F , 5′- GAGCTTATTCTGCTGAACCTGATA-3′ ) paired with an oligonucleotide specific for the IAP LTR ( IAPLTR5′ , 5′- GGCTCATGCGCAGATTATTT-′3 ) . Amplified fragments were visualized by agarose gel electrophoresis ( e . g . Figure 1C ) , excised and extracted from agarose using Qiagen minicolumns , cloned into Bluescript plasmid by T/A cloning ( Invitrogen ) , transformed into a suitable E . coli K12 host , miniprepped and subjected to Sanger sequencing using T7 and Sp6 vector primers . Total RNA was prepared from the whole brain of adult HeJ , FeJ , and FeJ-Gria4IAP and the hippocampus of adult FeJ- Gria4IAP either wildtype or carrying Pcnxl2 TALEN mutations with Trizol ( Invitrogen ) and treated with DNase I ( Promega ) under the manufacturer's suggested conditions . RNA ( 2 µg ) was reverse transcribed with AMV reverse transcriptase ( Promega ) . The cDNA was diluted 20-fold , and 2 µl was added to DyNAmo HS SYBR Green qPCR master mix ( Thermo Scientific ) with pairs of the following primers; beta-actinF ( 5′- ATGCTCCCCGGGCTGTAT-3′ ) and beta-actinR ( 5′- CATAGGAGTCCTTCTGACCCATTC-3′ ) , Pcnxl2exon19F ( 5′-GGATCTCACATCCTGTGCTC -3′ ) and Pcnxl2exon20R ( 5′-CCACACGTAGAGTCTCTCAAAC -3′ ) , Gria4exon15F ( 5′- GGTGGCTTTGATAGAGTTCTGTTACA-3′ ) and Gria4exon16R ( 5′- TCTTATGGCTTCGGAAAAAGTCA -3′ ) , Pcnxexon35F ( 5′-GAACAGCTGGAAAGACTGGA-3′ ) and Pcnxexon36R ( 5′-CGATGTGGGACCTTGTACTT-3′ ) . The PCR reactions were analyzed on an Applied Biosystems 7500 Real-Time PCR System . The PCR amplifications from three mice of each strain and/or genotype were run in triplicate . Amplification of the correct size products was confirmed by agarose gel electrophoresis . The ΔΔΧt method was adopted for the calculation of relative transcript levels . Somatosensory cortex or thalamus was dissected from wildtype or Scn8a8J/+ B6J and FeJ adult male mice in triplicate , and prepared for high-throughput sequencing on the Illumina HiSeq 2000 . The Jackson Laboratory Gene Expression Service prepared mRNA sequencing libraries using the Illumina TruSeq methodology . Tissue was placed in RNALater ( Qiagen , Inc , MD ) , RNA was extracted using TRIzol ( Invitrogen , CA ) . For mRNA-Seq , mRNA was purified from total RNA using biotin tagged poly dT oligonucleotides and streptavidin coated magnetic beads followed by quality control using an Agilent Technologies 2100 Bioanalyzer ( Agilent Technologies , Santa Clara , CA , USA ) . The mRNA was then amplified and double-stranded cDNA was generated by random priming . The ends of the fragmented DNA were converted into phosphorylated blunt ends . An ‘A’ base was added to the 3′ ends . Illumina-specific adaptors were ligated to the DNA fragments . Using magnetic bead technology , the ligated fragments were size selected and then a final PCR was performed to enrich the adapter-modified DNA fragments since only the DNA fragments with adaptors at both ends will amplify . The sequencing library was first validated using an Agilent Technologies 2100 Bioanalyzer to characterize DNA fragment sizes and concentration . The concentration of DNA fragments with the correct adapters on both sides was then determined using a quantitative PCR strategy , following the kit manufacturer's protocol ( Kapa Biosystem , Cambridge , MA ) . Following library quantitation , libraries were diluted and pooled as necessary . Using the Illumina cBot , libraries were added to the flow cells and clusters were generated prior to 100 bp paired end sequencing on the Illumina HiSeq 2000 ( Illumina , San Diego , CA , USA ) . During and after the sequencing run , sequence quality was monitored using the real time analysis ( RTA ) and sequence analysis viewer ( SAV ) software available by Illumina . Following sequencing , demultiplexed fastQ files were generated using the Illumina CASAVA software . FastQ files were aligned to the C57BL/6J reference genome on a high performance computing cluster using Tophat ( http://tophat . cbcb . umd . edu/ ) for the alignment and RSEM ( http://deweylab . biostat . wisc . edu/rsem/ ) for isoform assembly and quantitation , except that frequency of reads per kilobase was normalized based on quartile instead of the total number of mapped reads . Further analysis was done in R ( http://www . R- project . org ) using ANOVA and linear modeling to test expression differences by strain , genotype and tissue , and FDR analysis was done in Microsoft Excel ( Microsoft Corp ) . Adult mice aged between 6 and 8 weeks were anesthetized with tribromoethanol ( 400 mg/kg i . p . ) . Small burr holes were drilled ( 1 mm anterior to the bregma and 2 mm posterior to the bregma ) on both sides of the skull 2 mm lateral to the midline . Four teflon-coated silver wires were soldered onto the pins of a microconnector ( Mouser electronics , Texas ) . The wires were placed between the dura and the brain and a dental cap was then applied . The mice were given a post-operative analgesic of carprofen ( 5 mg/kg subcutaneous ) and allowed a minimum 48 h recovery period before recordings . Differential amplification recordings were recorded between all four electrode pairs , providing 6 channels for each subject . Mice were connected to the EEG Stellate Lamont Pro-36 programmable amplifier ( Lamont Medical Instruments , Madison , WI ) for a 2-hour period on 2 separate days , between the hours of 9 AM and 4 PM during the lights-on period . EEG data were recorded with Stellate Harmonie software ( Stellate Systems , Inc . , Montreal , Canada ) into a database . SWD consist of adjacent , connected spike-wave ( or wave-spike ) complexes . Recordings were reviewed using low/hi bandpass filters at 0 . 3 Hz and 35 Hz respectively , and SWD episodes were scored blinded to genotype using the following criteria: at least 2 connected spike-wave complexes ( typically spanning at least 0 . 5 seconds ) with amplitudes at least two fold higher than background and observed concurrently in the majority of the 6 recording channels per mouse .
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Absence seizures - also known as “petit-mal” - define a common form of epilepsy most prevalent in children , but also seen at other ages , and in related diseases such as juvenile myoclonic epilepsy . Absence seizures cause brief periods of unconsciousness , and are accompanied by characteristic abnormal brain waves called “spike-wave discharges” ( SWD ) due to their appearance in the electroencephalogram ( EEG ) . Although few genes are known for human absence seizures , perhaps because the underlying genetics are complex , several laboratory rodent models exist , including one caused by mutation of a gene called Gria4 . While studying Gria4 , we noticed that a mouse strain called C3H can suppress or enhance the frequency and severity of Gria4-associated SWD in a perplexing manner; such effects are generally attributed to “modifier” genes . Here we identify a novel modifier – called “pecanex-like 2” , or Pcnxl2 for short – that reduces the severity of SWD in the C3H substrain in which the Gria4 mutation originally arose . This finding directed us to use of related substrains to locate additional modifiers , one of which has an even more profound effect on SWD episodes . Modifier genes , nature's way of controlling seizure severity , are promising targets for better understanding seizure mechanisms and potential new therapies in the future .
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"genetics",
"genetic",
"disorders",
"retrotransposons",
"neurophysiology",
"neurotransmission",
"genetic",
"elements",
"genetics",
"transposable",
"elements",
"biology",
"and",
"life",
"sciences",
"brain",
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] |
2014
|
Unraveling Genetic Modifiers in the Gria4 Mouse Model of Absence Epilepsy
|
Natural killer ( NK ) cells play an important role in the detection and elimination of tumors and virus-infected cells by the innate immune system . Human NK cells use cell surface receptors ( KIR ) for class I MHC to sense alterations of class I on potential target cells . Individual NK cells only express a subset of the available KIR genes , generating specialized NK cells that can specifically detect alteration of a particular class I molecule or group of molecules . The probabilistic behavior of human KIR bi-directional promoters is proposed to control the frequency of expression of these variegated genes . Analysis of a panel of donors has revealed the presence of several functionally relevant promoter polymorphisms clustered mainly in the inhibitory KIR family members , especially the KIR3DL1 alleles . We demonstrate for the first time that promoter polymorphisms affecting the strength of competing sense and antisense promoters largely explain the differential frequency of expression of KIR3DL1 allotypes on NK cells . KIR3DL1/S1 subtypes have distinct biological activity and coding region variants of the KIR3DL1/S1 gene strongly influence pathogenesis of HIV/AIDS and other human diseases . We propose that the polymorphisms shown in this study to regulate the frequency of KIR3DL1/S1 subtype expression on NK cells contribute substantially to the phenotypic variation across allotypes with respect to disease resistance .
Natural killer ( NK ) cells play an important role in the detection and elimination of tumors and virus-infected cells by the innate immune system [1] . NK cells can identify stressed cells via cell surface receptors for class I MHC that sense alterations of these molecules on potential target cells [2] . Human NK cells express inhibitory receptors of the Killer cell Immunoglobulin-like Receptor family ( KIR ) that recognize HLA class I molecules [3] , [4] , whereas mouse NK cells use members of a lectin–related family ( Ly49 ) to recognize mouse class I MHC [5] . Both gene families contain activating counterparts; however , the ligands of these activating receptors are not well characterized [6] , [7] . Activating KIR lack the immunotyrosine inhibitory motif ( ITIM ) present in the intracellular domain of inhibitory KIR due to a carboxy-terminal truncation of the protein , and have thus been named as short forms of the receptors . For example , KIR3DS1 is an activating receptor highly related to the KIR3DL1 ( long form ) inhibitory receptor ( http://www . ebi . ac . uk/ipd/kir/align . html ) . The KIR genes are located on chromosome 19 in a head to tail cluster with approximately 2 kb separating the polyadenylation signal of one gene from the translation initiation codon of the next . The number of genes present in KIR haplotypes is variable , however four genes ( KIR3DL3 , KIR3DP1 , KIR2DL4 , KIR3DL2 ) are present on virtually all haplotypes , and are thus considered as framework genes . Two major classes of KIR haplotypes have been identified . The A haplotype contains four genes in addition to the framework genes ( KIR2DL1 , KIR2DL3 , KIR3DL1 , KIR2DS4 ) , representing a predominately inhibitory haplotype . There are many B haplotypes , containing various combinations of the activating KIR genes . The A haplotype and a representative B haplotype are shown in Figure 1 . Individual NK cells only express a subset of the available class I MHC receptors , presumably to generate specialized NK cells that can specifically detect alteration of a particular class I molecule or group of molecules [8]–[10] . The variegated expression of class I MHC receptors , KIR and Ly49 , by NK cells is a unique case of selective transcriptional activation of a subset of genes present within a cluster . The B cell , T cell , and olfactory receptors are examples whereby a single receptor is selected from a large repertoire , and only one type of receptor is expressed per cell [11] , [12] . In contrast , several KIR or Ly49 genes can be expressed by a single NK cell in a stochastic manner [9] , [10] . A considerable amount of information relating to the mechanisms controlling expression of the class I receptor genes has been acquired , and several general principles that apply to both the human and mouse systems have emerged . Expression is controlled by a stochastic mechanism; the probability of co-expression of two distinct inhibitory receptors is equal to the product of their individual frequencies , and NK cells appear to turn on class I MHC receptors until a self-reactive inhibitory receptor is present [13] , [14] . Active receptor genes are hypo-methylated and silent genes are methylated [15]–[18] . Multiple promoters are present within each gene in both the KIR and Ly49 clusters , including bi-directional promoters that are predicted to function as probabilistic switches controlling the probability of gene activation [19]–[22] . There is a high degree of polymorphism in the KIR gene family , including differences in haplotypic gene content among individuals [23] . Allelic variation has been observed for most KIR genes; however functional polymorphism within the promoter region of KIR genes has only been reported for KIR2DL5 alleles , where loss of an AML-binding site was associated with the lack of KIR2DL5 transcription [24] . Allelic variation in the KIR3DL1 promoter has been reported [16]; however the functional consequences were not investigated . A large number of KIR3DL1 alleles have been identified , including an activating allele , KIR3DS1 , making the KIR3DL1/S1 locus unique within the cluster ( http://www . ebi . ac . uk/ipd/kir/align . html ) . Numerous studies have demonstrated the effect of KIR3DL1 protein polymorphisms on the level of cell surface expression and the HLA recognition properties of the receptors [25]–[29] . There are currently at least four distinct categories of mean channel fluorescence intensity ( MFI ) of KIR3DL1 on the NK cell surface as detected by the DX9 and Z27 mAbs: low ( KIR3DL1*028 , *053 ) , intermediate ( KIR3DL1*005 , *006 , *007 ) , high ( KIR3DL1*001 , *002 , *003 , *008 , *015 , *020 ) and null ( KIR3DL1*004 ) [25]–[27] , [30] . The distinct MFIs observed were attributed to differences in the level of cell surface expression rather than altered antibody-binding affinity . Although KIR3DL1 is known to bind multiple HLA allotypes that possess the Bw4 public serological epitope defined by residues 77–83 of the HLA α1 domain , the degree to which KIR3DL1 binds individual HLA-A and B alleles is variable among the different KIR3DL1 allotypes [28] , [29] . Division of allelic groupings based on these differential characteristics of KIR3DL1/S1 molecules has associated strongly with HIV disease outcomes in genetic studies [31] , [32] . The genetically-linked variability of the frequency of NK cells that express KIR3DL1 was established over 10 years ago [33] , but the molecular basis of this variation has never been defined . The recent identification of bi-directional promoters in the KIR genes indicates that the relative strength of competing sense and antisense promoters may determine the probability of gene expression , similar to the model proposed for the control of Ly49 gene expression by the Pro1 probabilistic switch [19] , [20] , [22] . To test this hypothesis , promoter polymorphisms that affect promoter activity were identified , and the frequency of receptor expression associated with individual alleles was determined . The availability of a monoclonal antibody ( DX9 ) [34] that specifically recognizes KIR3DL1 and not KIR3DS1 provided the opportunity to specifically measure the frequency of expression of a single KIR3DL1 allele in heterozygous KIR3DL1/KIR3DS1 individuals and correlate the frequency of expression with specific promoter polymorphisms . The results reveal for the first time that specific KIR3DL1 promoter polymorphisms affect the frequency of expression , which has consequences in terms of NK cell function in disease resistance .
Three KIR genes were chosen for a detailed analysis of allelic variation in the promoter region based on the availability of specific antibodies to study their expression: KIR3DL1 ( detected by DX9 and Z27 mAbs ) ; KIR2DS4 ( detected by FES172 mAb ) ; KIR2DL3 ( detected by ECM41 mAb ) . Promoter polymorphisms were identified by sequencing PCR-generated clones of the core promoter region from individual donors , as well as analysis of KIR genomic sequences deposited in GenBank . Figure 2A shows the promoter polymorphisms observed in the donor population for the KIR3DL1/S1 and KIR2DS4 genes as well as KIR2DL5 polymorphisms identified in GenBank . The most frequently observed promoter sequence is shared by the KIR3DL1*002 , -*007 , -*008 , -*015 and -*020 alleles ( shown as the reference promoter sequence ) , and single nucleotide polymorphisms ( SNPs ) are shown for other KIR3DL1 alleles as well as the KIR2DS4 and KIR2DL5 alleles . The KIR2DL1 and KIR2DL3 genes are shown as examples of KIR promoters that were not found to be polymorphic in the donor population . KIR genotyping identified 73 individuals in the NCI-Frederick donor population possessing at least one copy of the KIR2DL3 gene; however , all of the KIR2DL3 promoter sequences were identical . The KIR3DL1*004 promoter is identical to the KIR3DL1*001 promoter , but there may be no functional role for the KIR3DL1*004 allele , since the KIR protein produced by this allele is not expressed on the NK cell surface [26] . The KIR3DL1/S1 alleles possess SNPs in the YY1 , E2F , and Sp1 transcription factor binding sites , predicting functional differences in promoter activity of these alleles . On the other hand , the SNPs present in the KIR2DS4 alleles and the KIR2DL3 promoter are not associated with any predicted transcription factor binding sites , suggesting that the promoter alleles of these genes should have a similar level of activity . Previous studies have shown that the presence of ligand or competition from other KIR receptor-ligand pairs can influence the percentage of NK cells expressing a given KIR [14] , [27] , [35] . However , we have proposed that the primary determinant of the frequency of KIR gene activation is related to the probability of sense or antisense transcription from the proximal promoter [20] , [22] . This model of probabilistic KIR expression predicts that there should be differences in the relative sense and antisense activities of individual KIR proximal promoter alleles to explain the observed differences in the percentage of NK cells that express different alleles of a given KIR gene . We previously observed that the KIR3DL1*001 and KIR3DL1*002 alleles have distinct bi-directional promoter characteristics [22] . To examine the effect of sequence differences observed in all of the KIR3DL1/S1 promoter alleles , DNA fragments containing the previously identified core bi-directional promoter region ( −229 to −1 ) [22] of the KIR3DL1*001 , KIR3DL1*002 , KIR3DL1*005 , and KIR3DS1 genes were cloned into the pGL3 vector in both orientations and the forward and reverse promoter activities were determined in transfected YT-Indy human NK cells . As shown in Figure 2B , the forward and reverse promoter activities of the individual KIR3DL1 promoter alleles are distinct . Since the ratio of forward to reverse promoter activities should determine the probability of forward transcription and gene activation , the ratio is shown for each allele ( Figure 2B ) . The KIR3DL1*001 promoter had the highest ratio of forward to reverse promoter activity and KIR3DL1*002 had the lowest , predicting that these two alleles should have the highest and lowest frequency of expression respectively . The transcriptional activities of the KIR3DL1*001 and KIR3DS1 promoters in the forward direction are higher than those of the KIR3DL1*002 and KIR3DL1*005 promoters . The Sp1 site is disrupted ( G→A ) in the KIR3DS1 , KIR3DL1*001 and KIR3DL1*005 alleles ( Figure 2A ) ; however , an increase in the strength of the forward promoter is not seen in the KIR3DL1*005 promoter , suggesting that the additional polymorphism within the E2F site at position −65 unique to the KIR3DL1*005 promoter ( A→G ) counteracts the positive effect of the SNP in the Sp1 site . A recent report has demonstrated that this polymorphism reduces E2F binding , resulting in reduced forward promoter activity [36] . The forward promoter activity of the KIR3DS1 promoter was highest of the four tested , including that of the KIR3DL1*001 allele . There is an additional SNP in the YY1 site of the KIR3DS1 promoter ( T→C ) , and the YY1 site has been shown to inhibit forward and reverse promoter activities [22] , [37] . Therefore , the increased forward and reverse activities of KIR3DS1 relative to KIR3DL1*001 is likely due to a disruptive effect of the additional SNP in the YY1 site unique to the KIR3DS1 promoter . Our previous characterization of the KIR2DL5*001 promoter indicated that disruption of the YY1 site resulted in a bidirectional element with dominant reverse promoter activity [22] . Examination of the KIR2DL5*003 promoter revealed the presence of an additional polymorphism in the Sp1 site . Figure 2C compares the promoter activity of KIR2DL5*003 to the *001 and *002 alleles . The disruption of the AML-binding site in the non-transcribed KIR2DL5*002 allele generates a promoter with weakened but balanced forward and reverse activity as previously shown for the KIR3DP1 promoter [22] . The novel polymorphism in the Sp1 site of the KIR2DL5*003 promoter results in a further decrease in forward promoter activity . A recent report by Estafania et al . [38] has revealed that KIR2DL5 is expressed by only a small percentage of NK cells ( ∼5% ) , consistent with the dominant reverse promoter activity observed with the promoters of the two expressed alleles ( *001 and *003 ) . The reduced forward promoter activity of KIR2DL5*003 suggests that it will be expressed on an even lower percentage of NK cells than the KIR2DL5*001 gene . Although there were several promoter polymorphisms observed within the KIR2DS4 genes , the SNPs observed were in regions lacking known transcription factor binding sites ( Figure 2A ) . The promoter activities of the KIR2DS4 alleles and the KIR2DL3 gene were determined , and there are only small differences in the forward and reverse promoter activities of the KIR2DS4 alleles and KIR2DL3 as predicted by the lack of SNPs in the known transcription factor binding sites ( Figure 2D ) . With the exception of the non-transcribed KIR2DL5*002 allele that has an altered AML-binding site [24] ( the only KIR allele known to have such a variant ) , the transcription factor binding sites within the core bi-directional KIR promoters are conserved between individual genes and alleles . Modulation of KIR bi-directional promoter activity appears to be due to polymorphisms in the YY1 and Sp1 sites that flank the core promoter region ( Figure 2 ) . The Sp1 transcription factor binding site is downstream of the major transcription start site of the KIR forward transcript and the YY1 binding site is downstream of the region where antisense transcription is initiated [22] . In vitro promoter assays demonstrated that disruption of the YY1 site is associated with increased promoter activity in the reverse orientation , whereas polymorphisms in the Sp1 site are associated with increased forward promoter activity [22] . These results indicate that Sp1 binding has an inhibitory effect on forward transcription whereas YY1 binding attenuates antisense transcription . The KIR3DL1*001 promoter has a SNP that disrupts the Sp1 site , possesses high forward transcriptional activity and low reverse activity . ( Figure 2 ) . The KIR2DL1 promoter has 3 SNPs , one in the YY1 site , and two in the Sp1 site ( Figure 2A ) . These changes result in a promoter with high transcriptional activity in both directions [22] . The KIR2DL5A*001 promoter has a single SNP that disrupts the YY1 site , leading to dominant reverse promoter activity . The additional Sp1 polymorphism present in the KIR2DL5*003 allele further suppresses forward transcriptional activity ( Figure 2C ) . Taken together , these observations support a model where the probability of transcription in the sense or antisense direction is controlled by the flanking YY1 and Sp1 sites . Since the analysis of several KIR promoters revealed a significant effect of SNPs in the Sp1 site spanning nucleotides −24 to −33 relative to the start of translation , EMSA analysis of this region was performed with oligonucleotide probes containing the polymorphisms observed in the various promoter alleles as well as unique Sp1 site polymorphisms found in the KIR2DS5 and KIR3DL2 genes ( Figure 3A ) . As shown in Figure 3B , polymorphisms associated with increased forward promoter activity ( KIR3DL1*001 G→A; KIR2DL1 G→T ) had reduced or undetectable Sp1 binding . The Sp1 site of the KIR2DL5*003 allele bound very strongly to Sp1 , consistent with the decreased forward promoter activity of this allele . These results are consistent with the proposed modulation of forward promoter activity by Sp1 binding downstream of the major forward transcription initiation site . In order to confirm the predicted effect of the observed changes in promoter activity associated with KIR3DL1 promoter polymorphisms , the in vivo levels of antisense transcript were measured by quantitative PCR . Primers specific for KIR3DL1 or KIR3DS1 antisense transcripts were used , along with NKp46 coding region primers to control for the percentage of NK cells present in individual donor's blood . Figure 4A shows the result of antisense transcript quantitation in donors with KIR3DL1 alleles that have either strong ( KIR3DL1*002 ) or weak ( KIR3DL1*001/*004 ) antisense promoter activity based on the transfection data shown in Figure 2B . There is a significant increase in the level of antisense transcript detected when an allele with a strong antisense promoter activity is present ( KIR3DL1*002 ) . The relationship between the frequency of receptor expression and antisense transcript levels was also studied ( Figure 4B ) . A significant negative correlation was found between antisense level and the frequency of NK cells expressing a given allele , supporting the hypothesis that antisense transcription blocks KIR gene activation . Although the percentage of NK cells that express individual KIR genes has been examined [27] , [35] , no evidence for allele-based differences in expression frequency based on promoter polymorphisms has ever been reported . In order to directly assess the frequency of expression of individual KIR3DL1 alleles , donors possessing a single copy of the allele of interest were studied . The expression of a single KIR3DL1 allele was examined with the DX9 antibody , which specifically reacts with KIR3DL1 and not KIR3DS1 in donors heterozygous for KIR3DL1 and KIR3DS1 . The Z27 monoclonal antibody , on the other hand , has weak reactivity with KIR3DS1 in addition to KIR3DL1 [29] , [39] , [40]; therefore , Z27 was used to measure the frequency of NK cells expressing KIR3DS1 . Figure 5A shows the results obtained from an analysis of KIR3DL1/KIR3DS1 heterozygous donors identified by KIR typing of individuals in the NCI-Frederick Research Donor Program . The KIR3DL1*001 allele is expressed by a significantly higher proportion of NK cells than any other KIR3DL1 allele , consistent with the high level of forward transcriptional activity and low reverse activity observed for KIR3DL1*001 ( F/R ratio of 8 . 7; Figure 2B ) . The frequency of expression of KIR3DS1 was analyzed in individuals that possessed the KIR3DL1*004 allele on the opposite haplotype ( Figure 5B ) , since the KIR3DL1*004 allele is not expressed on the cell surface , thus avoiding the detection of KIR3DL1 by the Z27 mAb that reacts with both KIR3DL1 and KIR3DS1 [29] , [39] , [40] . A single KIR3DS1 allele leads to the expression of KIR3DS1 on 30–50% of the NK cell population . This high frequency of expression was unexpected since the KIR3DS1 promoter possesses an intermediate ratio of forward to reverse promoter activity ( Figure 2B ) . The lack of functional polymorphisms in the KIR2DL3 and KIR2DS4 gene promoters suggests that the corresponding KIR proteins should be expressed at similar frequencies on NK cell populations . An analysis of KIR2DL3 and KIR2DS4 expression using antibodies specific for each receptor showed that the frequency of expression observed for KIR2DL3 ( Figure 5D ) is similar to that of KIR3DL1*002 ( Figure 5A ) , consistent with the similar promoter characteristics of these two genes ( Figure 2 ) . Remarkably , the frequency of expression of KIR2DS4 was very high ( mean = 48%; Figure 4C ) , even though the KIR2DS4 promoter is also functionally equivalent to the KIR3DL1*002 promoter in the luciferase assay ( Figure 2 ) . This discrepancy may be due to a difference in the post-expression selection of NK cells expressing activating KIR . Along these same lines , it is also possible that the high frequency of KIR3DS1 expression ( Figure 5B ) may be due at least in part to post-expression selection . The comparison of KIR2DL3 expression in individuals possessing one copy of the KIR2DL3 gene ( KIR2DL2/KIR2DL3 genotype ) with individuals carrying two copies ( KIR2DL3/KIR2DL3 genotype ) revealed a clear additive effect of gene dosage on expression frequency ( Figure 5D ) . The mean KIR2DL3 expression frequency in individuals with one copy of the gene was 18% , whereas those individuals possessing two copies had a mean expression frequency of 35% . This result is consistent with the independent regulation of the two alleles . The expression frequency of two alleles should equal the sum of the frequency of expression of each allele minus the predicted frequency of cells expressing both alleles . In the case of KIR2DL3 , since each allele should have the same probability of expression ( p ) , the predicted expression of two alleles is 2p-p2 ( . 36− . 03 ) or 33% , in close agreement with the observed frequency of 35% . The effect of two copies of KIR2DS4 on expression frequency could not be determined in this study since most donors possessing only the KIR2DS4*001 allele had a KIR B haplotype on the other chromosome and would not be expected to have two copies of KIR2DS4*001 . Like the gene dosage effect of KIR2DL3 , KIR3DS1 expression frequency also appears to be additive based on gene copy number . We had previously shown that individuals with two copies of KIR3DS1 have a mean expression frequency of 61% [40] consistent with the expected frequency of 62% ( . 76− . 14 ) predicted by the current observation that an individual KIR3DS1 allele has a mean expression frequency of 38% .
There are undoubtedly many factors that contribute to the generation of the KIR repertoire , including the presence of ligands and competition between inhibitory receptors . Reports by Shilling et al . [33] and Yawata et al . [27] have shown clear effects of HLA on the frequency of expression; however , both studies concluded that the major factor controlling the degree of KIR expression was somehow related to the KIR genotype , but the mechanism was not resolved . The current study demonstrates for the first time that SNPs in transcription factor binding sites , which can occur amongst alleles of a single KIR gene , produce differences in the functional activity of the bi-directional KIR promoters that are associated with distinct frequencies of receptor expression . A correlation between forward promoter activity and frequency of gene expression was observed for the bi-directional Pro1 promoter in the murine Ly49 genes , since the reverse promoter activity was similar in all Ly49 genes examined [19] . Forward transcription from the Pro1 promoter is required for activation of the downstream Pro2 promoter that is responsible for Ly49 expression in mature NK cells , since deletion of Pro1 abrogates Ly49 gene expression [41] . Although the probabilistic activation of KIR expression is also associated with the balance between sense and antisense transcription from a bi-directional promoter , the mechanism of gene activation must be distinct from the murine Ly49 system , since KIR expression in mature human NK cells originates from a bi-directional proximal promoter that appears to lose the ability to generate antisense transcripts in mature NK cells [22] . Perhaps antisense KIR transcription in developing human NK cells antagonizes the ability of sense transcripts from the upstream distal KIR promoter to open the locus , either by direct promoter competition or the production of double-stranded RNA in the KIR proximal promoter region . The variation in sense versus antisense promoter activity of the Ly49 probabilistic promoter is controlled by competition between overlapping C/EBP and TATA elements at either end of the bi-directional element . In contrast , the core bi-directional KIR promoter is conserved , and the variation in promoter strength between genes and alleles is controlled by flanking YY1 and Sp1 sites . The A→G substitution present in the upstream YY1 site of the KIR2DL1 and KIR2DL5 promoters has previously been shown to abrogate YY1 binding [37] , consistent with the observed increase in reverse promoter activity associated with this SNP shown herein . An additional SNP in the YY1 site is present in the KIR3DS1 gene , and this change is associated with an even higher level of forward promoter activity , but this is offset by an increased level of reverse activity as well . The frequency of KIR3DS1 expression was significantly higher than KIR3DL1*001; however , the analysis of the KIR2DS4 gene suggests that additional factors beyond the characteristics of the KIR proximal promoter may control the frequency of NK cells expressing activating receptors . Although the in vitro transcriptional activities of the KIR2DS4 promoter alleles are similar to the KIR3DL1*002 allele that is expressed on a low frequency of NK cells ( ∼10% ) , KIR2DS4 is expressed by 48% of NK cells on average . This discrepancy suggests that the KIR2DS4 and possibly KIR3DS1 subsets of NK cells undergo positive selection that increases the frequency of receptor expression in the NK pool . In this respect , it is worth noting that the mouse activating receptors Ly49D and Ly49H are co-expressed at a higher frequency than predicted by the “product rule” , suggesting that their expression is not governed by stochastic processes alone [42] . The current study provides the groundwork for further investigation of the role of promoter polymorphisms in KIR gene expression patterns . The identification and analysis of KIR promoter polymorphisms in more diverse donor populations together with the development of additional antibodies specific for individual KIR gene products will provide a more complete picture of the degree to which promoter polymorphisms modulate KIR expression frequency . Nevertheless , the information provided in this report is immediately applicable to studies of KIR locus variation on human disease and may explain some of the previous associations in this regard [43] . The quality of the NK response to a given pathogen is very likely to depend on the frequency of NK cells expressing the relevant KIR , which we have shown to be dependent on the specific promoter sequence driving transcription of the KIR allele/gene . KIR3DL1 allotypes that are expressed at a high level on the NK cell surface [25] associate with delayed progression to AIDS in HIV-infected individuals [31] . A high frequency of expression of these allotypes across the NK cell population would presumably lead to a larger population of mature , functional NK cells capable of detecting the loss of HLA-B . Indeed , KIR3DL1*001 and KIR3DS1 , which show high levels of expression frequency are both protective against HIV-1 , and KIR3DS1 also shows protection against hepatitis C virus [44] . Theoretically , an improved NK sensing of HLA-loss through a greater number of NK cells expressing the appropriate sensors would enhance the ability of the individual to detect and eliminate virally infected cells that have decreased/altered HLA class I expression . Thus , it will be of much interest to determine the potential influence of these functionally-significant promoter variants on HIV disease as well as other human diseases .
Healthy volunteers were recruited through the NCI-Frederick Research Donor Program ( http://www . ncifcrf . gov/rdp/ ) . The KIR genotype of each donor was determined as previously described [45] . The monoclonal antibodies ( mAb ) used in this study were: PE-conjugated anti-CD158a/h ( KIR2DL1/S1 , EB6 , IgG1 ) ; anti-CD158b1/b2/j ( KIR2DL2/2DL3/2DS2 , GL183 , IgG1 ) ; anti-CD158e1/e2 ( KIR3DL1/S1 , ZIN276 , IgG1 ) ; anti-CD158e1 ( KIR3DL1 , DX9 , IgG1 ) ; anti-CD158i ( KIR2DS4 , FES172 , IgG2a ) ; FITC-conjugated anti-CD3 ( IgG1 , UCHT1 ) ; APC or PC5-conjugated anti-CD56 ( IgG1 , NKH . 1 ) ( Beckman Coulter Inc , Miami , FL ) ; PE-Alexa Fluor 700-conjugated anti-CD3 ( S4 . 1 , IgG2a ) ( Invitrogen Caltag , Carlsbad , CA ) . The anti-CD158b2 ( KIR2DL3 , ECM41 , IgM ) [46] was kindly provided by Dr D . Mavilio , NIAID , Bethesda , USA . FITC-labeled goat IgG fraction to mouse IgM was purchased from MP Biomedical ( Solon , OH ) . Appropriately labeled mouse isotype control mAb were purchased from the respective companies . YT-Indy cells were cultured in RPMI 1640 media containing 10% fetal bovine serum , L-Glutamine , and 100 U/ml each of penicillin and streptomycin . The proportion of NK cells expressing a particular KIR receptor was assessed in whole blood by three- or four-color flow cytometry . Briefly , 100 µl of EDTA-treated blood was incubated with the appropriate cocktail of mAbs . Erythrocytes were lysed with an ammonium chloride solution and the remaining cells were analyzed with a FACSort flow cytometer ( Becton & Dickinson , San Jose , CA ) . Events ( 25 , 000 ) were collected in the lymphocyte gate and analyzed . NK cells were defined as CD3−CD56+ lymphocytes . KIR3DS1 expression on NK cells was investigated by using DX9 and ZIN276 ( Z27 ) mAbs as previously described [37] . KIR2DL3 expression on NK cells was obtained by using GL183 and ECM41 mAbs . Results were expressed as percentages of NK cells positive for one given KIR receptor . Promoter fragments were generated by PCR using a gene-specific forward primer starting at −229 and a reverse primer starting at −1 relative to the start codon of the gene . PCR products were cloned into the TOPO-TA vector ( Invitrogen , Carlsbad , CA , USA ) , and inserts were excised with either SacI/XhoI or XhoI/HindIII and cloned into pGL3 ( Promega , Madison , WI , USA ) to generate constructs in both forward and reverse orientations . All constructs were verified by sequencing with specific primers . Sequence analysis was performed with the SeqWeb package at the NCI- Frederick supercomputing center . YT-Indy cells were transfected by electroporation with a BTX ECM 830 ( Gentronics , San Diego , CA , USA ) set at 250 mV , with 3 pulses of 7 ms at an interval of 100 ms . A total of 5×106 cells in 0 . 5 ml of serum free RPMI medium were transfected with 10 µg of the specific reporter construct plus 0 . 1 µg of the Renilla luciferase pRL-SV40 vector . Luciferase activity was assayed at 48 hr using the Dual-Luciferase Reporter Assay System ( Promega ) according to the manufacturer's instructions . The luciferase activity of the KIR3DL1 promoter constructs was normalized relative to the activity of the Renilla luciferase produced by the pRL-SV40 control vector and each construct was tested in at least three independent experiments . Total cellular RNA was isolated from peripheral blood mononuclear cells with the RNAqueous-4PCR Kit ( Applied Biosystems/Ambion , Austin , TX ) , and further purified using DNase I according to the manufacturer's instructions . cDNA synthesis was carried out at 55°C using Oligo ( dT ) 18 or KIR3DL1/S1-specific- TGGTTTATT ( A ) GTCACAATTG-3′ RT primers with the Transcriptor First Strand cDNA Synthesis Kit ( Roche , Indianapolis , IN ) . Taqman real time RT-PCR primers and probes for target genes were: KIR3DL1 antisense transcript Fwd- ATTGTCACAATTGCTCTGAAAACC -3′; Rev- CATGGCTTCCTGGAAATTGCT -3′ and probe: 5′ ( FAM ) -CATGTTAGCACAGATTTTAGGCATCTCGTG - ( MGB ) 3′ . NKp46 Fwd- GGCTGTGTCTGAGTCAGAG -3′; Rev- GAGTTCATGTCCGGGATGTAG -3′ and probe 5′ ( VIC ) - CATCTGGGCCGAGCCCCATTTCATG - ( MGB ) 3′ . The PCR reactions were performed in 20 µl final volume containing 30 ng of cDNA , 1×Master Mix ( TaqMan Universal PCR Master Mix , ABI , CA ) , 500 nM of each primer and 100 nM of each probe , respectively . The thermal cycling conditions were 40 cycles of PCR amplification ( UNG incubation: 50°C , 2 min; AmpliTaqGold activation: 95°C , 10 min; denaturation: 95°C , 15 s; annealing/extension: 60°C , 1 min ) ( 7500 Fast Real-Time PCR System , Applied Biosystems , Foster City , CA ) . All assays were performed on the same plate in triplicate . Triplicate Ct values were analyzed using the comparative Ct ( ΔΔCt ) method as described by the manufacturer ( Applied Biosystems , Foster City , CA , USA ) . The relative amount of KIR3DL1 antisense transcript ( 2−ΔΔCt ) was obtained by normalization to NKp46 and relative to the level of YT-Indy . Nuclear extracts were prepared from YT-Indy cells using the CellLytic NuCLEAR extraction kit ( Sigma-Aldrich , St . Louis , MO ) . Protein concentration was measured with a Bio-Rad protein assay ( Hercules , CA ) and samples were stored at −70°C until use . All buffers contained a protease inhibitor cocktail ( 2 mM 4- ( 2-aminoethyl ) benzenesulfonylfluoride , 1 . 4 pM trans-epoxysuccinyl-l-leucylamido [4-guanidinobutane] , 130 pM bestatin , 1 µM leupeptin , and 0 . 3 pM aprotinin; Sigma-Aldrich ) . Eight double-stranded DNA oligonucleotide probes corresponding to the predicted Sp1-binding sequence of the KIR promoter alleles were synthesized ( Figure 3A , sense strand shown ) . Sense and anti-sense oligonucleotides were annealed to generate the double-stranded oligonucleotides and labeled with [α-32P]dCTP ( 3000 Ci/mmol; Perkin Elmer , Waltham , MA ) by fill-in using the Klenow fragment of DNA polymerase I ( Invitrogen , Carlsbad , CA ) . Radio-labeled double-stranded oligonucleotides were purified using mini Quick Spin Oligo Columns ( Roche , GmbH , Mannheim , Germany ) . DNA-protein binding reactions were performed in a 10 µl mixture containing 10 µg of nuclear protein and 1 µg of poly ( dI-dC ) poly ( dI-dC ) ( Sigma-Aldrich ) in 4% glycerol , 1 mM MgCl2 , 0 . 5 mM EDTA , 0 . 5 mM DTT , 50 mM NaCl , 10 mM Tris-HCl ( pH 7 . 5 ) . After a 10-min incubation on ice , samples were incubated with 1 µl 32P-labeled oligonucleotide probe ( 20 , 000 cpm ) at room temperature for 20 min , and then loaded on a 5% polyacrylamide gel ( 37∶5∶1 ) . Electrophoresis was performed in 0 . 5×TBE buffer for 2 hours at 130 V and the gel was visualized by autoradiography after 2 days exposure at −70°C . For antibody supershift experiments , nuclear extracts were incubated with 2 µl of anti-Sp1 antibody ( Santa Cruz Biotechnology , Santa Cruz , CA ) for 1 h on ice prior to the addition of 32P- labeled DNA probe . After addition of labeled DNA-probe , the binding reaction was incubated for additional 20 min at room temperature . The human Sp1 recombinant protein ( rhSP1 , Promega , Madison , WI ) was used as control . For competition analyses , unlabeled-competitor probe ( Sp1 consensus ) and AP2 probes were included in the binding reaction . Statistical analysis of allele expression frequencies was performed using GraphPad Prism software . Comparison of distributions was performed using a Mann-Whitney U test . The correlations between the level of KIR3DL1 antisense transcript and the frequency of NK cells expressing a given allele were assessed by Spearman's correlation coefficient . All p values reported were two-tailed , with significance defined as p<0 . 05 .
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Natural killer ( NK ) cells represent a specialized blood cell that plays an important role in the detection of virus-infected or cancer cells . NK cells recognize and kill diseased cells using receptors for self antigens ( HLA ) that are frequently altered on aberrant cells . The HLA receptors are known as Killer cell Immunoglobulin-like Receptors , or KIR . Humans possess from four to 14 KIR receptor genes in their genome , and individual NK cells express a subset of the available KIR genes , generating specialized NK cells that detect alterations in specific HLA proteins . The mechanism of this unusual selective gene activation was recently shown by our group to be controlled by a probabilistic bi-directional promoter switch that turns on a given gene at a pre-determined frequency in the NK cell population . The current study shows that the properties of the switches in terms of the relative activity of forward ( on ) versus reverse ( off ) promoter activity is directly correlated with the frequency at which a given gene is expressed within the NK cell population . These results have important implications for our understanding of the role of NK cells in viral resistance and bone marrow transplants .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunology/genetics",
"of",
"the",
"immune",
"system",
"immunology/leukocyte",
"signaling",
"and",
"gene",
"expression",
"immunology/innate",
"immunity"
] |
2008
|
Genetic Control of Variegated KIR Gene Expression: Polymorphisms of the Bi-Directional KIR3DL1 Promoter Are Associated with Distinct Frequencies of Gene Expression
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Plants have evolved strong innate immunity mechanisms , but successful pathogens evade or suppress plant immunity via effectors delivered into the plant cell . Hyaloperonospora arabidopsidis ( Hpa ) causes downy mildew on Arabidopsis thaliana , and a genome sequence is available for isolate Emoy2 . Here , we exploit the availability of genome sequences for Hpa and Arabidopsis to measure gene-expression changes in both Hpa and Arabidopsis simultaneously during infection . Using a high-throughput cDNA tag sequencing method , we reveal expression patterns of Hpa predicted effectors and Arabidopsis genes in compatible and incompatible interactions , and promoter elements associated with Hpa genes expressed during infection . By resequencing Hpa isolate Waco9 , we found it evades Arabidopsis resistance gene RPP1 through deletion of the cognate recognized effector ATR1 . Arabidopsis salicylic acid ( SA ) -responsive genes including PR1 were activated not only at early time points in the incompatible interaction but also at late time points in the compatible interaction . By histochemical analysis , we found that Hpa suppresses SA-inducible PR1 expression , specifically in the haustoriated cells into which host-translocated effectors are delivered , but not in non-haustoriated adjacent cells . Finally , we found a highly-expressed Hpa effector candidate that suppresses responsiveness to SA . As this approach can be easily applied to host-pathogen interactions for which both host and pathogen genome sequences are available , this work opens the door towards transcriptome studies in infection biology that should help unravel pathogen infection strategies and the mechanisms by which host defense responses are overcome .
During co-evolution with pathogens , plants have evolved multiple immune signaling mechanisms that successful pathogens have evolved to evade or suppress . The first layer is based on recognition of broadly conserved pathogen molecules ( pathogen/microbe-associated molecular patterns , PAMP/MAMPs ) by plant cell surface pattern-recognition receptors ( PRRs ) , resulting in PAMP- ( or pattern ) -triggered immunity ( PTI ) [1] . However , PTI can be suppressed by pathogen proteins , termed effectors , that are delivered into the apoplast or plant cell cytoplasm , resulting in effector-triggered susceptibility . Plants also carry a second layer of defense , so-called effector triggered immunity ( ETI ) , in which cytoplasmic disease resistance ( R ) proteins recognize directly or indirectly the presence of pathogen effectors . Recognized effectors are often known as avirulence ( AVR ) proteins [2] , [3] . A hallmark of ETI is the hypersensitive response ( HR ) , which involves programmed cell death at pathogen infection sites and helps resist biotrophic pathogens . In many oomycetes , such as Phytophthora spp . and downy mildews , the most common host-translocated effectors are the RxLR-type proteins that contain an N-terminal signal peptide and a RxLR ( or RxLR-EER ) motif involved in secretion and host uptake , and a C-terminal domain carrying the effector activity [3]–[5] . Hyaloperonospora arabidopsidis ( Hpa; formerly Peronospora parasitica or Hyaloperonospora parasitica ) is an obligate biotrophic oomycete that causes downy mildew in Arabidopsis thaliana . The Arabidopsis-Hpa pathosystem has been extensively used to study host/pathogen co-evolution , and has enabled identification of cognate host R and pathogen AVR genes , termed RPP ( recognition of Peronospora parasitica ) and ATR ( Arabidopsis thaliana recognized ) , respectively [6] . Genome analysis of Hpa isolate Emoy2 identified 134 high-confidence effector candidates ( HaRxL genes ) [7] . Comprehensive screening of HaRxL effectors revealed that the majority of HaRxLs contribute positively to pathogen fitness [8] , [9] . In addition , HaRxLs can be located in different subcellular compartments in planta [10] . Some have been shown by yeast two hybrid screens to interact with various plant proteins [11] . However , the mechanisms by which most Hpa effectors promote virulence remain to be elucidated . Salicylic acid ( SA ) is a phytohormone essential for the immune response against biotrophic pathogens [12] . SA biosynthesis is triggered during both PTI and ETI [13] . Signaling downstream of SA is largely controlled by the regulatory protein NON-EXPRESSOR OF PR GENES1 ( NPR1 ) , which upon activation by SA acts as a transcriptional coactivator of a large set of defense-related genes , such as PATHOGENESIS-RELATED GENE 1 ( PR1 ) [14] . Another phytohormone , jasmonic acid ( JA ) , is synthesized upon pathogen and herbivore attack , and is essential for the immune response against necrotrophic pathogens and herbivores [15] . Multiple studies revealed a mutually antagonistic interaction between SA- and JA-dependent signaling [16] , [17] . Some pathogens and herbivores appear to induce SA-JA crosstalk [18]–[23] . For example , Pseudomonas syringae produces coronatine , a toxin that mimics the bioactive jasmonate JA-isoleucine [24] and promotes stomatal reopening and bacterial propagation in both local and systemic tissues by inhibiting SA signaling and accumulation [20] , [23] . In addition to SA and JA , recent studies have revealed involvement of other phytohormones , such as ethylene ( ET ) , abscisic acid ( ABA ) , gibberellin and auxin , in biotic interactions [25] . Remarkably , several pathogens produce phytohormones and phytohormone mimics like coronatine in P . syringae . To dissect the Arabidopsis-Hpa interaction , changes in expression of Arabidopsis or Hpa genes during infection were previously investigated by microarray analysis for Arabidopsis genes [26]–[29] and by cDNA-amplified fragment length polymorphism and expressed sequence tag analysis for Hpa genes [30]–[32] . In Hpa , however , these approaches were not sensitive enough to enable genome-wide quantification of changes in gene expression during infection . Expression profiling in Arabidopsis or Hpa was carried out with different Arabidopsis accessions , Hpa isolates , plant ages and infection time courses , hindering comparison of these data . Recently , we established a high-throughput mRNA expression-profiling method ( Expression Profiling through Random Sheared cDNA tag Sequencing [EXPRSS] ) enabling the detection of differential expression of more genes , with higher sensitivity , than microarray and traditional RNA sequencing methods [33] . Briefly , EXPRSS is a restriction enzyme-independent tag-sequencing method and generates one tag per transcript at a relatively defined position from the 3′ end of a gene , ensuring no length-based data transformation and enabling expression data to be obtained at a ∼10× greater read depth than standard Illumina RNA sequencing . This is helpful when we investigate low-level transcripts , such as pathogen transcripts in host-pathogen interactions . Using EXPRSS , we monitored mRNA levels for both Arabidopsis and Hpa genes during infection . Here , we report the expression patterns of Hpa predicted effectors and Arabidopsis genes on the basis of transcriptome data in Arabidopsis Col-0 inoculated with the avirulent Hpa isolate Emoy2 ( recognized by RPP4 [34] ) or the virulent isolate Waco9 . From this analysis , we found that ATR1 ( recognized by RPP1 [35] ) is not expressed in Hpa Waco9 , and after resequencing the Waco9 genome , we found the ATR1 region is deleted . An Hpa effector HaRxL62 , previously shown to enhance host susceptibility [8] , [9] , was highly expressed in this assay , and was shown here to suppress responsiveness to SA .
Arabidopsis Col-0 was inoculated with either the avirulent isolate Emoy2 ( incompatible interaction ) or the virulent isolate Waco9 ( compatible interaction ) of Hpa , and infected plants were harvested at 1 , 3 and 5 days post-inoculation ( dpi ) prior to Illumina sequencing using EXPRSS [33] . Hpa haustoria are formed in both compatible and incompatible interactions till 1 dpi , and HR cell death is observed only in incompatible interactions [36] . HR was observed in Hpa Emoy2-inoculated leaves of Col-0 from 3 dpi , whereas no visible HR was observed at 1 dpi ( Figure 1A ) . After Hpa Waco9 inoculation , extensive growth of intercellular mycelium was evident on leaves from 3 dpi , and then sporulation ( conidiophores bearing conidiospores ) was observed at 5 dpi ( Figure 1A ) . In addition to the infectious stages , samples were taken from intact plants ( 0 dpi ) and water-sprayed ( mock-treated ) plants as control samples for transcriptome analysis in Arabidopsis . Further , to evaluate the expression pattern of Hpa genes , samples were taken from conidiospores before inoculation . The experiment was carried out with three independent biological replicates . Total RNA was prepared from infected plants , and libraries for EXPRSS were prepared . Although 36 bp sequencing reads are sufficient to identify Arabidopsis genes distinctly using EXPRSS [33] , longer sequencing reads ( 80 bp ) were used in this study to avoid cross-mapping to the Arabidopsis and Hpa genomes . The Illumina sequencing reads were mapped to the combined genome of Arabidopsis TAIR10 and Hpa Emoy2 v8 . 3 [7] ( Figure 1B ) . Mapped-reads to Arabidopsis and Hpa genomes were counted separately and the distribution of mean expression of each gene was represented as TPM ( tags per million ) of total reads mapped to Arabidopsis or Hpa genomes . To provide sufficient depth for expression analysis of Hpa genes in infected plants , Illumina sequencing was carried out twice for the incompatible interaction ( Hpa Emoy2-inoculated plants ) and for the early time point ( at 1 dpi ) of the compatible interaction ( Hpa Waco9-inoculated plants ) . In this study , we did the analyses using uniquely mapped or up to 10 matching reads ( Table S1 and Datasets S1 , S2 and S3; see Materials and Methods ) . Using only uniquely mapped reads would give a minimum estimate of high confidence in gene expression , but we might even discard the information for homologous genes . Although we cannot rule out the presence of some false positives and false negatives in the data using up to 10 matching reads , the data would contain more information including homologous genes . For these reasons , the data with up to 10 matching reads were used in the following analyses . Most reads in intact and mock-treated plants were mapped to the Arabidopsis genome ( i . e . % Hpa reads <0 . 005 ) , whereas most reads from Hpa conidiospores were mapped to the Hpa genome ( i . e . % Hpa reads >91 . 7 ) ( Table 1 and Figure S1 ) . The reads mapped to the Arabidopsis genome in samples from Hpa conidiospores are likely to be due to Arabidopsis contamination in the spore inoculum , as Hpa was propagated on susceptible Arabidopsis accessions and its conidiospores were collected from infected Arabidopsis leaf tissues . The results suggest high gene-identification accuracy between Arabidopsis and Hpa in this study . In the incompatible interaction , the number of Hpa reads clearly decreased from 1 dpi , whereas the population of Hpa reads increased in the compatible interaction ( Figure S1 and Table 1 ) . This indicates that Hpa Emoy2 dies upon recognition after 1 dpi , corresponding to visible HR from 3 dpi with Emoy2 ( Figure 1A ) . Hence , the data at 3 and 5 dpi with Emoy2 were omitted from the Hpa transcriptome data . The analysis of the overall transcriptome data revealed that out of 27 , 416 protein coding genes in Arabidopsis TAIR10 and 14 , 489 genes in Hpa v8 . 3 , 24 , 559 ( 89 . 6% ) for Arabidopsis and 11 , 394 ( 78 . 6% ) and 11 , 690 ( 80 . 7% ) for Hpa Emoy2 and Waco9 , respectively , were expressed in at least one of the samples ( Table 2 and Datasets S1 , S2 and S3 ) . The Hpa Emoy2 genome analysis revealed 134 high-confidence effector candidates ( HaRxLs ) with a signal peptide and canonical RxLR ( or RxLR-EER ) motif [7] . These include effector candidates HaRxL17 , HaRxL44 and HaRxL96 [10] , [18] , [37] and avirulent effectors ATR1 , ATR13 and ATR39 [35] , [38] , [39] . ATR5 containing a signal peptide and canonical EER motif , but not a canonical RxLR motif , was identified as an avirulence gene recognized by RPP5 [40] . This report suggests the existence of effector candidates without canonical RxLR motif . In our study , we defined a total of 475 genes as predicted effectors ( Table S2 ) . The selection criteria for predicted effectors were the following: ( 1 ) high-confidence effector candidates ( HaRxLs ) , ( 2 ) RxLR-like genes with at least one non-canonical feature , as for ATR5 ( HaRxLLs ) , ( 3 ) putative Crinkler-homologous genes with RxLR motif ( HaRxLCRNs ) [4] , ( 4 ) homologous genes based on amino acid sequence similarity over the 5′ region including a signal peptide and RxLR motif ( e . g . HaRxL1b ) . Transcriptome analysis of the compatible interaction revealed that 277 predicted effectors were expressed in at least one infection time point ( Table 2 ) . By quantifying the expression level , we found predicted effectors expressed highly during infection , e . g . HaRxL76 and HaRxL62 ( about 0 . 2% and 0 . 1% of total Hpa mRNA at 3 dpi , respectively ) . In addition , most of the highly-expressed predicted effectors were upregulated more than two fold at 3 dpi compared to the expression level in conidiospores ( Figure 2A ) . These findings suggested specific regulation of expression of some predicted effector genes upregulated at 3 dpi . To predict potential cis-regulatory elements in the upstream regions of Hpa genes , we categorized genes into five groups as follows; 87 predicted effectors which were induced more than two fold at 3 dpi ( induced effectors ) , 115 predicted effectors which were detected at 3 dpi but were not induced more than two fold at 3 dpi ( non-induced effectors ) , 1 , 880 genes excluding predicted effectors which were induced more than two fold at 3 dpi ( induced genes exc effectors ) , 4 , 776 genes excluding predicted effectors which were detected at 3 dpi but were not induced more than two fold ( non-induced genes exc effectors ) , and 14 , 489 genes predicted in Hpa v8 . 3 ( all genes ) ( Table S3 ) . The expression pattern of “induced effectors” and “non-induced effectors” was similar to “induced genes exc effectors” and “non-induced genes exc effectors” , respectively ( Figure 2B ) . The sets of promoters of “induced effectors” and “non-induced effectors” were searched separately for conserved motifs using MEME [41] , and then the motifs found were evaluated for over-representation in other groups using FIMO [42] . The INR-FPR motif , known as a core promoter element in oomycete genes [43] , [44] , was over-represented within 200 nt upstream of the start codon of “induced effectors” ( E-value = 9 . 3e-068 ) ( Figure 2C and D ) . The motif was also significantly over-represented in “non-induced effectors” and “induced genes exc effectors” ( Figure 2D and Table S4 ) , suggesting that INR-FPR motif is enriched in promoters of predicted effectors and genes induced during infection in Hpa . We also found two novel motifs ( Motif I and II ) within 500 nt upstream of the start codon that do not show any significant similarity to known motifs as determined by a TOMTOM search against the JASPAR database [45] . Interestingly , Motif I was overrepresented in only “induced effectors” ( E-value = 8 . 0e-003 ) , whereas Motif II was overrepresented in only “non-induced effectors” ( E-value = 1 . 1e-003 ) ( Figure 2F , G , I and J ) . The results suggest that Motif I and II might play a role in the regulation of the expression of predicted effector genes in Hpa . To evaluate whether these motifs are conserved in other oomycetes , we checked the presence of these motifs in promoters of Phytophthora infestans genes co-expressed during infection according to microarray data [46] . As reported previously [43] , [44] , INR-FPR was over-represented in P . infestans RxLR effectors and genes induced during infection as observed for Hpa ( Figure 2E and Table S4 ) . Motif I and Motif II were not significantly over-represented in promoters of P . infestans genes ( Figure 2H and K ) , suggesting that these novel motifs might be Hpa-specific cis-regulatory elements . Transcriptome analysis revealed that 355 and 366 predicted effectors were expressed in conidiospores and/or infections with Hpa Emoy2 and Waco9 , respectively ( Table 2 ) . Of these , 339 predicted effectors were expressed in both Hpa Emoy2 and Waco9 , whereas 16 and 27 predicted effectors were expressed in only Hpa Emoy2 and Waco9 , respectively ( Figure 3A and Table S5 ) . ATR5 , an effector recognized by RPP5 [40] , was found among the 339 predicted effectors expressed in both Hpa Emoy2 and Waco9 ( Figure 3B and Table S5 ) . The Waco9 allele of ATR5 is identical to the Emoy2 allele . Surprisingly , while ATR1 was expressed in Hpa Emoy2 , no tag corresponding to ATR1 in Hpa Waco9 was detected ( Figure 3B and Table S5 ) . We resequenced Hpa Waco9 genome using an Illumina Genome Analyzer II , and found that the genomic region that includes ATR1 is deleted in Waco9 ( Figure 3C ) . These results suggest that Hpa Waco9 can infect plants containing functional RPP1 , but not plants containing functional RPP5 . To evaluate this possibility , several Arabidopsis accessions were inoculated with Hpa Emoy2 and Waco9 . ATR1 from Hpa Emoy2 is recognized by RPP1-Nd from Arabidopsis Nd-1 accession and RPP1-WsA and RPP1-WsB from Arabidopsis Ws-2 accession ( the accession previously reported as Ws-0 in our laboratory is in fact Ws-2 ) [35] . As expected , Arabidopsis Nd-1 and Ws-2 are resistant to Hpa Emoy2 , but susceptible to Hpa Waco9 ( Figure S2 ) . We also checked the phenotype on an Arabidopsis RIL 3860 ( 3860 ) , a recombinant inbred line from a cross between Col-5 and Nd-1 that lacks RPP1-Nd , and a transgenic 3860 line containing the functional RPP1-Nd gene ( 3860:RPP1Nd ) [35] . Like Arabidopsis Nd-1 and Ws-2 , 3860:RPP1Nd is resistant to Hpa Emoy2 , but susceptible to Hpa Waco9 , whereas Arabidopsis 3860 is susceptible to both Hpa Emoy2 and Waco9 ( Figure 3D ) . On the other hand , no Hpa sporulation was observed on Arabidopsis Ler-0 accession containing functional RPP5 , RPP5-Ler , inoculated with Hpa Emoy2 and Waco9 ( Figure S2 ) . To confirm if Hpa Emoy2 and Waco9 are recognized by RPP5-Ler , Arabidopsis CW84 , a broadly Hpa-susceptible recombinant inbred line generated from a cross between Col-0 and Ws-2 [47] , and CW84 transformants containing RPP5-Ler ( CW84:RPP5Ler ) [40] were inoculated with Hpa Emoy2 and Waco9 . Like Arabidopsis Ler-0 , CW84:RPP5Ler is resistant to both Hpa Emoy2 and Waco9 , whereas Arabidopsis CW84 is susceptible to both Hpa isolates ( Figure 3D ) . These results indicate that Hpa Waco9 overcomes recognition by RPP1 , but not RPP5 , through the deletion of ATR1 from its genome . We investigated Arabidopsis gene expression during infection with Hpa Emoy2 and Waco9 . The expression of 24 , 559 Arabidopsis protein-coding genes ( 89 . 6% of the 27 , 416 protein-coding genes predicted in Arabidopsis TAIR10 ) was detected in at least one time point ( Tables 2 and Dataset S1 ) . Of these , 1 , 048 Arabidopsis genes showed significant changes in gene expression ( FDR = 0 . 001 ) after inoculation with Hpa Emoy2 or Waco9 ( Table S6 ) . To reveal compatible- or incompatible-interaction-specific changes in gene expression , we determined the level of overlap of differentially expressed Arabidopsis genes between infections with Hpa Emoy2 and Waco9 ( Figure 4A ) . We found that many genes were specifically upregulated at 1 dpi with Hpa Emoy2 ( 80 genes ) and at 3 and 5 dpi with Hpa Waco9 ( 335 and 863 genes , respectively ) ( Figure 4A ) . The Arabidopsis genes upregulated at 1 dpi with Hpa Emoy2 , but not Waco9 , might be induced upon recognition by RPP4 ( i . e . ETI ) , while the genes upregulated in the interaction with Hpa Waco9 , but not Emoy2 , might be genes targeted by Hpa to enhance susceptibility . Therefore , we focused on upregulated Arabidopsis genes at 1 dpi with Hpa Emoy2 and at 3 and 5 dpi with Hpa Waco9 , and categorized them into four groups: Group I , 81 upregulated Arabidopsis genes at 1 dpi with Hpa Emoy2; Group II , 98 upregulated Arabidopsis genes at only 3 dpi with Hpa Waco9; Group III , 297 upregulated Arabidopsis genes at both 3 and 5 dpi with Hpa Waco9; Group IV , 516 upregulated Arabidopsis genes at only 5 dpi with Hpa Waco9 ( Figure 4B , C and Table S7 ) . Interestingly , 86 . 4% of Arabidopsis genes in Group I ( 70 genes ) were also upregulated at 3 and/or 5 dpi with Hpa Waco9 . Gene Ontology ( GO ) term enrichment analysis showed that responses involved in disease resistance ( e . g . defense response , GO:0006952; response to salicylic acid stimulus , GO:0009751 ) were significantly enriched in all Groups ( Figure 4D left ) . These findings suggest that defense-related Arabidopsis genes upregulated at early time points in the incompatible interaction are similarly regulated at late time points in the compatible interaction . This is consistent with previous reports on expression profiling in Arabidopsis and Hpa interactions [26]–[29] . On the other hand , genes responsive to ET ( GO:0009723 ) and hormones ( GO:0009725 ) , such as ABA ( GO:0009737 ) and auxin ( GO:0009733 ) , were overrepresented in Group II , III and/or IV but absent in Group I , highlighting genes induced specifically during a compatible interaction ( Figure 4D right ) . In these Groups , we also found overrepresentation of genes related to nitrate transport ( GO:0015706 ) , water deprivation ( GO:0009414 ) and starvation ( GO:0042594 ) ( Figure 4D right ) . Defense-related Arabidopsis genes including SA-responsive genes were found to be upregulated not only at 1 dpi with Hpa Emoy2 but also at 3 and 5 dpi with Hpa Waco9 ( Figure 4 ) . Indeed , there was a positive correlation between these genes and genes upregulated by treatment with benzothiadiazole S-methylester ( BTH; a functional analog of SA ) [48] ( Figure 5A and Table S8 ) . At 1 dpi , BTH-inducible genes , such as PR1 , were upregulated by inoculation with Hpa Emoy2 , but not Hpa Waco9 , whereas these genes were upregulated at 3 and 5 dpi with Hpa Waco9 ( Figure 5A and B ) . Recently , we reported the cell-specific expression pattern of PR1 in a compatible interaction by infecting PR1::GUS lines with Hpa Waco9 [18] . PR1::GUS expression is suppressed in haustoriated cells , but not in non-haustoriated adjacent cells ( Figure 5C ) [18] , but this could arise either via suppression of SA biosynthesis or SA responsiveness in these cells . To distinguish these possibilities , we investigated the effect of Hpa infection on SA- and BTH-inducible PR1::GUS expression . PR1::GUS lines at 4 dpi with Hpa Waco9 or mock infected were treated with SA , BTH or water . As expected , we observed GUS staining in non-infected PR1::GUS lines after treatment with SA and BTH ( Figures 5D and S3 ) . In Hpa-infected PR1::GUS lines , although GUS staining was observed in non-haustoriated cells after SA and BTH treatment , Hpa-haustoriated cells were not stained ( Figures 5D and S3 ) . These results suggest that Hpa suppresses the expression of PR1 induced by treatment with SA and BTH . Thus , Hpa suppresses SA responsiveness by interfering with signaling , but not by promoting SA degradation . We also investigated the cell-specific expression pattern of PR1::GUS in the incompatible interaction . GUS staining was observed in cells that Hpa Emoy2 had infected and the surrounding cells at 1 dpi , and observed in the cell layer surrounding cells in which HR cell death had occurred at 2 dpi ( Figure 5C ) . These results are consistent with expression profiling data derived from whole Hpa-infected tissues ( Figure 5A and B ) . Histochemical GUS analysis in Hpa-infected PR1::GUS lines showed that Hpa suppresses SA-inducible PR1 expression specifically in the haustoriated cells into which RxLR effectors are delivered ( Figure 5D ) . To identify Hpa effectors which participate in the suppression , the level of PR1 expression after treatment with SA was checked in transgenic lines expressing Hpa predicted effectors and the SA-insensitive npr1 mutants [49] , as a positive control . Nine Hpa effector-expressing lines showed more susceptibility to Hpa compared to wild type ( WT ) Col-0 plants [8] , [10] ( Figure S4 and Table S9 ) . HaRxL62-expressing lines showed a five-fold reduction in expression level of PR1 compared to WT after SA treatment , whereas no significant reduction was observed in eight other Hpa effector-expressing lines , including HaRxLL464-expressing lines ( Figure 6A ) . To evaluate the effect of HaRxL62 on Hpa growth after treatment with SA , WT plants , npr1 mutants and HaRxL62- and HaRxLL464-expressing lines were treated with SA or water as a mock treatment and , 24 hours later , inoculated with Hpa Waco9 ( Figure 6B ) . Although water-treated WT plants were susceptible to Hpa Waco9 , no Hpa growth was observed in SA-treated WT plants . As expected , SA did not trigger resistance to Hpa in npr1 mutants . In HaRxLL464-expressing plants treated with SA , essentially no Hpa spores were observed as observed for WT plants , whereas there were countable Hpa spores in HaRxL62-expressing plants treated with SA ( Figure 6B ) , consistent with reduction in expression level of PR1 after treatment with SA ( Figure 6A ) . As shown in Figure 2A , HaRxL62 was the second-highest expressed Hpa effector at 3 dpi . These results suggest that HaRxL62 , a highly-expressed effector during infection , reduces responsiveness to SA .
A comprehensive understanding of host-pathogen interactions requires knowledge of the associated gene expression changes in both the host and the pathogen . However , in most cases , expression profiling has focused on either the host or the pathogen due to limitations and obstacles of older methods that involve microarrays [50] . In this study , using a high-throughput expression profiling method , EXPRSS [33] , the transcriptomes of both Arabidopsis and Hpa in compatible and incompatible interactions were analyzed in parallel . With comparative genomics , we revealed that Hpa Waco9 evades RPP1-mediated resistance through deletion of cognate AVR gene ATR1 . Histochemical analysis showed that Hpa suppresses SA-inducible PR1 expression specifically in infected cells . Finally , we found a highly-expressed Hpa effector candidate involved in suppression of responsiveness to SA . SA has been implicated as an important signal in plant immune signaling [51] , [52] . For example , Arabidopsis eds5/sid1 and ics1/sid2 mutants in which SA levels are reduced [53] , [54] are more susceptible to both virulent and avirulent forms of P . syringae and Hpa [51] . Expression profiling in Arabidopsis showed that SA-responsive genes including PR1 are activated not only at early time points in the incompatible interaction but also at late time points in the compatible interaction ( Figure 5A and B ) , consistent with previous reports [26]–[29] . Most recently , we reported that Hpa suppresses expression of PR1::GUS specifically in cells containing haustoria , into which host-translocated effectors are delivered , but not in non-haustoriated adjacent cells , which show high expression levels of PR1::GUS [18] . Here , we showed less PR1::GUS expression in Hpa-haustoriated cells after treatment with SA and BTH , indicating that Hpa interferes with the recognition of SA and/or downstream signaling after the recognition ( Figure 5D ) . HaRxL62-expressing plants showed significant reduction in SA-induced expression of PR1 and compromised resistance to Hpa after treatment with SA ( Figure 6 ) . HaRxL62 may make an important contribution to the virulence of Hpa because of its high expression levels during infection ( Figure 2A ) . However , the suppression of SA-inducible resistance to Hpa in HaRxL62-expressing plants was moderate even though HaRxL62-expressing plants and npr1 mutant plants showed comparable susceptibility to Hpa ( Figure 6B ) . These findings suggest that HaRxL62 also targets other defense pathway ( s ) than the SA pathway and other Hpa effectors must also participate in suppression of responsiveness to SA . Anderson et al . ( 2012 ) [37] showed that HaRxL96 suppresses PR1 expression , but not SA biosynthesis , induced by inoculation with an avirulent isolate of Hpa . HaRxL44 attenuates SA-dependent transcription through interfering with Mediator function by degrading MED19a , a transcriptional component involved in SA/JA crosstalk [18] . Our cell biology analysis also reveals a shortcoming of transcriptome analysis using whole tissues . We show that during Hpa infection , PR1 is expressed in non-haustoriated adjacent cells , but not in haustoriated cells . We presume that recognition of diffusible PAMPs from Hpa leads to PTI , resulting in SA biosynthesis and PR1 expression , and Hpa suppresses the responses in colonized cells by delivering effectors . Better methods are required for cell-type specific expression profiling specifically in haustoriated cells . In addition to SA and JA , other phytohormones , such as ET , ABA and auxin , are also implicated in plant immunity [25] . ETHYLENE INSENSITIVE3 ( EIN3 ) and ETHYLENE INSENSITIVE3-LIKE1 ( EIL1 ) , two closely related Arabidopsis transcription factors known to regulate the ET pathway , repress biosynthesis of SA by binding directly to the promoter of the SA biosynthetic gene ICS1/SID2 [55] . Consistent with this , plants mutated in EIN3/EIL1 and the key ET-signaling protein EIN2 exhibit enhanced resistance to P . syringae [55] in spite of suppressed signaling of FLS2 which recognizes the bacterial PAMP flagellin [56] . Increased susceptibility to P . syringae and Hpa is observed in plants treated with ABA and in ABA over-accumulating plants , and vice versa in ABA-deficient mutants [57]–[59] . Similarly , elevated auxin signaling correlates with increase in susceptibility to P . syringae and Hpa [60]–[63] . Collectively , these findings suggest that ET , ABA and auxin behave as negative regulators of defense responses . Some bacterial effectors appear to target these signaling systems . Conditional expression of P . syringae effector AvrPtoB increases in planta ABA levels and enhances bacterial growth [64] . AvrBs3 , a type three effector from Xanthomonas campestris pv . vesicatoria , induces auxin responsive genes , resulting in cell hypertrophy [65] . Our expression profiling in Hpa-infected Arabidopsis revealed overrepresentation of genes related to responses to ET ( GO:0009723 ) , ABA ( GO:0009737 ) and auxin ( GO:0009733 ) in Group II , III and/or IV , genes upregulated at 3 and/or 5 dpi with Hpa Waco9 , but not at 1 dpi with Hpa Emoy2 ( Figure 4 ) . Consistent with this finding , previous expression profiling using microarrays in Arabidopsis Ler-0 inoculated with compatible ( Cala2 ) and incompatible ( Waco9 , recognized by RPP5 ) Hpa isolates revealed that many compatible-specific genes are ABA responsive [28] . Interestingly , we also found that genes involved in nitrate transport ( GO:0015706 ) were overrepresented in Group III and IV ( Figure 4D ) . Hpa lacks genes for nitrate and nitrite reductases and a nitrate transporter [7] , which is also true for another obligate biotrophic powdery mildew fungi [66] . Expression profiling in Hpa revealed 202 and 252 predicted effectors expressed at 3 and 5 dpi with Hpa Waco9 , respectively ( Table 1 ) . Conceivably , some of these effectors target these phytohormone signaling and host nitrate transporter systems . This study also showed expression patterns and levels of Hpa predicted effectors , which may help select bona fide virulence effectors . Indeed , the second-highest expressed Hpa effector at 3 dpi , HaRxL62 , appears to enhance susceptibility at least in part by suppressing responsiveness to SA . In a previous screening of Hpa predicted effectors that enhance the virulence and/or that suppress PTI , HaRxL62 was selected as the most effective Hpa effector [8] , [9] . HaRxL76 , the highest-expressed Hpa effector at 3 dpi , was not in the list for our previous screenings . HaRxL76 and other highly-expressed Hpa predicted effectors will be investigated in future studies . To evade recognition by cognate R genes , the majority of RxLR effector genes are subject to diversifying selection , resulting in a diverse set of effector alleles in the pathogen population [4] , [5] . ATR1 and ATR13 have a high level of sequence polymorphism in the C-terminal regions that confer effector activity and are recognized by RPP1 and RPP13 , respectively [35] , [38] . In this study , we revealed that ATR1 is deleted in Hpa Waco9 genome , resulting in loss of recognition by RPP1 ( Figure 3 ) . Qutob et al . ( 2009 ) and ( 2013 ) [67] , [68] reported that virulent strains of Phytophthora sojae escape detection by R gene Rps3a through silencing a cognate AVR effector Avr3a . In virulent pathogens , the effectors recognized by cognate R genes would be deleted and polymorphic like ATR1 and ATR13 , or not expressed like Avr3a . These possibilities can be evaluated by comparative genomics and transcriptomics . In this study , we found overrepresentation of oomycete core element INR-FPR and two novel motifs , Motif I and II , in the promoter of Hpa predicted effectors ( Figure 2 ) . The INR-FPR motif is associated with higher levels of transcripts and pathogenesis-related genes including RxLR effectors in P . infestans [43] . Consistent with this , the genes with the INR-FPR motif were highly enriched for both Hpa predicted effectors and P . infestans RxLR effectors , especially effectors induced during infection referred to as “induced effectors” . On the other hand , we found association of Motif I and II with “induced effectors” and “non-induced effectors” , respectively , in Hpa , but not in P . infestans . While Hpa and P . infestans may have a common pre-initiation complex for transcription , there might be distinct regulatory mechanisms for specific gene expression , perhaps resulting from different lifestyles . Although the findings may be useful for predicting potential effectors in related oomycetes , it will be difficult to investigate functions of these motifs in Hpa because transformation of biotrophic oomycete pathogens is difficult . Here , we explored gene expression changes in both Arabidopsis and Hpa simultaneously during infection using a high-throughput RNA sequencing method , EXPRSS [33] . Although we cannot rule out the possibility that differences in effector sets between Hpa Emoy2 and Waco9 confer distinct transcriptional changes in Arabidopsis genes during infection , expression profiling of both pathogen effector genes and host genes involved in immunity allows us to suggest distinct mechanisms of effector-mediated susceptibility . When stably expressed in planta , some Hpa effectors cause diverse developmental phenotypes , highlighting that the effectors might interfere with fundamental plant regulatory mechanisms [69] . Further comparative investigations of transcriptional changes in Arabidopsis genes between Hpa infections and effector ( s ) -expressing plants would be interesting . Recently , using a custom-designed combined pathogen and host whole-genome microarray , Jupe et al . ( 2013 ) [70] reported a simultaneous overview of gene expression changes in both Phytophthora capsici and its host tomato during the infection . In comparison to their approach using a custom microarray , our approach using EXPRSS can be more easily applied to host-pathogen interactions for which both host and pathogen genome sequences are available . This work opens the door towards transcriptome studies in infection biology that should help unravel pathogen infection strategies and the mechanisms by which host defense responses are overcome .
Arabidopsis accessions used in this study were obtained from the Nottingham Arabidopsis Stock Centre . Arabidopsis RIL 3860 and 3860:RPP1Nd were kindly provided by Jim L . Beynon , University of Warwick , UK [35] , and Arabidopsis CW84 and CW84:RPP5Ler were from Bailey et al . ( 2011 ) [40] . PR1::GUS lines were from Caillaud et al . ( 2013 ) [18] , and plants expressing Hpa predicted effectors other than HaRxL62 were from Fabro et al . ( 2011 ) [8] and Caillaud et al . ( 2012 ) [10] ( Table S9 ) . A construct for expressing HaRxL62 in planta was generated by recombining the corresponding ORF from the signal peptide cleavage site cloned in pENTR/SD/D-TOPO ( Invitrogen ) into the Gateway destination binary vector pENS-StrepII-3×HA-GW under the control of Cauliflower mosaic virus 35S promoter [71] . The construct was transferred to Agrobacterium tumefaciens strain GV3101 ( pMP90 RK ) [72] and transformed into Arabidopsis accession Col-0 by the floral dipping method [73] . Primary transformants ( T1 ) were selected on soil containing BASTA ( Bayer CropScience , Wolfenbüttel , Germany ) and checked for expression of HaRxL62 by Western blot analysis as described by Asai et al . ( 2008 ) [74] . The progeny of the T2 generation was observed and 3∶1 ( BASTA-resistant/BASTA-susceptible ) segregating lines were taken further . Homozygous lines were selected by examining the BASTA resistance of T3 seedlings . Two independent transgenic lines were analyzed . For Hpa-inoculation assay , Arabidopsis plants were grown at 22°C and 60% humidity under a 10-h photoperiod and a 14-h dark period in environmentally controlled growth cabinets . For SA-induced PR1 expression analysis , Arabidopsis plants were grown on 0 . 7% agar plates of MS medium at 22°C under a 16-h photoperiod and an 8-h dark period in environmentally controlled growth cabinets . For Hpa infection , Arabidopsis plants were spray-inoculated to saturation with a spore suspension of 5×104 conidiospores/ml . Plants were covered with a transparent lid to maintain high humidity ( 90–100% ) conditions in a growth cabinet at 16°C under a 10-h photoperiod until the day for sampling . To evaluate hyphae growth and HR cell death , leaves inoculated with Hpa Emoy2 or Waco9 were stained with trypan blue as described by Asai and Yoshioka ( 2009 ) [75] . To evaluate conidiospore production , 5 pools of 3 plants for each Arabidopsis line were harvested in 1 ml of water . After vortexing , the amount of conidiospores released was determined using a haemocytometer . Total RNAs were extracted using TRI reagent ( Sigma ) and 1-bromo-3-chloropropane ( Sigma ) according to the procedure of the manufacturer . RNAs were precipitated with half volume of isopropanol and half volume of high salt precipitation buffer ( 0 . 8 M sodium citrate and 1 . 2 M sodium chloride ) . RNA samples were treated with DNaseI ( Roche ) and purified by RNeasy Mini Kit ( Qiagen ) according to the procedure of the manufacturers . Total RNAs ( 3 µg ) were used for generating cDNAs in a 20 µl volume reaction according to Invitrogen Superscript II Reverse Transcriptase protocol . The obtained cDNAs were diluted five times , and 1 µl were used for 10 µl qPCR reaction . qPCR was performed in 10 µl final volume using 5 µl SYBR Green mix ( Sigma ) , 1 µl diluted cDNAs , and primers . qPCR was run on the CFX96 Real-Time System C1000 thermal cycler ( Biorad ) using the following program: ( 1 ) 95°C , 3 min; ( 2 ) [95°C , 30 sec , then 60°C , 30 sec , then 72°C , 30 sec]×45 , 72°C , 10 min followed by a temperature gradient from 55°C to 95°C . The relative expression values were determined using EF-1α ( At5g60390 ) as a reference gene and the comparative cycle threshold method ( 2−ΔΔCt ) . Primers used for qPCR are listed in Table S10 . Genomic DNA was extracted from Hpa Waco9 conidiospores using a Nucleon PhytoPure DNA extraction kit ( GE Healthcare ) according to the procedure of the manufacturer . A paired-end 400 bp insert size library was prepared and sequenced on Illumina Genome Analyzer II . The sequence reads were aligned in a paired end fashion to the Hpa Emoy2 v8 . 3 [7] using BWA [76] . Trailing nucleotides with a quality score of less than 10 were trimmed using the -q option . In order to maximize the number of aligned reads , unaligned reads were aligned using a more sensitive aligner , Stampy [77] . SAMtools [78] was used to generate a BAM file that enables visualization of the alignment with the Integrative Genomics Viewer [79] , as seen in Figure 3C . For correction of Hpa genome by Waco9 SNVs , genetic variations between Hpa Emoy2 and Waco9 were predicted using SAMtools [78] . Hpa Emoy2 v8 . 3 genome sequence [7] was corrected by substituting Hpa Waco9 SNVs , using a custom Perl script . Insertion and deletion variations were ignored . The sequence data have been deposited in NCBI's Short Read Archive ( SRA ) and are accessible through SRA accession number SRX493773 . RNA sequencing was performed as described previously [33] . Purified double stranded cDNAs were subjected to Covaris shearing ( parameters: intensity , 5; duty cycle , 20%; cycles/burst , 200; duration , 60 sec ) . The libraries were sequenced on Illumina Genome Analyzer II . The sequence data have been deposited in NCBI's Gene Expression Omnibus ( GEO ) and are accessible through GEO Series accession number GSE53641 . Sequence reads to gene associations were carried out using the considerations described previously [33] . Quality-filtered libraries were aligned to the combined genome of Arabidopsis TAIR10 and Hpa Emoy2 v8 . 3 [7] using Bowtie version 0 . 12 . 8 [80] . Unaligned reads from previous step were aligned to the combined genome reference using Novoalign v2 . 08 . 03 ( http://www . novocraft . com/ ) . Remaining reads were aligned to transcript sequences of Arabidopsis Col-0 ( ftp://ftp . Arabidopsis . org/home/tair/Sequences/blast_datasets/TAIR10_blastsets/TAIR10_cdna_20101214_updated ) using Bowtie version 0 . 12 . 8 [80] . The reads with up to 10 reportable alignments or uniquely aligned reads were selected for downstream analysis . Differential expression analysis was performed using the R statistical language version 2 . 11 . 1 with the Bioconductor [81] package , edgeR version 1 . 6 . 15 [82] with the exact negative binomial test using tagwise dispersions . For identifying cis-regulatory elements , 200 and 500 nt upstream of the start codon of coexpressed Hpa genes categorized into five groups as shown in Figure 2B and Table S3 were extracted from Waco9-SNVs-corrected v8 . 3 genome sequence using a custom Perl script . The sets of sequences extracted from genes categorized into “induced effectors” and “non-induced effectors” were searched separately using MEME version 4 . 9 . 1 ( http://meme . nbcr . net/meme/cgi-bin/meme . cgi ) [41] . MEME was run with minimum width of 6 and maximum width of up to 20 and zero or one per sequence was allowed . The abundance of each motif found by MEME analysis in other groups was evaluated per individual motif using FIMO ( http://meme . nbcr . net/meme/cgi-bin/fimo . cgi ) [42] with a q-value cutoff 1e-4 . Similarity to known motifs was assessed using TOMTOM ( http://meme . nbcr . net/meme/cgi-bin/tomtom . cgi ) [45] against the JASPAR database . In P . infestans isolate T30-4 , genes were categorized into five groups according to whether genes were significantly upregulated at 2 and 3 dpi in microarray data of Cooke et al . ( 2012 ) [46] . As described above , 200 and 500 nt upstream of the start codon of coexpressed P . infestans genes were extracted , and then the abundance of each motif was evaluated using FIMO [42] . To investigate enrichment of specific gene ontologies in Arabidopsis genes categorized into four groups ( Group I to IV ) as shown in Figure 4D and Table S7 , the Singular Enrichment Analysis was done with FDR = 0 . 05 using AgriGO ( http://bioinfo . cau . edu . cn/agriGO/analysis . php ) . GUS activity was assayed histochemically with 5-bromo-4-chloro-3-indolyl-β-D-glucuronic acid ( 1 mg/ml ) in a buffer containing 100 mM sodium phosphate pH 7 . 0 , 0 . 5 mM potassium ferrocyanide , 0 . 5 mM potassium ferricyanide , 10 mM EDTA , 0 . 1% Triton . Arabidopsis leaves were vacuum-infiltrated with staining solution and then incubated overnight at 37°C in the dark . Destaining was performed in 100% ethanol followed by incubation in chloral hydrate solution . Stained leaves were observed using a Zeiss Axioplan 2 microscope ( Jena , Germany ) . For SA-induced PR1 expression analysis as shown in Figure 6A , ten-day-old plants grown on MS medium plates were used . The plants were equilibrated in water overnight , and water was changed for 100 µM SA ( Sigma ) solution in the morning . After 8 h of incubation with SA , the plants were quickly dried and flash-frozen in liquid nitrogen . Five plants per condition were used for RNA extraction . Sequence data of 475 Hpa predicted effectors can be found in NCBI's GenBank data library under accession numbers described in Table S2 .
|
A comprehensive understanding of host-pathogen interactions requires knowledge of the dynamics of gene expression changes in both the host and the pathogen during a time course of infection . However , expression profiling has often focused on either the host or the pathogen due to limitations of methods that involve microarrays . We report here gene expression changes in both Arabidopsis and its parasite Hyaloperonospora arabidopsidis ( Hpa ) simultaneously during infection using a high-throughput RNA sequencing method . By resequencing Hpa isolate Waco9 , we found it evades Arabidopsis resistance gene RPP1 through deletion of cognate recognized effector ATR1 . We also found that Hpa suppresses responsiveness to salicylic acid ( SA ) in haustoriated cells into which host-translocated effectors are delivered . An Hpa effector HaRxL62 , previously shown to enhance host susceptibility , was highly expressed in this assay , and we found it suppresses responsiveness to SA . Expression profiling of both pathogen effector genes and host genes involved in immunity allows us to suggest distinct mechanisms of effector-mediated susceptibility and reveals interesting Hpa effectors for detailed mechanistic investigation in future experiments .
|
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"Introduction",
"Results",
"Discussion",
"Materials",
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"Methods"
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2014
|
Expression Profiling during Arabidopsis/Downy Mildew Interaction Reveals a Highly-Expressed Effector That Attenuates Responses to Salicylic Acid
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African trypanosomes undergo a complex developmental process in their tsetse fly vector before transmission back to a vertebrate host . Typically , 90% of fly infections fail , most during initial establishment of the parasite in the fly midgut . The specific mechanism ( s ) underpinning this failure are unknown . We have previously shown that a Glossina-specific , immunoresponsive molecule , tsetse EP protein , is up regulated by the fly in response to gram-negative microbial challenge . Here we show by knockdown using RNA interference that this tsetse EP protein acts as a powerful antagonist of establishment in the fly midgut for both Trypanosoma brucei brucei and T . congolense . We demonstrate that this phenomenon exists in two species of tsetse , Glossina morsitans morsitans and G . palpalis palpalis , suggesting tsetse EP protein may be a major determinant of vector competence in all Glossina species . Tsetse EP protein levels also decline in response to starvation of the fly , providing a possible explanation for increased susceptibility of starved flies to trypanosome infection . As starvation is a common field event , this fact may be of considerable importance in the epidemiology of African trypanosomiasis .
African trypanosomes are protozoan parasites that cause sleeping sickness in humans and nagana in domestic livestock in sub-Saharan Africa . An epidemic involving several hundred thousand people that spread through Sudan , the Central African Republic , DRC and Angola in the 1990's , demonstrated how socially and economically devastating these diseases are [1] . Trypanosomes kill more than 3 million cattle annually and those animals that survive display low productivity due to the wasting effects of the disease [2] . The annual losses from trypanosomiasis in cattle amount to more than US $4 . 5 billion [3] . Trypanosomes , by influencing food production , natural resource utilization and the pattern of human settlement , are thus seen by the African Union as one of the greatest constraints to Africa's socio-economic development [4] . African trypanosomes are cyclically transmitted by tsetse flies ( Glossina spp . ) . Trypanosoma brucei and T . congolense undergo a complex cycle of development in the tsetse beginning almost immediately after ingestion of an infected bloodmeal when trypanosome bloodstream forms ( BSF ) differentiate to the procyclic form in the fly midgut lumen [5] , [6] , [7] . For the first three days following infection all flies contain trypanosomes . Between days 4 and 5 trypanosome infections are eliminated from most flies [7] through a process we term self-cure . The identified factors that influence vector competence ( the ability to transmit parasites ) include the age of the fly , the number of bloodmeals taken and the activation of fly immune processes , with both antimicrobial ( host defense ) peptides [8] , and lectins [9] , [10] , [11] implicated in parasite-vector interactions . More recently , antioxidants have been shown to increase fly susceptibility when administered to flies in an infective bloodmeal [12] . Most mature tsetse are resistant to trypanosome infection although the mechanisms involved in elimination of trypanosomes from the fly midgut ( self-cure ) are not understood [13] . As T . brucei BSF trypanosomes transform in the tsetse midgut the trypanosome surface coat changes from variant surface glycoproteins ( VSG ) to procyclins . At first the procyclins are a mixture of GPEET and EP forms and then expression of GPEET becomes repressed [14] . Our attention has been drawn to a fly protein called tsetse EP ( accession number CAC86027 ) , named for the extensive glutamic acid-proline dipeptide repeats that in Glossina morsitans morsitans comprise more than 40% of its length . The repeat section of this molecule shows remarkable sequence identity to the repeat section of the EP form of procyclin surface coat molecules of T . b . brucei [14] . These repeats are very rare in the protein databases and their co-incidence in two species showing such a close biological relationship is remarkable . Our knowledge of tsetse EP is limited although we do know that it is strongly up regulated following fly challenge with Gram-negative bacteria [15] suggesting a possible function in the insect immune response . In addition up regulation of the immune response by injection of E . coli also leads to a significant reduction in trypanosome prevalence [8] , [16] . For these reasons we have undertaken a series of experiments to see if these observations are connected . We provide evidence that tsetse EP protein has a powerful role in protecting the tsetse fly midgut from trypanosome infection .
Tsetse midguts were carefully dissected into distinct structural regions ( Figure 1 , Panel A ) to determine the location of tsetse EP mRNA and protein . Tsetse EP transcripts were detected in all sections tested . However , lower levels were consistently observed in the proventriculus ( PV ) ( Figure 1 , Panel B ) . The Western blot ( Figure 1 , Panel C ) overlay of the nigrosine-stained PVDF with the autoluminogram revealed the strong presence of tsetse EP protein in all tissues except for the PV . Given the presence of tsetse EP transcript in the PV we conclude that tsetse EP protein was either not produced in PV , was rapidly turned over in that organ or was rapidly translocated from there into the anterior gut . Similarly , tsetse EP transcript was detected in salivary glands from teneral and fed flies [17] but tsetse EP protein was only weakly detected by immunoblotting suggesting it may be rapidly translocated to midgut . Tsetse EP protein appears to be ubiquitous in Glossina spp . as its presence was confirmed in eight species of tsetse previously examined by Western blotting with the anti-EP repeat antibody ( mAb 247 ) [15] . Using the available sequence analysis of the amino acid sequence of tsetse EP protein [15] , [17] we designed effective double stranded RNA for knockdown experiments ( Figure 2 ) . A protein sequence comparison ( 87% similarity ) between two species ( G . m . morsitans and G . p . palpalis ) revealed that the outstanding sequence difference was in the length of the C-terminal EP repeat region [15] . The tsetse EP protein is probably a preproprotein containing a short ( 19 mer ) hydrophobic , N-terminal signal sequence as predicted by SignalP 3 . 0 [18] . Amino acids 20–48 appear to be removed from the remaining peptide during an undefined maturation process as determined by mass spectrometry and N-terminal sequencing [17] , [19] . The EP rich domain is extremely hydrophilic , and thus almost certainly is highly soluble in aqueous solvents . It is interesting that all 8 of the cysteine residues are situated up stream of the EP rich C-terminus , suggesting that this region may be highly folded . For our experiments , we designed dsRNA to target in RNA interference the homologous region 23 residues downstream from the N-terminus of the mature protein ( Figure 2 , red highlighted region: GKFASDKCAQEGQ ) . The dsRNA target varies only slightly between G . m . morsitans and G . p . palpalis ( 4/39 nucleotides differ and these are shown in yellow lettering in Figure 2 ) . Consequently the same dsRNA construct was used to achieve gene knockdown in both species . During RNAi experiments mRNA levels are often extrapolated to predict protein expression levels . However , this is often misleading as the correlation between transcript abundance and protein expression levels can often vary as much as 30 fold or more , leading to a grossly distorted analysis of a biological system [20] and this may be especially true in the midgut of blood sucking insects where post-transcriptional regulation may be a common phenomenon [21] . Consequently , we measured tsetse EP levels at both the mRNA and protein levels . We show that injection of dsRNA leads to significant reductions in transcript levels compared to controls ( Figure 3A ) . In addition , immunoblot analysis using the anti-EP repeat monoclonal antibody ( mAb247 ) to detect the tsetse EP protein in midguts of knockdown flies showed complete elimination of the endogenous protein following a single injection of 4 or more µg of dsRNA ( Figure 3B ) . We employed a reverse genetics approach to determine if tsetse EP influences parasite establishment in the midgut of the fly . We injected double-stranded RNA ( dsRNA ) into the thoracic haemocoel of male flies of different ages . Typically the flies were allowed to recover for 36–48 h after injecting dsRNA . This provides enough time for the dsRNA to start silencing tsetse EP protein transcription and for endogenous protein levels to decline [22] . After this point , flies were offered an infective bloodmeal containing virulent strains of either T . b . brucei ( TSW196 ) or T . congolense ( 1/148 ) BSF . Seven days after the infectious meal the midguts were dissected , examined microscopically , snap frozen , and the number of infections was recorded ( Table 1 ) . A complicating feature of this insect system is a natural decrease in susceptibility in older flies termed the teneral phenomenon . Typically more than 50% of flies establish midgut infections when fed trypanosomes in the first bloodmeal . However , if infected in the second bloodmeal , this susceptibility declines to ∼30% of the population . By the third bloodmeal , tsetse populations are predominantly refractory to infection with typical midgut establishment rates of 10% or less ( Table 1 ) . So , we investigated flies with differing feeding histories . Tsetse EP knockdown flies , infected at all feeding time points investigated , showed statistically significant increases in susceptibility to T . b . brucei establishment in the midgut when compared to the controls ( Table 1 ) . To determine if this phenomenon was present in other tsetse species we also investigated G . p . palpalis . Table 1 shows there are statistically significant increases in T . b . brucei establishment in the midgut of tsetse EP knockdown G . p . palpalis . We also conducted experiments to determine if the phenomenon extending to other trypanosome species . T . congolense also establishes higher midgut infections in EP knockdown flies ( Table 1 ) . Based on our current and previous [15] observations , the increase of vector competence to midgut inhabiting trypanosomes in tsetse EP knockdown flies is possibly a genus-wide phenomenon in Glossina . Male tsetse received 5 bloodmeals prior to starvation . Flies were killed at 24 h time points , up to 7 days after the last blood meal , and individual midguts were assayed by immunoblotting using an anti-EP antibody ( mAb247 ) ( Figure 4 ) . After 3 days of starvation a clear decline in tsetse EP protein levels is evident ( Figure 4 , asterisks ) . Fat body atrophy was also apparent in these flies when viewed with a dissection microscope . Tsetse EP protein levels increase again in flies 24 hours after feeding following a previous starvation period of 7 days ( Figure 4 , lane 8 ) . We have no data to show if the starvation-induced decrease in tsetse EP protein is specific or part of a general lowering of protein levels in the midgut in response to starvation .
Although RNA interference is an exquisite genetic technique to knockdown target genes , the success in achieving this post-transcriptional silencing appears to be gene-specific with variability due , in part , to the half-life of endogenous target protein and unexpected lethal secondary effects from depletion of gene specific product [23] . Our unpublished observations in Glossina reveal that , for some genes , transcript knockdown cannot be achieved regardless of the construct designed . This may relate to the lack of a spreading mechanism in Diptera and the difficulty of dsRNA reaching cells in complex organs [22] . We have previously shown and confirm here that thoracic injections of microgram amounts of specific dsRNA can effectively depress tsetse EP transcription in the tsetse midgut for up to 2 weeks [22] . Thus , the effect of persistent tsetse EP knockdown on trypanosome midgut establishment ( 7–10 day experiment ) could be confidently measured by microscopic examination . The data we present here shows that a tsetse molecule , tsetse EP protein , plays a role in protecting the midgut from infection with trypanosomes . Computer analysis of the translated protein sequences from both G . m . morsitans ( CAC86027 ) and G . p . palpalis ( AAL82540 ) , using multiple alignment tools and protein prediction algorithms , revealed that these proteins are highly conserved [15] . Including its signal peptide , tsetse EP protein from G . m . morsitans has a mass of 35 . 7 kDa and appears to form dimers and trimers and potentially larger oligomeric aggregates within the fly [15] , [17] . Apart from the EP sequence the tsetse EP protein has no currently defined protein domains [17] . However a possible clue to function may be suggested by the preliminary observation of weak agglutinating activity of the large molecular complex towards freshly collected , washed rabbit red blood cells , suggesting tsetse EP putatively has some lectin activity [17] . In addition it has been demonstrated that tsetse EP protein is strongly up regulated following immune stimulation with E . coli [15] providing good evidence that it is part of the immune response system . Given this it is interesting to note that the Imd immune regulatory pathway mainly responds to gram negative organisms [24] and the Imd pathway has been implicated in the response of dipterans to parasite infections [16] , [25] . Although all species of tsetse studied to date express tsetse EP protein [15] , orthologues are not found in the Anopheles , Aedes , Apis or Drosophila genomes . A search of non-redundant databases revealed only two eukaryotic protein hits ( apart from the procyclins ) : gi|94390895 [Mus musculus] and gi|109464874 [Rattus norvegicus] . These hypothetical proteins contain significant continuous EP repeat regions: e . g . 115 dipeptide repeats , representing 75% coverage of the rat protein . Unfortunately , no further functional information is available for these proteins . Remarkably , extensive regions of EP repeats ( also varying in length ) are contained in several procyclins that form the surface coat of procyclic trypanosomes of the T . brucei group [14] , [26] , [27] . Given the scarcity of EP repeats in organisms the chances of this happening coincidentally in trypanosomes and tsetse flies seem remote . To examine the possibility that the tsetse EP protein and the EP procyclins from T . b . brucei were involved in antigenic mimicry we investigated another trypanosome species that lacks EP procyclins . The procyclic coat of T . congolense contains no extensive dipeptidyl EP repeats although similar anionic motifs are present [28] . Despite the absence of EP repeats , establishment of T . congolense is similarly affected by tsetse EP protein knockdown ( Table 1 ) . Our experiments demonstrate that tsetse EP protein can partially protect against the midgut establishment of trypanosomes from both the Trypanozoon and Nannomonas group trypanosomes and thus , strictly sequence-specific interactions in tsetse and trypanosome are not likely at play . To assess if trypanosome establishment is altered by tsetse EP gene knockdown in tsetse species other than our G . m . morsitans laboratory model , we tested our RNAi protocol on G . p . palpalis . Knockdown of tsetse EP protein in G . p . palpalis also led to an increase in midgut infections ( Table 1 ) , confirming that tsetse EP protein influences trypanosome midgut establishment in both of these major vectors of trypanosomiasis . Given that tsetse EP has been demonstrated in a wide variety of Glossina species [15] this data suggests it may be a genus wide phenomenon . It has been demonstrated that up regulation of the immune response by injection of E . coli leads to a significant reduction in the ability of trypanosomes to establish in the tsetse midgut [8] , [16] . We have already demonstrated that tsetse EP protein is strongly up regulated upon introduction of Gram-negative bacteria into the fly [15] . Our demonstration here that knockdown of tsetse EP leads to increased fly susceptibility suggests that upregulation of tsetse EP protein upon injection of E . coli may be one explanation for the subsequent decrease in the susceptibility of the fly to trypanosomes . It is interesting to note that older flies in field populations show unexpectedly high levels of susceptibility compared to laboratory reared flies where susceptibility rapidly declines following eclosion [29] , [30] ( Table 1 ) ; the reasons remain unexplained . We have demonstrated here that starvation reduces tsetse EP levels in flies ( Figure 4 ) . It has already been demonstrated that starvation of mature flies results in an increase in parasite survival in the midgut [31] , [32] , [33] . Consequently , starvation , which is likely to be a common phenomenon in the field , could explain the differences in susceptibility seen between field and laboratory populations of flies . The observed reduction of tsetse EP protein expression and loss of parasite resistance upon starvation may have considerable epidemiological significance in African trypanosomiasis . In summary , this paper provides direct evidence for a tsetse-specific midgut molecule ( tsetse EP ) , which is an antagonist of trypanosome survival in the vector . RNAi-induced knockdown of the midgut-associated , immunoresponsive tsetse EP protein increased the frequency of trypanosome establishment in the fly midgut up to more than six fold . The precise mechanism by which tsetse EP protein influences the refractorial capacity of the midgut remains to be elucidated .
Tsetse ( G . m . morsitans ) were maintained in laboratory colony at the Liverpool School of Tropical Medicine ( LSTM ) at 26°C and 65–70% relative humidity . Glossina palpalis palpalis were supplied as puparia from the International Atomic Energy Agency ( IAEA ) Entomology Laboratories , Siebersdorf , Austria . Every 48 hours , male flies were fed horse blood through silicone membranes . For infectious bloodmeals blood stream forms ( BSF ) of Trypanosoma brucei brucei TSW196 MSUS/CI/78/TSW196 [CLONE A] , which is a fully fly-transmissible clone and able to undergo genetic exchange [34] , and T . congolense 1/148 ( Lister 1/148; isolated from a Zebu ox , Dongo River , Nigeria , Godfrey , 1960 ) were added to sterile defibrinated horse blood ( TCS Biosciences Ltd . , Buckingham , UK ) . Typically 200 µL of mouse blood ( containing 4×106 parasites ) were diluted in 5 mL of horse blood . Flies were dissected 6 days after the infectious bloodmeal . Midguts were dissected in saline on a glass slide and infection status determined by searching 10 random fields by light microscopy ( 125× magnification ) . Double stranded RNA was transcribed using a MEGAscript High Yield T7 Transcription kit ( Ambion , Huntingdon , UK ) . tsetseEP templates were available as clones from the tsetse EST program [35] . A double stranded fragment of the ampicillin resistance gene ( dsAMP ) was generated using pBluescript II SK+ as template . Template DNA was removed from the transcription reaction by DNase treatment and dsRNA was purified using MEGAclear™ columns ( Ambion ) and eluted in nuclease free water . Eluates were concentrated in a Christ ( Osterode , Germany ) 2–18 rotational vacuum concentrator to approximately 5 µg per µL . Primers were designed with the 20 base core T7 promoter sequence at the 5′ end . Primer sequences used were: AmpT7A TAATACGACTCACTATAGGGTTGCCGGGAAGCTAGAGTAAGTA; AmpT7B TAATACGACTCACTATAGGGAACGCTGGTGAAAGTAAAAGATG; EPT7A TAATACGACTCACTATAGGGTTCTGGCAAACCCTCAAT; EPT7B TAATACGACTCACTATAGGGCTACGATAAATATGTCCCTCTAAT . Borosilicate glass capillaries ( 2 . 00 mm outside diameter ) were formed into a fine point using a needle puller ( PC10; Narishige , Japan ) . To generate tsetse EP knockdowns , male flies were anaesthetized by chilling and intrathoracically injected with 10 µg ( 2 µL volume ) of dsRNA buffered in nuclease-free water . The primers used in semi-quantitative RT-PCR reactions for determination of transcript abundance in tsetse tissues were: Gm GAPDHA CTCAGCTTCTGTGCGTTG ( Tm°C 67 ) ; Gm GAPDHB AGAGTGCCACCTACGATG ( Tm°C 67 ) ; GmmEPA ACCGTTCGTTCGCTTTACTAC ( Tm°C 47 ) ; GmmEPB ACCCGCAGCCGTTTGACTTTC ( Tm°C 51 ) . Total RNA was extracted from individual tissues using Trizol ( Invitrogen , Paisley UK ) and treated with RQ1 RNase-Free DNase . RNA was quantified using a Nanodrop ND-1000 ( Wilmington , DE ) spectrophotometer . A Promega Access RT-PCR System ( Promega , Southampton , UK ) was used for amplification of transcripts . G . m . morsitans GAPDH ( Accession number DQ016434 ) was used to normalize samples . PCR cycling conditions were: 48°C for 45 minutes , 94°C for 2 minutes , followed by 30 cycles of 94°C for 30 seconds , 57°C for 1 minute , 68°C for 2 minutes and a final extension of 68°C for 7 minutes . TsetseEP gives a product of a larger size when genomic DNA ( indicative of a putative intron ) was used as template ( approximately 365 vs 315 bp . respectively ) and was used to ensure genomic DNA was removed from experimental templates . Immunoblotting using Hybond™-P polyvinylidene difluoride ( PVDF ) transfer membrane ( Amersham Biosciences , Amersham , UK ) was performed as previously described [36] . In brief , the primary antibodies used were either a 1∶20 dilution of anti-EP repeat mouse mAb TRBP1/247 [37] . The secondary ( detecting ) antibody was a 1∶50 , 000 dilution of horseradish peroxidase conjugated goat anti-mouse IgG/IgM ( H+L ) ( Caltag Laboratories , South San Francisco , CA ) . Kodak Biomax MR film ( Eastman Kodak Company , Rochester , NY ) was used to detect chemiluminescence . After development of the autoluminograms , proteins were stained on the PVDF membrane with 0 . 2% ( w/v ) nigrosine in PBS . The exposed film was superimposed on the stained PVDF membrane to reveal the precise location of the immunoreactive protein bands in relationship to the entire protein profile and to ensure equivalent protein loading . Tsetse , which had fed twice , were injected on day 4 post emergence with 7 µg of gene-specific dsRNA ( 2 µl injection volume ) . Flies in the control group were injected with nuclease free water . Injected flies were fed again on day 5 and midguts dissected on day 7 were snap frozen in liquid nitrogen in pools of 5 . The NorthernMax® formaldehyde-based system for Northern Blots ( Ambion ) was used . Total RNA ( 20 µg per lane ) was loaded on a 1% formaldehyde-agarose gel . The Strip-EZ™ PCR probe synthesis and removal kit ( Ambion ) was used to synthesize single stranded DNA probes , which were labeled with [α32P] dATP ( MP Biomedicals , Stretton Distributors , UK ) . Membranes were hybridized overnight at 42°C , given 2×5 minute low stringency washes and 2×15 minute high stringency washes before exposure to Kodak BioMax MR film .
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In Africa , tsetse flies transmit the trypanosomes causing the devastating diseases sleeping sickness in man and nagana in domesticated animals . These diseases are major causes of underdevelopment in Africa . Paradoxically , most , but not all , flies are resistant to infection with trypanosomes , but we do not have a clear picture of how flies fight off trypanosomes . Here we show that a particular , tsetse-specific immune responsive protein called tsetse EP acts as a powerful antagonist of trypanosome establishment in the fly midgut . It is known that starvation of flies leads to an increase in their susceptibility to trypanosomes and this may be a considerable factor in the epidemiology of the disease in Africa . Here we demonstrate that starvation leads to a decrease in tsetse EP levels , which may explain how starvation of the fly works to increase its susceptibility .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunology/innate",
"immunity"
] |
2010
|
Tsetse EP Protein Protects the Fly Midgut from Trypanosome Establishment
|
Horizontal Gene Transfer was long thought to be marginal in Mycoplasma a large group of wall-less bacteria often portrayed as minimal cells because of their reduced genomes ( ca . 0 . 5 to 2 . 0 Mb ) and their limited metabolic pathways . This view was recently challenged by the discovery of conjugative exchanges of large chromosomal fragments that equally affected all parts of the chromosome via an unconventional mechanism , so that the whole mycoplasma genome is potentially mobile . By combining next generation sequencing to classical mating and evolutionary experiments , the current study further explored the contribution and impact of this phenomenon on mycoplasma evolution and adaptation using the fluoroquinolone enrofloxacin ( Enro ) , for selective pressure and the ruminant pathogen Mycoplasma agalactiae , as a model organism . For this purpose , we generated isogenic lineages that displayed different combination of spontaneous mutations in Enro target genes ( gyrA , gyrB , parC and parE ) in association to gradual level of resistance to Enro . We then tested whether these mutations can be acquired by a susceptible population via conjugative chromosomal transfer knowing that , in our model organism , the 4 target genes are scattered in three distinct and distant loci . Our data show that under antibiotic selective pressure , the time scale of the mutational pathway leading to high-level of Enro resistance can be readily compressed into a single conjugative step , in which several EnroR alleles were transferred from resistant to susceptible mycoplasma cells . In addition to acting as an accelerator for antimicrobial dissemination , mycoplasma chromosomal transfer reshuffled genomes beyond expectations and created a mosaic of resistant sub-populations with unpredicted and unrelated features . Our findings provide insights into the process that may drive evolution and adaptability of several pathogenic Mycoplasma spp . via an unconventional conjugative mechanism .
Over the past decade , advances in metagenomics have uncovered the fascinating richness and diversity of bacterial taxa . As free-living cells or as parasites , bacteria colonize an impressive array of ecosystems , from those offering ideal conditions to those too extreme to support most life forms . To better understand the forces that have shaped bacterial evolution , tremendous efforts have been invested in decrypting their genomes . One main outcome is that our traditional view of bacterial clonality and species boundaries is currently being challenged by the many facets of horizontal gene transfer ( HGT ) , a key player of microbial diversification [1 , 2] . In this phenomenon , the role of mobile genetic elements ( MGE ) is central [3–6] and an increasing number of reports suggests that the transfer of these might only represent the tip of the iceberg [7–11] . Indeed , the conjugative transfer of large chromosomal fragments across genomes and their subsequent recombination might be more prominent and complex than first envisaged , with several new emerging mechanisms [7–11] that differ from the canonical Hfr- ( or oriT-based ) transfers . These latter ones were initially described in Hfr strains of Escherichia coli [12] and are initiated from an origin of transfer ( oriT ) integrated in the donor chromosome . oriT-based transfers are characterized by a gradient , with genes closer to the oriT being more reliably and more frequently transferred [13] , mainly because of physical constraints applying on large molecules during transfer . Usually , in oriT-based transfers , a single region of the chromosome is transferred and incorporated . HGT was long thought to be marginal in Mycoplasma ( class Mollicutes ) , a large group of wall-less bacteria often portrayed as minimal cells because of their reduced genomes ( ca . 0 . 5 to 2 . 0 Mb ) and their limited metabolic pathways [14 , 15] . Despite this simplicity , several mycoplasma species are important pathogens of human and a wide range of animals [15 , 16] . This situation reflects our failure in providing efficient preventive and therapeutic strategies and is due to the mycoplasma astonishing capacity to face the challenging host-environment , escape the immune response and develop antimicrobial resistance ( AMR ) [14 , 17 , 18] . Mycoplasmas live in close contact with their immunocompetent hosts on which they rely for nutrients and , in these wall-less bacteria , several loci have been selected over the course of their evolution that generate surface diversity [17 , 19] . These encode for a broad range of molecules that are key in host-interactions [17 , 20] and in escaping the host humoral response . The variation in expression and structure of these products relies on sophisticated genetic systems that combined large gene repertoires with high frequency , stochastic mutations or specific-recombination . In mycoplasmas , these systems account for extensive intra-clonal and inter-strains variability [17] . More recently , comparative genomic studies have uncovered the occurrence of massive HGT in between phylogenetically distant mycoplasma species , a phenomenon that may counteract erosion of the reduced mycoplasma genome and account for genome plasticity [21 , 22] . This finding was further supported by experimental data showing the conjugative exchange of large chromosomal fragments in Mycoplasma agalactiae , an important ruminant pathogen and a model organism [9] . Congruent in silico and in vitro data further demonstrated that these transfers equally affected all part of the chromosome via an unconventional mechanism , so that the whole mycoplasma genome is potentially mobile . While this has been formally demonstrated for M . agalactiae and M . bovis , increasing evidences point towards HGT also occurring in other species such as in M . pulmonis [23] , M . genitalium [24] and in other genera of the class Mollicutes such as in Ureaplasma or Spiroplasma [25–28] . HGT may have tremendous impact on the long and short-term evolution and adaptability of these minimal bacteria but has yet to be explored . Using antibiotics as selective pressure would offer a powerful approach for testing this question in vitro; at the same time understanding the emergence of antibiotic resistance in pathogenic mycoplasmas is of primary importance for public health [18 , 29] . The horizontal dissemination of MGE carrying AMR genes , within and across bacterial species is one main determinant of the antibiotic crisis [30 , 31] . In mycoplasmas , the role of HGT in acquiring AMR has long been ignored mainly because of ( i ) the paucity in MGEs and the total lack of known conjugative plasmids that could disseminate AMR genes and ( ii ) the scarcity of appropriate genetic tools which , combined to the mycoplasma fastidious culture hampered testing the hypothesis under laboratory conditions . The main genetic pathway described so far for the emergence of AMR in these organisms is the occurrence , selection and fixation of chromosomal mutations in target genes [18 , 29] . For instance , mutations conferring quinolone resistance have been reported in pathogenic mycoplasma species [32–34] as well as in several other bacterial taxa [35 , 36] . Quinolones , an important class of antibiotics effective on the wall-less mycoplasma cell , exert their antibacterial effect by preventing the DNA gyrase and the topoisomerase IV from unwinding and duplicating DNA [37 , 38] . Mutations occurring in genes encoding DNA gyrase subunits , gyrA and gyrB , and/or topoisomerase IV subunits , parC and parE , result in structural changes in the respective enzyme that limit antibiotic fixation , with the QRDR ( Quinolone Resistance Determining Region ) of GyrA and ParC being most often affected at key positions ( amino acids 83 for GyrA and 80 , 84 for ParC according to E . coli numbering ) [36] . In this study , we experimentally explored the impact of conjugative chromosomal transfer on mycoplasma evolution and adaptation using a fluoroquinolone , the enrofloxacin ( Enro ) , as selective pressure and M . agalactiae , as a model organism . For this purpose , we first generated spontaneous isogenic mutants displaying different combination of mutations in gyrA , gyrB , parC and parE together with various level of resistance to Enro . We then tested whether these mutations can be acquired by a susceptible population via conjugative chromosomal transfer knowing that in several Mycoplasma spp . , including our model organism , the 4 target genes are located in distinct chromosomal loci; in M . agalactiae these are separated by at least 250 kb , with parE and parC being part of a same operon . Under antibiotic selective pressure , spontaneous mutants emerged stepwise following a similar pathway with HGT acting as an evolutionary accelerator that reshuffled genomes and created a mosaic of resistant sub-populations with unpredicted and unrelated features . Our findings provide insights into the process that may drive evolution and adaptability of several pathogenic mycoplasma species and bring into the light an unconventional conjugative mechanism .
Prior to testing the impact of Mycoplasma Chromosomal Transfer ( MCT ) on AMR acquisition ( see below ) , we analysed the evolutionary pathway leading to high resistance to enrofloxacin ( Enro ) in a set of spontaneous mutants derived from PG2 55–5 . For this purpose , six lineages namely MF26 and MF29 to MF33 ( Fig 1 ) , were generated by rounds of single-colony bottleneck selection on solid medium containing stepwise concentrations of Enro ( 0 . 5 to 32 μg·ml-1 , with a two-fold step interval ) . At each step , single colonies were picked and analysed as described in Materials and Methods . Overall , 22 individual isogenic clones with MIC ( Minimal Inhibitory Concentration ) values ranging from 1 to 64 μg·ml-1 were selected and their parE- , parC- , gyrA- and gyrB-QRDR ( Quinolone Resistance Determining Region ) [39 , 40] were sequenced , directly from the chromosome . Sequence data revealed the occurrence of 1 to 3 single-point mutations in each of the 22 EnroR clones ( Fig 1 ) , with a total of 11 SNPs ( Single Nucleotide Polymorphisms ) detected in distinct positions , none being silent . All had at least one mutation within parC which always corresponded to a transition , C>T or G>A , and resulted in amino-acid changes at codon 80 and/or 84 of the QRDR ( further designated parC80 and parC84 , respectively ) . In 68% of the mutants , an additional point mutation was present in gyrA that also resulted in codon change within the QRDR , most often at codon 83 and in some cases , at codon 87 or 81 ( further designated gyrA83 , gyrA87 and gyrA81 , respectively ) . Finally , additional mutations were occasionally found in parE and gyrB , affecting the canonical QRDR only in mutants MF29-1-3-6 and MF31-1 , in parE codon 420 ( parE420 ) and gyrB codon 426 ( gyrB426 ) , respectively ( Fig 1 ) . Within each lineage , the MIC value was shown to increase over the stepwise selection process together with the number of accumulated mutations . This is illustrated in Fig 1 , with for instance the MF26 lineage acquiring a new mutation at each round of selection , in parC , gyrA and then parE , concomitantly to a MIC increase from 1 to 32 μg·mL-1 . This pattern was observed for all lineages , with a few cases of one-step MIC increase that were not linked to an additional mutation in the sequenced regions ( Fig 1 , MF30-1>MF30-1-4 and MF29-1>MF29-1-3 ) . Overall , EnroR mutations accumulate following a common pathway , emerging first in parC , then in gyrA ( or for MF31 in gyrB ) and last in parE . From these data , the contribution of parE mutations did not seem as critical towards resistance as those occurring in parC and gyrA , some mutants having a high MIC value and no parE mutation ( see for instance MF30-1-4-1 ) . Passaging of PG2 55–5 in broth medium containing increasing concentration of Enro , without intermediate rounds of sub-cloning onto solid medium , resulted in a selected PG2E10 population having a MIC of 64 μg·ml-1 and 3 SNPs located in parC80 , parC84 and gyrA83 , as in MF30-1-4-1 , MF30-1-4-8 and MF29-1-3-1 ( Fig 1 ) . Some discrepancies between the number of mutations and the MIC values ( see above ) raised the question of whether the level of resistance may be modulated by mutations occurring outside the QRDR regions . To address this issue , the genome of 13 clones belonging to 3 independent and representative lineages ( MF26 , MF33 , and MF30 ) was fully sequenced by Illumina with a mean coverage of 3100X . SNPs and indels were identified by variant calling analyses using the PG2 55–5 parent clone as reference ( Fig 2 ) . For 6 mutants ( MF33 , MF30 , MF33-1 , MF30-1-4 , MF30-1-4-1 , and MF30-1-4-8 ) , WGS ( Whole Genome Sequencing ) data revealed the occurrence of the SNPs parE86 , parE112 , parC291 , parC547 , gyrB29 and gyrB278 ( S2 Table ) in the 3 target genes; these SNPs are located outside the region previously sequenced with the Sanger method and thus are outside the QRDR . Data also suggested that gyrB mutations found in MF30 and MF33 lineages may have a negative impact on the further selection of highly resistant mutants . Indeed , the gyrB29 and gyrB218 mutations were only detected in the founders , MF33 and MF30 . Reversion of these mutations in progenies coincided with the emergence of mutations in gyrA which ones were further transmitted under increasing antibiotic pressure , a series of events that may reflect an epistatic phenomenon . As well , the reversion of parC291 and parC547 mutations in MF30 lineage was accompanied by the appearance in more resistant progenies of new mutations in parC and parE . Overall , these abrupt changes may be due to the constraints imposed by the interdependence of gyrA and gyrB or parE and parC subunits in forming a functional DNA gyrase or topoisomerase IV , respectively , that will best withstand the antibiotic pressure . A few mutations ( SNPs and indels ) were also detected outside of the classical quinolone target genes ( Fig 2 and S2 Table ) , with most occurring in homopolymeric tracts that are known as being prone to high frequency insertion-deletion [17] . For instance , nt-707306 and/or nt-711627 both underwent a C deletion within a polyC of the so-called spma locus which encode phase variable membrane proteins [41] . As well , the MF33-1-1 and -1-2 siblings both contained sub-populations displaying a large number of SNPs ( 14 and 11 ) within the highly variable vpma locus [42] . Overall , sequenced genomes contained from 3 to 23 mutations , with a mean of 10 . 2 ± 5 . 8 mutations , with approximately half being fixed in the population ( present in ≥95% of the reads ) . In parallel , the genome of the EnroR PG2E10 was analysed and a total of 8 mutations were detected , 3 fixed and 5 non-fixed ( present in <95% of the reads and further refer as polymorphic sites ) , when compared to the parental strain ( Fig 2 and S2 Table ) . As expected , 3 SNPs were found in the Enro target genes , parC80 , parC84 , gyrA83 , and an additional one was detected in parE112 , outside of the QRDR initially sequenced by Sanger ( see above ) . Among the 3 studied lineages , this combination of 4 SNPs was only found in MF30-1-4-1 and MF30-1-4-8 , which MIC of 64 μg·ml-1 is identical to that of PG2E10 . The other 4 mutations occurred outside the target genes and correspond to either highly variable loci ( see above ) or to mutations occurring only in minor subpopulations ( polymorphic sites ) . Based on competition fitness assays , these mutations did not appear to impose a cost on the PG2E10 fitness ( w = 1 . 03 ± 0 . 10 ) when compared to PG2 55–5 parent ( see Materials and methods ) . Our hypothesis is that horizontal conjugative chromosomal transfer may act as a driving force of mycoplasma short-term evolution . With this in mind , we tested whether multiple , distant chromosomal EnroR point-mutations can be simultaneously transferred by HGT from a resistant to a susceptible strain and further selected in presence of the antimicrobial . For this purpose , two independent mating experiments ( T5 and T6 ) were performed using as donor the EnroR PG2E10 mutant ( see above ) which displayed the highest MIC ( 64 μg . ml-1 ) but the smallest number of fixed mutations ( see Fig 2 and S2 Table ) . As recipient , we choose the 5632G3 clone previously derived from the EnroS 5632 strain ( MIC = 0 . 125 μg . mL-1 ) and in which the gentamicin-resistance marker ( Gm ) is stably inserted as a proxy [9] ( see Materials and methods ) ( Fig 3A ) . Transconjugants were selected on solid media containing 50 μg·mL-1 of gentamicin in addition to Enro at concentrations ranging from 0 . 25 to 8 μg·mL-1 ( equal to 2 to 64 fold the MIC 5632G3 ) . Repeated attempts consistently yielded transconjugants colonies on solid medium containing 0 . 25 μg·mL-1 of Enro ( except for T5-5 obtained at 0 . 5 μg·mL-1 ) with a low frequency ranging from 2 . 7 . 10−11 to 7 . 2 . 10−8 transconjugants per donor-CFU , depending on the 5632G3:PG2E10 initial ratio ( 1:10 or 10:1 , respectively , see Materials and methods ) . A total of 18 individual transconjugants were then picked and subjected to a series of PCR assays . These targeted the Gm marker and 11 distant loci that are distributed around the genome and discriminate 5632 from PG2 ( S1 Fig , S1 Table ) . Of these , 8 were previously described [9] and 3 were specifically designed in this study to distinguish PG2-parC , -gyrA and -gyrB from their 5632 counterparts . PCR data indicated that the transconjugant genotypes were a composite of PG2 and 5632 genomes ( S1 Fig ) except for T6-7 which was further shown by sequencing to have a chimeric gyrA ( see below ) . They further designated 5632G3 as the recipient chromosome , with a majority of the PCR products being 5632-specific and the Gm marker constantly detected at the same position , as in the parent . This finding was in agreement with our previous data showing that chromosomal transfers always occurred from PG2 ( donor ) to 5632 ( recipient ) [9] . Overall , 10 distinct PCR profiles were observed , with several transconjugants sharing identical profiles ( S1 Fig ) . Whole Genome Sequencing ( WGS ) by Illumina was performed with a subset of 13 transconjugants that were selected ( i ) to represent each of the 10 PCR profiles identified above and ( ii ) to include transconjugants with identical PCR profiles that were generated during independent ( T5-4 and T6-1 ) or during the same ( T6-4 , -8 , -9 ) mating experiments . Sequence data confirmed that all displayed the 5632G3 chromosome as genetic background designating the corresponding strain as the recipient ( Fig 3A ) . Further analyses demonstrated the systematic transfer of PG2E10 donor remote Enro target-genes containing ( i ) the mutated parE/parC operon ( 10/13 transconjugants ) together with either the mutated-gyrA or the wild-type gyrB ( wt ) or ( ii ) the mutated gyrA alone ( 3/13 ) ( Fig 3A ) . A close-up image of these regions is depicted in Fig 3A and shows that two fixed mutations , corresponding to parC80 and/or gyrA83 , were always associated to the transfer . Of note , the chimeric structure of T6-7 gyrA explains the PCR result obtained above ( S1 Fig ) . Of the 13 transconjugants analysed , 10 had received parE sequences from the donor . One , T5-2 , had acquired two mutations that were pre-existing in PG2E10 sub-populations ( 92% and 60% of the reads , respectively ) , parE112 and parC84 . The remaining 9 transconjugants displayed one mutation in parE , not previously detected in the donor , that was either ( i ) an insertion of 3 nt resulting in adding an Ala residue at codon 390 or ( ii ) a non-synonymous SNP corresponding to codon 423 or 625 . At least , mutations corresponding to codons 390 and 423 were independently confirmed by direct genome sequencing . Whether the occurrence of these mutations in some transconjugants reflects the heterogeneity of the PG2E10 donor population , with sub-populations being selected here , or whether they have arisen independently after transfer is not known . Of note , T5-2 parC and T6-5 gyrA were more chimeric than in other transconjugants as if multiple recombination events have occurred to produce mosaic genes composed of 5632 and PG2 intermingled sequences ( Fig 4A ) . Interestingly , none of the transconjugants accumulated all 4 SNPs described for the PG2E10 in the Enro target genes . As well , none reached the 64 μg·mL-1 MIC of the PG2E10 parental strain but their individual MIC value that ranged from 0 . 5 to 32 μg·mL-1 ( Fig 4A ) was always higher than the concentration used for selection ( 0 . 25 μg·mL-1 ) . More specifically , transconjugants having concomitantly acquired the two distant PG2E10 loci containing the mutated parE-parC and the mutated gyrA had the highest MIC ( 16 μg·mL-1 to 32 μg·mL-1 ) . In T5-1 and T5-2 that displayed the lowest MIC value ( 0 . 5 μg·mL-1 ) , the mutated parE-parC were co-transferred with the wild-type PG2E10-gyrB instead of the mutated PG2E10-gyrA . Since donor and recipient gyrB allelic sequences differ slightly , this event introduced amino acid changes in GyrB when compared to the parental 5632-background ( S5 Table ) . PCR genotyping indicates that this same combination was also observed in T6-2 , a transconjugant derived from the same partner but in an independent mating experiment ( see S1 Fig ) . Finally , it is interesting to note that the co-transfer of the PG2E10 mutated-gyrA and its wt-gyrB was never observed . In agreement with data obtained with the spontaneous mutants , the transfer of mutated-donor gyrA only was not sufficient to confer the recipient strain with the EnroR phenotype . Overall , mutations conferring resistance to Enro with MIC values ranging from 16–32 μg . mL-1 could be acquired by a susceptible population within one mating experiment via HGT , while reaching the same levels of EnroR MIC through spontaneous mutations would have required approximately 100 generations and multiple passages under selective pressure . The co-transfer of multiple loci and the possible occurrence of additional macro- and micro-heterogeneities were addressed in the 13 sequenced transconjugants . Reconstruction of the composite-genomes was performed as previously described with some minor modifications [9] ( see Materials and methods ) . Briefly , reads generated by NGS ( Next Generation Sequencing ) were mapped onto the 5632 and PG2 reference genomes and reads perfectly matching to one or the other genome were retained . Analyses of the reconstructed genomes and more specifically of the PG2 inherited sequences confirmed that other fragments , unrelated to topoisomerase genes carrying EnroR mutations , were also exchanged ( Fig 3A and S2 Fig ) . This resulted in complex mosaic genomes , containing an average of 18 ± 7 PG2E10 fragments ( Fig 3B ) which size varied from 77 bp to 53429 bp . All transconjugants display distinct patterns of transferred fragments , except for 3 , which had strictly identical genome sequences ( S2 Fig ) . These clones , namely T6-4 , -8 and -9 , were all selected from the same mating experiment and are most likely the result of the expansion of a single transconjugant as all were shown to be fitter than the parent or than other transconjugants produced during the same mating ( i . e . T6-5 and T6-1 ) ( Fig 3B ) . Competitive culture assays also indicated that there was no correlation between the number of fragments or the overall DNA amount that was exchanged and the fitness level ( Fig 3B ) , with some combinations imposing a fitness cost while other conferring a fitness benefit . Overall , the most frequently transferred regions were clustered within 20 kb around the selective EnroR determinants , but distant loci were also exchanged in all transconjugants . This suggested that multiple events of genomic replacements by recombination have occurred simultaneously . Although the PG2 and 5632 genomes are highly syntenic , some genes or regions are only present in one strain . Thus , in some cases , replacement of 5632 recipient genome by a PG2 fragment resulted in the loss or in the gain of strain-specific genes . On average , 13 ± 11 5632-specific genes were lost for 6 ± 4 PG2-specific genes that were gained ( S3 Table ) . One extreme case of replacement resulted in the deletion of a large region ( ca . 22 genes , 27 Kb ) which contained an integrated conjugative element ( ICE ) specific to 5632 and not present in PG2 [43] . This was observed in T5-2 , T5-5 , T6-1 and T6-7 transconjugants ( Fig 3A , S3 Table ) where the loss of the 22 genes was not due to the ICE excision but to recombination events occurring at homologous sites on each side of the ICE . In addition , micro-complexity events were observed , with transconjugants displaying short PG2-inherited fragments ( 180 ± 29 nt ) that were defined by only one or two PG2-specific variations ( SNPs or indels ) , and/or the occurrence within PG2-inherited fragments of short 5632 fragments defined by one or two 5632-specific variations . Overall , chromosomal exchanges by recombination of large or small fragments were shown to often occur within a coding sequence , resulting in chimeric PG2/5632 genes as illustrated above for parC and gyrA . On average 20 ± 7 genes were mosaic to various degrees , for each transconjugant . To evaluate the stability of the EnroR spontaneous mutants and transconjugants in absence of selective pressure , two different spontaneous mutants , namely , MF33-1-1 ( MIC = 32 μg·mL-1 ) and PG2E10 ( MIC = 64 μg·mL-1 ) , and two different transconjugants T5-1 ( MIC = 0 . 5 μg·ml-1 ) and T5-5 ( MIC = 16 μg·mL-1 ) , were submitted to serial passages in broth medium 40 times ( P0 to P40 ) . WGS was performed with DNA extracted at P10 and P40 that correspond to approximately 165 and 605 generations , respectively ( see Materials and methods ) . Based on comparative analysis , the genomes of the 2 mutants and the 2 transconjugants were remarkably stable over this period , in agreement with the overall stability of their MIC over passages ( Fig 4B and S4 Table ) . Interestingly , the two non-fixed mutations pre-existing in PG2E10 parE and parC , respectively , gradually faded in favour of the wildtype ( Fig 4B ) : one was detected in parE112 in 92% , 30% and 0% of the reads and the other in parC84 in 60% , 27% and 0% of the reads , at P0 , P10 and P40 respectively . Concomitantly , an indel emerged in parE390 at P10 ( 66% ) and P40 ( 93% ) that resulted in the insertion of an Ala residue . Interestingly , this same insertion occurred in 3 transconjugants: T5-1 , T5-3 and T6-5 , in which it was fixed . As shown in Fig 4A , the absence of mutation in parC84 in all but one transconjugant coincides with the presence of mutations in parE . Altogether , these data suggested that mutations in parC might have a slight fitness cost that tended to be compensated over passages by the introduction of mutations in parE , the functional partner of parC . Of note , other polymorphic sites were observed elsewhere in the genome during passages . In particular , MF33-1-1 with 9 polymorphic sites at P10 , displayed the highest number of non-fixed mutations ( excluding those in vpma locus ) most of which ( 6/9 ) being lost at P40 ( S4 Table ) . We then investigated the fitness of the two mutants and two transconjugants over passages in broth medium . Data presented in Fig 5 indicated that mutants MF33-1-1 and PG2E10 displayed a fitness similar to that of the PG2 55–5 ancestor ( P0 ) that remained stable over passages ( P10 and P40 ) . In contrast , the fitness of transconjugants T5-1 and T5-5 at P0 was reduced by 30 to 20% , respectively and increased over successive passaging to reach 120 and 100% when compared to the recipient strain , 5632G3 . WGS showed that a few different , polymorphic sites accumulate over passages in both the mutants and the transconjugants , without any obvious link to fitness ( see S4 Table ) .
Over the past decade , HGT has increasingly attracted attention and is now recognized as a main driver of microbial innovation , with conjugation as one prominent mechanism [2 , 44 , 45] . Yet , knowledge regarding the mechanisms and impacts of HGT in mycoplasmas is very limited , with only a few publications dedicated to this topic [9 , 21–23] . By combining next generation sequencing to classical mating and evolutionary experiments , this study uncovered the role of an unconventional mechanism of HGT in generating mosaic genomes in M . agalactiae . Under evolutionary experimental conditions , this phenomenon acted as an accelerator of AMR dissemination by providing susceptible mycoplasma cells with the ability to rapidly acquire , from pre-existing resistant populations , multiple chromosomal loci carrying AMR mutations . In M . agalactiae , high-level of Enro resistance can be reached via the emergence of spontaneous chromosomal mutations during propagation with increasing concentrations of the antimicrobial , as shown for other Mycoplasma species [32 , 34 , 46] . The comparison of several , independent lineages indicates that these mutations accumulate following a similar trajectory: first in parC resulting in a 8 to 16-fold increase in resistance , followed by additional mutations in gyrA to reach up to 128-fold increase . Higher resistance levels ( up to 500-fold ) were further achieved by combining either two mutations in parC with one in gyrA or , one mutation in each with one or more mutations in parE . WGS data further showed that only very few other mutations were selected and fixed outside of these genes , none that could account for the resistance phenotypes . Whether these played a role in counterbalancing a potential fitness cost during the selection process is not known but mutations in type II topoisomerase genes had no effect on PG2E10 fitness in vitro when compared to the ancestor strain ( w = 1 . 03 ± 0 . 10 ) . Overall , accumulation of fixed mutations in type II topoisomerase genes and high levels of resistance were reached over several weeks of propagation , after approximately 200 or 100 generations depending on whether selection was performed with or without bottleneck selection , respectively . A limited number of reports addressed evolutionary trajectories of fluoroquinolones resistance in bacteria and each identified species-specific mutational trajectories with identical target-site mutations emerging in different order [47–49] . In M . agalactiae , the convergent outcome of parallel independent experiments strongly suggested intermolecular epistatic interactions between DNA topoisomerases in the mechanism of fluoroquinolone resistance . The time scale of the mutational pathway leading to high-level of Enro resistance could be compressed into a single mating experiment , in which EnroR alleles were co-transferred from resistant to susceptible mycoplasma cells . Such event required the physical contact and a form of sexual competence of the pair [5 , 9] , as well as one partner being already highly resistant . Independent mating experiments generated progenies with chimeric genomes made of the 5632-recipient chromosome in which sequences of the resistant PG2E10-donor were transferred and recombined at homologous loci . Under the antimicrobial selective pressure , all transconjugants displayed the mutated parE-parC operon or/and the mutated gyrA of the donor , but resistance per se ( from 4 up to 250-fold-increase in resistance ) was reached only when both mutated loci were co-transferred . These data are in agreement with conclusions drawn from the analyses of co-evolved lineages ( see above ) : alteration of both the topoisomerase IV and the DNA gyrase subunit A is critical for mycoplasmas’ quinolones resistance . Mutations not previously detected in neither of the parents even as a minor population , were observed in the transconjugants having acquired parE-parC donor sequences ( corresponding to parE390 , parE423 and parE625 ) ( Fig 4A ) . The mutation affecting parE390 is also emerging in the donor strain after 40 serial passages in medium without Enro ( Fig 4B ) , raising the question of whether parE mutations ( i ) were pre-existing in the parent population at undetectable levels and were preferentially selected after mating or ( ii ) occurred de novo during or just after mating . It is interesting to note that in the donor strain , while the parE390 mutation emerged over passages , the mutations parE112 and parC84 were conversely being replaced by wild type ( wt ) sequences . Whether parE390 is being beneficial to the transconjugants in the context of the experiment , either towards resistance or fitness , is not known . Surprisingly , none of the selected transconjugants reached the MIC of the donor , most likely because none displayed the exact combination of parE-parC and gyrA mutations found in the predominant PG2E10 population . Whether such transconjugants did arise during mating but were outcompeted by others is one possible explanation . An interesting observation is that all transconjugants carried the mutated gyrA or the wt-gyrB of the donor , none having inherited both genes from a single parent . While the two strains , 5632 and PG2 , encode very similar GyrA and GyrB products these are not strictly identical , with 99 . 2 and 98 . 6% identity respectively ( S5 Table ) . Since the DNA gyrase is composed of two GyrA and two GyrB subunits , all transconjugants expressed a modified version when compared to that of the recipient cell prior to mating . Whether this provided an advantage in the context of our experiment , or whether it reflects an epistatic phenomenon [50 , 51] remains to be addressed . The most unexpected outcome of this study was the extent of combinatorial variation obtained after mating . Mycoplasma Chromosomal Transfer ( MCT ) was initially shown to differ from classical Hfr- or oriT-mediated transfer in that it affects nearly every position of the genome with equal efficiency [9] . Because NGS data had been obtained using pools of transconjugants , MCT was then thought to be limited to the transfer of one or two proximate loci in between two cells . Here , analyses of individual transconjugants revealed a much complex picture with the simultaneous transfer of small and large fragments distributed around the genome ( Fig 3A ) . Indeed , this phenomenon created within a single step a set of totally new genomes that were a combinatorial blend of the two parents . Thanks to the significant differences in genome sequence existing between the two parental strains ( average 1 variation every 26 nt ) , transconjugant genomes could be reconstructed with a high level of precision , revealing that besides the gain and loss of entire genes , MCT also generated chimeric genes . Overall , MCT affected from 6 to 17% of the genome regardless of whether these encoded housekeeping or accessory gene functions . An average of 18 donor-fragments co-exists in the new transconjugant genomes , of which only 2 carried the selectable EnroR mutations . This implies ( i ) that a large amount of unrelated fragments silently co-transferred along with the selectable marker , some of which may confer the cell with new , yet unpredictable phenotypes and ( ii ) that a large proportion of mosaic genomes have not been selected and that most likely , the combinatorial possibilities of conjugative MCT are endless . Because MCT introduces variation instantly , one limitation of this phenomenon is the viability and adaptability rate of the resulting chimeric cells . Although both parents are of the same species , the overall success of the transconjugants depends on how well the donor and new chimeric genes interact with the remaining recipient’s genes in a particular environment [52] . For a few generations , the cell may have to cope with multimeric enzymes or products which sub-units are not of a perfect match depending of the protein turn-over of the recipient cell . To a lower extent , this situation resembles genome transplantation used to engineer mycoplasmas and thus faced a number of similar issues [53] . Although several transconjugants turned out to be highly resistant to Enro , their initial selection could only be achieved in low concentration of Enro ( 0 . 25 μg . mL-1 ) . This raised the question of whether growth on selective media was impaired because of the low turnover of recipient wt GyrA/ParC or because of a synergistic effect of the Enro with the gentamicin used for selecting transconjugants . Incorporating large amount of incoming donor DNA had a fitness cost for most transconjugants but not all . Surprisingly , this was counterbalanced after a few passages in media with even one transconjugant ending with a higher fitness than the recipient or the donor cell . In contrast , passaging had no effect on the fitness of EnroR spontaneous mutants derived from the donor ( Fig 5 ) suggesting that new genome configurations may require a certain period of time for fine-tuning . In search for compensatory mutations , comparative analyses of WGS before and after passages were conducted that indicated the emergence of subpopulations with an overall low number of mutations , none of which could explain the improved fitness . Quantifying pathogen fitness in its entire life cycle is not trivial [54] and whether transconjugants selected in this study perform better than their parents in the animal host remains to be addressed . Clues on the impacts of MCT were provided by our earlier in silico work that revealed massive HGT in between phylogenetically distant ruminant Mycoplasma spp . [55] . Loci that were exchanged in M . agalactiae accounted for 18% of its genome and often encompassed gene cluster with highly conserved organisation that were distributed around the genome [55] . Rather than successive independent HGT events , this picture might reflect the concomitant transfers of multiple unrelated fragments during mating . Within the ruminant host , M . agalactiae and some members of the M . mycoides cluster are often re-isolated from a same organ where they co-habit [14] , a prerequisite to conjugative transfers . Throughout the process of infection , these populations have to face a series of bottlenecks applied by the host-response and the host-hostile environment . The mycoplasma minimal cell may be particularly vulnerable to the deleterious effect of Muller’s ratchet due to its limited genetic content and lack in DNA repair components . MCT may provide these organisms with a means to rescue their injured genomes by restoring deleted or inactivated genes . Yet , the repertoire of mosaic genomes produced in the host is likely to be limited by a low MCT frequency , although some parameters such as stress may trigger the phenomenon , and the viability of the chimeric genomes within the hostile host-environment . While sharing the same ecological niche is an obvious facilitator of HGT , MCT was shown in M . agalactiae to rely on ICE , most likely because these conjugative transfers being dependent on the ICE-encoded conjugal pore [3 , 5 , 9] . Although ICE occurrence varies among strains of a same species [25 , 56] , conserved ICE-elements have been detected in about 50% of the mycoplasma species with sequenced genome [25] suggesting that MCT might not be restricted to ruminant mycoplasmas but may occur in species that colonize man and swine . Horizontal chromosomal transfers that do not conform to the canonical Hfr- ( or oriT-based ) model are increasingly being reported [7 , 8 , 57 , 58] and mosaic genomes were recently described in Mycobacterium smegmatis as the result of Distributive Conjugative Transfer ( DCT ) [52] . As for MCT , the exact molecular mechanism driving these events remains to be fully elucidated . Overall , our study unravelled the astonishing capacity of MCT to generate unlimited genome diversity . While this process may contribute to counteract the erosion of the small mycoplasma genome [59] , it can also rapidly promote the mycoplasma short-term adaptability to changing environment . Our findings reinforce the central role played by HGT in promoting evolutionary adaptation but also challenge our view on the boundaries of bacterial species and on our capacity in predicting the emergence of new phenotypes .
The PG2 clone 55–5 [57] and 5632 clone C1 [41] , further referred as PG2 and 5632 for simplicity , were previously derived from the PG2 and the 5632 strains of Mycoplasma agalactiae respectively . The 5632 gentamicin-resistant clone ( 5632G3 ) , designated as 5632G-3 in previous publication [9] , was obtained by stable insertion at nucleotide 919899 of the gentamicin-resistance gene , aacA-aphD [9] . All strains were propagated at 37°C in SP4 medium [60] supplemented with 5 mM pyruvic acid ( Sigma-Aldrich ) and 45 μg . mL-1 cefquinome ( cobactan 4 . 5% , MSD Animal Health ) and , when needed , with enrofloxacin ( Sigma-Aldrich ) and/or gentamicin ( Sigma-Aldrich ) at specified concentrations . Based on CFU counts taken at different times of the exponential growth phase , the doubling time ( or generation time , G ) of M . agalactiae PG2 55–5 was calculated to be equal to 3 . 3 ± 0 . 14 hours per generation ( ca . 7 . 2 generations per 24h ) . The number of generations needed to generate the mutants and the transconjugants was estimated using this value as reference multiplied by their time of growth in broth medium only ( the number of generations needed for a single cell to form a colony was not taken into account ) . Mycoplasma PG2 55–5 cells from a 1 mL of mid-exponential culture were centrifuged for 15 min at 8000 g at room temperature and re-suspended in SP4 medium containing 0 . 5 μg . mL-1 enrofloxacin . After 48h , 10 μL cultures of 108 to 109 CFU . mL-1 were plated on SP4 agar plates with increasing enrofloxacin concentrations ( 0 . 5 to 2 μg . mL-1 ) . Colonies were only obtained on plates containing 0 . 5 μg . mL-1 enrofloxacin . They were picked and propagated in SP4 broth medium with the same antimicrobial concentration . This represented the first step of selection used in this study and constituted the basis of the lineages . One to 3 additional rounds of selection were similarly performed with increasing concentration of enrofloxacin ( ranging from 0 . 5 to 32 μg . mL-1 ) , with colonies growing on the highest concentration being picked and subjected to the next round . A total of 108 clones were obtained , among which 22 clones corresponding to 6 lineages were analysed . In parallel , PG2 55–5 cultures ( 108 to 109 CFU . mL-1 ) were propagated by serial passaging ( dilution 1/50 or 1/100 ) , in broth medium containing increasing enrofloxacin concentration ( 0 . 25 , 0 . 5 , 1 and 10 μg . mL-1 ) to generate the resistant PG2E10 population . Mutations in Quinolone Resistance Determining Region ( QRDR ) sequences of the target genes were identified by direct sequencing using the BigDye Terminator chemistry [61 , 62] and by whole genome sequencing . Direct sequencing of genomic DNA was performed at the genomic platform of Get-Purpan ( Toulouse , France ) using primers listed in S1 Table and genomic DNA extracted with chloroform , as previously described [63] . Of note , amino-acid positions of type II topoisomerases were numbered according to the Escherichia coli K-12 strain nomenclature , GyrA ( AAC75291 . 1 ) , GyrB ( AAT48201 . 1 ) , ParC ( AAC76055 . 1 ) and ParE ( AAA69198 . 1 ) . Mating experiments were performed as previously described [5] . Briefly , the donor strain ( PG2E10 ) and the recipient strain ( 5632G3 ) were grown individually in SP4 medium , during 24 h . The two cultures were mixed at a 5632G3:PG2E10 cell ratio of 1:10 for one experiment ( T5 ) and 10:1 for the second ( T6 ) and then centrifuged for 5 min at 8000 g at room temperature . Cells were re-suspended in SP4 medium , incubated during 16 h at 37°C and an aliquot of 300μl was plated in SP4 agar containing gentamicin ( 50 μg . mL-1 ) and different enrofloxacin concentrations ( from 0 . 25 to 8 μg . mL-1 ) . After several days of incubation at 37°C , single colonies were picked from plates with the highest antibiotic concentration before being propagated in SP4 liquid medium with the same concentration of enrofloxacin . Of note , colonies were only observed on solid media containing 0 . 25 μg . mL-1 of enrofloxacin , with the exception of one transconjugant , T5-5 , which grew at 0 . 5 μg . mL-1 . Mating experiments using PG2 55–5 ( EnroS ) and 5632G3 were used as negative control , to test the absence of enrofloxacin spontaneous resistant clones . The frequency of transconjugants was determined as the number of transconjugants , divided by the number of PG2E10 donor parental cells . PG2- or 5632-specific PCR assays were used to determine the parental origin of 11 genomic loci across the transconjugant genomes ( S1 Table ) . Of these , 8 were previously described [9] and 3 were specifically designed for this study that targeted parC , gyrA and gyrB . PCR assays were conducted using genomic DNA extracted with the chloroform method [63] and primers listed in S1 Table . The presence and position of the 5632G3-specific gentamicin resistance marker ( Gm ) was confirmed by a specific PCR using one primer inside the marker and the other in the flanking chromosomal sequence ( S1 Table ) . All PCR amplifications were performed according to the recommendations of the Taq DNA polymerase suppliers ( M0267S , New England Biolabs ) . The enrofloxacin MICs were determined according to the recommendation of Hannan 2000 [64] using the agar dilution method as previously described [32] . Briefly , 1 μL of each clone diluted to 104−105 CFU . mL-1 was spotted on agar plates containing serial two-fold dilution of enrofloxacin ( from 0 . 0625 to 64 μg . mL-1 ) . MIC assays were performed in triplicates for each clone , and the median value was retained . The MIC was defined as the lowest concentration of enrofloxacin that prevented visible growth after 5 days at 37°C while , in parallel 30 to 300 CFU were observed on the antimicrobial-free control plate . Based on Hannan 2000 [64] , we considered in this study isolates with MIC of ≤0 . 5 μg . mL-1 as susceptible ( EnroS ) while MIC ≤1 μg . mL-1 and ≥2 μg . mL-1 corresponded to intermediate and resistant isolates ( EnroR ) . Here , isolates with MIC ≥16 μg . mL-1 were further referred as being highly resistant . A pairwise competition assay was performed to estimate the relative fitness of evolved strains versus their ancestor ( i . e . transconjugants versus 5632G3 or spontaneous mutants versus PG2 55–5 ) . For each pair , the evolved and the ancestor clones were mixed in SP4 medium at a 1:1 cell ratio ( 104 CFU . mL-1 ) . Serial dilutions of the starting ( 0h , T0 ) and final ( 18h , T18 ) co-cultures were plated on SP4 plates containing none or 4 μg . mL-1 of enrofloxacin . After 5 days at 37°C , the number of CFU was determined for the ancestor and the evolved clones . Fitness of each clone relative to its ancestor was calculated according to the equation: w = Fitness evolved/ancestor = ln ( evolved at T18/evolved at T0 ) /ln ( ancestor at T18/ancestor at T0 ) [65 , 66] . Concerning the fitness of EnroS GentaR transconjugants with MIC ≤0 . 5 μg . mL-1 , selection onto enrofloxacin solid media was obviously not feasible . These were then performed using 5632 as the ancestor and the gentamicin as selective antimicrobial ( 50 μg . mL-1 ) . Their fitness ratio was then corrected by multiplying by 1 . 17 , a value equal to the fitness ratio of 5632 versus 5632G3 . At least three replicates were performed for each assay and the mean value and the standard deviation ( SD ) were calculated . A value of 1 indicated a fitness of the evolved strain similar to the ancestor ( 5632G3 or PG2 55–5 ) , a ratio lesser than 1 or greater than 1 indicated a fitness-cost or -benefit for the evolved strain , respectively . Genomic DNA was extracted from mycoplasma cells using the phenol-chloroform method [67] . Whole genome sequencing was performed at the GATC Biotech facility ( Konstanz , Germany ) using Illumina technology HiSeq ( paired-end , 2x150 bp ) . An average of 2x107 reads by mutants or transconjugants was obtained , corresponding to an average of 3100X for coverage depth . One exception is the PG2E10 population that was sequenced by the Genome-Transcriptome facility of Bordeaux ( France ) using HiSeq ( paired-end , 2x100 bp , 1 . 6x107 reads , coverage 1700X ) . All bioinformatics analyses were performed using the galaxy platform hosted by Genotoul , Toulouse , France ( bioinfo . genotoul . fr ) and default parameters unless specified ( see workflow S6 Table ) . The reads of each clone ( fastq file ) were mapped on the reference genome M . agalactiae PG2 ( NC_009497 . 1 ) or 5632 ( NC_013948 . 1 ) , using Burrows-Wheeler Aligner ( BWA , MEM algorithms , Galaxy version 0 . 8 . 0 ) [68] . The quality of the alignments was controlled with Qualimap 2 . 2 . 1 [69] . Calling variant analyses were performed using successively RealignerTargetCreator , IndelRealigner , PrintReads and HaplotypeCaller of GATK3 ( Galaxy version 3 . 5 . 0 ) for SNPs and indels detection [70] . Variations with a quality lower than 10000 were excluded ( Filter VCF file tool , Galaxy Version 1 . 0 . 0 ) . Variations were considered as ( i ) fixed when present in ≥95% of the reads or ( ii ) non-fixed when present in <95% of the reads , as a result of coexisting sub-populations [71] . The percentage of each variation was calculated using the ratio AD/DP ( AD: Allelic depths for the reference and alternative alleles; DP: Approximate read depth ) provided by GATK . Alignments ( bam file ) and variations ( vcf file ) were visualized using the Integrative Genome Viewer ( IGV 2 . 3 . 93 ) [72] , Artemis 16 . 0 . 0 [73] and ACT 13 . 0 . 0 [74] . Reconstruction of the composite genome of transconjugants ( PG2/5632 ) was possible because of the frequent polymorphisms existing between PG2 and 5632 , on average 1 variation every 26 nt calculated using Nucmer [75] ( S2 Fig ) . This was performed by PG2 specific reads detection as follows: transconjugants reads were aligned on the 5632 genome , reads with mismatch were recovered ( select lines tool , Galaxy version 1 . 0 . 1 ) and these reads were then aligned on the PG2 genome . Only reads with no mismatch and regions with a coverage higher than 15 reads were conserved . These mapped reads , corresponding to PG2 transferred regions , were manually curated using Artemis ( S2 Fig , S6 Table ) . This consisted in removing ( i ) false-positive fragments ( also present in the negative control 5632G3 ) , ( ii ) the vpma and hsd gene families which ones spontaneously undergo high-frequency , intraclonal recombination in propagating population [41] and ( iii ) fragments having no SNPs based on Bam files . Of note , the absence of contaminations between DNA libraries was ensured by ( i ) treating separate DNA batches , ( ii ) by matching PCR genotyping with sequence data ( S1 Fig ) and ( iii ) by independent Sanger sequencing of regions containing mutations detected by WGS in quinolone target genes .
|
Genome downsizing is often viewed as a degenerative process of evolution . Such erosion has left current mycoplasmas with a minimal genome: for some species its size barely exceeds the amount of information needed for sustaining autonomous life . Despite such limitations , these simple bacteria showcase a baffling capacity for adaptation to complex environments such as that provided by the animal host . By using the enrofloxacin antibiotic as selective pressure , we performed a genome scale analysis of macro- and micro-events leading to antimicrobial resistance in mycoplasmas . Sexually competent cells were found to shortcut this process by using an unconventional mechanism of chromosomal transfer driving massive exchanges of DNA materials . Remarkably , this powerful mechanism was associated with a profound genomic reorganization that reshuffled parental features and created mosaicism . This finding emphasizes the extraordinary adaptability of some pathogenic Mycoplasma spp . and provides major insights into the processes that contribute to shaping the evolution of their minimal genome . While unconventional conjugative mechanisms are being documented in more complex bacteria , the reduced mycoplasma genome may provide a simplified model to study mosaicism and its role in bacterial evolution .
|
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2019
|
Mycoplasmas under experimental antimicrobial selection: The unpredicted contribution of horizontal chromosomal transfer
|
Cells transition from spread to rounded morphologies in diverse physiological contexts including mitosis and mesenchymal-to-amoeboid transitions . When these drastic shape changes occur rapidly , cell volume and surface area are approximately conserved . Consequently , the rounded cells are suddenly presented with a several-fold excess of cell surface whose area far exceeds that of a smooth sphere enclosing the cell volume . This excess is stored in a population of bleb-like protrusions ( BLiPs ) , whose size distribution is shown by electron micrographs to be skewed . We introduce three complementary models of rounded cell morphologies with a prescribed excess surface area . A 2D Hamiltonian model provides a mechanistic description of how discrete attachment points between the cell surface and cortex together with surface bending energy can generate a morphology that satisfies a prescribed excess area and BLiP number density . A 3D random seed-and-growth model simulates efficient packing of BLiPs over a primary rounded shape , demonstrating a pathway for skewed BLiP size distributions that recapitulate 3D morphologies . Finally , a phase field model ( 2D and 3D ) posits energy-based constitutive laws for the cell membrane , nematic F-actin cortex , interior cytosol , and external aqueous medium . The cell surface is equipped with a spontaneous curvature function , a proxy for the cell surface-cortex couple , that is a priori unknown , which the model “learns” from the thin section transmission electron micrograph image ( 2D ) or the “seed and growth” model image ( 3D ) . Converged phase field simulations predict self-consistent amplitudes and spatial localization of pressure and stress throughout the cell for any posited stationary morphology target and cell compartment constitutive properties . The models form a general framework for future studies of cell morphological dynamics in a variety of biological contexts .
Cells maintain their structural integrity while being flexible enough to adopt a variety of shapes . In general , it is the cytoskeleton of eukaryotic cells that drives shape transformation leading to cell movement and provides the structural support to the cytoplasm and the means to resist external forces . The periphery of cells , consisting of the plasma membrane ( PM ) and the acto-myosin cortex , is highly dynamic to accommodate shape change . The plasma membrane ( PM ) consists of a high density of proteins [1] embedded in a phospholipid bilayer of 5–10 nm thickness , with a very limited ability to extend without rupture [2 , 3] but highly amenable to bending [4 , 5 , 6] . The thin ( 50–500 nm ) layer of cytoskeleton structure immediately subjacent to the plasma membrane , known as the cell cortex , consists of a dense F-actin network that is cross-linked by actin binding proteins and is amenable to contractility mediated by myosin motors . Interposed between the cortex and the PM is a thin spectrin-actin network , forming a ‘fishnet’ with a mesh size of ~100 nm [7 , 8] . This structure is anchored both to the PM and cortex by adaptor proteins . In the following , we term the plasma membrane and spectrin-actin network as the “cell surface” . Previously we [9] suggested that most dynamical shape changes exhibited by non-spread ( rounded ) cells originate from a membrane-cortex folding-unfolding process and an excess of cell surface area is a necessary requirement for such changes . We investigated the dynamics of periodically protruding cells and hypothesized that the plasma membrane and thin cortical layer remain coupled during all stages of shape transformation . We also assumed that densely compressed cell surface folds and small protrusions could be kept intact by the underlying actin-myosin network residing in the cortex proper . While this notion may be applicable to many shape transitions occurring in non-spread cells , in this paper we reconsider this hypothesis in context of one of the most drastic changes of cell shape: the transformation from a fully spread to a rounded state . If a cell transitions from a spread to rounded state while maintaining a constant volume , it will experience an excess of surface area over the minimum needed to cover the enclosed volume . Because this process typically happens rapidly ( ~30s- ) , there is insufficient time for excess membrane to be internalized by endocytosis . Thus , another mechanism for storing surface area at the plasma membrane must exist . Indeed , there is significant evidence from both electron and fluorescence microscopy that during the rounding process the cell surface adopts a tightly folded morphology [9 , 10 , 11 , 12] . While there are a number of models for cell shape , most of them treat the cell surface as smooth [13 , 14 , 15 , 16 , 17] and do not take into account the possibility that rounded cells store excess surface area in a dense distribution of bleb-like protrusions ( BLiPs ) . Thus , new modeling approaches are needed to understand the dynamics of cell shape changes that involve active use of this surface storage . We introduce three complementary modeling approaches , each incorporating the concept of excess surface area . The first approach is a 2D model based on a thin cell surface structure that is coupled to a thicker , contractile actomyosin layer . This model allows us to investigate the folding of the excess surface and to estimate the bending energy in different configurations . The second approach is a random “seed and growth” model that produces 3D morphologies consistent with the distributions of BLiP size and number estimated from scanning electron micrographs . This model yields insight into how large numbers of BLiPs are efficiently packed on the cell periphery . The third approach is a multi-compartment phase field model . By faithfully capturing the physical properties of the cortex , cytosol , and cell surface , the model predicts the stress and pressure distributions associated with a highly folded 2D morphology and a dense distribution of 3D BLIPs . Phase field models have been widely used to study complex systems comprised of distinct material phases and their adjacent interfaces . When the separate material phases are immiscible , the phase field approach is to prescribe a finite thickness of a “diffuse” interface within which there is a mixture of the two materials [18 , 19] . The phase field method is an alternative to sharp interface methods; in both methods the shape and evolution of the sharp versus diffuse interface are part of the solution . For every pair of adjacent material components , a phase field variable is introduced that interpolates from one material phase to the other through the finite thickness boundary . Phase field models have been employed to describe shapes of lipid bilayer vesicles in which the surface tension and Helfrich bending energy are approximated using a bulk energy defined within the diffuse interfacial layer [19] . Phase field models have been applied to many interfacial problems including liquid drops , multiphase complex fluids [20] , and fractures in solid-state materials [21] . The phase field model simulations achieve separate goals . From either a 2D transmission electron micrograph or a 3D image reconstruction of the cell morphology , the model “learns” the spontaneous curvature functional of the rounded , BLiP-rich , morphology . Since the phase field model faithfully captures material properties of each cellular compartment , the model converges to the cell target morphology while constructing self-consistent stress and isotropic pressure distributions for the cell surface , cortex and cytoplasm , as well as estimating the nematic orientation within the cortex . Storage of excess cell surface in folds or bleb-like protrusions at the periphery is likely to be important for a variety of rapid cell shape changes , taking place over a time scale of a few minutes or less , such as those that occur in forms of amoeboid migration or either within or in the transitions between the phases of mitosis . It seems likely that rapid cell shape changes can be accomplished more quickly by calling upon a reserve of excess membrane stored in the BLiP distributions rather than relying on extensive membrane-cortex remodeling and exocytosis . Thus , the theoretical approaches presented here should be applicable in a number of different biological contexts .
When spread cells ( Fig 1A ) are chemically detached from an underlying substrate , they rapidly transition to a rounded state on a characteristic time scale of ~30-60s ( Fig 1B ) . Numerous studies suggest that in media with constant osmolarity , cell volume is stable [3 , 22] . We estimated cell volume by reconstructing 3D geometries from Z-stacks of spinning disc fluorescence images of cells undergoing rounding . The mean volume for Chinese hamster ovary ( CHO ) cells in the spread state is 6 . 5±2 . 82*103 μm3 , while the mean volume in the rounded state is 5 . 7±2 . 30*103 μm3 , indicating a slight decrease in cell volume after rounding . Because this slight decrease in cell volume would only increase excess surface area , in all our models , we assume that cell volume remains constant during rounding . The surface area of a spread cell is estimated as twice the area measured from images to account for dorsal and ventral surfaces . In reality cells are not completely flat and have more surface area due to finite thickness , particularly around the nucleus . Therefore , we are underestimating the surface area of a spread cell . In the rounded state , the minimal surface area needed to enclose the measured cell volume can be found by assuming the cell is spherical and calculating the radius . For example , the cell 1 in Fig 1A has a surface area of ~ 31000 μm2 while the surface area needed to enclose the rounded state is only ~ 2200 μm2 . Therefore after rounding , this cell has ~ 14 times more surface area than is required to enclose its volume . This image presents an extreme case of surface area excess . For cell 2 in Fig 1A , which is less spread before detachment , an excess surface area of about five times the required amount is accumulated following rounding . It is important to note that the amount of excess surface area that is accumulated during rounding depends on cell type and characteristics of the spreading and detachment for individual cells . Using DIC and fluorescence microscopy , we studied populations of cells before and after detachment , and individual cells rounding during trypsinization . The histogram in Fig 1C presents the distribution of surface areas for spread CHO cells ( blue bars; population mean = 4310±3600 μm2 , N = 199 ) and cells immediately after rounding ( red bars; population mean = 892±284 μm2 , N = 1646 ) . The distribution of rounded cell sizes is narrow with majority of cell radii ( Fig 1E ) being between 7 and 9 . 5 μm ( mean = 8 . 36±1 . 24 μm; N = 1646 ) . Separate experiments , where we followed the change in morphology of individual cells during rounding , demonstrated that for CHO cells the average excess surface , defined as the ratio of spread cell area to that required to smoothly cover a sphere with radius corresponding to that of the rounded cell , accumulated due to detachment and rounding is 3 . 8± 2 . 06 with maximum value of 12 ( N = 99 ) . To gain insight into how much excess surface area can be stored in BLiPs , we first consider the case of a rounded cell uniformly covered with equally sized sphererical BLiPs . It is easy to show ( S1 Appendix ) that as spherical BLiPs become smaller , more excess surface area can be accommodated . The maximum possible surface excess that can be stored in the equally sized sphererical BLiPs is 5 ( in the limit of BliP radius r→0 ) . The fact that we observed rounded cells with the surface excess as high as 14 , means that cells utilize a more efficient packing strategy . Also no limits on the surface excess ratio would be imposed if we did not require BLiPs to be spherical , but rather allow for an arbitrarily high curvature of the surface , as occurs , for example , in tubules . Yet , the majority of BLiPs appear to be rounded immediately after detachment . These considerations suggest that the actual morphology of the folded cell surface is dictated by a balance between the necessity to pack tightly a very large number of BLiPs and the necessity to generate , regulate , and maintain significant surface curvature . To better understand the process of packing cell surface excess into a convoluted surface morphology , we constructed a 2D geometric model designed to produce BLiPs . We hypothesize that the cell surface and underlying contractile cortex form a two-layer structure that is coupled at certain fixed points . The first layer , which we term the cell surface , is passive and consists of the plasma membrane and membrane associated cytoskeleton . This layer is assumed to be similar to the spectrin-actin network that is coupled to the plasma membrane of red cells [23] . Such structures have been shown by Kusumi and co-workers to exist in many other cell types [8] . The membrane associated cytoskeleton has been termed the membrane skeleton fence [8] . It is basically a very thin filamentous meshwork that provides a “fishnet” with a mesh size of approximately 100 nm immediately underlying the PM . This layer is coupled to the plasma membrane via adaptor proteins including the ankyrin and ERM families as well as by interactions of the membrane skeleton fence with lipids in the inner monolayer of the PM . The layer is thought to be passive undergoing only thermal motions , serving to anchor some transmembrane proteins and restrict the free diffusion of others . We assume that this thin layer is coupled via adaptor proteins to a thicker , active contractile layer containing actin and myosin . This view of the cortex-cell surface couple is consistent with that advanced by Charras et al ( 2006 ) [24] in the context of spontaneously blebbing cells . Additional evidence for this structure comes by imaging employing confocal and electron microscopy . Fig 1F shows a confocal image of the actin-myosin cortex in rounded cells as visualized with GFP-lifeact and RFP-myosin merged with a DIC image of the same cell . The green signal for Lifeact marks F-actin filaments associated with the folded morphology of the cell periphery and this fluorescent signal originates from both the thin layer immediately subjacent to the membrane and the thicker contractile layer . The fluorescent signal from myosin ( red ) shows that this protein is localized mainly to a thin circle located below the BLiPs and more toward the cell interior . S1 Fig presents Z-stack images of the same cell . From this image , it is clearly visible that the convoluted morphology covers the whole cell . Fig 1G and 1H shows immunogold TEM images of GFP-lifeact where F-actin is seen underlying BLiPs ( arrow ) and also in a layer closer toward the center of the cell ( arrowhead ) . To construct the 2D geometric model introduced qualitatively above , we implement a two-layer architecture in a 2D bead-spring model of the cell membrane and cortex ( model description in Methods ) . The bead-spring model consists of two-layers ( Fig 2A and S2 Fig ) , where one layer ( outer layer ) represents the membrane and underlying actin mesh ( i . e . the cell surface ) and the other layer ( inner layer ) represents the actomyosin-rich contractile cortex . In each layer , beads are connected pairwise by springs and contact points serve to connect the two layers . By minimizing the bending energy we explored the steady-state shapes of BLiPs generated during cell rounding when the cell is rapidly presented with a substantial excess surface . For simplicity we define excess surface ratio ( ER ) as a ratio of perimeters of the surface layer and contracted cortex . Here we define a normalized total bending energy ( E ) as for ( 1 ) where κ is the local curvature measured between two neighboring beads , L is the perimeter , and N is the number of beads in the outer layer [25 , 26 , 27 , 28 , 29] . The number of contact points determines the number of folds ( M ) . In the simulation the total Hamiltonian of the two-layer system is minimized with the result that a folded configuration of outer layer is produced . Fig 2B shows the resulting shapes as a function of both M and ER . Fig 2C shows a portion of a model cell with BLiPs at steady state where the gold line represents the contractile part of the cortex with contact points . While the appearance of folds is expected , the shape of folds and the bending energy stored in each configuration is of particular interest . Fig 2D gives a comparison of the fold configuration for several different sets of parameters with the calculated bending energy for each shape . Inspection of the fold shapes shows that in order to accommodate more surface , the folds tend to develop long necks . ( Note that in case where there is heterogeneity in the size of folds , this effect would allow small folds to grow under larger ones , an effect that permits accommodation of more excess surface . ) The smallest possible bending energy will be achieved when ER = 1 and M = 0 ( no surface excess and no BLiPs ) . For a given value of ER , the energy increases with the number of BLiPs ( Fig 2D and S2 Fig ) . However , the bending energy is decreasing while the excess surface ratio is increasing . Although this result looks counterintuitive , it can be explained . The local curvature is the inverse of local radius . Folds with a longer perimeter have bigger inner radii which substantially decreases bending energy with the square of local radius ( Fig 2D and S3 Fig ) . Thus , morphologies with longer perimeters corresponding to larger ERs will have lower energy compared to the shapes with the same number of folds but with shorter perimeters ( i . e . smaller ERs ) . The analysis of the area which is stored inside the folds ( i . e . , volume in 3D ) shows that for the same surface excess , more area is stored in folds when the number of folds used for accommodation of this surface surplus is smaller ( S3D Fig ) . At the resolution achievable by standard fluorescence microscopy , the convoluted cell surface often appears as a thickening of membrane and cortical stains ( Fig 3A and 3B ) . However , surface morphology can be imaged at higher resolution using both scanning ( SEM ) and thin-section transmission ( TEM ) electron micrographs ( Figs 3C and 1H , respectively ) . At this scale , bleb-like protrusions ( BLiPs ) and other cell surface protuberances are clearly visible . Note that only fully spread cells have a smooth surface essentially devoid of protrusions ( S4 Fig ) . To determine length , area and volume metrics of BLiPs , we manually segmented SEM images of cells that were fixed after rounding ( S5 Fig ) . Each protrusion was approximated as a sphere and the area of the protrusion visible on the image was interpreted as a two dimensional projection of that sphere . We calculated the radius that corresponds to a projection of that size , and consider it as the radius of the BLiP . The distributions of BLiP radii derived from 10 SEM images ( 25 cells ) that include 7096 BLiPs is presented in Fig 3D . We find that the distribution of radii is skewed with a preponderance of small BLiPs and a decreasing frequency of larger BLiPs . The mean BLiP radius is R = 0 . 25 μm with a median of 0 . 22 μm and mode of 0 . 19 μm . It is important to mention that during the processes of detachment and rounding some part of the cell surface can be lost due to incomplete detachment from the substrate or because it remains in retraction fibers . However , the area remaining in retraction fibers is quite small . Using SEM images from cells that spread for 24 h and rounded 5 minutes before fixation , we estimated that the surface area that might be stored in retraction fibers represents between ~0 . 5–5% of the cell surface area in the spread state . To investigate how the large number of BLiPs required to accommodate the excess cell surface are packed on the cell periphery , we constructed two models . As a plausible starting point , we employed a Voronoi approach , in which a spherical ball of radius R=S/4π contains the surface area , S of the spread cell before detachment and rounding; n seed point locations are randomly sampled from a uniform spatial distribution on the ball . The ball is then partitioned by a Voronoi tessellation according to the n seed points ( Fig 4A ) such that any point in each Voronoi cell is closer to the parent seed point than any other seed point . The area of each Voronoi cell is then determined . In this configuration , we assumed that , upon the cell rounding to its final state with the “BLiPed” morphology , each Voronoi cell of area v morphs into a spherical BLiP with radius r=ν/4π . Fig 4D demonstrates that , constructed in this way , the distribution of BLiP radii has a well-defined length scale with the bell-shape distribution , which is not consistent with the skewed distribution of experimental data . This result arises from the fact that Voronoi cells corresponding to two very closely positioned seeds are not necessarily small themselves , as might be expected from two closely positioned BLiPs . In order to mitigate this effect , we introduce an alternative 3D “seed and growth” model ( Fig 4B and 4C ) , in which BLiP radii are proportional to spacing between randomly distributed seeds . In this model , spheres are generated from each seed point by increasing their radii at a uniform rate . Simultaneously , the locations of the seed points are moved outward radially at the same rate , so that the spheres always remain tangent to the cell . When one sphere encounters another , it stops growing . When all spheres have stopped growth , a spherical cell coated by different sized BLiPs is produced ( Fig 4B ) . The resulting BLiP radius distribution in Fig 4D is more consistent with the experimental distribution than that produced by the Voronoi model . The generated structure is also consistent with SEM image data , which show approximately spherical BLiPs largely covering the cell but with some areas devoid of BLiPs . We reproduced 2D cross-sectional views from the simulated 3D geometries ( Fig 4F ) ; these show similarity to the thin section TEM images of rounded cells ( Fig 4G ) where some of the BLiPs appear to be detached from the cell body because BLiPs are not always sectioned through their centers . In the “seed and growth” model , a larger number of BLiPs results in a higher surface area excess ratio ( Fig 4E ) and a smaller percentage of the cell volume stored within the BLiPs , which is consistent with our simplified estimations based on an 2D equal-sized BLiP distribution . In principle , BLiPs that are not of equal size could allow a more efficient packing of excessive cell surface ( with smaller BLiPs filling the space between larger ones ) , which is important for accommodating very high excess ratios ( >5 ) . However , in this model the packing is still inefficient because it always generates areas devoid of BLiPs . A potential improvement to our model might be to incorporate stochastic seeding of new BLiPs and occasional “shrinking” BLiPs that are in contact , so that BLiPs are dynamic and continue to adjust themselves toward the most efficient filling of the available space . Such a “seed , growth , and shrinking” model would be consistent with the BLiP dynamics observed in our experiments , but is beyond the scope of the current paper . Another potential improvement would be to make final BLiP size proportional to the rate of expansion of the BLiP; this has been found to be the case in an earlier study of blebbing cells [30] . The preceding models help build mechanistic intuition , yet , while predictive , they do not capture all of the essential physics of the rounded phenotype . In order to approach this goal , we formulated 2D and 3D phase field models for a cell immersed in the aqueous extracellular environment . The model is formulated in 3 space dimensions ( 3D ) , but it also restricts to 2D for purposes of modeling a cell cross-section . In our case , we have three phases ( exterior aqueous medium , cortex , interior cytosol ) and two diffuse interfaces . The external aqueous medium and interior cytosol are modeled as viscous fluids with specified viscosities and the cortex is modeled as a nematic ( liquid crystal ) gel [31] . The first diffuse ( i . e . , finite thickness ) interface is what we have termed the cell surface , consisting of the plasma membrane and very thin underlying filamentous “fishnet” , that separates the aqueous medium and the cortex proper . As described below in Methods , a particular level set function in the phase field formulation will afford our definition of the “cell surface” . The second diffuse interface is the cortex-cytosol transition layer . Fig 5 is a 2D schematic of a 3D cell cross-section with individual components and diffuse interfaces labeled , along with the phase variables defined below . ( We do not explicitly model the nucleus within the cytoplasm for this paper since we are primarily concerned with the stationary rounded morphology . ) A more complete mechanical formulation giving the total system free energy in terms of its components is found in Methods . We summarize the key numerical results of the phase field modeling of a 2D cell surface morphology due to an imposed excess arc length enclosing the 2D area . We require a 2D image of the membrane morphology , taken from 2D transmission electron micrographs . From the image file , we posit an initial smooth membrane boundary , and then evolve the phase field model while adjusting the spontaneous curvature function C1 until the model converges to the image dataset . We first illustrate the ability of the phase field model to match an arbitrary specified 2D boundary by “learning” the spontaneous curvature function; the results are shown in Fig 6A for an illustrative benchmark in which the cell perimeter contains 25 regularly spaced , uniform “BLiPs” . ( In 2D , this is achieved by superimposing the appropriate Fourier mode on a circle . ) Next , we used as input the actual periphery of a rounded cell from a 2D transmission electron micrograph ( TEM ) image . The results in Fig 6B show the convergence of the phase field membrane morphology to the TEM image , where the nematic phase ordering ( representing F-actin orientation ) in the cortex is depicted . This result assumes tangential anchoring condition of F-actin at both cortical diffuse interfaces . Fig 6C–6E shows the phase field predictions of the pressure distribution ( C ) and the first invariant of the dominant stored stress , the Ericksen stress , for the converged stationary morphology ( D , E ) shown in Fig 6B . These results reveal the orders of magnitude as well as spatial localization of pressure and stored stress for the target 2D morphology . A 3D simulation is depicted in Fig 7 to demonstrate the capability of our phase field model to converge to a target 3D cell surface morphology . The excess surface area ratio for this illustration is s0 = 3 . Because it is impossible to reproduce a full 3D morphology of a rounded cell from a single scanning electron micrograph , we use the “seed and growth” model to simulate a 3D cell surface target . This model ( Fig 4 ) provides a 3D surface morphology consistent with the measured BLiP size distribution data; therefore , we posit the output image from this model as the target morphology for the 3D phase field simulation . As shown earlier in 2D and here in Fig 7 in 3D , the phase field model converges to the target 3D morphology from an initial posited surface , while satisfying the volume and excess surface area constraints . The model does so by iterating the spontaneous curvature function until all constraints are satisfied; once converged , the model then yields the pressure and stresses within the cell surface and cortex that are self-consistent with the 3D surface morphology and constitutive properties of the exterior and cell compartments . In Fig 7A , the target cell morphology is shown . The evolution of cell morphology , from an initial rounded cell guess to the target cell shape , is provided in ( Fig 7B and 7C ) . Fig 7D depicts 2D projections in three mutually orthogonal planes of the cell surface morphology as well as the cortical layer and interior cytosol domains , displaying the values of the phase field variables for each domain . In Fig 7E and 7F the model predictions for hydrostatic pressure distributions and stored stress in the same orthogonal planar sections for the stationary morphology are given correspondingly . The pressure values are not unreasonable ( e . g . a 1 mm depth of water at atmospheric pressure exerts a hydrostatic pressure of 9 . 8 Pa ) . The pressure is low and positive in the external aqueous medium and cytosol; therefore , an inward pressure is exerted from the external medium to the cell surface and an outward pressure from the cell interior ( cytoplasm ) to the cortex . The pressure is negative in the plasma membrane and cortical layer meaning that this layer experiences an inward compressive pressure from the external aqueous medium and cytosol . The highest ( compressive ) pressures arise in the cell surface “interphase” nearby high curvature BLiPs , with about an order of magnitude lower values within the cortical layer itself . These stationary pressure gradients suggest a propensity for fluid absorption from the exterior aqueous medium into the cortex phase and cell surface interface . It is important to note that our simulations assume a stationary morphology , and the pressure and stress distributions are a consequence of the stationary assumption . In reality , these morphologies are non-stationary , and in particular there is flow of the cytoplasm that fills the BLiPs . The new balance of pressure and stress will then dictate the directional flux and flow of the external aqueous medium and cytosol through the various cell compartments . The 3D orientational distributions of cortical actin-filaments , comprising the nematic cortical phase , are given in Fig 8A; planar 2D slices are shown at the specified positions in Fig 8B–8D . These figures convey the degree and direction of order within the F-actin filaments of the cortex , and their strong correlation with the cell surface morphology . Note that cortical F-actin is assumed to prefer parallel alignment at both cortical interfaces for this illustration of the 3D phase field model . The anchoring energy at the interfaces together with the presumed strength of the nematic potential are responsible for the relatively high degree of alignment of the F-actin; these parameters are tunable to match experimental data on nematic order within the cortex . The nematic order was changed by varying Flory order parameter from 1 to values approaching 0 . The results of these simulations are shown in S6 Fig which shows the nematic order superimposed on the 3D morphology for order parameters that range from 1 . 0 to 0 . 01 . Cross-sections taken at three orthogonal planes are shown in S7 Fig . Note that the values assumed for K ( the Franck elastic constant ) , and h1 and h2 will not significantly affect the pressure or the Ericksen stress of the stationary morphology; this is because the dominant contribution to the Ericksen stress is from spatial gradients of the level set function ϕ1 = 1/2 that defines the cell surface . As shown in S6 and S7 Figs , h1 and h2 only affect the nematic order of the stationary state and K does not affect any of the predictions for the stationary morphology . However , for dynamic processes , these parameter values will strongly dictate the results of the simulation .
Using comparative measurements of many individual cells in two distinct configurations , spread on a substrate versus in a rounded state detached from the substrate , we showed that the cell surface area in the rounded state is highly convoluted and far exceeds the surface area of a sphere that would enclose the volume of the cortex , cytosol and nucleus . We analyzed the size and distribution of bleb-like protrusions ( BLiPs ) on the cell periphery that served as storage for the excess of surface area . We then developed three complementary modeling approaches that incorporate the concept of excess surface area on rounded cells in different ways . Employing a 2D discrete geometric model , we tested whether the two-layer composition of the cortex , with the outer layer termed the cell surface , giving the local shape of the BLiPs and the inner layer responsible for contraction , is sufficient to reproduce the highly folded surface observed after rounding . This model demonstrated that morphologies with longer perimeters corresponding to larger ERs will have lower energy compared to the shapes with the same number of folds but with shorter perimeters ( i . e . smaller ERs ) . This result predicts that during cell rounding , larger folds , which are energetically more favorable , should appear early in the process . On the other hand , smaller folds will appear later because it can require time to build structures that will support the higher curvature folds . Indeed , several preliminary experiments in which cells are detached and imaged appear to support this notion . In the early stages of rounding , big folds often appear on the cell surface but , as time goes on , the cell breaks the large folds into the smaller ones . This is supported by the fact that we see a prevalence of small BLiPs in the distribution of fold sizes from the SEM and fluorescence imaging . It is also possible that smaller BLiPs are required as the cell approaches a rounded , steady state because the large folds stored too much of the cell volume ( S3D Fig ) which could disrupt normal cell functioning . In addition , the ability to create smaller folds would be advantageous in storing large surface excesses in that small BLiPs could form under larger ones for more efficient packing . Our 3D random “seed and growth” model of BLiPs approximated the BLiP number density and size distribution from 3D scanning electron micrographs . The model provided insight into the SEM image analysis that revealed skewed size distributions of the BLiPs , with a preponderance of small-scale features and successively fewer large-scale protuberances . Moreover , this model demonstrates that efficient packing of BLiPs requires that heterogeneity of BLiP sizes is needed to recapitulate 3D morphologies . To begin to capture the physical properties underlying the convoluted morphology of the rounded cell , we introduced a generalized phase field formulation . The model accounted for the cell surface as a diffuse interface between the exterior aqueous phase and the interior cortical phase and cytoplasm . In the model , the cell surface is equipped with a Helfrich bending elastic energy that includes a spontaneous curvature function that encodes the bending energy associated with the BLiPs . The spontaneous curvature function is a consequence of molecular components ( the spectrin-actin “fishnet” ) that mediate the attachment between the cell surface and cortex . However , this molecular information is implicit at this stage of the phase field model , with future extensions aimed at coupling these molecular origins of the spontaneous curvature . At this juncture , our multi-compartment , phase field model accepts 2D or 3D images of the cell morphology as input targets and “learns” the membrane curvature of that target morphology . Since each cell compartment is endowed with constitutive properties , the phase field model predicts physical consequences of the target morphology throughout the cell compartments , restricted for this study to input stationary morphologies . In particular , the model predicts pressure and stress distributions that are concentrated within the cell surface diffuse interface and highly correlated with membrane-cortex interface gradients associated with BLiPs . In future model developments , when the dynamics of the rounded phenotype are introduced , the pressure-stress distributions will evolve in time , and the consequences of constitutive properties of each compartment will dominate the evolution , unlike the stationary predictions where viscous and nematic stresses relax to zero . How do these three distinct approaches relate to one another ? We postulate that cell surface regions rich in adaptor proteins bind the cell surface to the cortex , inheriting the mean curvature of the cortex . We have termed these regions attachment or contact points . Although the species composing these regions have not been identified , presumably they would belong to groups such as the ERM family of cytoskeletal-membrane adaptors as previously suggested in [24] . Moreover , one would expect that these contact points would be transient and regulatable leading to more dynamic behavior than we capture in the current models . Domains with less binding proteins allow the cell surface to detach from the contractile part of cortex forming BLiPs . We assume in our models that the distribution of binding protein species dictates the surface morphology , which in turn dictates the spontaneous curvature function . In the discrete geometric Hamiltonian model , the binding sites forming the attachments between the cell surface and the cortex are explicitly modeled , leading to an induced cell surface morphology . In the phase field model , we choose the level set ϕ1 = 0 . 5 to define and match the surface morphology captured in 2D micrographs or reconstructed in 3D using the seed-and-growth model . Thus discrete Hamiltonian and phase field modeling approaches are complementary: the discrete Hamiltonian model is based on postulated attachment points that determine morphology , whereas the phase field model , in which specific molecular features are coarse-grained , is based on a spontaneous curvature function specific to and constructed from the morphology itself . In 3D , micrographs are not sufficient to provide a 3D image file due to significant occluded cell surface . Thus , we used images produced by seed-and-growth model that yields 3D images of the surface morphology that are statistically consistent with the measured 3D BLiP distribution data from scanning electron microscopy images . The phase field model then uses the 3D surface construction as an imposed target morphology , and the model evolution adapts the spontaneous curvature function until the target morphology is reached . The phase field model then predicts the stationary stresses and energies within the cell surface and cortex self-consistent with that surface morphology . It is important to note the stationary aspect of the model predictions which identify stress contributions that are due to spatial gradients of the fitted membrane morphology . Indeed , since the model simulation converges to the input stationary morphology , the stored stresses due to nematic elasticity all relax and are negligible . I . e . , the stress components are insensitive to the nematic parameters , and are dominated by the gradients of the level set function ϕ1 = 0 . 5 learned from the morphology . The power of the model will be further revealed when we investigate the dynamics of the highly convoluted morphology , where nematic parameters and constitutive properties of all compartments will then have significance . It will be important from a biological standpoint to learn the bounds on these parameters . An additional caveat is that the models presented are purely mechanical or steric in nature . It is certainly possible that active processes other than cortical contraction , giving rise to cortical tension [32] during rounding could play a role even in the short time span of cell rounding from a spread state . For example , in our model , we did not include the actin polymerization process explicitly although but it is true that smaller BLiPs , which are the majority of the population , have higher bending energy so that they require stronger cortical support perhaps requiring additional actin nucleation and polymerization [33] on short time scales . Highly convoluted surface morphologies are often apparent in three-dimensional tissue contexts and in cells that are not fully spread on a two-dimensional surface . The storage of the cell surface in folds or bleb-like protrusions at the cell periphery is likely to be crucial to a variety of rapid cell shape changes such as those that occur in cell migration . It seems more feasible and energetically favorable that rapid cell shape changes can be accomplished quickly by calling upon and pulling out the excess surface stored in the BLiP distributions as an alternative to large scale endo- and exocytosis accompanied by membrane-cortex remodeling . Although the results presented here are derived for stationary BLiP-laden morphologies , these models form the foundation for future studies of cell surface dynamics regulated by coupling to reaction-diffusion kinetics of various molecular species . These kinetics can be expected to be controlled by signal transduction in many cases . The theoretical approaches presented here should find application in a number of different biological contexts .
The bead-spring model consists of two-layers ( Fig 2A ) , where one layer ( outer layer ) represents the membrane and underlying actin mesh ( i . e . the cell surface ) and the other layer ( inner layer ) represents the myosin-rich contractile cortex . Within each layer , beads are connected pairwise by springs . Special contact points serve to connect the two layers via springs . At the beginning of simulation both layers have the same perimeter . During the simulation the inner layer ( cortex ) shrinks in order to reach the target enclosed area with smaller perimeter , imitating cortex contraction . The presence of contact points between two layers enforces outer layer bending ( S2 Fig ) . Although the more correct definition of the excess surface ratio is ε2D=L¯2πR¯=L¯/2πAtotal , where L¯ is the perimeter of the surface layer and R¯ is the radius of the circle that would enclose the area inside this surface layer ( Atotal ) , for the simplicity we define excess surface as a ratio between the perimeters of surface layer and contracted cortex . Let the surface layer with the perimeter L be represented by N beads ( S2 Fig ) , with the notational convention that bead 0 corresponds to bead N ( representing a closed contour ) . Then the Hamiltonian for this outer layer of beads and springs ( i . e . the cell surface ) is: Hout=c1Σi=1Nκi2+c2Σi=1N ( li−L¯/N ) 2 , ( 2 ) where ki is the local curvature of the surface at bead i; li is the length of the spring between beads i and i+1; and c1 and c2 are free parameters that define relative contributions of the energy terms . The first term in Eq 2 is the energy cost for bending the surface layer . The second term ensures that the outer layer does not significantly stretch or contract during the simulated process . c1 and c2 are chosen with c2 ≫ c1 so that as the system approaches a steady ( minimum energy ) state , the first term tends to a configuration that minimizes curvature and the second term tends to zero . The Hamiltonian of the inner , contractile layer ( i . e . the cortex ) is: Hinn=c3Σj=1Mpj2+c4 ( A−A¯ ) 2 , ( 3 ) where M is the number of beads in the inner cortex ( M<N ) ; pj is the length of the spring between inner beads j and j+1; A is the area of the polygon formed by the inner beads with perimeter P; A¯ is the target area; and c3 and c4 are scaling parameters that define relative contributions of the energy terms . At the steady state this layer approaches the circular shape with A→A¯ and lj→2πA¯/M . The total Hamiltonian of the two-layer system contains three additional terms: Htot=Hout+Hinn+Hcontact+Hcross+Hself . ( 4 ) Hcontact is the energy stored in the springs between inner and outer cortex contact points: Hcontact=c5Σi=1P∥ti−τi∥2 , ( 5 ) where ti denote the contact points on the outer cortex and τi denote the corresponding contact points on the inner cortex . This term ensures that these contact points remain close . Hcross penalizes crossing of outer cortex beads into the inner cortex polygon: Hcross=c6Σi=1M∥ti−l¯∥1pi∈Inn , ( 6 ) where Inn denotes the interior of the polygon formed by the points of the inner cortex , and the indicator function 1pi∈Inn = 1 if the outer cortex point pi is in Inn and 0 if outside . ti denote the points on the outer cortex and l¯ denotes the segment closest to each point . We calculate this function by computing the point’s winding number . Lastly , Hself penalizes self-crossing of the outer polygon . Let l¯ denote the line segment connecting beads i and i+1 . Hself=c7Σi≠jcross ( li¯ , lj¯ ) , ( 7 ) where the function cross ( li¯ , lj¯ ) =1 if li¯ and lj¯ cross and 0 if they do not . We let c7 = ∞ with the convention 0∙∞ = 0 , effectively preventing any self-crossings of the outer cortex . In practice , this condition is enforced by considering all other energy terms and keeping bead i fixed if li¯ crosses any lj¯ for any j ≠ i in the next iteration . As the system approaches steady state each of these additional terms tends to zero . While the target area ( A¯ ) constraint is more aptly applied to the outer layer , it is numerically more feasible to apply the target area constraint to the inner layer Hamiltonian and to scale the final simulated result by multiplying the coordinates of each point by a multiplicative factor to match the target area . With this scaling the overall shapes of the “cell surface” and cortex do not change but all simulated shapes get the same area inside their surface layer which includes cortex and folds . We introduce phase variables ϕi , i = 1 , 2 , 3 ( Fig 5 ) that denote the volume fractions of phase 1 ( the exterior aqueous medium surrounding the rounded cell ) , phase 2 ( cortex ) and phase 3 ( interior cytosol ) , respectively . Clearly , in any pure phase i , the respective ϕi = 1 , whereas in diffuse interfaces between phases i and j , ϕi+ϕj = 1 , with ϕk = 0 , k ≠ i , j , and everywhere the total volume fraction is 1 . Thus in the external aqueous medium , ϕ1 = 1; in the F-actin rich , cell cortical layer , ϕ2 = 1; and in the interior cytoplasm , ϕ3 = 1 . The phase boundaries are: the cell surface , as defined above , that separates the external aqueous medium and cortical layer , where 0<ϕ1 , ϕ2<1; and , the transition layer between the cortex and interior cytosol where 0<ϕ2 , ϕ3<1 . For graphical purposes and for matching 2D TEM and 3D simulated topology images from the seed and growth model , the cell surface is defined by the level sets ϕ1 = ϕ2 = 0 . 5 , while the cortex-cytosol interface is defined by ϕ2 = ϕ3 = 0 . 5 . We do not allow all three phases to come into contact in this model , achieved by an energy penalty term . Therefore the level set ϕ1 = 0 . 5 , in domains where ϕ3 = 0 , determines the cell surface . Below , we illustrate how to constrain this level set function to match the experimentally measured cell surface , in both shape and surface area . We note that for this paper the external aqueous medium and interior cytosol are modeled as viscous fluids with specified viscosities and the cortex is modeled as a nematic ( liquid crystal ) gel [31] . Viscoelasticity of the interior cytoplasm is easily incorporated into our phase field formulation [34] , but for the purposes of the stationary morphology any stored elastic stress in the cell interior relaxes to zero . Thus we simplify to a viscous cytosol for this paper . The governing equations for the three phases and two diffuse interfaces are presented next . The phase field method is an energy-based variational theory , comprised of free energy functionals for each phase and diffusive interface . Swiss 3T3 cells ( obtained from Tissue culture facility UNC Chapel Hill ) were cultured in DMED ( Gibco ) with 10% FBS ( Gibco ) . CHO-wt cells ( from ATTC ) were grown in medium DMEM/F12 ( Gibco ) containing 10% FBS . CHO cells stably expressing Lifeact-GFP ( the small 17-amino-acid peptide , Lifeact , fused to green fluorescent protein , GFP ) were obtained from the James Bear laboratory ( UNC-Chapel Hill ) . CHO-wt cells were transiently transfected by GFP-PH-delta domain ( gift from Con Beckers , UNC-CH ) using Lipofectamine Plus reagent ( Invitrogen ) and images were taken 24–48 hours after transfection .
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Individual cells must have the capability for rapid morphological transformations under various physiological conditions . One of the most drastic shape transformations occurs during the transition from spread to rounded morphologies . When this transition occurs rapidly , there is insufficient time for significant changes in surface area to occur , although the final size of the rounded cell indicates a significant reduction in apparent cell surface area at light microscope resolution . By contrast , high-resolution scanning electron micrographs of rapidly rounded cells reveal that a large amount of surface area is stored in a highly convoluted surface morphology consisting of bleb-like protrusions ( BLiPs ) and other small structures that are unrecognizable at lower resolution . This surface reserve is an important part of the mechanism that allows rapid and efficient large scale transformations of cell shape . Remarkably , although this convoluted morphology has been observed for decades , there has been very little effort recognizing and including this surface surplus in mathematical modeling of cell morphology and physiology . In this paper , we develop three complementary models to fill this void and lay the foundation for future investigations of the mechanisms that drive cellular morphological dynamics .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"crystallization",
"techniques",
"perimeters",
"biological",
"cultures",
"radii",
"geometry",
"interface",
"diffusion",
"mathematics",
"cellular",
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] |
2016
|
Modeling the Excess Cell Surface Stored in a Complex Morphology of Bleb-Like Protrusions
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The apicomplexan moving junction ( MJ ) is a highly conserved structure formed during host cell entry that anchors the invading parasite to the host cell and serves as a molecular sieve of host membrane proteins that protects the parasitophorous vacuole from host lysosomal destruction . While recent work in Toxoplasma and Plasmodium has reinforced the composition of the MJ as an important association of rhoptry neck proteins ( RONs ) with micronemal AMA1 , little is known of the precise role of RONs in the junction or how they are targeted to the neck subcompartment . We report the first functional analysis of a MJ/RON protein by disrupting RON8 in T . gondii . Parasites lacking RON8 are severely impaired in both attachment and invasion , indicating that RON8 enables the parasite to establish a firm clasp on the host cell and commit to invasion . The remaining junction components frequently drag in trails behind invading knockout parasites and illustrate a malformed complex without RON8 . Complementation of Δron8 parasites restores invasion and reveals a processing event at the RON8 C-terminus . Replacement of an N-terminal region of RON8 with a mCherry reporter separates regions within RON8 that are necessary for rhoptry targeting and complex formation from those required for function during invasion . Finally , the invasion defects in Δron8 parasites seen in vitro translate to radically impaired virulence in infected mice , promoting a model in which RON8 has a crucial and unprecedented task in committing Toxoplasma to host cell entry .
Host cell invasion by apicomplexan parasites is a highly specialized feature of this phylum that is crucial to their survival in humans ( Toxoplasma gondii , Plasmodium spp . , Cryptosporidium spp . ) and animals ( Neospora caninum , Eimeria spp . ) [1] , [2] . Following initial contact with a target host cell via GPI-anchored parasite surface proteins , the pathogen reorients its apical end toward the host cell membrane and secretes the contents of microneme , rhoptry , and dense granule secretory organelles in an intricate , coordinated manner to enable parasite invasion and concomitant formation of a unique vacuole within the host [3] . A key stage in invasion is the formation of a tight apposition between parasite and host cell membranes known as the MJ , first visualized in electron micrographs of invading Plasmodium [4] . Initially a punctate focus , this interface rapidly resolves into a ring that slides posteriorly over the parasite in conjunction with host membrane invagination and eventual engulfment of the invading pathogen . The MJ is essential for this process , as it anchors the parasite to the host surface while the parasite's actin-myosin motor ( the “glideosome” [5] ) provides forward motion into the host cell . In addition , studies of invading Toxoplasma parasites using fluorescently labeled lipids and host membrane proteins have demonstrated molecular sieving at the junction , presumably responsible for the non-fusogenic nature of the PV that precludes destruction by host lysosomes [6] , [7] . Although the mechanism by which the MJ carries out these functions is unknown , the identification of a complex of rhoptry neck proteins that specifically localize to the Toxoplasma MJ provided a significant advance in the characterization of the unique invasion process used by apicomplexan parasites [8] , [9] , [10] , [11] . Rhoptries are subdivided into mottled bulbous bodies and tapered electron-dense necks , corresponding to storehouses of proteins that play distinct roles in invasion [12] . Rhoptry bulb proteins ( ROPs ) are injected into the host cell where they contribute to the formation of the parasitophorous vacuole [13] and co-opt host cell processes to create a favorable environment for the parasite [14] , [15] . Several neck proteins , by contrast , assemble with the microneme protein AMA1 to constitute the Toxoplasma MJ [8] , [10] . Orthologs of most known Toxoplasma MJ/RON components localize to the Plasmodium rhoptry neck [16] , [17] , [18] , [19] and traffic to the Plasmodium moving junction [20] . While this establishes the generally conserved nature of the complex across the Apicomplexa , MJ proteins lack identifiable domains or motifs that could provide clues to their function in this enigmatic invasion machinery . A novel rhoptry neck protein , RON8 , was recently identified in Toxoplasma and Neospora as a coccidia-specific component of the MJ [9] , [11] . RON8 associates with RONs 2/4/5 in a preformed complex within the rhoptry necks that is injected into the MJ with RONs 4/5/8 exposed to the cytoplasmic face of the host cell membrane tethered to RON2 , which is thought to span the host plasma membrane via its transmembrane domains [9] . This topology could be facilitated by RON2′s integration within multi-lamellar whorls detected in Plasmodium rhoptries by electron microscopy , which could insert into the host plasma membrane during invasion , enabling soluble RONs bound to RON2 to be exposed to the host cytoplasm [21] . Here they are ideally poised to carry out filtration or anchoring roles for the parasite , as molecular sieving by the moving junction is restricted to this face of the host membrane [6] , [7] . These findings are supported by exogenous expression of RON8 within mammalian cells , as RON8 in the absence of other complex members traffics to its site of action at the cell periphery via its C-terminal region [11] . In addition to cleavage of the signal peptide , RON8 is processed at the N-terminus , like other MJ/RONs , and the proforms of these proteins can associate in vitro with each other and with pro-AMA1 [9] . AMA1 coprecipitates RON2 under harsh conditions and likely has a favored association with this RON , supporting a model where the universal “receptor” RON2 grants Toxoplasma access to a wide variety of host cells through binding its universal “ligand , ” AMA1 , lodged in the parasite membrane [9] . Aside from establishing contact between RON2 and AMA1 , however , precise roles for the MJ/RON proteins during invasion remain elusive . In this study , we present RON8 as the first RON junction component to be disrupted in Toxoplasma , and show that RON8-deficient parasites have severe defects in host cell entry . Junction proteins secreted from invading Δron8 parasites are often disorganized , suggesting an imperfect separation of the PV from the host cell membrane after invasion . We further show through complementation of knockout parasites that the RON8 C-terminus is processed , and also identify functional domains of RON8 that are sufficient for rhoptry neck targeting and MJ complex association . RON8-ablated parasites are greater than five logs less virulent in mice , reinforcing the devastating consequences of lacking RON8 for infection in vivo and subsequent formation of disease . Taken together , this work identifies crucial adherence and organizational roles for RON8 , demonstrating the importance of this protein within the junction for committing Toxoplasma to host cell entry .
Our initial attempts to disrupt RON8 from the RH strain of T . gondii were not successful [11] , consistent with the necessity of the MJ complex for parasite invasion [8] , [22] . The recent development of Toxoplasma strains lacking the non-homologous end-joining protein KU80 virtually eliminates heterologous DNA insertion and enables highly efficient gene knockouts [23] , [24] . We therefore investigated whether this strain would be more receptive to a direct deletion of RON8 . The RON8 deletion construct was transfected into Δku80Δhpt parasites [25] , ( referred to as wildtype strain ) to replace the sequence encoding residues 1-1716 with the selectable marker hypoxanthine-xanthine-guanine phosphoribosyl transferase ( HPT ) ( Figure 1A ) . We observed that parasites lacking RON8 in transfected populations were fully outcompeted by drug-resistant RON8+ parasites in less than four passages , consistent with a defect in invasion . We were able to isolate a clonal line of knockout parasites by cloning early following transfection , named Δron8 ( 1-1716 , +HPT ) strain parasites . To remove the remainder of the RON8 locus and the HPT selectable marker , a second construct ( HPT KO , Figure 1A ) was transfected into Δron8 ( 1-1716 , +HPT ) parasites . Following negative selection with 6-thioxanthine and cloning , PCR confirmed both the absence of RON8 coding sequences and the concomitant shortening of the distance between RON8 5' and 3′UTRs normally separated by ∼20 kb ( Figure 1B ) . We thus generated Δku80ΔhptΔron8 parasites , referred to as Δron8 ( Figure 1A ) . Immunofluorescence assays ( IFA ) of intracellular Δron8 parasites demonstrated that RON8 was not present in the rhoptry necks ( Figure 1C ) , and Western blot analysis confirmed this strain to be devoid of RON8 ( Figure 1D , note that in lysates of wildtype parasites an ∼230 kDa breakdown product of RON8 is frequently seen as previously described [11] ) . RON4 could still be detected in the moving junction of invading knockout parasites ( Figure 1E , arrow ) , demonstrating that the ability of these parasites to form junctions was not completely compromised by the loss of RON8 . We observed that higher inoculums were necessary for obtaining infection levels similar to wildtype parasites; however , no gross change in the time needed for intracellular parasites to replicate was observed ( data not shown ) . While these results establish RON8 is not absolutely required for propagation of Toxoplasma , the rapid loss of Δron8 parasites from transfected populations suggested a severe invasion defect in parasites lacking this junction component . Complementing knockout parasites at the RON8 locus would not strictly eliminate the possibility that the observed defect in invasion was due to polar effects resulting from the ablation of the gene . Given that Δku80 parasites greatly favor homologous integration of transfected DNA , we executed a novel strategy for complementing Δron8 by targeting a RON8 expression cassette containing a C-terminally tagged version of RON8 driven from its endogenous promoter to the ablated KU80 locus ( Figure 2A ) . A clonal line of complemented parasites ( referred to as R8c ) showed exogenous RON8 both trafficked correctly to the rhoptry necks of intracellular parasites ( Figure 2B ) and localized to the moving junction during invasion ( Figure 2B , arrow ) . Western blot analysis demonstrated slightly higher levels of RON8 expression in this line compared to wildtype parasites ( Figure 2C ) . PCR confirmed integration of the RON8 expression cassette at the KU80 and not the RON8 locus ( not shown ) . Thus , the KU80 locus appears to be a suitable site for complementation in this recently-developed strain of T . gondii parasites . We observed that the C-terminal HA tag on RON8 was frequently not detectable in intracellular R8c parasites by immunofluorescence ( Figure 2D , top panels ) . When HA was observed , it did not colocalize with RON8 in the parasite apex but instead stained two slightly more posterior dots indicative of pro-rhoptries formed in dividing parasites ( Figure 2D , bottom panels , arrows ) . The HA staining within these posterior dots colocalizes with the pro-form of ROP4 in R8c parasites ( Figure 2E , arrows ) [26] , confirming pro-rhoptry localization for HA-tagged RON8 and indicating C-terminal processing of the protein during rhoptry development . To investigate the extent of proteolytic cleavage , we compared the size of the protein detected by anti-RON8 antibodies with that given by anti-HA ( Figure 2F ) . No distinguishable difference in size was observed between these two forms of the protein , suggesting that the cleavage site does not lie far from the extreme C-terminus , although determination of the extent of cleavage is difficult given RON8's large size ( ∼330 kDa ) . To examine defects in invasion displayed by Δron8 parasites in detail , we performed red/green invasion assays [27] , in which wildtype , Δron8 , or R8c parasites were allowed to infect for a one-hour time period and extracellular and intracellular parasites were detected by staining and microscopic counting . A dramatic 70% reduction in penetration was observed in knockout parasites compared to the wildtype control , a defect which is completely reversed in R8c parasites ( Figure 3A ) . While we anticipated seeing a concomitant increase in attached parasites that failed to invade in the Δron8 strain , we intriguingly saw only a mild increase in the number of extracellular parasites ( Figure 3A ) . This could be due to a failure of the knockout parasites to attach to host cells , a function previously associated solely with microneme proteins . To assess whether gross perturbation of microneme function was detectable in Δron8 parasites , we examined the localization of MIC2 and also assessed the levels of both parasite-associated and secreted MIC2 and saw no apparent differences ( Figure S1A–C ) . We additionally assessed gliding motility by examining deposits of parasite surface antigens onto FBS-coated slides from wildtype versus knockout parasites and again saw no noticeable differences ( Figure S1D ) . While we cannot exclude the possibility that loss of RON8 impacts other microneme functions , these data suggest that microneme protein generation and function of the actin-myosin motor central to driving T . gondii invasion are not grossly affected in Δron8 parasites . Although RON8 contributions to the initial stages of host cell attachment would explain lower than expected counts of extracellular Δron8 parasites , this protein could alternatively be important for maintaining a secure contact with host cells , the absence of which leads to abortive invasion and subsequent parasite detachment . To differentiate between these possibilities , we incubated wildtype , Δron8 , or R8c parasites in cytochalasin D ( cytD ) to permit only the initial stages of attachment , thereby isolating these steps from the rest of invasion ( Figure 3B ) [29] . Similar levels of attachment were observed for all three strains , suggesting that RON8-deficient parasites can properly attach , but do not form a stable grip and eventually release the host cell . As treatment with cytD does not inhibit the secretion of rhoptry bulb proteins in evacuoles [29] , we also stained for these vesicles using anti-ROP2/3/4 antisera . We counted noticeably fewer evacuoles secreted from knockout parasites than either wildtype or R8c strains suggesting that the knockout parasites are less able to advance to the stage at which they are committed to invasion , although variability in numbers between experiments precluded assessing statistical significance ( not shown ) . Together , these experiments demonstrate that invasion is impacted both at the steps of parasite attachment and entry , and that the attachment defect is likely due to parasites failing to obtain the firm grip on host components that is necessary for a commitment to host cell entry . We have previously shown that exogenously expressed RON8 traffics to the periphery of host cells , its predicted site of action during invasion . We tested whether host-expressed RON8 could complement the knockout by comparing infections of wildtype and Δron8 parasites in cells expressing RON8 , but saw no apparent rescue of the invasion defect ( data not shown ) . This is most likely due to the inability to incorporate RON8 into the rest of the complex during the rapid process of parasite entry , but also could be due to differences in processing or other modifications of the parasite-derived form of RON8 that are not present in the exogenously expressed protein . Having established the diminished ability of Δron8 parasites to invade host cells , we used immunofluorescence to further examine the junction morphology in those knockout parasites that could successfully invade . Whereas the junction appears as a punctate residual focus on the PV membrane of newly invaded wildtype and R8c parasites ( Figure 4A ) , ∼15% ( 15% and 16% in two independent experiments ) of Δron8 parasites displayed short trails of RON4 extending from the posterior end upon entering the host cell ( arrows in Figure 4B ) . In addition to RON4 , the other MJ/RONs RON2 and RON5C also specifically localized to these trails ( Figure 4C ) . The trails of MJ components are distinct from so called “slime trails” deposited by gliding parasites on FBS coated slides as the MJ components are not present in slime trails or in staining of extracellular parasites . The trails also appear to be specific to RON/MJ proteins as the non-junction rhoptry proteins ( ROP2/3/4 ) are not present within these structures ( data not shown ) . The appearance of these trails in the absence of RON8 suggests this protein is important for maintaining the integrity of the MJ complex or for pinching the nascent PV off from the plasma membrane at the end of invasion . Our success in rescuing Δron8 parasites encouraged us to use selective complementation of the knockout to discern RON8 functional domains as well as regions necessary for trafficking to the neck subcompartment . As RON8 has been shown to have a N-terminal prodomain and such prodomains are known to function in rhoptry targeting , we first tested whether the first 262 amino acids are sufficient for targeting to the rhoptry necks by fusing this region to the reporter protein mCherry ( R8promCherry ) . We expressed the fusion and a full-length control by targeting each back to the ablated RON8 locus in an expression cassette driven from the RON8 promoter ( Figure 5A , B ) . In stably transformed parasite clones , the R8promCherry fusion only partially targeted to the rhoptry necks as assessed by colocalization with the rhoptry neck protein RON1 whereas the full-length control targeted perfectly . mCherry that was not localized to the rhoptry necks was found in punctate spots that localized in both apical and basal regions of the parasite . This data suggests that the RON8 prodomain does play some role in trafficking , but additional sequences are also needed for efficient targeting of the protein to the rhoptry necks . We then assessed whether addition of the C-terminal half of the protein would improve targeting by fusing this portion to the C-terminus of the R8promCherry protein ( Figure 5C ) . Addition of the C-terminal region of RON8 to the fusion construct ( termed R8promCherryR8C ) restored efficient rhoptry neck targeting as seen by colocalization with RON1 . Having completely restored rhoptry neck targeting , we determined whether the R8promCherryR8C fusion was incorporated into the MJ complex by immunoprecipitating the complex with RON2 antisera from parasites expressing the fusion protein , using parasites rescued with the full length protein as a control ( Figure 5D/E ) . Both the control and the R8promCherryR8C fusion were efficiently co-precipitated with RON2 and the other RONs in the MJ complex . Thus , the RON8 N-terminal prodomain combined with the C-terminal region is sufficient for rhoptry neck targeting and MJ complex formation . While trafficking and complex association were restored , parasites expressing R8promCherryR8C showed the same defect in invasion as Δron8 parasites ( not shown ) , demonstrating that the N-terminal region of RON8 is necessary for function . With the significant defects in host cell entry observed in vitro , we assessed the degree to which virulence was affected in parasites lacking RON8 . To that end , CD1 mice were infected with either a sufficiently lethal dose of wildtype parasites ( LD100 = 1 ) or increasing doses of either Δron8 or R8c ( Figure 6 ) . While all mice infected with a low dose of 50 wildtype parasites succumbed to infection by day 9 , mice subjected to doses of <5×104 knockout parasites did not show any visible signs of infection . Mice infected with 5×104 parasites did show symptoms of infection , but three of the four mice survived the acute infection . The mice generally became moribund with 5×105 parasites injected; however , even at this high dose , one animal out of the group of four recovered . The virulence phenotype was completely reversed in the complemented strain , demonstrating that the defect was specifically due to the lack of RON8 . Mice infected with even the lowest doses of Δron8 parasites developed an immune response against the parasite as seen by seroconversion , and all mice infected with the knockout survived a challenge with 1×104 wildtype tachyzoites ( data not shown ) . Statistical analysis of these virulence experiments yields an LD50 for Δron8 parasites = ∼2 . 6×105 parasites , a decrease of more than five logs compared to the wildtype strain . These experiments demonstrate that the inability of Δron8 parasites to firmly attach and thereby commit to invading host cells in vitro produces a dramatically impoverished ability to cause disease in vivo .
RON8 is the first moving junction rhoptry neck protein to be functionally analyzed by direct knockout in an apicomplexan parasite . This overturns the previous paradigm that all MJ components are essential for parasite survival , which had been postulated with the need for conditional approaches to study AMA1 [22] and technical difficulties in ablating other junction partners [8] , [11] . Deleting RON8 in the Δku80 strain reinforces both the major technical advance in promoting homologous recombination in parasites lacking KU80 and the potential produced by this advance to examine the moving junction with greater capacity than previously thought . Our success in ablating RON8 is a first step toward ultimately establishing the “minimal” junction complex required by T . gondii for host cell entry . AMA1 is already known to be essential for tachyzoite invasion [22] . RON2 , which displays a privileged association with AMA1 and likely spans the host membrane [9] , will probably also prove to be essential in Toxoplasma . This leaves the soluble junction components RON4 and RON5 ( which is processed into N and C-terminal fragments ) , which we are currently attempting to knockout from Δku80 and Δron8 parasites to explore their roles in invasion . RON8's dispensability in Toxoplasma agrees with increasing evidence suggesting the moving junction is constructed using different complements of junction proteins deployed during particular life cycle stages in apicomplexan parasites . RON4 can be detected in the ring-shaped junctions of egressing parasites , but AMA1 is absent from these structures , and parasites depleted of AMA1 expression are crippled in invasion , but not affected in egress [8] . Proteomic analysis of Eimeria tenella suggests that its ortholog of RON5 is expressed in sporozoites but not in merozoites , while sporozoites appear to lack AMA1 and RON4 [30] . Similarly , PfRON2 is expressed in all Plasmodium invasive forms except for ookinetes , which do not form moving junctions and lack rhoptries [31] . Distinct arsenals of junction proteins tailored for specific life cycle stages within each apicomplexan may reflect the evolutionary fine-tuning of this structure that enables phylum members to exploit unique host niches . Our attachment and invasion experiments ( Figure 3 ) suggest that RON8 may have evolved within the coccidia to anchor the invading parasite to common host cytoskeletal proteins [11] , although a biochemical demonstration of RON8's link with the host cell will firmly establish this scenario . The current model of Toxoplasma invasion presents attachment as a series of steps increasing in strength , beginning with low affinity interactions between GPI-anchored parasite surface antigens and unknown host contacts , followed by host surface protein association with transmembrane microneme proteins on the parasite surface , and finally moving junction formation through AMA1's interaction with RON2 embedded in the host membrane [9] , [19] . The work presented here adds a further critical step to invasion: the secure connection of the parasite to the host through RON8 contacts within the host cell , supported by the topology of the RON proteins in the MJ , the exogenous expression of RON8 in mammalian cells [9] , [19] , and the localization of host F-actin rings observed at Toxoplasma and Plasmodium moving junctions [32] . According to this updated model ( Figure 7 ) , the moving junction in wildtype parasites ( Figure 7 , left ) serves as a two-fold lock both inside and outside the host cell , irreversibly directing the parasite toward complete penetration of its target . In knockout parasites ( Figure 7 , right ) , the loss of the intracellular clasp provided by RON8 severely weakens the moving junction's hold on its target , resulting in frequent detachment from host cells and thereby debilitating the invasive capacity of Toxoplasma . In addition to disrupting contacts with host components important for parasite adherence , the loss of RON8 imparts further structural abnormalities in the moving junctions of parasites that overcome this defect , as evidenced by the appearance of MJ components in trails dragging behind newly invaded Δron8 parasites ( Figure 4B , C ) . Disorganized trails are not ubiquitous during pulse invasion of these parasites ( ∼20% ) , suggesting that the structure is not entirely disorganized , a finding which is supported by the recovery of the remainder of the junction complex by co-immunoprecipitation ( Figure 5 ) . Junctions that are compromised enough to form trails could indicate difficulties in PVM separation from the host membrane without RON8 , and the low numbers of evacuoles observed in cytD-arrested Δron8 parasites suggests RON8 function is a prerequisite for rhoptry bulb secretion . The latter phenotype is reminiscent of defects observed in parasites depleted of AMA1 , which reorient after initial contact with the host cell but generally do not form a moving junction [8] or inject evacuoles [22] . Similarly , preventing AMA1-RON complex formation using inhibitory peptides abolishes evacuole secretion in Plasmodium [19] . While this indicates some signal occurs after microneme and rhoptry neck secretion to allow rhoptry bulb secretion , it is unclear how such a signal is transmitted between apical organelles of the invading parasite . We have demonstrated that RON8 is processed at its C-terminus ( Figure 2D ) at a site that cannot be readily resolved by Western analysis comparing the size of the HA-tagged RON8 precursor with its mature form . This cleavage event is distinct from RON8's processing at the N-terminus demonstrated in Besteiro et al [9] , which also showed all MJ proteins are subject to proteolysis , probably at ROP1-like SφXE sites which are believed to be processed by the subtilisin protease TgSUB2 [33] . There are two candidate processing sites near the C-terminus that match this consensus site ( SAME at residues 2652–2655 and SAGE at 2702–2705 ) , but cleavage at these sites would remove ∼30–35 kDa which might be resolvable by SDS-PAGE . Additional sites are also possible as we have seen some minor variations in the residues present in these cleavage sites ( Hajagos and Bradley , unpublished results ) and others have highlighted a potential cleavage site at the extreme C-terminus of RON8 that would remove only nineteen amino acids from the protein ( residues 2958–2961 , SFLQ , [9] ) . Our HA-tagged construct and additional fusions will undoubtedly aid in determining the precise cleavage site and the role of processing in RON8 targeting , complex formation and function . RON8's exposure to the host cytosol during invasion makes it a prime candidate for restricting access of host transmembrane proteins to the nascent PVM , which may occur in conjunction with the stabilizing grip on host components identified in this work . Our initial attempts to examine the ability of Δron8 parasites to exclude host Na+/K+ ATPase ( known to be sieved during invasion ) by immunofluorescence has not revealed any gross differences from the wildtype strain , although minor changes would be difficult to detect ( data not shown ) . While we cannot exclude a role for this protein in some aspect of molecular sieving , it is possible that RON8's sole function is to firmly grip host peripheral components to completely anchor the parasite , leaving other cytosolic-exposed junction proteins ( RON4 and RON5 ) to conduct filtration . It is currently unknown whether the individual RON proteins of the MJ complex each contain their own rhoptry neck targeting information or if one protein can escort the others as is seen in adhesive complexes secreted from the micronemes and for the Plasmodium rhoptry protein RAP1 [34] , [35] , [36] . While the precise cleavage site of RON8's N-terminal prodomain has not been determined , this region does appear to contain some but not complete rhoptry targeting information . Addition of the C-terminal region fully restores rhoptry neck targeting , which is likely due to the ability to associate with other complex members ( Figure 5E ) . The lack of a functional rescue in parasites lacking the N-terminal region of RON8 indicates that this region contains domains that are critical for invasion . These domains could play a direct role in the mechanics of invasion or provide additional interaction domains , thus strengthening the integration of RON8 into the complex . Deletion of the N-terminal region could also impact folding of the remaining C-terminal portion in the complex , which our previous exogenous expression data suggests is binding to host components at the periphery of the cell [11] . While a large number of functional domains are likely to be present in the 330 kDa RON8 protein , the data presented here demonstrate that functional complementation promises to be a powerful tool to further dissect specific regions of RON8 involved in targeting , complex formation , and function . We have shown that the RON8-deficient strain is more than five logs less virulent than wildtype Δku80 parasites in outbred CD1 mice ( Figure 6 ) . The contribution of MJ/RONs to the host response during in vivo Toxoplasma infection is likely minimal , due to the short period of time the moving junction is present during invasion ( ∼30 sec ) as well as the topology of RON8 and other soluble MJ/RONs beneath the plasma membrane in this structure [9] , [11] . Studies of RON4 antigenicity in Plasmodium falciparum [37] and P . yoelii [18] support this , as this protein displays extensive sequence conservation with concordantly little apparent immune pressure . The dramatic impact on virulence we observe in Δron8 parasites is therefore in all likelihood a consequence of the significant defects in host cell entry displayed in vitro rather than any enhanced host reaction to the parasite . In conclusion , we have utilized RON8 knockouts to make greater inroads into analyzing rhoptry neck protein function than previously achieved in Toxoplasma . In the process , we have expanded the model of T . gondii invasion by implicating at least one rhoptry protein in ensuring the parasite's commitment to entering its target cell . Our complementation strategy will enable further dissection of RON8 functional and interaction domains , which promises a more detailed understanding of the molecular architecture of this phenomenal structure . Future experiments will also examine RON8's contribution to egress , as moving junctions are observed during pathogen exit from the host cell [8] . Although RON8 is a coccidia-specific member of the complex , identifying host contacts with this protein will likely prove useful in preventing invasion across the phylum , as host proteins regulating cytoskeletal filament assembly are recruited to the junctions of invading Plasmodium and Toxoplasma [32] . Through this work and upcoming studies , a complete understanding of the moving junction's participation in Toxoplasma's remarkable success at a parasitic lifestyle will be at hand .
Toxoplasma infections in mice and antibodies raised in mice were performed under the guidelines of the Animal Welfare Act and the PHS Policy on Humane Care and Use of Laboratory Animals . Specific details of our protocol were approved by the UCLA Animal Research Committee ( ARC# 2004-055 ) . The RHΔhpt and RHΔhptΔku80 strains of Toxoplasma have been previously described [25] , [38] , and were maintained on confluent monolayers of human foreskin fibroblasts ( HFFs ) grown in Dulbecco's modified eagle medium supplemented with 5% fetal bovine serum , 5% Cosmic Calf Serum ( Hyclone ) , and 2 mM glutamine [12] . The following antibodies were used in immunofluorescence and Western blot assays: mouse polyclonal anti-RON8 ( 1∶400 ) [11] , rabbit polyclonal anti-RON4 ( 1∶7000 ) [8] , mouse polyclonal anti-RON2 ( 1∶800 ) [12] , rabbit polyclonal anti-RON2 ( 1∶1000 ) ( described below ) , mouse polyclonal anti-RON5N and RON5C ( both 1∶600 ) [11] , mouse polyclonal anti-RON1 ( 1∶300 ) [8] , mouse monoclonal anti-ISP1 [25] , rabbit polyclonal anti-SAG1 ( 1∶100000 ) , a gift from John Boothroyd , rabbit polyclonal anti-SAG2 ( 1∶4000 , John Boothroyd ) , mouse monoclonal T34A7 against ROP2/3/4 ( 1∶300 ) [39] , rabbit polyclonal anti-ROP13 ( 1∶1000 ) [40] , mouse monoclonal 1B10 anti-ROP7 ( 1∶1000 ) [41] , rabbit polyclonal anti-pro-ROP4 ( 1∶1000 ) [26] , rabbit polyclonal anti-HA ( 1∶300 ) ( Invitrogen ) , and mouse monoclonal anti-HA ( 1∶500 ) ( Covance ) . SDS-PAGE gels were used to resolve proteins by Western blot analysis as previously described [42] . Secondary antibodies were horseradish peroxidase ( HRP ) -conjugated goat anti-mouse and goat anti-rabbit used at a dilution of 1∶500–2000 ( Sigma ) and detected using the ECL Western Blot Detection Kit ( Thermo Scientific ) . To generate a RON2 fusion protein with a N-terminal 6xHis tag for antibody production , the RON2 cDNA encoding amino acids 25 to 313 ( residue numbers are from Genbank accession HQ110093 with residue 1 as the start methionine ) was amplified from a Toxoplasma RH strain cDNA library using primers P21 and P22 ( Table S1 ) . The amplified product was cloned into pET28a ( + ) ( Novagen ) using the HindIII and XhoI sites encoded in the primers . Production and purification of rRON225–313 from E . coli strain Rosetta ( Novagen ) using a nickel-nitrilotriacetic acid matrix were done essentially following manufacturer's instructions ( Qiagen ) . Antibodies to rRON225–313 were generated in rabbits by Covance , Inc . Specific antisera against mCherry ( used at 1∶4000 ) was generated in mice using recombinant 6xHis tagged protein purified by denaturing nickel agarose chromatography . mCherry was amplified using primers p23 and p24 and the amplified product was cloned into the pET101 ( Invitrogen ) bacterial expression plasmid which encodes a 6xHis tag in frame with the C-terminus of the gene . BL21-DE3 cells were transformed with the construct and induced with 1 mM IPTG for 5 hours before the cells were collected and mCherry-6xHis was purified and dialyzed against PBS . BALB/c mice ( Charles River ) were immunized with ∼70 µg of recombinant protein on a 21 day immunization schedule . The resulting mouse polyclonal antiserum were collected and tested by Western blot analysis . The initial RON8 KO vector was previously described [11] . The construct was linearized by KpnI digestion , and 30 µg of DNA were transfected by electroporation into Δku80xΔhpt strain parasites [25] . Following selection of transformants in media containing 50 µg/ml MPA and 50 µg/ml xanthine for five days , the knockout populations were scrape-syringed and cloned by limiting dilution . GFP negative parasites were screened by immunofluorescence using anti-RON8 polyclonal antisera , and a clonal line of GFP/RON8 double negative parasites was named strain Δron8 ( 1-1716 , +HPT ) . For generation of the HPT KO vector , the HPT cassette from RON8 KO was removed by digestion with XbaI and re-ligated , generating RON8KOΔHPT . To remove the remaining RON8 coding region along with the HPT marker , a new 3′ flank downstream of the RON8 stop codon was amplified from RH strain genomic DNA using primers P9 and P10 and subcloned into RON8KOΔHPT using KpnI and XbaI to make HPT KO . This plasmid was linearized by KpnI digestion and 50 µg of DNA transfected by electroporation into Δron8 ( 1-1716 , +HPT ) parasites . Exclusion of HPT was selected for using 350 µg/ml 6-thioxanthine ( Sigma ) for 5 weeks , after which populations were cloned by limiting dilution . Clones were then screened for the absence of RON8 and susceptibility to medium containing 50 µg/ml MPA and 50 µg/ml xanthine , to identify Δron8 parasites . An HPT cassette was amplified from the pMini-GFP . ht plasmid [43] using primers P11 and P12 and cloned into pCR2 . 1-TOPO ( Invitrogen ) as per manufacturer's instructions . Sequences upstream ( using primers P13 and P14 ) and downstream ( primers P15 and P16 ) of the ablated KU80 locus were amplified from RHΔhptΔku80 genomic DNA and subcloned into pCR2 . 1-TOPO+HPT using KpnI and SpeI for the 5′ flank ( ∼1 . 5 kb ) and NsiI and XbaI for the 3′ flank ( ∼1 . 1 kb ) . The 12 kb RON8-HA expression cassette was subcloned into this vector at an EcoRV site following excision from pGRA-HA_HPT-RON8 [11] by PciI and DraIII digestion and blunting with Klenow fragment ( NEB ) . Sequencing established the inverse orientation of the cassette relative to the KU80 flanks and confirmed placement of the HPT and RON8-HA cassettes between these flanks . This vector ( R8compHPT ) was then linearized by PmeI digestion , and 25 µg of DNA transfected into Δron8 parasites , followed by MPA/xanthine selection . Resistant clones were screened by IFA and RON8 complemented parasites were confirmed by Western blot analysis , identifying R8c strain parasites . The HPT cassette described above was also subcloned back into the HPT KO vector after blunting at the XbaI site . This vector , HPTKO+HPT , was then digested by NotI and ApaI , blunted , and ligated with the blunted 12 kb RON8 expression cassette described above to make a complementation vector encoding HPT and RON8-HA adjacent to RON8 flanks lying outside the entire RON8 coding sequence . After confirming forward orientation of the RON8-HA cassette relative to the RON8 flanks and placement of the HPT and RON8-HA cassettes between these flanks by sequencing , this vector ( R8HPTKO+HPT ) was linearized by AflII digestion and transfected ( 25 µg ) into Δron8 parasites , followed by MPA/xanthine selection for 9 days and cloning . Resistant clones were screened by IFA and RON8 complemented parasites were confirmed by Western blot analysis , with positive clones named Δron8 + RON81–2980 parasites . To make fusions of RON8 fragments with mCherry , primers P17 and P18 were used to amplify the mCherry coding sequence using pmCherry Vector ( Clontech ) as a template . This PCR product contains SmaI at one end and AvrII/PacI sites on the other end; after SmaI/PacI digestion , it was subcloned into R8HPTKO+HPT digested at SmaI and PacI endogenous sites in RON8-HA . This replaced sequences encoding residues 263–2980 and the HA tag with mCherry . This vector ( R8proMCHERHPTKO ) was linearized with AflII and 25 µg of DNA transfected into Δron8 parasites prior to 9 days MPA/xanthine selection , cloning , and IFA screening as above , generating R8promCherry parasites . For introduction of the RON8 C-terminus into R8proMCHERHPTKO , the sequence encoding residues 1318–2980 and the C-terminal HA tag flanked by AvrII/PacI sites was amplified from template pGRA-HA_HPT-RON8 using primers P19 and P20 . This fragment was subcloned into R8proMCHERHPTKO at AvrII/PacI sites , sequenced to confirm both junctions with mCherry were in frame , linearized with AflII , transfected , drug selected , cloned , and screened by IFA as above to identify Δron8 + R8promCherryR8C parasites . Template DNA was extracted from harvested wildtype Δku80 , Δron8 , or R8c parasites using the Wizard Genomic DNA Purification Kit ( Promega ) as per the manufacturer's protocol . RON8 coding sequence removal in Δron8 parasites was confirmed using primers P1 and P2 using either Δku80 or Δron8 genomic DNA . The removal of the entire RON8 genomic locus with concomitant bridging of flanking sequences normally separated by ∼20 kb was confirmed using primers P3 and P4 using Δku80 , Δron8 , or R8c genomic DNA as a template . Correct targeting of the full-length RON8 complementation cassette at the KU80 locus was confirmed using primers P5 with P6 and P7 with P8 . Intracellular parasites were examined by immunofluorescence as per [11]; confluent HFFs grown on glass coverslips were infected with parasites and incubated for 24–30 hours at 37°C , washed with PBS , and then fixed with either ice-cold methanol for 3 minutes or 3 . 7% formaldehyde/PBS for 15 minutes prior to quenching with phosphate-buffered saline ( PBS ) plus 0 . 1 M glycine for 5 min . Coverslips were then washed in PBS and blocked with PBS/3% bovine serum albumin ( BSA ) or a completely permeabilizing solution of PBS/3%BSA/0 . 1% Triton X-100 ( PBT buffer ) for 30 minutes . Primary antibodies were diluted in PBS/3%BSA or PBT buffer for 1 hour . Coverslips were washed five times in PBS and incubated with secondary antibodies Alexa-488 goat anti-mouse and Alexa-594 goat anti-rabbit ( or Alexa 594 goat anti-mouse and Alexa 488 goat anti-rabbit , Molecular Probes , OR ) diluted 1∶2000 in PBS/3%BSA for 1 hour . Following secondary washes in PBS , coverslips were then mounted onto slides using Vectashield mounting medium for fluorescence microscopy using a Zeiss upright light microscope ( Zeiss Axio Imager Z1 ) using either a 100x or 63x oil immersion objective . All images were rendered using Axiovision software in conjunction with a Zeiss digital CCD camera ( AxioCam MRm ) . Early invasion experiments were performed using low temperatures as per [11] . Invasion assays were conducted to distinguish between extracellular parasites and internalized parasites via a red/green invasion assay as described [27] . In brief , equivalent cultures of wildtype Δku80 , Δron8 , and R8c parasites were scraped and passed through a 27-gauge needle to collect strictly intracellular parasites for use in the invasion assay . After washing with fresh medium , ∼3×106 tachyzoites were resuspended in 500 µl of fresh prewarmed DMEM and placed onto separate chambers of an 8-well chamber slide ( Falcon ) containing confluent HFF monolayers . These parasites were allowed to invade for one hour at 37°C , after which monolayers were washed three times in PBS and fixed with EM-grade 3 . 7% formaldehyde/PBS ( Biosciences , Inc . ) . After quenching as above , the samples were blocked in PBS/3%BSA for 25 minutes and incubated with rabbit anti-SAG1 diluted in PBS/3%BSA for 1 hour , then washed five times in 1X PBS and permeabilized with PBT buffer for 30 minutes prior to incubation with mouse anti-ROP7 diluted in PBT buffer as a second primary step . Secondary staining and fluorescence microscopy then proceeded as above . Parasites staining with both anti-SAG1 and ROP7 denoted attached but uninvaded parasites , while those staining only for ROP7 were scored as internalized . Nine fields were randomly counted for each chamber , yielding total counts of 250–800 parasites for all strains . Invasion experiments were conducted in triplicate and repeated at least twice . To examine whether the Δron8 parasites could be rescued by host cells exogenously expressing RON8 , we used a competition growth assay in cells with and without RON8 expression . No difference in the rate at which the wildtype parasites outcompeted the knockout was observed , indicating that exogenously expressed RON8 cannot complement the invasion defect . Attachment and evacuole assays utilizing cytochalasin D were performed as described [22]; wildtype , Δron8 , and R8c parasites were scrape-syringed as above and resuspended in Endo buffer ( 44 . 7 mM K2SO4 , 10 mM Mg2SO4 , 106 mM sucrose , 5 mM glucose , 20 mM Tris , 0 . 35% wt/vol BSA , pH 8 . 2 ) containing 1 µM cytochalasin D ( Sigma-Aldrich ) . After incubation at room temperature for 10 min , ∼3×106 parasites from each strain were used to infect HFF monolayers grown on 8-chamber slides and incubated for 20 min at 37°C . Media was replaced with prewarmed DMEM/10% FBS containing 1 µM cytochalasin D and incubation continued for another 15 min at 37°C before fixation with formaldehyde and immunofluorescence/counting as described above for red/green invasion assays ( except that instead of anti-ROP7 antibody , evacuoles were labeled with monoclonal anti-ROP2/3/4 antibody ) . To examine whether MJ component trails are present in extracellular parasites , intracellular wildtype or Δron8 parasites were scrape-syringed , washed once in PBS , and allowed to adhere to slides prior to coating with 3 . 7% formaldehyde/PBS fixative for 15 min prior to quenching with 0 . 1 M glycine/PBS as above . Following fixation , blocking/permeabilization in PBT buffer and staining with rabbit anti-RON4 was performed as before . Rabbit polyclonal anti-RON2 was cross-linked to Protein A-Sepharose beads ( Amersham ) using dimethyl pimelimidate as described [8] . RON2-linked beads were then incubated with parasite lysates made in a modified RIPA buffer + Complete Protease Inhibitor described in [8] ( 50 mM Tris-Cl pH 8 . 0 , 5 mM EDTA , 75 mM NaCl , 1% NP40 , 0 . 5% DOC , 0 . 005% SDS ) . In brief , ∼2×108 extracellular parasites were centrifuged at 3000 g for 20 min . The parasites were washed once in 1X PBS and then lysed on ice for 20 min prior to removing insoluble material by centrifugation at 10000 g for 20 min . Antibody-coupled beads were incubated with lysate at 25°C for 3 hours before four washes with lysis buffer . The bound proteins were eluted using 100 mM triethylamine pH 11 . 5 , and lyophilized to remove the triethylamine and concentrate the eluate . The resulting products were analyzed by Western blot using antibodies against RON/MJ proteins . Motility experiments were performed largely as described [44]; wildtype or Δron8 parasites were resuspended in Hank's buffered saline solution ( HBSS ) and allowed to glide on serum-coated glass coverslips for 20 min at 37°C . Coverslips were rinsed twice in PBS and fixed with EM-grade formaldehyde for 15 min prior to immunofluorescence with rabbit anti-SAG2 antibodies as described above . Intracellular wildtype ( Δku80Δhpt parental ) , Δron8 , and R8c parasites were scrape-syringed from infected HFF monolayers and resuspended in Opti-MEM prior to intraperitoneal injection of 50 wildtype ( Δku80Δhpt parental ) ; 50 , 500 , 5×103 , 5×104 , or 5×105 Δron8; or 50 , 500 , or 5×103 R8c strain tachyzoites in outbred CD1 female mice , making a total of 9 groups of 4 mice each . Mice in each group were bled both prior to injection and surviving mice bled 15 days post infection , and serum used in Western blot analysis lysates from wildtype parasites to test for seroconversion . Prism GraphPad software was used to determine the LD50 of Δron8 parasites from an analysis of the results by a standard sigmoidal dose-response curve . Mice were monitored for 25 days , and surviving mice “protected” by Δron8 immunization were challenged by intraperitoneal injection with 1×104 wildtype tachyzoites at day 30 and assessed for an additional 30 days . All care and handling of animals was in accordance with institutional guidelines and approved by the UCLA Animal Research Committee .
|
Apicomplexan parasites actively invade host cells to survive , with an important step being the formation of a tight interface between parasite and host cell membranes called the moving junction ( MJ ) . Passing over the length of the invading parasite , the MJ anchors the pathogen to enable propulsion into a parasitophorous vacuole ( PV ) formed from host membrane . This structure also selectively filters transmembrane proteins from the membrane surrounding the PV , preventing its targeting to host lysosomes . The MJ's molecular nature is understood as an association between proteins secreted from rhoptry and microneme organelles , but the functional significance of the rhoptry neck ( RON ) components that predominate within this complex is entirely unknown . Our study describes the first functional analysis of any MJ/RON protein in Toxoplasma , RON8 . RON8 knockout parasites are severely deficient in both attachment and entry , likely due to the inability of the parasite to firmly engage the host cell . When Δron8 parasites do invade , MJ proteins are often secreted in disorganized trails , indicating the MJ is unstably formed without RON8 . From this data , we propose that loss of RON8 produces a crippled parasite frequently incapable of firm attachment , drastically retarding the establishment of vacuoles in vitro and subsequent disease in vivo .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"toxoplasma",
"gondii",
"microbiology",
"host-pathogen",
"interaction",
"plasmodium",
"falciparum",
"parasitic",
"diseases",
"parasitology",
"parastic",
"protozoans",
"infectious",
"diseases",
"microbial",
"pathogens",
"biology",
"protozoan",
"infections",
"pathogenesis",
"toxoplasmosis",
"protozoology",
"malaria"
] |
2011
|
The Moving Junction Protein RON8 Facilitates Firm Attachment and Host Cell Invasion in Toxoplasma gondii
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The considerable uncertainty regarding cancer risks associated with inherited mutations of BRCA2 is due to unknown factors . To investigate whether common genetic variants modify penetrance for BRCA2 mutation carriers , we undertook a two-staged genome-wide association study in BRCA2 mutation carriers . In stage 1 using the Affymetrix 6 . 0 platform , 592 , 163 filtered SNPs genotyped were available on 899 young ( <40 years ) affected and 804 unaffected carriers of European ancestry . Associations were evaluated using a survival-based score test adjusted for familial correlations and stratified by country of the study and BRCA2*6174delT mutation status . The genomic inflation factor ( λ ) was 1 . 011 . The stage 1 association analysis revealed multiple variants associated with breast cancer risk: 3 SNPs had p-values<10−5 and 39 SNPs had p-values<10−4 . These variants included several previously associated with sporadic breast cancer risk and two novel loci on chromosome 20 ( rs311499 ) and chromosome 10 ( rs16917302 ) . The chromosome 10 locus was in ZNF365 , which contains another variant that has recently been associated with breast cancer in an independent study of unselected cases . In stage 2 , the top 85 loci from stage 1 were genotyped in 1 , 264 cases and 1 , 222 controls . Hazard ratios ( HR ) and 95% confidence intervals ( CI ) for stage 1 and 2 were combined and estimated using a retrospective likelihood approach , stratified by country of residence and the most common mutation , BRCA2*6174delT . The combined per allele HR of the minor allele for the novel loci rs16917302 was 0 . 75 ( 95% CI 0 . 66–0 . 86 , ) and for rs311499 was 0 . 72 ( 95% CI 0 . 61–0 . 85 , ) . FGFR2 rs2981575 had the strongest association with breast cancer risk ( per allele HR = 1 . 28 , 95% CI 1 . 18–1 . 39 , ) . These results indicate that SNPs that modify BRCA2 penetrance identified by an agnostic approach thus far are limited to variants that also modify risk of sporadic BRCA2 wild-type breast cancer .
After more than a decade of clinical testing for mutations of BRCA1 and BRCA2 , there remains considerable uncertainty regarding cancer risks associated with inherited mutations of these genes . This variable penetrance is most striking for BRCA2 [1]–[4] , and it affects medical management [5] . Women with the same BRCA2 mutation may develop breast , ovarian or other cancers at different ages or not at all [6] . In a segregation analysis of families identified through breast cancer cases diagnosed before age 55 , the residual familial clustering after accounting for BRCA1 and BRCA2 mutations could be explained by a large number of low penetrance genes with multiplicative effects on breast cancer risk [7] , [8] . A candidate gene approach in BRCA2 mutation carriers led to the discovery of loci that modify the penetrance of BRCA2 mutations , such as RAD51 135 G>C [9] and perhaps CASP8 [10] , [11] and IGFBP2 [12] , if replicated . To investigate whether other common single nucleotide polymorphisms ( SNP ) , copy number variants ( CNV ) , or copy number polymorphisms ( CNP ) modify penetrance for BRCA2 mutation carriers , we undertook a two-staged genome-wide association study ( GWAS ) in BRCA2 mutation carriers from the international Consortium for Investigators of Modifiers of BRCA1/2 ( CIMBA ) and other international studies . We hypothesized that an agnostic search for breast cancer loci in an enriched population of BRCA2 mutation carriers , the first among this high risk population , would provide greater power than a sporadic population of equal number , and would yield associations specific to BRCA2 carriers and/or the general population .
In stage 1 , genotype data were available for 899 young ( <40 years ) affected and 804 older ( >40 years ) unaffected carriers of European ancestry after quality control filtering and removal of ethnic outliers ( Figure S1 ) . A total of 592 , 163 filtered SNPs genotyped using the Affymetrix Genome-Wide Human SNP Array 6 . 0 platform passed quality control assessment . In stage 1 , comparison of the observed and expected distributions ( quantile-quantile plot: Figure S2 ) showed little evidence for an inflation of the test statistics ( genomic inflation factor λ = 1 . 01 ) , thereby excluding the possibility of significant hidden population substructure , cryptic relatedness among subjects or differential genotype calling between BRCA2 affected and BRCA2 unaffected carriers . Multiple variants were found to be associated with breast cancer risk ( Figure S3 ) : 3 SNPs had p<10−5 and 39 SNPs had p<10−4 . The most significant association ( ) was observed for FGFR2 rs2981582 ( Table 1 ) , a variant previously shown to be associated with increased risk of BRCA2-related breast cancer [13] . A positive association was also observed with rs3803662 ( Table 1 ) , near TOX3 , which has also been associated with sporadic breast cancer risk [13] . Using the stage 1 data , we also performed a GSEA as implemented in MAGENTA [14] to evaluate whether a functionally-related set of genes relevant to BRCA2 function ( Table S1 ) was enriched for relative risk associations ( see Statistical Methods ) . The 59 genes selected are related to the Fanconi anemia pathway [15] as well as other pathways reported in the literature to regulate or interact with BRCA1/2 [16] . These showed no enrichment of associations with the breast cancer risk ( p = 0 . 56 ) . In addition , eight of 125 known cancer susceptibility alleles identified by previous GWAS of other cancers [17] were associated with BRCA2 modification in the current study , a number not greater than expected ( Kolmogorv-Smirnov p = 0 . 60 ) by chance alone . Of the 113 most significantly associated SNPs ( p<10−3 ) in our study , three showed significant association ( p<0 . 05 ) with BRCA1-associated breast cancer risk in a complimentary GWAS [18] . In the combined stage 1 and stage 2 results , four independent SNPs ( pairwise ) were associated with increased risk of breast cancer risk with p-values<10−4 ( Table 1 ) . Previously identified breast cancer susceptibility loci [13] , [19] , [20] had the most significant associations among BRCA2 mutation carriers ( FGFR2: per allele and TOX3: per allele ) . Novel loci , rs16917302 on chromosome 10 and rs311499 on chromosome 20 , had HRs in stage 2 that were in the same direction as those observed for stage 1 ( Figure 1 , Table 1 ) , but were smaller in magnitude ( HR = 0 . 67 ( 95% CI:0 . 56–0 . 80 ) vs . 0 . 85 ( 95% CI: 0 . 70–1 . 04 ) for rs16917302; HR = 0 . 60 ( 95%CI:0 . 50–0 . 78 ) vs . 0 . 84 ( 95%CI: 0 . 67–1 . 06 ) for rs311499 ) perhaps reflecting a “winner's curse” effect” [21] . The associations for these SNPs were not statistically significant in stage 2 ( Table 1 ) . In the combined stage 1 and stage 2 dataset , the C allele of rs16917302 was associated with lower risk of breast cancer ( per allele HR = 0 . 75 , 95% CI 0 . 66–0 . 86; ; Table 1 ) , and the C allele of rs311499 was associated with a reduced risk ( per allele HR = 0 . 72 , 95% CI 0 . 61–0 . 85; ; Table 1 ) . A full list of stage 2 results can be found in Table S2 . Using the combined stage 1 and stage 2 data , there was no evidence that the HR for SNP rs16917302 changes with age ( p = 0 . 63 ) , but there was some evidence that the per-allele HR for rs311499 may increase with age ( p = 0 . 034 ) . We also examined the association of both high-frequency CNPs and low-frequency CNVs to case-control status using the stage 1 data . After performing standard quality control measures including a minor allele frequency ( MAF ) threshold of 5% , we identified 191 polymorphisms with reliable genotypes . No associations were found between CNVs and the phenotype; there was no inflation or deflation of the test statistic , and the best p-value was . We similarly assessed less common CNPs , and found neither the overall burden of events ( or any subclass thereof , such as large deletions overlapping genes ) nor any specific locus associated with breast cancer risk ( Figure S4 ) . Because of the prior evidence of significant LD extent around the 6174delT ( c . 5946delT ) founder mutation in the Ashkenazi Jewish population [22] , we explored the potential excess sharing of the genome compared to the BRCA2 region in both Ashkenazi Jewish and non-Jewish European ancestries . Using GERMLINE [23] , shared segments of greater than 5 cM were computed based on the imputed genotype dataset . In the BRCA2 region , we observed a significant excess of sharing amongst both Ashkenazi ( n = 304 ) and non-Jewish ( n = 1331 ) individuals compared to samples from an autism study ( n = 808 ) suggesting common founders for BRCA2 mutations . Examining sites across the genome every 2 . 5 cM ( excluding telomere and centromere regions ) , we observed possible pairs share segments greater than 5 cM that on average 0 . 005% ( u = 50 . 17 , s . d = 55 . 5 , max = 491 ) for non-Jewish individuals and 0 . 12% ( u = 141 . 11 , s . d = 57 . 32 , max = 525 ) for Ashkenazi Jewish individuals . Comparing cases and controls , we did not observe a significant difference in number of pairs of samples sharing segments greater than 5cM across the genome excluding chromosome 13 . That is , there was no evidence of overall excess sharing across the genome other than for the BRCA2 locus within the Ashkenazi Jewish and non-Ashkenazi Jewish populations in the study .
In this GWAS of BRCA2 mutation carriers , the first in this high risk population , we found previously identified breast cancer susceptibility loci modified risk of BRCA2-associated breast cancer with similar magnitude of association . Although FGFR2 ( rs2981575 ) was the only locus to reach genome-wide statistical significance , novel loci , rs16917302 and rs10509168 were each associated with breast cancer risk . rs16917302 is located on chromosome 10 , in the zinc finger protein 365 gene ( ZNF365 ) . A recent multistage GWAS of 15 , 992 sporadic breast cancer cases and 16 , 891 controls also observed an inverse association ( per allele OR = 0 . 82 , 95% CI 0 . 82–0 . 91 , ) between breast cancer risk and rs10509168 , a SNP 18kb from rs16917302 ( pairwise ) and located in intron 4 of ZNF365 [24] . Of the 3 , 659 cases and 4 , 897 controls in phase 1 of that study , imputation revealed that the locus identified in our BRCA2 study , rs16917302 , was significantly associated with risk for breast cancer ( p = 0 . 02 ) ( Easton DF , personal communication ) . The second novel SNP in the current study , rs311499 , is located on chromosome 20 , within a region containing several possible candidate genes including GMEB2 , SRMS , PTK6 , STMN3 , and TNFRSF6 . The functional significance of both of these regions with breast carcinogenesis is unknown; further research is warranted . There was some evidence that the HR associated with rs311499 may change with age . We also observed that the stage 1 HR for this SNPs was larger in magnitude compared to the stage 2 HR , consistent with a winner's curse effect [21] . Since stage 1 of our experiment included mostly BRCA2 mutation carriers diagnosed at a young age , and stage 2 mutation carriers diagnosed an older age , the “winner's curse” and age-specific effects are confounded and may be difficult to distinguish . Fitting the age-dependent HR model for SNP rs311499 using the stage 2 data yielded no significant variation in the HR by age ( p = 0 . 47 ) , but the sample size for this analysis was relatively small . Future larger studies should aim to clarify this . Mutations in known genes ( BRCA1 , BRCA2 , TP53 , CHEK2 , PTEN , and ATM ) explain only 20–25% of the familial clustering of breast cancer; the residual familial clustering may be explained by the existence of multiple common , low-penetrance alleles ( ‘polygenes’ ) [25] . Perhaps because the majority of BRCA2-associated breast tumors are estrogen receptor ( ER ) -positive , as are the majority of non-hereditary breast cancers [26] , risk alleles for sporadic breast cancer are more likely to be modifiers of risk of BRCA2-associated hereditary breast cancer . Of the seven GWAS-identified breast cancer-associated SNPs examined in a BRCA2 background [13] , [19] , [20] , SNPS in FGFR2 ( rs2981575 ) , TOX3 ( rs3803662 ) , MAP3K1 ( rs889312 ) , and LSP1 ( rs3817198 ) have been shown to modify BRCA2 penetrance , in contrast with BRCA1 tumors , in which only two of these same SNPs ( based on a 2 degrees of freedom model ) modified risk of these largely ER-negative tumors [26] . As previously noted [13] , [20] , the stage 1 HRs among BRCA2 mutation carriers , reported here , were nearly identical to odds ratio estimates observed in sporadic breast cancer studies , consistent with a simple multiplicative interaction between the BRCA2 mutant alleles and the common susceptibility SNPs . If replicated , the two additional SNPs identified here would only explain about 1 . 7% of the variance in breast cancer risk among BRCA2 mutation carriers . Taken together , the combined effects of all the common and putative risk modifiers in this study only account for ∼4% of the variance of BRCA2 mutations , compared with 1 . 1% for the single RAD51 135 G>C variant , which is rare and biologically-linked to BRCA2 function , as shown by candidate gene studies [9] . Thus , the common alleles that modify risk in BRCA1 and BRCA2 backgrounds appear to have comparable associated risks in sporadic ER-positive and ER-negative tumors , respectively [18] . While individual SNPs are unlikely to be used to guide radiographic screening and risk-reducing surgical strategies , the combined effect of these SNPs may ultimately be used for the tailor management of subsets of BRCA mutation carriers [5] . While we took great efforts to collect all of the possible known BRCA2 mutation carriers , there were insufficient numbers to stratify by race and BRCA2 mutations with the exception of BRCA2*6174delT mutations . Due to the small numbers of women of non-European ancestry who have participated in the individual studies represented here , the current analysis was based only on women who had genetic backgrounds consistent with HapMap CEU samples . While we expect that SNPs identified among women of European ancestry might also be applicable to women of other genetic backgrounds , additional research in these populations will be needed . Similarly , the observed associations represented across all types of mutations , and specifically a weighted average of BRCA2*6174delT and non-delT mutations . It is possible that the observed associations may only modify the penetrance of specific BRCA2 mutations due to differential effects on function or differences in genetic background . Our analysis was stratified on the basis of the most common BRCA2 mutation , BRCA2*6174delT , which is prevalent in individuals with an Ashkenazi Jewish ancestry . Large numbers of mutation carriers will be necessary to calculate mutation-specific estimates . In addition , there was a drop-out of SNPs in the two phases of this study . While we were able to achieve a representative coverage of the genome , it is also possible that additional studies using denser arrays may provide further information . As expected , we observed associations with some of the major common genetic variants seen in genome-wide scans of breast cancer in a non-BRCA1/2 mutation background . However , we found no evidence for loci with stronger effects than FGFR2 . Although we observed an association with a novel locus at ZNF365 that appears also to be a risk factor for sporadic breast cancer , overall , our results suggest that there are no common variants with major effects ( i . e . , OR>2 . 0 ) that are specific in BRCA2 carriers . Similarly , in a recent report of SNPs from sporadic breast cancer GWAS genotyped in a restricted set of BRCA1/2 carriers [27] , loci in LOC134997 ( rs9393597: per allele HR = 1 . 55 , 95% CI 1 . 25–1 . 92 , ) and FBXL7 ( rs12652447: HR = 1 . 37 , 95% CI 1 . 16–1 . 62 , ) were associated with BRCA2 breast cancer risk with p-values weaker than FGFR2 reported here ( per allele ) , although the magnitudes of the associations were slightly stronger than FGFR2 ( HR = 1 . 28 ) . Although these SNPs were not in our genotyped panel of SNPs at stage 1 , imputation results indicate that SNP rs9393597 has a p-value of 0 . 008 and SNP rs12652447 a p-value of 0 . 04 for association with breast cancer risk for the BRCA2 mutation carriers in our stage1 . However , there is substantial overlap between our study and the study of Wang et al . [27] . Replication in larger datasets will be necessary to precisely estimate the magnitude of the associations of suspected loci identified from our study , candidate gene analysis [10]–[12] , and other selection approaches [27] . It is of interest , however , that when utilizing an agnostic approach in BRCA2 mutation carriers in this study , the major determinants of risk variation in mutation carriers are those that also modify risk in subsets of sporadic , BRCA1/2 wild type , breast cancer . However , it remains possible that unique variants with smaller effects , or rarer variants ( not evaluated in this experiment ) , may be specific modifiers of breast cancer risk in BRCA2 carriers . Their detection would require study populations much larger than the current analysis , which is presently the largest such cohort assembled .
|
The risk of breast cancer associated with BRCA2 mutations varies widely . To determine whether common genetic variants modify the penetrance of BRCA2 mutations , we conducted the first genome-wide association study of breast cancer among women with BRCA2 mutations using a two-stage approach . The major finding of the study is that only those loci known to be associated with breast cancer risk in the general population , including FGFR2 ( rs2981575 ) , modified BRCA2-associated risk in our high-risk population . Two novel loci , on chromosomes 10 in ZNF365 ( rs16917302 ) and chromosome 20 ( rs311499 ) , were shown to modify risk in BRCA2 mutation carriers , although not at a genome-wide level of significance . However , the ZNF365 locus has recently independently been associated with breast cancer risk in sporadic tumors , highlighting the potential significance of this zinc finger-containing gene in breast cancer pathogenesis . Our results indicate that it is unlikely that other common variants have a strong modifying effect on BRCA2 penetrance .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion"
] |
[
"genetics",
"and",
"genomics/genomics",
"genetics",
"and",
"genomics/gene",
"discovery",
"genetics",
"and",
"genomics/functional",
"genomics",
"genetics",
"and",
"genomics/genetics",
"of",
"disease",
"genetics",
"and",
"genomics/gene",
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"genomics/medical",
"genetics",
"genetics",
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"genomics/cancer",
"genetics"
] |
2010
|
Common Genetic Variants and Modification of Penetrance of BRCA2-Associated Breast Cancer
|
The mission of the Encyclopedia of DNA Elements ( ENCODE ) Project is to enable the scientific and medical communities to interpret the human genome sequence and apply it to understand human biology and improve health . The ENCODE Consortium is integrating multiple technologies and approaches in a collective effort to discover and define the functional elements encoded in the human genome , including genes , transcripts , and transcriptional regulatory regions , together with their attendant chromatin states and DNA methylation patterns . In the process , standards to ensure high-quality data have been implemented , and novel algorithms have been developed to facilitate analysis . Data and derived results are made available through a freely accessible database . Here we provide an overview of the project and the resources it is generating and illustrate the application of ENCODE data to interpret the human genome .
Interpreting the human genome sequence is one of the leading challenges of 21st century biology [1] . In 2003 , the National Human Genome Research Institute ( NHGRI ) embarked on an ambitious project—the Encyclopedia of DNA Elements ( ENCODE ) —aiming to delineate all of the functional elements encoded in the human genome sequence [2] . To further this goal , NHGRI organized the ENCODE Consortium , an international group of investigators with diverse backgrounds and expertise in production and analysis of high-throughput functional genomic data . In a pilot project phase spanning 2003–2007 , the Consortium applied and compared a variety of experimental and computational methods to annotate functional elements in a defined 1% of the human genome [3] . Two additional goals of the pilot ENCODE Project were to develop and advance technologies for annotating the human genome , with the combined aims of achieving higher accuracy , completeness , and cost-effective throughput and establishing a paradigm for sharing functional genomics data . In 2007 , the ENCODE Project was expanded to study the entire human genome , capitalizing on experimental and computational technology developments during the pilot project period . Here we describe this expanded project , which we refer to throughout as the ENCODE Project , or ENCODE . The major goal of ENCODE is to provide the scientific community with high-quality , comprehensive annotations of candidate functional elements in the human genome . For the purposes of this article , the term “functional element” is used to denote a discrete region of the genome that encodes a defined product ( e . g . , protein ) or a reproducible biochemical signature , such as transcription or a specific chromatin structure . It is now widely appreciated that such signatures , either alone or in combinations , mark genomic sequences with important functions , including exons , sites of RNA processing , and transcriptional regulatory elements such as promoters , enhancers , silencers , and insulators . However , it is also important to recognize that while certain biochemical signatures may be associated with specific functions , our present state of knowledge may not yet permit definitive declaration of the ultimate biological role ( s ) , function ( s ) , or mechanism ( s ) of action of any given genomic element . At present , the proportion of the human genome that encodes functional elements is unknown . Estimates based on comparative genomic analyses suggest that 3%–8% of the base pairs in the human genome are under purifying ( or negative ) selection [4]–[7] . However , this likely underestimates the prevalence of functional features , as current comparative methods may not account for lineage-specific evolutionary innovations , functional elements that are very small or fragmented [8] , elements that are rapidly evolving or subject to nearly neutral evolutionary processes , or elements that lie in repetitive regions of the genome . The current phase of the ENCODE Project has focused on completing two major classes of annotations: genes ( both protein-coding and non-coding ) and their RNA transcripts , and transcriptional regulatory regions . To accomplish these goals , seven ENCODE Data Production Centers encompassing 27 institutions have been organized to focus on generating multiple complementary types of genome-wide data ( Figure 1 and Figure S1 ) . These data include identification and quantification of RNA species in whole cells and in sub-cellular compartments , mapping of protein-coding regions , delineation of chromatin and DNA accessibility and structure with nucleases and chemical probes , mapping of histone modifications and transcription factor ( TF ) binding sites by chromatin immunoprecipitation ( ChIP ) , and measurement of DNA methylation ( Figure 2 and Table 1 ) . In parallel with the major production efforts , several smaller-scale efforts are examining long-range chromatin interactions , localizing binding proteins on RNA , identifying transcriptional silencer elements , and understanding detailed promoter sequence architecture in a subset of the genome ( Figure 1 and Table 1 ) . ENCODE has placed emphasis on data quality , including ongoing development and application of standards for data reproducibility and the collection of associated experimental information ( i . e . , metadata ) . Adoption of state-of-the-art , massively parallel DNA sequence analysis technologies has greatly facilitated standardized data processing , comparison , and integration [9] , [10] . Primary and processed data , as well as relevant experimental methods and parameters , are collected by a central Data Coordination Center ( DCC ) for curation , quality review , visualization , and dissemination ( Figure 1 ) . The Consortium releases data rapidly to the public through a web-accessible database ( http://genome . ucsc . edu/ENCODE/ ) [11] and provides a visualization framework and analytical tools to facilitate use of the data [12] , which are organized into a web portal ( http://encodeproject . org ) . To facilitate comparison and integration of data , ENCODE data production efforts have prioritized selected sets of cell types ( Table 2 ) . The highest priority set ( designated “Tier 1” ) includes two widely studied immortalized cell lines—K562 erythroleukemia cells [13]; an EBV-immortalized B-lymphoblastoid line ( GM12878 , also being studied by the 1 , 000 Genomes Project; http://1000genomes . org ) and the H1 human embryonic stem cell line [14] . A secondary priority set ( Tier 2 ) includes HeLa-S3 cervical carcinoma cells [15] , HepG2 hepatoblastoma cells [16] , and primary ( non-transformed ) human umbilical vein endothelial cells ( HUVEC; [17] ) , which have limited proliferation potential in culture . To capture a broader spectrum of human biological diversity , a third set ( Tier 3 ) currently comprises more than 100 cell types that are being analyzed in selected assays ( Table 2 ) . Standardized growth conditions for all ENCODE cell types have been established and are available through the ENCODE web portal ( http://encodeproject . org , “cell types” link ) . This report is intended to provide a guide to the data and resources generated by the ENCODE Project to date on Tier 1–3 cell types . We summarize the current state of ENCODE by describing the experimental and computational approaches used to generate and analyze data . In addition , we outline how to access datasets and provide examples of their use .
Cis-regulatory regions include diverse functional elements ( e . g . , promoters , enhancers , silencers , and insulators ) that collectively modulate the magnitude , timing , and cell-specificity of gene expression [35] . The ENCODE Project is using multiple approaches to identify cis-regulatory regions , including localizing their characteristic chromatin signatures and identifying sites of occupancy of sequence-specific transcription factors . These approaches are being combined to create a comprehensive map of human cis-regulatory regions . ENCODE is also generating additional data types to complement production projects and benchmark novel technologies . An overview of these datasets is provided in Table 1 . With the aim of ensuring quality and consistency , ENCODE has defined standards for collecting and processing each data type . These standards encompass all major experimental components , including cell growth conditions , antibody characterization , requirements for controls and biological replicates , and assessment of reproducibility . Standard formats for data submission are used that capture all relevant data parameters and experimental conditions , and these are available at the public ENCODE portal ( http://genome . ucsc . edu/ENCODE/dataStandards . html ) . All ENCODE data are reviewed by a dedicated quality assurance team at the Data Coordination Center before release to the public . Experiments are considered to be verified when two highly concordant biological replicates have been obtained with the same experimental technique . In addition , a key quality goal of ENCODE is to provide validation at multiple levels , which can be further buttressed by cross-correlation between disparate data types . For example , we routinely perform parallel analysis of the same biological samples with alternate detection technologies ( for example , ChIP-seq versus ChIP-chip or ChIP-qPCR ) . We have also compared our genome-wide results to “gold-standard” data from individual locus studies , such as DNase-seq versus independently performed conventional ( Southern-based ) DNaseI hypersensitivity studies . Cross-correlation of independent but related ENCODE data types with one another , such as DNaseI hypersensitivity , FAIRE , transcription factor occupancy , and histone modification patterns , can provide added confidence in the identification of specific DNA elements . Similarly , cross-correlation between long RNA-seq , CAGE , and TAF1 ChIP-seq data can strengthen confidence in a candidate location for transcription initiation . Finally , ENCODE is performing pilot tests for the biological activity of DNA elements to the predictive potential of various ENCODE biochemical signatures for certain biological functions . Examples include transfection assays in cultured human cells and injection assays in fish embryos to test for enhancer , silencer , or insulator activities in DNA elements identified by binding of specific groups of TFs or the presence of DNaseI hypersensitive sites or certain chromatin marks . Ultimately , defining the full biological role of a DNA element in its native chromosomal location and organismic context is the greatest challenge . ENCODE is beginning to approach this by integrating its data with results from other studies of in situ knockouts and/or knockdowns , or the identification of specific naturally occurring single base mutations and small deletions associated with changes in gene expression . However , we expect that deep insights into the function of most elements will ultimately come from the community of biologists who will build on ENCODE data or use them to complement their own experiments . A catalog of ENCODE datasets is available at http://encodeproject . org . These data provide evidence that ∼1 Gigabase ( Gb; 32% ) of the human genome sequence is represented in steady-state , predominantly processed RNA populations . We have also delineated more than 2 million potential regulatory DNA regions through chromatin and TF mapping studies . The assessment of the completeness of detection of any given element is challenging . To analyze the detection of transcripts in a single experiment , we have sequenced to substantial depth and used a sampling approach to estimate the number of reads needed to approach complete sampling of the RNA population ( Figure 6A ) [104] . For example , analyzing RNA transcripts with about 80 million mapped reads yields robust quantification of more than 80% of the lowest abundance class of genes ( 2–19 reads per kilobase per million mapped tags , RPKM ) [24] . Measuring RNAs across multiple cell types , we find that , after the analysis of seven cell lines , 68% of the GENCODE transcripts can be detected with RPKM >1 . In the case of regulatory DNA , we have analyzed the detection of regulatory DNA by using three approaches: 1 ) the saturation of occupancy site discovery for a single transcription factor within a single cell type as a function of sequencing read depth , 2 ) the incremental discovery of DNaseI hypersensitive sites or the occupancy sites for a single TF across multiple cell types , and 3 ) the incremental rate of collective TF occupancy site discovery for all TFs across multiple cell types . For detecting TF binding sites by ChIP-seq , we have found that the number of significant binding sites increases as a function of sequencing depth and that this number varies widely by transcription factor . For example , as shown in Figure 6B , 90% of detectable sites for the transcription factor GABP can be identified by using the MACS peak calling program at a depth of 24 million reads , whereas only 55% of detectable RNA Pol2 sites are identified at this depth when an antibody that recognizes both initiating and elongating forms of the enzyme is used . Even at 50 million reads , the number of sites is not saturated for RNA Pol2 with this antibody . It is important to note that determinations of saturation may vary with the use of different antibodies and laboratory protocols . For instance , a different RNA Pol2 antibody that recognizes unphosphorylated , non-elongating RNA Pol2 bound only at promoters requires fewer reads to reach saturation [105] . For practical purposes , ENCODE currently uses a minimum sequencing depth of 20 M uniquely mapped reads for sequence-specific transcription factors . For data generated prior to June 1 , 2010 , this figure was 12 M . To assess the incremental discovery of regulatory DNA across different cell types , it was necessary to account for the non-uniform correlation between cell lines and assays ( see Figure 6C legend for details ) . We therefore examined all possible orderings of either cell types or assays and calculated the distribution of elements discovered as the number of cell types or assays increases , presented as saturation distribution plots ( Figure 6C and 6D , respectively ) . For DNase hypersensitive sites , we observe a steady increase in the mean number of sites discovered as additional cell types are tested up to and including the 62 different cell types examined to date , indicating that new elements continue to be identified at a relatively high rate as additional cell types are sampled ( Figure 6C ) . Analysis of CTCF sites across 28 cell types using this approach shows similar behavior . Analysis of binding sites for 42 TFs in the cell line with most data ( K562 ) also shows that saturation of the binding sites for these factors has not yet been achieved . These results indicate that additional cell lines need to be analyzed for DNaseI and many transcription factors , and that many more transcription factors need to be analyzed within single cell types to capture all the regulatory information for a given factor across the genome . The implications of these trends for defining the extent of regulatory DNA within the human genome sequence is as yet unclear .
The ENCODE Data Release and Use Policy is described at http://www . encodeproject . org/ENCODE/terms . html . Briefly , ENCODE data are released for viewing in a publicly accessible browser ( initially at http://genome-preview . ucsc . edu/ENCODE and , after additional quality checks , at http://encodeproject . org ) . The data are available for download and pre-publication analysis of any kind , as soon as they are verified ( i . e . , shown to be reproducible ) . However , consistent with the principles stated in the Toronto Genomic Data Use Agreement [106] , the ENCODE Consortium data producers request that they have the first publication on genome-wide analyses of ENCODE data , within a 9-month timeline from its submission . The timeline for each dataset is clearly displayed in the information section for each dataset . This parallels policies of other large consortia , such as the HapMap Project ( http://www . hapmap . org ) , that attempt to balance the goal of rapid data release with the ability of data producers to publish initial analyses of their work . Once a producer has published a dataset during this 9-month period , anyone may publish freely on the data . The embargo applies only to global analysis , and the ENCODE Consortium expects and encourages immediate use and publication of information at one or a few loci , without any consultation or permission . For such uses , identifying ENCODE as the source of the data by citing this article is requested . After curation and review at the Data Coordination Center , all processed ENCODE data are publicly released to the UCSC Genome Browser database ( http://genome . ucsc . edu ) . Accessioning of ENCODE data at the NCBI Gene Expression Omnibus ( GEO; http://www . ncbi . nlm . nih . gov/geo/info/ENCODE . html ) is underway . Primary DNA sequence reads are stored at UCSC and the NCBI Sequence Read Archive ( SRA; http://www . ncbi . nlm . nih . gov/Traces/sra/sra . cgi ? ) and will also be retrievable via GEO . Primary data derived from DNA microarrays ( for example , for gene expression ) are deposited directly to GEO . The processed data are also formatted for viewing in the UCSC browser . Metadata , including information on antibodies , cell culture conditions , and other experimental parameters , are deposited into the UCSC database , as are results of validation experiments . Easy retrieval of ENCODE data to a user's desktop is facilitated by the UCSC Table Browser tool ( http://genome . ucsc . edu/cgi-bin/hgTables ? org=human ) , which does not require programming skills . Computationally sophisticated users may gain direct access to data through application programming interfaces ( APIs ) at both the UCSC browser and NCBI and by downloading files from http://genome . ucsc . edu/ENCODE/downloads . html . An overview of ENCODE data types and the location of the data repository for each type is presented in Table 4 .
Many users will want to view and interpret the ENCODE data for particular genes of interest . At the online ENCODE portal ( http://encodeproject . org ) , users should follow a “Genome Browser” link to visualize the data in the context of other genome annotations . Currently , it is useful for users to examine both the hg18 and the hg19 genome browsers . The hg18 has the ENCODE Integrated Regulation Track on by default , which shows a huge amount of data in a small amount of space . The hg19 browser has newer datasets , and more ENCODE data than are available on hg18 . Work is in progress to remap the older hg18 datasets to hg19 and generate integrated ENCODE tracks . On either browser , additional ENCODE tracks are marked by a double helix logo in the browser track groups for genes , transcripts , and regulatory features . Users can turn tracks on or off to develop the views most useful to them ( Figure 7 ) . To aid users in navigating the rich variety of data tracks , the ENCODE portal also provides a detailed online tutorial that covers data display , data download , and analysis functions available through the browser . Examples applying ENCODE data at individual loci to specific biological or medical issues are a good starting point for exploration and use of the data . Thus , we also provide a collection of examples at the “session gallery” at the ENCODE portal . Users are encouraged to submit additional examples; we anticipate that this community-based sharing of insights will accelerate the use and impact of the ENCODE data . Numerous genome-wide association studies ( GWAS ) that link human genome sequence variants with the risk of disease or with common quantitative phenotypes have now become available . However , in most cases , the molecular consequences of disease- or trait-associated variants for human physiology are not understood [107] . In more than 400 studies compiled in the GWAS catalog [108] , only a small minority of the trait/disease-associated SNPs ( TASs ) occur in protein-coding regions; the large majority ( 89% ) are in noncoding regions . We therefore expect that the accumulating functional annotation of the genome by ENCODE will contribute substantially to functional interpretation of these TASs . For example , common variants within a ∼1 Mb region upstream of the c-Myc proto-oncogene at 8q24 have been associated with cancers of the colon , prostate , and breast ( Figure 8A ) [109]–[111] . ENCODE data on transcripts , histone modifications , DNase hypersensitive sites , and TF occupancy show strong , localized signals in the vicinity of major cancer-associated SNPs . One variant ( rs698327 ) lies within a DNase hypersensitive site that is bound by several TFs and the enhancer-associated protein p300 and contains histone modification patterns typical of enhancers ( high H3K4me1 , low H3K4me3; Figure 8B ) . Recent studies have shown enhancer activity and allele-specific binding of TCF7L2 at this site [112] , with the risk allele showing greater binding and activity [113] , [114] . Moreover , this element appears to contact the downstream c-Myc gene in vivo , compatible with enhancer function [114] , [115] . Similarly , several regions predicted via ENCODE data to be involved in gene regulation are close to SNPs in the BCL11A gene associated with persistent expression of fetal hemoglobin ( Figure S2 ) . These examples show that the simple overlay of ENCODE data with candidate non-coding risk-associated variants may readily identify specific genomic elements as leading candidates for investigation as probable effectors of phenotypic effects via alterations in gene expression or other genomic regulatory processes . Importantly , even data from cell types not directly associated with the phenotype of interest may be of considerable value for hypothesis generation . It is reasonable to expect that application of current and future ENCODE data will provide useful information concerning the mechanism ( s ) whereby genomic variation influences susceptibility to disease , which then can then be tested experimentally . All ENCODE datasets to date are from populations of cells . Therefore , the resulting data integrate over the entire cell population , which may be physiologically and genetically inhomogeneous . Thus , the source cell cultures in the ENCODE experiments are not typically synchronized with respect to the cell cycle and , as with all such samples , local micro-environments in culture may also vary , leading to physiological differences in cell state within each culture . In addition , one Tier 1 cell line ( K562 ) and two Tier 2 cell lines ( HepG2 and HeLa ) are known to have abnormal genomes and karyotypes , with genome instability . Finally , some future Tier 3 tissue samples or primary cultures may be inherently heterogeneous in cell type composition . Averaging over heterogeneity in physiology and/or genotype produces an amalgamation of the contributing patterns of gene expression , factor occupancy , and chromatin status that must be considered when using the data . Future improvements in genome-wide methodology that allow the use of much smaller amounts of primary samples , or follow-up experiments in single cells when possible , may allow us to overcome many of these caveats . The use of DNA sequencing to annotate functional genomic features is constrained by the ability to place short sequence reads accurately within the human genome sequence . Most ENCODE data types currently represented in the UCSC browser use only those sequence reads that map uniquely to the genome . Thus , centromeric and telomeric segments ( collectively ∼15% of the genome and enriched in recent transposon insertions and segmental duplications ) as well as sequences not present in the current genome sequence build [116] are not subject to reliable annotation by our current techniques . However , such information can be gleaned through mining of the publicly available raw sequence read datasets generated by ENCODE . It is useful to recognize that the confidence with which different classes of ENCODE elements can be related to a candidate function varies . For example , ENCODE can identify with high confidence new internal exons of protein-coding genes , based on RNA-seq data for long polyA+ RNA . Other features , such as candidate promoters , can be identified with less , yet still good , confidence by combining data from RNA-seq , CAGE-tags , and RNA polymerase 2 ( RNA Pol2 ) and TAF1 occupancy . Still other ENCODE biochemical signatures come with much lower confidence about function , such as a candidate transcriptional enhancer supported by ChIP-seq evidence for binding of a single transcription factor . Identification of genomic regions enriched by ENCODE biochemical assays relies on the application of statistical analyses and the selection of threshold significance levels , which may vary between the algorithms used for particular data types . Accordingly , discrete annotations , such as TF occupancy or DNaseI hypersensitive sites , should be considered in the context of reported p values , q values , or false discovery rates , which are conservative in many cases . For data types that lack focal enrichment , such as certain histone modifications and many RNA Pol2-bound regions , broad segments of significant enrichment have been delineated that encompass considerable quantitative variation in the signal strength along the genome .
Development and implementation of algorithms and pipelines for processing and analyzing data has been a major activity of the ENCODE Project . Because massively parallel DNA sequencing has been the main type of data generated by the Consortium , much of the algorithmic development and data analysis to date has been concerned with issues related to producing and interpreting such data . Software packages and algorithms commonly used in the ENCODE Consortium are summarized in Tables 3 and S1 . In general , the analysis of sequencing-based measurements of functional or biochemical genomic parameters proceeds through three major phases . In the first phase , the short sequences that are the output of the experimental method are aligned to the reference genome . Algorithm development for efficient and accurate alignment of short read sequences to the human genome is a rapidly developing field , and ENCODE groups employ a variety of the state-of-the-art software ( see Tables 3 and S1 ) . In the second phase , the initial sequence mapping is processed to identify significantly enriched regions from the read density . For ChIP-seq ( TFs and histone modification ) , DNase-seq or FAIRE-seq , both highly localized peaks or broader enriched regions may be identified . Within the ENCODE Consortium , each data production group provides lists of enriched regions or elements within their own data , which are available through the ENCODE portal . It should be noted that , for most data types , the majority of enriched regions show relatively weak absolute signal , necessitating the application of conservative statistical thresholds . For some data , such as those derived from sampling RNA species ( e . g . , RNA-seq ) , additional algorithms and processing are used to handle transcript structures and the recognition of splicing events . The final stage of analysis involves integrating the identified regions of enriched signal with each other and with other data types . An important prerequisite to data integration is the availability of uniformly processed datasets . Therefore , in addition to the processing pipelines developed by individual production groups , ENCODE has devoted considerable effort toward establishing robust uniform processing for phases 1 and 2 to enable integration . For signal comparison , specific consideration has been given to deriving a normalized view of the sequence read density of each experiment . In the case of ChIP-seq for TFs , this process includes in silico extension of the sequence alignment to reflect the experimentally determined average lengths of the input DNA molecules that are sampled by the short sequence tag , compensation for repetitive sequences that may lead to alignment with multiple genomic locations , and consideration of the read density of the relevant control or input chromatin experiment . ENCODE has adopted a uniform standardized peak-calling approach for transcription factor ChIP-seq , including a robust and conservative replicate reconciliation statistic ( Irreproducible Discovery Rate , IDR [117] , to yield comparable consensus peak calls . As the project continues , we expect further standardizations to be developed . There are many different ways to analyze and integrate large , diverse datasets . Some of the basic approaches include assigning features to existing annotations ( e . g . , assigning transcribed regions to annotated genes or Pol2-binding peaks to likely genes ) , discovery of correlations among features , and identification of particular gene classes ( e . g . , Gene Ontology categories ) preferentially highlighted by a given annotation . Many software tools exist in the community for these purposes , including some developed within the ENCODE Project , such as the Genome Structure Correction statistic for assessing overlap significance [3] . Software tools used for integration by ENCODE are summarized in Tables 3 and S1 .
The challenge of achieving complete coverage of all functional elements in the human genome is substantial . The adult human body contains several hundred distinct cell types , each of which expresses a unique subset of the ∼1 , 500 TFs encoded in the human genome [118] . Furthermore , the brain alone contains thousands of types of neurons that are likely to express not only different sets of TFs but also a larger variety of non-coding RNAs [119] . In addition , each cell type may exhibit a diverse array of responses to exogenous stimuli such as environmental conditions or chemical agents . Broad areas of fundamental chromosome function , such as meiosis and recombination , remain unexplored . Furthermore , ENCODE has focused chiefly on definitive cells and cell lines , bypassing the substantial complexity of development and differentiation . A truly comprehensive atlas of human functional elements is not practical with current technologies , motivating our focus on performing the available assays in a range of cell types that will provide substantial near-term utility . ENCODE is currently developing a strategy for addressing this cellular space in a timely manner that maximizes the value to the scientific community . Feedback from the user community will be a critical component of this process . To understand better and functionally annotate the human genome , ENCODE is making efforts to analyze and integrate data within the project and with other large-scale projects . These efforts include 1 ) defining promoter and enhancer regions by combining transcript mapping and biochemical marks , 2 ) delineating distinct classes of regions within the genomic landscape by their specific combinations of biochemical and functional characteristics , and 3 ) defining transcription factor co-associations and regulatory networks . These efforts aim to extend our understanding of the functions of the different biochemical elements in gene regulation and gene expression . One of the major motivations for the ENCODE Project has been to aid in the interpretation of human genome variation that is associated with disease or quantitative phenotypes . The Consortium is therefore working to combine ENCODE data with those from other large-scale studies , including the 1 , 000 Genomes Project , to study , for example , how SNPs and structural variation may affect transcript , regulatory , and DNA methylation data . We foresee a time in the near future when the biochemical features defined by ENCODE are routinely combined with GWAS and other sequence variation–driven studies of human phenotypes . Analogously , the systematic profiling of epigenomic features across ex vivo tissues and stem cells currently being undertaken by the NIH Roadmap Epigenomics program will provide synergistic data and the opportunity to observe the state and behavior of ENCODE-identified elements in human tissues representing healthy and disease states . These are but a few of many applications of the ENCODE data . Investigators focused on one or a few genes should find many new insights within the ENCODE data . Indeed , these investigators are in the best position to infer potential functions and mechanisms from the ENCODE data—ones that will also lead to testable hypotheses . Thus , we expect that the work of many investigators will be enhanced by these data and that their results will in turn inform the development of the project going forward . Finally , we also expect that comprehensive paradigms for gene regulation will begin to emerge from our work and similar work from many laboratories . Deciphering the “regulatory code” within the genome and its associated epigenetic signals is a grand and complex challenge . The data contributed by ENCODE in conjunction with complementary efforts will be foundational to this effort , but equally important will be novel methods for genome-wide analysis , model building , and hypothesis testing . We therefore expect the ENCODE Project to be a major contributor not only of data but also novel technologies for deciphering the human genome and those of other organisms .
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The Encyclopedia of DNA Elements ( ENCODE ) Project was created to enable the scientific and medical communities to interpret the human genome sequence and to use it to understand human biology and improve health . The ENCODE Consortium , a large group of scientists from around the world , uses a variety of experimental methods to identify and describe the regions of the 3 billion base-pair human genome that are important for function . Using experimental , computational , and statistical analyses , we aimed to discover and describe genes , transcripts , and transcriptional regulatory regions , as well as DNA binding proteins that interact with regulatory regions in the genome , including transcription factors , different versions of histones and other markers , and DNA methylation patterns that define states of the genome in various cell types . The ENCODE Project has developed standards for each experiment type to ensure high-quality , reproducible data and novel algorithms to facilitate analysis . All data and derived results are made available through a freely accessible database . This article provides an overview of the complete project and the resources it is generating , as well as examples to illustrate the application of ENCODE data as a user's guide to facilitate the interpretation of the human genome .
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2011
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A User's Guide to the Encyclopedia of DNA Elements (ENCODE)
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Mayaro virus ( MAYV ) is an arbovirus that circulates in Latin America and is emerging as a potential threat to public health . Infected individuals develop Mayaro fever , a severe inflammatory disease characterized by high fever , rash , arthralgia , myalgia and headache . The disease is often associated with a prolonged arthralgia mediated by a chronic inflammation that can last months . Although the immune response against other arboviruses , such as chikungunya virus ( CHIKV ) , dengue virus ( DENV ) and Zika virus ( ZIKV ) , has been extensively studied , little is known about the pathogenesis of MAYV infection . In this study , we established models of MAYV infection in macrophages and in mice and found that MAYV can replicate in bone marrow-derived macrophages and robustly induce expression of inflammasome proteins , such as NLRP3 , ASC , AIM2 , and Caspase-1 ( CASP1 ) . Infection performed in macrophages derived from Nlrp3–/– , Aim2–/– , Asc–/–and Casp1/11–/–mice indicate that the NLRP3 , but not AIM2 inflammasome is essential for production of inflammatory cytokines , such as IL-1β . We also determined that MAYV triggers NLRP3 inflammasome activation by inducing reactive oxygen species ( ROS ) and potassium efflux . In vivo infections performed in inflammasome-deficient mice indicate that NLRP3 is involved with footpad swelling , inflammation and pain , establishing a role of the NLRP3 inflammasome in the MAYV pathogenesis . Accordingly , we detected higher levels of caspase1-p20 , IL-1β and IL-18 in the serum of MAYV-infected patients as compared to healthy individuals , supporting the participation of the NLRP3-inflammasome during MAYV infection in humans .
Arboviruses are one of the public health authorities major concerns , contributing to an increasing awareness of emerging infections worldwide . After the spread of chikungunya virus ( CHIKV ) to new areas of the globe and the emergence of Zika virus ( ZIKV ) , surveillance systems worldwide are focusing much attention on tracking the next arboviral epidemic [1] . Reported human cases of Mayaro virus ( MAYV ) infection have been limited to Central and South America , particularly to the regions around the Amazon basin [2–5] . Recent studies revealed the emergence of MAYV recombinants in Brazil and Haiti , and adaptation to a broad host and vector range , placing these countries as high-risk areas for the emergence of MAYV epidemics [6 , 7] . MAYV belongs to the Togaviridae family and Alphavirus genus , which consists of well-known pathogenic viruses , such as CHIKV , Ross River virus ( RRV ) , Eastern ( EEEV ) , Western ( WEEV ) , and Venezuelan equine encephalitis ( VEEV ) viruses [8] . Haemagogus mosquitoes have been documented as the main vectors of MAYV , and Aedes aegypti has also been found to be a competent vector , a feature that has alerted authorities to the eminent possibility that MAYV emerge as a global pathogen [1 , 9] . MAYV is the causative agent of Mayaro fever , a neglected endemic infection that is characterized by nonspecific symptoms such as high fever , rash , arthralgia , myalgia and headache . Similar to CHIKV infection , MAYV is associated with a prolonged arthralgia which can last for months or even years , possibly due to chronic inflammation [10 , 11] . However , the mechanisms underlying these clinical signs are still not elucidated . Inflammation is a key event during the pathogenesis of many diseases [12–15] , and is also the case of those caused by arbovirus [11] . Several studies have characterized the inflammatory process of dengue virus ( DENV ) , CHIKV and ZIKV infection , both in animal models and humans [16–18] . For example , the acute phase of CHIKV infection is associated with high production of inflammatory mediators including IL-6 , IL-8 , IL-12 and MCP-1 , while the chronic phase is associated with other inflammatory cytokines , such as IL-17 , IFN-γ and IL-1β [11] . Overall , these cytokines are mainly produced upon recognition of pathogens by pattern recognition receptors ( PRRs ) , such as Toll-like receptors ( TLRs ) and Nod-like receptors ( NLRs ) [19 , 20] . While TLRs are found within cellular and endosomal membranes , NLRs are located in the cytoplasm . They can be activated by different types of pathogens and their associated molecular patterns ( PAMPs ) [21 , 22] , or by generation of damage-associated molecular patterns ( DAMPs ) such as potassium efflux , reactive oxygen species ( ROS ) production , and cathepsin B release [12 , 23] . Upon activation , NLRs trigger the assembly of cytosolic protein complexes called inflammasomes , which consists of a NLR protein , an adaptor protein , and caspase-1 . This enzyme is capable of cleaving pro-IL-1β , producing its mature form ( IL-1β ) , and inducing an inflammatory type of cell death called pyroptosis [23 , 24] . Although several inflammasomes have been described , the NLRP3 inflammasome is the most studied . It is involved in a wide variety of diseases , such as autoimmunity , cancer and neurodegenerative and infectious diseases [12 , 23 , 25] . Arboviral infections , including ZIKV and CHIKV can trigger NLRP3 inflammasome activation in myeloid cells , such as macrophages , leading to an increased production of IL-1β , contributing to pathological inflammatory events that drive the development of both diseases [16 , 17] . However , whether MAYV infection leads to activation of NLRs and inflammasome assembly has never been reported in vitro or in vivo . MAYV-induced inflammation is likely to play a key role during MAYV pathogenesis , as suggested by a study conducted in patients [11] . Although some studies have evaluated MAYV infection in different models of mice [26 , 27] , the mechanisms governing the pathogenesis of the disease remain largely unexplored . Here , we used primary bone marrow-derived macrophages ( BMDMs ) to investigate infection and replication of MAYV , we also established an adult mouse model of acute inflammation that allows the evaluation of effects of inflammasomes during the pathogenesis of MAYV . Our study highlights the key role of the NLRP3 inflammasome for pathogenesis of MAYV infection .
Arboviruses such as CHIKV and ZIKV induce inflammasome activation [16 , 17 , 28–30] . We thus hypothesized that MAYV could also trigger inflammasome activation in macrophages . As an experimental in vitro model , BMDMs were employed , since these cells are a highly pure population of macrophages ( S1A Fig ) . First , we primed BMDMs derived from C57BL/6 ( WT ) mice with PAM ( 3 ) CSK ( 4 ) , a TLR2 agonist , for 4 hours and infected the macrophages with different multiplicities of infection ( MOI ) of CHIKV , ZIKV or MAYV . These three viruses induced IL-1β production ( Fig 1A–1C ) and LDH release ( Fig 1D–1F ) in a MOI-dependent manner . MAYV infection in unprimed BMDMs triggered Il1b expression at 3 and 6 hours after infection ( S1B Fig ) , but it did not induce the release of significant levels of mature IL-1β ( S1C Fig ) . Thus , priming with a TLR agonist is required to achieve robust release of IL-1β in macrophage cultures ( S1C Fig ) . It is possible that in vivo , cytokines such as TNF-α are able to prime the cells . We assayed kinetics of IL-1β release by BMDMs and found a time-dependent secretion of IL-1β ( S1D Fig ) . Additionally , we measured the kinetics of viral replication in unprimed and PAM ( 3 ) CSK ( 4 ) primed BMDMs . The virus has a fast replication cycle , as MAYV RNA levels reach its intracellular peak at 6 to 12 hours , while the extracellular peak is achieved at 12 hours after infection ( S1E and S1F Fig ) . Priming BMDMs did not affect virus infectivity in BMDMs but affected virus output at 24 hours ( S1E and S1F Fig ) . Activation and processing of caspase-1 is a key event during inflammasome activation [23] . Therefore , we stained infected BMDMs with FAM-YVAD , a fluorescent dye that specifically binds to active Caspase-1 ( CASP1 ) . By FACS analysis , we gated in the FAM-YVAD+ population ( S1G Fig ) and found that MAYV induces robust CASP1 activation , as shown by the percentage of FAM-YVAD+ cells and the integrated mean of fluorescence ( Fig 1G–1I ) . We determined that approximately 25% of the BMDMs are infected by MAYV ( S2A and S2B Fig ) or and 10% with CHIKV ( S2D and S2E Fig ) . Interestingly , we found that approximately 50% of MAYV-infected BMDMs display active caspase-1 ( S2C Fig ) , while 30% of CHIKV-infected BMDMs are FAM-YVAD+ ( S2F Fig ) . Corroborating these data , we performed western blotting and detected the cleaved form of CASP1 ( p20 ) and IL-1β ( p17 ) in the supernatants of MAYV-infected BMDMs ( Fig 1J ) . These data established that MAYV induces CASP1 activation and secretion of IL-1β in infected macrophages . Different inflammasomes , including AIM2 and NLRP3 , are activated in response to viral infections [28–35] . To address which inflammasome is activated upon MAYV infection , we assessed the mRNA expression of different inflammasome molecules in BMDMs by qPCR . At 3 and 6 hours post infection , MAYV induced increased expression of Casp1 ( Fig 2A ) , Nlrp3 ( Fig 2B ) , Aim2 ( Fig 2C ) and Asc ( Fig 2D ) . Next , we tested whether the AIM2 or NLRP3 inflammasomes were required for IL-1β release in macrophages . We observed robust IL-1β production in BMDMs from WT and Aim2–/–mice in response to infection with MAYV , but Nlrp3–/– , Asc–/–and Casp1/11–/–macrophages failed to induce IL-1β secretion ( Fig 2E ) . Importantly , live virus is required for NLRP3 inflammasome activation , since neither heat-inactivated nor UV-irradiated MAYV were capable of inducing IL-1β release ( Fig 2F ) . However , both NLRP3 and Casp1/11 were dispensable for LDH release ( S3A Fig ) . By using western blot we found that NLRP3 was required for CASP1 ( p20 ) and IL-1β ( p17 ) cleavage in response to infection ( Fig 2G ) . Accordingly , NLRP3 was also required for CASP1 activation measured by FAM-YVAD as shown by FACS analysis ( Fig 2H–2J ) . To address whether NLRP3 activation by MAYV influenced viral replication , we infected Nlrp3–/–and Casp1/11–/–BMDMs with MAYV and found that NLRP3 inflammasome does not affect MAYV intracellular or extracellular RNA levels ( S3B and S3C Fig ) . Cellular processes such as potassium efflux and ROS production are known to be important for NLRP3 inflammasome assembly and activation [12 , 23] . Therefore , we tested whether potassium efflux and ROS are induced by MAYV in BMDMs . MAYV triggered both total ( Fig 3A and 3B ) and mitochondrial ROS production ( Fig 3C and 3D ) , as shown by the representative histograms and iMFI . In addition , MAYV infection also induced a significant decrease in intracellular potassium in BMDMs ( Fig 3E ) . To address the importance of ROS production in NLRP3 inflammasome activation , we used apocynin , which inhibits NADPH oxidase activity [36] . We found that apocynin effectively blocked ROS production upon infection with MAYV or PMA stimulation ( Fig 3F ) , and this effect resulted in a dose-dependent blockage of IL-1β secretion induced by MAYV infection ( Fig 3G ) . We then tested the effect of K+ efflux on activation of the NLRP3 inflammasome in response to MAYV infection . We treated cells with KCl to increase extracellular K+ and used NaCl as a control [37] . Treatment with NaCl did not interfere in inflammasome activation in response to MAYV infection ( Fig 3H ) . In contrast , KCl ( Fig 3I ) inhibited IL-1β release upon MAYV infection . Importantly , we measured viral infectivity and replication upon stimulation with the different treatments used and found that neither apocynin , nor NaCl or KCl affected viral infectivity at 1 hour ( Fig 3J–3L ) , or viral load at 24 hours ( Fig 3M–3O ) . Taken together , these results demonstrate that potassium efflux and ROS production are necessary for activation of the NLRP3 in response to MAYV infection . Mouse models of CHIKV infection are well established in the literature [38] . Of note , CHIKV injection into the footpad of mice induces acute inflammation , partially mimicking the pathogenesis of the disease in humans [17] . Patients infected by MAYV develop symptoms very similar to CHIKV-infected individuals [10 , 11 , 39] . Because of the similarity of clinical signs and previous reports showing that MAYV induce ankle or foot swelling in 4 weeks old C57BL/6 [27] or A129 mice [26] , we tested whether MAYV was able to induce inflammation in the footpad of 6–8 weeks old WT C57BL/6 mice . We injected 106 PFU of MAYV into the footpad of C57BL/6 mice , and footpad thicknesses were measured through eight days of infection . Conditioned media was used in mock infections as a negative control and 107 PFU of CHIKV was used as a positive control . The magnitude of inflammation for both viruses was very similar , with the peak of MAYV-induced swelling at 5 to 6 days of infection ( Fig 4A and 4B ) . We also performed the von-Frey test , which measures the mechanical withdrawal threshold , to assay pain in mice infected with MAYV . The results show that mice felt pain after 1 day of infection , until the end of the experiment at day 8 post infection ( Fig 4C ) . The kinetics of MAYV replication were also measured in the infected footpad , spleen and leg muscle . Although viral copies were considerably high at 1 day after infection in all tissues analyzed , the viral load dropped considerably in the footpad and muscle up to day 10 of infection but remained stable in the spleen ( Fig 4D ) . To evaluate the inflammatory infiltrate at the peak of infection , we infected WT mice with mock or MAYV and performed histological analyses of the footpads . Our results show that MAYV-induced footpad swelling was followed by a strong inflammatory infiltrate , composed primarily of myeloid cells ( Fig 4E ) . Together , these results validate our in vivo model for MAYV-induced acute inflammation . This model resembles the well standardized CHIKV model of infection in the footpad , which is known to mimic infections in patients [17] . The NLRP3 inflammasome has been implicated in the development of CHIKV-induced inflammation in the footpad of injected mice [17] . We injected MAYV at 105 or 106 PFU dose in the footpad of WT and Nlrp3-/- mice and observed that Nlrp3–/–mice had increased footpad swelling compared to WT mice at 5 days post infection with a dose of 105 PFU and at 6 days post infection with a dose of 106 PFU ( Fig 5A , 5B and 5C ) . Representative images of infected WT and Nlrp3–/–mice are shown for both mock and MAYV-infected animals ( Fig 5D ) . We next infected two independently generated Nlrp3–/–mice [40 , 41] and Casp1/11-/- mice with 105 PFU dose of MAYV and we observed that footpad swelling was significantly higher in these deficient strains compared to WT mice ( Fig 5E ) . Of note , Nlrp3–/–mice showed reduced pain hypersensitivity at earlier time points upon infection but developed pain hypersensitivity similar to WT mice after 5 days of infection ( Fig 5F and 5G ) . Our data indicate that inflammasome signaling is involved with the pathogenesis of this acute model of MAYV infection . Thus , we tested whether the inflammasome plays a role in control of viral replication in vivo . To test this hypothesis , WT and Nlrp3–/–mice were injected with MAYV and euthanized at an early time point ( 1 day after infection ) or at the peak of inflammation ( 6 days after infection ) , and virus RNA levels were measured by qPCR . NLRP3 deficiency did not alter viral loads in the footpad ( Fig 5H ) , spleen ( Fig 5I ) or muscle ( Fig 5J ) of mice at 1 or 6 days post infection ( Fig 5K ) . We next assessed whether activation of the NLRP3 inflammasome in vivo also occurs upon MAYV infection . We measured IL-1β and IL-18 levels in the supernatants obtained from footpad homogenates of Mock or MAYV-infected WT , Nlrp3-/- and Casp1/11-/- mice . These cytokines are produced upon infection in a NLRP3- and Caspase1/11-dependent manner ( Fig 5L and 5M ) . Taken together , our results suggest that NLRP3 inflammasome activation by MAYV impacts the pathogenesis of an acute in vivo model of infection but does not play a role in viral control in macrophages or in vivo . To further investigate the differences in footpad swelling between WT and Nlrp3-/- mice we performed histopathological analysis of mock and MAYV infected mice . Unexpectedly , the pronounced footpad swelling observed in Nlrp3-/- mice was not followed by a greater infiltration of mononuclear cells and neutrophils in the tissue ( Fig 6A and 6B ) . As expected , we found a robust neutrophil infiltration both in Nlrp3-/- and WT mice infected with MAYV at 6 dpi ( Fig 6A and 6B ) . In addition , MAYV-infected WT mice presented a higher tissue damage score when compared to Nlrp3-/- mice ( Fig 6C ) . In order to assess the inflammatory infiltrate in the joint tissue , we obtained articular lavages from the knees of mock and MAYV-injected mice . Although a considerable number of inflammatory cells was found in MAYV-infected WT mice , NLRP3 deficiency increased cellular infiltration into this joint tissue ( Fig 6D ) . We next addressed the role of this inflammatory platform in the recruitment of specific cellular subsets found in the footpad of MAYV-infected mice ( S4 Fig ) . Although the percentage of neutrophils ( CD11b+Ly6G+ ) ( Fig 7A and 7B ) and inflammatory monocytes ( CD11b+Ly6Chigh ) ( Fig 7A and 7D ) infiltrating the tissue is not affected by the absence of NLRP3 or Casp1/11 , all these populations decrease in absolute numbers compared to WT mice ( Fig 7C and 7E , respectively ) . Besides myeloid cells , NK and T lymphocytes were also abundant in the footpad of MAYV-infected mice . Thus , we investigated whether NLRP3 play a role in the recruitment of these populations . We found that MAYV enhanced the percentage ( Fig 8A and 8B ) and absolute numbers ( Fig 8C ) of NK cells ( CD45+NK1 . 1+CD3- ) in WT mice and strikingly , the deficiency of NLRP3 and Caspase1/11 promoted an increased infiltration of this subset of cells to the infected tissue . We found that MAYV did not affect the percentage and absolute numbers of T ( CD3+ ) ( Fig 8D and 8E ) , NKT cells ( CD45+NK1 . 1+CD3+ ) ( Fig 8F and 8G ) and B cells ( CD19+ ) ( Fig 8H and 8I ) . In addition , NLRP3 and Casp1/11 do not play a role in the recruitment of these populations . Taken together , our data demonstrate that NLRP3 activation by MAYV is important for recruitment of specific cells , induction of pain and inflammation in a mouse model of Mayaro infection . MAYV infection elicits robust immune responses in patients during acute and convalescent phase of the disease . In addition , secretion of pro-inflammatory immune mediators during the disease has been reported [11] . We evaluated inflammasome-related cytokines levels in sera of confirmed MAYV-infected patients presenting acute febrile illness for five days or less during the early phase of the disease . We found that active Caspase-1 ( Caspase-1 p20 ) levels were higher in sera of infected individuals when compared to healthy controls samples ( Fig 9A ) . Additionally , IL-1β and IL-18 levels in the sera of MAYV patients were higher than those found in healthy individuals ( Fig 9B and 9C ) . These data indicate that MAYV infection is associated with the production of inflammasome-derived components such as Caspase-1 p20 , IL-1β and IL-18 , supporting our assertion that the NLRP3 inflammasome is important for MAYV clinical setting .
In this study , we evaluated whether MAYV activates the NLRP3 inflammasome and the possible mechanisms involved in the pathogenesis of this acute mouse model . Our findings demonstrate that MAYV triggers NLRP3 activation in macrophages , which have been implicated in the pathogenesis of many viruses [31 , 32 , 42–45] . Of note , MAYV induces IL-1β production similar to ZIKV and CHIKV , which are known to trigger the inflammasome [16 , 17 , 28–30] . Importantly , we showed that the NLRP3 inflammasome plays an important role during MAYV infection in our mouse model . In addition , the serum of MAYV-infected individuals contains elevated levels of active caspase-1 , IL-1β and IL-18 , supporting the participation of the NLRP3 inflammasome in the development of MAYV fever in humans . Although the inflammasome is triggered by several viruses and plays an important role in the outcome of infections [31 , 32 , 42–45] , our study is the first to identify the mechanisms of NLRP3 activation by an alphavirus . The finding that MAYV induces ROS production and potassium efflux , that are important to activate NLRP3 , suggest that upon viral entry into the host cell , several PRRs may recognize the virus and affect cellular homeostasis , triggering mitochondrial and other organelle’s damage and inducing pore formation . IL-1β protein secretion was abrogated by inactivated MAYV , indicating that MAYV infection is required for the NLRP3 inflammasome activation . MAYV infection increased the levels of inflammasome-related mRNAs , including Aim2 . However , infection did not activate the AIM2 inflammasome , since there was no alteration on secreted IL-1β levels using cells deficient in Aim2 . Although MAYV infection induced an increase in steady-state Il1b transcript levels , priming with a TLR agonist prior to infection is needed for significant IL-1β secretion , in agreement with previously published literature with other viruses [45] . Besides cytokine production , cell death is another common consequence of inflammasome activation . However , our LDH release data suggest that cell death is NLRP3-independent . MAYV replication in BMDMs paralleled the kinetics of IL-1β production , which suggests that viral replication cycle itself may account for macrophage death . The cell death mechanism remains to be determined in future studies , since other forms of inflammatory cell death , such as necroptosis , might be triggered by MAYV . Our in vivo model of MAYV acute infection demonstrates that NLR3 inflammasome signaling is associated with pain generation early during infection . This is consistent with previous studies in which NLRP3 inflammasome activation and pain induction in many disease models [46 , 47] . On the other hand , the finding that NLRP3 signaling was protective for the footpad swelling at the peak of MAYV inflammation was unexpected , since it has been demonstrated that inflammasome blockage is protective against CHIKV infection [48] . However , our FACS data demonstrate that NLRP3 is involved in the recruitment of neutrophils and inflammatory monocytes to the site of MAYV infection , while limiting infiltration of NK cells . Of note , it has been reported that caspase-1-specific antagonist Z-YVAD-FMK treatment leads to a reduction in cutaneous neutrophils recruitment during Semliki Forest virus infection [49] . The influx of inflammatory neutrophils are associated with worsened outcomes during CHIKV infection , since they support CCR2-dependent entry of myeloid cells and increase swelling during CHIKV infection [50] . However , while neutrophils initiate counterproductive responses at mosquito bites , they are required at later stages of disease to prevent mice from succumbing to Semliki Forest virus infection [49] , demonstrating that they could have a dual role during alphavirus infections . Interestingly , NK cells were shown to contribute to CHIKV pathogenesis [51] , and may explain the increased footpad swelling in NLRP3-deficient mice infected with MAYV . During the acute phase of CHIKV infection there is an increased frequency of cytolytic NK cells in patients [52 , 53] , which could indicate a role in the control of virus-infected target cells . Nevertheless , a greater infiltration of NK cells expressing granzyme B has been correlated with increased edema and footpad swelling during the early acute phase of CHIKV infection in a mouse model [54] . Depletion of NK cells significantly reduced the first peak of swelling during CHIKV infection ( 3dpi ) , while not affecting the second peak of swelling ( 6dpi ) of the biphasic pattern of footpad swelling [54] . Although the greater infiltration of NK cells could lead to the increased swelling in NLRP3-deficient mice , we cannot not discard the participation of the inflammasome in edema formation , cell activation and cytokine production , given that we found no roles for NLRP3 in controlling viral titers , a finding consistent with previous studies [48 , 55] . Inflammasome may present a dual role in alphavirus infections , having protective or deleterious effects . Against other viruses , such as influenza , the NLRP3 activation can be either protective or detrimental depending on the stage of infection and virus load [31 , 32 , 55 , 56] . In addition , mosquito saliva was shown to enhance inflammasome activation and Semliki Forest virus disease [49] . The NLRP3 inflammasome may act alongside other pathways during the development of disease in MAYV-infected patients . We found higher levels of caspase-1 , IL-1β and IL-18 in MAYV-infected patients during the acute phase of the disease , showing that the NLRP3-inflammasome might be relevant during MAYV infection in the clinical setting . Recent epidemics of arboviral diseases , such as ZIKV and CHIKV , have emphasized the need for health agencies to anticipate potential emerging pathogens transmitted by arthropods . MAYV infections have been emerging in South America representing a potential threat to public health . There is an urgent need to elucidate the basic processes of MAYV pathogenesis , which is essential to better understand the disease and manage patients . In this scenario , this study advances toward the understanding of the pathogenesis of the disease caused by this virus . We identified the NLRP3 inflammasome as an important pathway related to MAYV pathogenesis , paving the way to future studies exploring the mechanisms governing this disease .
The care of the mice was in compliance with the institutional guidelines on ethics in animal experiments; approved by CETEA ( Comissão de Ética em Experimentação Animal da Faculdade de Medicina de Ribeirão Preto , approved protocol number 014/2016 ) . CETEA follow the Brazilian national guidelines recommended by CONCEA ( Conselho Nacional de Controle em Experimentação Animal ) . All proceedings involving human samples and data were approved by the Julio Muller University Hospital ethics committee ( process number 1 . 164 . 656 ) . This institution follows the recommendation from CONEP ( Comissão Nacional de Ética em Pesquisa ) . Samples were obtained with written informed consent from each patient . All participants were adults . Virus strains used in this study comprised MAYV BeAr 20290 , CHIKV BzH1 and ZIKV ZikaSPH2015 . Genomic sequences of these 3 viruses were deposited in NCBI ( National Center for Biotechnology Information ) database under the GenBank accession numbers KT754168 , KT581023 and KU321639 respectively [57 , 58] . Virus stocks were produced after infecting Vero cells ( ATCC CCL-81 ) with a MOI of 0 . 1 PFU and cultured in DMEM with 2% heat-inactivated FCS , 1% glutamine and 1% Pen-Strep . The supernatant was collected after 2–3 days of infection for MAYV and CHIKV and 5 days for ZIKV , clarified by centrifugation to remove cell debris ( 5500g ) and aliquots were kept at −80°C . Conditioned media used for mock infections was prepared from uninfected Vero cells in a similar manner . Virus stocks were titrated by plaque assay in Vero cells using 10-fold serial dilutions of virus stocks [59] . To inactivate MAYV by ultraviolet light ( UV ) , the virus was dispersed in a tissue culture dish , and a compact UV lamp was placed directly above the dish for 30 minutes . Heat-inactivated MAYV was prepared by incubating the virus at 70°C for 15 minutes . Conditioned media used for mock infections was treated in a similar manner . Virus complete inactivation was verified by the lack of virus plaques after titration by standard plaque assays on Vero cells . The patients samples used were previously described and confirmed for MAYV acute infection by RT-nested-PCR , sequencing , virus isolation and IgM and IgG detection [60 , 61] . The samples were obtained from May 2015 to March 2016 from patients of Mato Grosso state of Brazil , presenting acute febrile illness with symptoms for five days or less . Patients age presented a median of 40 years old ( ranging from 24 to 66 years ) and most of them were women ( 77% ) . Control serum samples were collected from healthy subjects not infected with arboviruses . Mice used were C57BL/6J mice ( JAX 000664 ) , Nlrp3−/− [41] , Casp1/11−/− [62] , Aim2−/− [63] , Nlrc4−/− [64] , Asc−/− [65] and Il1r−/− ( JAX 003245 ) , all on a C57BL/6 mouse background . When indicated , the JAX Nlrp3−/− ( JAX stock #017969 ) was also used [40] . All mice were bred and maintained under specific-pathogen-free conditions at the Animal Facilities of the Medical School Ribeirão Preto ( FMRP-USP ) . For in vitro experiments bone marrow were collected from 6–12 week old female and/or male mice . The in vivo experiments were conducted using 6–8 week old mice . 6–8 week old male mice ( n = 5 mice per group ) were inoculated subcutaneously ( 10 μL ) in the ventral side of the footpad with 105 or 106 PFU of MAYV . Mock-infected mice were inoculated with the conditioned media ( 10 μL ) . MAYV-induced footpad swelling was assessed every 24 h by measuring height ( H ) or the height ( H ) X width ( W ) of the perimetatarsal area of the hind foot using Kincrome digital vernier calipers . The total area ( H x W ) was normalized for day 0 measurements . BMDMs were prepared using tibia and femur from 6- to 12-week-old mice as previously described [66] . Briefly , progenitor cells were isolated by flushing femurs and tibia with cold sterile incomplete RPMI 1640 ( Gibco ) . The cells were then cultured in differentiation medium: RPMI 1640 supplemented with 20% heat-inactivated FCS and 30% L-929 cell-conditioned medium ( LCCM ) as a source of M-CSF . After 7 days in culture , BMDMs were harvested and seeded at the required density for each experiment . An MOI of 5 and 24 hours of infection were used in the experiments in vitro using BMDMs unless otherwise stated in the figure legends . For in vitro cytokine determination , BMDMs were seeded overnight at a density of 2 × 105 cells/well in 48-well plates and prestimulated with 300 ng/ml of PAM ( 3 ) CSK ( 4 ) ( Invivogen ) for 4 h , and subsequently infected with MAYV . The cytokines in the supernatants were assayed using a mouse IL-1β ELISA kit ( BD Biosciences ) according to the manufacturer’s instructions . For in vivo cytokine determination , footpads were processed and the supernatants from total homogenates were obtained . The levels of IL-1β and IL-18 in the homogenates were detected using a mouse IL-1β ELISA kit ( BD Biosciences ) and a mouse IL-18 ELISA kit ( RD-MBL ) , respectively , according to the manufacturer’s instructions . Detection of IL-1β , IL-18 and Caspase 1 ( p20 subunit ) in human serum samples was accomplished by using human IL-1β ELISA kit II ( BD Biosciences ) , human total IL-18 ( RD ) and human caspase-1 ELISA kit ( RD ) respectively . Total RNA was extracted from 1x106 BMDMs using RNeasy Mini Kit ( Qiagen ) , according to the manufacturer’s instructions . RNA concentrations were determined in a NanoDrop One spectrophotometer ( Thermo Fisher Scientific ) and 1 μg of the extracted RNA was used for cDNA conversion using the iScriptTM cDNA Synthesis kit ( BIO-RAD ) in a thermal cycler . Primers used were AscForward: 5’-CCAGTGTCCCTGCTCAGAGT-3’; AscReverse: 5’-TCATCTTGTCTTGGCTGGTG-3’; Casp1Forward: 5’-AGATGCCCACTGCTGATAGG-3’; Casp1Reverse: 5’-TTGGCACGATTCTCAGCATA-3’; Nlrp3Forward: 5’-GTGGTGACCCTCTGTGAGGT-3’; Nlrp3Reverse: 5’-TCTTCCTGGAGCGCTTCTAA-3’; Aim2Forward: 5’-TCTGTCCTCAAGCTAAGCCTCA-3’; Aim2Reverse: 5’-GTGACAACAAGTGGATCTTTCTGTA-3’; Il1bForward: 5’-CCAAGCAACGACAAAATACC-3’; Il1bReverse: 5’-GTTGAAGACAAACCGTTTTTCC-3’; HprtForward: 5’-CAGTCCCAGCGTCGTGATTA-3’; HprtReverse: 5’-GGCCTCCCATCTCCTTCATG-3’ . The quantification of Asc , Casp1 , Nlrp3 and Il1b produtcs was performed using 50 ng of cDNA , 10 μM of each primer and PowerUp SYBR Green Master Mix ( Applied Biosystems ) according to the manufacturer’s instructions . The reactions were performed in the QuantStudio 3 Real-Time PCR System ( Applied Biosystems ) . Quantitation was performed by normalizing target gene mRNA levels to Hprt ( hypoxanthine guanine phosphoribosyl transferase ) levels , and infected sample values are expressed relative to the mean of mock values . Statistical significance between-groups was calculated with ΔCT values that provides the estimates of ΔΔCT values ( Log2 fold change ) [67 , 68] . A total of 107 BMDMs were seeded in 6-well plates overnight and then primed with 300 ng/ml PAM ( 3 ) CSK ( 4 ) ( InvivoGen , tlrl-pms ) for 4 hours prior to infection with MAYV or mock infection . After 24 hours the supernatants were harvested and proteins were precipitated with ice-cold 50% trichloroacetic acid followed by acetone . Cells were lysed in RIPA buffer ( 10 mM Tris-HCl , pH 7 . 4 , 1 mM EDTA , 150 mM NaCl , 1% Nonidet P-40 , 1% deoxycholate and 0 . 1% SDS ) in the presence of a protease inhibitor cocktail ( complete , Roche ) . Precleared lysates and supernatants were boiled in Laemmli sample buffer , resolved by SDS-PAGE and transferred ( Semidry Transfer Cell , Bio-Rad ) to a 0 . 22-μm nitrocellulose membrane ( GE Healthcare ) . The membranes were blocked in Tris-buffered saline ( TBS ) with 0 . 01% Tween-20 and 5% nonfat dry milk . Rat monoclonal antibody to CASP1 p20 ( 1:250 , Genentech , 4B4 ) , goat antibody to IL-1β p-17 ( 1:200 , Sigma Aldrich , I3767 ) , mouse anti-β-Actin ( 1:1000 , C4 , Santa Cruz sc-47778 ) and specific horseradish peroxidase–conjugated antibodies ( 1:3 , 000 , KPL , 14-16-06 and 14-13-06 ) were diluted in blocking buffer for the incubations . Enhanced chemiluminescence luminol reagent ( GE Healthcare ) was used for antibody detection . Active CASP1 was measured by fluorochrome inhibitor of caspases assay ( YVAD-FLICA , ImmunoChemistry Technologies ) , a green fluorescent dye that binds specifically to active CASP1 . Briefly , 106 BMDMs were seeded in 12-well plates overnight and then infected with MAYV ( MOI of 5 ) or mock infected for 24 hours . As a positive control , we used 20 μM of Nigericin ( Sigma-Aldrich ) for 40–60 minutes . After that , cells were harvested and stained for 1 h with YVAD-FLICA , following the manufacturer's instructions . For intracellular virus staining , BMDMs were infected with either MAYV or CHIKV ( MOI of 5 ) or mock-treated for 8 hours . After that , cells were fixed in paraformaldehyde 4% and permeabilized with 0 , 1% saponin . Cells were incubated for 1 hour with mouse hyperimmune sera to MAYV , CHIKV or isotype control and with FAM-YVAD fluorescent dye , following the manufacturer's instructions . Mouse hyperimmune sera to MAYV strain BeAr20290 and to CHIKV strain S27-African had their reactivity previously confirmed by indirect immunofluorescence assay [69] . Secondary antibody anti-mouse stained with Alexa-594 was added and incubated for 40 minutes . Cells were then detached from the plates and the data were acquired on a FACS ACCURI C6 flow cytometer ( BD Biosciences ) and analyzed with FlowJo software ( Tree Star ) . Intracellular concentration of potassium was determined by fluorescence emission of asante potassium green-2 ( APG-2 , TEFLabs ) . BMDMs ( 2×104 ) were seeded in black , clear-bottom 96-well plates , and treated with PAM ( 3 ) CSK ( 4 ) for 4 hours , then infected with MAYV ( MOI of 5 ) . After 2 hours of infection , BMDMs were incubated with 5 μM APG-2 in RPMI without FBS and phenol red for 30 min . The cells were washed with PBS , and RPMI without phenol red was replaced . Nine images per well were recorded at 40× magnification with the ImageXpress Micro High-Content Imaging System and processed with MetaXpress High-Content Image Acquisition and Analysis ( Molecular Devices ) . To detect intracellular and mitochondrial ROS production , we seeded 106 BMDMs in 12-well plates overnight . Cells were infected with MAYV ( MOI of 5 ) or stimulated with PMA ( 200 ng/ml ) or rotenone ( 50 μM ) for 90 minutes . Next , H2DCFDA ( 10 μM ) and MitoSOX Red dye ( 2 . 5 μM ) were added to the cells for 30 min at 37°C , and then they were harvested and analyzed by flow cytometry . The data were acquired on a FACS ACCURI C6 flow cytometer ( BD Biosciences ) and analyzed with the FlowJo software ( Tree Star ) . BMDMs were primed with 300 ng/ml of PAM ( 3 ) CSK ( 4 ) ( InvivoGen ) for 4 hours , treated for 2 hours with 0–50 nM of NaCl or KCl , and then infected with MAYV ( MOI of 5 ) . To inhibit NADPH oxidase , cells were treated with 50 or 100 μM apocynin for 1 hour prior to infection . After 24 hours of infection , supernatants were collected and the levels of IL-1β were measured by ELISA ( BD Biosciences ) . The effect of these treatments on virus infectivity and virus production were measured at 1 hour , intracellularly , and 24 hours , extracellularly , after infection with MAYV ( MOI of 5 ) by viral RNA detection by qPCR as described below . BMDMs were seeded overnight in 48-well plates ( 2x105 cells/well ) , primed with 300 ng/mL of PAM ( 3 ) CSK ( 4 ) for 4 hours , and then infected for 24 hours with MAYV , CHIKV or ZIKV in RPMI1640 medium without phenol red , 15 mM HEPES and 2 g/l NaHCO3 supplemented with 2% FBS . At the end of the infection , supernatants were collected and assayed using the CytoTox 96 Non-Radioactive Cytotoxicity Assay ( Promega ) following the manufacturer’s instructions . Cells were incubated with 9% Triton X-100 ( Fisher Scientific ) for 15 min as a positive control for complete cell lysis . The percentage of LDH release was calculated as ( mean OD value of sample / mean OD value of Triton X-100 control sample ) × 100 , and is shown in the figures as the percentage of cell death compared to Triton X-100 . At the times indicated after infection , mice were euthanized and perfused by intracardial injection with PBS . Tissues were dissected , weighed , and homogenized in sterile PBS using a TissueLyser II ( Qiagen ) . The homogenized tissues were diluted 1:3 in serum-free DMEM ( Thermo Fisher Scientific ) and seeded onto a monolayer of Vero cells in 24-well plates . The plaque assay was performed as described previously for the quantitation of viral stocks [59] . For viral RNA quantification , aliquots from the same samples used for plaque assay were extracted using QIAamp Viral RNA Mini Kit ( QIAGEN ) according to the manufacturer's recommendations . RT-qPCR was performed in one-step using TaqMan Fast Virus 1-Step Master Mix ( Applied Biosystems ) , following the manufacturer's recommendations . Primers and probe used were designed to detect a 99 bp region of MAYV Nsp2 gene ( Nsp2Forward: 5’-GGCATTGCATCCTTTAGCGG-3’; Nsp2Reverse: 5’-GGGAGTAGAACACGGCCATC-3’; Probe: FAM TACCCACAAAGGTCGTGCAGGGCGATACCAAG BHQ1 ) . The reaction was performed in the QuantStudio 3 Real-Time PCR System ( Applied Biosystems ) . Standard curves were generated using titrated virus stocks . qPCR results were normalized to the amount of virus in PFU . Each sample was assayed in duplicate . After 6 days of infection , animals were deeply anesthetized with ketamine and xylazine and perfused through the ascending aorta with PBS , followed by 4% paraformaldehyde ( PFA ) . After perfusion the mouse footpad tissue was immediately removed ( skin and muscle ) . Pieces of the footpad tissue were post-fixed for 24 h in PFA and then replaced with 20% sucrose for 4 days . The tissues were embedded in Tissue-Tek O . C . T . compound and sectioned at 15μm thickness . The specimens were dehydrated in ascending grades of ethyl alcohol , cleared in xylene and stained with Harris haematoxylin and eosin ( H&E ) for histopathology [70] . Image acquisition was performed by using light microscopy ( DM6000B; Leica Microsystems , Buffalo Grove , IL , USA ) . From each slide , twenty representative photographs were randomly taken ( magnification 400x ) for analysis of the histological changes . In each high-power field , the degree of tissue damage was determined by a modified score [71]: ( 1 ) tissue edema ( 2 ) infiltration or aggregation of inflammatory cells , and ( 3 ) muscle necrosis . Each item was graded according to the following five-point scale: 0 , no damage; 1 , minimal damage; 2 , mild damage; 3 , moderate damage; 4 , severe damage . The degree of tissue disease was assessed by the sum of scores ranging from 0 to 12 for each high-power field . In addition , quantification of subcutaneous tissue thickness was performed by histomorphometric analysis using ImageJ software ( National Institutes of Health , U . S . A . ) , and the total number of polymorphonuclear neutrophils infiltrated into the tissues was counted . Knee cavities were surgically opened , and synovial lavage was obtained by flushing the joint cavity with 10μl . Cytospin preparations were acquired by Cytospin 4 ( Thermo Scientific ) with 70 μl of diluted joint lavage per slide and centrifugation at 200 r . p . m . for 7 min . The slides were air-dried and Giemsa-stained ( Laborclin , Pinhais , Brazil ) and counted under a light microscope with a 40X objective to determine the total numbers of cells in the lavage fluid . Mice were infected as detailed in In vivo infections and footpad swelling section , and after 5 days of infection for MAYV or 6 days for CHIKV , the animals were sacrificed , and footpads were removed . Footpads homogenates were obtained after 2 hours incubation in collagenase VIII at 1mg/mL and passed thought a 70 μm cell strainer . Cells were counted and plated in 96-well U bottom . Cells were blocked with Fc Block ( BD Biosciences ) and then stained for flow cytometry analysis . The antibodies employed were anti-CD3ε-PerCP ( BioLegend ) , anti-CD19-APC ( BioLegend ) , anti-NK1 . 1-FITC ( BioLegend ) , anti-CD45-APC ( BD Biosciences ) , anti-CD11b-FITC ( BioLegend ) , anti-Ly6G-PerCP ( BioLegend ) and anti-Ly6C-PE ( BioLegend ) . Cells were also stained with a viability dye ( Live/Dead fluorescent dye , Pacific Blue , Life Technologies ) . Samples were acquired on a FACS Verse flow cytometer ( BD Biosciences ) and analyzed with FlowJo software ( Tree Star ) . To evaluate the mechanical nociceptive threshold , mice were placed on an elevated wire grid and the plantar surface of the ipsilateral hind paw was stimulated perpendicularly with a series of von Frey filaments ( Stoelting , Chicago , IL , USA ) with logarithmically increasing stiffness ( 0 . 008–2 . 0 g ) and the basal mechanical withdrawal threshold was measured one day before infection . Each one of these filaments was applied for approximately 3–4 s to induce a paw-withdrawal reflex . The weakest filament able to elicit a response was taken to be the mechanical withdrawal threshold . The log stiffness of the filament is reported as log10 of the mass of the filament in mg and ranged from 0 . 903 ( 8 mg or 0 . 008 g ) to 3 . 0 ( 1000 mg or 1 g ) [72 , 73] . Data were plotted and analyzed with GraphPad Prism 8 . 1 software ( GraphPad , San Diego , California ) . Log-transformed values for viral load data were used for statistical comparisons . For comparisons of multiple groups , two-way analysis of variance ( ANOVA ) followed by the Bonferroni's post test were used . The differences in values obtained for two different groups were determined using an unpaired , two-tailed Student’s t test with a 95% confidence interval . Differences were considered statistically significant when the P < 0 . 05 .
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Viruses transmitted by mosquitoes have recently received huge attention from the media because the epidemics caused by Zika and chikungunya virus rapidly spread to new areas and infected a large number of individuals . Mayaro is a virus transmitted by mosquitoes that circulates in the Caribbean and tropical regions of Latin America . It causes a highly inflammatory disease , called Mayaro fever , and acute disease is often followed by a prolonged arthralgia mediated by chronic inflammation that can last months or years . The spread of Mayaro to urban areas is a major concern by the authorities , given that the virus has potential to cause an epidemic if spread in high-risk areas . Thus , understanding the mechanisms related to the pathogenesis of this infectious agent would be of great value to treat and prevent the disease . Here , we established an adult mouse model of Mayaro infection and demonstrated that the virus activates the NLRP3 inflammasome , which is important to regulate this viral disease . Our study provides molecular targets for a future treatment of Mayaro fever and provides an experimental model to understand the pathology caused by this emerging viral pathogen .
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2019
|
The NLRP3 inflammasome is involved with the pathogenesis of Mayaro virus
|
Subnuclear promyelocytic leukemia ( PML ) nuclear bodies ( NBs ) are targeted by many DNA viruses after nuclear delivery . PML protein is essential for formation of PML NBs . Sp100 and Small Ubiquitin-Like Modifier ( SUMO ) are also permanently residing within PML NBs . Often , large DNA viruses disassemble and reorganize PML NBs to counteract their intrinsic antiviral activity and support establishment of infection . However , human papillomavirus ( HPV ) requires PML protein to retain incoming viral DNA in the nucleus for subsequent efficient transcription . In contrast , Sp100 was identified as a restriction factor for HPV . These findings suggested that PML NBs are important regulators of early stages of the HPV life cycle . Nuclear delivery of incoming HPV DNA requires mitosis . Viral particles are retained within membrane-bound transport vesicles throughout mitosis . The viral genome is released from transport vesicles by an unknown mechanism several hours after nuclear envelope reformation . The minor capsid protein L2 mediates intracellular transport by becoming transmembranous in the endocytic compartment . Herein , we tested our hypothesis that PML protein is recruited to incoming viral genome prior to egress from transport vesicles . High-resolution microscopy revealed that PML protein , SUMO-1 , and Sp100 are recruited to incoming viral genomes , rather than viral genomes being targeted to preformed PML NBs . Differential immunofluorescent staining suggested that PML protein and SUMO-1 associated with transport vesicles containing viral particles prior to egress , implying that recruitment is likely mediated by L2 protein . In contrast , Sp100 recruitment to HPV-harboring PML NBs occurred after release of viral genomes from transport vesicles . The delayed recruitment of Sp100 is specific for HPV-associated PML NBs . These data suggest that the virus continuously resides within a protective environment until the transport vesicle breaks down in late G1 phase and imply that HPV might modulate PML NB assembly to achieve establishment of infection and the shift to viral maintenance .
Promyelocytic leukemia ( PML ) nuclear bodies ( NBs ) are highly dynamic nuclear structures that have been associated with numerous cellular processes , including apoptosis , transcriptional regulation , and innate and intrinsic immune responses [1] . While their size and number of residing proteins vary according to the cell condition , the main component of PML NBs is PML protein [2] . It is present in seven isoforms that constitute the main scaffold of PML NBs , with the exception of PML VII that lacks a nuclear localization signal and remains in the cytosol [3] . PML protein is required for the formation and stability of PML NBs , as cells knocked down for PML protein fail to form these structures [4] . SUMOylation of PML protein with SUMO-1 and SUMO-2 is necessary for this process and SUMOylated PML proteins then recruit other PML NB-residing proteins that are either SUMOylated themselves or contain SUMO interacting motifs [2 , 4 , 5 , 6 , 7] . Transcriptional repressors Sp100 and Daxx are two additional proteins that permanently reside in PML NBs [1] . PML NBs are modified during cell cycle progression [1 , 8 , 9] . They disassemble upon the onset of mitosis and PML protein forms large aggregates in the cytosol , also referred to as mitotic accumulations of PML proteins ( MAPPs ) . MAPPs do not contain Sp100 or Daxx but only de-SUMOylated PML protein and are recycled after completion of mitosis . Following nuclear envelope reformation , released PML protein molecules are translocated back into the nucleus to form new PML NBs in the daughter cell nuclei and recruit other proteins . Despite extensive research on the role of PML NBs , their specific function in the cell remains unclear . However , they have been shown to be involved in innate and intrinsic immunity as both repressors of viral gene expression and coregulators of the type I interferon pathway [10] . Consequently , many DNA viruses , such as herpes simplex 1 ( HSV-1 ) , human cytomegalovirus ( HCMV ) , simian virus 40 ( SV40 ) and adenovirus 5 ( ADV5 ) , target PML NBs during primary infection and induce the reorganization and degradation of the residing proteins , including PML protein and Sp100 [11–15] . Specifically , HSV-1 and ADV5 encode early immediate proteins , ICP0 and E1A-13S , respectively , which target PML protein isoforms for degradation and thus enhances viral gene expression [15 , 16] . Similarly , HCMV targets Sp100 for degradation through its immediate early protein IE1 to prevent their transcriptional repression activity and enhance the early stages of infection [14] . It is thought that PML NBs are sensors of DNA/protein complexes and are thus recruited to virally-induced foci [17 , 18] . In the case of HSV-1 infection , PML NBs have recently been shown to be recruited to incoming HSV-1 genomes following nuclear delivery [18 , 19] . Furthermore , high-resolution microscopy showed that PML NB-residing proteins engulfed viral genomes shortly after nuclear entry . As SUMOylation and SUMO interaction are critical for the formation and dynamics of PML NBs , HSV-1 ICP0 is thought to target PML protein through SUMO interaction or recognition of their SUMO-1 conjugation motifs [16 , 20] . Similar to most other DNA viruses , papillomaviruses ( PVs ) associate with PML NBs at several stages of their life cycle . PV genomes along with minor capsid protein L2 have been observed to associate with PML NBs after infectious delivery into the nucleus of target cells . L2 protein also localizes to PML NBs in natural productive lesions , although transiently , and when over-expressed in cell culture [21–24] . However , while PML NBs restrict gene expression of most viruses , PVs , such as bovine papillomavirus 1 ( BPV1 ) and human papillomavirus ( HPV ) types 16 and 18 , have been shown to require PML protein for efficient transcription [25–27] . Transcription driven by both PV and heterologous promoters delivered by PV particles was repressed in the absence of PML protein , suggesting that PML protein does not function in a promoter-specific manner [25] . In addition , Sp100 was shown to restrict HPV18 transcription and replication [26 , 28] . These findings suggest that PML NBs may play an important role in the regulation of the PV life cycle . Following infectious entry , the HPV capsid uncoats within acidified endocytic vesicles . This is facilitated by host cell cyclophilin which allows for the partial dissociation of the major capsid protein L1 from the minor capsid protein L2 , which remains in complex with the viral genome [29–31] . While most of the L1 protein appears to be degraded in the late endosome , a subset of L1 protein , likely arranged as capsomeres , remains associated with the viral genome [30–32] . L2 protein assumes a transmembranous configuration , which is promoted by a newly described chaperone function of γ-secretase [33] . A putative transmembrane region spanning from residues 45 to 65 separates a small luminal domain from the large carboxy-terminal region that can interact with cytosolic factors , including the machinery mediating retrograde transport along microtubules ( MTs ) towards the trans-Golgi network ( TGN ) [34 , 35] . Prior to associating with PML NBs , the HPV genome needs to be delivered to the nucleus . Rather than utilizing nuclear pores and the nuclear import machinery , HPV takes advantage of nuclear envelope breakdown during mitosis to gain access to the nucleus [36 , 37] . HPV-harboring vesicles likely rely on L2 protein , which retains its transmembranous configuration during vesicular mitotic transport , to interact with motor proteins , such as dynein and kinesins , for transport along astral and spindle MTs , respectively [32 , 38–40] . Surprisingly , the viral genome resides within the transport vesicle in the nucleus for several hours after completion of mitosis and nuclear envelope reformation , resulting in delayed transcription when compared to delivery of naked DNA [25 , 30] . More recently , we reported that viral pseudogenomes delivered by HPV16 particles were lost after successful nuclear delivery in the absence of PML protein in the spontaneously immortalized HaCaT keratinocytes but not in HPV18 transformed HeLa cells [27] . Viral genome loss in HaCaT cells was prevented by inhibitors of the Jak/Stat signaling axis , although transcription was not restored . These findings pointed towards a protective role of PML protein in the immediate early stages of the HPV life cycle . Thus , we were prompted to pose the following questions: 1 ) when does the association of viral genome with PML protein and other PML NB-residing proteins occur; 2 ) which viral factors may play a role mediating this interaction during infection; and 3 ) whether viral genomes target preformed PML NBs or rather PML NB-residing proteins are recruited to incoming HPV genomes . Given that DNA successfully delivered to the nucleus by HPV particles is lost in the absence of PML protein , we hypothesized that PML protein is recruited to HPV-harboring transport vesicles prior to release from this membrane-bound environment and that likely L2 protein is mediating this association . L2 protein harbors a SUMO conjugation domain on its N-terminus , as well as one highly conserved SUMO interactive motif ( SIM ) and two additional putative SIMs on its C-terminus , which we hypothesized might be involved in recruitment of PML protein [24 , 41] . We also hypothesized that the recruitment of Sp100 is delayed and occurs after release of the viral genome from the transport vesicle . Herein , we utilized differential staining of viral pseudogenomes in combination with high-resolution immunofluorescence to determine the spatio-temporal recruitment of PML protein , Sp100 , and SUMO-1 to incoming viral genomes during infectious entry and establish an order of events following nuclear delivery of HPV genomes [30 , 32] .
To investigate the order of events following nuclear delivery of viral genomes , we needed to estimate the amount of time that has passed throughout mitosis and interphase . To achieve this , we observed the morphology of the nucleus and the localization of PML protein by immunofluorescent microscopy ( Fig 1 ) . During mitosis , PML protein forms MAPPs that are observed around mitotic chromosomes at all stages of mitosis ( Fig 1A ) [8 , 9 , 42] . Using DAPI staining , we define late telophase as cells exhibiting decondensing DNA and reforming nucleus , as well as the presence of PML protein aggregates ( Fig 1B ) . As MAPPs translocate back into the nucleus after nuclear envelope reformation , early interphase cells harbor large cytosolic aggregates of PML protein and a few , typically small , PML protein foci in the newly formed nucleus . The number and size of MAPPs decrease , while the number and size of PML protein foci inside the nucleus increase , throughout interphase . To estimate how much time has elapsed after the completion of mitosis , we counted the number of nucleoli , as it is inversely correlated with time , a method we have used in our previously published work [30 , 43] . Therefore , we estimated that 7+ nucleoli are present in the nucleus of early interphase cells , while late interphase cells are characterized by 1–6 nucleoli . Although the association of HPV genomes with PML protein has been known for decades , how it occurs is still unclear [22] . It has been assumed that incoming viral genomes are targeted to preformed PML NBs rapidly after nuclear delivery [22 , 25] , despite the now known dynamics of PML protein [8 , 9] . However , in HSV-1 infection , PML protein was shown to be recruited to and engulf incoming viral genomes following nuclear entry by high resolution microscopy [19] . Furthermore , our previously published findings suggest that PML protein provides a protective environment for the viral genome [27] . Therefore , we wanted to determine whether PML protein is recruited towards viral genomes or vice versa and to visualize the architecture of this association . We acquired high resolution images of HaCaT cells infected with HPV16 pseudovirions ( PsVs ) harboring an EdU-labeled pseudogenome after immunofluorescent staining . Images are z-stacks combined with 3D reconstruction for PML protein ( green ) and EdU-labeled viral pseudogenomes ( red ) ( Fig 2A ) . While previous confocal microscopy could only show EdU puncta adjacent to PML protein [25 , 27] , high resolution microscopy allows us to observe the structure of PML protein in association with incoming viral genomes in the nucleus of infected cells . As expected , PML protein aggregates in cytosolic MAPPs in mitotic cells , whereas the EdU-labeled pseudogenomes are present throughout the cell with some in the vicinity of mitotic chromosomes as previously reported [30 , 36] . Majority of EdU puncta did not co-localize with PML protein aggregates during mitosis ( for quantification , see Fig 3D ) , such as metaphase or late telophase , unlike previously reported [44] . However , we observed EdU puncta co-localizing with PML protein foci of different sizes in early interphase cells , which is then engulfed in the later interphase cells . In order to quantify these observations , we measured the distance between the center of EdU puncta and the center of PML protein puncta in early ( 7+ nucleoli ) or late ( 1–6 nucleoli ) interphase and found that PML protein is very closely co-localizing with EdU in late interphase cells , while the distance is very variable and overall greater in early interphase cells ( Fig 2B ) . In addition , as a measurement of engulfment , we calculated the ratio of PML intensity over EdU intensity , each normalized to the area of co-localizing foci ( Fig 2C ) . While the intensity of EdU puncta remained similar throughout , the intensity of PML puncta significantly increased in late interphase as we observe entrapment of EdU signal . Taken together , these data indicate that PML protein targets incoming viral genomes and forms around them , rather than viral genomes being recruited to preformed PML protein structures . We previously described that incoming viral genomes are lost in cells depleted for PML protein , implying that PML protein provides a protective environment for the viral genomes . If this is the case , we would predict that PML protein accumulates around incoming pseudogenomes before the release from the transport vesicles . To test this assumption , we employed a differential staining technique that has been previously described by our lab [30 , 32] . HaCaT cells were infected with EdU-labeled PsVs for 24 h . Following fixation , cells were permeabilized with a low concentration of digitonin , which only permeabilizes cholesterol-rich membranes , such as the plasma membrane and endocytic vesicles directly derived from the plasma membrane . Next , the cells were subjected to the Click-iT reaction using AlexaFluor ( AF ) 555 as reactive dye to stain the viral genome ( green ) . Subsequently , cells were completely permeabilized with Triton X-100 ( TX-100 ) and subjected to another round of Click-iT reaction , this time using AF647 dye to stain the viral genome ( red ) . Only EdU-labeled genomes either present on the cell surface , in early endocytic vesicles , or after egress from the endocytic compartment will be stained with AF555 , whereas all genomes will be stained with AF647 after TX-100 permeabilization . As a positive control , we treated cells with TX-100 instead of digitonin prior to the first staining for total permeabilization ( S1 Fig , Fig 3B ) . To control for intracellular membrane integrity , we tested the reactivity of an antibody recognizing a luminal epitope of TGN46 ( cyan ) in digitonin- and TX-100-treated cells ( S1 Fig ) . We observed that the luminal epitope of TGN46 was recognized in TX-100-treated cells , but not in digitonin-treated cells , suggesting that the plasma membrane but not internal membranes were permeabilized with the low concentration of digitonin . Representative images of infected HaCaT cells in various stages of mitosis demonstrate the inaccessibility of pseudogenomes to AF555 after digitonin but not TX-100 permeabilization ( S1 Fig ) . We combined differential staining of EdU-labeled pseudogenomes with immunofluorescent staining for PML protein ( cyan ) and quantified the presence of PML protein as a function of genome accessibility in different phases of the cell cycle ( Fig 3 ) . Once again , we observed essentially no co-localization of PML protein with EdU in late telophase cells and it was not until after mitosis was completed that viral genomes were shown co-localizing with PML protein ( Fig 3A and 3B ) . In early interphase cells , PML protein co-localized with nuclear-localized EdU puncta that were inaccessible to AF555 after digitonin permeabilization , whereas EdU puncta were accessible to both dyes and co-localized with PML protein in late interphase cells . We quantified the number of single red ( inaccessible ) or dual green/red ( accessible ) EdU puncta in mitosis and early ( 7+ nucleoli ) and late ( 1–6 nucleoli ) interphase ( Fig 3C ) . Chromosome-localized EdU puncta were 95% inaccessible in mitosis ( 5% accessible ) , and become more accessible over time after completion of mitosis in digitonin-treated cells ( 45% and 80% accessible EdU in early and late interphase , respectively ) . In the TX-100-treated control cells , EdU was consistently 95% accessible throughout the cell cycle ( Fig 3C ) . These results are consistent with our published findings suggesting that accessibility of the viral genome is delayed after completion of mitosis [30] . Next , we quantified the number of inaccessible ( In ) or accessible ( Ac ) EdU puncta that co-localized with PML protein in mitosis , early interphase ( 7+ nucleoli ) , and late interphase ( 1–6 nucleoli ) in digitonin-treated cells ( Fig 3D ) . During mitosis , EdU puncta did not co-localize with PML protein , which was visible as large cytosolic aggregates . In interphase cells , accessible EdU puncta largely co-localized with PML protein ( 79% and 76% in early and late interphase , respectively ) . Interestingly , we observed 70% of inaccessible EdU puncta co-localized with PML protein in early interphase . This implies that EdU puncta co-localize with PML protein while the viral genomes are still inaccessible immediately after completion of mitosis and remains associated to a comparable level in late interphase ( 62% ) . The differences in PML protein co-localization with inaccessible and accessible EdU puncta in early and late interphase cells were not determined to be statistically significant , thereby suggesting that EdU and PML protein associate in early interphase and remain associated throughout interphase . Taken together , these data suggest that , as PML protein translocates back into the nucleus following completion of mitosis , PML protein is subsequently recruited to incoming viral genomes when still inaccessible and they remain associated as the cell progresses through interphase and viral genomes become accessible . PML protein is SUMOylated and interacts with and recruits other proteins by non-covalent SUMO interactions , mainly through SUMO-1 , which is essential for PML NB formation , stability , and localization [2 , 4] . Therefore , we sought to examine the recruitment of SUMO-1 to incoming viral genomes . To address this , we performed immunofluorescent staining on EdU-labeled PsV-infected HaCaT cells to detect EdU-labeled viral pseudogenomes ( red ) , PML protein ( cyan ) , and SUMO-1 ( green ) and acquired high resolution images of z-stacks combined by 3D reconstruction ( Fig 4A ) . We observed very limited detection of SUMO-1 aggregates in cells undergoing mitosis , with little to no co-localization with PML protein; whereas , following the completion of mitosis , SUMO-1 was detected co-localizing with PML protein in the nucleus of interphase cells . In addition , EdU puncta co-localized with these SUMO-1/PML protein foci in the nucleus of interphase cells . SUMO-1 was also observed encompassing the EdU signal in a similar manner as PML protein alone previously was ( Fig 2 ) . Next , we investigated SUMO-1 co-localization as a function of viral genome accessibility . We combined immunofluorescent staining of SUMO-1 with the same differential staining technique described in S1 Fig and Fig 3 . HaCaT cells were infected with EdU-labeled PsVs and differentially stained to detect inaccessible ( red ) and accessible ( green/red ) EdU-labeled pseudogenomes along with SUMO-1 ( cyan ) in mitotic and interphase cells ( Fig 4B ) . EdU accessibility was quantified in mitosis , early interphase ( 7+ nucleoli ) , and late interphase ( 1–6 nucleoli ) in digitonin- and TX-100-treated cells . Just like we observed in Fig 3 , we reproduced the same pattern of EdU genome accessibility throughout the cell cycle ( Fig 4C ) . Next , we quantified the number of inaccessible ( In ) or accessible ( Ac ) EdU puncta that co-localized with SUMO-1 in mitosis , early interphase ( 7+ nucleoli ) , and late interphase ( 1–6 nucleoli ) in digitonin-treated cells ( Fig 4D ) . Not surprisingly , SUMO-1 co-localization with EdU puncta was very similar to PML protein in Fig 3D . EdU puncta did not co-localize with SUMO-1 during mitosis ( 2% and 1% of inaccessible and accessible EdU , respectively ) . However , 51% of inaccessible EdU puncta co-localize with SUMO-1 in early interphase and 61% in late interphase . Accessible EdU puncta also largely co-localize with SUMO-1 in early and late interphase ( 66% and 76% , respectively ) . Taken together , these data suggest that SUMO-1 is recruited to incoming viral genomes prior to becoming accessible , likely along with PML protein . Our recently published work suggested that a subset of the L1 protein traffics and is delivered into the nucleus along with the L2/viral genome complex [31] . Our data also indicated that L1 protein resides within the transport vesicle during trafficking and is lost when viral genomes become accessible in the nucleus . Therefore , we hypothesized that PML protein would associate with L1 together with viral genomes in early interphase . To test this , we performed immunofluorescent staining on HaCaT cells infected with EdU-labeled PsVs ( Fig 5 ) . Representative images of infected HaCaT cells in mitosis , early , and late interphase stained for PML protein ( cyan ) , EdU-labeled pseudogenomes ( red ) , and L1 protein ( green ) are displayed ( Fig 5A ) . We quantified the total number of chromosome-associated or nuclear EdU puncta co-localizing with L1 protein in following phases of the cell cycle: mitosis , early interphase ( 7+ nucleoli ) , and late interphase ( 1–6 nucleoli ) ( Fig 5B ) . During mitosis , the majority of EdU puncta co-localized with L1 ( 81% ) . The L1 signal is still present with EdU in early interphase ( 56% ) but is dramatically reduced in late interphase ( 30% ) , which is consistent with our published results [31] . Next , we quantified the number of EdU-L1 puncta co-localizing with PML protein during mitosis , early ( 7+ nucleoli ) and late ( 1–6 nucleoli ) interphase ( Fig 5C ) . Once again , PML protein did not co-localize with EdU puncta during mitosis ( 0% ) . However , EdU/L1 signal did co-localize with PML protein in the nucleus of early interphase cells ( 53% ) . In late interphase , while EdU puncta remained co-localized with PML protein , L1 signal is lost ( 17% ) . Taken together , these data suggest that L1 remains associated with the viral genome as PML protein is recruited in early interphase but is lost in later stages of interphase , which corresponds to when viral genomes become accessible . In contrast to L1 , we had showed that L2 proteins remained with the viral genome for a longer period of time , even after viral genomes become accessible [31] . Logically , we sought to investigate whether L2 remained at PML NBs along with the viral genomes as interphase progresses and performed the same analysis as for L1 protein ( Fig 6 ) . Representative images of infected HaCaT cells in mitosis , early , and late interphase stained for PML protein ( cyan ) , EdU-labeled pseudogenomes ( red ) , and L2 protein ( green ) are displayed ( Fig 6A ) . We quantified the total number of chromosome-associated or nuclear EdU puncta co-localizing with L2 protein in the different phases of the cell cycle ( Fig 6B ) . Although there was a decrease in the amount of L2 co-localized with EdU between mitosis and early interphase , similarly to what was observed with L1 , the L2 signal remained constant with about 50% L2 viral genomes after completion of mitosis and was still present in late interphase . Then , we quantified the number of EdU-L2 co-localizing with PML protein in the same conditions as previously described ( Fig 6C ) . As expected , L2 also remained with EdU co-localized with PML protein even in late interphase cells . Taken together , these data suggest that , while L1 is lost when viral genomes become accessible in later stages of interphase , L2 remains associated with the now accessible viral genomes . Another major component of PML NBs is Sp100 . While the presence of PML protein is critical for HPV transcription , Sp100 is known to restrict HPV transcription and replication [26 , 28] . Therefore , we hypothesized that Sp100 is recruited with a delay compared to the recruitment of PML protein after the viral genomes becomes accessible . To address this , we performed immunofluorescent staining on EdU-labeled PsV-infected HaCaT cells to detect EdU-labeled viral pseudogenomes ( red ) , PML protein ( cyan ) , and Sp100 ( green ) and acquired high resolution images of several z-stacks combined by 3D reconstruction ( Fig 7A ) and confocal images ( Fig 7B ) . During mitosis , PML protein formed cytosolic aggregates and Sp100 signal was not detected . In early interphase , Sp100 was detectable but was not seen co-localized with EdU puncta co-localizing with PML protein . During late interphase , both PML protein and Sp100 co-localized with EdU puncta , engulfing the signal in a similar manner as observed in Fig 2B . We quantified the number of Sp100-containing PML foci for the presence or absence of EdU puncta in the same infected cells ( Fig 7C ) . In early interphase cells , 53% of EdU puncta co-localized with PML protein foci containing Sp100 , while the rest co-localized with PML protein only . In contrast , in late interphase cells , nearly all EdU puncta co-localized with PML protein and Sp100 ( 92% ) . Interestingly , EdU-negative PML/Sp100 foci are nearly as abundant in early and late interphase cells ( 82% and 89% , respectively ) . It is important to note that EdU puncta were never seen to co-localize with Sp100 alone . Therefore , our data suggest that Sp100 is recruited with a delay to the viral genome and PML protein . More fascinating , this delay seems to be specific for PML NBs forming around incoming viral genome . Next , we examined the recruitment of Sp100 with PML NBs as a function of viral genome accessibility using differential staining to distinguish between inaccessible EdU-labeled pseudogenomes ( red ) , accessible pseudogenomes ( red/green ) , and Sp100 ( cyan ) within cells undergoing mitosis or during early or late interphase ( Fig 8A ) . EdU puncta showed a very reproducible pattern of accessibility as previously observed ( Fig 8B ) . We quantified the number of inaccessible ( In ) or accessible ( Ac ) EdU puncta that co-localized with Sp100 in mitosis , early interphase ( 7+ nucleoli ) , and late interphase ( 1–6 nucleoli ) in digitonin-treated cells ( Fig 8C ) . Sp100 signal was not detected in mitotic cells . Only a marginal number of inaccessible EdU co-localized with Sp100 during early interphase ( 26% ) , which significantly increases in late interphase ( 56% ) . Sp100 co-localized with accessible EdU in early interphase and more in late interphase ( 49% and 72% , respectively ) . Taken together , these results suggest that Sp100 is recruited to incoming viral genomes and PML protein after viral genomes become accessible . Other DNA viruses have been shown to target PML protein via SUMO interaction with viral proteins , such as HSV-1 ICP0 and ADV E1A [15 , 16] . Considering that HPV L2 protein is interacting with cellular factors during trafficking [32 , 39] , we hypothesized that L2 is also interacting with and recruiting PML protein and possibly Sp100 . A SUMO conjugation motif ( K35 ) , one highly conserved SIM ( aa286-289 ) , and two putative SIMs ( aa105-109 and aa145-148 ) have been identified on L2 protein [24 , 41] . Therefore , we sought to investigate whether at least one or more of these sites played a role in recruiting PML NB proteins to viral genomes . To test this , we generated EdU-labeled PsVs carrying mutations in L2 protein ( S1 Table ) . We performed site-directed mutagenesis on our L2 expression plasmid to disrupt the SUMO conjugation motif with a residue substitution K35R [41] . The L2 mutants disrupting each SIM ( SIM 105-9A , SIM 145-8A , SIM 286-9A ) have recently been described [24] . All mutant L2 proteins were efficiently incorporated into PsVs and the mutations did not affect PsV binding to the cell surface ( S2 Fig ) . Next , we infected HaCaT cells with WT or L2 mutant EdU-labeled PsVs and performed immunofluorescent staining to detect PML protein ( cyan ) and viral pseudogenomes ( red ) ( Fig 9A ) . Although cells were infected with same amounts of viral genome equivalents ( vge ) and comparable numbers of EdU puncta were detected in whole cells ( Fig 9B ) , the number of EdU puncta present in the nucleus was dramatically decreased in cells infected with L2 mutant PsVs compared to WT ( Fig 9C ) . Consequently , we observed a significant reduction in the number of L2 mutant EdU puncta co-localized with PML protein compared to WT . However , when we normalize the number of EdU co-localized with PML protein to the total number of EdU puncta in the nucleus of infected cells , there is no significant difference between WT and L2 mutants , apart from SIM 286-9A as almost no EdU puncta were observed in the nucleus ( Fig 9D ) . These data imply that L2 mutant PsVs are not delivered into the nucleus as efficiently as WT PsVs . To test this , we examined the association of viral genomes with mitotic microtubules and chromosomes in HaCaT cells infected with WT and L2 mutant EdU-labeled PsVs by immunofluorescent staining to detect EdU ( red ) and microtubules ( white ) ( Fig 9E ) . We observed a significant loss of EdU puncta associated with condensed chromosomes during mitosis in cells infected with L2 mutant PsVs compared to WT PsV-infected cells ( Fig 9F ) . Taken together , these findings suggest that the mutations on L2 protein render the PsVs deficient for nuclear delivery , in a similar phenotype to a previously identified mutant R302/5A [30] .
PML protein has been shown to be critical for the retention of HPV genomes in the nucleus of host cells and transcription [25 , 27] . However , the temporal recruitment of PML NB proteins and how they associate with incoming viral genomes is still poorly defined . The findings described herein suggest that PML protein and SUMO-1 are recruited to and assemble around incoming viral genomes after nuclear delivery and completion of mitosis but prior to the genome becomes accessible in the nucleus . Furthermore , L1 protein accompanies the viral genome into the nucleus followed by PML protein recruitment during early interphase , but L1 protein becomes lost as the cell progresses through interphase , while L2 protein remains with the viral genome . The transcriptional repressor Sp100 showed a delayed recruitment to viral genomes after the viral genome becomes accessible . Lastly , we determined that disruptions in the SIMs of L2 protein result in varying degrees of deficient nuclear delivery of incoming viral genomes . The recruitment of PML protein towards incoming viral genomes rather than viral genomes targeting preformed PML NBs is a critical aspect for understanding how HPV genomes are delivered to the nucleus . During mitosis , HPV-harboring vesicles do not associate with MAPPs , or only what seems to be incidental and only transient . MAPPs are still predominantly cytosolic when viral genomes are delivered to the nucleus . This was specifically brought to light using high resolution microscopy as only rotating the images in the three-dimensional plane could reveal that EdU puncta do not truly co-localize with PML protein aggregates in mitotic cells , whereas confocal microscopy could not always distinguish large PML protein aggregates from EdU puncta [44] . At the moment , we cannot completely rule out that PML protein below our limit of detection is co-localizing with viral genomes . However , it seems unlikely as PML protein-deficient HaCaT and HeLa cells are fully capable of delivering viral genomes to the nucleus [25 , 27] . We observed that PML protein then translocates into the nucleus in early interphase and targets viral genomes to form PML NBs . Therefore , unlike what was recently suggested by Broniarczyk et al . , our findings suggest that PML protein is not involved in nuclear delivery of HPV genomes , but rather starts playing a role in the nucleus , after viral genomes have already been delivered , which confirms other previous findings [25 , 27 , 44] . This is further supported by the recent findings by the Boutell group who observed the recruitment of PML protein and other PML NB-residing proteins to HSV-1 incoming genomes after nuclear entry but prior to the initiation of lytic replication [19] . High resolution microscopy also showed the structure of PML protein entrapping HSV-1 genomes similarly to our observation with PML protein engulfing HPV pseudogenomes . The temporal recruitment and structure of PML protein surrounding viral genomes offer support for our hypothesis that PML protein provides a protective environment for the viral genome against innate and intrinsic immune sensors , rather than an environment favoring transcription as previously suggested [25] . Indeed , our previous findings suggest that incoming PV genomes can be sensed in PML protein-deficient cells and subsequently targeted for degradation [27] . IFI16 ( IFN Gamma Inducible Protein 16 ) has been a major candidate for viral DNA sensing in host cell nucleus and restricts HSV-1 and HPV18 replication and transcription [45] . However , more recently , the repression of HSV-1 replication was shown to occur rapidly after association with PML NBs and independently of IFI16 and induction of ISG ( IFN-stimulated gene ) expression [19] . However , in the context of HPV infection , knocking down IFI16 did not prevent genome loss in HaCaT cells [27] . Another possible candidate is the Myb-related transcription factor MYPOP that has recently been shown to sense incoming HPV DNA and L2 protein and subsequently inhibit early gene expression [46] . Here , PML protein may compete for binding of such restriction factors to L2 protein to protect the infectious HPV complex and prevent transcriptional repression . Nevertheless , PML protein seems to protect the viral genome from such a fate . Our previous work demonstrated that the viral genomes are delivered to the nucleus of target cells in a membrane-bound vesicular compartment [30 , 32] . We have previously shown that the egress from the transport vesicle by an unknown mechanism is slow , resulting in a delay of transcription by four to five hours when compared to a transfection method that requires mitosis for nuclear delivery [30] . The delay strongly suggests that transcriptional activity requires an additional step , which is presumably the egress from the transport vesicles . However , we are aware that a minority ( 5% ) of viral genomes present on mitotic chromosomes is accessible to staining in differentially permeabilized cells and could be the infectious ones . However , we believe that this level of background is most likely due to the extensive processing required for two sequential immunofluorescent stainings . Indeed , in the absence of immunofluorescent processing , viral genome is completely protected from degradation by nucleases when the cells were arrested in mitosis [30] . Furthermore , the work done by the Schelhaas , Campos , DiMaio , and Tsai groups , using a BirA-based system , supports the importance of the transport vesicle for productive infection [47–50] . Therefore , upon nuclear delivery , the viral genome is already presumably protected from DNA sensors within the transport vesicle . The formation of the PML protein structure around the genome-harboring vesicle allows for egress of the viral genome , while remaining protected . We speculate that this step allows for the initiation of transcription responsible for the primary amplification of viral genomes resulting in the establishment of infection [51] . Interestingly , we observed a delayed recruitment of Sp100 to viral genomes when compared to PML protein . This delayed recruitment of Sp100 seems to be specific for HPV-harboring PML NBs and occurs mostly after viral genomes become accessible , although , due to the unknown specificity of our Sp100 antibody , we cannot rule out that low but undetectable levels of Sp100 are present early . It has been demonstrated that Sp100 restricts HPV18 early transcription during establishment of infection [26] . It is attractive to speculate that HPV exploits PML NBs to regulate early transcription , PML protein allowing early transcription to establish infection and delayed Sp100 recruitment helping transition to the maintenance phase . The McBride group also demonstrated that Sp100 does not seem to be involved in the maintenance phase [28] . Additionally , it restricts viral processes in later stages of infection , during the differentiation-induced viral amplification [28] . Our data strongly suggest that L2 protein mediates the recruitment of PML protein to viral genome prior to complete release within the nucleus . It has become clear in recent studies that L2 protein is ultimately lost after orchestrating viral delivery [31] . Therefore , we assume that PML NB association is lost after the next round of mitosis , which would be consistent with the findings by Stepp et al . [26 , 28] . However , further experimentation is needed to test this assumption and to link PML NB composition and transcriptional activity of incoming viral genome . Our recent work also focused on the role of the capsid proteins during trafficking and nuclear delivery , more specifically the L1 protein . We have shown that L1 protein remains associated with the viral genome in the nucleus of infected cells [31] . We demonstrated that L1 protein directly interacts with the viral DNA and a transmembranous L2 protein while inside a transport vesicle during trafficking and after nuclear delivery . In addition , reactivity of conformationally-dependent antibodies provided evidence that L1 protein was likely arranged as capsomeres while it accompanies the viral genome to the nucleus . Herein , we observed that L1 protein remains associated with the viral genome within the nucleus and that PML protein is recruited and assembles around it . In late interphase , L1 protein dissociates , timed with release of the viral genome , while the viral genome remains at PML NBs . At this time , there is no evidence to suggest that L1 protein plays a role beyond just incidental trafficking . Therefore , we only refer to the loss of L1 protein as a marker for the point in time that coincides with release of the viral genome in the nucleus . Considering that L1 protein resides within the lumen of the transport vesicle with the viral genome during trafficking into the nucleus , our lab and others have hypothesized that L2 protein is involved in recruiting PML NB proteins . Indeed , L2 protein is already known to contain many domains involved in interacting with cellular factors to facilitate trafficking [32 , 38 , 39] . These domains also include SUMO conjugation and interacting motifs [24 , 41] . SUMOylation is such a critical step in the formation of PML NBs and the recruitment of additional proteins , therefore it is thought that L2 protein may be responsible for recruiting PML protein and Sp100 via SUMO interactions . The Florin group identified three SIMs on L2 protein , at residues 105–9 , 145–8 , and 286–9 . The latter was found to interact with cellular SUMO-1/2 and to be essential for interaction with PML protein as PsVs carrying a disrupted SIM resulted in a decrease in PML co-localization [24] . However , they also noted that infection with the mutant PsVs exhibited reduced amounts of L2 protein and viral genomes in the nucleus , suggesting the mutation may affect events upstream of PML accumulation , such as nuclear localization and retention . The Schelhaas group also investigated the nuclear delivery of the SIM 286–9 and observed that the mutant PsVs were impaired in interacting with mitotic chromosomes , although the SIM 286–9 mutation had been shown to result in clear nuclear accumulation and loss of PML co-localization after L2 overexpression [24 , 47] . Herein , we show that all of the mutants used in the study were deficient in nuclear delivery . These mutants had a similar pattern to the nuclear retention mutant , 302/5A , that failed to traffic along spindle microtubules , which also resulted in lower infectivity [30] . L2 protein is very compact and harbors many important domains on its C-terminus [23 , 29 , 30 , 32 , 38 , 39 , 47 , 52] . Therefore , it is a possibility that by mutating the SIMs , we may have disrupted other domains on L2 protein that are essential for cellular trafficking or nuclear delivery . We also cannot exclude defects in proper assembly and early events of the infectious entry process . Therefore , at this time , we are unable to directly test whether the SIMs are responsible for PML recruitment , although these regions are still of particular interest as they all seem to be important for the delivery of the viral genome to the nucleus . In summary , we present herein a promising model defining the order of events following nuclear delivery of HPV genomes . We demonstrated that PML protein and SUMO-1 are recruited to and assemble around viral genomes that still reside within the transport vesicle in early interphase cells . As L1 protein accompanies the viral genome to the nucleus , it also localizes at PML protein , but is lost in later stages of interphase along with the transport vesicle and release of the viral genome into the nucleus . Then , Sp100 is recruited to viral genomes and also engulfs them in a PML NB structure . Further studies will be necessary to link HPV transcriptional regulation to PML NB composition . However , our study defines these events , thus providing new insights into the kinetics of the primary HPV infection and how HPV relies on the specific temporal recruitment of various factors necessary to promote infection .
The 293TT cells ( a kind gift of Dr . John T . Schiller , Laboratory of Cellular Oncology , National Cancer Institute , Bethesda ) used for the generation of pseudovirions were cultured in Dulbecco’s Modified Eagle Medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) , non-essential amino acids , L-Glutamax , and antibiotics . HaCaT cells ( purchased from the American Type Culture Collection ) used in the infection studies were grown in low glucose DMEM containing 5% FBS and antibiotics . HPV16 pseudoviruses ( PsVs ) encapsidating a green fluorescent protein ( GFP ) expression plasmid ( pfwB ) were generated in 293TT cells as previously described using puf16L1 and puf16L2HA-3’ [53–56] . The pfwB plasmid was a kind gift from Dr . John Schiller ( National Cancer Institute , Bethesda , MD ) . The L1 and L2 expression plasmids harbor codon-optimized genes and were kindly provided by Dr . Martin Müller ( Deutsches Krebsfoschungszentrum , Heidelberg , Germany ) [57] . For detection of pseudogenomes by IF staining , pseudogenomes were labeled by supplementing the growth media with 100 μM 5-ethylnyl-2’-deoxyuridine ( EdU ) at 6 hours post transfection during PsV generation , as previously described [58] . Viral DNA within the virions was isolated using NucleoSpin Blood QuickPure ( Machery-Nagel; #740569 . 250 ) supplemented with 4 μM Ethylenediamineteraacetic acid ( EDTA ) and Dithiothreitol ( DTT ) and genome copy number was quantified by quantitative PCR ( qPCR ) . Capsid composition was verified by western blot analysis . For all experiments , 100 to 300 vge/cell were added . Point mutation K35R in L2 [41] was generated by site-directed mutagenesis using a pair of complementary PCR primers specific to codon-optimized plasmid puf16L2HA-3’ using the PfuUltra II Hotstart DNA Polymerase 2x Master Mix ( Stratagene; #600630 ) following manufacturer’s protocol . The entire plasmid was amplified during the PCR reaction and the PCR products were digested with DpnI to remove methylated template DNA . The PCR products were transformed and the mutation was confirmed by sequencing ( Macrogen ) . L2 expression plasmids harboring SIM mutations ( pCDNA16L2-ΔSIM105-109 , pCDNA16L2-ΔSIM145-148 , pShell16L1L2-ΔSIM286-289 ) were a kind gift from Dr . Luise Florin ( University of Mainz , Germany ) [24] . Mutant L2 expression plasmids were used to generate mutant PsVs as described above . Capsid composition and vge were determined by western blot and qPCR as described above . Same amounts of vge were used in experiments comparing WT and mutant PsVs . Antibodies used for the IF studies were as follows: rabbit polyclonal antibody ( pAb ) anti-PML ( BETHYL; #A301-167A ) , mouse monoclonal antibody ( mAb ) anti-PML ( Santa Cruz Biotechnology; #sc-966 ) , rabbit pAb anti-TGN46 ( Thermo Scientific; #PA5-23068 ) , rabbit pAb anti-SUMO-1 ( abcam; #ab11672 ) , AlexaFluor ( AF ) 488-conjugated rabbit pAb anti-GFP ( Molecular Probes; #A21311 ) , mouse mAb anti-CD147 ( Affinity BioReagents; #MA1-19202 ) , mouse mAb anti-alpha-Tubulin ( Cell Signaling; #3873S ) , AF647-conjugated phalloidin ( Molecular Probes; #A22287 ) , and goat AF-labeled secondary antibodies ( Life Technologies; #A11034 , #A21236 ) . Rabbit polyclonal anti-Sp100 antibody was a kind gift from Dr . Hans Will ( Heinrich Pette Institute , Hamburg , Germany ) [59] . L1-specific mouse mAb 33L1-7 and rabbit pAb K75 were described previously [60 , 61] . L2-specific mouse mAb 33L2-1 was also previously described [62] . Click-iT EdU Imaging Kit ( Molecular Probes; #C10338 ) was used for Click-iT reactions to label EdU-labeled pseudogenomes . For western blot analysis , L1 was detected with mouse mAb HPV16 312F and L2 with mouse mAb 33L2-1 , combined with peroxidase-conjugated AffiniPure pAb goat secondary anti-mouse ( Jackson ImmunoResearch; #115-035-003 ) . HaCaT cells were grown on coverslips at approximately 50% confluency and infected with EdU-labeled PsVs at approximately 106 viral genome equivalents per coverslips for 24 h at 37°C . Cells were fixed with 4% paraformaldehyde ( PFA ) for 15 min at room temperature , washed with phosphate-buffered saline ( PBS; pH 7 . 5 ) , permeabilized with 0 . 5% TX-100 in PBS for 5 min at room temperature , washed with PBS , and blocked with 5% normal goat serum ( NGS ) for 15 min at room temperature . The Click-iT reaction containing AF555 followed for 30 min at room temperature and protected from light to specifically detect EdU-labeled pseudogenomes [58] . After cells were washed with PBS , they were incubated with primary antibodies in 2 . 5% NGS for 30 min at 37°C in a humidified chamber . After extensive washing with PBS , the cells were incubated with AF-labeled secondary antibodies in 2 . 5% NGS for 30 min at 37°C in a humidified chamber . After another round of extensive washing with PBS , the cells were mounted in ProLong Gold antifade reagent with DAPI ( 4’ , 6’-diamidino-2-phenyllindole; Invitrogen; #P36931 ) . Confocal images were acquired in single-slices or z-stacks with Nikon A1R confocal microscope using a 100X objective and NIS Elements software . Number of EdU-labeled pseudogenomes in nuclei was quantified in z-stacks spanning the whole nucleus . Results are expressed in average number of EdU or percent of EdU-labeled viral pseudogenome in the nucleus ± standard error of the mean ( SEM ) . High resolution images were acquired with Nikon N-SIM E Super Resolution microscope using 100X objective . Several z-stacks spanning the whole nucleus were acquired and assembled using 3D reconstruction in NIS Elements software . All images from individual experiments were acquired under the same laser power settings and enhanced uniformly in Adobe Photoshop . HaCaT cells were grown on coverslips to 50% confluency and infected with EdU-labeled PsVs for 24 h at 37°C . Cells were fixed with 4% PFA for 15 min at room temperature , washed with PBS , and selectively permeabilized with 0 . 625 μ/mL of digitonin in water for 5 min at room temperature , washed with PBS , and blocked with 5% NGS for 15 min at room temperature . Cells were treated with the first Click-iT reaction with AF555 for 30 min at room temperature protected from light . Cells were washed with PBS , permeabilized with 0 . 5% TX-100 for 5 min at room temperature , and blocked with 5% NGS for 15 min at room temperature . Cells were treated with the second Click-iT reaction with AF647 for 30 min at room temperature protected from light . Cells were washed with PBS and incubated with primary and secondary antibodies to detect protein of interest as described above . Cells were extensively washed and mounted with DAPI . Differential staining of EdU-labeled pseudogenome using sequential Click-iT reactions in selectively permeabilized HaCaT cells was previously described in greater details [30] . A control experiment was performed by treating the cells with 0 . 5% TX-100 in both permeabilization steps . In a parallel experiment , cells were incubated with primary Ab anti-TGN46 in 2 . 5% NGS for 30 min at 37°C in a humidified chamber following the first Click-iT reaction . Cells were washed extensively and incubated with secondary AF488-labeled Ab in 2 . 5% NGS for 30 min at 37°C . This parallel experiment acts as a permeabilization control , as previously described [32] . All images were acquired in z-stacks spanning the whole nucleus with Nikon A1R confocal microscope using a 100X objective and NIS Elements software . All images from individual experiments were acquired under the same laser power settings and enhanced uniformly in Adobe Photoshop . Co-localization of EdU puncta with protein of interest was quantified by counting the number of nuclear EdU puncta as a function of single or double EdU staining and co-localization with protein of interest and expressed as percent co-localization of total nuclear accessible or inaccessible EdU puncta ± SEM . HaCaT cells were grown on coverslips to 70% confluency and equal number of PsVs were allowed to bind to the cell surface for 1 h at 37°C . Cells were stained as described above without the Click-iT reaction . Instead , conformational L1 protein was detected with K75 Ab . Assay was quantified as pixel sum ratio of L1-specific signal on the cell surface to region of interest ( ROI ) area and expressed as percent of WT ( 100% ) ± SEM . All images were acquired in single slice through the cell with Nikon A1R confocal microscope using a 100X objective and NIS Elements software . All images from individual experiments were acquired under the same laser power settings and enhanced uniformly in Adobe Photoshop .
|
Promyelocytic leukemia ( PML ) nuclear bodies ( NBs ) are often targeted and reorganized by DNA viruses to counteract their antiviral activity . Human papillomavirus ( HPV ) also associates with PML NBs after infectious entry . While PML protein is required for nuclear retention and efficient transcription of incoming HPV genomes , Sp100 , another PML NB component , was identified as a restriction factor . HPV virions are delivered to the nucleus during mitosis while continuously residing in membrane-bound transport vesicles . L2 protein directs trafficking via its carboxyl terminus by becoming transmembranous in the endocytic compartment . Herein , we demonstrate that PML protein associates with viral particles still residing in transport vesicles after nuclear delivery , possibly to provide a continuous protective environment after disruption of the membrane bilayer of the transport vesicle . In contrast , Sp100 recruitment is delayed specifically for PML NBs forming around HPV particles , suggesting that HPV transiently modulates PML NB composition . In contrast to large DNA viruses , which encode factors capable of reorganizing PML NBs , HPV seems to take advantage of the disassembly occurring at the onset of mitosis . As such , it utilizes well-established cellular pathways to orchestrate the regulation of viral transcription during the immediate early events of the viral life cycle .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"urology",
"medicine",
"and",
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"vesicles",
"immune",
"physiology",
"cell",
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"cell",
"division",
"cell",
"processes",
"immunology",
"microbiology",
"mitosis",
"viral",
"genome",
"sexually",
"transmitted",
"diseases",
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"nucleus",
"nuclear",
"bodies",
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"organelles",
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"research",
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"preparation",
"and",
"treatment",
"viral",
"genomics",
"staining",
"infectious",
"diseases",
"immune",
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"proteins",
"human",
"papillomavirus",
"infection",
"chromosome",
"biology",
"proteins",
"biochemistry",
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] |
2019
|
PML nuclear body-residing proteins sequentially associate with HPV genome after infectious nuclear delivery
|
The human specific poxvirus molluscum contagiosum virus ( MCV ) produces skin lesions that can persist with minimal inflammation , suggesting that the virus has developed robust immune evasion strategies . However , investigations into the underlying mechanisms of MCV pathogenesis have been hindered by the lack of a model system to propagate the virus . Herein we demonstrate that MCV-encoded MC80 can disrupt MHC-I antigen presentation in human and mouse cells . MC80 shares moderate sequence-similarity with MHC-I and we find that it associates with components of the peptide-loading complex . Expression of MC80 results in ER-retention of host MHC-I and thereby reduced cell surface presentation . MC80 accomplishes this by engaging tapasin via its luminal domain , targeting it for ubiquitination and ER-associated degradation in a process dependent on the MC80 transmembrane region and cytoplasmic tail . Tapasin degradation is accompanied by a loss of TAP , which limits MHC-I access to cytosolic peptides . Our findings reveal a unique mechanism by which MCV undermines adaptive immune surveillance .
Molluscum contagiosum virus ( MCV ) is a phylogenetically distinct poxvirus with a significant global disease burden [1 , 2] . MCV infections are thought to be restricted to humans , producing cutaneous lesions which often lack signs of an inflammatory response and persist for months to years in otherwise healthy individuals . This is in stark contrast to well characterized orthopoxviruses and parapoxviruses , which generally have a broader tropism and present as acute inflamed infections [1 , 3] . The persistence of MCV appears to be coupled to its ability to remain undetected by the immune system , as inflammatory responses have been implicated in spontaneous regression of MCV lesions [4 , 5] . Consistently , immunodeficient individuals are prone to exacerbated MCV infections [6 , 7] . The clinical features of MCV infections highlight a unique interplay between the virus and the human immune system . MCV has clearly become well adapted to its niche , encoding a repertoire of proteins which are capable of evading the immune responses of the human epidermis . Yet , of the 59 open reading frames ( ORFs ) which distinguish MCV from orthopoxviruses [8 , 9] , only nine have been well characterized [10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18] . Studies regarding the pathogenesis and immune evasion mechanisms employed by MCV have been severely limited by the lack of an animal model or cell line to propagate the virus [19] . Nevertheless , the ORFs unique to MCV likely play a major role in ensuring human-specific epidermal persistence , and should thus be more thoroughly characterized . Among the MCV ORFs without a known function , MC80R shares moderate sequence similarity with the α1–3 domains of classical MHC-I . Given the central role of MHC-I and MHC-I-like proteins in inhibiting natural killer cells ( NKs ) , it was long suspected that MC80 may be involved in NK subversion [9 , 20] . However , unlike classical MHC-I which is presented on the cell surface to NK and T cells , MC80 appeared to be retained in the endoplasmic reticulum ( ER ) [20] . As MC80 did not come to the cell surface , its host target and function have remained elusive . Several large DNA viruses , particularly herpesviruses , have been found to repurpose the MHC-I fold in order to evade cell-mediated immune defenses . The specific function of each of these viral MHC-I-like proteins is highly related to its cellular localization . Cell surface viral MHC-I-like proteins ( e . g . murine cytomegalovirus ( MCMV ) m157 , human cytomegalovirus ( HCMV ) UL18 ) generally function as ligands for NK-inhibitory receptors without concurrently presenting viral peptides to cytotoxic T lymphocytes ( CTL ) [21] . However , intracellular MHC-I-like proteins ( e . g . MCMV m145/m152/m155 ) are not directly exposed to NKs or CTLs . Instead , these proteins tend to localize to the ER/Golgi/lysosomal compartments where they retain or lead to the degradation of NK-activating ligands and , in some cases , classical MHC-I . Additionally , secreted viral MHC-I-like proteins have been identified which act as competitive antagonists of TNFα signaling and NKG2D-mediated NK activation ( tanapox 2L and cowpox OMCP , respectively ) [22 , 23 , 24 , 25] . Thus , through prolonged co-evolution , large DNA viruses have used the MHC-I fold for a diverse array of immune evasion functions . As opposed to viral MHC-I-like proteins , vertebrates utilize classical MHC-I to display a repertoire of peptides on the cell surface to surveilling CTLs [26 , 27] . This pivotal role demands rapid yet stringent quality control in the assembly of MHC-I/peptide complexes . To accomplish this , host cells utilize a multi-subunit peptide loading complex ( PLC ) ; comprised of tapasin ( Tpn ) , transporter associated with antigen processing ( TAP ) , ERp57 , and calreticulin ( CRT ) [28 , 29] . In the ER , nascent MHC-I heavy chains ( HC ) are initially stabilized by a chaperone , calnexin ( CNX ) , and subsequently by heterodimerization with β2m [30] . However , HC/β2m must assemble with a high-affinity peptide , usually via the PLC , in order to efficiently traffic from the ER to the cell surface [31] . HC/β2m accomplishes this by directly binding Tpn/ERp57 , which plays a central role in both transiently stabilizing the unloaded conformation of MHC-I and bridging the interaction between MHC-I and TAP [32 , 33] . TAP functions by transporting short cytosolic peptides into the ER , allowing PLC-associated MHC-I to sample the repertoire of proteasome-degraded proteins within the cell [34 , 35] . Once an MHC-I molecule has been loaded with a high-affinity peptide , it dissociates from the PLC and traffics to the cell surface . Given the critical role played by the PLC in peptide loading , both MHC-I itself and components of the PLC provide attractive targets for viruses seeking to subvert CTL responses [28] . However , the magnitude of MHC-I downregulation depends on the specific target and the MHC-I alleles expressed by the host cell . Understanding these viral mechanisms provides insight into the pathogenesis of viral infections , as well as the underlying cellular pathways that these viruses exploit . Here we demonstrate that expression of MC80 results in ER-retention and consequent surface downregulation of classical MHC-I in human and mouse cells . Mechanistically , we found that MC80 interacts with Tpn via its luminal domain and targets Tpn for ER-associated degradation in a transmembrane ( TM ) - and cytoplasmic tail-dependent manner . The loss of Tpn coincides with a loss of TAP , further impeding the assembly of MHC-I with high-affinity peptides . Our findings reveal a strategy employed by MCV to disrupt antigen presentation and thereby CTL responses by exploiting MHC-I fold recognition by the PLC .
While MCV does not encode an ORF with sequence-similarity to any viral protein known to downregulate MHC-I , previous studies have suggested that MCV may be downregulating MHC-I and β2m in human lesions [5 , 36] . Given that some poxviruses do not appear to subvert MHC-I antigen presentation [1 , 37] , we hypothesized that the MCV ORF ( s ) responsible for MHC-I downregulation may be unique to MCV . Additionally , as MHC-I traffics through the ER/Golgi to the plasma membrane , we limited our initial screen to the four MCV-specific ORFs which are predicted to encode type-1 transmembrane proteins ( MC3 , MC33 , MC80 , and MC157 ) . We cloned the respective MCV-1 variants into an IRES-GFP retroviral vector ( pMXsIG ) , replacing each predicted signal peptide with the mouse β2m signal peptide and an N-terminal Flag tag . Following transient transfection of human embryonic kidney ( HEK-293T ) cells with these constructs or vector control , we found that MC80 dramatically decreased the level of cell surface MHC-I by 2–4 days post-transfection ( Fig 1A ) . MC80 is well conserved among known MCV strains , sharing 24–36% amino acid identity to the ectodomains of human classical and non-classical MHC-I . The MC80 ORF has at least two potential start codons N-terminal to the MHC-I like α1 domain , termed MC80L and MC80S , which both provide unusually long signal peptides ( S1A Fig ) . While the ectodomains of MC80 share moderate sequence-similarity with MHC-I , functionally distinct regions exhibit varying levels of conservation ( S1A Fig ) . Briefly , residues involved in peptide binding and PLC interactions are not well conserved between MC80 and classical MHC-I ( S1A Fig ) [38 , 39 , 40] . In contrast , residues known to be involved in β2m binding are well-conserved , consistent with a previous study which found that MC80 associates with β2m [20 , 41] . This same study found that MC80 did not traffic to the cell surface , which we were able to recapitulate by flow cytometry and EndoH sensitivity assays , indicating that MC80 is retained in the ER ( S1B and S1C Fig ) . The cell surface half-life of MHC-I can be greater than 24 hours depending on the specific cell line and peptide ( s ) displayed [42 , 43] . We investigated the kinetics of MHC-I surface downregulation using a transient transfection system , finding that maximal expression of MC80 at 24 hours post transfection ( hpt ) did not coincide with maximal downregulation of MHC-I surface expression ( Fig 1B ) . Instead , we found a significantly lower level of surface MHC-I at 72hpt than 24hpt , even though there was less MC80 at the later time point . Additionally , our data demonstrated that continued expression of MC80 was necessary to maintain MHC-I downregulation ( Fig 1B ) . As extended HLA class I half-lives may have played a role in the apparent time-dependence of MC80-mediated MHC-I downregulation ( Fig 1B ) , we employed a stable retroviral transduction system to achieve a steady state of MHC-I downregulation in further analyses . This time-dependence may also provide insight into why the previous MC80 study reported no change in surface expression of HLA-A2 12 hours post-infection of an MC80-expressing vaccinia virus [20] . Since viral MHC-I evasion mechanisms can downregulate MHC-I with varying levels of promiscuity [44 , 45] , we next sought to determine the specificity of MC80-mediated MHC-I downregulation . Flow cytometry analysis demonstrated that expression of MC80 markedly decreased the surface levels of classical MHC-I in multiple human cell lines , including HEK 293T , Hela ( human cervical cancer cell line ) , and HFF-1 ( a human foreskin fibroblast cell line ) ( Fig 2 , S2 Fig ) . A comparable effect was observed with untagged MC80 constructs with the canonical signal peptide , indicating that the Flag tag did not significantly affect MC80 function ( Fig 2A ) . Interestingly , MC80 also downregulated all tested alleles of classical MHC-I in murine cell lines ( Fig 2B , 2C , 2D and 2E ) . Additionally , of the non-classical MHC-I proteins examined , MC80 significantly decreased the surface expression of Qa-1 but did not significantly affect surface expression of CD1d or the NKG2D ligands MICA and Rae1a ( Fig 2C , 2D and 2E ) . Like classical MHC-I and Qa-1 , CD1d requires β2m for stable expression , indicating that MC80 is unlikely to function by competing for β2m . Instead , MC80 appears to be specifically downregulating peptide-binding MHC-I through a cellular component/pathway that is conserved between humans and mice . As viruses are well known to have strategies to downregulate classical MHC-I by altering the cellular trafficking of MHC-I [28] , we next examined the maturation state of Ld by EndoH sensitivity in the presence or absence of MC80 ( Fig 3A ) . We found that MC80 did not appear to affect the steady state expression level of Ld . However , compared to the 35% of EndoH-resistant Ld in the control , we found that Ld was completely EndoH-sensitive in MC80-expressing cells ( Fig 3A , S3A Fig ) . This indicates that MC80 interferes with MHC-I trafficking to the Golgi , which consequently decreases the extent of surface MHC-I . Virally-encoded MHC-I saboteurs are further dichotomized into PLC-dependent and PLC-independent mechanisms; as exemplified by the cowpox virus CPXV012 and CPXV203 proteins , respectively [46 , 47 , 48 , 49] . To determine which strategy is utilized by MC80 , we next tested whether MC80 downregulates MHC-I in murine embryonic fibroblasts ( MEF ) expressing SIINFEKL , a Kb-specific peptide of egg ovalbumin , either in the cytosol or in the ER . Cytosolic SIINFEKL requires TAP to be transported into the ER for MHC-I loading , whereas ER-SIINFEKL is able to load onto MHC-I independent of TAP function . Because CPXV203 does not require the PLC in order to downregulate MHC-I surface expression , it dramatically affects Kb/SIINFEKL expression in both cell lines ( S3B Fig ) . However , CPXV012 only induces significant downregulation of Kb/SIINFEKL in cells expressing cytosolic SIINFEKL , as its mechanism of action is dependent on TAP [49] . Similar to CPXV012 , MC80-mediated downregulation of Kb/SIINFEKL could be rescued by expression of SIINFEKL in the ER ( Fig 3B , S3B Fig ) . SIINFEKL localization had only a marginal effect on the surface expression of Db in the presence of MC80 , indicating that this effect was specific to Kb/SIINFEKL ( Fig 3B ) . While TAP- and Tpn-deficient cells display low levels of MHC-I on the cell surface , these levels can be further decreased by PLC-independent viral mechanisms , such as CPXV203 . However , we found that MC80 functionally relies on the presence of TAP and Tpn , as murine MHC-I alleles were not further downregulated by MC80 in TAP- or Tpn-deficient cells , relative to vector control ( Fig 3C ) . Together , ( 1 ) the lack of MHC-I-maturation , ( 2 ) the specific rescue of MHC-I by an ER targeted peptide , and ( 3 ) the TAP-/Tpn-dependence collectively suggest that MC80 sabotages the PLC-assisted peptide transport/loading of MHC-I in the ER . Given the central role of Tpn in peptide loading and the partial conservation of Tpn-binding residues within MC80 ( S1A Fig ) , we hypothesized that MC80 may subvert peptide loading by competitively binding Tpn to block the interaction between MHC-I and the PLC . A Flag-IP of MC80 followed by western blotting ( WB ) for PLC components supported this hypothesis; demonstrating that Tpn , TAP , CRT and CNX co-immunoprecipitate ( co-IP ) with the full length MC80 constructs ( Fig 4B ) . While the N-terminal Flag-tag did not appear to impact MHC-I downregulation in Fig 2A , we observed that an anti-Flag WB of F-MC80S ( N-terminally Flag-tagged MC80S ) produced a laddering effect in non-reduced samples . Therefore , we used C-terminal Flag-tagged constructs , encoding the canonical signal peptide of MC80 , for all further co-IP experiments . Notably , a truncated MC80S protein which lacks the putative TM and cytoplasmic tail ( sol MC80S-F ) could also co-IP Tpn , CRT and CNX but not TAP1 in murine cells ( Fig 4B ) . This suggests that MC80 primarily associates with the PLC via the luminal domain and that the interaction between MC80 and TAP1 may be further stabilized by the TM and cytoplasmic tail . Using TAP1-deficient MEFs treated with interferon gamma ( IFNɣ ) , we were able to recapitulate the association of the MC80 luminal domain with Tpn , CRT and CNX . However , in the absence of Tpn , we could not detectably co-IP TAP1 or CRT with MC80 , even when the Tpn-/- MEFs were treated with IFNɣ . These data suggest that MC80 interacts with CNX and Tpn via the luminal domain; and the latter association may bridge the interaction of MC80 with CRT and TAP1 , reminiscent of classical MHC-I assembly in the ER . A co-IP of PLC components with MC80 in 293T cells demonstrated that soluble MC80S-F interacts with both Tpn and TAP1 , further suggesting that the TM and tail of MC80 may not be necessary for the association of MC80 and TAP ( S4A Fig ) . Despite the observed PLC-associations , flow cytometry demonstrated that soluble MC80 constructs do not markedly downregulate surface MHC-I in MEFs or HFF-1s ( Fig 4C , S2B Fig ) . As binding to the PLC appears to be insufficient for MC80 function , Tpn-competition/blockade is unlikely to be the mechanism of MHC-I downregulation , as we had hypothesized . To determine the role of the transmembrane and tail in MC80-mediated MHC-I downregulation , we assessed the steady state levels of PLC components in the presence of various MC80 constructs . Remarkably , we found that MC80L and MC80S , but not soluble MC80S , dramatically reduced the steady state levels of Tpn and TAP compared with vector control in MEFs and HEK 293Ts ( Fig 5A and 5C ) . However , CNX , CRT , and ERp57 were not downregulated by the expression of MC80 in MEFs . While the soluble MC80S construct slightly increased β2m levels , they appeared unchanged by active forms of MC80 . These data suggest that MC80 selectively destabilizes Tpn and TAP in a TM/tail-dependent manner . While full-length forms of MC80 downregulated Tpn , the completion of tapasin degradation appears to be cell-line specific . For instance , the MEF-Ld cells in Fig 4B ( left panel ) were not treated with drug or cytokine to rescue Tpn levels , yet all MC80 constructs associated with Tpn . However , only soluble MC80S-F appeared to associate with Tpn in untreated TAP-/- MEFs and 293T cells , presumably due to the degradation of Tpn by the functional MC80 constructs ( S4 Fig ) . Therefore , to demonstrate the ability of functional MC80 to associate with Tpn in the absence of TAP , we treated the Tpn-/- and TAP-/- MEFs with mIFNɣ ( Fig 4B; right panel ) . F-MC80L also downregulated Tpn in Hela cells in the presence of IFNɣ , but did not markedly affect the steady-state levels of TAPBPR , a structural relative of Tpn known to interact with classical MHC-I ( Fig 5D ) [40] . Given the interdependence of Tpn and TAP , we next sought to determine whether MC80 primarily targeted one component or both equivalently . Using TAP1-/- MEFs , we were able to recapitulate the MC80 TM/tail-dependent degradation of Tpn observed in wildtype MEFs ( Fig 5B ) . However , the level of TAP in Tpn-/- cells expressing MC80 was comparable to the vector control . This was more readily observable when the Tpn-/- cells were treated with IFNɣ for 24hr prior to harvesting , to upregulate TAP expression ( Fig 5B , right panel ) . Thus , while the destabilization of TAP by MC80 depends on the presence of tapasin , the MC80-mediated loss of tapasin is independent of TAP , indicating that tapasin is the primary target of MC80 in murine cells . TAP destabilization is potentially a consequence of the loss of Tpn , given that both this and previous studies demonstrate that TAP is generally unstable in the absence of Tpn ( Fig 5B , left panel ) [50 , 51] . We hypothesize that tapasin is also the primary target in human cells due to the homology of murine and human PLC components . However , as murine TAP is apparently more Tpn-dependent than human TAP , our data does not rule out the possibility that MC80 directly targets TAP for degradation in human cells . While previous work demonstrated that MC80 associates with β2m , it was not clear whether this association was functionally relevant . To determine whether β2m was necessary for MC80-mediated downregulation of Tpn , we used a classical MHC-I ( H2-Kb/H2-Db ) and β2m triple knock-out MEF cell line ( 3KO ) with or without stably transduced β2m . As observed in wild-type MEFs , the soluble form of MC80S-F did not cause Tpn degradation in either cell line . However , β2m expression was necessary for MC80S-F to induce Tpn degradation ( Fig 6 ) . In addition , the level of MC80 in 3KO cells without β2m is lower than that in 3KO cells with β2m transcomplementation . Given that the cistronically-translated GFP was expressed at similar levels , these findings indicate that , ( 1 ) β2m can stabilize MC80 , ( 2 ) β2m is required for MC80 function , and ( 3 ) MC80-mediated degradation of Tpn is classical MHC-I-independent . The majority of eukaryotic protein degradation is mediated through proteasomal and lysosomal pathways [52] . Autophagy has also been implicated in trafficking proteins from the ER to the lysosome/autophagosome for degradation [53] . To determine which host degradation pathway was being exploited by MC80 , we assessed the effects of two inhibitors of proteasomal degradation ( MG132 and Epoxomicin ) and one inhibitor of lysosomal degradation ( chloroquine ) . We also assessed an Atg5-/- murine microglial cell line , which is deficient in classical autophagy . Due to the toxicity of the tested drugs and the slow intrinsic turnover of Tpn , we treated the cells with IFNɣ prior to drug exposure to increase the synthesis of Tpn . Following a nine hour incubation , MG132 treatment partially but significantly rescued the expression of Tpn in the presence of MC80 in murine cells ( Fig 7A , S5A Fig ) . The proteasome-dependence of MC80 was also demonstrated in hIFNɣ-stimulated Hela ( human ) cells using the more specific inhibitor , Epoxomicin ( Fig 7D , S5C Fig ) . In contrast , neither the Atg5-/- cell line nor chloroquine treatment had a discernable effect on MC80 function ( S5A and S5B Fig ) . Furthermore , upon treatment with IFNɣ and MG132 , anti-Tpn antibody co-immunoprecipitated ubiquitinated bands corresponding to the size of multi/poly-ubiqutinated Tpn , specifically in the presence of MC80 ( Fig 7B ) . This suggests that the expression of MC80 leads to the ubiquitination of Tpn . Aside from ubiquitination , ER-associated degradation ( ERAD ) requires retrotranslocation of targeted proteins to the cytoplasm for proteasomal degradation to occur . While multiple retrotranslocation complexes exist in the ER , we have observed that MC80 associates with Derlin-1 in the presence of MG132 ( Fig 7C ) . Given that MC80 is retained in the ER , these data suggest that MC80 selectively destabilizes Tpn by recruiting ER-associated degradation ( ERAD ) components for the ubiquitination and retrotranslocation of Tpn ( Fig 7E ) .
Through this study , we demonstrate that ( 1 ) the MHC-I-like MCV protein , MC80 , associates with the PLC via its luminal domain; ( 2 ) MC80 remains localized to the ER , where it induces the degradation of Tpn and TAP to impede peptide loading and consequent MHC-I surface expression; ( 3 ) MC80 requires β2m to degrade Tpn; ( 4 ) MC80 primarily targets Tpn , with our data suggesting that MC80 induces ERAD through a mechanism that requires its transmembrane and cytoplasmic tail . Taken together , these findings support a model wherein MC80 directly interacts with Tpn via its luminal domain and presumably recruits cellular ERAD machinery via its transmembrane and/or tail to facilitate the degradation of Tpn; which secondarily destabilizes TAP . The loss of Tpn/TAP in turn dramatically affects the ability of nascent MHC-I to load high affinity peptides and subsequently traffic to the cell surface . Our experiments indicate that MC80L and MC80S both downregulate classical MHC-I , associate with Tpn , and degrade Tpn/TAP in human and mouse cells . Thus , the extended MC80 signal peptide does not appear necessary for MHC-I sabotage but may have an as-yet-unknown independent function . To our knowledge , this is the first example of a viral protein that primarily targets Tpn for degradation . Given the central role of Tpn in PLC organization and function , it is not surprising that multiple virally-encoded proteins have been found to undermine its function to evade CTL killing . HCMV US3 directly competes for Tpn binding to prevent peptide loading , while adenovirus E3-19K obstructs the TAP interface to prevent Tpn from bridging classical MHC-I to the PLC [54 , 55] . Unlike these mechanisms , our data indicates that soluble MC80 can associate with the PLC , but does not prevent MHC-I presentation . Therefore , at the expression levels tested in our retroviral system , MC80 does not appear to be functioning as a competitive inhibitor of PLC-mediated peptide-loading . Instead , we find that Tpn is degraded in the presence of MC80; but Tpn levels can be partially rescued in MC80-expressing cells by inhibiting proteasomal degradation . We also found that Tpn was multi/poly-ubiquitinated in the presence of MC80 , suggesting that MC80 induces the ER-associated degradation of Tpn . The fact that we can detect the association between functional MC80 and Tpn in our wild-type MEFs and interferon-induced TAP-/- cells indicates that the downstream steps of ERAD ( ubiquitination , retrotranslocation , and/or degradation ) may be rate-limiting . However , this hindrance may be cell-line/species specific , as the association between MC80 and tapasin is only detectable for soluble ( non-functional ) MC80 in HEK 293T cells and untreated TAP-/- MEFs . In comparison to other viral MHC-I-evasion mechanisms which utilize ERAD , HCMV US2/US11 are only known to target MHC while MHV68 mK3 primarily targets MHC-I with only a slight effect on TAP levels [56 , 57] . Recently , a virally encoded ER-resident ubiquitin E3 ligase , RHVP pK3 , was found to degrade MHC-I , Tpn , and TAP [58] . However , the degradation of Tpn and TAP were found to be secondary effects of the pK3-mediated degradation of MHC-I . Conversely , MC80 appears to degrade Tpn independent of TAP or MHC-I . Thus , the MC80-mediated destabilization of Tpn is distinct from other known viral MHC-I-evasion mechanisms . While US2 and US11 specifically target MHC , their mechanisms are reminiscent of MC80 . Indeed , while none of these viral proteins appear to have a ubiquitin E3 ligase domain , all three appear to induce ubiquitination-mediated ERAD . The luminal domains of US2 and US11 are able to associate with MHC-I , but without their transmembrane or tail domains these viral proteins cannot induce MHC degradation [59 , 60] . The transmembrane and tail sequences that distinguish US2 and US11 are thought to be responsible for recruiting distinct ERAD pathways . Of note , Derlin-1 associates with US11 and is essential for US11-mediated , but not US2-mediated , ER-associated degradation of MHC-I [61 , 62 , 63] . Intriguingly , MC80 contains two glutamic acid residues in its predicted TM domain [64] , which are exceedingly rare in human type I TM proteins [65] . While MC80 is also found to associate with Derlin-1 , the observation that neither US2 nor US11 TMs contain a negatively-charged residue raises the question of whether MC80 co-opts a distinct ERAD pathway . The current data cannot rule out the possibility that MC80 is able to actively degrade TAP through the proximal interaction with Tpn or direct interaction with TAP . However , considering that the loss of Tpn has been previously shown to destabilize TAP in human and murine cells , it is attractive to speculate that the loss of TAP is a secondary effect of specific ubiquitination and degradation of Tpn [50 , 51] . Interestingly , MC80 is retained in the ER despite lacking a putative ER-retention motif [66]; even when expressed as a truncated protein without a TM or tail . MC80's ability to associate with β2m and members of the PLC suggest that it maintains an MHC-I-like fold , and thus may exploit host machinery which canonically retains unloaded MHC-I in the ER . The association of soluble MC80 with CNX and CRT supports this hypothesis , as both chaperones have been previously shown to bind and retain immature MHC-I in the ER [29 , 30 , 31] . Furthermore , four of the eight residues critical to sequence-independent association of MHC-I with peptides are not conserved in MC80 , suggesting that MC80 may not bind peptides in an analogous manner . Potentially , the divergence of MC80 from MHC-I functions to mimic the peptide-receptive conformation of MHC-I to continually associate with CNX/PLC . As such , a structural analysis of MC80 may provide insight into the mechanism underpinning PLC-assisted peptide loading of classical MHC-I; particularly regarding the poorly understood interaction between MHC-I and Tpn . Recent studies have made significant progress toward a structural understanding of MHC-I peptide loading by employing a protein with sequence similarity to Tpn , TAPBPR [40 , 67] . Like Tpn , TAPBPR is capable of peptide editing through association with MHC-I; however , its functional role in antigen presentation has not yet been fully resolved . It is interesting to note that , while expression of MC80 in Hela cells treated with IFNɣ dramatically decreased Tpn levels , TAPBPR levels remained unchanged compared to vector control . We hypothesize that this specificity is a result of the fact that E3 ligases usually conjugate ubiquitin with lysine residues [68]; whereas the cytoplasmic tail of Tpn has four lysines , the cytoplasmic tail of TAPBPR does not have any . However , it is possible that MC80 preferentially interacts with Tpn over TAPBPR; or that TAPBPR may be present in other cellular compartments where MC80 is absent . Regardless , MC80 expression appears to cause the specific degradation of Tpn and not TAPBPR . While MC80 was originally predicted to be involved in NK-subversion , the mechanism described herein suggests that MC80 is involved in subverting CTL responses by downregulating MHC-I , which may in turn increase NK killing [69] . However , our data does not preclude MCV from encoding additional ORFs which subvert NK and CTL responses through distinct mechanisms . One such MCV protein , MC148 , is known to function as an inhibitor of CCR8-mediated chemotaxis , limiting T cell migration into sites of infection [70] . MC80 likely works in concert with MC148 to prevent the activation of surveilling T cells , specifically those which have been able to localize to the MCV lesion . Comparatively , cowpox virus encodes at least seven distinct proteins suspected of antagonizing host chemokines [71] , while also downregulating MHC-I expression by two independent mechanisms [72] , and encoding at least one separate protein to prevent NK activation [24] . We therefore believe it unlikely that MC80 and MC148 make up the complete repertoire of immune evasion proteins that allow for apparent MCV subversion of both T and NK cell surveillance .
Murine embryonic fibroblasts ( MEF ) cell lines including B6/WT3 , TAP1-deficient ( TAP1-/-; also referred to as FT1- ) , tapasin-deficient ( Tpn-/- ) , triple knock-out ( Kb-/- , Db-/- , β2m-/-; 3KO ) , and L cells were gifts from Dr . Ted Hansen , and have been described previously [73] . Baf3 cells [74] , a murine proB lymphocyte was obtained from Dr . Anthony French . The BV2 microglial cell line was a gift from Dr . Anthony Orvedahl , and have been described previously [75] . The murine T lymphocyte cell line RMA ( ATCC: TIB-39 ) , human embryonic kidney 293T cell line ( HEK 293T; ATCC: CRL-3216 ) , human cervical cancer cell line ( Hela; ATCC: CCL-2 ) , and human foreskin cell line ( HFF-1; ATCC: SCRC-1041 ) , were obtained from the American Type Culture Collection ( ATCC , Manassas , VA ) . Hela cells used in this work were stably transfected with HLA-A2 , indicated as Hela-A2 . Cyt-SIINFEKL and ER-SIINFEKL MEFs were produced by stably transfecting a construct encoding SIINFEKL peptide conjugated to ubiquitin or a signal peptide , respectively . Atg5-KO BV2 cell line isolation was performed as described [76] . All cell lines were cultured in 5% CO2 at 37°C with RPMI-1640 ( Gibco ) supplemented with 10% fetal bovine serum ( Gibco ) , 2mM L-glutamine , 10mM HEPES pH 7 . 2 , 1mM sodium pyruvate , and 100U/mL penicillin/streptomycin . Where indicated , prior to harvesting for immunoprecipitation and immunoblotting , cells were cultured for 24–48 h with 100–150 units/mL of mouse or human interferon gamma ( mIFNɣ , Invitrogen; hIFNɣ , R&D Systems ) , followed by an 8-9h incubation with 100nM Epoxomicin , 30μM MG132 ( Calbiochem , MA ) or 100μM chloroquine ( Sigma ) . Cells were harvested and washed in PBS containing 20mM iodoacetamide twice before freezing cell pellets at -80°C for storage prior to processing . Where indicated dithiobis-succinimidyl propionate ( DSP; Pierce ) was added to wash buffer at a concentration of 2mM . The MC80 ( MCV genotype 1 ) sequence was PCR amplified starting at M1 and M33 for constructs without the N-terminal Flag-tag or starting at Q18 and H72 for constructs with the N-terminal Flag-tag . Constructs with an N-terminal Flag-tag were inserted in frame with the canonical mouse β2m signal peptide and a Flag-tag into the pMXsIG vector ( CellBioLabs ) by overlap PCR and Gibson Assembly ( NEB ) . In constructs that lacked an N-terminal Flag-tag , including the untagged construct and C-terminally Flag-tagged constructs , the native signal peptide was used for both the long and short forms of MC80 . The soluble construct only included up to residue A342 of MC80 , to truncate the predicted transmembrane and cytoplasmic tail . All constructs were confirmed by Sanger sequencing ( GeneWiz ) . Retrovirus-containing supernatants were produced as per manufacturer instructions using either ( i ) the pVPack-GP and pVSVG vector ( Agilent ) in 293T cells to generate virus which infects human cell lines or ( ii ) the retroviral-packaging plat-E cells [77] to generate virus which infects murine cell lines . When necessary , retrovirally-transduced cells were enriched by cell sorting for GFP-positive cells ( MoFlo ) . Rabbit anti-mouse TAP1 and ERp57 , rabbit anti-human TAP1 and tapasin , and hamster anti-mouse tapasin ( 5D3 ) were gifts from Dr . Ted Hansen and have been described previously [33 , 35 , 78] . The rabbit anti-Derlin-1 antibody was a gift from Dr . Yihong Ye and has been described previously [62] . Anti-human tapasin ( TO-3 ) , anti-human TAPBPR ( 42-L ) , and anti-ubiquitin ( P4D1 ) antibodies were purchase from Santa Cruz Biotechnology . Anti-β-actin ( AC-74 ) and anti-Flag ( M2 ) ; anti-GFP; rabbit anti-CRT and rabbit anti-calnexin; anti-CD1d and anti-Qa-1 were purchased from Sigma , Covance , Stressgen , and BD Pharmingen , respectively . Rae1a and MICA were detected using an NKG2D-tetramer ( a gift from Dr . Sytse Piersma ) . All MHC-I mAbs including 11-4-1 ( α-H-2Kk ) , B8-24-3 ( α-H-2Kb ) , 30-5-7 ( α-H-2Ld ) , B22/249 ( α-H-2Db ) , 25-D1-16 ( α-H-2Kb-SIINFEKL ) , BBM . 1 ( α-β2m ) , and W6/32 ( HLA-ABC ) were previously described and available from the ATCC collection . Staining was performed as described previously [79] . Phycoerythrin-conjugated goat anti-mouse IgG ( BD Pharmingen ) was used to visualize primary antibody staining . Intracellular staining was conducted using the BD cytofix/cytoperm kit ( BD Pharmingen ) following the manufacturer’s instructions . All flow cytometric analyses were performed using a FACSCalibur ( Becton Dickinson ) . Data was analyzed using FlowJo 10 ( Tree Star ) and Prism 7 ( GraphPad ) . Relative surface MHC-I expression % was calculated using the equation: [Mean fluorescence intensity ( MFI ) of MC80 positive population ( GFP+ ) /MFI of MC80 negative population ( GFP- ) ]*100 . Error bars represent the standard deviation of 2 to 3 independent replicates . Cells were lysed in PBS buffer containing 20mM iodoacetamide , 1% IGEPAL CA-630 ( Sigma ) , and cOmplete protease inhibitors ( Roche ) . For coimmunoprecipitations , IGEPAL CA-630 was replaced with digitonin ( Wako ) . After lysis for at least 30min on ice , homogenized lysates were incubated for 1hr with a saturating concentration of antibody that was either directly conjugated to resin or associated via resin-conjugated protein A . Beads were then washed four times with 0 . 1% IGEPAL-CA-630 or digitonin , and eluted with Flag peptide or LDS sample buffer ( Invitrogen ) . If endoglycosidase H treatment followed the immunoprecipitation , bound proteins were instead eluted by boiling in 10mM TrisCl , pH 6 . 8 , 0 . 5% SDS . Supernatants were then incubated with an equal volume of 100mM sodium acetate , pH 5 . 4 , and 1–5 μU endoglycosidase H ( NEB ) for 1hr at 37°C . Immunoblotting was performed following SDS-PAGE separation of precipitated proteins or cell lysates as described previously [79] . Following primary blotting with mouse or hamster primary antibodies , membranes were blotted with biotin-conjugated goat anti-mouse IgG ( Invitrogen ) or goat anti-hamster IgG ( Jackson ImmunoResearch ) , respectively , followed by blotting with streptavidin-horseradish peroxidase ( Invitrogen ) . In cases where the biotin-conjugated anti-mouse system produced a high background , m-IgGk BP-HRP was substituted ( Santa Cruz Biotechnology ) . For rabbit primary antibodies , HRP-conjugated mouse anti-rabbit IgG light chain ( Jackson ImmunoResearch ) was used instead . Specific proteins were visualized by chemiluminescence using ECL ( Thermo ) . Statistical significance compared with the control group was calculated using ANOVA with Dunnett’s multiple comparisons test or unpaired t test and annotated as * = P<0 . 05; ** = P<0 . 01; *** = P<0 . 001; **** = P<0 . 0001 . Protein alignments were conducted using the ESPRESSO webserver [80] . Signal peptide cleavage sites were predicted using the SignalP 4 . 1 webserver [81] .
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The presentation of antigenic peptides by classical MHC-I to cytotoxic T-cells is a cornerstone of antiviral immunity . As such , viruses have devised a plethora of strategies to target MHC-I or cellular components involved in MHC-I antigen presentation in order to block effective T-cell surveillance . Molluscum contagiosum virus ( MCV ) is a human-specific poxvirus that produces skin lesions that can persist for months on a healthy individual . Herein , we demonstrate that MCV encodes a protein , MC80 , which disrupts MHC-I antigen presentation by associating with the peptide loading complex , a set of proteins responsible for transporting peptides into the endoplasmic reticulum and loading them into a groove on MHC-I proteins . These MHC-I/peptide complexes are then trafficked to the plasma membrane for presentation to T-cell receptors . MC80 shares sequence similarity with classical MHC-I , and like MHC-I , requires β2m to function . We have found that MC80 engages a peptide loading complex specific chaperone , tapasin , which leads to its ubiquitination and subsequent degradation along with the TAP peptide transporter . This prevents peptides from being trafficked into the endoplasmic reticulum and severely limits peptide loading onto MHC-I . Thus , MCV-infected cells expressing MC80 can be protected from T cell killing due to the sabotage of cell surface MHC-I/peptide presentation .
|
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2019
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Molluscum contagiosum virus MC80 sabotages MHC-I antigen presentation by targeting tapasin for ER-associated degradation
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Lymphocytic choriomeningitis virus ( LCMV ) causes a variety of diseases , including asymptomatic infections , meningitis , and congenital infections in the fetus of infected mother . The development of a safe and effective vaccine against LCMV is imperative . This study aims to develop a new candidate vaccine against LCMV using a recombinant replication-incompetent rabies virus ( RV ) vector . In this study , we have generated a recombinant deficient RV expressing the LCMV glycoprotein precursor ( GPC ) ( RVΔP-LCMV/GPC ) which is lacking the RV-P gene . RVΔP-LCMV/GPC is able to propagate only in cells expressing the RV-P protein . In contrast , the LCMV-GPC can be expressed in general cells , which do not express RV-P protein . The ability of RVΔP-LCMV/GPC to protect mice from LCMV infection and induce cellular immunity was assessed . Mice inoculated intraperitoneally with RVΔP-LCMV/GPC showed higher survival rates ( 88 . 2% ) than those inoculated with the parental recombinant RV-P gene-deficient RV ( RVΔP ) ( 7 . 7% ) following a LCMV challenge . Neutralizing antibody ( NAb ) against LCMV was not induced , even in the sera of surviving mice . CD8+ T-cell depletion significantly reduced the survival rates of RVΔP-LCMV/GPC-inoculated mice after the LCMV challenge . These results suggest that CD8+ T cells play a major role in the observed protection against LCMV . In contrast , NAbs against RV were strongly induced in sera of mice inoculated with either RVΔP-LCMV/GPC or RVΔP . In safety tests , suckling mice inoculated intracerebrally with RVΔP-LCMV/GPC showed no symptoms . These results show RVΔP-LCMV/GPC might be a promising candidate vaccine with dual efficacy , protecting against both RV and LCMV .
Arenaviruses ( Genus Arenavirus , Family Arenaviridae ) are enveloped , ambisense RNA viruses containing small ( S ) and large ( L ) RNA segments [1] . The S-segment encodes a nucleoprotein ( NP ) and a glycoprotein precursor ( GPC ) . The L-segment encodes an RNA-dependent RNA polymerase and a small RING finger protein ( Z ) that functions as a matrix protein . The GPC is cleaved into 2 subunits , GP1 and GP2 , and forms a mature complex [2] . Arenaviruses are divided into 2 groups , New World and Old World arenaviruses . Junin virus ( New World arenavirus ) , Lassa virus and Lujo virus ( Old World arenavirus ) causes viral hemorrhagic fever ( VHF ) in humans , with a relatively high fatality rate [3] . Lymphocytic choriomeningitis virus ( LCMV ) belongs to Old World arenaviruses and causes mild and self-limited disease in humans , with symptoms such as headache , fever , chills , and muscle aches . Humans can be infected with LCMV through exposure to rodent feces . LCMV also can be transmitted via solid organ transplantation and causes fatal infections in immunosuppressed recipients [4 , 5] . In addition , LCMV infection during pregnancy can result in abortion and cause congenital defects in infants infected in utero [6 , 7] . Therefore , a vaccine to protect humans against arenavirus-associated VHF and LCMV infection is needed . An inactivated , whole-virion vaccine is reported to strongly induce a humoral immune response against viral antigens but fails to protect animals from a lethal challenge of Lassa [8] or Junin [9] virus . DNA or live-attenuated vaccines expressing the arenavirus GPC and/or NP would be appropriate vaccine candidates for eliciting effective cellular immunity against the arenavirus infection . To date , only the live-attenuated Junin virus vaccine has been developed; this vaccine is presently used in Argentina [10] . No vaccines for other arenaviruses have been approved in clinical use , although candidate vaccines against Lassa virus infection has been reported . Recombinant vaccinia viruses [11 , 12 , 13 , 14] , recombinant vesicular stomatitis viruses ( VSV ) [15] , virus-like particles [16 , 17] , and DNA vaccines [18] have been shown to provide complete or partial protection against lethal Lassa virus challenge . In the quest for an LCMV vaccine , recombinant viral vectors [19] , DNA vaccines [20 , 21] , virus-like particles [22] , and an attenuated live vaccine [23] have been developed . These vaccines for arenaviruses target the NP and GPC proteins as antigens , and studies using recombinant virus in a nonhuman primate model suggest that the full-length GPC is necessary and sufficient for protection [14 , 15 , 24] . Rabies virus ( RV ) causes rabies , a zoonotic viral disease of the central nervous system that can infect almost every mammalian species . Once symptoms appear , the mortality rate is 100% . It is estimated that approximately 55 , 000 individuals die of rabies each year [25] . Rabies vaccine can prevent rabies at a rate of nearly 100% , if it is administered to a person immediately after they have been exposed . Inactivated vaccines against rabies are widely used globally . These vaccines were developed from laboratory strains , which efficiently propagated in cultured cells and were highly attenuated by serial passage in animal brains , chicken embryo , and cultured cells . RV belongs to the genus Lyssavirus of the family Rhabdoviridae and has unsegmented , negative-sense RNA as its genome . Reading from the 3′ to 5′ end , the genome encodes the genes of five structural proteins: nucleoprotein ( N ) , phosphoprotein ( P ) , matrix protein ( M ) , glycoprotein ( G ) , and polymerase ( L ) [26] . The genomic RNA plus the N , P , and L proteins form the ribonucleoprotein ( RNP ) , which is the component that is active in transcription and replication . The M protein and the G protein are membrane-associated proteins . A reverse genetics system for rabies was developed by Schunel et al . in 1994 [27] . This system has been extensively used not only as a tool for investigating the virulence of RV but also as a viral vector for other antigens and a tracer for studying neuronal networks [28 , 29] . Recombinant RVs , which lack one of the viral genes , were shown to be safe and immunogenic in mice and nonhuman primates [29] . In fact , replication-incompetent RV was shown to be a powerful tool for developing RV vaccines [30 , 31 , 32 , 33] . Additionally , M- or G-deficient RVs with the antigens of foreign pathogens have been used as vectors for vaccines against simian immunodeficiency virus [34] and Ebola virus , respectively [35 , 36] . Moreover , a previously developed P-gene-deficient rabies virus ( RV ) ( RVΔP ) was confirmed to be efficacious in protecting mice against lethal RV infection [37 , 38] . We hypothesized that RVΔP , which neither replicates nor produces infectious RV , might be suitable as a vector for the development of recombinant RVΔP that expresses a foreign gene . HEP-Flury is the parental strain of RVΔP ( GenBank: AB085828 . 1 ) ; it has been applied for human use in an inactivated rabies vaccine as it is one of the most attenuated RV strains . HEP-Flury causes no symptoms in adult mice , but kills suckling mice infected by intracerebral inoculation [39] . In contrast , RVΔP does not kill suckling mice , indicating a marked further attenuation in its in vivo pathogenicity . Despite such high attenuation , the inoculation of mice with RVΔP induces a high level of NAb and confers protective immunity against lethal RV infection [37] . In addition , Cenna et al . reported that P-gene-deficient RV elicited a rapid and potent IgG2a-dominated immune response and was completely safe in Rag2 knock-out mice inoculated intramuscularly [40] . It has been reported that an RVΔM vector expressing RV-G protein more efficiently elicited protective immunity against RV than RVΔP and that RVΔM is effective as an RV vaccine even upon inoculation at a low dose , namely , 103 focus-forming units ( FFU ) /mouse . Nonetheless , RVΔP shows the same efficacy as RVΔM at a high dose , namely , 105 FFU/mouse [31] . The main advantage of RVΔP compared with RVΔM is the ease of establishing and maintaining stable RV-P protein expression in cells . Since RV-M protein is known to be highly cytotoxic [41] , it would be necessary to use an expression control system to establish cell lines that stably express it . In contrast , RV-P protein-expressing cells are easy to establish without the need for any complicated system [38] . In addition , since RV-P protein was identified as a major and multifunctional type 1 interferons ( IFNs ) antagonist [42] , the RVΔP vector probably cannot inhibit type 1 IFNs expression in infected cells . Although this would probably be unfavorable for viral replication , it could be suitable for a vaccine , since it has been reported that CD8+ T cells were significantly activated in mice immunized with an RV vaccine vector expressing IFN-β as an adjuvant [43] . Since type 1 IFNs directly promotes the proliferation of antigen-specific CD8+ T cells [44] , it is expected that RVΔP would stimulate type 1 IFN expression and enhance host immune responses . In this study , we have developed replication-incompetent RVΔP-LCMV/GPC that contains the LCMV-GPC gene in a P-gene-deficient RV genome . The efficacy of RVΔP-LCMV/GPC in protecting mice from LCMV infection was evaluated . Anti-RV NAb titers were measured . Furthermore , the safety of RVΔP-LCMV/GPC was evaluated by suckling mice inoculated intracerebrally with RVΔP-LCMV/GPC . The mechanism of vaccine protection using a recombinant RVΔP expressing LCMV antigen was evaluated virologically and immunologically .
This study used Neuro-2a cells gifted from Dr . Satoshi Inoue [National Institute of Infectious Diseases ( NIID ) , Tokyo , Japan] and HEK-293 cells purchased from RIKEN BioResource Center ( Ibaraki , Japan ) , and Vero cells ( ATCC CCL-81 ) purchased from ATCC ( Manassas , VA , USA ) . Neuro-2a cells were grown in Dulbecco’s modified Eagle’s medium ( D5796 , D-MEM , Sigma-Aldrich , St . Louis , MO , USA ) supplemented with 10% fetal bovine serum ( FBS; Biowest , Nuaillé , France ) , 100 U/mL penicillin , and 100 μg/mL streptomycin ( Thermo Fisher Scientific Inc . Kanagawa , Japan ) ( D-MEM-10FBS ) . HEK-293 cells and Vero cells were grown in Eagle’s minimum essential medium ( M4655 , E-MEM , Sigma-Aldrich ) containing 10% ( E-MEM-10FBS ) or 5% ( E-MEM-5FBS ) FBS , 0 . 1-mM nonessential amino acids , 100 U/mL penicillin , and 100 μg/mL streptomycin ( Thermo Fisher Scientific Inc . ) . BHK-21 cell lines expressing RV P protein ( BHK-P ) [37] were grown in D-MEM-10FBS and 200 μg/mL Zeocine . During exposure to RV , BHK-P cells were cultured in D-MEM containing 2% FBS and antibiotics ( D-MEM-2FBS ) . The recombinant RVΔP-LCMV/GPC developed in this study was also grown in BHK-P cells . Neuro-2a and BHK-P cells were passaged generally twice per week at split ratios of 1:6 and 1:20 , respectively . HEK-293 and Vero cells were passaged once per week with a split ratio of 1:8 and 1:10 , respectively . Neuro-2a cells were seeded into 24- or 96-well culture plates ( Techno Plastic Products AG , Trasadingen , Switzerland ) at a density of 6 × 104 cells/cm2 1 day before virus inoculation . BHK-21 , BHK-P , and HEK-293 cells were seeded into plates at a density of 1 × 105 cells/cm2 . All cultures were cultured in a humidified incubator at 37°C with 5 . 0% CO2 . The full-length GPC gene of the LCMV WE strain ( LCMV-WE ) was amplified and inserted into the plasmid p5 . 1-defP , which has BsiWI and PstI restriction enzyme sites on the N-P intergenic region of the p3 . 1-defP [37] . The plasmid was designated as p5 . 1-defP-LCMV/GPC . Recombinant virus RVΔP-LCMV/GPC was rescued with p5 . 1-defP-LCMV/GPC and 4 helper plasmids ( pH-N , pH-P , pH-G , and pH-L ) using a previously described reverse genetics method [45] . To generate the recombinant adenovirus expressing the GPC of the LCMV-WE , the GPC gene was amplified and cloned into SwaI digested cosmid vector pAxCAwtit2 ( WAKO , Osaka , Japan ) and named pAx-LCMV/GPC . Recombinant adenoviruses , Ax-LCMV/GPC and Ax-empty , were generated as described before [46] . Briefly , HEK-293 cells were transfected with linearized cosmid vectors pAx-LCMV/GPC or pAxCAwtit2 ( RDB05213 , RIKEN BioResource Center ) , respectably , and cloned by limiting dilution . A wild type recombinant HEP-Flury strain ( rHEP ) was generated from full-length cDNA of the HEP-Flury strain by reverse genetics method as described previously [45] ( Fig 1 ) . The HEP-Flury strain is highly attenuated and is used as inactivated rabies vaccine for humans in Japan . RVΔP , which lacks the P gene in the RV genome [37] , and newly prepared recombinant RVΔP-LCMV/GPC were also generated by reverse genetics method in BHK-P cells . After viral inoculation , inoculated cells were cultured in a humidified incubator at 33°C with 5 . 0% CO2 because RV grows better at temperatures below 37°C [47] . The RVΔP and RVΔP-LCMV/GPC used in the animal experiments were propagated in hyper flasks ( Corning , NY , USA ) , concentrated by precipitation with 7% polyethylene glycol 6000 ( PEG: WAKO , Osaka , Japan ) , purified by ultracentrifugation ( 83 , 000×g , 90 min ) with 60% and 20% sucrose ( WAKO ) , and treated with Amicon Ultra-15 ( Merck Corporation , Darmstadt , Germany ) . The purified virus stocks were stored at −80°C until use . To inactivate RVΔP-LCMV/GPC , the purified virus stock was irradiated with UV light in a Falcon 35-mm culture dish ( Corning ) for 15 min . Virus inactivation was confirmed by absence of positive fluorescent focus by focus-forming assay using inoculated BHK-P cells . LCMV-WE was grown in BHK and Vero cells as described previously [48] . Briefly , cells were seeded in tissue culture bottles and infected with each of the recombinant viruses at a multiplicity of infection ( MOI ) of 0 . 03 and incubated for 3 days . After incubation , the culture medium was collected and centrifuged; the supernatant fraction stored at −80°C until use . Recombinant adenoviruses were grown in HEK-293 cells and titrated as described elsewhere [49] . Recombinant adenoviruses were enriched and purified using ViraBind Adenovirus purification kit ( Cell Biolabs Inc . , San Diego , CA , USA ) . The titer of Ad was evaluated by standard plaque forming assay with HEK-293 cells [50] . Specific pathogen-free 3-week-old female inbred C57BL/6j mice were purchased from Japan SLC Inc . ( Shizuoka , Japan ) and allowed to acclimate for 1 week . One-day-old specific pathogen-free outbred ICR mice were purchased from Japan SLC Inc . Eight suckling mice were placed in a cage with their untreated mother . BHK-P cells seeded on culture plates were inoculated with each of the viruses at 37°C for 1 h and cultured in D-MEM-2FBS at 33°C for 48 h . After incubation , the cells were fixed with Mildform10N ( WAKO , Osaka , Japan ) for 20 min and washed with phosphate buffered saline solution ( PBS ) 5 times . The cells were permeabilized in 0 . 5% Triton-X100 for 20 min at room temperature and washed with PBS 3 times . To detect the LCMV-GPC expression , cells blocked in PBS containing 2% FBS for 1 h were stained for 1 h at 37°C with an anti-LCMV-GP1 mAb ( clone KL25 ) [51] ( kindly provided by Dr . Daniel Pinschewer , University of Basel ) , washed 3 times with PBS , and stained with the secondary antibody Dylight 549-conjugated polyclonal anti-mouse IgG ( H+L ) ( Vector Laboratories , CA , USA ) . The cells were observed with a fluorescence microscope OLYMPUS X-81 ( Olympus Co . , Tokyo , Japan ) . Images were captured by an ORCA-R2 ( Hamamatsu Photonics K . K . Shizuoka , Japan ) and colored with LuminaVision ( MITANI Corporation , Tokyo , Japan ) . For detection of RV-N , the PBS-washed cells were stained with fluorescein isothiocyanate ( FITC ) -labeled anti-RV mAb ( Fujirebio Inc , Tokyo , Japan ) for 1 h at 37°C . The cells were also observed as described above . Titers of RVΔP and newly generated RVΔP-LCMV/GPC were determined by focus assay in BHK-P cells . BHK-P cells cultured in 96-well plates for 1 day were inoculated with each virus solution diluted 10-fold serially and incubated for 3 days at 33°C . The cells were then fixed with 80% acetone for 20 min at room temperature . Fixed cells were stained with FITC-labeled anti-RV mAb , observed under a fluorescence microscope , and foci were counted . The infectious doses of RVΔP and RVΔP-LCMV/GPC were calculated and shown as FFU per mL . The infectious titers of LCMV solution were determined using the plaque assay as described elsewhere [48] . Briefly , Vero cells were seeded onto 6-well plates and were inoculated with 10-fold serially diluted virus solutions . After removing the virus solutions , 0 . 5% agarose overlay medium were added on the cells . The cells were incubated for 4 days at 37°C and stained with neutral red . The infectious dose was calculated and shown as plaque forming unit ( PFU ) per mL . Besides plaque assay , the infectious titers of LCMV were determined by focus forming assay described before [52] for neutralizing antibody assay . Briefly , Vero cells seeded in 96-well plates were inoculated with each of the virus solutions 10-fold serially diluted and then incubated for 40–48 h at 37°C . The cells were fixed with Mildform10N and permeabilized with 0 . 5% Triton-X100 and were stained with mAb KL-25 , and then HRP-anti-mouse IgG ( H+L ) ( Thermo Fisher Scientific Inc . ) after washing the cells . Foci in the cells were counted . The infectious dose was calculated and shown as FFU per mL . Cells infected with either of RVΔP-LCMV/GPC or RVΔP were incubated for 48 h at 33°C . Cells were then washed with PBS twice and lysed with lysis buffer and centrifuged at 14 , 000×g for 10 min at 4°C . Sample supernatants were mixed with an equal volume of sample buffer containing 2-ME and incubated at 98°C for 2 min . Samples were separated with precast 10% gel SDS-PAGE ( ATTO , Tokyo , Japan ) and transferred onto polyvinylidene difluoride ( PVDF ) membranes . Membranes were incubated with anti-RV-G mAb15-13 [53] , kindly distributed by Dr . Nobuyuki Minamoto ( University of Gifu ) , or the anti-LCMV-GP1 mAb . After incubation followed by washing in PBS , the membranes were incubated with HRP anti-mouse IgG ( H+L ) ( Thermo Fisher Scientific Inc . ) , stained with SuperSignal West Femto ( Thermo Fisher Scientific Inc . ) , and visualized with LAS-3000 ( Fujifilm , Tokyo , Japan ) . Groups of five mice were intraperitoneally inoculated with 106 FFU/0 . 1 mL of RVΔP , RVΔP-LCMV/GPC , UV-irradiated RVΔP-LCMV/GPC , or PBS containing 2% BSA twice at 1-week intervals . Mice were infected with 10 PFU of LCMV-WE under isoflurane anesthesia 1 week after the last inoculation . After challenge , the mice were observed for 2 weeks . The number of surviving mice was recorded daily . To determine the serum anti-LCMV and anti-RV NAb titers , blood samples were collected from the facial vein of the mice using an animal lancet ( Medipoint Inc , Mineola , NY , USA ) 1 day before the days of inoculation , challenge , and completion of the observation period . Groups of eight suckling mice were inoculated intracerebrally with 30 μL of rHEP RV , RVΔP , or RVΔP-LCMV/GPC , each of which contained 2 × 105 FFU of the viruses , using a double-hub needle and 0 . 3 mL glass syringe ( Hoshiseido Medical Appliance Industry Co . , Ltd . , Tokyo , Japan ) . The suckling mice were observed for clinical signs for 3 weeks . The infectious dose of LCMV was determined using a focus reduction assay [52] . Sera were diluted 1:16 with E-MEM-5FBS and heat-inactivated at 56°C for 30 min and were further serially 2-fold diluted in E-MEM-5FBS . Each of the diluted serum samples was mixed with an equal volume ( 0 . 05 mL ) of virus solution containing 50 FFU of LCMV-WE in a well of a 96-well plate . After incubation at 37°C for 1 h , 2 . 5 ×105 Vero cells in 0 . 05 mL were added to each well and incubated for 4–6 h . Then , 0 . 1 mL of overlay medium ( E-MEM-5FBS with 1% methylcellulose ) was added to each well . The cells were further incubated for 42–48 h at 37°C . The cells were fixed with 4% formaldehyde and permeabilized with 0 . 5% Triton X-100 . FBS ( 10% ) was added to each well and incubated for 60–90 min at 37°C for blocking . After washing with PBS , the cells were stained with the anti-LCMV-GP1 mAb , followed by treatment with HRP-labeled anti-mouse IgG ( H+L ) ( Thermo Fisher Scientific Inc . ) . DAB solution ( Wako Pure Chemical Industries , Ltd . Osaka , Japan ) with 0 . 1M imidazole ( Wako Pure Chemical Industries ) was added and incubated at room temperature for 10–30 min . Neutralization was determined by counting the number of foci in each well . The NAb titer was defined as the reciprocal of the highest dilution level at which the number of foci was less than half that of the control ( wells infected with a mixture of LCMV and pre-immunized serum collected from the same mice ) . The IgG antibody titers of LCMV in mouse sera were determined using an IFA . HEK-293 cells were inoculated with Ax-LCMV/GPC or Ax-empty and incubated at 37°C for 48 h . The cells were collected , mixed with two volumes of mock-inoculated HEK-293 cells , and placed onto 48-well slide glasses . Cells were fixed with 4% formaldehyde , permeabilized with 0 . 5% Triton X-100 , and stored at −20°C until use . As a negative control , HEK-293 cells infected with empty adenovirus were used . Sera were serially diluted 2-fold ( from 1:8 to 1:2048 ) and used as the primary antibody , and 400-fold diluted FITC-labeled anti-mouse IgG ( H+L ) ( Cappel , MP Biochemicals LLC , Santa Ana , CA , USA ) was used as the secondary antibody . The antibody titers against the LCMV-GPC were determined in terms of the highest dilution of serum for which IFA positivity of cells could be demonstrated . Titers of anti-RV NAb in mouse sera were determined using a modified rapid fluorescent focus-forming inhibition test . Briefly , sera were diluted 16-fold with D-MEM-10FBS and inactivated at 56°C for 30 min . The heat-inactivated sera were serially diluted 2-fold and mixed with an equal volume ( 0 . 05 mL ) of virus solution containing 50 FFD50 ( 50% focus-forming dose ) . After incubation at 37°C for 1 h , 2 ×105 cells/0 . 05 mL of Neuro-2a cells were added . Cells were incubated at 37°C for 48 h and then fixed with 80% acetone for 20 min at room temperature . The cells were stained with FITC-labeled anti-RV antibody for 40 min at 37°C . The NAb titer was defined as the highest serum dilution that neutralizes 50% of the challenge virus . This value was normalized to international units ( IU ) using the World Health Organization anti-rabies immunoglobulin . Groups of six mice were intraperitoneally inoculated with 106 FFU/ 0 . 1 mL of RVΔP-LCMV/GPC . The mice were inoculated again with the same amount of RVΔP-LCMV/GPC 3 weeks after the first inoculation . Four days and 7 days after the last RVΔP-LCMV/GPC inoculation , the mice were injected with 500 μg of anti-mouse CD8α Clone 53–6 . 72 ( Bio X Cell , Lebanon , NH , USA ) for CD8+ T-cell depletion or 500 μg of Rat IgG2b isotype ( anti-Trinitrophenol Clone 2A3 , Bio X Cell ) as the control . One week after the last RVΔP-LCMV/GPC inoculation ( 3 days after the anti-mouse CD8α Clone 53–6 . 72 or 500 μg of Rat IgG2b isotype control injection ) , the mice were infected intracerebrally with 10 PFU of LCMV-WE and observed for 3 weeks . The animal studies were carried out in strict accordance with the Guidelines for Proper Conduct of Animal Experiments of the Science Council of Japan and with animal husbandry and welfare regulations . All animal experiments were reviewed and approved by the Committee on Experimental Animals at the National Institute of Infectious Diseases ( NIID ) ( approval nos . 214091 , 215084 , 114122 , and 116130 ) . All mice infected with LCMV-WE were handled in biosafety level 3 animal facilities , in accordance with the guidelines of the NIID . The mice were inoculated with virus under proper anesthesia with isoflurane . During the observation period , the mice were monitored daily , and moribund mice were euthanized with isoflurane . The data were analyzed using JMP 11 ( SAS ) software . The growth of virus was analyzed using the Kruskal–Wallis test . Differences in survival and recurrences between groups were compared using Kaplan–Meier curves and tested using the log-rank test . The Steel–Dwass nonparametric test was used for multiple comparisons of NAb titers .
RVΔP-LCMV/GPC was successfully generated ( Fig 1 ) . No mutations were found in the nucleotide sequence of the LCMV-GPC gene inserted into the virus . BHK-P cells were inoculated with RVΔP-LCMV/GPC at an MOI of 0 . 1 and incubated at 33°C for 48 h . LCMV-GP1-positive cells were observed in BHK-P cells infected with RVΔP-LCMV/GPC ( Fig 2B ) but not in those infected with RVΔP ( Fig 2E ) . In contrast , RV-N antigen-positive foci were observed both in RVΔP-LCMV/GPC- and RVΔP-infected BHK-P cells ( Fig 2A and 2D ) . LCMV-GP1 was also detected in RVΔP-LCMV/GPC-infected Neuro-2a cells ( Fig 2H ) , which do not supply RV-P protein . The expression of the LCMV-GPC- and GP1 proteins in RVΔP-LCMV/GPC-infected BHK-P cells was examined by western blotting ( Fig 2J ) . The LCMV-GPC was definitely detected in RVΔP-LCMV/GPC-infected BHK-P cells , although it was lower expression level than that in LCMV-WE-infected Vero cells . GP1 antigen was detected in the PEG-precipitated fraction and the purified virion of RVΔP-LCMV/GPC ( Fig 2K ) . In contrast , the LCMV-GPC and GP1 were not detected in the PEG-precipitated fraction and the purified virion of RVΔP ( Fig 2K ) . RV-G was detected in the PEG-precipitated and the purified virions of RVΔP-LCMV/GPC , RVΔP and HEP infected Neuro-2a cells ( Fig 2L ) . BHK-P cells were inoculated with RVΔP-LCMV/GPC or RVΔP at an MOI of 0 . 01 and incubated at 33°C for 7 days . Titers of the progeny viruses were determined on day 1 , 3 , 5 and 7 days ( Fig 3 ) . These strains demonstrated similar growth curves , while the growth capacity differed significantly between RVΔP-LCMV/GPC and RVΔP strains on day 5 ( p = 0 . 0459 ) and 7 ( p = 0 . 0459 ) , The titers of RVΔP-LCMV/GPC and RVΔP were highest on day 5 , reaching a maximum titer of 105 FFU/mL . To address the safety of RVΔP-LCMV/GPC , 4-day old suckling ICR mice were inoculated intracerebrally with RVΔP-LCMV/GPC , RVΔP , or rHEP and observed for 3 weeks . Mice inoculated with RVΔP-LCMV/GPC or RVΔP showed no clinical signs during the observation period ( Fig 4 ) . In contrast , all mice inoculated with rHEP died or were sacrificed moribund within 7 days of inoculation . Statistically significant differences were observed in survival rates between mice inoculated with rHEP and those inoculated with RVΔP-LCMV/GPC or RVΔP . To examine the efficacy of RVΔP-LCMV/GPC as a vaccine against LCMV , mice were intraperitoneally inoculated twice with RVΔP-LCMV/GPC , RVΔP , or UV-irradiated RVΔP-LCMV/GPC at 1-week intervals . One week after the last immunization , the mice were intracerebrally inoculated by injection with 10 PFU of LCMV-WE ( Fig 5A ) . As shown in Fig 5B , the survival rate of mice inoculated with RVΔP-LCMV/GPC was 88 . 2% ( 15 out of 17 mice ) , while those of mice inoculated with RVΔP , UV-irradiated RVΔP-LCMV/GPC , and mock inoculated were 7 . 7% ( 1/13 ) , 50% ( 5/10 ) , and 10% ( 1/10 ) , respectively . The survival rate of mice inoculated with RVΔP-LCMV/GPC was significantly higher than that of mice inoculated with RVΔP ( p < 0 . 0001 ) or mock-inoculated controls ( p = 0 . 0005 ) . No significant difference was observed between RVΔP-LCMV/GPC and UV-irradiated RVΔP-LCMV/GPC ( p = 0 . 0552 ) . Inoculation with UV-irradiated RVΔP-LCMV/GPC exhibited a moderate efficacy for the LCMV challenge infection . These survival rates did not show significant difference between mice inoculated with UV-irradiated RVΔP-LCMV/GPC and those inoculated with RVΔP ( p = 0 . 1306 ) or mock- inoculated controls ( p = 0 . 3136 ) . Anti-LCMV and anti-RV antibody titers in the sera were examined in mice inoculated with RVΔP-LCMV/GPC , RVΔP , UV-irradiated RVΔP-LCMV/GPC , or PBS . As shown in Fig 5C , IgG titers against the LCMV-GPC were undetectable in the sera of mice inoculated with RVΔP-LCMV/GPC , RVΔP , or UV-irradiated RVΔP-LCMV/GPC , even after a second inoculation . But all mice that survived after LCMV challenge have definitely elicited the antibodies against the LCMV-GPC in the sera . Titers of IgG anti-LCMV-GPC antibody were up to 1:2048 in the sera of surviving mice . However , the NAb titers against LCMV did not detect throughout the observation period any of the groups ( Fig 5D ) . In contrast , the NAb titers against RV increased after inoculation with RVΔP-LCMV/GPC , RVΔP , or UV-irradiated RVΔP-LCMV/GPC mice and were considerably higher than those of mock-inoculated mice ( Fig 5E ) . There were no statistically significant differences among the anti-RV NAb titers of mice inoculated with RVΔP-LCMV/GPC , RVΔP , or UV-irradiated RVΔP-LCMV/GPC . To elucidate the involvement of CD8+ T cells in the protection against 10 PFU of LCMV infection in mice inoculated with RVΔP-LCMV/GPC , mice were inoculated with RVΔP-LCMV/GPC twice and injected with anti-CD8+ antibody before and after the LCMV challenge to deplete CD8+ T cells ( Fig 6A ) . Alternatively , rat IgG2b immunoglobulin ( isotype control ) was administrated as a negative control . As shown in Fig 6B , depletion of CD8+ T cells apparently reduced to protect the mice inoculated with RVΔP-LCMV/GPC . The percent survival of CD8+ T-cell-depleted mice ( 33% ) was significantly lower ( p = 0 . 0185 ) than that of mice injected with isotype control ( 100% ) ( Fig 6B ) . The NAb titers against RV ( Fig 6D ) produced in sera of mice injected with anti-CD8+ antibody were the same as those in sera of mice injected with rat IgG2b immunoglobulin . This result indicated that the administration with these immunoglobulins did not affect the humoral immunity . Since the antigen expressed by viral vector is an important factor to elicit protective immunity against target virus . We confirm the antigenic efficacy of the expressed the LCMV-GPC which we used in this study , mice were immunized with Ax-LCMV/GPC and Ax-empty twice and challenged with 10 PFU of LCMV ( Fig 7A ) . As shown in Fig 7B , all the mice inoculated with Ax-LCMV/GPC were survived after LCMV challenge , whereas all the mice inoculated with Ax-empty died ( p = 0 . 0046 ) . This result suggested that the LCMV-GPC sequences , which inserted in the genomes of RVΔP-LCMV/GPC and Ax-LCMV/GPC is sufficient for the antigen against LCMV protection .
The present study clearly demonstrated that RVΔP-LCMV/GPC , which cannot multiply but expresses LCMV and RV antigen , elicited protective immunity against LCMV and humoral immunity against RV in mice . Most of the mice inoculated with RVΔP-LCMV/GPC survived after LCMV challenge . RVΔP-LCMV/GPC simultaneously induced strong humoral immunity against RV . Moreover , we demonstrated that RVΔP-LCMV/GPC remained attenuated in suckling mice . The main advantages of RV vectors are as follows: 1 ) RV can infect almost all mammals; 2 ) RV inoculation can elicit strong humoral and cellular immunity; 3 ) RV genomic RNA never integrates into the host genome because its replication cycle does not involve a DNA genome stage; and 4 ) globally , most people and animals have no immunity against RV . In addition , replication-incompetent RV is considered to be highly safe . Considering that the probable target population for receiving LCMV vaccine would include immunodeficient individuals , the safety profile of a candidate LCMV vaccine is extremely important . As we showed here , RVΔP-LCMV/GPC exhibited no pathogenicity in suckling mice inoculated intracerebrally . This suggests that RVΔP-LCMV/GPC would be avirulent in other mammals . Furthermore , it indicates that the presentation of endogenously synthesized viral antigens might stimulate responses of the cellular immunity in mice inoculated with RVΔP . Therefore , RVΔP-LCMV/GPC is considered to have the potential to elicit cellular immunity against LCMV . Although , the candidates of viral vector vaccines against LCMV have been reported such as adenovirus [54] , vaccinia virus [55] , influenza virus [56] and VSV [57] , RV have some advantages mentioned above and have a long successful history as an inactivated vaccine for human and as a live vaccine for animals . Thus , RV could be a valuable alternative approach to the currently existing viral vectors . Expression of the LCMV-GPC protein was clearly detected in BHK-P cells infected with RVΔP-LCMV/GPC . However , expression of the LCMV-GPC protein in infected Neuro-2a cells was not intensely detected by IFA , which indicated modest expression of the antigens even in cells without supply of the RV-P protein . In principle , RVΔP-LCMV/GPC did not multiply in cells not supplied with the RV-P protein , a constituent of the viral polymerase complex . The RV-P protein is incorporated into the RVΔP particle , so RVΔP could perform the primary transcription of viral mRNA even in cells without de novo synthesis of the RV-P protein [37] . Although the protein expression level in vivo was probably much lower than in the in vitro experiment , it would be sufficient to provoke an effective immune response . In fact , we showed that most of the mice immunized with RVΔP-LCMV/GPC survived the LCMV challenge and the sera from these mice had high neutralizing titers against rabies virus . Compared with adenovirus vector , the protection rate of RVΔP-LCMV/GPC was slightly lower than that of Ax-LCMV/GPC , this could be due to a difference of the expression level of the LCMV-GPC . We need further improvement to archive the higher expression level of the LCMV-GPC in the RVΔP RV vector system . The slightly lower growth capacity of RVΔP-LCMV/GPC compared with that of RVΔP might be inversely correlated with the length of their RNA genomes . This modest decrease in viral growth has been observed in other studies [34 , 35] . Since the growth capacity of RVΔP-LCMV/GPC ( highest titer 105FFU/mL ) is considerably lower than that of ordinary tissue-culture-adapted propagation-competent strains ( e . g . , 108 FFU/mL ) , we are considering improving our preparation protocol or exploit other potential cell lines to obtain higher yields of virus stocks for vaccine production . Cells infected with RVΔP-LCMV/GPC or RVΔP showed no detectable cytopathic effects ( CPE ) and survived; thus , the culture could be maintained for nearly a month . This feature allows the repeated collection ( up to three times ) of culture fluid after sequential 6-day incubation periods . Unfortunately , it is expected that an extremely high dose of virus would be required for human use , so we should consider new strategies to overcome this limitation . In this study , intraperitoneal inoculation twice with 106 FFU of RVΔP-LCMV/GPC was shown to confer protective immunity against a challenge with LCMV . Anti-LCMV-GPC IgG antibodies were not detected before the LCMV challenge , demonstrating that neither NAb nor IgG antibodies were induced by the RVΔP-LCMV/GPC inoculation . Regarding LCMV infection , induction of NAbs against LCMV antigen is not important for protection and sometimes develops very late , namely , 60 to 120 days after infection [58] . It was reported that the administration of convalescent serum failed to prevent re-infection [1] . In fact , another study reported that Lassa-convalescent plasma did not significantly reduce mortality in any of the high-risk groups [59] . In contrast , Lassa-virus-specific cytotoxic T-cell responses were evident in patients who recovered from Lassa fever [60] . Anti-LCMV-GPC IgG antibodies were detected in the sera from surviving mice after the LCMV challenge , however these antibodies did not neutralize the LCMV . These results agree with the previous study in which mice exposed with high titer of LCMV developed GP-1-specific antibodies by day 8 but the antibodies failed to neutralize the virus [61] . As reported in a previous study [57] , the LCMV-GPC itself , not the viral backbone , is responsible for the poor NAb response observed in mice infected with LCMV or recombinant VSV expressing the LCMV-GP . These findings suggest that cytotoxic T-cell responses are more important than the NAb titers for the protective immune response against arenaviruses . Accordingly , our results suggested that CD8+ T cells in mice inoculated with RVΔP-LCMV/GPC were essential to protect mice against a LCMV infection by the in vivo depletion of CD8+ T cells . As shown in Fig 6B , even though 33% of mice with CD8+ T cell depletion survived after LCMV challenge , the survival rate of those mice was significantly lower than that of the control mice ( 100% ) . Although , direct evidences of antigen-specific T cell responses should be monitored using ELISPOT assay or intracellular cytokine staining , it seems that CD8+ T cells play a critical role in the protective immunity against LCMV induced by RVΔP-LCMV/GPC inoculation . It is expected that RVΔP-LCMV/GPC would be a promising vaccine candidate for the practical development of a vaccine against LCMV . Interestingly , inoculation with UV-irradiated RVΔP-LCMV/GPC exhibited a moderate efficacy for protection against LCMV challenge , it suggests that the inactivated preparation has provoked protective immunity against LCMV infection to some extent . Although , only a small amount of GP1 protein was detected in purified RVΔP-LCMV/GPC virus particles , it was clearly confirmed that its incorporation into these particles does occur . We suspect that this incorporated GP1 protein would stimulate immune responses in mice inoculated with UV-irradiated RVΔP-LCMV/GPC to some extent , albeit inefficiently , conferring partial protection against LCMV challenge . Inactivated vaccines are commonly believed to be weak inducers of cellular immunity . However , Papaneri et al . have reported that live and inactivated RV vaccines expressing Ebola virus glycoprotein ( EBOV-GP ) induced primary EBOV-GP specific T-cells and a robust recall response as measured by interferon-gamma ELISPOT assay [62] . This would be another unique feature of RV vectors . It is necessary to clarify how UV-irradiated RVΔP-LCMV/GPC could elicit any T-cell responses against the LCMV-GPC . Protection against RV infection depends primarily on humoral immunity . A titer of NAbs greater than 0 . 5 IU/mL is indicative of fully positive seroconversion [63] . As expected , titers of anti-RV NAbs in mice inoculated with RVΔP-LCMV/GPC increased efficiently after the first and second inoculations . NAb titers remained above 0 . 5 IU during the post-challenge period . These results demonstrated that RVΔP-LCMV/GPC would induce high humoral immunity sufficient to protect mice against RV infection . Although RV is distributed worldwide , human mortality due to RV occurs mainly in Asia and Africa [64] , which is somewhat similar to the mortality pattern observed for Old World arenaviruses , which is also concentrated in regions where they are endemic ( West Africa and South America ) . We have demonstrated that RVΔP-LCMV/GPC immunization successfully induced protective immunity against LCMV , probably associated with CD8+ T cells , concurrent with effective RV seroconversion . We are planning to precisely examine how RV vector elicits the T cell responses in future studies . We conclude that RVΔP-LCMV/GPC is a promising bivalent vaccine candidate against LCMV and RV . We expect that the RVΔP vector can be applied to develop highly attenuated vaccines against other arenaviruses , such as Lassa , Machupo , and Lujo viruses .
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Lymphocytic choriomeningitis virus ( LCMV ) causes infections that are often asymptomatic but can be fatal in immunocompromized persons . In addition , LCMV infection during pregnancy can cause spontaneous abortion or severe birth defects . Humans are exposed to LCMV by direct or indirect contact with wild or pet rodents such as mice , hamsters , and guinea pigs . There is no vaccine against LCMV infection . Because of the importance of cellular immunity , inactivated vaccines are not considered effective for protection against LCMV infection . In contrast , protection against rabies , one of the most lethal zoonotic diseases , depends primarily on humoral immunity . In this study , we have developed a recombinant rabies virus ( RVΔP-LCMV/GPC ) that cannot multiply but expresses LCMV and rabies antigens in the inoculated mice . Hence , we expected that both humoral and cellular immunity would be induced . Most of the mice ( 88 . 2% ) inoculated with RVΔP-LCMV/GPC survived after a LCMV challenge , whereas only 7 . 7% of the empty vector inoculated mice survived . Simultaneously , RVΔP-LCMV/GPC induced strong humoral immunity against rabies virus . In conclusion , this study indicates that RVΔP-LCMV/GPC may be useful as a bivalent vaccine against LCMV and RV .
|
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2018
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Replication-incompetent rabies virus vector harboring glycoprotein gene of lymphocytic choriomeningitis virus (LCMV) protects mice from LCMV challenge
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Helminth infections are known to influence T cell responses in latent tuberculosis ( LTBI ) . Whether helminth infections also modulate B cell responses in helminth-tuberculosis co-infection is not known . We assessed Mycobacterium tuberculosis ( Mtb ) –antigen specific IgM and IgG levels , circulating levels of the B cell growth factors , BAFF and APRIL and the absolute numbers of the various B cell subsets in individuals with LTBI , LTBI with coincident Strongyloides stercoralis ( Ss ) infection ( LTBI/Ss ) and in those with Ss infection alone ( Ss ) . We also measured the above-mentioned parameters in the LTBI-Ss group after anthelmintic therapy . Our data reveal that LTBI-Ss exhibit significantly diminished levels of Mtb-specific IgM and IgG , BAFF and APRIL levels in comparison to those with LTBI . Similarly , those with LTBI-Ss had significantly diminished numbers of all B cell subsets ( naïve , immature , classical memory , activated memory , atypical memory and plasma cells ) compared to those with LTBI . There was a positive correlation between Mtb—antigen specific IgM and IgG levels and BAFF and APRIL levels that were in turn related to the numbers of activated memory B cells , atypical memory B cells and plasma cells . Finally , anthelmintic treatment resulted in significantly increased levels of Mtb—antigen specific IgM and IgG levels and the numbers of each of the B cell subsets . Our data , therefore , reveal that Ss infection is associated with significant modulation of Mtb-specific antibody responses , the levels of B cell growth factors and the numbers of B cells ( and their component subsets ) .
Helminth infections are powerful modulators of the immune response and typically elicit both Type 2 and regulatory cytokine responses [1 , 2] . Helminths can influence the host immune response to co-existent infections because of their propensity to establish longstanding , persistent infections that in turn can modulate host immunity [3] . For example , helminth infections are known to modulate the immune response to Mycobacterium tuberculosis ( Mtb ) in a variety of ways [4] including: 1 ) the down modulation of Th1 responses with diminished production of the cytokines IFNγ , TNFα and IL-2 [5 , 6 , 7]; 2 ) the down regulation of the Th17 ( IL-17A , IL-17F and IL-22 ) response [5 , 6 , 7]; and 3 ) the induction of regulatory T cell responses [8] . While the T cell-mediated response is the cornerstone of the protective immune response to Mtb , recent evidence suggests that B cells can also play an important role [9 , 10] . Thus , human studies have demonstrated that antibodies in LTBI are functionally more competent than antibodies in those with active TB [11 , 12] . Moreover , active TB is characterized by altered levels of the B cell growth factors , BAFF and APRIL [13] , that are crucial factors for peripheral B cell survival and antibody production [14] . In addition , those with active pulmonary tuberculosis ( TB ) are also known to have a dysfunctional circulating B cell compartment that can be reset following successful TB treatment [15] . Since helminth infections are also known to influence B cell survival and function [1] , we postulated that helminth infections could affect Mtb-specific B cell responses in LTBI . We , therefore , sought to examine the B cell arm of the immune response in LTBI and how it is influenced by the presence of Strongyloides stercoralis , an intestinal helminth known to infect about 50–100 million people worldwide [16] . In so doing , we demonstrate that S . stercoralis infection is associated with alterations in the levels of Mtb–specific IgM and IgG , levels of BAFF and APRIL , and the number of B cells ( and their component subsets ) in LTBI and that most of these changes are reversible following anthelmintic therapy .
All individuals were examined as part of a natural history study protocol ( 12IN073 ) approved by Institutional Review Boards of the National Institute of Allergy and Infectious Diseases ( USA ) and the National Institute for Research in Tuberculosis ( India ) . Informed written consent was obtained from all participants . We studied 132 individuals in Tamil Nadu , South India; 44 with LTBI and clinically asymptomatic S . stercoralis infection ( hereafter LTBI/Ss ) , 44 with LTBI only ( hereafter LTBI ) and 44 with S . stercoralis infection alone ( hereafter Ss ) ( Table 1 ) . None had previous anthelmintic treatment nor HIV . Follow up was performed at 6 months following recruitment and treatment . Those with LTBI were clinically asymptomatic with a positive QuantiFERON Gold-in-tube tests and normal chest radiographs . Active TB was excluded by sputum smear negativity . Ss infection was diagnosed by the presence of IgG antibodies to the recombinant NIE antigen as described previously [17 , 18] . None of the study population had other intestinal helminths ( based on stool microscopy ) . All LTBI/Ss and Ss individuals were treated with single doses of ivermectin ( 12mg ) and albendazole ( 400 mg ) and follow–up blood draws from LTBI/Ss individuals were obtained six months later . Treated individuals were Ss infection negative by stool microscopy at six months post–treatment . All LTBI alone individuals were anti- Ss-NIE negative and negative for other intestinal helminths . Leukocyte counts and differentials were performed on all individuals using an AcT5 Diff hematology analyzer ( Beckman Coulter , Brea , CA , USA ) . Whole blood was used for ex vivo phenotyping . Briefly , 250μl aliquots of whole blood was added to a cocktail of monoclonal antibodies specific for B cell subtypes and memory markers . B cell phenotyping was performed using antibodies directed against CD45-PerCP ( clone 2D1 , BD ) , CD19-Pacific Blue ( clone H1B19; Biolegend , San Diego , CA , USA ) CD27-APC-Cy7 ( clone M-T271; BD ) , CD21-FITC ( clone B-ly4; BD ) CD20-PE ( clone 2H7; BD ) and CD10-APC ( clone H110a; BD ) . Naive B cells were classified as CD45+ CD19+ CD21+ CD27-; classical memory B cells as CD45+ CD19+ CD21+ CD27+; activated memory B cells as CD45+ CD19+ CD21- CD27+; atypical memory B cells as CD45+ CD19+ CD21-CD27-; immature B cells as CD45+ CD19+ CD21+ CD10+; and plasma cells as CD45+ CD19+ CD21- CD20- [19 , 20] ) . Following 30 min of incubation at room temperature , erythrocytes were lysed using 2 ml of FACS lysing solution ( BD Biosciences , San Jose , CA , USA ) , and cells were washed twice with 2 ml of 1XPBS and suspended in 200 μl of PBS ( Lonza , Walkersville , MD , USA ) . Eight- color flow cytometry was performed on a FACS Canto II flow cytometer with FACSDIVA software , version 6 ( Becton Dickinson , Franklin Lakes , NJ , USA ) . The gating was set by forward and side scatter , and 100 , 000 gated events were acquired . Data were collected and analyzed using FLOW JO ( TreeStar , Ashland , OR , USA ) . Leukocytes were gated using CD45 expression versus side scatter . Absolute counts of the subpopulations were calculated from flow cytometry and hematology data . A representative flow cytometry plot showing the gating strategies for B cell subsets is shown in the S1 Fig . Plasma levels of BAFF ( B cell activating factor ) and APRIL ( A proliferation-inducing ligand ) ( R&D Systems , Minneapolis , MN , USA ) were measured using ELISA kits , according to the manufacturer's instructions . Plasma levels of human TB antibody IgM and IgG ( CUSABIO , College Park , MD , USA ) were measured using ELISA kits , according to the manufacturer's instructions . The TB antigens used in the kit include both membrane and secreted antigens from Mtb H37Rv . The values are expressed as OD units . Data analyses were performed using GraphPad PRISM 6 ( GraphPad Software , Inc . , San Diego , CA , USA ) . Geometric means ( GM ) were used for measurements of central tendency . Statistically significant differences were analyzed using the nonparametric Mann-Whitney U test and Wilcoxon matched pair test . Multiple comparisons were corrected using the Holm’s correction . Correlations were calculated by the Spearman rank correlation test .
The baseline demographics of the study population are shown in Table 1 . As can be seen , there were no differences in age or sex between the groups . As expected , all of the individuals in the LTBI/Ss and Ss groups had IgG antibodies to the NIE antigen , while those in the LTBI ( only ) group did not have IgG antibodies to NIE . Similarly , those in the LTBI/Ss and LTBI groups were positive by QuantiFERON in–tube testing , indicative of latent M . tuberculosis infection , whereas those in the Ss group were not . The baseline hematological characteristics of the study populations are shown in Table 2 . As can be seen , compared to the LTBI group , those with LTBI/Ss or Ss had significantly higher eosinophil and basophil counts . No significant differences in the other hematological parameters were observed . To characterize the antibody responses in LTBI/Ss co-infection , we first measured the levels of Mtb–specific IgM and IgG in LTBI/Ss and compared these to levels in LTBI or Ss . As shown in Fig 1A , the circulating levels of Mtb–specific IgM ( GM of 0 . 11U ) and IgG ( GM of 0 . 89 U ) in LTBI/Ss were significantly lower than in LTBI ( GM IgM of 0 . 4 U and IgG of 1 . 67 U ) , but were no different from those in Ss ( GM IgM 0 . 09 U and GM IgG of 1 . 18 U ) . This suggests that the coincident Ss infection in LTBI/Ss is associated with a reduction in the levels of Mtb-specific antibodies to those seen in Ss alone . When the circulating levels of the B cell growth/differentiation factors BAFF and APRIL were measured in the 3 groups ( Fig 1B ) , the systemic levels of BAFF ( GM of 584 . 6 pg/ml in LTBI/Ss vs . 1118 pg/ml in LTBI and 1007 pg/ml in Ss ) and APRIL ( GM of 468 . 4 pg/ml in LTBI/Ss vs . 607 . 9 pg/ml in LTBI and 242 . 4 pg/ml in Ss ) were significantly lower in LTBI/Ss group compared to those in the in LTBI and Ss groups . To examine the ex vivo B cell ( and B cell subset ) phenotype LTBI/Ss co-infection , we analyzed the absolute numbers of each of the important B cell subsets in the 3 groups . As shown in Fig 2 , the absolute numbers of naïve B cells and classical memory B cells were significantly lower in the LTBI/Ss group compared to the LTBI group . The absolute numbers of immature B cells , activated memory B cells , atypical memory B cells and plasma cells were significantly also lower in LTBI/Ss when compared to LTBI , but they were also significantly lower than the levels seen in Ss . Next , we performed correlation analyses between the levels of IgM and IgG with those of BAFF and APRIL and with the numbers of certain B cell subsets . As shown in Fig 3A , the circulating levels of Mtb–specific IgM showed a significant positive relationship with the systemic levels of BAFF and APRIL; the circulating levels of IgG only showed a significant relationship with BAFF ( but not APRIL ) levels . As shown in Fig 3B , the levels of IgM exhibited a significant positive relationship with numbers of activated and atypical memory B cells as well as plasma cells . Finally , as shown in Fig 3C , the circulating levels of IgG were positively associated with activated memory B cells and plasma cells . These data suggest that both IgM and IgG levels reflect levels of B cell growth factors and B cell memory subsets / plasma cells in LTBI/Ss coinfection . To examine whether the modulation of B cell responses in LTBI/Ss co-infection is reversible following anthelmintic therapy , we measured the levels of Mtb–specific IgM and IgG , the circulating levels of BAFF and APRIL and the numbers of various B cell subsets in LTBI/Ss individuals six months following anthelmintic treatment . As shown in Fig 4A , the circulating levels of Mtb–specific IgM and IgG levels increased significantly six months following anthelmintic treatment whereas there were no significant changes in levels of BAFF and APRIL ( Fig 4B ) in the LTBI/Ss group . Finally , as seen in Fig 4C , the absolute numbers of naïve B cells , immature B cells , classical memory B cells , activated memory B cells , atypical memory B cells and plasma cells were all significantly increased following treatment .
Infection caused by Mtb induces a strong humoral response in humans [10] . Although , T cell responses are considered the main driver of protective immunity , antibodies , may in part , contribute to protective immunity as well [9 , 10] . Thus , human antibodies have been shown to exert inhibitory activity against the growth of Mtb and to neutralize certain mycobacterial antigens ( including virulence factor associated-antigens ) that play important roles in host infection [12 , 21] . Based on an antibody profiling approach , it has also been demonstrated that in LTBI , there are distinct antibody profiles compared to the antibody profiles in those with active TB and that these antibodies can promote processes pivotal in host immunity , including enhanced phagolyosomal maturation , inflammasome activation and macrophage killing of intracellular Mtb [11] . In addition , antibodies , plasma cells and Fc receptor-bearing cells are abundant in TB granulomas [22 , 23] and antibodies against Mtb lipoarabinomannan induce increased bacterial opsonization and restrict growth [21 , 24] . Finally , mice lacking B cells or the ability to secrete antibodies are more susceptible to Mtb infection [25 , 26 , 27] . We tested the hypothesis that helminth co-infection can alter B cell responses in LTBI . Our data clearly reveal that Ss co-infection is associated with major effects on three different arms of the humoral immune response–antibody production , B cell growth factor levels and absolute numbers of B cells among the various subsets . Our data clearly illustrate that Ss co-infection is associated with significant modulation of the systemic levels of Mtb-specific IgM and IgG antibodies in the context of LTBI . IgM and IgG have the ability to opsonize antigens for complement mediated clearance , induce FcR mediated phagocytosis , direct anti-microbial activity by engagement of Fc receptors and augment cell mediated immune responses [28 , 29] . Therefore , the diminished levels of IgM and IgG in LTBI/Ss could potentially have detrimental effects in the immune response to TB . Interestingly , our post-treatment data also confirm a direct association of helminth infections on the modulation of B cell function in TB as the diminished levels of both IgM and IgG increased following successful anthelmintic treatment . Our data also reveal important associations of Ss infection with BAFF and APRIL levels in LTBI/Ss . BAFF and APRIL are TNF-like cytokines that support the survival and differentiation of B cells [14] . BAFF is known to support naïve B cell survival and influences the development of other B cell subsets [14 , 30] . During antigen activation , BAFF upregulates TLR expression , promotes B cell survival and in collaboration with other cytokines , costimulatory signals , or TLR signals , promotes antibody class switching [30 , 31] . Moreover , BAFF in conjunction with inflammatory cytokines causes the induction of memory B cell differentiation into plasma cells [30 , 31] . APRIL is known to mainly function by amplifying the effects of BAFF on B cells [30 , 31] . Thus , BAFF and APRIL can profoundly influence B cell function; our data suggest that Ss infection is associated with significant modulation of their circulating levels . Studies examining peripheral B cell numbers have suggested that B cell numbers are decreased in active TB compared to LTBI [15 , 32 , 33] , a decrease that can normalize following definitive anti-tuberculous treatment [15] . Thus , LTBI appears to be characterized by higher numbers of different B cell subsets , and our data suggest a significant reduction in these numbers is associated with concomitant Ss infection . These results suggest a significant compromise in B cell distribution in the periphery . Combined with the finding of Ss-associated changes in absolute numbers of certain B cell subsets that are associated with changes in Mtb-specific IgM and IgG levels , our data indicate that Ss infection is associated with impaired functional responses of B cells . In summary , our study has demonstrated clearly that Ss infections are associated with altered B cell responses and B cell subset numbers in the context of LTBI coinfection . While our study does not prove causation , it does provide evidence of a significant association of Ss infection with modulation of B cell function . With increasing data supporting a role of antibodies in protective immune responses to TB , our data add to the growing list of immunological mechanisms by which co-existent helminth infections can modulate responses in LTBI . They also suggest that treatment of helminth infection would make for a prudent first step in the conduct of TB vaccine trials in countries endemic for both TB and helminths .
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Helminth infections and tuberculosis are two of the major health care problems worldwide and share a great deal of geographical overlap . Moreover , helminth infections are known to induce immune responses that are antagonistic to the protective immune responses elicited by Mycobacterium tuberculosis . Having previously demonstrated that helminth infections can profoundly alter protective T cell responses needed to control tuberculosis infection , we examined how Strongyloides stercoralis ( Ss ) infection influences B cell responses in latent tuberculosis infection ( LTBI ) in the context of co-infection and showed the Ss infection is associated with dramatic alterations in mycobacterial-specific IgG and IgM responses and levels of B cells and their growth factors BAFF and APRIL . These alterations in B cell responses could have implications for vaccine-induced immune responses to tuberculosis in helminth—endemic countries .
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2017
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Modulation of Mycobacterium tuberculosis-specific humoral immune responses is associated with Strongyloides stercoralis co-infection
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The prevalence of obesity ( body mass index ( BMI ) ≥30 kg/m2 ) is higher in African Americans than in European Americans , even after adjustment for socioeconomic factors , suggesting that genetic factors may explain some of the difference . To identify genetic loci influencing BMI , we carried out a pooled analysis of genome-wide admixture mapping scans in 15 , 280 African Americans from 14 epidemiologic studies . Samples were genotyped at a median of 1 , 411 ancestry-informative markers . After adjusting for age , sex , and study , BMI was analyzed both as a dichotomized ( top 20% versus bottom 20% ) and a continuous trait . We found that a higher percentage of European ancestry was significantly correlated with lower BMI ( ρ = −0 . 042 , P = 1 . 6×10−7 ) . In the dichotomized analysis , we detected two loci on chromosome X as associated with increased African ancestry: the first at Xq25 ( locus-specific LOD = 5 . 94; genome-wide score = 3 . 22; case-control Z = −3 . 94 ) ; and the second at Xq13 . 1 ( locus-specific LOD = 2 . 22; case-control Z = −4 . 62 ) . Quantitative analysis identified a third locus at 5q13 . 3 where higher BMI was highly significantly associated with greater European ancestry ( locus-specific LOD = 6 . 27; genome-wide score = 3 . 46 ) . Further mapping studies with dense sets of markers will be necessary to identify the alleles in these regions of chromosomes X and 5 that may be associated with variation in BMI .
Obesity is a highly prevalent condition that increases the risk of many illnesses such as cardiovascular disease , diabetes , and some cancers . Familial aggregation studies have shown that both genetic and environmental factors are involved in the development of common forms of obesity , and heritability estimates suggest that approximately 40% of variation in body mass index ( BMI ) can be attributed to genetic factors [1] , [2] . The current increase in prevalence of obesity in the U . S . has been hypothesized to be the result of genetic susceptibility in an environment that promotes obesity [3] . James V . Neel in 1962 proposed the “thrifty gene hypothesis” to put these epidemiological observations in an evolutionary context [4] . He suggested that the genetic factors that predispose to weight gain might have been selectively advantageous in ancient environments where food was scarce , but might have become deleterious in modern environments where food is plentiful and lifestyles are generally sedentary . Based on epidemiologic evidence , specific racial/ethnic groups seem to be particularly susceptible to obesity in the U . S . , especially African Americans , Pima Indians , and Pacific Islanders [3] , [5] . Data from the 2003–2004 National Health and Nutritional Examination Survey ( NHANES ) indicate that African Americans are about 1 . 5 times more likely to be obese ( defined as BMI ≥30 kg/m2 ) than European Americans even in homogeneous socioeconomic groups [6] , [7] . Recent genome-wide association studies have shown that variants in the fat mass and obesity-related gene ( FTO ) are significantly associated with obesity in populations of European origin [8]–[10] . It was estimated that a ∼0 . 4 kg/m2 rise in BMI is associated with each copy of the A allele at rs9939609 in populations of European descent [8] . While the association was replicated in East Asian populations [11]–[13] , no association was observed in African Americans [9] , although there is evidence that another SNP ( rs3751812 ) affects the risk of obesity in African Americans as well [14] . These results suggest that the genetic factors predisposing to obesity in African Americans at FTO may be different from that in other populations , although an alternative explanation for these observations is that the causal variant has not been identified , and that the linkage disequilibrium patterns to the causal variant are different in African and non-African populations . To screen for genetic variants modulating BMI in African Americans , we used admixture mapping , a technique that scans the genomes of recently admixed populations and searches for genomic regions in people with disease where there is substantial deviation in one of the parental ancestries compared with the genome average [15]–[20] . To maximize power to detect variants affecting BMI , we carried out a pooled admixture mapping analysis of 15 , 280 African-American samples from 14 studies , including the Atherosclerosis Risk in Communities ( ARIC ) Study , the Breast Cancer Family Registry ( BCFR ) , the Los Angeles component of the Women's Contraceptive and Reproductive Experiences ( CARE ) Study , the Dallas Heart Study ( DHS ) , the Family Investigation of Nephropathy and Diabetes ( FIND ) Study , the Genomics Collaborative ( GCI ) Study , the Health , Aging and Body Composition ( Health ABC ) Study , the Jackson Heart Study ( JHS ) , the Learning the Influence of Family and the Environment ( LIFE ) Study , the Multiethnic Cohort of Los Angeles and Hawaii ( MEC ) , the Osteoporotic Fractures in Men Study ( MrOS ) , the San Francisco Bay Area Breast Cancer Study ( SFBABCS ) , the Study of Osteoporotic Fractures ( SOF ) , and the Women's Circle of Health Study ( WCHS ) .
Our analysis was carried out in 15 , 280 African Americans . Samples were scanned with at least one of three iteratively improved and partially overlapping panels of ancestry-informative markers . The Phase 1 panel was published in Smith et al . 2004 [20] and Reich et al . 2005 [21] . The Phase 2 panel was first published in Reich et al . 2007 [22] . The Phase 3 panel was first published in Nalls et al . 2008 [23] ( http://www . illumina . com/downloads/AfricanAmericanAdmixture_DataSheet . pdf ) . Altogether 4 , 372 markers were genotyped in the present study , with a median of 1 , 411 markers genotyped per sample . We found in practice that all marker panels provided at least 60% of the maximum possible information about ancestry . The samples were assembled from 14 studies ( Table 1 ) . Of these , six ( ARIC , DHS , Health ABC , JHS , MrOS and SOF ) were prospective cohort studies that did not oversample any particular phenotype , and eight ( BCFR , CARE , FIND , GCI , LIFE , MEC , SFBABCS and WCHS ) were studies that oversampled individuals with particular phenotypes , such as breast cancer , end-stage renal disease , type 2 diabetes , hypertension , and prostate cancer . Brief description of each study as well as the number of samples we analyzed after applying various data quality filters are provided in Text S1 . In the six prospective cohort studies , anthropometric measurements were performed using study-specific standardized protocols , and BMI was calculated as weight ( in kg ) divided by height ( in meters ) squared . In the BCFR , SFBABCS and WCHS , BMI was also calculated from height and weight measures taken at the time of study interview by trained research staff . In the remaining studies , BMI was calculated using self-reported weight and height . We used previously published genotyping data to estimate the frequency of each SNP in West Africans and European Americans [20] , [24] , [25] . We only used SNPs for which we were able to obtain data from both West African ( Yoruba ) and European American ( CEU ) populations from the International Haplotype Map . For SNPs in the Phase 1 panel , we also added additional genotyping data from African and European samples , which was the same as the data collected in Smith et al . 2004 [20] . To decrease the likelihood of false-positives in our admixture scans , we applied a series of filters that had the goal of detecting and removing any SNPs with problematic genotyping , as described previously [20]–[22] . Briefly , we applied three previously published filters . ( 1 ) We applied a “mapcheck” filter that tests whether the estimate of ancestry obtained based on the information from that SNP alone is consistent with the estimate of ancestry obtained from neighboring markers; SNPs with discrepancies are removed from analysis . ( 2 ) We applied a “freqcheck” filter that tests whether the observed frequency of a SNP in African Americans is statistically consistent with being a mixture of the frequencies observed in the West Africans and European American samples that we used to represent the ancestral populations . ( 3 ) We finally applied an “ldcheck” filter that for each sample , iteratively removes SNPs that are less informative ( in terms of the information content about ancestry ) until none are within 200 kilobases of each other or are in detectable linkage disequilibrium with each other in the ancestral West African or European American populations [21] , [25] . We required all individuals included in the study to have complete phenotypic information , including BMI , age at the time of measurement , and gender . We also required all individuals to have a full admixture scan , and we removed samples that were outliers with respect to others in the same cohort in the sense of having many fewer genotypes , as we found that this predicts less reliable data . The data for the great majority of the samples we analyze in this study was reported previously [22]–[26] , and hence we do not report further details of the sample genotyping here . We estimated the European and African ancestry at each locus and genome-wide using the ANCESTRYMAP software [19] . ANCESTRYMAP uses a Hidden Markov Model ( HMM ) to combine the weak information about local ancestry that is provided by each marker , into a more confident estimate that takes into account the information from many neighboring markers . The HMM is nested within a Markov Chain Monte Carlo method , which accounts for uncertainty in the unknown parameters: SNP allele frequencies in the West African and European American ancestral populations , the number of generations since mixture and the average proportion of ancestry inherited from ancestral populations . All Markov Chain Monte Carlo runs used 100 burn-in and 200 follow-on iterations , as previously recommended [19] , except for one longer run of 1 , 000 burn-in and 2 , 000 follow-on iterations , which we carried out to check the stability of our results . Samples with an estimated percentage of European ancestry of more than 0 . 85 ( n = 27 ) were excluded from this analysis . Body mass index was defined as described above . For most of our admixture analysis runs , BMI was adjusted for age , age-squared , sex and study , using multivariate linear regression analyses , and the residuals that emerged from this regression analysis were used for subsequent analysis . ANCESTRYMAP [19] was used to test whether individuals with high or low BMI had a proportion of ancestry that was significantly different from the genome average in the same samples . For the dichotomous admixture scans , we defined the top 20% of samples with the highest residuals of BMI as cases and the bottom 20% as controls . Because a prior distribution on risk models is required for the Bayesian statistical analysis in ANCESTRYMAP [19] , we tested a total of 24 pre-specified risk models and assessed overall evidence of association by averaging all models . The first eight models specified 0 . 5- , 0 . 6- , 0 . 7- , 0 . 8- , 1 . 3- , 1 . 5- , 1 . 7- and 2 . 0-fold increased risk due to inheritance of one copy of European ancestral allele for cases , with a control risk of 1 . The next eight models used the same set of risk models for cases , and the control risks were set to be the reciprocal of the case risks . The last eight models used a case risk of 1 , but specified that controls had risks of 0 . 5 , 0 . 6 , 0 . 7 , 0 . 8 , 1 . 3 , 1 . 5 , 1 . 7 and 2 . 0 . These risk models equally tested for both positive and negative associations of BMI with African ancestry [19] . To assess statistical significance , the ANCESTRYMAP software provided two scores: a locus-specific score and a case-control score . A locus-specific score is obtained in cases ( i . e . , case-only analysis ) by calculating the likelihood of the genotyping data at the SNPs at the locus under the risk model and comparing it to the likelihood of the genotyping data at the SNPs at the locus assuming that the locus is uncorrelated to the phenotype [19] . The ratio of these two likelihoods is the “likelihood ratio” , and the log-base-10 of this quantity is the “LOD” score . A locus-specific LOD score of >5 has been recommended as criterion for genome-wide significance and >4 has been recommended as a criterion for genome-wide suggestiveness [27] . To obtain an assessment of the evidence for a risk locus anywhere in the genome—which we call the “genome-wide score”—we averaged the likelihood ratio for association across all loci in the genome , and took the log10 to obtain a genome-wide score . We interpret a genome-wide score>2 as significant and >1 as suggestive as previously recommended [27] . A case-control score was calculated by comparing locus-specific deviations in European ancestry in cases versus controls at each locus across the genome . This score tests whether any deviation in ancestry from the genome-wide average is significantly different comparing cases with controls [19] . If there is no locus associated with disease , the case-control score is expected to be distributed approximately according to a standard normal distribution . For loci identified by this score , the level of genome-wide significance was defined as a case-control Z score<−4 . 06 or >4 . 06 ( i . e . , an uncorrected nominal P<5×10−5 , or a corrected nominal P<0 . 05 after conservatively correcting for 1 , 000 hypotheses tested , corresponding to independent chromosomal chunks assigned to either African or European ancestry ) . The case-control score is particularly important for X chromosome analyses . Case-only admixture analyses of the X chromosome are complicated by the fact that African Americans tend to have lower proportions of European ancestry on the X chromosome than on the autosomes , and thus an X-chromosome-wide-specific estimate of ancestry is required [19] . However , such an X-chromosome-wide estimate of ancestry is difficult to obtain because of the relatively short size of the X chromosome . By contrast , a case-control score is robust to uncertainty in the X-chromosome-wide European ancestry proportion . A systematic bias in the estimate of ancestry at a locus is expected to affect controls as much as cases , and hence is not expected to generate a significant difference between cases and controls . We have now extended ANCESTRYMAP to also allow for association analyses of quantitative traits ( Text S2 ) . Briefly , we applied a normal-quantile transformation to the covariate-adjusted BMI to obtain normally distributed values for subsequent quantitative admixture scans and regression-based association analysis . To test for association to a quantitative trait , we applied a feature , “qtmode” , in ANCESTRYMAP ( see Text S2 for mathematical details ) . In qtmode , each risk model represented a correlation coefficient ( ρ ) of European ancestry with the normally distributed value of the trait . For this analysis , we tested equally spaced risk models of ρ = 0 . 1 , 0 . 08 , 0 . 06 , 0 . 04 , 0 . 02 , −0 . 02 , −0 . 04 , −0 . 06 , −0 . 08 and −0 . 1 . To determine statistical significance , we used the same thresholds of locus-specific LOD and genome-wide scores as described above for the dichotomous analyses . To calculate a 95% credible interval ( CI ) for the position of a locus , we obtained the likelihood ratio for association at each marker across the chromosome where we found an association . This provided a Bayesian posterior probability for the position of the underlying causal variant assuming a flat prior distribution across the region for the position of the disease locus . The central region of this peak containing 95% of the area was used as the CI . Local estimates of ancestry at the admixture peak were obtained using the ANCESTRYMAP software [19] . Heterogeneity of the correlations between the local ancestry and BMI across studies was quantified using the I2 inconsistency metric [28] . To determine the association of BMI with local ancestry at the admixture peak , we performed a linear regression analysis , with the transformed BMI as the dependent variable and the local estimates of ancestry as independent variables . To determine whether there was evidence of residual association with local ancestry after adjustment for global ancestry , we included each individual's percentage of genome-wide European ancestry as a covariate in the regression models . This enabled us to increase power by including all samples in a quantitative analysis , rather than using only a subset of samples with the highest 20% and lowest 20% values in the dichotomous admixture scans described above . This study was conducted according to the principles expressed in the Declaration of Helsinki . All sample collections were carried out according to institutionally approved protocols for study of human subjects and written informed consent was obtained from all subjects .
The average percentage of genome-wide European ancestry in these samples was 19 . 3±11 . 5% based on estimates from the autosomes , and the average percentage of European ancestry on the X chromosome was 15 . 5±8 . 4% . Because the study samples came from different resources and locations across the U . S . , there was significant variation in average European ancestry , either estimated from autosomes or the X chromosome , across studies ( P<0 . 001 ) . The relationship between BMI and percentage of European ancestry is shown in Figure 1 . BMI was inversely correlated with European ancestry as estimated from autosomes , an effect that was weak ( ρ = −0 . 042 ) but statistically significant ( P = 1 . 6×10−7 ) given the large sample size . It was also significantly correlated with European ancestry as estimated from the X chromosome ( ρ = −0 . 046 , P = 1 . 2×10−8 ) . The dichotomous admixture scans detected evidence of genome-wide significant associations between markers on the X chromosome and higher BMI ( Table 2 and Figure 2 ) . By comparing the 20% of samples with the highest and lowest covariate-adjusted BMI , we identified the strongest association for high BMI at Xq25 ( locus-specific LOD = 5 . 94 ) . The genome-wide score was 3 . 22 , substantially exceeding our genome-wide threshold for significance . At the same locus , we also observed a case-control Z score of −3 . 94 standard deviations ( nominal P = 8 . 1×10−5 ) , which also supported an association at this locus , with obese cases having lower European ancestry than non-obese controls . Interestingly , we found another admixture peak at Xq13 . 1 . Although the LOD score at Xq13 . 1 was far from significant ( locus-specific LOD = 2 . 22 ) , this locus had the strongest case-control Z score , −4 . 62 ( nominal P = 3 . 8×10−6 ) , anywhere in the genome . The associations at Xq13 . 1 was nominally genome-wide significant ( P = 3 . 8×10−3 ) after conservatively correcting for 1 , 000 hypotheses tested . To examine the potential impact of heterogeneity across the studies on our admixture-generated signals , we carried out a series of subgroup analyses ( Table 2 ) . When BMI was adjusted for diabetes in samples with information on diabetes status , the association signal at Xq25 grew stronger , with the locus-specific LOD score rising to 6 . 92 , the genome-wide score rising to 3 . 98 , and the case-control Z-score becoming less significant at −3 . 25 . To take into account potential measurement errors from self-reported BMI in 40% of the samples , we also performed admixture scans restricting the samples to those from the six prospective cohort studies where BMI was clinically measured . Similarly strong evidence of association at Xq25 ( locus-specific LOD = 6 . 00; genome-wide score = 3 . 03 ) was found . We also carried out an analysis in which we excluded individuals with diabetes to avoid problems related to co-morbidity and treatment . After removing these samples ( a drop of 23 . 4% of the sample size ) , the signal of association became weaker but remained suggestive ( locus-specific LOD = 4 . 21 ) . Because the admixture peaks we identified were located on chromosome X , which has a different copy number in men and women , we also performed analyses for each gender separately to explore whether the strength of association differed significantly between males and females . We found that the evidence of association at Xq25 was stronger in females ( locus-specific LOD = 4 . 15; N = 1 , 703 ) than in males ( locus-specific LOD = 0 . 96; N = 1 , 351 ) , and that the association signal at Xq13 . 1 in males grew stronger with the local LOD score rising to 4 . 40 ( Run 6 and 7 in Table 2 ) . In the more comprehensive linear regression analysis of local ancestry , there was a significant gender difference at Xq13 . 1 ( P<0 . 026; see below for details ) . In addition to the two peaks on chromosome X , using dichotomous admixture scans we observed a few interesting regions ( Figure 2 and Table S1 ) , particularly locus 5q13 . 3 ( locus-specific LOD = 2 . 48 , Table 2 ) . This locus is unique in that even though its LOD score was far from statistical significance , it had the strongest increase in European ancestry in individuals with high BMI compared to individuals with low BMI ( case-control Z score = 4 . 03 , nominal P = 5 . 6×10−5 ) . The case-control score was marginally significant at genome-wide level , suggesting that higher BMI was , though counter-intuitively , associated with greater European ancestry at 5q13 . 3 locus . By including all African-American samples and using BMI as a continuous trait , our quantitative admixture scan supported and strengthened the evidence of association at 5q13 . 3 locus ( Figure 3 ) . The peak locus-specific LOD score was 6 . 27 and the genome-wide score was 3 . 46 , both reaching the thresholds for genome-wide significance . The local estimate of European ancestry was also extracted for each individual at each of the three admixture peaks and analyzed for association with continuous BMI ( Model 1 in Table 3 ) . Higher local European ancestry both at Xq13 . 1 and Xq25 was significantly and inversely associated with lower values of transformed BMI ( P = 2 . 2×10−11 and P = 4 . 5×10−10 , respectively ) . To examine whether these associations could be fully explained by the significant association between BMI and genome-wide ancestry ( discussed above ) , we further adjusted for genome-wide European ancestry in the multivariate analysis . The residual association of local ancestry with BMI after adjusting for genome-wide ancestry remained significant at both Xq13 . 1 ( P = 1 . 9×10−7 ) and Xq25 ( P = 4 . 1×10−6 ) ( Model 2 in Table 3 ) , indicating that local ancestry had an effect on BMI above and beyond genome-wide ancestry . Both associations were nominally genome-wide significant ( P = 1 . 9×10−4 and P = 4 . 1×10−3 ) after conservatively correcting for 1 , 000 hypotheses tested . A naive analysis suggests that each additional copy of a European ancestral allele at either the Xq13 . 1 or the Xq25 peak is independently associated with a BMI decrease of ∼0 . 1 Z-score units on average ( equivalent to ∼0 . 64 kg/m2 and accounting for 0 . 3% of the variance in BMI , after adjusting for age , age-squared , sex and study ) . The true genetic effects are expected to be somewhat weaker because of discovery bias . The association at the 5q13 . 3 peak was particularly interesting in that it did not achieve statistical significance until the genome-wide estimate of European ancestry was added into the analysis . This was presumably because the locus effect was in the opposite direction to the genome-wide ancestry effect ( thus , the effects cancel in the unadjusted analysis ) . Each additional copy of a European ancestral allele at 5q13 . 3 was significantly ( P = 5 . 8×10−7 ) associated with an increase in BMI of 0 . 09 Z-score units ( naively equal to ∼0 . 59 kg/m2 , accounting for 0 . 3% of the variance in BMI ) , which was nominally significant ( P = 5 . 8×10−4 ) after correcting for the approximately 1 , 000 independent hypotheses tested . For the two peaks on chromosome X , we further examined whether the effects of the local ancestry on BMI were modified by gender . The local ancestry at Xq13 . 1 tended to be more strongly associated with BMI in males than in females . After adjusting for genome-wide European ancestry , the gender difference at Xq13 . 1 was significant ( P for heterogeneity = 0 . 026 , Model 2 in Table 3 ) , which was in line with our results of dichotomous admixture scans . At Xq25 , the effects of local ancestry did not show significant heterogeneity ( P>0 . 05 ) between the two gender groups , either before or after adjusting for genome-wide European ancestry . A potential mechanism for the difference in the strength of association in men and women at the Xq13 . 1 locus is that women carry two copies of chromosome X whereas men carry only one , and hence this may simply reflect a difference in the genetics of the two genders on chromosome X . Since our analysis pooled data from 14 studies , we also examined whether the strength of the admixture associations to BMI on chromosomes X and 5 differed across studies . Local ancestry estimates at each of the three admixture peaks were used to check for homogeneity of their correlation with BMI across studies . There was no evidence of heterogeneity across studies ( all P>0 . 05 , I2 = 0% ) at any of the three peaks ( Table S2 ) . We constructed 95% CI for each of the three loci identified . The 95% CI for the chromosome 5 locus spanned from 69 . 2 to 77 . 2 Mb ( an ∼8 Mb region ) on build 35 of the human genome reference sequence . The 95% CI for the higher admixture peak on chromosome X spanned from 114 . 4 to 124 . 4 Mb ( an ∼10 Mb region ) , and then 95% CI for the other chromosome X admixture peak spanned from 47 . 8 to 89 . 2 Mb , a much broader region ( ∼40 Mb ) .
We have carried out admixture mapping analyses to search for genomic regions associated with BMI . This pooled analysis of samples from 14 studies is the largest admixture scan reported to date . In more than 15 , 000 individuals , we identified a locus on chromosome 5 where greater local European ancestry was associated with higher levels of BMI ( P = 5 . 8×10−7 ) , and two regions on chromosome X where greater local European ancestry was associated with lower levels of BMI ( both P<5 . 0×10−6 ) . Each of these three associations was above and beyond the contribution of genome-wide European ancestry , and each reached genome-wide significance . One of the major strengths of this study is its large sample size , with over 15 , 000 African Americans . However , the large sample also introduced complications in that it required the pooling of several studies which potentially introduced various types of heterogeneity to the study samples . For example , we included individuals with either self-reported BMI or clinically measured BMI in the present study . It is well known that individuals tend to under report their body weight and that this measurement error is potentially more common among heavier individuals . Moreover , this type of measurement error can reduce the statistical power of a study . To assess the potential effects of such measurement error , we performed subgroup analysis by restricting the samples to those from the six population-based cohort studies , where body weight and height were clinically measured according to study protocols ( samples in the six cohorts represented 94% of all samples with measured BMI ) and found the two sets of results to be largely comparable . Additional subgroup analyses , as shown in Table 2 , also confirmed the robustness of our findings [21] , [25] , [26] . The inverse correlation between BMI and percentage of European ancestry estimated on the genome-wide scale confirmed the results from previous studies of smaller sample size and fewer markers [29] , [30] . However , while genome-wide ancestry is likely correlated with local ancestry , it cannot fully capture ancestry information at each locus as there exists variation across the genome in the effects of locus-specific ancestry on obesity . In particular , local European ancestry at 5q13 . 3 was positively associated with BMI , providing the first evidence of a genome-wide significant ancestry association being in the opposite direction to the overall epidemiological association . The 95% CI for the chromosome 5 peak harbors a number of genes , including the cocaine and amphetamine regulated transcript ( CART ) gene , which is a candidate for modulating obesity . CART is a hypothalamic neuropeptide that transmits a physiological anorexigenic signal and is involved in appetite regulation [31] , [32] . Experiments have also shown that CART knock-out mice have increased body weight compared with wild type mice [33] . Genomic regions containing the CART gene have also been linked to both BMI and serum leptin levels in a study of French Caucasian families [34] . SNPs in the 5′ upstream region have been reported to be associated with obesity in Japanese [35] and French [36] . However , association studies in European-related populations [37] , [38] and Pima Indians [39] have not found associations between BMI and the CART gene in these populations , and to our knowledge no published studies have studied CART variants in African Americans . Further mapping work is needed to determine whether the CART gene or other genetic variants in the interval may influence the risk of obesity . There have been very few studies reporting linkage of obesity with markers on the X chromosome [40] , yet three prior studies also reported either suggestive or significant linkage of obesity to the q arm of chromosome X [41]–[43] . Although these three studies were performed in European-American families , they all mapped the obesity locus to the Xq23–q24 region , which overlaps with the 95% CI of the highest admixture peak on chromosome X in our study . The 95% CI in our study contains one particular gene that may be a candidate for obesity susceptibility . The gene solute carrier family 6 member 14 ( SLC6A14 ) is involved in serotonin synthesis and serotonergic receptor mechanisms that have been implicated in appetite control and body weight regulation [44]–[46] . Nominally significant evidence of association between BMI and a SNP ( 22510C/G ) in SLC6A14 was observed in ∼1 , 800 samples from Finland and Sweden ( P = 0 . 003 ) , and females were found to contribute most to this particular observed association [43] . The gender difference observed in the previous study [43] is in line with the results from our dichotomous admixture scans at this locus , although the difference observed between men and women in our study did not reach statistical significance . Another potential candidate gene near the highest admixture peak is the cullin 4B ( CUL4B ) gene . CUL4B was recently identified as a causative gene for an X-linked mental retardation syndrome , which was associated with several clinical features , including central obesity [47] . Although we did not detect a significant association in the region of the FTO gene , we noticed that the second highest admixture peak ( locus-specific LOD = 3 . 68 ) identified in our quantitative scans was on chromosome 16 , about 5 . 6 Mb away from the FTO gene , and its 99% CI spanned 51 . 5 to 66 . 8 Mb ( on build 35 of the human reference sequence ) , which is a region that includes the FTO gene . ( However , FTO is outside the 95% CI . ) Further fine-mapping analysis may determine whether additional variations in FTO may explain the intriguing admixture signal in this region . The melanocortin-4 receptor ( MC4R ) gene , located on chromosome 18q21 . 32 , is the second obesity-susceptibility gene discovered by genome-wide association studies in individuals of European origin [48] , [49] . However , our dichotomous and quantitative admixture scans did not identify any admixture signals on chromosome 18q . In summary , we have carried out a genome-wide admixture mapping scan in 15 , 280 African Americans and have identified three loci , 5q13 . 3 , Xq13 . 1 and Xq25 , that may harbor genetic variants associated with variations in BMI . The local ancestry associations to BMI at each of the three admixture-generated peaks were statistically significant , suggesting the presence of a genetic effect at these loci above and beyond the effects of genome-wide ancestry . Follow-up fine mapping and focused analysis of each locus using data that emerge from genome-wide association studies in African Americans with measured BMI will be crucial to determine whether these regions harbor genetic variants predisposing to obesity . The present study is also methodologically significant in illustrating how searches for genes in African Americans and diverse populations can result in the detection of genetic loci that have eluded discovery in European-derived populations , perhaps because the underlying variants are too rare in the latter populations . However , there is no reason to think that the three loci we have identified are biologically important only in African Americans . Replication and fine-mapping studies in other ethnic groups , including Hispanic Americans and Pacific Islanders , with a similar risk of obesity to African Americans , and even European Americans and East Asians with a lower but still important rate of this condition , may further elucidate these regions of the genome . Studying multiple populations to fine-map a locus highlighted in an admixture scan can be more informative than studying any one population , as was previously demonstrated by our use of a multi-ethnic cohort to fine-map prostate cancer risk factors at 8q24 [50] .
|
Obesity is about 1 . 5-fold more prevalent in African Americans than European Americans . To determine whether genetic background may contribute to this observed disparity , we scanned the genomes of African Americans , searching for genomic regions where obese individuals have a difference from the average proportion of African ancestry . By examining genetic data from more than 15 , 000 African Americans , we show that the proportion of European ancestry is inversely correlated with BMI . In obese individuals , we detect two loci with increased African ancestry on chromosome X ( Xq13 . 1 and Xq25 ) and one locus with increased European ancestry on chromosome 5 ( 5q13 . 3 ) . The 5q13 . 3 and Xq25 regions both contain genes that are known to be involved in appetite regulation . Our results suggest that genetic factors may contribute to the difference in obesity prevalence between African Americans and European Americans . Further studies of the regions may identify the causative variants affecting susceptibility to obesity .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"public",
"health",
"and",
"epidemiology/epidemiology",
"diabetes",
"and",
"endocrinology/obesity",
"genetics",
"and",
"genomics/complex",
"traits",
"genetics",
"and",
"genomics/population",
"genetics"
] |
2009
|
Admixture Mapping of 15,280 African Americans Identifies Obesity Susceptibility Loci on Chromosomes 5 and X
|
G-quadruplexes are non-canonical nucleic-acid structures that control transcription , replication , and recombination in organisms . G-quadruplexes are present in eukaryotes , prokaryotes , and viruses . In the latter , mounting evidence indicates their key biological activity . Since data on viruses are scattered , we here present a comprehensive analysis of potential quadruplex-forming sequences ( PQS ) in the genome of all known viruses that can infect humans . We show that occurrence and location of PQSs are features characteristic of each virus class and family . Our statistical analysis proves that their presence within the viral genome is orderly arranged , as indicated by the possibility to correctly assign up to two-thirds of viruses to their exact class based on the PQS classification . For each virus we provide: i ) the list of all PQS present in the genome ( positive and negative strands ) , ii ) their position in the viral genome , iii ) the degree of conservation among strains of each PQS in its genome context , iv ) the statistical significance of PQS abundance . This information is accessible from a database to allow the easy navigation of the results: http://www . medcomp . medicina . unipd . it/main_site/doku . php ? id=g4virus . The availability of these data will greatly expedite research on G-quadruplex in viruses , with the possibility to accelerate finding therapeutic opportunities to numerous and some fearsome human diseases .
G-quadruplexes ( G4s ) are nucleic-acid secondary structures that may form in single-stranded DNA and RNA G-rich sequences under physiological conditions [1] . Four Gs bind via Hoogsteen-type base-pairing to yield G-quartets: stacking of at least two G-quartets leads to G4 formation , through π-π interactions between aromatic systems of G-quartets . K+ cations in the central cavity relieve repulsion among oxygen atoms and specifically support G4 formation and stability [2] . In the human genome , potential quadruplex-forming sequences ( PQS ) are clustered at definite genomic regions , such as telomeres , oncogene promoters , immunoglobulin switch regions , DNA replication origins and recombination sites [3] . In RNA , G4s and PQSs were mapped in mRNAs and in non-coding RNAs ( ncRNAs ) [4] , such as long non-coding RNAs ( lncRNAs ) [5] and precursor microRNAs ( pre-miRNAs ) [6] indicating the potential of RNA G4s to regulate both pre- and post-transcriptional gene expression [7 , 8] . Viruses are intracellular parasites that replicate by exploiting the cell replication and protein synthesis machineries . Viruses that infect humans are very diverse and , according to the Baltimore classification , they can be divided in seven groups based on the type of their genome and mechanism of genome replication: DNA viruses with 1 ) double-stranded ( ds ) and 2 ) single-stranded ( ss ) genome; RNA viruses with 3 ) ds genome , or ss genome with 4 ) positive ( ssRNA ( + ) ) or 5 ) negative ( ssRNA ( - ) ) polarity; 6 ) RNA or 7 ) DNA viruses with reverse transcription ( RT ) ability , whose genome is converted from RNA to DNA during the virus replication cycle ( Table 1 ) . Each of these classes possesses a peculiar replication cycle [9] . The presence of G4s in viruses and their involvement in virus key steps is increasingly evident in most of the Baltimore groups [10 , 11] . In the dsDNA group , G4s were described in both Herpesviridae and Papillomaviridae families [12–20] . In ssDNA viruses , the presence of G4s was reported in the adeno-associated virus genome [21] . RNA G4s were described in the genomes of both ssRNA ( + ) ( i . e . Zika , hepatitis C virus ( HCV ) [22 , 23] , and the severe acute respiratory syndrome ( SARS ) coronavirus [24 , 25] ) and ssRNA ( - ) viruses ( i . e . Ebola virus [26] ) . A G4 was also detected in hepatitis B virus ( HBV ) genome , the only member of dsDNA viruses with RT activity [27] . Finally , functionally significant G4s were identified both in the RNA and DNA proviral genome of the human immunodeficiency virus ( HIV ) , a retrovirus belonging to group 6 ( Table 1 ) [28–35] , and [33 , 34]in the LTR region of lentiviruses in general ( ssRNA RT ) [36] . Given this amount of scattered data , we here aimed at analyzing the presence of PQSs in the genome of all known viruses that can cause infections in humans . The analysis is performed at two distinct levels , globally for each viral genome and individually for each detected PQS . We asked the following: is the number of PQSs found in a viral genome simply due to chance , hence trivially reflecting genomic G/C content ? And how much is each PQS conserved among the strains belonging to a viral species ? To address these questions , we collected the whole viral genomes deposited in databanks , scanned them to detect all PQSs , and performed different statistical evaluations following the data analysis workflow shown in Fig 1 . The detailed information on PQSs present in each human virus is available in an easily accessible web site with interactive graphics and genome browser visualization tools ( http://www . medcomp . medicina . unipd . it/main_site/doku . php ? id=g4virus ) .
All known viruses that cause infections in humans , according to the Viral Zone ExPASy web site ( http://viralzone . expasy . org/all_by_species/678 . html ) , were grouped in 7 classes according to Baltimore classification , which takes into account the viral genome nature: dsDNA , ssDNA , dsRNA , ssRNA ( + ) , ssRNA ( - ) , ssRNA ( RT ) and dsDNA ( RT ) . Different replication strategies and structural similarities allow to further divide viruses in families ( Table 1 ) . The complete list of reference sequences for each virus included in the analyses is reported in S1 Table . PQSs in viral genomes were searched by looking for the following patterns: [G ( 2 ) N ( 1–7 ) ] ( 3 ) G ( 2 ) , [G ( 3 ) N ( 1–12 ) ] ( 3 ) G ( 3 ) and [G ( 4 ) N ( 1–12 ) ] ( 3 ) G ( 4 ) , where both island and loop lengths were chosen to provide a comprehensive detection . We decided to expand the search to PQSs with very short islands and quite extended loops for the following reasons: first , the folding of PQS with GG-islands has been previously demonstrated in viruses [32]; second , since many viruses possess a RNA genome , and considering that RNA G4s are more stable than their DNA counterparts [37] , PQSs with only two tetrads have a reasonable chance to fold in viral RNA genomes or in their intermediates . Finally , while long loops are known to destabilize G4 structures , their presence is anyway compatible with the folding of stable G4s at physiological temperature [38] . PQSs with bulged islands [39] and intermolecular G4s are not considered in the present study . PQSs were searched in the positive and negative strand of each virus genome sequence , since both filaments are present and important in different stages of the viral replicative cycle of all virus classes . As the length of virus genomes greatly varies , i . e . from 235 , 646 nucleotides ( nts ) of the human cytomegalovirus ( HCMV ) to 1 , 682 nts of hepatitis delta virus ( HDV ) , we reported the number of PQS independently of the genome length by normalizing their number per 1 , 000 nts ( Fig 2 ) . The PQS distribution for both the positive and negative strands is shown as a box plot for each Baltimore virus class , whereas the PQS count for each virus within each class is shown as a dot besides the box plot ( Fig 2 ) . The negative strand of retroviruses ( ssRNA ( RT ) viruses ) , ssDNA viruses and both strands of dsDNA viruses showed the largest presence of PQSs made of GG- , GGG- and GGGG-islands ( box plots , Fig 2 ) . Both strands of genomes of single virus families belonging to these groups and to ssRNA ( + ) and ssRNA ( - ) were enriched in PQSs of all G-islands types ( dot plots , Fig 2 ) . Conversely , dsRNA and dsDNA ( RT ) viruses notably lacked the presence of PQSs . Then , we evaluated the conservation of PQSs among different strains of each viral species , hypothesizing that the presence of a conserved PQS within a less conserved genome environment could be an indication of a G4 with a biological function [40] . To allow for the evaluation of PQS conservation in the local context of viral genomes , we computed the “G4 scaffold conservation index” ( G4_SCI ) for each PQS in each virus species . This value measures the degree of conservation of G-islands that are necessary and sufficient to form a PQS: the higher the score , the higher the conservation of the PQS . An example of the results from such analysis is reported in Fig 3 for the lymphocytic choriomeningitis virus ( segment S ) : all PQSs detected in the virus are plotted as vertical bars , the height and position of which represent the G4_SCI on the y-axis and the genome coordinates on the x-axis , respectively . In addition , the local sequence conservation ( LSC ) of the viral genome , calculated with a sliding window approach on all available viral sequences , is reported alongside as a red broken line . This visualization method allows the prompt identification of the presence , position , and conservation of G-islands within PQSs , together with the overall local conservation of the genomic context . Moreover , the degree of conservation of the connecting regions ( loops ) with respect to G-islands ( the loop_conservation value ) was calculated as the difference between G4_SCI and LSC . Positive and negative loop_conservation scores indicate , respectively , lower and higher conservation of connecting regions compared to the conservation of G-islands . Values close to zero mean that both G-islands and connecting loops show the same level of sequence conservation . In Fig 3 , three PQSs formed by highly conserved GG-islands are shown for the S segment of lymphocytic choriomeningitis virus , present in genomic regions both well and less well conserved ( Fig 3 at positions 1 , 790 in the positive strand , 1 , 760 and 2 , 680 in the negative strand ) . This kind of analysis is available for all PQSs of all human virus species at http://www . medcomp . medicina . unipd . it/main_site/doku . php ? id=g4virus ( loop_conservation values are included in tarballs downloadable for each viral class of the Baltimore classification , whereas each virus species has a dedicated page displaying all graphical representations ) . To assess the results , we retrieved from the literature all the available experimentally validated G4s detected in human viruses . All patterns were confirmed also by our analysis and the complete list is reported in S2 Table , together with the genomic coordinates of the predicted PQSs . G4 formation may be largely affected by G/C content , which greatly varies in viral genomes ( from 76% of Cercopithecine 2 herpes virus to 27% of Yaba like disease virus ) . Moreover , it has been shown that some di- and trinucleotides are over- or under-represented in certain viruses [41 , 42] and , in the context of PQSs , this means that their abundance could be biased by unexpected frequencies of guanine homopolymers . G-island frequencies higher or lower than expected would lead to a potential over- or under-representation of PQSs , respectively . To check whether the presence of PQSs was statistically relevant or whether it occurred by pure chance , we compared the results obtained from real viral genomes with those obtained by two different simulation strategies . The first one ( single nucleotide assembling ) assumes that the occurrence of each DNA base in the genome is independent [43]; the second ( G-island reshuffling ) considers that short sequences of a given length ( k-mer ) could be over- or under-represented in certain viral genomes [41 , 42] . In the former case , sequences were generated with the same composition of nucleotides but different order with respect to references; in the latter , sequences were produced by reshuffling the positions of G-islands while keeping constant their number . For each virus and simulation strategy , we produced 10 , 000 random sequences , which were screened with our PQSs detection pipeline . Real and simulated data were compared by computing a P-value , defined as twice the smaller proportion of simulated sequences that exhibit , respectively , a higher and lower count of PQSs as compared to the median value of all the available complete genome sequences for a certain virus . Hence , a P-value close to 1 means that the median PQS content in real viral sequences is not significant if compared to a random distribution; conversely , a P-value close to 0 means that PQS content is highly significant . This interpretation holds independently of the length of the genome and/or of the prevalence of either G/C bases or G-islands , as we compare the number of PQSs in a viral genome with the one we would expect in a simulated genome of the same length and of either the same base or G-island composition . To account for possible high discreteness of the data , a less conservative version of the P-value , called the mid-P value [44] , was used . Segment diagrams of the mid-P values of the Baltimore grouped viruses are reported in S1 and S2 Figs [45] . The number of viruses whose median PQS count is significant at the 10% level is listed in Table 2 ( virus names in S3 Table ) with the indication of whether this median count is either higher or lower than the PQS count in simulated sequences . Our data show that most members of the dsDNA , ssDNA , and ssRNA ( RT ) present a highly significant content of PQSs formed by GG- , GGG- and/or GGGG-islands in one or both strands . ssRNA ( - ) and ssRNA ( + ) classes are heterogeneous since some viruses are highly significant in any PQS category ( from GG- to GGGG-islands ) , while others are not ( see below ) . The presence of PQSs in members of the dsRNA group is notably less significant . Interestingly , few viruses display a smaller amount of PQSs than expected: both Sagiyama virus and Human coronavirus HKU1 are depleted of PQSs belonging to GG-islands category in the positive genome strand when compared with both simulation strategies based on single nucleotide assembling and GG-island reshuffling . In addition , Human parainfluenza virus 2 is poor of PQSs made of GG-island in the positive genome strand but is enriched in both GG- and GGG-type PQSs in the negative strand . Overall , if we consider the viruses that contain at least one PQS in either the real or the simulated genomes , we observe that the increase in G-islands’ length corresponds to a decrease in the absolute number of viruses containing PQSs , but it also corresponds to a dramatic increase in the fraction of them that is statistically significant . By looking at the family level of viral classification , which is far more homogeneous than the Baltimore groups , some virus families emerge as prominently enriched in PQSs . Among them , Herpesviridae is not only the one with the highest PQS content , but most of its members display significantly more PQSs than expected in both genome strands and in all considered G-island lengths . Notably , some of the viruses belonging to Herpesviridae and showing the highest G/C content are statistically enriched in PQSs . This suggests that simply having a high G/C content is not a sufficient condition to justify the presence of such a high number of PQSs . Other viral families that are consistently enriched in PQSs are Adenoviridae and Papillomaviridae , especially in GG- ( both strands ) and GGG-island ( positive strand ) types . Poxviridae and Parvoviridae show an enrichment of GG-type PQSs in both genome strands , whereas the same pattern is enriched in the positive strand of all Anelloviridae members and in the negative strand of most Paramyxoviridae and Retroviridae viruses . All other families are generally not enriched in PQSs in any of the evaluated categories , with only a few exceptions that are listed in the following: L segments of Lassa virus and Lymphocytic choriomeningitis virus ( Arenaviridae ) , Wu and Merkel cell polyomaviruses ( Polyomaviridae ) , Salivirus ( Picornaviridae ) , M and S segments of respectively Crimean-Congo hemorrhagic fever virus and Rift Valley fever virus ( Bunyaviridae ) . By comparing the results obtained independently from the two simulation strategies it is possible to draw additional conclusions . First , in most cases the results are concordant , meaning that both simulations show similar trends in the statistical significance . Nonetheless , the overall number of viruses whose PQS content is significantly different with respect to simulated data is higher when real viral genomes are compared to those generated by single nucleotide assembling . This difference indicates that viral genome k-mer composition is indeed affecting the probability of randomly finding PQSs , at least in a proportion of viruses as shown in Fig 4: in the heatmaps , viruses that are significant in only one of the two simulations are reported for GG- and GGG-island patterns , whereas no such cases were found for GGGG-type PQSs . Finally , some remarkable exceptions exist where both simulations return a significant p-value , but with an opposite meaning . This is the case of two members of the Poxviridae family , namely Molluscum contagiosum virus and Orf virus , which are enriched in GG- and GGG-type PQSs in both strands of their genomes if compared with the islands reshuffling simulation but show the opposite behavior when compared with the single nucleotide assembling ( they are also reported in Fig 4 ) . While the full meaning of this observation is not clear to us , it seems that these viruses possess far less PQSs than they could have , but at the same time they are able to cluster their relatively few G-islands in more PQSs than expected . To check the prevalent positions of PQSs in virus genomes , we compared the coordinates of predicted PQSs with the available information regarding viral genome features . Genome coordinates were extracted for coding sequences ( CDS ) , repeat regions ( RR ) , 5’- and 3’-untranslated ( UTR ) , and promoter regions . While CDS and RR are explicitly defined in RefSeq and GenBank databases , the annotation of UTRs and promoters is more inconsistent , being defined only for some viral species . For this reason , the annotations of genes and CDSs were exploited to indirectly extract the coordinates of 5’–and 3’–regulatory regions ( see Materials and methods for details ) . To determine the localization of PQSs in viral genomes , the overlap extent between PQSs and genomic features was computed . Given the vast heterogeneity of the annotations reported in the feature fields , a manual revision was required to fix potential inconsistencies in annotations , regarding both keywords and coordinates . A revision was performed when possible , while controversial and uncertain annotations were not considered . These analyses are presented as bar charts for individual viral classes and G-island pattern types ( GG- , GGG- , GGGG-island ) ( S3–S5 Figs ) . As regards the GGG-island type , the herpesvirus family of dsDNA viruses presents PQSs distributed along all the four identified genomic features , with a particularly high concentration in RR and , in some members , in the 5’–regulatory region . This feature is consistent with the reported extent of G4s in HSV-1 , which are mainly clustered in the RR of the virus genome [12 , 13] . Conversely , viruses belonging to the ssRNA ( + ) and ssRNA ( - ) classes show PQSs mainly grouped in CDS and in the 3’- and 5’-regulatory regions , respectively . HIV-1 , belonging to ssRNA ( RT ) virus class , presents PQSs of the GGG-island type mainly in the RR and 3’-regulatory regions and in part in CDS . This distribution confirms previous data [28 , 32] . Conversely , other retroviruses ( ssRNA ( RT ) ) such as HTLV-1 and HTLV-2 , display PQSs in the CDS . Given the lower stringency of PQSs of the GG-island type , these are more widely distributed along the four identified genomic features , whereas the most stringent PQSs of the GGGG type , present only in herpesviruses ( dsDNA ) and HTLV-1 ( ssRNA ( RT ) ) , show a clear-cut localization in the RR and CDS , respectively . These data indicate that the localization of PQSs in the viral genomes differs in virus classes . In this line of thinking , we asked whether the observed number of PQSs , and more precisely its statistical significance with respect to the two random assembling scenarios , is representative for a specific Baltimore class . To answer this question , we checked whether it is possible to classify each virus to one of the six classes considered , that is , dsDNA , ssDNA , dsRNA , ssRNA ( + ) , ssRNA ( - ) and ssRNA ( RT ) , based on the information of how significant its median PQS counts are . We used a classifier built on multinomial logistic regression , as this method is both interpretable and robust to unbalanced group sizes as long as the group sizes are large enough . To avoid the latter drawback , we excluded from the model fit the hepatitis B virus , the only virus classified as dsDNA ( RT ) , and the two unclassified hepatitis delta and hepatitis E viruses . Six features were used to classify the viruses , i . e . the six mid-P values ( those calculated for GG- , GGG- , GGGG- , both in the positive and negative strand ) which qualify the PQS content of the real viral sequences . The values were multiplied by 1 or -1 depending on whether the median PQS count was over- or under-represented . Since real and corresponding simulated sequences contain the same base or G-islands composition , the classification model based on PQS content does not depend on the highly variable genome length and G/C content in the different virus classes but is specifically designed on the peculiar presence or absence of PQSs in each viral class . Furthermore , 34 viruses with no PQS count in all three G-island types in both the positive and negative strand and non-significant mid-P values at the 10% level were excluded from the analysis . We re-classified every viral genome used in our assessment using the discriminant function obtained from a leave-one-out analysis . This latter technique allowed us to accurately estimate how our classifier performs without the need to split our data into a training and a test set . The corresponding confusion matrix is given in Table 3 from where it is possible to extract the overall percentage of correct classifications that amount to 66 . 7% for the single nucleotide assembling model and 68 . 1% for the G-island reshuffling model . The agreement is good for the dsDNA , ssDNA , dsRNA , ssRNA ( + ) and ssRNA ( - ) classes . The two unclassified genomes of the hepatitis delta and hepatitis E viruses were classified as ssRNA ( + ) .
In this work we provide: i ) the list of PQSs present in all human virus genomes ( both positive and negative strands ) , ii ) their position in the viral genome , iii ) the degree of conservation of both G-islands and loops vs . the genome , iv ) the statistical significance of PQS abundance in each virus . Our data show that viruses belonging to dsDNA , ssDNA , ssRNA ( RT ) and , to a less extent , ssRNA ( + ) and ssRNA ( - ) display the largest presence of GG- , GGG- and GGGG-type PQSs ( box plots , Fig 2 ) and that the presence of PQSs in all these virus groups is statistically significant ( segment diagrams , S1 and S2 Figs ) . Both results support a role of G4s in the virus biology: indeed , some G4s predicted in this work were already reported in viruses and were shown to possess specific and different functions . We evidenced some general trends and exceptions that are worth noting if seen in comparative terms among all viruses considered in this study . Starting from the general features we noted: i ) high G/C content is not sufficient per se to generate a high number of PQSs , as observed in G/C rich members of Herpesviridae family that are richer of PQSs than expected . ii ) The statistical significance of PQSs found in real sequences tends to decrease when G-islands reshuffling ( ISL ) is compared with the corresponding single nucleotide assembling ( SN ) , as is appreciable from the heatmap in Fig 4 ( more intense color in the heatmap boxes ) . This suggests that short sequences of a given length ( k-mer ) could be over- or under-represented in certain viral genomes , as already reported in the literature [41 , 42] . We observed that viral genomes enriched in PQSs also contain a higher number of G-islands than expected from mere nucleotide composition , especially evident in the GG-islands . iii ) The unevenly distribution of PQSs can be used to classify membership of a virus in its corresponding category . This was not predictable a priori but up to two-thirds of unequivocal assignments suggest that for some viruses the PQS content works as a distinctive feature . iv ) PQS localization shows differences in some virus classes , but this outcome is still incomplete due to lack of information in the databases about virus genomic features and partitioning into regulatory and coding regions . Some other interesting observations are worth reporting as either special cases or exceptions . To start with , the ssRNA ( - ) group is the most heterogeneous one , since some viral species are significantly enriched in PQSs up to the most extended G-island type ( GGGG ) , while others lack this feature . Surprisingly , two viruses of the dsDNA group , which was generally highly enriched in PQSs , show a significantly lower presence of PQSs than expected in a random sequence with the same G/C content ( SN , S3 Table ) , even though the opposite result was observed vs . simulated genomes with the same G-islands content as the real ones ( ISL , S3 Table ) . These two viruses , i . e . Molluscum contagiosum virus and Orf virus , are the only ones belonging to genera other than the Orthopoxvirus within the Poxviridae family that cause skin lesions . Finally , dsRNA and dsDNA ( RT ) viruses are notably poor in PQSs and with mostly null statistical significance; however , single PQSs are highly conserved ( e . g . rotavirus a segment 6 ) , therefore still conveying potential biological interest . These data indicate that PQSs are mainly present in ss-genome viruses , which in principle are more suitable to fold into G4s since they do not require unfolding from a fully complementary strand . The major exception to this evidence is the Herpesviridae family of dsDNA viruses . In this case , most PQSs reported here and also previously described [12 , 13] form in repeated regions . It is possible that repeated sequences are more prone to alternative folding , as shown by several non-canonical structures that form in repeated regions of the DNA [46–49] . However , for some herpesviruses many PQSs are also present in regulatory regions , which may indicate yet undiscovered functional roles . To note that the investigation of PQSs was performed on a maximum window of 52 nucleotides in the case of isolated G4s . Alternatively , when more than four G-islands are found complying with the maximum distance allowed between consecutive islands , this window is extended as long as the rules are satisfied , thus including multiple distinct PQSs or potential isoforms . However , it is possible , especially for the ss genomes , that bases more distant in the primary sequence interact among each other , therefore expanding the repertoire of G4 structures . The significant enrichment of PQSs in many viruses with respect to the corresponding randomized genomes is an indication that the clustering of G-islands did not occur by pure chance , suggesting a specific biological role of the G4 structures [40] . Complementary to this , the analysis of the PQS conservation highlights every PQS that is conserved among viral strains . Since one of the peculiarities of viral genomes is their fast mutation rate , the strong conservation of a specific G-island pattern among strains is per se an indication of the biological relevance of a PQS . In light of this , single conserved PQSs in viruses that do not display statistically significant PQS enrichment may retain key functional roles . The meaning of PQS conservation can have different explanations for the different viruses analyzed in this study . Given the high variability in the number of full-genome sequences available for each species , a more general evaluation of PQS conservation at higher taxonomic ranks ( e . g . at the family level ) could have been more informative . Nonetheless , generating and analyzing whole-genome multiple alignments involving different viral species , even if belonging to the same family , is almost prohibitive given the huge variability that is usually present in their genome sequences . Hence , the conservation of each PQS has to be considered on a case by case basis , exploiting the visualization tools provided in the website . As an example , an interesting approach could be looking at the discrepancy between the conservation of G-islands and connecting loops ( loop_conservation ) as an additional indication on the likeliness of biological implications of a specific PQS . A positive loop_conservation value highlights G-islands more conserved than their connecting loops , suggesting that only the PQS scaffold is required for mechanisms that are important for the virus life cycle , while the loops are dispensable . Considering the high mutation rate of viruses , this type of conservation indicates sequences where G4 formation is most likely essential . Equally conserved G-islands and loops ( loop_conservation value = 0 ) imply that both the PQS scaffold and connecting loops are potentially relevant for the virus and probably involve interactors that specifically recognize them . In this case , the high sequence conservation , especially in CDS , may depend on the required conservation of that peculiar gene product rather than the presence of a G4 structure . Nonetheless , the option of targeting these conserved G4s for therapeutic purposes remains unaltered and the availability of specific and conserved loops may only enhance the chance of finding selective ligands [50] . Therefore , from this point of view , the ‘zero’ class is the best scenario for the development of specific drugs . The “negative” loop_conservation value scenario is of less immediate interpretation: it is possible that false positive hits fall in this category as it is unexpected that G-islands are less conserved than their connecting loops . The evidence provided here , the previous studies on G4s in viruses , and the possibility to correctly classify the majority of viruses based on their PQSs ( Table 3 ) suggest that most of the virus classes adopted G4-mediated mechanisms to control their viral cycles . Together with the associated database , which is projected to be periodically updated to keep up with the fast-growing list of novel sequenced viruses , this work offers comprehensive data to guide researchers in the choice of the most significant PQSs within a human virus genome of interest . Hopefully , this will accelerate research in this area with the identification of new G4-mediated mechanisms in viruses and the development of effective and innovative therapeutics .
The complete list of viral species able to infect humans was retrieved from http://viralzone . expasy . org/all_by_species/678 . html ( accessed in April 2016 ) and , for each of them , all available complete genome sequences were downloaded from GenBank . Multiple alignments were built for each virus with usearch8 [51] , using a permissive identity threshold ( 60% ) to account for viral variability . Since in some cases nucleotide heterogeneity within viral species exceeded this value , multiple clusters of aligned sequences were obtained for some viruses , representing distinct genotypes . Considering the difficulty of obtaining high quality alignments beyond this limit of nucleotide similarity , all clusters obtained with this method were kept separate , manually assigned to specific genotypes and independently processed in the downstream analyses . One genome per each group of aligned sequences was chosen to serve as reference sequence , possibly belonging to the manually curated RefSeq database [https://www . ncbi . nlm . nih . gov/refseq/] . The complete list of selected reference sequences is reported in S1 Table . PQSs were searched in all multiple-aligned nucleotide sequences with an in-house developed tool , as previously described [36 , 52] . Briefly , a PQS was reported when at least four consecutive guanine islands ( G-islands ) were detected . If ‘l’ is the minimum number of G in every G-island of a PQS and ‘d’ is the maximum distance allowed between two consecutive G-islands , the following combinations of ‘l’ and ‘d’ were searched: l = 2 and d = 7; l = 3 and d = 12; l = 4 and d = 12 . Patterns in the negative strands of viral genomes were searched by looking for cytosines ( Cs ) instead of Gs . The conservation of each PQS in the multiple aligned genomes of the viruses was determined by looking at the conservation not only of the G-islands but also their connecting loops . We computed different indexes to measure the nucleotide sequence conservation of viral genomes and PQSs: LSCG4 is calculated as the average of LSC windows overlapping the PQS . LSC measure is computed within a sliding window of fixed length ( length 20 , shift 10 ) , averaging the conservation values of each position extracted from the multiple sequence alignments with Jalview [53] . They are formally defined as: LSC=∑i=120cmaxi20 LSCG4=LSC1+ . . . +LSCnn where cmax i is the maximum conservation at position i of the multiple aligned sequences and n is the number of windows overlapping the PQS . The results of these analyses are presented individually for each virus and PQS ( http://www . medcomp . medicina . unipd . it/main_site/doku . php ? id=g4virus ) , together with the calculated profiles of simple linguistic complexity and Shannon entropy that can highlight other potential local features of the sequence ( e . g . repeats and low complexity regions ) [54] . All charts were generated with Plotly [https://plot . ly] , exploiting Pandas [55] and Numpy Python libraries [56] . Multiple alignments are visualized with MSAViewer[57] and genomic features with JBrowse 1 . 15 . 0 [58] . Unless otherwise stated , analyses were conducted with ad hoc developed Python and Perl scripts , which are available in the website ( scripts . tar . gz ) . To determine whether the presence of PQSs in a virus is a conserved feature or it is only a consequence of its nucleotide composition , simulated viral genomes were generated and compared with real data . Two different strategies were adopted to generate simulated data: The simulated sequences were scanned for the presence of PQSs as previously described . The 10 , 000 counts obtained for each simulation formed the empirical distribution for PQS prevalence under the hypothesis of random assembling of the genome in the SN and ISL models respectively . The mid-P value was calculated using a homemade function . The semiparametric classifier used to assign the virus to its exact class relying on its PQS content was based on the ‘multinom’ function of the R package ‘nnet’ . The feature tables containing viral genome annotations were downloaded from RefSeq or GenBank for all the reference sequences reported in S1 Table . Genome coordinates were extracted for coding sequences ( ‘CDS’ ) , repeat regions ( ‘repeat_region’ ) , 5’- and 3’-untranslated ( UTR ) and promoter regions . Given the annotation inconsistency of promoters and UTRs , two new feature categories were created , 5’–and 3’–regulatory regions that were defined by exploiting the annotation of genes and CDSs . We calculated boundaries for genes in the positive strand of viral genomes as follows: 5’–regulatory = Sgene − Scds; 3’–regulatory = Ecds—Egene . For the genes in the negative strand of viral genomes we defined: 5’–regulatory = Scds—Sgene; 3’–regulatory = Egene—Ecds . Sgene , Scds , Egene and Ecds are the Start ( S ) and End ( E ) of genes and CDSs as extracted from the feature tables . These newly defined features contain both UTRs and promoters . Since the positive genomic strands are deposited in RefSeq for most of the viruses belonging to the ssRNA ( - ) class , the following sequences available as negative strands were converted into their inverse complement , together with the coordinates of their genomic features: Junin arenavirus segment S ( NC_005081 ) and segment L ( NC_005080 ) , Lassa virus segment L ( NC_004297 ) , lymphocytic choriomeningitis virus segment S ( GQ862982 ) , Machupo virus segment S ( AY924208 ) and L ( AY624354 ) , Pichinde virus segment S ( NC_006447 ) , Rift Valley fever virus segment S ( NC_014395 ) , and Toscana virus segment S ( NC_006318 ) . The overlap extent between PQSs and genomic features was computed by intersecting the genomic coordinates of each PQS with the genomic features extracted from the corresponding virus , and a positive count was recorded every time an overlap of at least one nucleotide was detected . Finally , to compare the enrichment in different feature classes , characterized by different sizes , a normalization step was performed . The total extension of each feature class ( i . e . CDS , repeat_region , 5’–regulatory and 3’–regulatory ) was calculated by summing the lengths of individual features . The total count of PQS overlapping a feature class was then divided by the total length of the class and multiplied by a factor 1 , 000 to obtain the number of PQS present every 1 , 000 nucleotides . This procedure was performed considering only the PQSs conserved in at least 80% of sequences for each viral species . All feature tables files were manually revised to fix inconsistencies in the use of keywords and coordinates .
|
G-quadruplexes are nucleic acid non-canonical structures that have been implicated in the regulation of different biological processes of many organisms . Their presence has been demonstrated also in several viral pathogens , providing new insights into viruses’ biology and potentially serving as drug targets . Although experimental validation is needed to confirm the actual folding of G-quadruplexes , they can be inferred in silico directly from the nucleotide sequence . Several computational methods exist for this purpose , but they are all limited to the analysis of independent sequences . Since viral genomes can be highly variable , G-quadruplexes with important functional roles are expected to be conserved among strains and isolates belonging to the same viral species . Here we aimed at characterizing the potential quadruplex-forming sequences ( PQS ) content in the genome of viral human pathogens and assess their degree of conservation in each viral species . We demonstrate that many viruses possess more PQSs than expected from their nucleotide composition and some of them are highly conserved within single viral species , claiming some biological roles . We provide a website where the results of our analyses are displayed for each virus with interactive graphics . This work is intended as a resource that can guide scientists in the choice of the most promising candidates for functional characterization .
|
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"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
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"methods"
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] |
2018
|
G-quadruplex forming sequences in the genome of all known human viruses: A comprehensive guide
|
Colonies of bacterial cells can display complex collective dynamics , frequently culminating in the formation of biofilms and other ordered super-structures . Recent studies suggest that to cope with local environmental challenges , bacterial cells can actively seek out small chambers or cavities and assemble there , engaging in quorum sensing behavior . By using a novel microfluidic device , we showed that within chambers of distinct shapes and sizes allowing continuous cell escape , bacterial colonies can gradually self-organize . The directions of orientation of cells , their growth , and collective motion are mutually correlated and dictated by the chamber walls and locations of chamber exits . The ultimate highly organized steady state is conducive to a more-organized escape of cells from the chambers and increased access of nutrients into and evacuation of waste out of the colonies . Using a computational model , we suggest that the lengths of the cells might be optimized to maximize self-organization while minimizing the potential for stampede-like exit blockage . The self-organization described here may be crucial for the early stage of the organization of high-density bacterial colonies populating small , physically confined growth niches . It suggests that this phenomenon can play a critical role in bacterial biofilm initiation and development of other complex multicellular bacterial super-structures , including those implicated in infectious diseases .
The past few decades witnessed an emergence of the realization that bacterial cells in their natural environments are not asocial , but can exist as colonies with complex organization and exhibit sophisticated and highly regulated collective behaviors [1–5] . Consequently , significant efforts have been made to investigate the collective behavior of bacteria in various settings , with a particular emphasis on the formation of highly organized , multicellular super-structures . Instances of such colony formation include tightly packed bacterial “pods” in epithelial cells , colonies of luminescent bacteria in light organs of marine animals , or biofilms forming on plastic or glass surfaces in various high-humidity environments [6–10] . One important aspect of these naturally occurring tightly packed bacterial colonies ( henceforth referred generically to as biofilms ) is that they frequently arise despite , and possibly in response to , unfavorable environmental conditions including various types of chemical stress , variable temperature , fluid flow , the host immune system , and limited supply of nutrients [5] . In the initial stages of the biofilm development , it is crucial for bacterial cells to overcome the above-mentioned adverse environmental conditions , while laying foundations for highly ordered , mature biofilm structures . Recent studies have revealed that one of the important initial steps in this process might be for bacterial cells to actively seek out small cavities and populate them , reaching very high densities [11–13] . In addition to providing partial shelter , physical confinement might facilitate the onset of quorum sensing that is thought to be important for the successful progression of biofilm development . However , there are also severe potential disadvantages to forming packed colonies that are partially isolated from the surrounding environment , including increasingly poor nutrient supply and waste removal , as well as the possibility of disorganized expansion leading to cell damage and even blockage of cell escape from the growth niches . How cells cope with these constraints to successfully initiate biofilm development is currently unclear . A clue to understanding cell behavior in these early stages of biofilm development might come from the high degree of multicellular organization found in stalk formation of yeast cells emerging from microscopic pits in agar gels [14 , 15] . Initial confinement of cells to small cavities and mechanical interaction between cells and the cavity walls appeared to be essential not only for the formation of complex tall structures uncharacteristic of lab yeast strains , but also for the high degree of functional order and differentiation within these structures . Colony organizations in various organisms such as Bacillus subtilis and Escherichia coli , that were visualized using scanning and transmission electron microscopy [2] , also show strikingly well-ordered multicellular arrays within the colonies . Recently , with the aid of advanced microscopy systems , biofilm structures were also found to comprise millions of bacterial cells in regular arrangements that facilitate the physiological interactions within the community [9] . It is of interest therefore to investigate whether this functional organization in biofilms , as in yeast stalks , might emerge from the initial ordering of bacterial colonies growing in small cavities—ordering that might facilitate nutrient exchange and relieve the mechanical stress stemming from cell proliferation . Although these dynamical reorganizations of the biofilm-like bacterial colonies are likely to be controlled by cell–cell interactions , lack of convenient experimental platforms allowing a dynamic analysis of large , tightly packed colonies on the single cell level has hampered research progress in this area . Recently , we developed a microfluidic device with chemostatic microchambers , which allows bacterial cells to grow over multiple generations in controlled microenvironments [16] . However , these chambers were relatively deep ( ∼6 μm ) , making it difficult to reach single-cell resolution at high cell densities . In addition , the device was designed to prevent the escape of cells from the chambers , and the packed colonies could only be monitored over a very limited number of cell generations . In this study , we sought to overcome these limitations and create a device for monitoring tightly packed colonies of actively dividing cells in chambers with different geometries over at least 24 h with single-cell resolution . Using a combination of modeling and experiments in these new devices , we found that developing E . coli colonies achieve progressively higher levels of spatial organization , enabling them to increase the nutrient supply and the efficiency of escape from the chamber . The results of the study suggest added importance of the asymmetrical shape of some bacterial species and have direct implications for biofilm organization . The device we describe can also have wider applications for robust long-term culture of bacterial cells under controlled conditions with real-time microscopy at single-cell resolution .
Similar to the previously described microfluidic chemostat [16] , the main functional area of the microfludic device ( Figure 1 ) is an array of flow-through channels and chambers between the channels . The symmetric binary branching of the flow-through channels from a single channel on both the inlet and outlet sides leads to highly balanced pressures at opposite sides of the chambers . Because the depth of the chambers is much smaller than the depth of the flow-through channels ( ∼1 . 5 μm versus 15 μm; Figure 1A ) , the chambers are much more resistant to flow than the channels . Therefore , there is practically no active flow through the chambers ( flow at 0 . 1–0 . 2 μm/s for a typical channel flow rate of ∼100 μm/s ) , and the exchange of chemicals between them and the flow-through channels occurs by diffusion only . There are three important differences between the device in Figure 1 and that described in [16] . First , the depth of the chambers of our device is close to the diameter of E . coli cells ( ∼1 μm ) and substantially smaller than the cell length ( ∼3 μm ) . Therefore , all cells in the chambers are oriented in the plane of the device and create a monolayer , making it possible to detect cellular responses at a single-cell resolution . Second , there are no capillaries impermeable for cells between the flow-through channels and the chambers . Therefore , there are no physical barriers for cells to enter and exit the chambers . Third , although the distance between all flow-through channels is the same ( 140 μm ) , the chambers greatly vary in shapes ( Figure 1B ) . The shapes of the outer boundaries of the chambers include circles , squares , diamonds , triangles , and strips . Furthermore , many chambers have one or several posts inside , adding inner boundaries to the geometries ( Figure 1B ) . The posts have different sizes and are shaped as circles , squares , diamonds , triangles , parallel rectangles , and crosses . As in the cells in a microfluidic chemostat [16] , the E . coli cells loaded into the chambers were found to readily form microcolonies . The microcolonies started from as few as 1–2 cells and eventually filled the chambers completely , with cells being in a densely packed state . However , since cells could freely exit the chambers into the adjacent flow-through channels , cell proliferation was not limited in time and continued for as long as the flow of fresh medium in the flow-through channels was maintained . The combination of cell proliferation and escape led to a continuous collective motion of cells of the densely packed colonies toward the microchamber exits . The rate of flux of cells through the exits is proportional to the rate of colony expansion and , given constant volume of the chamber and constant number of cells in the colony in a steady state , the rate of flux is also proportional to the mean cell growth rate in the colony . We did not observe any noticeable decrease in the growth rates of high-density colonies during prolonged continuous incubation , as judged by the characteristic rate of motion of cells toward chamber exits ( see , e . g . , Figure S3A ) . For effective visualization of the organization of microcolonies , we used a strain of E . coli , transformed with a low-copy plasmid carrying green fluorescent protein ( GFP ) controlled by the truncated version of the Vibrio fischeri lux operon responsive to exogenously added V . fischeri–specific autoinducer [17] , N-3-oxo-hexanoyl homoserine lactone ( HSL ) , but deficient in endogenous AI production ( Figure S1 ) . In the presence of exogenously added autoinducer supplied with the medium ( [AI] = 10 nM ) , the GFP mediated fluorescence allowed us to visually identify individual cells and perform analysis of their shape , size , and orientation . We analyzed images of the colonies in different chambers ( see Videos S1–S8 for typical examples ) and were surprised to observe that , at high densities , the orientations of cells were often anisotropic and highly correlated over distances much larger than the cell length . Typical evolution of a colony in one of the chambers is shown in Figure 2A . Both inner and outer boundaries of the chamber are shaped as rhombi ( “rhombus in a rhombus” or RIR chamber ) . The cell growth initiated in region I , and the orientations of cells appeared random in the beginning . The microcolony then filled the entire chamber to dense cell packing in 8–9 h of growth . ( Dense packing in a subcolony was defined as a state in which there was no cell-free region with the area equal to or greater than the area of a daughter cell following a cell division . ) As the colony reached dense packing , most cells gradually became oriented along the direction of the collective cell flow toward the chamber exits , which frequently coincided with the directions of the internal and external walls of the chamber . To quantify the orientation of cells , we processed fluorescence images of the chambers using two different methods that we termed the “gradient analysis” and the “major-axis analysis” ( Protocol S1 and Figure S2 ) . In the major-axis analysis , the images were segmented to outline individual cells . Following this , the orientations of the major axes of the cells were determined . In the gradient analysis , cell orientation was estimated by determining the direction of the fluorescence gradients from the pixel intensity matrices , without explicit identification of individual cells . Sharp changes of fluorescence occur at cell boundaries , and the directions of fluorescence gradients are perpendicular to the orientation of cells . We applied both methods to sequences of images taken with an interval of 1 h in selected regions of the chambers and found that the two methods consistently yielded similar results ( Figure S2 ) . In region I of the RIR chamber ( Figure 2A ) , at the early time points 0 h and 2 h , the orientation histograms had nearly uniform distributions , indicating that cells were oriented randomly ( Figure 2B ) . As the cell density in the colony increased , the distribution of cell orientations evolved to a shape with a peak at 45° , the direction of the chamber walls and of the collective cell flow in this region . Histograms of the orientation of cells in densely packed states in the other selected regions of the chamber ( Figure 2A ) were similar to the distribution in region I ( Figure 2C ) . In particular , all histograms had peaks at angles corresponding to directions of the chamber walls—135° , 135° , and 45° for regions II , III , and IV , respectively . The shapes of the orientation histograms of densely packed colonies at distinct time points differed relatively little ( Figure 2C ) , and there were no detectable tendencies in the variation of their shapes with time ( Figure 2B ) . This invariance of histograms indicated that the microcolony reached a steady state in terms of cell orientation . To assess the evolution of cell orientation , we introduced an order parameter , β , calculated as the fraction of cells oriented within ±45° of the angle at the peak of the eventual steady state histogram ( e . g . , at 0°−90° for region I ) . We plotted the time evolution of β for the four regions of the chamber ( Figure 2D ) . The value of β is 0 . 5 for randomly oriented cells and it is 1 when all cells are aligned in the preferred direction , which is parallel to the chamber walls . Values of β ∼ 0 . 5 with large variations in time , suggesting basically random orientation of cells with large fluctuations , were typical at the early stages of colony development when cell densities were low . As time progressed and cell densities increased , variations of β became minimal , as expected for a microcolony at a steady state . In this steady-state regime , the values of β were 0 . 85 on average , indicating a major bias in the orientation of cells toward the directions of the nearby chamber walls . ( Calculation of the order parameter using a reduced range of angles around the preferred orientation resulted in smaller values of β at the steady state but did not change the shapes of the curves . ) The large value of the order parameter was attained in spite of the fact that the distance between the walls ( ∼30 μm ) was much larger than the cell diameter . The cells in the region I reached a highly ordered state earlier , and in the region II reached it later , than cells in other regions , in accordance with the relative timing of expansion of the colony into these regions . In addition to the RIR chamber , we used the same methods to analyze the cell orientation in chambers with other shapes . Two typical examples are described here . One chamber was shaped as a rhombus with no internal boundary ( Figure 3A ) , and the other was shaped as a circle with a square internal boundary ( Figure 3B ) . In both chambers , the walls were substantially further apart from each other than in the RIR chamber , which could be expected to lead to less order in the orientation of cells . Surprisingly , the analysis of images of densely packed colonies in both chambers showed that , in most regions , the orientation of cells was highly correlated over large distances . In a chosen region of interest ( e . g . , regions I , III , IV , and V in Figure 3A and 3B ) , the preferred orientation of cells usually coincided with the orientation of a nearby wall and always coincided with the direction of flow of cells toward chamber exits . In the central region of the rhombus-shaped chamber ( region I and inset I in Figure 3A ) , close to 95% of cells were oriented within ±45° from the vertical axis , which was the direction of flow toward the chamber exits ( Figure 3C and 3D ) . The orientations of cells in region I were highly correlated over a distance ∼20 times the cell length , in spite of the absence of chamber walls nearby . The orientation order was similarly high in most other regions of the chamber . Exceptions were the side corners ( region II and inset II in Figure 3A ) , where cells appeared to be oriented nearly randomly , as indicated by a close-to-uniform distribution of angles ( Figure 3C ) and the order parameter β ≈ 0 . 5 in the steady-state regime . In the “square within a circle” chamber , the distributions of cell orientations in regions IV and V reached the steady states rapidly , and the order parameter attained high values β > 0 . 9 ( Figure 3C and 3D ) . In region III , the steady state was reached considerably later than in regions IV and V , and the cell orientation was less anisotropic , as indicated by a lower value of the order parameter , β ≈ 0 . 8 ( Figure 3C and 3D ) . In contrast to regions III and V , in region IV , the direction of flow of cells toward the exit ( the flow direction defined the preferred orientation of cells ) was orthogonal to the orientation of a nearby chamber wall . As a result , the flow pattern in region IV was rather complicated , with two fluxes of cells ( from the left and from the right in Figure 3B ) merging into one and with an analogue of a separation line in the middle . The observation that a high degree of correlation in cell orientation occurred only at high cell densities , in the absence of active cell movement , suggested that the correlation originated from purely mechanical forces . Locally , these forces result from contact interactions of cells with each other and from the constraint of 2-D motion in the plane of the chamber . Globally , the cells are mechanically influenced by their collective motion toward chamber exits . The pattern of this motion depends on the position and orientation of the chamber walls and exits . To investigate if cell self-organization could be solely due to these contact interactions , we simulated the development of a colony in a 2-D region corresponding to the footprint of a chamber , with cells modeled by 2-D objects with shapes , lengths , and widths typical for E . coli cells . The model simulations were based on our previously reported analysis of yeast colony growth [11] with appropriate modifications accounting for differences in cell shape , for symmetric cell division , for the presence of boundaries , etc . ( see Methods for more details ) . Cells were assumed to grow steadily along their major axes , to divide at even time intervals , and to have spring-like deformation potentials . Forces experienced by cells were due to cell–cell and cell–wall interactions . We found that this simple mechanical model reproduced all the major features of colony expansion in different geometries , including the gradual onset of anisotropic orientation of cells with long-range spatial correlations at high cell densities . Both the time evolution and the steady-state distributions of the cell orientation angles in the RIR chamber strongly resembled the corresponding experimental results ( Figures 2 and 4A ) . Similar evolution and steady-state patterns were obtained in the other modeled geometries ( Videos S9–S11 and unpublished data ) . The agreement between the model and experiments corroborated the suggestion that the self-organization of cells within the colonies originated from purely mechanical effects . Since a preferred direction of orientation can only exist for nonspherical , elongated objects , the self-organization of E . coli cells in the colony and the anisotropy in their orientation are closely related to the elongated shape of E . coli cells . We used the model to examine how the orientation anisotropy and β in the high-density steady states depend on the ratio between the length and width of cells and simulated colonies composed of cells with maximum lengths shorter ( 0 . 5L ) and longer ( 2L , 4L ) than “normal” ( the normal length , L , corresponded to the average aspect ratio 3 . 75:1 calculated given that the cell lengths vary from half-maximal to maximal in the course of cell growth ) . We found that cells with the reduced aspect ratio had smaller orientation order and lower β than the “normal” cells ( 0 . 81 vs . 0 . 99 for region I ) . On the other hand , we found no significant difference in the anisotropy of orientation between the “normal” cells and the cells with the increased aspect ratios , with the β values of 1 . 00 and 0 . 97 for 2L and 4L cells versus 0 . 99 for the normal cells . The results of the simulation suggest that the actual aspect ratio of E . coli ( 3:1–4:1 ) might be close to the minimal aspect ratio sufficient to ensure a high level of coordination of cell orientation within a colony . We further interrogated the model to evaluate the forces experienced by cells of different lengths at different stages of colony development ( see Methods , Videos S12–S15 ) . For cells of 0 . 5L length simulated within a RIR chamber , the stress at the steady state increased with the distance from the chamber exits and was maximal along the horizontal axis of symmetry of the chamber ( Figure 4C ) . The distribution of stress in the simulated colony was similar to the distribution of pressure in a model of a Newtonian fluid with spatially uniform source term , corresponding to a uniform and steady growth of cells in a chamber of the same geometry ( Figure S3B ) . By contrast , for the longer cells ( 2L and 4L ) than “normal” cells , the simulations indicated the existence of areas of high stress near the chamber exits . Near the exits , the directions of flow of cells in two merging streams change abruptly by 45° , the total width of the cell stream decreases , and the flow lines merge , causing many cells to become misaligned . The misalignment can lead to high stresses due to both growth of cells in the direction perpendicular to the flow and possible “stampede”-like exit blockage exacerbated by the convergence of flow lines ( Videos S13 and S14 ) . Additionally , we observed that if the cells were oriented perpendicularly to a nearby chamber wall and their growth was not directed toward one of the exits , the cells experienced high localized forces ( Figure 4D , left and right corners of the chamber ) . This observation suggested that self-organization of colony expansion in parallel to the chamber walls may decrease the mechanical stress induced by cell growth . A high degree of anisotropy achieved in the steady-state colonies might affect the efficiency of diffusive transport of nutrients into and metabolites out of the bulk of the colony through the chamber exits . Indeed , a recent theoretical study suggested that increased anisotropy of a porous tissue can lead to a dramatic reduction of its tortuosity and enhancement of diffusion in a preferred direction [18] . The study also analyzed diffusion in a 2-D medium with an array of excluded regions having shapes of identical extended rectangles , oriented parallel to each other , which is a good approximation of the high-density steady-state colonies in the microchambers . The effective diffusion coefficient in the direction along the rectangles was found to be , where D is the diffusion coefficient of the medium without excluded regions , L1 is the longer , and L2 is the shorter axes of the rectangles . For E . coli cells with the aspect ratios 3:1–4:1 , in a highly ordered steady state with β ≈ 1 , one might thus expect an effective diffusion coefficient of 0 . 75D to 0 . 8D along the directions from the chamber's exit into the bulk of the colony . For randomly oriented cells , Deff would drop to 0 . 5D or even less , due to possible “dead-end pores” , i . e . , blockages of openings between pairs of parallel cells by perpendicularly oriented cells . Thus , increasing the anisotropy of cell orientation within a colony is expected to enhance the supply of nutrients to internal regions of the colony and the evacuation of metabolites from the internal regions . To test this prediction , we took advantage of the sensitivity of the lux operon to glucose levels . Transcription in this operon is positively regulated by the cAMP-receptor protein ( CRP ) [19] , which is progressively activated at decreasing glucose levels due to elevated intracellular cAMP concentrations . As a result , the level of GFP expression substantially increased at reduced glucose levels that were expected to be found in densely populated microfluidic chambers with impaired diffusive transport ( Figure P3 of the Protocol S1 ) . We found that , for all chamber shapes , fluorescence of individual cells increased as the colony became denser and filled the chamber , which was consistent with the expected reduction of glucose concentration . Moreover , in the high-density steady-state colonies , well-formed gradients of fluorescence were often observed , with cells becoming progressively less fluorescent toward the chamber exits ( Figures 2 and 3 and Figure S5 ) . Because the exits were a source of the exogenously added autoinducer , this type of distribution of fluorescence in the colony was inconsistent with the possibility that autoinducer did not reach cells in the internal regions . The variation of the glucose level appeared to be the only plausible explanation of this fluorescence distribution , and we concluded that the level of cell fluorescence could be used as an indicator of the local concentration of glucose . This conclusion was further corroborated by a high degree of correlation between the expression of GFP driven by the lux operon promoter and the expression of HcRed protein under exclusive control of cAMP-CRP complex ( both expressed in the same strain of E . coli ) in different cells growing in the same chamber [20] ( Figure P4 of Protocol S1 ) . We then investigated the evolution of GFP fluorescence in different regions of the previously analyzed chambers ( Figure 5 ) . In the case corresponding to region I in Figure 3A ( rhombus-shaped chamber ) , we found that , following a maximum level reached around 13 h , the fluorescence intensity gradually dropped by approximately 30% ( Figure 5A ) . Additionally , the fluorescence intensity gradient along a line connecting region I with a chamber exit gradually became shallower , indicating better penetration of the nutrients into internal regions of the colony ( Figure 5A-ii ) . These observations could be explained by noting that the alignment of cells in the rhombus chamber reached its highest level at least 4 h after the colony became densely packed . Therefore , initially , nutrient transport through the colony would be hampered by decreasing intercellular spaces and increasing nutrient consumption leading to increased fluorescence intensity . However , over time , the transport efficiency can be improved by increased anisotropy within the colony , leading to a progressive decrease in the GFP signal at the colony interior . Consistent with this hypothesis , the drops in the GFP fluorescence intensity from the maximal levels were much more limited in the other two chambers analyzed , where by the time the colonies became densely packed , cells were already arranged in a highly anisotropic fashion ( Figure 5B and 5C ) . Taken together , the results suggest that fluctuations in the nutrient supply available to the colony can depend on whether the colony self-organization accompanies or follows achieving the densely packed state . A significant increase in the colony growth rate , as judged by the rate of cell movement out of a chamber , which was observed in the rhombus but not RIR chamber at a later stage of experiment provided independent further support for this suggestion ( Figure 5A-i and Figure S3A ) .
This study has demonstrated that long-term growth of E . coli colonies within small enclosed spaces can be accompanied by self-organization of the colonies into states characterized by highly correlated cell orientation . A simple agent-based model incorporating the details of asymmetric cell shape , mechanical interactions arising from cell growth and division , and the confinement of colonies within specific boundaries has reproduced the salient features of this organization process . The success of the model strongly suggests that self-organization is due to localized mechanical cell–cell and cell–chamber wall interactions , and most importantly , elongated cell shape . When viewed on a coarse scale—that of hundreds of cells—the dynamics of steady-state colonies bear a marked similarity to the patterns of flow of Newtonian fluid continuously generated within confined spaces of various shapes . This similarity is exemplified by the coincidence of the streamlines of Newtonian fluid with the directions of the collective cell motion ( Figure S3B ) . In addition , the areas corresponding to high fluid pressure overlap with those of increased stress experienced by cells with low aspect ratio ( Figure S3B ) . These simple patterns are further refined on the scales comparable to the size of a single cell , where the effects of elongated shape of the cells become especially pronounced . In particular , it becomes evident that not only the direction of movement but also the orientations of individual cells become aligned with the predicted streamlines and also become mutually correlated . The directional correlation increases with increasing cell aspect ratio . This self-organization behavior , involving hundreds to thousands cells , contrasts with the previously reported transient local alignment of relatively small cell clusters ( [21] , Figure S4 ) , and is strongly dependent on the geometry of the confining boundaries and chamber exits and on the direction of the collective cell flow ( Figure 3D and Figure S5 ) . Based on the results described here , we propose that colony self-organization can endow the constituent cells with at least two important advantages . First , orientation of cell growth toward the exits connecting microchambers to external space can lead to the relaxation of the stress experienced by the cells due to constriction by the chamber boundaries . In a cell population lacking such an orientational order , cell growth toward chamber walls can create local foci of high stress ( see e . g . , Figure 4D ) . Interestingly , increasing the cell aspect ratios beyond the values comparable to those of the wild-type E . coli does not seem to affect the degree of self-organization , but can increase the cellular stresses at the chamber exits due to rapid changes in cell orientation . In this regard , it is interesting to contrast the collective behavior of bacterial cells to the self-driven movement of a large crowd in a confined space . The model in [22] suggests that a combination of a relatively narrow bottleneck coupled with self-propelled , panic-like movement of people within a confined space can easily result in a blockage of the exits , leading to potentially widespread injuries . Arguably , bacterial cells in tightly packed colonies populating small cavities or other confining environments might face similar stampede-like blockage challenges . Our results suggest , however , that the potential for “bacterial stampedes” arises only for relatively long cells , which have feature sizes comparable to the dimension of the cavity exit openings . Therefore , from the standpoint of stress relief , one can speak of an optimal cell length , such that cells are long enough to undergo robust self-organization , and yet short enough to avoid increased stress at the exits from a confined space , and potential blockage of cell escape . It can be proposed that these constraints on cell shape , in addition to the potential importance of elongated cell shape for cell motility and other functional responses [23] , might have contributed to the evolutionary pressure defining the aspect ratios of cells in many extant bacterial species , in particular those that have morphology similar to E . coli and form biofilms . The second advantage of anisotropic colony organization is the reduction of the tortuosity of the intercellular spaces progressively enhancing the diffusion of nutrients into the colony . Efficient diffusive transport of nutrients and metabolites is an important determinant of the well-being of bacterial cells within the bulk of the colony , ensuring sufficient energy supply for cell division and other functions . A corollary of this finding is that signal molecules might also diffuse more easily along the flow lines formed by the cells , thus introducing anisotropy into cell-cell communication , e . g . , through quorum sensing . Our findings provide a fresh perspective on possible mechanisms of emergence of the structurally complex biofilms and other types of multicellular super-structures . Although the order inherent in biofilms is clearly shaped by a complex interplay of multiple processes , including intercellular signaling [24] and regulated migration of cell sub-populations within the developing and differentiating colonies [25] , this order and the chances of survival of bacterial biofilms might critically depend on the initial self-organization of cells during their anchorage and expansion within small cavities and other niches in complex growth substrata . The complex mechanical interactions between cells themselves and between cells and physical boundaries confining them , determined in large part by cell morphologies , can help create patterns of extensive organization and enhanced chemical transport , which can lay the foundation of complex functional multicellular ensembles . The understanding of these processes might help control biofilm growth , and ultimately facilitate treatment of the related diseases . Finally , we note that the microchamber device presented here provides a convenient and effective way to observe bacterial cells over multiple generations and track the evolution of a colony at single-cell resolution . It enabled us to observe the individual cellular response in both fluorescence and phase-contrast microscopic settings . We envision that the flexibility of design and convenience of use will make this type of devices a platform of choice for many scientific and biotechnological applications .
In our study , we defined the “orientation” of a cell as the angle that the cell forms with respect to the x-axis of the Cartesian coordinates of each image ( inset in Figure 2B ) . Its range is [0° , 180°] . The custom software package to determine the cell orientations was developed and implemented in Matlab 7 . 0 . 0 . , including the image-processing toolbox . Time-lapse fluorescence images in the 16-bit TIFF format indexed by location and time stamps were used as the input . In the package , two different routines were applied to find the orientation of individual cells . ( Gradient analysis and Major axis analysis; see Protocol S1 for a detailed description ) . Individual cells are explicitly modeled as separate agents and are assumed to have an area enclosed by two semi-circles attached to opposite sides of a rectangle of constant width but variable length . Positions and lengths of the cells are defined by two generalized coordinates designating the centers of the semi-circles attached to the rectangle . The dynamics of the colonies is considered to be dominated by viscous friction , with velocity rather than acceleration being proportional to the force . See Protocol S1 and Videos S17 and S18 for details of the model set-up and simulations .
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Bacterial cells form colonies with complex organization ( aka biofilms ) , particularly in response to hostile environmental conditions . Recent studies have shown that biofilm development occurs when bacterial cells seek out small cavities and populate them at high densities . However , bacteria in cavities may suffer from poor nutrient supply or waste removal , or disorganized expansion leading to blockage of cell escape . In this study , we observed Escherichia coli in a microfluidic device that allows direct observation of the growth and development of cell colonies in microchambers of different shapes and sizes through multiple generations . Combining this experimentation with computational analysis of colony growth and expansion , we characterize a process of colony self-organization that results in a high degree of correlation between the directions of cell orientation and growth of collective cell movement . We also find that this self-organization can significantly facilitate efficient escape of cells from the confines of cavities where they reside , while improving the access of nutrients into the colony interior . Finally , we suggest that the aspect ratio of the shape of E . coli and other similar bacteria might be generally subject to a constraint related to colony self-organization .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"biotechnology",
"physics",
"developmental",
"biology",
"infectious",
"diseases",
"cell",
"biology",
"microbiology",
"computational",
"biology",
"biophysics"
] |
2007
|
Self-Organization in High-Density Bacterial Colonies: Efficient Crowd Control
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The Ebola virus causes an acute , serious illness which is often fatal if untreated . However , factors affecting the survival of the disease remain unclear . Here , we investigated the prognostic factors of Ebola virus disease ( EVD ) through various statistical models . Sixty three laboratory-confirmed EVD patients with relatively complete clinical profiles were included in the study . All the patients were recruited at Jui Government Hospital , Sierra Leone between October 1st , 2014 and January 18th , 2015 . We first investigated whether a single clinical presentation would be correlated with the survival of EVD . Log-rank test demonstrated that patients with viral load higher than 106 copies/ml presented significantly shorter survival time than those whose viral load were lower than 106 copies/ml ( P = 0 . 005 ) . Also , using Pearson chi-square test , we identified that chest pain , coma , and viral load ( >106 copies/ml ) were significantly associated with poor survival of EVD patients . Furthermore , we evaluated the effect of multiple variables on the survival of EVD by Cox proportional hazards model . Interestingly , results revealed that patient’s age , symptom of confusion , and viral load were the significantly associated with the survival of EVD cases ( P = 0 . 017 , P = 0 . 002 , and P = 0 . 027 , respectively ) . These results suggest that age , chest pain , coma , confusion and viral load are associated with the prognosis of EVD , in which viral load could be one of the most important factors for the survival of the disease .
In the year of 2014 , Ebola virus disease ( EVD ) was quickly widespread and caused the whole world to pay attention [1 , 2] . By the end of 2014 , more than eleven thousand cases were reported from West African countries such as Guinea , Sierra Leone , Liberia , Senegal , Nigeria , and Mali [1 , 2] . However , Ebola did not stop in West Africa only , it has gone globally as cases were diagnosed in the United States and Spain [1 , 2] . The Ebola outbreak had many clinical management challenges due to its high fatality rate ( 45–90% ) and easy transmission [3 , 4] . Because of this , health care professionals are put at great risk when helping Ebola infected patients [5] . Such risks are greatly higher than regular daily practices . Although supportive care such as the use of antibiotics and administrating intravenous fluids is believed to be helpful , there is no clinically approved treatment to Ebola [6 , 7] . The survival rate of EVD can increase in places with advanced medical care because of constant maintenance of blood pressure , body fluids volume and hydration [8] . However , the most severely affected countries , Guinea , Liberia and Sierra Leone , have very weak health systems , lack human and infrastructural resources , and have only recently emerged from long periods of conflict and instability , which makes it extremely difficult to prevent and treat the disease . Previous studies have provided some information regarding the prognosis of EVD [9 , 10] . Bah et al . reported that EVD patients who were 40 years of age or older had a higher risk of death compared with those under the age of 40 years using Poisson regression analysis [9] . Also , the viral load appeared to be higher in non-survivors compared to survivors by univariate analyses [9] . Schieffelin et al . showed that EVD patients under the age of 21 years had a lower case fatality rate than those over the age of 45 years , and patients presenting with fewer than 105 EBOV copies/ml had a lower case fatality rate than those with 107 EBOV copies/ml [10] . Also , weakness , dizziness , and diarrhea were the symptoms that were significantly associated with a fatal outcome [10] . In addition , Towner et al . presented that viral load could be correlated with disease outcome [11] . However , these studies were conducted using relatively small number of patients . Therefore , independent research is required to confirm these findings . Moreover , since it is extremely difficult to obtain completely detailed patient information during the outbreak [12 , 13] , and it is biased and wasting of data to rule out patients with relatively uncompleted information , it would be necessary to apply COX's proportional hazard model to deal with this kind of data set . In this study , we investigated the prognostic factors of EVD using various statistical models .
The institutional review board at Beijing 302 Hospital and the Sierra Leone Ethics and Scientific Review Committee approved this project . These committees waived the requirement to obtain informed consent during the West African Ebola outbreak . From Oct . 1st , 2014 to Jan . 18th , 2015 , the Chinese Medical Team in the Jui Government Hospital , Sierra Leone admitted 661 patients and 269 of them were diagnosed EVD . Noticeably , Jui Government Hospital was positioned as a holding center from Oct 1st , 2014 to Dec 31st , 2014 , and upgraded as an Ebola Treatment Center ( ETC ) on Jan 1st , 2015 . The admission of patients was coordinated by the National Emergency Response Center ( NERC ) . First of all , venous blood of the patients was immediately taken for sampling . They were then hospitalized and the samples were examined in the Chinese portable biosafety level 3 laboratories within the Jui Government Hospital . While waiting for the test results , patients were treated based on their clinical presentations . All the patients were given oral rehydration salts as a routine treatment , and the dose was dependent on the severity of dehydration . Intravenous administration of supplements was given under certain conditions . Patients with headache and/or muscle pain were given Acetaminophen or Ibuprofen . Patients with fever were given Cefixime or anti-infective Ciprofloxacin and anti-malaria Compound Naphthoquine Phosphate Tablets . Patients with upper abdominal pain or burning sensation were given antacid drugs such as Ranitidine or Omeprazole . Patients feeling fretful or insomnia were given Diazepam . A few patients were offered intravenous lactated Ringer’s solution . All the treatments were performed in compliance with ‘Clinical management of patients with viral haemorrhagic fever A pocket guide for the front-line health worker’ ( http://www . who . int/csr/resources/publications/clinical-management-patients/en/ ) and ‘Manual for the care and management of patients in Ebola Care Units/ Community Care Centres Interim emergency guidance’ ( http://www . who . int/csr/resources/publications/ebola/patient-care-CCUs/en/ ) . Patients confirmed with Ebola infection were reported to NERC , and were transported to other treatment centers before December 31st , 2014 , or stayed in the special treatment zone of Jui Government Hospital after the hospital was upgraded to ETC . Ebola-negative patients were arranged for corresponding treatments or discharge . Sierra Leone is one the most severely affected countries . However , due to its weak health system and lack of human resources , it was extremely difficult to obtain complete patient data during the outbreak . Holding center faced even worse situation because EVD confirmed patients were transferred to different ETCs after the diagnosis . Of the 269 EVD cases , 201 subjects were diagnosed when Jui Government Hospital functioned as a holding center . Only 7 patients were obtained relatively complete information . Sixty eight patients were confirmed EVD between January 1st and January 18th , 2015 after the hospital was upgraded to ETC , in which 56 cases provided relatively complete information , and the other 12 patients died soon after the admission and did not give enough information for the study . Patient data included demographic information such as gender , age and job; epidemiological history of attending traditional funerals; history of contacting with EVD patients; symptoms; clinical signs , etc . Patients were sent to the hospital ward after the healthcare workers evaluated the general situations and filled out the Ebola case investigation form or viral hemorrhagic fever case investigation form formulated by NERC . The EVD Patient Observation Sheet was completed daily after routine check , and all the sheets were transported either via closed-circuit surveillance system or by WiFi after being photographed . In order to keep the results accurate , all the data was recorded and compared separately in the Excel database by two healthcare professionals . Discharged patients were followed by calling randomly to patients themselves or their families or related treatment centers and the information of their conditions ( whether they were cured/ discharged or died , and the date of cure or death ) were being accessed . Viral load was examined upon the admission to the hospital . Detection of virus was performed by the Chinese-CDC portable biosafety level 3 laboratories based on previously published method [14] . These laboratories accepted specimens from the Jui Government Hospital as well as specimens transported by NERC from other ETC and holding centers . After recording the sign-in information in the biosafety level 3 laboratory , technicians inactivated the specimens by incubating in the water bath at 60 degree for 1 hour . Then 50 ul of inactivated specimen was used to isolate RNA by Viral RNA Isolation Kit ( Life Technologies , Grand Island , NY , USA ) and DNA by Automatic DNA Extractor ( Life Technologies , Grand Island , NY , USA ) . The isolated RNA and DNA were tested in biosafety level 2 laboratory . Thermal cycling parameters of the real-time reverse transcription-polymerase chain reaction were 30 min at 42°C followed by 10 min at 95°C and a 40 cycles of amplification . The range of cycle threshold was between 18 . 34 and 35 . 81 and the range of viral load was between 103 and 3 . 9x108 copies/ml in our study population . Data were analyzed by PASW statistics 18 . 0 software . Pearson's chi-squared test was applied to compare dichotomous variables . The Yates’s correction was applied when the expected ( theoretical ) frequency was <5 and ≥ 1 . The survival curve was estimated by Kaplan-Meier method . The Cox proportional hazard model was used for multi-factor analysis .
In our study population , businessman ( 17 cases ) and students ( 13 cases ) were the two major professions . Of the 63 patients , 27 subjects were non-survivors and 36 subjects were survivors; 35 subjects had clear contact history , in which 11 cases were through funerals and 10 cases were through family members . As shown in Table 1 , common symptoms included fever ( 80 . 95% ) , diarrhea ( 49 . 21% ) , vomiting ( 50 . 79% ) , fatigue ( 90 . 48% ) , anorexia ( 87 . 30% ) , abdominal pain ( 49 . 21% ) , chest pain ( 55 . 56% ) , muscle pain ( 74 . 60% ) , joint pain ( 77 . 78% ) , headache ( 65 . 08% ) , cough ( 39 . 68% ) , difficulty in breathing ( 41 . 27% ) , difficulty in swallowing ( 42 . 86% ) , sore throat ( 38 . 10% ) , jaundice ( 26 . 98% ) , redeye ( 28 . 57% ) , skin rash ( 6 . 35% ) , hiccups ( 19 . 05% ) , pain behind eye ( 12 . 70% ) , coma ( 7 . 94% ) , confusion ( 14 . 29% ) , and bleeding ( 9 . 52% ) . We first investigated patient’s age and the prognosis of EVD . Based on WHO and other literatures [9] , patients were divided into two groups ( ≥40 years old and <40 years old ) . Log-rank test showed that the ≥40 years old group had moderately shorter survival time than the <40 years old group ( P = 0 . 087 ) . We further examined the viral load and the survival of EVD patients . Since 106 is the closest integer to the median value of the viral load of our study population , we divided the cases into two subsets according to the value . Data showed that patients with viral titer higher than 106 copies/ml presented significantly shorter survival time than those whose viral titer were lower than 106 copies/ml ( P = 0 . 005 , Fig 1 ) . We also examined the correlation between clinical symptoms and the survival of EVD . Using Pearson chi-square test , we found that fever , diarrhea , vomiting , fatigue , anorexia , abdominal pain , chest pain , muscle pain , joint pain , headache , cough , difficulty in breathing , difficulty in swallowing , sore throat , jaundice , redeye , skin rash , hiccups , pain behind eye , coma , confusion , and bleeding were not significantly associated with EVD death , whereas symptoms such as chest pain , coma and confusion showed significantly association with EVD mortality ( P = 0 . 040 , P = 0 . 007 , and P = 0 . 022 , Table 1 ) . However , value of confusion lost statistical significance after corrected for continuity ( P = 0 . 055 , Table 1 ) . Furthermore , we analyzed the positive predictive values ( PV+ ) and negative predictive values ( PV- ) of chest pain , coma and confusion . Data showed that chest pain presented 54 . 3% of PV+ and 71 . 4% of PV-; coma presented 100% of PV+ and 62 . 1% of PV-; confusion presented 60 . 6% of PV+ and 76 . 7% of PV- . In addition , we investigated whether coma , chest pain , confusion , and viral load were associated with each other using Pearson's chi-squared test . No significant difference was identified ( coma vs . chest pain , p = 0 . 371; coma vs . confusion , p = 0 . 704; chest pain vs . confusion , p = 0 . 469 , coma vs . viral load , p = 0 . 286; chest pain vs . viral load , p = 0 . 269; confusion vs . viral load , p = 0 . 847 ) . One-way ANOVA analysis sometimes cannot reflect the combined effect of multiple variables on EVD . Since it is difficult to obtain complete patient information during the outbreak of EVD , whereas it is biased and wasting of data to rule out patients with relatively non-detailed information , applying COX's proportional hazard model to deal with this kind of data set is necessary . We found that that patient’s age , the symptom of confusion , and viral load were the significantly associated with the survival of EVD cases ( P = 0 . 017 , P = 0 . 002 , and P = 0 . 027 , respectively , Table 2 ) .
In this study , we identified significant differences between survivors and non-survivors in terms of chest pain and coma . Moreover , the p value was close to 0 . 05 for symptoms such as diarrhea , anorexia and fever . These data indicate that the current Ebola outbreak is similar to previous ones [15 , 16] . Meanwhile , we observed some differences . First of all , EVD used to be called Ebola hemorrhagic fever , but most cases did not show bleeding or just had little fever in the current Ebola outbreak . It might be due to different strains of the Ebola virus . Secondly , occurrence of chest pain , coma and confusion were statistically significantly correlated with EVD death by Pearson’s chi-squared test ( Table 1 ) , whereas only confusion showed correlation to the survival of EVD by Cox proportional hazard model ( Table 2 ) . There could be two reasons causing the discrepancy: 1 ) sample size was too small; 2 ) Cox proportional hazard model takes survival time into consideration . Different survival time had different effects on the model , even though the clinical outcome of the patients was the same , whereas one-way ANOVA analysis only considers the clinical outcome of the patients . In terms of age and the prognosis , we found that the survival time was shorter and the mortality was higher in older people through various statistical analyses . These data suggest that we should pay more attention to elderly patients and give them more efficient treatments . In terms of viral load and prognosis , we found that shorter survival time and higher mortality happened to the patients with higher viral load , when the patients were at similar age . These results suggest that it might be important to increase the efficiency of anti-viral treatment in order to lower the mortality and improve prognosis . Although the existing anti-viral drug such as Zmapp seems to be effective , we need larger scale clinical trials to prove it . We treated the patients mainly with oral therapy . Although we evaluated the severity of dehydration by asking and observing the amount of urine , the amount of vomiting , heart rate , the condition of peripheral limbs circulation and skin elasticity , it was difficult to adjust the dosing of the drugs without biochemical test and blood routine test . This also happened in other treatment centers [17 , 18] . There were several reasons causing the lack of intravenous therapy . First of all , we had a shortage of experienced healthcare personnel . Similar to previous disease outbreak , many healthcare staff , especially the well-trained and skilled nurses , got infected at the beginning of the outbreak , and were forced to leave their positions [19] . Secondly , different from routine medical work , it was mandatory for the healthcare staff to wear multi-layered personal protective equipment ( PPE ) when treating the patients . The multi-layered gloves and goggles made the intravenous injection much more difficult . There are some difficulties in conducting the research . At the beginning of the Ebola outbreak , the Jui Government Hospital was in paralysis as lots of experienced professionals left their positions . The data were messy as there was no formulated mode to record patients’ information . It turned better after we trained the medical team repeatedly . However , it was difficult to statistically compare the mortality with that from other reports since the outcome of the patients remained largely unknown . Also , as some of the local people don’t speak English , the communication with the Chinese doctors was through the translation from Sierra Leone nurses , which increased the possibility of misunderstanding and led to misjudgment of some symptoms . In addition , before upgraded to ETC , Jui Government Hospital transported most of the patients confirmed with Ebola virus to other ETCs , which made it difficult to get access to the treatment and outcome of these patients . In conclusion , our data indicate that clinicians should pay close attention and give efficient treatment for elderly EVD patients and whose with high viral load . Future studies should focus on how to carry out intravenous therapy efficiently and safely as well as to develop novel antiviral drugs .
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The current outbreak of Ebola virus disease ( EVD ) in West Africa is the largest and most complex Ebola outbreak since the virus was first discovered in 1976 . Factors affecting the survival of the disease remain unclear . Here , we investigated the prognostic factors of EBV from 63 cases with relatively complete clinical profiles in Sierra Leone . Using different statistical models , we found that age , chest pain , coma , confusion and viral load were associated with the prognosis of EVD , in which viral load could be one of the most important factors for the survival of the disease .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Prognostic Analysis of Patients with Ebola Virus Disease
|
The interplay between hippocampus and prefrontal cortex ( PFC ) is fundamental to spatial cognition . Complementing hippocampal place coding , prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making . We model a prefrontal network mediating distributed information processing for spatial learning and action planning . Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns . We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs . The recurrent nature of the network supports multilevel spatial processing , allowing structural features of the environment to be encoded . An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning . The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks . We illustrate the link from single unit activity to behavioral responses . The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik . Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning , including prospective coding and distance-to-goal correlates .
Spatial cognition requires long-term neural representations of the spatiotemporal properties of the environment [1] . These representations are encoded in terms of multimodal descriptions of the animal-environment interaction during active exploration . Exploiting these contextual representations ( e . g . through reward-based learning ) can produce goal-oriented behavior under different environmental conditions and across subsequent visits to the environment . The complexity of the learned neural representations has to be adapted to the complexity of the spatial task and , consequently , to the flexibility of the navigation strategies used to solve it [2] , [3] . Spatial navigation planning —defined here as the ability to mentally evaluate alternative sequences of actions to infer optimal trajectories to a goal— is among the most flexible navigation strategies [3] . It can enable animals to solve hidden-goal tasks even in the presence of dynamically blocked pathways ( e . g . detour navigation tasks , [4] ) . Experimental and theoretical works have identified three main types of representations suitable for spatial navigation planning , namely route-based , topological , and metrical maps [2] , [3] , [5]–[7] . Route-based representations encode sequences of place-action-place associations independently from each other , which does not guarantee optimal goal-oriented behavior ( e . g . in terms of capability of either finding the shortest pathway or solving detour tasks ) . Topological maps merge routes into a common goal-independent representation that can be understood as a graph whose nodes and edges encode spatial locations and their connectivity relations , respectively [2] . Topological maps provide compact representations that can generate coarse spatial codes suitable to support navigation planning in complex environments . Metrics-based maps go beyond pure topology in the sense they embed the metrical relations between environmental places and/or cues —i . e . their distances and angles— within an allocentric ( i . e . world centered ) reference frame [5] . Here , we model a spatial memory system that primarily learns topological maps . In addition , the resultant representation also encodes directional-related information , allowing some geometrical regularities of the environment to be captured . The encoding of metric information favors the computation of novel pathways ( e . g . shortcuts ) even through unvisited regions of the environment . In contrast to the qualitative but operational space code provided by topological maps , metrical representations form more precise descriptions of the environment that are available only at specific locations until the environment has been extensively explored [5] . However , purely metric representations are prone to errors affecting distance and angle estimations ( e . g . path integration [8] ) . Behavioral and neurophysiological data suggest the coexistence of multiple memory systems that , by being instrumental in the encoding of routes , topological maps and metrical information , cooperate to subserve goal-oriented navigation planning [9] . An important question is how these representations can be encoded by neural populations within the brain . Similar to other high-level functions , spatial cognition involves parallel information processing mediated by a network of brain structures that interact to promote effective spatial behavior [3] , [9]–[11] . An extensive body of experimental work has investigated the neural bases of spatial cognition , and a significant amount of evidence points towards a prominent role of the hippocampal formation [12] . This limbic region has been thought to mediate spatial learning functions ever since location-selective neurons —namely hippocampal place cells [1] , and entorhinal grid cells [13]— and orientation-selective neurons —namely head-direction cells [14]— were observed by means of electrophysiological recordings from freely moving rats . Yet , the role of the hippocampal formation in goal representation and reward-dependent navigation planning remains unclear [15] . On the one hand , the hippocampus has been proposed to encode topological-like representations suitable for action sequence learning [16] ( see [15] for a review of models ) . This hypothesis mainly relies on the recurrent dynamics generated by the CA3 collaterals of the hippocampus [17] . On the other hand , the hippocampal space code is likely to be highly redundant and distributed [18] , which does not seem adequate for learning compact topological representations of high-dimensional spatial contexts . Also , the experimental evidence for high-level spatial representations mediated by a network of neocortical areas ( e . g . the posterior parietal cortex [19] and the prefrontal cortex [20] ) suggests the existence of an extra-hippocampal action planning system shared among multiple brain regions [21] , [22] . The model presented here relies on the hypothesis of a distributed spatial cognition system in which the hippocampal formation would contribute to navigation planning by conveying redundant spatial representations to higher associative areas , and a cortical network would elaborate more compact representations of the spatial context —accounting for motivation-dependent memories , action cost/risk constraints , and temporal sequences of goal-directed behavioral responses [23] . Among the cortical areas involved in map building and action planning , the prefrontal cortex ( PFC ) is likely to play a central role , as suggested by anatomical PFC lesion studies showing impaired navigation planning in rats [24] , [25] and neuroimaging studies [26] , [27] . Also , the anatomo-functional properties of the PFC seem appropriate to encode multimodal contextual memories that are not merely based on spatial correlates . The PFC receives direct projections from sub-cortical structures ( e . g . the hippocampus [28] , the thalamus [29] , the amygdala [30] and the ventral tegmental area [31] ) , and indirect connections from the basal ganglia through the basal ganglia - thalamocortical loops [32] . These projections convey multidimensional information onto the PFC , including ( but not limited to ) emotional and motivational inputs [33] , reward-dependent modulation [34] , and action-related signals [32] . The PFC seems then well suited to ( i ) process manifold spatial information [35] , ( ii ) encode the motivational values associated to spatiotemporal events [15] , and ( iii ) perform supra-modal decision making [36] , [37] . Also , the PFC may be involved in integrating events in the temporal domain at multiple time scales [38] . Indeed , its recurrent dynamics , regulated by the modulatory action of dopaminergic afferents , may maintain patterns of activity over long time scales [39] . Finally , the PFC is likely to be critical to detecting cross-temporal contingencies , which is relevant to the temporal organization of behavioral responses , and to the encoding of retrospective and prospective memories [38] . This article presents a neurocomputational model of the PFC columnar organization [40] and focuses on its possible role in spatial navigation planning . The cortical column model generates compact topological maps from afferent redundant spatial representations encoded by the hippocampal place cell activity patterns as modeled by Sheynikhovich et al . [41] . The model exploits the multimodal coding property offered by the possibility to refine the cortical architecture by adding a sublevel to the column , i . e . the minicolumn . It also exploits the recurrent nature of the columnar organization to learn multilevel topological maps accounting for structural regularities of the environment ( such as maze alleys and arms ) . It shows how specific connectivity principles regulated by unsupervised Hebbian mechanisms for synaptic adaptation can mediate the learning of topological neural representations in the PFC . Then , the model uses the underlying topological maps to plan goal-directed pathways through a neural implementation of a simple breadth-first graph search mechanism called activation diffusion or spreading activation [42]–[44] . The activation diffusion process is based on the propagation of a reward-dependent signal from the goal state through the entire topological network . This propagation process enables the system to generate action sequences ( i . e . trajectories ) from the current position towards the goal . We show how the modeled anatomo-functional interaction between the hippocampal formation and the prefrontal cortex can enable simulated rats to learn detour navigation tasks such as Tolman & Honzik's task [4] . The model presented here aims at shedding some light on the link between single-cell activity and behavioral responses . We perform a set of statistical and information theoretical analyses to characterize the encoding properties of hippocampal and PFC neuronal activity —in terms of both main correlates ( e . g . location , distance-to-goal , and prospective coding ) and functional time course changes . We interpret and validate the results of these analyses against available experimental data ( e . g . extracellular electrophysiological recordings of PFC units ) .
Cortical maps consist of local circuits —i . e . the cortical columns [40]— that share common features in sensory , motor and associative areas , and thus reflect the modular nature of cortical organization and function [45] . Cortical columns can be divided in six main layers including: layer I , which mostly contains axons and dendrites; layers II-III , called supragranular layers , which are specialized in cortico-cortical connections to both adjacent and distant cortical zones; layer IV , which receives sensory inputs from sub-cortical structures ( mainly the thalamus ) or from columns of cortical areas involved in earlier stages of sensory processing; and layers V–VI , called infragranular layers , which send outputs to sub-cortical brain areas ( e . g . to the striatum and the thalamus ) regulating the ascending information flow through feedback connections . According to the cytoarchitectonic properties of the rat medial PFC [32] , no layer IV is considered in the model of cortical column described henceforth . Neuroanatomical findings ( see [45] for a review; see [46] , [47] for anatomical data on rat PFC ) suggest that columns can be further divided into several minicolumns , each of which consists of a population of interconnected neurons [48] . Thus , a column can be seen as an ensemble of interrelated minicolumns receiving inputs from cortical and sub-cortical areas . It processes these afferent signals and projects the responses both within and outside the cortical network . This twofold columnar organization has been suggested to subserve efficient computation and information processing [45] , [49] . Several models have been proposed to study the cortical columnar architecture , from early theories on cortical organization [50]–[52] to recent computational approaches ( e . g . the blue brain project [53] ) . These models either provide a detailed description of the intrinsic organization of the column in relation to cytological properties and cell differentiation or focus on purely functional aspects of columnar operations . The approach presented here attempts to relate the columnar organization to decision making and behavioral responses using a highly simplified neural architecture which does not account for cell diversity and biophysical properties of PFC neurons . Fig . 1A shows an overview of the model architecture based on this notion of cortical column organization . As aforementioned , the underlying hypothesis is that the PFC network may mediate a sparsification of the hippocampal place ( ) representation to encode topological maps and subserve goal-directed action planning . The model exploits the anatomical excitatory projections from hippocampus to PFC [28] to convey the redundant state-space representation to the columnar PFC network , where a sparse state-action code is learned . Within a column , each minicolumn becomes selective to a specific state-action pair , with actions representing allocentric motion directions to perform transitions between two states . Each column is thus composed by a population of minicolumns that represent all the state-action pairs experienced by the animal at a location . This architecture is consistent with data showing that minicolumns inside a column have similar selectivity properties [54] and that some PFC units encode purely cue information while others respond to cue-response associations [55] . The model employs the excitatory collaterals between minicolumns [45] , [56] to learn multilevel topological representations . Egocentric self-motion information ( provided by proprioceptive inputs ) biases the selectivity properties of a subpopulation of columns to capture morphological regularities of the environment . Unsupervised learning also modulates the recurrent projections between minicolumns to form forward and reverse associations between states . During planning , the spreading of a reward signal from the column selective for the goal through the entire network mediates the retrieval of goal-directed pathways . Then , a local competition between minicolumns allows the most appropriate goal-directed action to be inferred . The following sections provide a functional description of the model columnar structure , connectivity and input-output functional properties . A more comprehensive account –including equations , parameter settings and explanatory figures– can be found in Supplementary Text S1 . We demonstrate the ability of the model to learn topological representations and plan goal-oriented trajectories by considering a navigation task: the Tolman & Honzik's detour task . The behavioral responses of simulated rats are constraint by intersecting alleys , which , in contrast to open field mazes , generate clear decision points and permit dynamic blocking of goal-directed pathways .
We first examined the behavioral responses of simulated animals solving the 1∶1 version of Tolman & Honzik's task ( see Sec . sec:tolmantask and Fig . 2 for details on the experimental apparatus and protocol ) . The qualitative and quantitative results shown on Figs . 3A and B , respectively , demonstrate that the model reproduced the behavioral observations originally reported by Tolman & Honzik [4] . During the first 12 training trials ( Day 1 ) the simulated animals learned the topology of the maze and planned their navigation trajectories in the absence of blocks A and B . Similar to Tolman & Honzik's findings , the model selected the shortest pathway P1 significantly more than alternative paths P2 and P3 ( ANOVA , ; Figs . 3A , B left column ) . During the following 156 training trials ( Days 2–14 ) , a block at location A forced the animals to update their topological maps dynamically , and plan a detour to the goal . The results reported by Tolman & Honzik provided strong evidence for a preference for the shortest detour path P2 . Consistently , we observed a significantly larger number of transits through P2 compared to P3 ( ANOVA , ; Figs . 3A , B central column ) . The simulated protocol included 7 probe trials ( Day 15 ) during which the block A was removed whereas a block at location B was added . This manipulation aimed at testing the “insight” working hypothesis: after a first run through the shortest path P1 and after having encountered the unexpected block B , will animals try P2 ( wrong behavior ) or will they go directly through P3 ( correct behavior ) ? In agreement with Tolman & Honzik's findings , simulated animals behaved as predicted by the insight hypothesis , i . e . they tended to select the longer but effective P3 significantly more often than P2 ( ANOVA , ; see Figs . 3A , B , right column ) . The patterns of path selection during this task is explained by the ability of the model to choose shortest paths . When a block is added into the environment , the goal propagation signal is also blocked at the level of the column network , and hence the simulated animals choose the shortest unblocked pathways . We then tested the robustness of the above behavioral results with respect to the size of the environment . We considered a 4∶1 scaled version of Tolman & Honzik's maze and we compared the performances of simulated animals with intact populations ( “control” group ) against those of simulated animals lacking the cortical population ( “no ” group ) . The latter group did not have the multilevel encoding property provided by the – recurrent dynamics ( see Sec . Recurrent cortical processing for multilevel topological mapping ) . Fig . 3C compares the average path selection responses of the two simulated groups across the different phases of the protocol . During Day 1 ( i . e . no blocks in the maze ) both groups selected the shortest path P1 significantly more often ( ANOVA , ; Fig . 3C left ) . However , the action selection policy of subjects without began to suffer from mistakes due to the enlarged environment , as suggested by lower median value corresponding to P1 . During Days 2–14 ( with block A ) , the group without did not succeed in solving the detour task , because no significant preference was observed between P2 ( shortest pathway ) and P3 ( ANOVA , ; Fig . 3C center ) . By contrast , control animals coped with the larger environmental size successfully ( i . e . P2 was selected significantly more often than P3 , ANOVA , ) . During the probe trials of Day 15 ( with a block at B but not at A ) , the group without was impaired in discriminating between P2 and P3 ( ANOVA , ; Fig . 3C right ) , whereas control subjects behaved accordingly to the insight hypothesis ( i . e . they selected the longer but effective P3 significantly more than P2; ANOVA , ) . The better performances of control subjects were due to the fact that back-propagating the goal signal through the cortical network benefited from the higher-level representation encoded by the population and from the - interaction during planning ( see Supplementary Text S1 Sec . Exploiting the topological representation for navigation planning , Fig . S2 ) . Indeed , an intact population allowed the goal signal to decay with a slower rate compared to , due to the smaller number of intermediate columns in ( i . e . planning could benefit from a more compact topological representation ) . Henceforth we demonstrate how the modeled neural processes can be interpreted as elements of a functional network mediating spatial learning and decision making . We show that the neural activity patterns of all types of neurons in the cortical model are biologically plausible in the light of PFC electrophysiological data [20] , [35] , [59]–[66] . We studied to what extent the neural populations of the model ( i . e . , , , and neurons ) could be quantitatively segregated on the basis of a set of statistical measures . We then compared the results to those obtained by applying the same clustering analysis to a population of neurons recorded from the medial PFC of navigating rats ( see Materials and Methods Sec . Statistical analysis of neural activities ) . We first gathered all non-silent simulated neurons recorded during the 4∶1 version of Tolman & Honzik's task . All types of units ( i . e . , , , , ) were pulled together in a data set . We characterized each neuron's discharge by measuring its mean firing rate , standard deviation , skewness , lifetime kurtosis , spatial information per spike and spatial mutual information ( see Supplementary Text S2 ) . Then , we performed a principal component analysis ( PCA ) on the multidimensional space containing the values provided by these measures per each neuron ( see Figs . S4 A , C for details ) . Fig . 9A shows the resulting data distribution in the space defined by the first three principal components . Interestingly , model neurons with different functional roles tended to occupy distinct regions of the PCA space . For instance , neurons , whose function in the model is to propagate goal information and code for the distance to the goal , were located within the same portion of the PCA space ( blue and cyan crosses and circles ) . All neurons , which primarily code for spatial locations , were also clustered within the PCA space ( red crosses ) . Neurons ( and also ) , responsible for forward signal propagation and local decision making , respectively , were aggregated within the same region ( gray and black crosses , and black circles ) . Finally , neurons , mainly involved in high-level mapping and navigation planning , were also separated from other units in PCA space ( gray and red circles ) . Figs . 9B , C , D display the mean values , averaged over each population of the model , of three statistical measures ( out of six ) used to perform the PCA . These diagrams can help understanding the data point distribution of Fig . 9A . When considering the mean spatial information per spike ( Fig . 9B ) , at least three groups could be observed: neurons whose activity had nearly no spatial correlate ( ) , neurons conveying intermediate amounts of spatial information ( and ) , and neurons with larger spatial information values ( ) . The mean firing rate parameter ( Fig . 9C ) allowed two distinct groups to be clearly identified: one with low average firing ( neurons ) , and one with high firing rates ( neurons ) . Together with Fig . 9A , this diagram can help understanding why neurons , which had almost no spatial correlate and very high firing rates compared to other populations of the model , were well segregated within the same region of the PCA space ( Fig . 9A , blue and cyan crosses and circles ) . Finally , when comparing the mean skewness values of all neural populations ( Fig . 9D ) , neurons and were pulled apart , according to their distribution in the PCA space ( Fig . 9A , gray and black crosses , and black circles ) . As a control analysis , we extended the data set used for the PCA by adding a population of neurons with random Poisson activities . As shown in supplementary Figs . S5 A–B , the population of Poisson neurons ( light green data points ) was well separated from all model neurons within the space defined by the first three principal components , suggesting that the variability of model discharge properties could not be merely explained by a random Poisson-like process . We then applied an unsupervised clustering algorithm ( k-means clustering method with ) to partition the distribution of data points of Fig . 9A , based on the discharge characteristics of model neurons . This blind clustering analysis ( i . e . without any a priori knowledge on neural populations ) allowed us to identify three main groups ( Fig . 10A ) . The first cluster ( blue data points ) corresponded to non-spatial , reward-related neuronal activities ( i . e . neurons ) . The second cluster ( green points ) represented location-selective activity ( mainly from neurons , but also including some neurons ) . The third cluster ( red data points ) corresponded to location-selective activity of neurons in the cortical network ( i . e . mainly ) . See supplementary Fig . S6 for details on the composition of the three identified clusters . We performed the same series of analyses on a dataset of medial PFC neurons recorded from navigating rats ( see Materials and Methods , Sec . Statistical analysis of neural activities ) . We characterized every recorded activity according to the same set of statistical measures used for model neurons ( i . e . mean firing rate , standard deviation , skewness , lifetime kurtosis , spatial information per spike and spatial mutual information , see Supplementary Text S2 ) . Then , we applied a PCA on the resulting high dimensional space containing , per each neuron , the resulting values of these measures ( see Figs . S4 B , D for details ) . Finally , we used the same unsupervised k-mean clustering algorithm to partition the data distribution in the space defined by the first three principal components . As for simulated data , the clustering method identified three main classes ( Fig . 10B; with red , green , and blue data points corresponding to three subsets of electrophysiologically recorded activities in the PFC ) . We then compared model and experimental clusters ( Figs . 10C , D , E ) in order to investigate whether real and simulated data points belonging to the same clusters shared some discharge characteristics . In terms of mean spatial information ( Fig . 10C ) , we found similar non-homogeneous distributions between model and real clusters . Both red clusters encoded the largest spatial information content . Recall that the model red cluster mainly contained activities from location-selective neurons ( as quantified in supplementary Fig . S6 B ) . When looking at mean firing rates averaged over each cluster ( Fig . 10D ) , we found that both real and simulated activities within the blue clusters had significantly larger frequencies than others . The model blue cluster was mainly composed by neurons propagating reward-related information . Finally , when comparing the mean absolute values of the skewness of receptive fields ( Fig . 10E ) , we found both model and experimental populations with asymmetric fields ( i . e . non-zero skewness ) . Model-wise , the red and green clusters ( containing neurons , Fig . S6 B ) had the largest mean skewness . Similarly , experimental red and green subpopulations had larger skewness values than the blue population . As a control analysis , we computed the three mentioned measures ( i . e . information per spike , mean firing rate and skewness of the receptive field ) for two populations of neurons with random Uniform and Poisson activities . As shown in supplementary Fig . S7 , unlike model data , the two populations of random neurons could not explain the experimental data in terms of information content and skewness of the receptive field . Taken together , these results indicated that , within the data set of experimental PFC recordings , subpopulations of neurons existed with distinct discharge properties , and that these subpopulations might be related to distinct functional groups predicted by the model .
Our model is based upon three main assumptions . First , the model relies on the columnar organization of the cortex . Although this concept is supported by many experimental studies [45] , [48] , no clear general function for columns has emerged to explain their role in cortical processing [69] . In addition , Rakic [70] stressed that the size , cell composition , synaptic organization , expression of signaling molecules , and function of various types of columns are dramatically different across the cortex , so that the general concept of column should be employed carefully . In our model , we call “column” a local micro-circuit composed by neurons processing common spatial information , and we propose that this columnar organization may be a substrate suitable to implement a topological representation of the environment . Second , our planning model relies on an activation diffusion mechanism . At the neural level , this suggests that strong propagation of action potentials should occur in the neocortex . This is not a strong assumption , since several studies have demonstrated such phenomena as propagating waves of activity in the brain [71] , [72] . For example , Rubino et al . [73] suggested that oscillations propagate as waves across the surface of the motor cortex , carrying relevant information during movement preparation and execution . Third , the multiscale representation is based on a putative signal . There are several potential candidates for its implementation in the brain . One of these candidates is habit learning involving the striatum [74] , [75] . Indeed , if for instance the rat always turns left at a particular location it may acquire a corresponding habit . The neural activity corresponding to this stimulus-response association may serve as the signal . In this case , the time scale of learning in the population should correspond to the time scale of habit acquisition ( potentially many trials , see e . g . [75] ) . Topological map learning and path planning have been extensively studied in biomimetic models ( see [6] for a general review; see also [76] for theoretical discussions on hierarchical cognitive maps ) . These models aimed at either providing more efficient path planning algorithms or , like our work , establishing relations between anatomical substrates , electrophysiology and behavior . In particular , several studies took inspiration from the anatomical organization of the cortex and used the activation diffusion mechanism to implement planning . Burnod [49] proposed one of the first models of the cortical column architecture , called “cortical automaton” . He also described a “call tree” process that can be seen as a neuromimetic implementation of the activation diffusion principle . Some subsequent studies employed the cortical automaton concept [77] , [78] , while others used either connectionist architectures [16] , [79]–[84] or Markov decision processes [85] . Our approach is similar to that of Hasselmo [44] , who also addressed goal-directed behavior by modeling the PFC columnar structure . Both Hasselmo's and our model architectures employ minicolumns as basic computational units to represent locations and actions , to propagate reward-dependent signals , and mediate decision making . Yet , the two models differ in the encoding principles underlying the learned representations , which generate , consequently , distinct behavioral responses . The connectivity layout of Hasselmo's model allows state-response-state chains to be encoded , with each minicolumn representing either a state or an action . In our model , a state and its related actions are jointly encoded by a set of minicolumns within a column . Similar to Koene and Hasselmo [44] , [86] , we compared the discharge of simulated PFC units against experimental recordings exhibiting place- , action- and reward-related correlates . As explained henceforth , we focused further on the functional relationship between the hippocampus and the PFC in encoding complementary aspects of spatial memory , with a quantitative approach based on the analysis of statistical properties and information content of the neural codes . We also put the emphasis on the time course analysis of neural responses mediating place coding vs . decision making . The successful performance of our model in large environments relies on its ability to build a multiscale environment representation . This is in line with the proposal by McNamara et al . [87] who have suggested that humans can solve complex spatial problems by building a hierarchical cognitive map , including multiple representations of the same environment at different spatial scales . Moreover , animals may be able to chunk available information and build hierarchical representations to facilitate learning [88]–[92] . Recently , multiscale spatial representations have been identified at the neural level . For example in the entorhinal cortex , Hafting et al . [13] have shown that grid cells have spatial fields forming grids with different spacing and place field sizes . Kjelstrup et al . [93] have provided neural recordings of place cell activities in a large maze , supporting the multiscale coding property in the hippocampus . These entorhinal and hippocampal multiscale representations are likely to encode spatial contextual information at variable resolution . Complementing this code , we suggest that multiscale representations related with space , action and reward should also be found in neocortical areas such as the prefrontal cortex , commonly associated with high-level cognitive processes . Moreover , there are several works suggesting a role of the PFC in the learning of hierarchical representations . For example , Botvinick [94] reviewed how the hierarchical reinforcement learning framework [95] could explain the mechanism by which the PFC aggregates actions into reusable subroutines or skills . The multiscale property is applied there for actions instead of states as in our approach . From a biological point of view , recent studies directly pointed out the role of the PFC for hierarchical representations , with a possible anatomical mapping of the hierarchical levels along the rostro-caudal axis of the PFC [96] . In spite of a possible complementary role for the PFC and the hippocampus in multiscale space coding , our work focuses on different roles of the PFC and the hippocampus in the planning process . Namely , we propose that the hippocampus is more involved in the representation of location [1] and , possibly , route learning [97] , [98] , while the PFC is responsible for topological representations and action selection . From a more general perspective , a route could be seen as an example of navigation from a point to another , an episode . In contrast , the more integrated topological representation would be more similar to a set of navigation rules . This hypothesis is in accordance with data showing that the hippocampus would be involved in instance-based episodic memory , whereas the PFC would be linked to rule learning from examples [99]–[101] . Our model is consistent with recent studies suggesting a role for the PFC in prospective memory [102] , [103] . Goto and Grace [102] showed that , depending on the dopamine receptors activation , PFC either incorporates retrospective information processed by the hippocampus or processes its own information to effect preparation of future actions . This is in accordance with our model which includes hippocampal information to localize itself in the environment , and then propagates reward signal to generate a goal-directed sequence of action . Moreover , Mushiake et al . [104] showed that activity in the PFC reflects multiple steps of future events in action plans . They suggested that animals may be engaged in planning sequences in a retrograde order ( starting from the goal to the first motion ) , in conjunction with a sequence planning with an anterograde order . At the cognitive level , the activation diffusion planning process provides a capacity of mental simulation of action selection: the back-propagated goal signal allows possible navigation trajectories to be identified , whereas the forward-propagated path signal actually simulates the execution of the selected action sequence . Schacter et al . [103] recently reviewed theories on simulation of future events and neural structures associated with this cognitive ability . They showed that the same core network , which plays a role in remembering , is also implied in mental simulation . This network involves prefrontal as well as medial temporal regions including the hippocampus , which is also involved in prospective and retrospective memory coding ( [105] , [106]; see also [107] , [108] for theoretical works modeling the role of this core network in memory retrieval and mental imagery ) . Our simulation results on the Tolman and Honzik detour task show that the behavior of the model is consistent with an “insight” demonstrated by rats in this task . The insight , as defined by Tolman and Honzik , is the ability to conceive that two paths have a common section , and so when a passage through the common section is blocked , both of these paths are necessarily blocked and a third , alternative pathway , should be chosen . The realization that a common section exists leads to two conclusions . First , animals do not act exclusively according to stimulus-response associations , but use some kind of mental representation of the environment [109] . For example , in the conditions of the detour task ( Fig . 2A ) , the rats chose path 3 without actually testing path 2 during probe trials and so they did not have a chance to form the correct stimulus-response associations to solve the task . In order to choose the correct path 3 , rats had to mentally replay path 2 and realize that it was blocked , suggesting the existence of a spatial representation . Second , a representation of the environment in terms of routes is not sufficient to solve the task . Indeed , if after training animals store separate representations of routes via paths 1–3 , then the fact that route 1 is blocked should not lead to the conclusion that route 2 is also blocked . In summary , the results of this experiment suggest the existence of a topological graph-like representation in which common points ( nodes ) and common sections ( edges ) are identified . The model presented here proposes a plausible way of how such a representation can be built ( see below ) . In terms of the model , the insight capability in the detour task is mediated by the propagation of the goal signal through the nodes of the spatial graph , in which the common section of paths 1 and 2 is blocked . The other important question addressed by the present study is whether the requirements of the proposed model are consistent with the neural activities observed in the PFC . We show that all types of neurons that are required by the model , have actually been observed in the PFC . Namely , ( i ) the state-encoding neurons in the model correspond to spatially selective prefrontal neurons with different receptive field sizes ( Fig . 4D , see also [20] ) ; ( ii ) the distance-to-goal , or value , neurons correspond to the PFC neurons with constant discharge rate ( Fig . 6B ) , giving rise to the prediction that neurons with higher ( constant ) discharge rates can code for locations closed to reward; ( iii ) the prospective-coding neurons in the model correspond to PFC neurons with the firing rate that increases when the animal moves toward the goal ( Figs . 8B , D , see also [62] , [65] ) ; and , finally , ( iv ) neurons and , which together encode state-action values , show activity patterns similar to strategy-switching neurons observed by Rich and Shapiro [37] . Indeed , the authors reported that in their task ( i . e . strategy switching in a plus-maze ) during the periods before and after reward contingency change , different subsets of PFC neurons were highly active . This is exactly what was observed in our model . For example , neurons and that were more active than neurons and before the contingency change ( Figs . 7B , C at 4 s ) became relatively less active after the change ( Figs . 7B , C at 5 s ) . The model provided a vantage point to interpret PFC electrophysiological data in terms of quantitative clustering of population activity . On the basis of a set of statistical measures , we performed a principal component analysis on both simulated and real data sets of PFC recordings . This study gave rise to comparative results based on the identification of clusters of characteristic discharge properties . We could put forth some hypotheses about the functional meaning of the observed clusters —in terms of their role in spatial localization and planning , reward coding , and prospective memory . For instance , model neurons mediating planning in large scale mazes ( i . e . belonging to the cortical population of the model ) could be segregated from other simulated units ( red cluster in Fig . 10 ) . A corresponding cluster was found when analyzing real recordings , corroborating the hypothesis of the presence of neurons with similar discharge properties in the PFC . We also identified another cluster of real PFC activities which contained both pyramidal cell and interneuron responses ( and , respectively ) . This cluster corresponded to goal propagating neurons of the model ( blue cluster in Fig . 10 ) , leading to the prediction that interneurons may contribute to decision making by participating to the propagation of information relevant to the next decision to be taken . Interestingly , in their study of spatial navigation , Benchenane et al . [60] showed that the inhibitory action of PFC interneurons onto pyramidal cells is enhanced during periods of high coherence in theta oscillations between hippocampus and PFC occurring at decision points . In this model , the simulated hippocampal population does not account for the full range of place cell firing properties that have been extensively studied during the past decades . Particularly , the dynamics of the model hippocampal population in terms of learning , extrafield firings and rhythmic discharges are not reproduced . Experimental data show that the introduction or the removal of a barrier in the environment may induce learning-related changes in the hippocampal population ( remapping ) . For example , previously silent cells may discharge and previously active cells may be silent when the animal visits the modified environment [110] , [111] . In addition , complementing their location selectivity , hippocampal place cells may have extrafield firings , and neural ensembles in the hippocampus may transiently encode paths forward of the animal [112] . Finally , it has been shown that hippocampal place cell discharges are modulated by theta oscillations ( e . g . phase precession phenomena , [113] ) and that the hippocampus and the PFC seem to synchronize at behaviorally relevant places in a maze , such as decision points [114] . Although the scope of the presented model is targeted to address the PFC firing patterns , these experimental data suggest that improving our hippocampal place cell model is relevant to provide plausible predictions about the interactions between the hippocampus and the PFC during decision making in spatial navigation tasks . The second limitation of the model is related to the issue of goal representation in the PFC . The model makes decisions based on an appetitive motivational signal only ( i . e . the reward at the goal site ) . Clearly , there are other variables apart from the reward size that influence the planning process . For example , there is evidence that physical efforts required to reach the goal or delay in reward delivery influence PFC-dependent behavioral decisions [115] . Moreover , the model can merely deal with a single goal at present and can not estimate relative values of different goals [63] . In order to address these limitations , the activation diffusion mechanism in the model can be extended to propagate several motivational signals , the intensities of which are proportional to their subjective values . In this case , a goal-related effort or delayed reward can be modeled by adjusting the relative values of motivation signals at different locations in the maze . We limited our study to a structured maze ( i . e . Tolman & Honzik's maze [4] ) to focus on the adaptive response to dynamic blocking of goal-directed pathways , a required property to validate detour-like navigation behavior . Furthermore , Tolman & Honzik's maze provided us with the possibility to investigate the neural dynamics of the modeled network at clear decision points –i . e . at the intersections between corridors . Several models have addressed spatial navigation in open-field environments based on place-triggered-response strategies ( i . e . locale navigation ) , in which hippocampal place cell activity is associated to the best local action leading to the goal ( e . g . [57] , [116] , [117] ) . In fact , two components are relevant to avoid the combinatorial explosion of the space in open-fields: ( i ) the reliability of the spatial code in terms of minimum hidden-state probability , to avoid , for instance , that a same place cell population can code for different locations –a problem often arising from sensory-aliasing phenomena in purely topological maps; ( ii ) the use of a discrete action space , meaning that a finite set of actions are available at each state ( location ) . Our hippocampal-PFC model satisfies these requirements . We already used a highly simplified version of the model presented in this paper to solve open-field navigation problems ( e . g . Morris water maze [11] ) . Note that , however , Dollé et al . ( 2010 ) focused on navigation strategy switching and did not model the PFC columnar organization and the ( possibly ) involved computational processes ( e . g . multiscale coding ) to drive planning behavior [11] . In open-field environments with no obstacles our model predicts -like units with uniform activity across the whole space –as a result of a uniform signal reflecting equal probability of turning at each location . Adding borders or barriers would result in the “recruitment” of new units preferentially active on either one side or the other of the barriers . In more structured environments such as interconnected arenas ( e . g . [15] ) , the model predicts separate units for each space . To our knowledge , there is no direct experimental evidence in favor or against the existence of such PFC units . Another interesting direction of future work is to study the encoding of task-related information in the PFC during sleep . Although it is likely that information is transferred during task learning , memory consolidation during sleep also appears to play a central role [59] . In particular , sharp wave-ripple complexes in the hippocampus seem prominent for transferring labile memories from the hippocampus to the neocortex for long-term storage [118] . A key issue for modeling approaches is to understand computational properties of this learning mechanism .
|
We study spatial cognition , a high-level brain function based upon the ability to elaborate mental representations of the environment supporting goal-oriented navigation . Spatial cognition involves parallel information processing across a distributed network of interrelated brain regions . Depending on the complexity of the spatial navigation task , different neural circuits may be primarily involved , corresponding to different behavioral strategies . Navigation planning , one of the most flexible strategies , is based on the ability to prospectively evaluate alternative sequences of actions in order to infer optimal trajectories to a goal . The hippocampal formation and the prefrontal cortex are two neural substrates likely involved in navigation planning . We adopt a computational modeling approach to show how the interactions between these two brain areas may lead to learning of topological representations suitable to mediate action planning . Our model suggests plausible neural mechanisms subserving the cognitive spatial capabilities attributed to rodents . We provide a functional framework for interpreting the activity of prefrontal and hippocampal neurons recorded during navigation tasks . Akin to integrative neuroscience approaches , we illustrate the link from single unit activity to behavioral responses while solving spatial learning tasks .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"circuit",
"models",
"cognitive",
"neuroscience",
"cognition",
"computational",
"neuroscience",
"decision",
"making",
"neural",
"networks",
"biology",
"computational",
"biology",
"neuroscience",
"learning",
"and",
"memory"
] |
2011
|
Spatial Learning and Action Planning in a Prefrontal Cortical Network
Model
|
The toxin components of toxin-antitoxin modules , found in bacterial plasmids , phages , and chromosomes , typically target a single macromolecule to interfere with an essential cellular process . An apparent exception is the chromosomally encoded toxin component of the E . coli CbtA/CbeA toxin-antitoxin module , which can inhibit both cell division and cell elongation . A small protein of only 124 amino acids , CbtA , was previously proposed to interact with both FtsZ , a tubulin homolog that is essential for cell division , and MreB , an actin homolog that is essential for cell elongation . However , whether or not the toxic effects of CbtA are due to direct interactions with these predicted targets is not known . Here , we genetically separate the effects of CbtA on cell elongation and cell division , showing that CbtA interacts directly and independently with FtsZ and MreB . Using complementary genetic approaches , we identify the functionally relevant target surfaces on FtsZ and MreB , revealing that in both cases , CbtA binds to surfaces involved in essential cytoskeletal filament architecture . We show further that each interaction contributes independently to CbtA-mediated toxicity and that disruption of both interactions is required to alleviate the observed toxicity . Although several other protein modulators are known to target FtsZ , the CbtA-interacting surface we identify represents a novel inhibitory target . Our findings establish CbtA as a dual function toxin that inhibits both cell division and cell elongation via direct and independent interactions with FtsZ and MreB .
In E . coli , as in most other bacteria , cell shape is defined by the peptidoglycan sacculus [1] , which is built by the coordinated efforts of two major protein complexes , the cell elongation complex and the cell division complex ( reviewed in [2–4] ) . The cell elongation complex directs the insertion of new cell wall material into the E . coli lateral sidewall , causing a newly divided rod cell to increase in length ( while maintaining a constant width ) . Once the elongated cell has approximately doubled its mass , the division complex ( or divisome ) builds a new septal wall at mid-cell , forming two equivalently sized rod-shaped daughter cells [2 , 5 , 6] . Functional disruption of either of these two complexes in E . coli results in striking cell morphological alterations . Cells that fail to divide form long filaments [7] , whereas cells that are blocked for cell elongation lose their rod-shape and become spherical [8 , 9] . Peptidoglycan insertion by the cell division and cell elongation complexes is directed by a dedicated bacterial cytoskeletal element . Cell division is governed by the broadly conserved bacterial tubulin homolog and GTPase , FtsZ . FtsZ polymerizes into dynamic filaments that coalesce into a ring structure ( referred to as the Z ring ) at mid-cell . Once properly assembled at mid-cell , this Z ring serves as a scaffold for a large set of essential and non-essential protein components , resulting in formation of the mature division complex , which constructs the new septum ( reviewed in [7 , 10] ) . Cell elongation in the majority of rod-shaped bacteria is mediated by the actin-homolog and ATPase , MreB [11–14] . MreB polymerizes to form antiparallel double filaments [15] that are peripherally associated with the inner leaflet of the cytoplasmic membrane [16] . In vivo fluorescence imaging studies have shown that MreB forms dynamic filament patches that move circumferentially along the long axis of the cell , directing the lateral incorporation of cell wall material [17–19] . The polymerization , assembly , and dynamics of these bacterial cytoskeletal elements are dictated by their inherent biochemical properties and further influenced by diverse modulatory proteins . FtsZ assembly is controlled by a complex set of positive and negative “house-keeping” regulators that spatiotemporally coordinate Z ring formation with the cell cycle [7 , 20–25] . FtsZ is also the target of several inhibitors that block its assembly in response to specific environmental cues . For example , in response to cellular DNA damage , the SOS inhibitor SulA blocks FtsZ assembly by sequestering FtsZ monomers [26–28] . Several exogenous inhibitors of FtsZ function , including phage-encoded proteins and small molecule inhibitors , have also been described in recent years [29–31] . The best-characterized MreB inhibitor is the small molecule antibiotic A22 , which binds within the nucleotide-binding pocket of MreB and blocks double filament formation [15] . However , relatively few protein modulators of MreB function have been described [32–37] and the physiological relevance of their effects is unknown . Among proteins that can alter cell shape , the CbtA ( formerly known as YeeV ) protein of E . coli is unusual in being able to inhibit both cell division and cell elongation . Previously proposed to target both FtsZ and MreB [32] , CbtA is the toxin component of the prophage-encoded CbtA/CbeA chromosomal toxin-antitoxin system found in E . coli and other closely related species [38] . Toxin-antitoxin systems are genetic modules that encode a small , stable toxin protein and a labile , cotranscribed antitoxin ( reviewed in [39–41] ) . Capable of causing growth arrest or cell death , the toxins typically target essential cellular processes . Toxin-antitoxin systems are abundant in prokaryotic genomes [42] and have been implicated in the bacterial stress response [40 , 43 , 44] . Overexpression of the cbtA toxin gene in E . coli was shown by Tan et al . [32] to result in a cell growth defect and a loss of rod shape . Over the course of several hours , cells induced for cbtA expression formed swollen lemon-shaped cells with distinct poles; with prolonged induction , these lemon-shaped cells eventually lysed [32] . This morphology is reminiscent of the change in cell shape induced by a simultaneous block of cell division and cell elongation pathways in E . coli–specifically by the combined inhibition of FtsZ ( via overexpression of sulA ) and MreB ( with A22 treatment ) [45] . Consistent with the striking lemon-like morphology seen with cbtA overexpression , Tan et al . detected interactions between the CbtA toxin and both FtsZ and MreB in vivo ( by yeast two-hybrid ) and in vitro ( by pull-down assay ) [32] . Nonetheless , whether or not these interactions are directly responsible for the effects of CbtA overproduction on cell shape and cell growth has not been established; in particular , it is not known if CbtA , a small protein of only 124 amino acids , interacts independently with FtsZ and MreB to mediate its effects on cell shape and whether its interaction with these or other proteins is responsible for its toxic effects . Moreover , in light of evidence that in E . coli cell division and septum formation depend on an interaction between FtsZ and MreB [46] , CbtA might conceivably exert its effects by interacting directly with only one or the other of these cytoskeletal elements [32] . Here , we genetically dissect the reported interactions of CbtA with FtsZ and MreB . Our analysis indicates that these interactions are direct and independent . We show further that both of these interactions are functionally relevant , contributing independently to CbtA-mediated toxicity and cell-shape perturbations . Our findings thus establish CbtA as a bona fide dual inhibitor of bacterial cell elongation and cell division . Moreover , by identifying the surface of each cytoskeletal element that is bound by CbtA , our findings describe new inhibitory surfaces that can be targeted to block cytoskeletal function .
Consistent with previous reports , we observed that overproduction of CbtA ( as a His6-CbtA-GFP fusion protein ) under the control of a hybrid T5-lac promoter [47] in E . coli resulted in a severe decrease in viability ( Fig 1A ) . Furthermore , time-lapse microscopy confirmed that upon overproduction of His6-CbtA-GFP , cells failed to divide , adopting a morphology resembling swollen lemons , and eventually lysed ( Fig 1B ) . Observation of GFP fluorescence in these cells revealed that the His6-CbtA-GFP fusion protein was distributed diffusely throughout the entire bloated cell ( Fig 1C ) ; a high background of diffuse cytoplasmic fluorescence even early after the induction of fusion protein synthesis obscured any possible co-localization with cytoskeletal elements at earlier time points . Importantly , we found that overproduction of untagged CbtA yielded an identical lemon-shape phenotype ( Fig 1D ) . As the various images in Fig 1 illustrate , whereas essentially all the cells visualized manifested drastic morphological change , the individual lemon-shaped cells displayed striking heterogeneity . Many cells resembled smooth lemons , while others had pronounced tubular projections at one or both poles; bi-lobed lemon-shaped cells ( such as the one shown in Fig 1C ) were seen by time-lapse microscopy to form from pre-constricted cells ( see S1A Fig ) . These morphologies are consistent with the varied cell shapes observed by Varma et al . upon combined FtsZ and MreB inhibition [45] . Tan et al . detected interaction between CbtA and its proposed cytoskeletal targets in a yeast two-hybrid system [32] . Similarly , we detected interaction between CbtA and both FtsZ and MreB in a bacterial two-hybrid system developed in our lab [48 , 49] . In this assay , contact between a protein domain ( X ) fused to the α subunit of E . coli RNA polymerase and a partner domain ( Y ) fused to the CI protein of bacteriophage λ ( λCI ) activates transcription of a lacZ reporter gene under the control of a test promoter bearing an upstream λCI-binding site ( Fig 2A ) . In this case , we fused CbtA to λCI and either FtsZ or MreB to α . We detected an 18-fold increase in lacZ expression in the presence of the λCI-CbtA and α-FtsZ fusion proteins ( Fig 2B ) and a 3-fold increase in lacZ expression in the presence of the λCI-CbtA and α-MreB fusion proteins ( Fig 2C ) . Whereas these results are consistent with the idea that CbtA can interact directly with both FtsZ and MreB , they do not exclude the possibility that chromosomally encoded FtsZ may serve as a protein bridge linking the fused CbtA and MreB moieties . Our genetic analysis below addresses this possibility . To determine if CbtA can interact independently with FtsZ and MreB and to examine whether these interactions contribute directly to CbtA-mediated toxicity , we sought to identify mutations that specifically disrupt each of these interactions . We began by testing a CbtA variant that we had isolated on the basis of reduced toxicity ( S1 Text and S1 Fig ) , which bore the substitution F65S . Although further analysis revealed that mutant CbtA-F65S was only slightly less toxic than wild-type CbtA when overproduced in E . coli ( Fig 3A ) , bacterial two-hybrid analysis revealed that substitution F65S in the CbtA moiety of the λCI-CbtA fusion protein specifically disrupted its interaction with FtsZ ( Fig 3B ) , without compromising its interaction with MreB ( Fig 3C ) . Morphological observations were consistent with these results; upon overproduction of His6-CbtA-F65S-GFP to an intracellular level comparable to that of the wild-type protein ( S1C Fig ) , cells adopted a sphere-like rather than a lemon-like morphology ( Fig 3D ) . As seen by time-lapse microscopy ( S1A Fig ) , over the course of a three-hour induction period , cells producing His6-CbtA-F65S-GFP lost their rod shape , becoming spheroidal . These spheroidal cells continued to divide for one to two generations , gradually increasing in diameter until they lysed , a phenotype that mirrors that observed upon depletion of MreB [8 , 9] . We conclude that CbtA’s ability to block cell division is due to a direct interaction with FtsZ . In addition , these findings suggest that CbtA-F65S is able to interact with MreB and mediate a block in cell elongation even in the absence of an interaction with FtsZ . To further evaluate the proposition that CbtA’s ability to block cell elongation is due to a direct interaction with MreB , we sought to identify a CbtA variant with the opposite interaction profile: strong FtsZ interaction and abrogated MreB interaction . To do this , we used a two-hybrid-based screening strategy ( see Materials and Methods ) . Specifically , we introduced random mutations into the gene fragment encoding the CbtA moiety of the λCI-CbtA fusion protein , transformed the mutagenized library into reporter strain cells containing the α-MreB fusion protein , and screened for clones with reduced expression of the lacZ reporter gene . λCI-CbtA mutants identified in this manner were then counter-screened to identify those that supported high levels of lacZ expression in the presence of the α-FtsZ fusion protein . Using this two-step screening procedure , we identified substitution R15C , which specifically disrupted the interaction of CbtA with MreB ( Fig 3C ) , without compromising its interaction with FtsZ ( Fig 3B ) . Consistent with these two-hybrid data , induction of His6-CbtA-R15C-GFP production was toxic and caused the cells to form filaments , rather than adopting the lemon shape observed when the wild-type protein was produced at comparable levels ( Fig 3A , Fig 3D , S1C Fig ) . We conclude that CbtA’s ability to block cell elongation is due to a direct interaction with MreB , such that the CbtA-R15C variant , which still interacts with FtsZ , blocks cell division without blocking cell elongation . Together , the two-hybrid data and morphological phenotypes produced by the CbtA-F65S and CbtA-R15C variants demonstrate that the inhibitory functions of the CbtA toxin are independent and genetically separable . Although cells overproducing either His6-CbtA- F65S-GFP or His6-CbtA-R15C-GFP were still inviable ( Fig 3A ) , we found that overproduction of the His6-CbtA-R15C/F65S-GFP double mutant , which accumulated to comparable levels as the wild-type protein ( S1C Fig ) , did not influence viability ( Fig 3A ) . Furthermore , cells producing this variant maintained their rod shape , exhibiting only minor morphological perturbations ( Fig 3D ) . These findings establish the functional relevance of both the CbtA-FtsZ interaction and the CbtA-MreB interaction , each of which contributes to the lemon-like morphology and the viability defect observed upon CbtA overproduction . As an additional readout of the physiological perturbations caused by each of our CbtA variants , Z ring formation was monitored in cells producing the untagged mutant proteins . We first used a strain that constitutively produces a ZapA-GFP fusion from its native locus [50] . Because ZapA-GFP forms fluorescent ring structures that require proper assembly of the FtsZ ring , this fusion protein can serve as a proxy for FtsZ localization [50 , 51] . When the ZapA-GFP strain was transformed with an empty vector , fluorescent ZapA-GFP bands were observed at mid-cell in the majority of cells ( S1D Fig ) . In contrast , after two hours of cbtA expression , ZapA-GFP exhibited patchy , cloud-like localization throughout the resulting lemon-shaped cells , suggesting disruption of Z ring formation ( S1D Fig ) . When ZapA-GFP localization was observed in cells producing CbtA-R15C , the majority of cell filaments did not contain visible ring structures , and ZapA-GFP again formed cloud-like structures ( S1D Fig ) . In cells overproducing the less toxic CbtA-R15C/F65S variant , rod shape was maintained , and ZapA-GFP rings were observed in most cells ( S1D Fig ) . Importantly , overproduction of the untagged CbtA variants resulted in identical morphologies to those observed with the His6/GFP constructs ( compare Fig 3D phase contrast images with those shown in S1D Fig ) . A recent study reported similar ZapA-GFP cloud-like structures under conditions where FtsZ assembly and localization were disrupted , indicating that ZapA is able to localize in an FtsZ-independent manner [52] . To determine whether the patchy ZapA-GFP localization we saw upon overproduction of CbtA and CbtA-R15C was similarly occurring in an FtsZ-independent manner , we examined the localization of GFP-FtsZ ( overproduced in a strain also containing wild-type endogenous ftsZ ) in the presence of our untagged CbtA variants . Consistent with our ZapA-GFP data , we saw that after two hours of induction of CbtA or CbtA-R15C production , the majority of cells did not contain Z rings; however , unlike ZapA-GFP , GFP-FtsZ exhibited diffuse localization throughout the cell with no cloud-like structures observed ( S1E Fig ) . Thus , it seems likely that the ZapA-GFP patches seen in S1D Fig are forming independently of FtsZ . GFP-FtsZ was found to localize to mid-cell ring structures in cells transformed with an empty vector and in cells producing the CbtA-F65S/R15C double mutant variant ( S1E Fig ) . Taken together , the ZapA-GFP and GFP-FtsZ localization patterns suggest that wild-type CbtA and CbtA-R15C are able to disrupt FtsZ assembly and localization , blocking cell division , whereas the double mutant variant does not . The results of these analyses are consistent with the genetic evidence indicating that CbtA inhibits cell division and cell elongation via independent and separable interactions . We next sought to identify the CbtA interaction sites on FtsZ and MreB required for CbtA to mediate its inhibitory effects on cell division and cell elongation . Tan et al . reported that removal of the last 66 residues of FtsZ eliminated the yeast two-hybrid interaction detected between CbtA and FtsZ as well as the interaction between MreB and FtsZ [32] . The last 66 residues of E . coli FtsZ include the conserved 15 amino acid C-terminal tail domain ( CTT ) , which serves as a site of interaction for a variety of protein factors that regulate FtsZ assembly [53–62] , raising the possibility that CbtA too binds the CTT . We sought to test this possibility using our bacterial two-hybrid assay . As a positive control , we first tested the ability of the FtsZ membrane-anchoring protein ZipA ( its cytoplasmic C-terminal domain ) to interact with FtsZ . Structural data indicate that the C-terminal domain ( CTD ) of ZipA ( ZipACTD ) binds the FtsZ-CTT [57] . We detected a strong interaction ( resulting in a 10-fold increase in lacZ expression ) between FtsZ and the ZipACTD , and this interaction was compromised by removal of the C-terminal 66 residues of the FtsZ moiety ( Fig 4A ) , consistent with the structural data [57] and previously reported yeast two-hybrid analysis [63] . However , surprisingly , we found that FtsZ-Δ66 maintained an interaction with CbtA comparable to that of the wild-type protein ( Fig 4A ) , suggesting that the FtsZ-CTT is not necessary for the CbtA-FtsZ interaction . We were unable to detect an interaction between wild-type FtsZ and E . coli MreB in our two-hybrid system ( S2A Fig ) and thus could not determine whether or not this C-terminal truncation had any effect on that reported interaction . Because , in our bacterial two-hybrid system , the last 66 residues of FtsZ did not appear to mediate the interaction with CbtA , we sought to identify substitutions in FtsZ that specifically disrupt its interaction with CbtA . To do this , we introduced random mutations into the gene fragment encoding the FtsZ moiety of the α-FtsZ fusion protein , introduced the mutagenized library into reporter strain cells containing the λCI-CbtA fusion protein and screened for colonies with reduced lacZ expression on appropriate indicator medium ( see Materials and Methods ) . To identify FtsZ mutants specifically deficient for interaction with CbtA as opposed to generally destabilized variants , we performed a counter-screen based on the ability of FtsZ to interact with itself . Specifically , we detected a 4-fold increase in lacZ expression in reporter strain cells containing both the α-FtsZ fusion protein and a λCI-FtsZ fusion protein ( Fig 4A ) ; a similar interaction was previously reported in the context of both the yeast two-hybrid system [58] and an alternative bacterial two-hybrid system [46] . Thus , we screened for amino acid substitutions in the FtsZ moiety of the α-FtsZ fusion protein that reduced lacZ reporter gene expression in cells containing the λCI-CbtA fusion protein , but not in cells containing the λCI-FtsZ fusion protein . Among those amino acid substitutions that reduced lacZ expression by at least 60% in cells containing λCI-CbtA and by less than 25% in cells containing λCI-FtsZ , all localized to a small region encompassing residues 169–182 ( Fig 4A ) . These amino acid substitutions did not compromise the interaction between FtsZ and the ZipACTD ( Fig 4A ) . FtsZ residues 168–182 make up a loop region connecting α-helices 6 and 7 ( the H6/H7 loop ) in the GTP-binding N-terminal domain of FtsZ . FtsZ protofilament crystal structures from several bacterial species show that the H6/H7 loop ( shown in yellow in Fig 4B ) lies at the longitudinal interface formed by stacked FtsZ monomers [64 , 66 , 67] . To evaluate whether or not the H6/H7 loop residues identified in our genetic screen are functionally important for the CbtA-FtsZ interaction , we sought to test the effect of CbtA overproduction in an E . coli strain bearing one of the H6/H7 loop mutations at the endogenous ftsZ locus . We found that the ftsZ-L169P allele was able to support growth when introduced into the chromosomal ftsZ locus ( S2 Fig ) . Although this strain did not fully support cell division in fast-growth conditions ( LB at 37°C ) –we saw a subset of filamented cells and notable heterogeneity in cell length ( S2C Fig ) –this division defect was partially rescued by slower growth in LB at 30°C and fully rescued by growth in M9 minimal medium at 30°C ( S2C Fig ) . Indeed , in minimal medium , we observed comparable cell lengths for the wild-type and ftsZ-L169P strains ( S2D Fig ) . Western blot analysis with an FtsZ-recognizing antibody indicated that the FtsZ-L169P mutant protein accumulated within cells to levels comparable to that of the wild-type protein ( S2E Fig ) . We were therefore in a position to test whether or not the FtsZ L169P substitution specifically blocked the ability of CbtA to inhibit cell division . We found that when wild-type His6-CbtA-GFP was overproduced in the ftsZ-L169P strain , in M9 maltose at 30°C , cells lost their rod shape , but formed small spherical or sphere-like cells rather than lemon-shaped cells ( Fig 4C ) , the expected phenotype for a defect in cell elongation . In particular , previous studies have shown that growth in minimal medium at low temperature can suppress the lethality of a cell elongation defect , such that the cells do not form large spheres and lyse , but instead are able to propagate as small spheres [9] . Overproduction of His6-CbtA-GFP in the wild-type background in these same growth conditions resulted in the formation of lemon-shaped cells , as expected ( Fig 4C ) . We quantified these morphological observations by measuring cell roundness ( width divided by length ) , confirming that His6-CbtA-GFP overproduction in the ftsZ-L169P strain caused a more pronounced increase in roundness than in the wild-type strain ( Fig 4D ) . These findings are consistent with the two-hybrid data and provide strong support for the idea that H6/H7 loop region is functionally implicated in the CbtA-FtsZ interaction . As a complementary approach , we developed a Bacillus subtilis heterologous system with which to evaluate the importance of residues in the FtsZ H6/H7 loop in enabling CbtA to interact functionally with FtsZ to inhibit cell division . Although the E . coli and B . subtilis FtsZ proteins share ~47% amino acid identity , comparison of the H6/H7 loop sequences reveals several non-conservative amino acid differences ( Fig 5A ) . Thus , we surmised that if the H6/H7 loop mediates the interaction of CbtA with FtsZ , then the CbtA toxin should not interact with B . subtilis ( Bsu ) FtsZ . As shown in Fig 5B , CbtA was unable to interact with Bsu FtsZ by two-hybrid analysis; however , replacement of the B . subtilis H6/H7 loop with the E . coli loop ( Bsu ftsZ ( loopEco ) ) resulted in a strong interaction between Bsu FtsZ and CbtA . To test whether or not CbtA could inhibit cell division in B . subtilis cells containing either wild-type FtsZ or an FtsZ chimera bearing the E . coli H6/H7 loop region , we constructed strains with either the wild-type or chimeric ftsZ ( loopEco ) ( linked to spec ) at the endogenous locus . These strains additionally harbored gfp , wild-type cbtA or cbtA-F65S ( both alleles encode an N-terminal His6 tag preceding cbtA followed by a C-terminal GFP moiety ) at the ycgO locus under the control of a strong inducible promoter ( pHYPERSPANK ) . Overproduction of wild-type CbtA in the strain bearing the wild-type ftsZ allele had no effect on cell growth ( Fig 5C and 5D ) or any detectable effect on cell division ( S3A Fig ) , consistent with our inability to detect an interaction between CbtA and Bsu FtsZ by two-hybrid analysis . The chimeric ftsZ ( loopEco ) allele itself caused a slight growth defect manifest as decreased colony size ( Fig 5C ) and decreased growth rate in liquid medium ( Fig 5D ) ; in addition , microscopic analysis of cells containing the chimeric ftsZ ( loopEco ) allele revealed a cell division defect ( S3B and S3C Fig ) . However , CbtA overproduction in this strain caused a severe growth defect both on plates ( Fig 5C ) and in liquid ( Fig 5D ) and resulted in increased cell lysis ( S3C Fig ) , but overproduction of CbtA-F65S to comparable levels ( S3D Fig ) did not ( Fig 5C and 5D and S3C Fig ) . We conclude that residues in the H6/H7 loop region of FtsZ dictate whether or not CbtA can interact functionally with FtsZ . We next sought to determine whether CbtA makes direct contact with the H6/H7 loop of FtsZ . To do this , we aimed to identify compensatory substitutions in CbtA that restored its interaction with specific FtsZ H6/H7 loop mutants , using our two-hybrid system to screen for such mutant-suppressor pairs . To facilitate this analysis , we first sought to identify a charge reversal substitution in the H6/H7 loop that disrupted the CbtA-FtsZ interaction . Having identified substitution D180N in our original screen for disruptive mutations , we tested the effect of a charge reversal substitution at the same position ( D180K ) . We found that this charge reversal substitution almost completely eliminated the two-hybrid interaction between FtsZ and CbtA ( Fig 6 ) , making it a suitable starting point for seeking to identify compensatory substitutions in CbtA . Accordingly , we transformed reporter strain cells containing the α-FtsZ-D180K fusion protein with a mutagenized library of plasmids encoding the λCI-CbtA fusion protein ( bearing random mutations in the cbtA moiety ) and screened for clones with elevated expression of the lacZ reporter gene . These candidate suppressor mutants were then pooled and counter-screened to identify those that maintained a low level of lacZ expression in the presence of the wild-type α-FtsZ fusion protein , and thus to identify substitutions that enabled CbtA to interact with FtsZ-D180K but not wild-type FtsZ . With this two-step screening procedure , we identified CbtA substitution V48E , which partially restored the interaction between CbtA and FtsZ-D180K ( resulting in a 9-fold increase in lacZ expression ) ( Fig 6 ) . The effect of this substitution was allele-specific , as CbtA-V48E was unable to interact with wild-type FtsZ or other H6/H7 loop mutants identified in our original screen ( L169P , S177P , D180N ) ( Fig 6 ) . We conclude that CbtA interacts directly with the H6/H7 loop of FtsZ . Next , in an attempt to identify the MreB surface targeted by CbtA , we sought to isolate MreB variants reduced for their interaction with CbtA in our bacterial two-hybrid system . Specifically , we introduced random mutations into the gene fragment encoding the MreB moiety of the α-MreB fusion protein , introduced the mutagenized library into reporter strain cells containing the λCI-CbtA fusion protein and screened for colonies with reduced lacZ expression . To identify MreB mutants that were specifically deficient for CbtA interaction , each candidate was counter-screened for interaction with the cytoplasmic N-terminal domain of RodZ ( RodZNTD ) . RodZ is a component of the cell elongation complex , and its interaction with MreB has been established by bacterial two-hybrid and structural studies [68 , 69] . Using our two-hybrid system , we detected a 3 to 4-fold increase in lacZ expression in reporter strain cells containing both the α-MreB fusion protein and a λCI-RodZNTD ( residues 2–84 ) fusion protein ( Fig 7A and 7B ) . We confirmed the biological relevance of this interaction with the introduction of a charge reversal substitution ( E319K in E . coli MreB , corresponding to E318K in C . crescentus MreB , shown in wheat in Fig 8 ) at the previously defined MreB-RodZ interface [69] ( S4A Fig ) . Using this two-step screening procedure , we identified four amino acid substitutions in MreB ( I126V , V173A , E196G , and E262G ) that reduced lacZ expression substantially in cells containing λCI-CbtA , but by less than 30% in cells containing λCI-RodZNTD ( Fig 7A ) . We mapped these residues onto the C . crescentus ( Cc ) MreB double filament structure recently determined by the Lowe group [15] and found that they clustered at or near the interface formed by the paired protofilaments ( Fig 8 ) . This interface is formed by an interaction between the flat sides of the protofilaments , which pair in an antiparallel fashion . Stabilizing this antiparallel arrangement is an interaction between juxtaposed alpha helices ( α-helix 3 in subdomain IA from one MreB subunit stacks onto α-helix 3 from the opposed subunit , with residue V121 ( corresponding to Cc residue V118 ) playing a particularly important role; Fig 8 ) . Two of the residues identified in our screen ( E196 and E262 , corresponding to Cc residues E193 and E261 , respectively ) are surface exposed on the flat side of the Cc MreB protofilament at the inter-protofilament interface . Although the other two residues ( I126 and V173 , corresponding to Cc I123 and Cc V170 , respectively ) are buried , they are located near the inter-protofilament interface and residue I126 , in particular , lies within the critical dimerization helix ( α3 ) ( Fig 8 ) [15] . The Lowe group demonstrated that the interaction of MreB protofilaments via their flat sides is necessary for proper MreB function in E . coli [15]; thus , if CbtA is indeed interacting with the flat side of MreB , it may be blocking MreB function by preventing the formation of the essential double filament . To further evaluate the proposition that CbtA interacts with the flat side of MreB , we performed a targeted mutagenesis of other residues located on its flat side . Charge reversal substitutions were introduced at several positions ( e . g . D192K ) and non-conservative changes were made at additional positions ( e . g . F84A ) . The ability of each MreB mutant ( bearing a single amino acid substitution ) to interact with both CbtA and RodZNTD was assessed in our two-hybrid system . S1 Table summarizes the two-hybrid interaction profiles of the complete set of MreB variants that was tested . Among those MreB variants tested ( as α-MreB fusion proteins ) , we identified three additional inter-protofilament interface mutants with altered CbtA-binding . The mutants α-MreB-F84A and α-MreB-D192K were unable to interact with λCI-CbtA , but maintained strong interaction with λCI-RodZNTD ( Fig 7A ) . Conversely , the α-MreB-S269F variant was greatly increased in its ability to interact with λCI-CbtA , yielding a 25-30-fold increase in lacZ expression as compared to the highest empty vector control ( Fig 7B , S4B Fig ) . This fold-change value is ~10 times higher than the 3-fold increase in lacZ expression consistently measured with wild-type α-MreB and λCI-CbtA . Importantly , the effect of the S269F substitution was specific to CbtA; α-MreB-S269F yielded a λCI-RodZNTD interaction profile identical to that of wild-type α-MreB across multiple induction levels ( Fig 7B ) . Because MreB and CbtA are both known to interact with FtsZ [46] , we considered the possibility that the S269F substitution might actually promote interaction between α-MreB and FtsZ; enhanced bridging of α-MreB-S269F and λCI-CbtA by endogenous FtsZ molecules could potentially lead to an apparent increase in the CbtA-MreB interaction . However , this explanation seems unlikely as α-MreB-S269F interacted similarly strongly with the λCI-CbtA-F65S variant , which is unable to interact with FtsZ ( S4B Fig ) . Single amino acid substitutions at various positions along the flat side of MreB ( including several affecting residues that lie directly at the double protofilament interface ) altered its interaction with CbtA in the context of our two-hybrid system . These data suggest that the MreB inter-protfilament interface may be the binding surface utilized by CbtA to inhibit cell elongation . To determine whether or not the MreB interface residues identified in our two-hybrid analyses are critical for the toxic block in cell elongation mediated by CbtA , we aimed to overproduce CbtA-F65S ( whose toxicity derives exclusively from its ability to interact with MreB ) in strains producing the various mutants as the sole source of endogenous MreB . Overproduction of CbtA-F65S results in a lethal ( under rapid growth conditions ) loss of rod shape , causing cells to become spherical and lyse . Accordingly , we predicted that in strains bearing MreB substitutions that disrupt the CbtA-MreB interaction , overproduction of CbtA-F65S would be less toxic and would not induce a spherical morphology . Additionally , we hypothesized that CbtA-F65S toxicity might be increased in a strain producing the “up” variant MreB-S269F , resulting in more severe growth defects and morphological perturbations . Importantly , this strategy required the use of MreB variants capable of supporting rod-shaped growth . We tested the abilities of several of our isolated mutant alleles to complement the growth and morphological defects of an mreBCD depletion strain ( FB30/pFB174 ) when expressed in an IPTG-dependent manner from a multi-copy plasmid along with operon partners mreC and mreD ( Fig 9A ) [9] . Only cells expressing mreB-E262G or mreB-S269F were able to support growth to a similar extent as the wild-type allele ( Fig 9B ) ; strains expressing these mutant alleles also maintained a rod shape comparable to that of the wild-type mreB-expressing strain ( Fig 9C ) . We thus proceeded with these two alleles , testing the effect of overproducing CbtA-F65S in cells containing wild-type mreB , mreB-E262G or mreB-S269F as the sole source of MreB ( see Materials and Methods ) . All three strains exhibited similar growth rates in liquid medium ( S4C Fig ) , and Western blot analysis of these strains using an MreB antibody indicated that both mutant proteins were produced at levels comparable to that of the wild-type MreB protein ( S4D Fig ) . To test the effect of disruptive substitution E262G , we transformed our wild-type and mreB-E262G strains with either a plasmid producing CbtA-F65S ( untagged ) under the control of an arabinose-inducible promoter or an empty vector control and monitored cell growth and cell morphology in the presence of arabinose . Cells of both strains bearing the empty vector maintained rod shape in the presence of arabinose , and , as expected , cells containing wild-type MreB and the CbtA-F65S plasmid became spherical within two hours of arabinose addition ( Fig 9D ) . In marked contrast , cells containing MreB-E262G did not become round , maintaining rod-like shape after two hours of arabinose addition ( Fig 9D ) ; CbtA-F65S-dependent growth inhibition was also reduced in the mreB-E262G strain ( Fig 9E ) . Thus , MreB substitution E262G , which lies at the double protofilament interface and disrupted the two-hybrid interaction between MreB and CbtA , also interfered with the ability of CbtA-F65S in inhibit cell elongation . To test the effect of substitution S269F , which strengthened the two-hybrid interaction between MreB and CbtA , we transformed our wild-type and mreB-S269F strains with either a plasmid producing CbtA-F65S ( untagged ) under the control of a tetracycline-inducible promoter or an empty vector control . We used a tetracycline-inducible system for these experiments to achieve a finer range of CbtA-F65S concentrations that might enable us to observe a wider range of growth and morphology phenotypes . Nonetheless , we did not observe any obvious morphological differences between these two strains at either 30°C or 37°C at multiple anhydrous-tetracycline ( ATC ) concentrations; both strains similarly transitioned from rod-shaped to spherical cells over the course of one hour ( S4E Fig ) . However , we did see a more pronounced CbtA-F65S-dependent growth defect in the mreB-S269F strain ( Fig 9F ) . In particular , expression of cbtA-F65S at a low ATC concentration ( 15 ng/mL ) in the wild-type mreB strain caused a relatively modest growth defect on LB agar at 37°C , whereas expression of cbtA-F65S at the same ATC concentration in the mreB-S269F strain resulted in a 2-3-log decrease in plating efficiency ( Fig 9F ) . Expression of the cbtA-R15C/F65S double mutant ( recall that substitution R15C specifically disrupts the interaction of CbtA with MreB ) had no effect on the growth of either strain , confirming that the increased toxicity of CbtA-F65S in the mreB-S269F background was dependent on the CbtA-MreB interaction ( Fig 9F ) . Thus , MreB substitution S269F , which lies at the double protofilament interface and substantially strengthened the CbtA-MreB two-hybrid interaction , also sensitized MreB to the toxic cell elongation block mediated by CbtA-F65S . Taken together , our analyses of the MreB-E262G and MreB-S269F variants strongly suggest that the flat surface of MreB is critical for CbtA-dependent cell elongation inhibition and likely forms the inhibitory surface directly targeted by CbtA . CbtA has two homologs in E . coli: the YkfI toxin of the YkfI/YafW toxin-antitoxin system , and the YpjF toxin of the YpjF/YfjZ toxin-antitoxin system [38] . The three toxins are encoded on different cryptic prophage elements within the E . coli genome , and have high amino acid sequence identity ( 58% identity between CbtA and YkfI , 62% identity between CbtA and YpjF , 78% identity between YpjF and YkfI ) [38] ( S5A Fig ) . Overexpression of either ykfI or ypjF was previously shown to be toxic [38 , 70] and results in the formation of lemon-shaped cells [70] . Consistent with these previous results , we found that overexpression of his6-ypjF-gfp or his6-ykfI-gfp under the control of the hybrid pT5-lac promoter resulted in a decrease in viability ( Fig 10A ) and led to the formation of lemon-shaped cells ( Fig 10B ) . We were also able to detect strong interactions between both toxins and FtsZ ( Fig 10C ) , and between YpjF and MreB in our bacterial two-hybrid system ( Fig 10D ) . Although YkfI is 78% identical to YpjF and blocks cell elongation when overproduced , we were unable to detect an interaction between YkfI and MreB in our bacterial two-hybrid system . In order to determine whether YkfI and YpjF interact independently with FtsZ and MreB and utilize the same inhibitory surfaces as CbtA , we repeated many of the two-hybrid analyses described above . Residue F65 is conserved in all three toxins ( S5A Fig ) , and as we saw with CbtA-F65S , overproduction of YpjF-F65S and YkfI-F65S yielded sphere-like rather than lemon-shaped cells ( Fig 10B ) . Furthermore , we found that substitution F65S disrupted the two-hybrid interactions between YpjF and FtsZ and between YkfI and FtsZ ( Fig 10C ) , but did not compromise the two-hybrid interaction between YpjF and MreB ( Fig 10D ) . These analyses suggest that the FtsZ interaction determinants for all three toxins are conserved . Importantly , the morphology data also suggest that toxin interaction with FtsZ contributes to the striking lemon-shape phenotype . Interestingly , R15 is not a conserved residue; YpjF and YkfI both have a cysteine at this position ( S5A Fig ) . Because substitution R15C decreased the interaction between CbtA and MreB , we wondered if the reverse substitution ( C15R ) in YpjF would increase its interaction with MreB , and in the case of YkfI , might allow for a detectable interaction . We found that substitution C15R had no effect in either case ( S5B Fig ) . Thus , the genetic determinants within YpjF and YkfI that specify their abilities to inhibit cell elongation remain unknown . To assess whether residues in the H6/H7 loop are necessary for the YpjF-FtsZ and YkfI-FtsZ interactions , we assayed the abilities of YpjF and YkfI to interact with wild-type Bsu FtsZ and our Bsu FtsZ chimera . We found that neither YkfI nor YpjF interacted with wild-type Bsu FtsZ , but both toxins interacted strongly with the Bsu FtsZ chimera containing the H6/H7 loop of E . coli ( Bsu ftsZ ( loopEco ) ) ( Fig 10E ) . These findings provide strong support for the idea that all three homologous toxins interact directly with the H6/H7 loop of E . coli FtsZ . Similarly , as was seen with CbtA , single amino acid substitutions affecting residues on the flat side of MreB altered its interaction with YpjF . The E262G substitution disrupted the YpjF-MreB interaction , whereas the S269F substitution more than doubled the detected interaction ( Fig 10F ) . Furthermore , we found that a strain harboring the mreB-E262G allele was less susceptible than the corresponding wild-type mreB strain to cell shape changes induced by YpjF-F65S ( Fig 10G ) , suggesting that CbtA and YpjF both require residues lying at the double protofilament interface to inhibit the function of MreB . Taken together , these results strongly suggest that like CbtA , YpjF and YkfI act as dual inhibitors that block cell division and cell elongation in a genetically separable manner; furthermore , all three toxins appear to inhibit these processes by targeting the same surfaces of FtsZ and MreB .
Our analysis of the interaction between CbtA and FtsZ uncovered the H6/H7 loop as a new target for inhibitors of FtsZ function . In particular , using our two-hybrid assay to screen for FtsZ mutations that specifically disrupted its interaction with CbtA , we found that the identified mutations mapped to the H6/H7 loop . We then showed that the identities of loop residues dictated whether or not CbtA could inhibit cell division both in E . coli and in B . subtilis . Finally , we identified an amino acid substitution in CbtA ( V48E ) that functioned as an allele-specific suppressor of a disruptive charge reversal substitution in the FtsZ H6/H7 loop ( D180K ) , providing strong evidence for a direct physical interaction between CbtA and the H6/H7 loop . FtsZ subunits assemble as protofilaments by stacking vertically in a head-to-tail fashion , and the H6/H7 loop lies at the longitudinal interface formed by pairs of stacked subunits ( see Fig 4B ) [64 , 66 , 67] . We thus suggest that CbtA likely exerts its inhibitory effect on cell division by interfering with FtsZ protofilament formation . Additionally , residues in the H6/H7 loop have been implicated in FtsZ lateral interactions and bundling [71 , 72] , raising the possibility that CbtA could also inhibit such higher order interactions of FtsZ . We note that the cognate antitoxin of CbtA , CbeA ( previously YeeU ) , has been shown to interact with both FtsZ and MreB and stabilize protofilament bundling in vitro , suggesting that it neutralizes CbtA toxicity in vivo by stabilizing a higher order assembly of each of its targets , rather than by interacting directly with the toxin [33] . Interestingly , CbeA was also found to neutralize the toxicity of several other protein inhibitors of FtsZ function with distinct modes of action , suggesting that CbeA’s ability to stabilize a higher order assembly of FtsZ has a general protective effect [33] . Our findings do not support the previous proposal that CbtA interacts with the C-terminal region of FtsZ . Specifically , we found that removal of this C-terminal region ( FtsZΔ66; Fig 4A ) had no effect on the CbtA-FtsZ interaction , in contrast with previously reported results [32] . The FtsZ monomer consists of a globular core comprising two independently folded domains separated by α-helix H7; appended to this globular core is an unstructured linker region terminating in the highly conserved 15-residue C-terminal tail ( CTT ) . Like their tubulin counterparts , the FtsZ N-terminal domain binds GTP , whereas the C-terminal domain contains the so-called synergy ( T7 ) loop , which stimulates GTP hydrolysis in the context of an assembled protofilament by contacting the GTP-binding pocket of the next subunit . Specifically , the vertical stacking of FtsZ subunits enables the T7 loop of one subunit to insert into the GTP-binding pocket of the subunit just beneath [64] ( see Fig 4B ) . Most previously characterized regulators of FtsZ assembly , including inhibitory factors such as SlmA [53 , 55] and the C-terminal domain of MinC [62] , bind to the CTT , establishing it as an important hub of regulation [54 , 56–61] . Other protein regulators , such as SulA , the B . subtilis sporulation factor MciZ , and the N-terminal domain of MinC bind within the C-terminal domain ( for example , in the vicinity of the T7 loop ) and inhibit FtsZ assembly through diverse mechanisms [28 , 73–75] . CbtA and its homologs YpjF and YkfI provide the first example of protein inhibitors that target the FtsZ N-terminal domain , binding the H6/H7 loop . The identification of an FtsZ inhibitor that binds to the H6/H7 loop could facilitate studies aimed at probing the polarity of protofilament assembly and disassembly . Both the GTP-binding pocket and the H6/H7 loop are located at the FtsZ plus end ( defined by analogy with tubulin ) , whereas the T7 loop is located at the minus end [64 , 76] . Evidence from one study , in which plus-end and minus-end mutants were tested for their abilities to function as FtsZ cappers , suggested that FtsZ filaments assemble and disassemble with a polarity opposite that of microtubules , with FtsZ subunits being added primarily to the minus end and dissociating primarily from the plus end [76] . However , evidence from a more recent study , in which the sporulation-specific Z ring inhibitor MciZ was defined as a minus-end capper , was more consistent with designation of the plus end as the primary addition site [74] . CbtA , a plus-end binder , could provide a useful new tool for addressing this problem . Our genetic analysis of the CbtA-MreB interaction identified amino acid substitutions that mapped to the flat side of the MreB monomer . Specifically , we identified six substitutions affecting flat-side residues ( F84A , I126V , V173A , D192K , E196G , E262G ) that disrupted and one ( S269F ) that strongly increased the two-hybrid interaction between CbtA and MreB . Among the affected residues , F84 , E196 , and E262 are surface-exposed , suggesting that they may contact CbtA directly; in addition , S269 is surface-exposed , suggesting that the mutant phenylalanine residue may form a new interaction at the CbtA-MreB interface . In the case of residue D192 , which lies in a small pocket beneath residue E196 , we speculate that the introduction of a positively charged lysine residue in its place may alter the position of surface-exposed E196 , indirectly perturbing its interaction with CbtA . We found that cells containing either MreB-E262G or MreB-S269F ( in the absence of wild-type MreB ) grew as rods , enabling us to assess the effects of these substitutions on CbtA-dependent morphological changes , as well as CbtA-dependent growth inhibition . Our findings indicated that MreB substitution E262G mitigated the effects of CbtA on growth and cell shape , whereas MreB substitution S269F potentiated the toxic effect of CbtA ( Fig 9 ) . Taken together with the two-hybrid data , these findings support the idea that CbtA inhibits cell elongation by binding directly to the flat surface of MreB . The discovery that pairs of MreB protofilaments associate in an antiparallel fashion along their flat sides to form a double filament that is required for MreB function in E . coli [15] leads us to propose that CbtA inhibits cell elongation by interfering with double filament formation . This proposed mechanism appears to be shared by other MreB inhibitors , as well . The small molecule inhibitor A22 ( and its derivative MP265 ) was found to prevent double filament formation , evidently by displacing the main dimerization helix ( α-helix 3 formed by Q120 to A133 ) that participates in essential inter-protofilament contacts ( though the inhibitors also block nucleotide hydrolysis in the active site ) [15] . Moreover , a recent study of B . subtilis sporulation factors YodL and YisK suggests that they can influence cell shape by targeting MreB and the MreB-like protein Mbl , respectively; specifically , Mbl substitution E250K ( affecting the residue corresponding to E262 in E . coli MreB ) was found to suppress the cell-shape defects caused by YisK [37] . Assuming that Mbl adopts a similar double-filament architecture as E . coli MreB , YisK may also disrupt double filament formation . CbtA is the first example of a cytoskeletal inhibitor capable of independently targeting the cell division and cell elongation apparatuses . Given its small size ( 124 amino acids ) , CbtA’s ability to function as a dual inhibitor is particularly striking . It will be interesting to learn whether a single toxin molecule can interact simultaneously with FtsZ and MreB or whether inhibition of cell division and cell elongation depends on the combined action of subsets of molecules that interact with one or the other target . Whereas our work sheds light on the molecular basis for the effects of CbtA and its homologs YpjF and YkfI on cell shape and cell growth , it remains to be learned what roles these toxins might play in cellular physiology . A recent study reported that an E . coli strain deleted for all three toxin genes exhibited increased susceptibility to oxidative stress [70] , raising the possibility that these toxin-antitoxin systems , like other chromosomally encoded toxin-antitoxin systems , contribute to the bacterial stress response . As these toxins are encoded on cryptic prophages , it is also interesting to consider what roles they might have played in the context of phage biology . A number of phage-encoded factors are known to block bacterial cell division , some of which ( for example , the Kil peptide of bacteriophage λ ) have been shown to target FtsZ [29] . It is of particular interest to note that the lytic phage T7 has recently been shown to encode separate inhibitors of FtsZ ( Gp0 . 4 ) and MreB ( Gp0 . 6 ) [30 , 36] , one of which ( Gp0 . 4 ) was shown to provide a growth advantage to the phage in dividing cells [30] . Kiro et al . [30] suggest that inhibiting cell division early after infection ensures that all the cell’s resources are available for phage replication by preventing daughter-cell escape . Whether or not the changes in cell size , cell shape and/or cell wall integrity that result from the combined inhibition of FtsZ and MreB have the potential to enhance phage production remains to be investigated . FtsZ has been validated as a clinically relevant antibiotic target [31] . Our identification of its H6/H7 loop as a new epitope that can be exploited by FtsZ inhibitors may therefore have implications for the development of new antibiotics that target this essential protein . More speculatively , future structural studies of CbtA in complex with its partners could potentially inform the design of antibiotics that target both FtsZ and MreB ( the latter also representing a potentially effective target for antibacterial agents ) . Such dual-function agents would be attractive due to the greater barrier towards the development of resistance .
A complete list of the bacterial strains used in this chapter is provided in S2 Table . Additionally , lists of the plasmids and oligonucleotides used in this chapter can be found in S3 and S4 Tables , respectively . NEB5-α F’Iq ( New England Biolabs ) was used as the cloning strain for all plasmid constructions outlined below . Two-hybrid studies were performed in FW102 OL2-62 [77] or BN30 [78] . Morphology observations were made primarily in strain BW27785 [79 , 80] . This strain also served as template for all colony PCRs . ZapA-GFP microscopy was performed using strain NP1 [50] . GFP-FtsZ microscopy was performed using strain TB28 HKHC488; this strain contains wild-type ftsZ at the endogenous locus and an IPTG-inducible allele of sfgfp-ftsZ ( encoding FtsZ fused to superfolder GFP ) integrated at the attHK site . E . coli strains were grown in LB ( 1% NaCl ) broth at 37°C or 30°C , and on LB plates supplemented with appropriate antibiotics at the following concentrations ( unless otherwise noted ) : carbenicillin ( Carb ) , 100 μg/mL; chloramphenicol ( Cm ) , 25 μg/mL; kanamycin ( Kan ) , 50 μg/mL; spectinomycin ( Spec ) , 50 μg/mL; streptomycin ( Strep ) , 25 μg/mL; tetracycline ( Tet ) , 5 μg/mL . Where noted , strains were grown in M9 minimal liquid medium or M9 agar ( 1 mM MgSO4 ) supplemented with either 0 . 4% maltose and 0 . 01% casamino acids , or 0 . 2% maltose and 0 . 2% casamino acids . B . subtilis strains were grown at 37°C in LB ( 0 . 5% or 1% NaCl ) broth without antibiotic or on LB plates supplemented with spectinomycin ( 100 μg/mL ) or MLS ( mixture of 1 μg/mL erythromycin and 25 μg/mL lincomycin ) . B . subtilis PY79 genomic DNA was used as template for all B . subtilis ftsZ constructs . p3-37 is a derivative of the ASKA overexpression vector , pCA24N [47] , encoding His6-YkfI-GFP under the control of the pT5-lac promoter . In this construct , his6-ykfI-gfp contains two SfiI sites flanking the ykfI sequence . Empty vector plasmid pMT136 encoding His6-GFP was made by cloning in a linker sequence composed of annealed oligonucleotides , oSG623 and oSG624 , into SfiI-digested p3-37 . This linker sequence contains ClaI and XbaI sites and encodes for the additional residues “IDAAASR” in between the SfiI sites in the His6-GFP sequence . To construct plasmids pMT138 ( encoding His6-YpjF-GFP ) and pMT139 ( encoding His6-CbtA-GFP ) , colony PCR products generated using primer pair oSG639/oSG640 or oSG641/oSG642 , respectively , were digested with AclI and XbaI and ligated into pMT136 digested with ClaI and XbaI . Plasmids pMT144 ( encoding His6-YkfI-F65S-GFP ) and pMT146 ( encoding His6-CbtA-F65S-GFP ) were generated by ligation of AclI/XbaI-digested overlap PCR products amplified with internal mutagenic primers ( oSG663/oSG664 and oSG667/oSG668 ) and outside primers ( oSG659/oSG660 and oSG641/oSG642 ) into ClaI/XbaI-digested pMT136 backbone . To construct plasmid pDH253 , the cbtA-R15C allele was amplified from the two-hybrid construct pDH246 using primers oSG641 and oSG642 , digested with AclI and XbaI , and ligated into pMT136 ClaI/XbaI backbone . Plasmid pDH262 ( encoding His6-CbtA-R15C/F65S-GFP ) was constructed in the same manner as pMT146 except using pDH253 as PCR template . All bacterial two-hybrid α fusion constructs were cloned by restriction digest into the parent plasmid pBRα-β flap; all two-hybrid λCI fusion constructs were cloned by restriction digest into the parent plasmid pACλCI-β flap . Briefly , the parent plasmids were digested with NotI and BamHI to generate backbone . These backbones were ligated to relevant inserts generated by NotI/BamHI digestion of PCR products amplified using a NotI-containing forward primer and BamHI-containing reverse primer . Forward primers all contain an extra “A” base after the NotI site to maintain the reading frame . Reverse primers all encode a stop codon preceding the BamHI site . Mutant alleles ( both point mutants and chimeric alleles ) of ftsZ , mreB , or toxin genes were generated using internal mutagenic primers ( see S4 Table for specific sequences ) . Oligonucleotides pBRα_F , pBRα_R , pACλCI_F , and pACλCI_R were used to sequence all two-hybrid constructs . To generate plasmids pDH325 , pDH326 , pDH327 , and pDH328 encoding untagged CbtA variants , the relevant cbtA allele was amplified from the appropriate pCA24N-derived construct described above using primers oDH446 and oDH447 , digested with EcoRI/HindIII , and ligated into pSG360 ( EcoRI/HindIII ) backbone . To construct cbtA-F65S and ypjF-F65S arabinose-inducible overexpression vectors ( pDH212 and pDH289 ) , alleles were amplified from pMT146 and pMT188 using primer pair oDH285/oDH286 or oDH380/381 , respectively . PCR products were digested with NdeI/XbaI and ligated into the pBAD33 ( NdeI/XbaI ) backbone . To construct cbtA-F65S and cbtA-R15C/F65S tet-inducible overexpression vectors ( pDH335 and pDH337 ) , the EcoRI/HindIII inserts from plasmids pDH326 and pDH328 were ligated into pSG369 ( EcoRI/HindIII ) backbone . pFB149 ( plac-mreBCD lacIQ ) contains an XbaI site upstream of mreB and a naturally occurring BamHI site within mreD that is unique on plasmid pFB149 . To construct pFB149-derivatives for MreB mutant expression studies , mreB mutant alleles were generated by overlap PCR using pFB149 as template , outside primers oDH372/oDH369 , and allele-specific internal mutagenic primers . PCR products were digested with XbaI/BamHI and ligated into the pFB149 ( XbaI/BamHI ) backbone . All pFB149- derivatives were verified by sequencing using primers oDH355 , oDH369 , oDH373 , oDH374 , and oDH375 . ( The ftsZ-L169P allele was cloned into pCX41 ( digested with HindIII/ClaI ) in place of wild-type ftsZ by restriction digest ( HindIII/ClaI ) and ligation of overlap PCR products generated using wild-type pCX41 as template , internal mutagenic primers ( oDH34_F and oDH35_R for L169P ) and flanking primers oDH36_F , oDH37_R , which anneal within ftsA and lpxC , respectively . This generated plasmid pDH35 , replication of which is controlled by a temperature-sensitive origin of replication . Plasmid is maintained at 30°C and lost at 42°C . Attempted integration of these mutant alleles into the endogenous chromosomal locus was performed essentially as described in [81] . Briefly , pDH35 was transformed into E . coli strain BW27785 in parallel with a pCX41 derivative encoding FtsZ-F268C . ftsZ-F268C is a known complementing allele and thus serves as a control for chromosomal integration . Transformants were plated on LB agar supplemented with Cm ( 10 μg/mL ) and incubated overnight at 30°C . Several colonies were restreaked onto LB ( Cm ) and incubated at 42°C overnight in order to identify single crossover integrants . After an additional round of restreaking on LB ( Cm ) at the nonpermissive temperature , candidates were streaked onto LB ( Cm ) and incubated at 30°C overnight . Firing of the plasmid origin of replication on the chromosome causes a severe growth defect , and double crossover integrants that had looped out the plasmid were identified as healthy revertants within poorly growing streaks . These candidates were purified by restreaking , and were cured of plasmid by growth on LB ( without Cm ) at 42°C . The ftsZ locus was PCR amplified and sequenced ( using sequencing primers generously provided by H . Cho and T . Bernhardt ) from Cm-sensitive candidates in order to identify those in which allelic replacement occurred . All subsequent propagation of this strain was done at RT or 30°C to minimize growth defects . Multiple isolates of this strain exhibited identical phenotypes . To construct strains DH118/pFB149 , DH118/pDH278 , and DH118/pDH332 , plasmids pFB149 , pDH278 , and pDH332 were transformed into strain BW27785 . To introduce the mreBCD::kanR deletion , a P1 lysate was grown on strain FB30/pFB174 and used to infect each recipient strain . Transductants were selected on M9 maltose plates ( 0 . 2% maltose , 0 . 2% casamino acids , 1 mM MgSO4 ) supplemented with 5 mM sodium citrate and 250 μM IPTG ( for expression of mreBCD ) . Growth on minimal medium is known to suppress mreBCD defects and was used to prevent acquisition of suppressor mutations . Strains were checked for proper kanR insertion by colony PCR using primers oDH289 and oDH307 . We note that in all DH118 strains , about 5% of cells failed to grow as rods , forming large spheres ( as observed by microscopy ) ; this is likely the result of plac-mreBCD plasmid loss . Growth of DH118 strains was monitored at 37°C over the course of 4 h . Four replicate M9 maltose overnight cultures were back diluted to a starting OD600 of 0 . 02 in 200 μL LB ( Carb ) supplemented with 250 μM IPTG in a 96-well microtitre plate . The plate was incubated shaking at 900 rpm in 90% humidity in a Multitron incubation shaker ( Infors HT ) ; OD600 readings were taken every 30 min with a microtitre plate reader ( Molecular Devices ) . B . subtilis strains were generated by directly transforming a PY79 derivative with either a linearized plasmid containing homology to the chromosomal locus where integration was desired or a PCR fragment containing chromosomal homology . In order to generate B . subtilis strains with gfp or various cbtA alleles integrated into the chromosome , plasmids pDH84 ( pHYPERSPANK-his6-gfp ) , pDH85 ( pHYPERSPANK-his6-cbtA-gfp ) , and pDH102 ( pHYPERSPANK-his6-cbtA-F65S-gfp ) were constructed . These plasmids were generated by restriction digest ( HindIII/NheI ) and ligation of PCR products amplified from pMT136 , pMT139 , or pMT146 using primers oDH108 and oDH116 into QER167 ( generous gift of D . Rudner ) HindIII/NheI digested backbone . These plasmids all contain homology to the ycgO locus flanking the insert . Plasmids were linearized by digestion with ScaI . DH84 ( ycgO:: pHYPERSPANK-his6-gfp erm ) , DH85 ( ycgO:: pHYPERSPANK-his6-cbtA-gfp erm ) , and DH104 ( ycgO:: pHYPERSPANK -his6-cbtA-F65S-gfp erm ) were generated by transformation of linearized plasmids pDH84 , pDH85 , and pDH102 , respectively , into PY79 ycgO::spec . Transformants were selected on LB supplemented with MLS . The wild-type ftsZ allele linked to a spec resistance cassette was assembled by Gibson assembly [82] of three PCR products with >20bp of overlapping homology: 1 ) part of the ftsA locus and the entire ftsZ locus amplified from PY79 genomic DNA using oligos oDH130 and oDH131 , 2 ) amplification of spec from pDR111 using oDH132 and oDH133 , and 3 ) 2 kb chromosomal sequence downstream of the ftsZ locus amplified from PY79 genomic DNA using oligos oDH134 and oDH135 . This assembled PCR product was transformed directly into PY79 to generate strain DH98 . Transformants were selected for on LB ( Spec ) . The chimeric ftsZ allele ( containing E . coli ftsZ residues 169–182 ) linked to a spec resistance cassette , was assembled by Gibson assembly of three PCR products ( all with at least 20 bp of overlapping homology: 1 ) 2 kb upstream of ftsZ amplified from PY79 genomic DNA using oligos ODH141 and ODH142 , 2 ) the ftsZ chimeric allele amplified from pDH69 ( Bsu ftsZ ( loopEco ) ) using oligos oDH143 and oDH131 3 ) 2 kb downstream of ftsZ , including spec , amplified from DH98 genomic DNA using oDH132 and oDH144 . This assembled PCR product was transformed directly into PY79 to generate strain DH99 . Transformants were selected for on LB ( Spec ) . The ftsZ loci from DH98 and DH99 were PCR amplified ( using oDH167 and oDH168 ) and sequenced using oDH127 , oDH172 , and oDH173 . oDH124 and oDH125 , which anneal inside the ftsZ ORF were also used for PCR and sequencing . Strains DH100 , DH101 , and DH105 were generated by direct transformation of DH98 genomic DNA into strains DH84 , DH85 , and DH104 , respectively . Strains DH102 , DH103 , and DH106 were generated by direct transformation of DH99 genomic DNA into DH84 , DH85 , and DH104 , respectively . Transformants were selected on LB ( Spec ) and patched on LB ( MLS ) to ensure the ycgO locus was unchanged . The ftsZ loci were re-sequenced after transformation . cbtA and mreB gene fragments ( located on pMT154 and pMT151 , respectively ) were mutagenized by error-prone PCR using Taq polymerase ( Promega ) and the outside primers pACλCI_F and pACλCI_R . The ftsZ gene fragment ( located on pMT153 ) was amplified using Taq polymerase and the outside primers pBRα_F and pBRα_R . The mutagenized cbtA alleles were cloned into the pAC-λCI fusion vector; mreB and ftsZ alleles were cloned into the pBRα fusion vector . To identify λCI-CbtA variants with a decreased ability to interact with α-MreB , the λCI-cbtA mutant library was transformed into a modified two-hybrid reporter strain ( BN30 ) bearing pBRα-MreB ( pMT151 ) . Strain BN30 contains an F’ episome bearing a two-hybrid reporter with the λCI operator positioned at -42 , 20 bp closer to the transcription start site than in the standard two hybrid strain FW102 OL2-62 . This positioning allows for an additional stabilizing contact between λCI and region 4 of σ70 bound to the -35 promoter element and results in an elevated level of lacZ expression [78] , which afforded us a better color range for blue-white screening than our standard reporter . Transformants were plated on LB ( KanCarbCm ) indicator medium containing IPTG ( 25 μM ) and X-gal ( 40 μg/mL ) . Plates were incubated overnight at 30°C and refrigerated ( 4°C ) for an additional 8–16 h . α-MreB mutants with decreased λCI-CbtA interaction were isolated under identical conditions . For each screen , several thousand colonies were screened to identify those exhibiting lower lacZ expression ( white or light blue color ) as compared to the dark blue colonies producing wild-type α-MreB and λCI-CbtA fusions . For each screen , candidate mutants were counter-screened to identify those that maintained the ability to interact with a second partner protein ( FtsZ , in the case of CbtA , and the NTD of RodZ , in the case of MreB ) . Specifically , colonies containing prospective λCI-CbtA mutants were pooled into a single overnight culture , grown at 30°C; a pooled plasmid prep generated from this overnight culture was transformed into FW102 OL2-62/pBRα-FtsZ . Transformants were plated on LB ( KanCarbCm ) indicator medium supplemented with 5 μM IPTG , 40 μg/mL X-gal , and 250 μM TPEG ( a competitive inhibitor of β- galactosidase; Gold Biotechnologies ) ; dark blue candidates were selected and the pACλCI-CbtA plasmids were isolated and fusion gene sequenced . For colonies containing prospective α-MreB mutants , individual cultures of candidate clones were grown overnight at 30°C . Plasmids were prepped from these cultures , most likely generating a mixed prep of α and λCI plasmids in each case . Individual mixed preps were used to transform FW102 OL2- 62 cells containing either pACλCI-CbtA or pACλCI-RodZNTD . Transformants were selected on LB ( CmCarbKan ) . β-galactosidase assays ( see below ) were performed to measure interaction between each α-MreB mutant and λCI-CbtA ( at 100 μM IPTG ) or λCI-RodZNTD ( at 25 μM IPTG ) . pBRα-MreB plasmids were isolated from candidates that were down for λCI-CbtA interaction but maintained >60% of the wild-type λCI-RodZNTD interaction; the fusion genes were sequenced and the mutant plasmids re-tested by β-galactosidase assay . To identify α-FtsZ mutants with a decreased ability to interact with λCI-CbtA , our α-ftsZ mutant library was transformed into FW102 OL2-62/pACλCI-CbtA; several thousand colonies were screened on medium containing 5 μM IPTG and X-gal ( 40 μg/mL ) at 37°C . Colonies that were pale blue or white were selected and the plasmids were isolated and transformed into FW102 OL2-62 cells containing either pACλCI-CbtA or pACλCI-FtsZ . Transformants were selected on LB ( CmCarbKan ) . β-galactosidase assays were performed to measure interaction between each α-FtsZ mutant and λCI-CbtA ( at 100 μM IPTG ) or λCI-FtsZ ( at 100 μM IPTG ) . Those candidates that exhibited at least a 60% decrease in interaction with CbtA but maintained greater than 75% FtsZ-FtsZ self-interaction were sequenced and further assayed for their interaction with λCI-ZipACTD by β-galactosidase assay . To identify CbtA variants with a restored ability to interact with FtsZ-D180K , the λCI-cbtA mutant library was transformed into FW102 OL2-62 cells pre-transformed with pBRα-FtsZ-D180K , and transformants were plated on LB ( KanCmCarb ) indicator medium supplemented with IPTG ( 5 μM ) , X-gal ( 40 μg/mL ) , and TPEG ( 250 μM ) ( Gold Biotechnology ) . Plates were incubated at 30°C overnight . Several thousand colonies were screened in order to identify those that exhibited increased lacZ expression as compared to the pale blue control colonies producing wild-type λCI-CbtA and α-FtsZ-D180K . Dark blue candidate colonies were pooled into a single overnight culture , grown at 30°C . In order to identify those candidates that specifically interact with α-FtsZ-D180K , a pooled plasmid prep generated from this overnight culture was transformed into FW102 OL2-62 cells containing pBRα-FtsZ . Transformants were plated on the same indicator medium as before; pale blue candidates were selected and the pACλCI-CbtA plasmids were isolated and the fusion genes sequenced . The interaction between λCI-CbtA-V48E and all relevant α-FtsZ fusions was assayed by β-galactosidase assay . All β-galactosidase assays were performed in our standard two-hybrid reporter strain , FW102 OL2-62 . FW102 OL2-62 cells were co-transformed with plasmids encoding the relevant α and λCI fusions . Cultures inoculated with transformants were grown in 1 mL LB ( KanCmCarb ) in deep-well 96-well plates at 37°C , 900 rpm , 90% humidity in a Multitron incubation shaker ( Infors HT ) overnight . Overnight cultures were back diluted 1:100 or 1:40 in LB ( KanCmCarb ) supplemented with the appropriate concentration of IPTG in sterile microtitre plates ( total volume of 200 μL ) ; subcultures were grown , shaking at 37°C until they reached mid-log phase ( OD600 0 . 4–0 . 8 ) . A 100 μL aliquot of subculture was lysed by addition of 10 μL PopCulture reagent ( Novagen ) supplemented with rlysozyme ( 400 mU/μL ) . LacZ levels were determined by β-galactosidase assay performed in microtitre plates with a microtitre plate reader ( Molecular Devices ) , as described in [83] . All assays were done in triplicate and were repeated independently at least twice . All Miller Unit values shown are from a single representative experiment and represent averages of triplicate measurements . Fold-change values were calculated by normalizing to the highest relevant empty vector control . For E . coli spot dilution assays , strain BW27785 ( or the appropriate derivative ) was transformed with the appropriate plasmid ( s ) . Transformants were selected either on LB supplemented with appropriate antibiotic at 30°C or 37°C , or , for assays done with DH118 strains , on M9 maltose ( 0 . 2% maltose , 0 . 2% casamino acids , 1 mM MgSO4 ) plates supplemented with 250 μM IPTG at 30°C . Overnight cultures ( grown in either LB or M9 maltose + IPTG ) were back diluted in fresh medium + antibiotics to a starting OD600 of 0 . 03–0 . 05 and grown at the indicated temperature until cultures reached an OD600 of 0 . 5–1 . Cultures were normalized by OD600 value and 1:10 serial dilutions were made in fresh LB or sterile phosphate buffered saline ( PBS ) in a microtitre plate . 5 μL of each culture were spotted on LB plates containing the appropriate antibiotics with or without the indicated level of inducer ( IPTG , arabinose , or ATC ) . See figure legends for details on each experiment . For B . subtilis spot dilution analysis ( Fig 5C ) , relevant strains were streaked from glycerol stocks onto LB ( Spec ) agar and incubated at 37°C overnight followed by additional overnight incubation at RT . LB cultures were inoculated with single colonies , which were grown at 37°C until they reached an OD600 ~1 . Cultures were normalized by OD600 value , and 1:10 serial dilutions were made in fresh LB in a microtitre plate . 5 μL of each dilution were spotted on LB agar supplemented with 100 μg/mL spectinomycin and LB agar supplemented with 100 μg/mL spectinomycin and 1 mM IPTG . Plates were incubated overnight at 37°C . Spot dilution analysis was done on both LB Miller ( 1% NaCl ) and LB Lennox ( 0 . 5% ) agar with identical results . For B . subtilis growth curves , 5 mL LB cultures ( no antibiotics ) were inoculated with single colonies of relevant strains . Several dilutions of these cultures were made and grown with shaking at RT overnight . The next day , cultures that were in early to mid-log phase were back diluted to a starting OD600 of 0 . 01 in 5 mL fresh LB medium supplemented with 1 mM IPTG . Growth was monitored every 30–60 min by transferring 200 μL to a sterile microtitre plate and taking OD600 measurements on a plate reader ( Molecular Devices ) . All strains were grown in triplicate , and growth curve experiments were repeated independently several times . Growth curve cultures were imaged at the indicated times using the same microscopy protocol as described below . Cultures used for microscopy were handled as described in the corresponding figure legends . Briefly , BW27785 , DH73 , NP1 , or TB28 attHKHC488 cells were transformed with the relevant plasmids and transformants were selected for on LB containing the appropriate antibiotic ( s ) . FB30 or DH118 transformants were selected for on M9 maltose supplemented with the appropriate antibiotic ( s ) and inducer . For most experiments , all growth incubation steps were done at 30°C . LB or M9 overnight cultures were back diluted to a starting OD600 of 0 . 02–0 . 05 , grown without induction for 1 h ( cultures had reached an OD600 of ~0 . 1–0 . 2 ) , and then induced for toxin expression with the addition of IPTG ( 50 , 100 , or 200 μM ) , arabinose ( 0 . 2% ) , or ATC ( 10 , 15 , or 25 ng/mL ) . Cultures were typically in an OD600 range of 0 . 4–1 at the time of imaging . For all snapshot images , cells were mounted on 2% agarose pads containing PBS , and microscopic observation was performed using an Olympus BX61 microscope ( objective UplanF1 100x ) . Images were captured with a monochrome CoolSnapHQ digital camera ( Photometrics ) using Metamorph software version 6 . 1 ( Universal Imaging ) . Cropping and minimal adjustment was performed with ImageJ [65] or Adobe Photoshop . Cell roundness quantification was done manually in ImageJ with the ObjectJ plugin . Briefly , the length of the cell was measured along the long axis , and the width was measured as the axis roughly perpendicular to the long axis . Angle measurements were spot-checked to ensure the axes intersected at an angle close to 90° . Cell roundness data were compiled from three independent experiments; 200–300 cells of each strain from each independent experiment were measured . For time-lapse imaging of CbtA-induced morphology changes , pMT136 , pMT139 , and pMT146 were individually transformed into BW27785 cells . Overnight cultures were back diluted to a starting OD600 of 0 . 05 and grown at 30°C for 1 h without induction . Cells were concentrated 5x , and 2 μL were spotted on the bottom of a glass-bottomed dish ( Willco dish HBSt-5040; Willco Wells ) . A 2% agarose pad containing LB growth medium supplemented with 100 μM IPTG was placed on top of the culture aliquot . Cells were imaged on a Nikon Ti inverted microscope using Nikon Elements software , a Photometrics CoolSNAP HQ2 Interline CCD camera , and a Well Plate Holder stage ( TI-SH-W; Nikon ) equipped with a humid , temperature-controlled incubator ( TC-MIS; Bioscience Tools ) . The objective was heated to ~30°C using a Bioptechs objective heater system . Images were acquired every 3 min for 3 h . Image analysis was performed in FIJI and ImageJ . To compare levels of His6-CbtA-GFP , MreB , or FtsZ variants in E . coli cells , 2 mL of each culture grown as described in the relevant figure legends were pelleted and resuspended in various amounts of BugBuster lysis buffer ( EMD ) to normalize by OD600 value ( OD600 of 1 = 100 μL lysis buffer ) . rlysozyme ( 3 kU; EMD ) and Omnicleave ( 20U; Epicentre ) were added to equal volumes of cell suspensions for a typical final concentration of 60U/μL and 0 . 4U/μL , respectively . Cells were lysed at room temperature for 30 min . Total protein concentration was measured by Bradford assay , and lysate volumes were adjusted using lysis buffer such that all samples contained equivalent amounts of protein . To compare levels of His6-CbtA-GFP and His6-CbtA-F65S- GFP in B . subtilis cells , Western blot analysis was performed on whole-cell lysates generated from growth curve cultures . Briefly , 2 mL of each B . subtilis culture were pelleted and resuspended in various amounts of lysis buffer ( 20 mM Tris-HCl pH 7 . 5 , 50 mM EDTA , 100 mM NaCl ) to normalize by OD600 value ( OD600 of 1 = 100 μL lysis buffer ) . rlysozyme ( 30kU ) and Omnicleave ( 20U ) were added to cell suspensions , which were lysed for 30 min at 37°C . All lysates were diluted 1:2 in 2x Laemmli buffer + BME ( final concentration 1% ) and boiled for 10 min; further dilutions were made in 1x Laemmli buffer + BME . Duplicate 10–20% Tris-glycine gels ( Thermo Fisher ) were run in MOPS-SDS buffer and transferred to nitrocellulose membranes using a wet transfer system ( Life Technologies ) . Membranes were incubated with primary antibodies α-GFP 1:5 , 000 ( Roche ) , α-RpoA 1:10 , 000 ( Neoclone ) , α-FtsZ 1:10 , 000 ( T . Bernhardt ) , α-MreB 1:5 , 000 ( T . Bernhardt ) , α-SigA 1:5 , 000 ( D . Rudner ) , or α-Spo0J 1:5 , 000 ( D . Rudner ) and HRP-conjugated α-mouse or α-rabbit secondary antibodies ( Cell Signaling ) . Chemiluminescent signal was detected using ECL Plus reagent ( GE Healthcare ) on a ChemiDock XRS+ system ( Bio-Rad ) .
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Bacterially encoded toxin-antitoxin systems , which consist of a small toxin protein that is co-produced with a neutralizing antitoxin , are a potential avenue for the identification of novel antibiotic targets . These toxins typically target essential cellular processes , causing growth arrest or cell death when unchecked by the antitoxin . Our study is focused on the CbtA toxin of E . coli , which was known to inhibit both bacterial cell division and also bacterial cell elongation ( the process by which rod-shaped bacteria grow prior to cell division ) . We report that the effects of CbtA on cell division and cell elongation are genetically separable , and that they are due to direct and independent interactions with its targets FtsZ and MreB , essential cytoskeletal proteins that direct cell division and cell elongation , respectively . Our genetic analysis defines the functionally relevant target surfaces on FtsZ and MreB; in the case of FtsZ this surface represents a novel inhibitory target . As a dual-function toxin that independently targets two essential cytoskeletal elements , CbtA could guide the design of dual-function antibiotics whose ability to independently target more than one essential cellular process might impede the development of drug resistance , which is a growing public health threat .
|
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2017
|
CbtA toxin of Escherichia coli inhibits cell division and cell elongation via direct and independent interactions with FtsZ and MreB
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Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype . However , even for simple bacteria , whole-cell models will contain thousands of parameters , many of which are poorly characterized or unknown . New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models . We organized the Dialogue for Reverse Engineering Assessments and Methods ( DREAM ) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models . We asked participants to identify a subset of parameters of a whole-cell model given the model’s structure and in silico “experimental” data . Here we describe the challenge , the best performing methods , and new insights into the identifiability of whole-cell models . We also describe several valuable lessons we learned toward improving future challenges . Going forward , we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation .
Mechanistic modeling is a powerful tool for understanding and engineering biological behavior at the molecular level . Davidson et al . have used Boolean modeling to understand Drosophila developmental patterning [1]; Orth et al . have used flux-balance analysis ( FBA ) to predict Escherichia coli metabolism at the genomic scale [2]; Barkai and Leibler have used ordinary differential equations ( ODEs ) to model E . coli chemotaxis [3]; Arkin et al . have used stochastic ODEs to understand the bacteriophage λ lysis/lysogeny switch [4]; and many others have used mechanistic models to study a wide range of cell physiology . Despite these successes , no one mathematical formalism is capable of explaining all biological behaviors . Consequently , a comprehensive predictive understanding of biology behavior has remained elusive . Recently , Karr et al . developed an integrative modeling approach that enabled them to construct the first whole-cell model by combining submodels of 28 cellular processes [5] . This approach enabled them to model each process using the most appropriate mathematics . For example , they modeled metabolism using FBA [6] and cytokinesis using ODEs . Mathematically , the model is a stochastic , discrete–continuous hybrid , nonlinear , dynamical system . Furthermore , the model is computationally expensive . The model accounts for the function of every annotated gene product of the gram-positive bacterium Mycoplasma genitalium and predicts the dynamics of every molecular species . The model has enabled researchers to gain insights into cell cycle regulation , as well as to predict kinetic parameters [7] . Predictive models begin with a list of molecular components [8] . This can be captured using unbiased high-throughput experiments including DNA sequencing and mass spectrometry . Molecular components are then connected through interactions into wiring diagrams . These interactions can be assembled from prior knowledge or inferred from high-throughput experiments such as microarrays or flow cytometry [9–12] . Next , wiring diagrams are translated into quantitative mathematical models . This introduces quantitative parameters such as transition probabilities , reaction turnover numbers , and binding affinities . Lastly , parameter values are curated from prior knowledge or estimated from experimental data . Accurate parameter values are essential for reliable prediction [13] . Unfortunately , many parameters have not been characterized . Consequently , parameter estimation is critical for model construction . In principle , parameters can be estimated using numerical optimization . Many techniques are available , including derivative-based initial value methods and stochastic multiple shooting methods [14] . However , few techniques are tractable for computationally expensive models . Numerical optimization must be combined with additional techniques such as surrogate modeling , model reduction , distributed optimization , or automatic differentiation . Surrogate modeling and model reduction minimize the computational cost of optimization by replacing the original function with a cheaper , approximate function [15–18] . Surrogate modeling , which is also referred to as function approximation , metamodeling , response surface modeling , and model emulation , uses statistical models including artificial neural networks , splines , and support vector machines . Model reduction uses lower fidelity physical models . Surrogate modeling and model reduction have been used in several fields , including aerospace engineering [19] , hydrology [20] , and petroleum engineering [21] . Distributed optimization is also a promising approach for optimizing computationally expensive models . It uses multiple agents , each simultaneously employing the same algorithm on different regions , to quickly identify optima [22 , 23] . Typically , agents cooperate by exchanging information so that agents learn from each other’s experiences . Distributed optimization has also been used in several fields including aerospace and electrical engineering [24 , 25] and molecular dynamics [26] . Another potential approach for optimizing computationally expensive models is automatic differentiation , an efficient technique for analytically computing the derivative of a computational model by decomposing the model into elementary functions to which the chain rule can be applied [27] . Automatic differentiation can be used to make derivative-based optimization methods tractable in cases where finite difference calculations are prohibitively expensive . It has been used to identify parameters in chemical engineering [28] , biomechanics [29] , and physiology [30] . Estimating the parameters of whole-cell models is further complicated by limited experimental data , stochastic variation , and measurement error [14] . Taken together , parameter estimation is an important problem in systems biology , as researchers pursue increasingly comprehensive and accurate models . We organized the Dialogue for Reverse Engineering Assessments and Methods ( DREAM ) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation methods for whole-cell models . Stolovitzky and Califano founded DREAM to foster collaborative efforts by computational and experimental biologists to reverse engineer cellular networks from high-throughput data [31] . DREAM challenges have repeatedly demonstrated the “wisdom of crowds” to produce high-quality methods [32–34] . This challenge focused on developing and assessing methods for estimating parameters of computationally expensive hybrid mathematical models . Previous challenges , in contrast , have focused on lower dimensional models , although some previous challenges have asked participants to estimate more parameters [35] . To mimic real-life whole-cell model parameter estimation , we challenged participants to identify a subset of parameters of a slow-growing mutant in silico strain of a recent whole-cell model of M . genitalium . We created the mutant strain by modifying its parameters to increase its predicted doubling time . We used the mutant strain to simulate several commonly available experimental datasets . We encouraged participants to form teams and gave participants 15 weeks to identify the unknown parameters . We provided participants the model’s structure , its wild-type strain parameter values , and mutant strain in silico “experimental” data . We also allowed participants to obtain a limited amount of perturbation data . This was designed to mimic the real-life scenario of limited experimental resources and encourage participants to identify the most informative data types and perturbations . To foster collaboration among teams , we divided the competition into four subchallenges and required teams to share their methodology to compete in each subchallenge . To maximize participation , we provided participants the BitMill cloud computing service ( http://bitmill . numerate . com ) to evaluate the model . Participants used BitMill to calculate model predictions and errors . Unintentionally , BitMill also provided information about the distance between the submitted and true parameter values , which is never available in real-world parameter estimation . Unfortunately , we were not alerted to this mistake until the end of the challenge , at which point it was too late to change the challenge . Ten teams participated in the challenge . Six teams pursued the true parameter estimation problem using only the training data and the prediction errors computed by BitMill . Four teams also used the parameter errors returned by BitMill , instead focusing on an artificial parameter estimation problem . The teams used a variety of parameter estimation techniques . All of the teams , including those that focused on the artificial parameter estimation problem , generated valuable ideas about how to best identify whole-cell models and about the tractability of the true parameter estimation problem . Here we describe the challenge setup and the top performing methods . We examine the submissions to identify the most identifiable parameters and reproducible predictions . We conclude by discussing the remaining obstacles to identifying whole-cell models and by describing how to improve future challenges .
We asked participants to identify a modified model of the gram-positive bacterium M . genitalium [5] . The model is composed of submodels of 28 cellular processes , each of which was modeled independently at short time scales using different mathematical representations . For example , the metabolism submodel was modeled using FBA , whereas the transcription submodel was modeled using stochastic methods . The submodels were integrated through 16 cell state variables that represented the instantaneous configuration of the cell and its external environment , including metabolite , RNA , and protein copy numbers , reaction fluxes , nascent RNA and protein sequences , and DNA-binding protein locations . Mathematically , the model is a stochastic , discrete–continuous hybrid , nonlinear , dynamical system . Each model simulation predicts the dynamics of each molecular species over the life cycle of one in silico cell . Each simulation requires approximately one core day . The whole-cell model contains 1 , 462 quantitative parameters including average metabolite concentrations , RNA polymerase promoter binding affinities , RNA half-lives , and reaction kinetics ( S1 Table ) . The wild-type values of these parameters were initially set according to published experimental measurements . However , the model’s predictions based on these initial values were inconsistent with the measured doubling time . Consequently , Karr et al . modified the model’s parameters to match the physiological data . Numerical optimization methods that require large numbers of model evaluations were prohibitively expensive . Instead , Karr et al . optimized the model’s parameters using a reduced model . First , Karr et al . constructed a reduced physical model that approximates the temporal and population average of the full model . The reduced model has the same parameters as the full model , but is computationally cheaper . Second , they minimized the reduced model’s prediction error by numerically optimizing its parameters . Next , they calculated the full model’s prediction error with the optimized parameter values . Lastly , they manually tuned the full model’s parameters to reduce its prediction error . Their model reduction approach is described in Data S1 of Karr et al . , 2012 [5] . We challenged participants to identify an in silico mutant strain with a significantly altered phenotype from that of the wild-type strain . Because the original model was primarily used to investigate the molecular determinants of the growth rate , we decided to ask participants to identify a slow-growing mutant strain . To limit the difficulty of the challenge , we decided to modify only 15 parameters . The precise number of modified parameters was chosen arbitrarily . Furthermore , we only modified three types of parameters: the RNA polymerase promoter binding probabilities and RNA half-lives , which control RNA expression and in turn metabolic enzyme expression , and the metabolic reaction turnover numbers . We focused on these three types of parameters because these parameters uniquely map onto changes in specific observables and are therefore structurally identifiable , and because these parameters have the most direct influence on the metabolic submodel , and in turn the predicted growth rate . We constructed the mutant in silico strain by modifying a subset of the model’s parameter values . First , we calculated the sensitivity of the predicted doubling time to the RNA polymerase binding probabilities , RNA half-lives , and reaction turnover numbers . Second , we used the sensitivities to estimate the parameter value changes required to increase the predicted doubling time by 1 . 9% . We chose 1 . 9% so that iteratively modifying the 15 parameters would together increase the predicted doubling time by 33% . Third , we randomly selected a single parameter to modify , weighted by its estimated fold value changes from the previous step . Next , we modified the value of the selected parameter . We iteratively repeated this to achieve a mutant strain with a 33% increased doubling time . The mutant strain construction procedure selected three polymerase promoter binding probabilities , three RNA half-lives , and nine metabolic reaction turnover numbers . The procedure increased the values of two of these parameters 3%–95% and decreased the values of the remaining 13 12%–91% . To further limit the difficulty of the challenge , we told participants the identities of the 15 modified parameters plus the identities of 15 additional unmodified parameters of the same three types ( S2–S4 Tables ) . This was designed to increase the tractability of the challenge by reducing the dimensionality of the search space , as well as to determine if the participants were able to distinguish between modified and unmodified parameters . The precise number of unknown parameters was chosen arbitrarily . We constructed eight sets of in silico “experimental” data for parameter estimation . These mimicked the experimental data available for real-world parameter estimation . They included one single-cell data set: growth , mass , and volume time courses and replication initiation , replication , and cytokinesis times . They also included seven temporal and population average data sets: metabolite concentrations , DNA-seq , RNA-seq , ChIP-seq , RNA expression arrays , protein expression array , and metabolic reaction fluxes . We simulated the eight in silico data sets for the mutant strain , as well as for 2-fold up and down perturbations to each of the 30 unknown parameters . Each mutant strain data set was simulated using a population of 32 in silico cells; each perturbation data set was simulated using eight cells . In total , we simulated eight mutant strain data sets and 480 perturbation data sets . The eight data sets were chosen such that each of the unknown parameters were expected to be practically identifiable . The ChIP-seq data contains information about the unknown RNA synthesis rates , together the ChIP-seq and RNA half-life data contain information about the RNA synthesis rates , and the reaction flux data contains information about the metabolic kinetic rates . It is important to note that the unknown parameters would have been substantially more difficult to identify with the scalar prediction error alone . The in silico data sets contain valuable information for parameter identification . We provided participants all eight mutant strain data sets . In addition , to mimic the real-life scenario of limited experimental resources , we allowed participants to obtain up to 50 perturbation data sets . We provided participants the BitMill cloud computing service to simulate the in silico data sets and calculate prediction errors . To ensure equal access to BitMill , we limited participants to eight simultaneous simulations during the first ten weeks and 40 during the final five weeks . To mimic real-life collaborative research , we created an online forum to help participants find teammates . Teams were allowed to pool in silico perturbation data and BitMill resources . To foster collaboration among teams , we divided the competition into four subchallenges and required participants to share their methodology to compete in each subchallenge . This enabled teams to learn from the best performing methods throughout the challenge . For the first subchallenge , we ranked submissions by their log ratio parameter error , eparam=1N∑i=1N ( log10viestvitrue ) 2 , ( 1 ) where vitrue and viest are the true and estimated parameter values , and N = 30 is the number of unknown parameters . For the third subchallenge , we ranked submissions by their least squares prediction error , epredict=1M∑i=1M ( vitrue−viestσitrue ) 2 , ( 2 ) where vitrue and viest are the true and estimated values of simulated experimental measurement i , σitrue is the true variance of measurement i , and M = 2 , 810 , 064 is the total number of simulated experimental measurements . We judged the creativity of the participants’ methodologies for the second subchallenge . We scored the final challenge by combining the parameter and prediction errors used for the first and third subchallenges . First , we calculated the parameter and prediction p-values of each submission , pparam and ppredict , using empirical parameter and prediction error distributions . We constructed these empirical distributions by calculating the errors of meta parameter and prediction vectors formed by randomly sampling the submitted parameter vectors and simulated prediction vectors [35] . Next , we computed an overall score , s , by combining the parameter and prediction p-values multiplicatively , We motivated participants to compete in the final subchallenge by offering winners the opportunity to present their methodology at the annual Research in Computational Molecular Biology ( RECOMB ) Conference on Regulatory and Systems Genomics and in this manuscript . In addition , we offered small cash awards , scientific software , and other small prizes for the winners of the first three subchallenges . We organized the challenge using the Synapse workspace ( https://www . synapse . org/# ! Synapse:syn1876068 ) . We used Synapse to distribute challenge materials , administer the perturbation data , collect submissions , and announce winners . We used GitHub ( http://github . com/CovertLab/WholeCell/tree/parameter-estimation-DREAM-challenge-2013 . ) to distribute the model to participants . We used a Get Satisfaction forum ( http://getsatisfaction . com ) , GoToWebinar ( http://www . gotomeeting . com ) , and YouTube to communicate with participants through a webinar ( https://www . youtube . com/watch ? v=VQA9YwsAgQk ) .
Ten teams comprising 45 researchers from 16 institutions and six countries participated in the challenge . The researchers represented a broad variety of disciplines , including biology , computer science , mathematics , physics , and statistics . The researchers also spanned a wide range of experience levels ranging from undergraduate students to senior faculty . In total , nine teams submitted 691 solutions , including 682 solutions from the five top performing teams . One team obtained all of the perturbation data and performed simulations on their own computers , but did did not submit a solution . Three teams collected 586 perturbation experiments . One of the top four teams collected all 60 single-cell data sets , as well as 19 of 20 metabolic reaction flux and DNA-seq measurements of increased turnover numbers . A second team collected all 20 metabolic reaction flux measurements of perturbed turnover numbers . A third team collected all 480 data sets . However , this team did not submit any solutions . Surprisingly , seven teams did not collect any perturbation data , including four of the top five teams . Overall , participants used the perturbation data minimally . Only two of nine teams that submitted solutions obtained perturbation data . Both of these teams focused on the metabolic turnover rate perturbations and metabolomic data , possibly because the mutant strain exhibited a metabolic , slow-growth phenotype . However , neither team discussed the perturbation data in their write-ups . Together , this suggests that teams did not use experimental design strategies to focus on the most likely informative data , or use the data to estimate parameters . This contrasts what has been observed in other DREAM challenges for smaller models [35] . Instead , the model’s stochasticity led most of the teams to focus on generating more precise training data by running and averaging large numbers of their own simulations . Participants used the BitMill cloud computing service extensively . During the first 10 weeks when participants were limited to eight simultaneous simulations , participants requested 100 simulations per week . Participants submitted simulations 5-fold more frequently after the BitMill limit was increased 5-fold at the end of the tenth week . We believe that BitMill was critical to the success of the challenge . Nine teams submitted 691 solutions , including 682 solutions from the five most active and top performing teams . We began analyzing the submissions by inspecting the distribution of parameter and prediction errors across all 691 solutions ( Fig 1A ) . Interestingly , we found that although participants were able to reduce the parameter error by over 18 orders of magnitude from the wild-type parameter values , they were only able to reduce the prediction error 30-fold . As discussed below , several participants were able to perform substantially better on the parameter error metric than on the prediction error metric by using information to directly minimize the parameter error rather than indirectly minimizing the parameter error using the prediction error as a proxy . We also found that the parameter and prediction errors are only moderately correlated ( log–log R2 = 0 . 57 ) . This is primarily because the prediction error is sensitive to the model’s stochastic variation . Importantly , this suggests that the prediction error must be evaluated over a large number of model simulations to minimize its sensitivity to stochastic variation . Unfortunately , this magnifies the large computational cost of whole-cell model parameter estimation . The moderate correlation is also due in part to practical parameter unidentifiability given the limited training data , both in terms of phenotypic diversity and small numbers of samples , and therefore large stochastic variation . Interpreted biologically , this means that multiple sets of parameters can produce different molecular phenotypes but have similar systems-level phenotypes . Fortunately , this practical unidentifiability can typically be overcome for whole-cell models by using additional types of training data , which contain additional molecular information . For example , participants who only used the RNA-seq data , which provides information about the product of RNA synthesis rates and half-lives , would have found these parameters practically unidentifiable . However , participants who also used the ChIP-seq , which provides information about RNA synthesis rates , would have found these parameters identifiable . In the context of real-world whole-cell modeling research , an easy way to make parameters more identifiable is to collect additional molecular data which provides information about individual parameters . For example , an easy way to estimate RNA half-life parameters is to measure the decay rate of each individual RNA species . In contrast , additional systems level data typically does not significantly increase the practical identifiability of whole-cell models . Next , we examined the participants performance over the duration of the challenge ( Fig 1D and 1E ) . Despite the formidable difficulty of the challenge , we found that performance improved throughout the challenge . Notably , we observed that participants improved their parameter performance by over 13 orders of magnitude between submissions 654 and 666 . Reviewing the participants’ write-ups , we learned that the dramatic improvement was due to a change in parameter estimation strategy by Team Crux ( see below ) . Furthermore , the dramatic improvement occurred with little concomitant decrease in the prediction error , underscoring the weak correlation between the parameter and prediction errors . Ultimately , primarily using the parameter error information , participants accurately identified the parameters . Table 1 lists each team’s methodology and performance . Next , we inspected the individual contributions of the unknown parameters to the parameter errors ( Fig 1F ) . We found that the error distribution of the unknown , unmodified parameters is centered over two orders of magnitude left of that of the modified parameters , indicating that participants successfully differentiated the unmodified and modified parameters . More importantly , we found that the error distribution was very broad , suggesting that the parameters are unequally practically identifiable . As discussed below , this is likely because the predicted phenotypes are unequally sensitive to the parameters . Going forward , this suggests that broader phenotypic profiling is needed to identify whole-cell models . To gain additional insight into the broad distribution of individual parameter errors , we plotted the ratio of each parameter’s true and predicted values for each team’s top scoring solution ( Fig 2 ) . This showed that Team Crux identified every parameter . Moreover , the analysis showed that teams had the most difficulty estimating the metabolic reaction turnover rates . Teams likely had the most difficulty estimating these parameters because they were changed significantly relative to the wild-type values and because they affect the in silico data nonlinearly . This suggests that additional types of experimental data that respond linearly to the turnover rates may improve turnover rate estimation . Next , we analyzed the participants’ prediction performance of the individual in silico phenotypes ( Fig 3A ) . We found that even the top scoring solutions produced some phenotypes that differed by more than 25 standard deviations from that of the mutant strain . In particular , we found that participants had difficulty reproducing several of the mutant strain reaction fluxes , protein expression values , and metabolite concentrations . We believe this is because these phenotypes are not only highly sensitive to the modified parameters but also highly variable and thus poorly sampled by the small number of simulations . Surprisingly , we also found that participants were able to reproduce some of the most variable in silico data including the ChIP-seq data ( Fig 3B ) . This is because although the ChIP-seq data is highly variable across individual cells , it is relatively insensitive to the modified parameters and thus can be predicted relatively easily . In contrast , some of the least variable phenotypes , including the protein expression data , were difficult to reproduce because they are highly sensitive to modified probabilities and half-lives . Overall , the fact that participants had trouble reproducing the mutant phenotype , even with the help of parameter error metric , implies that whole-cell model parameter estimation requires large numbers of simulations to accurately compare model predictions and experimental training data . In turn , this means that whole-cell parameter estimation methods must be highly computationally efficient . Broadly , participants used two families of strategies: ( 1 ) participants tried to solve the real-world problem of estimating the unknown parameter values using only the mutant and perturbation experimental data and the prediction error metric , and ( 2 ) participants tried to solve the artificial problem of identifying the parameters primarily using the parameter error . Initially , all teams pursued the first class of strategies . Together , they employed a variety of techniques including differential evolution and derivative-based approaches , as well as manual tuning guided by mathematical and biological intuition ( Table 1 ) . Table 2 summarizes the advantages and disadvantages of the methods used by the participants . Team Whole-Sale Modelers submitted the top scoring solution from this first class of strategies using an innovative technique combining differential evolution with random forests ( Box 1 ) . In addition , a few teams used reduced physical models to estimate specific model parameters from specific in silico data . Team CU estimated the RNA polymerase promoter binding probabilities from the RNA polymerase ChIP-seq data using the DNA-seq data to correct for DNA copy number differences along the chromosome from the oriC to terC . Team CU then used the estimated binding probabilities to estimate the unknown RNA half-lives from the RNA expression data . Team Alucinatori estimated the unknown reaction turnover rates using short time scale simulations of the metabolic submodel . Team Alucinatori refined the parameters by matching metabolic fluxes between the reduced and full models . Four teams focused on the artificial problem of estimating the unknown parameters using parameter error information from BitMill . Although these four teams did not focus on the real-world parameter estimation problem , their methods may be applicable to the real-world parameter estimation problem . Further work is needed to assess their methods on real-world parameter estimation . We analyzed the teams’ error trajectories to better understand their relative merits , including their performance and efficiency . We found that Team Crux’s derivative-based approach not only achieved the lowest parameter error but also was the most efficient strategy , arriving at the top solution using the smallest number of model iterations among the top performing teams ( Fig 5A ) . In contrast to Team Crux , Teams New Dream , ICM Poland , and Alucinatori used methods that wandered through the error landscape , causing them to slowly and inefficiently approach the true parameter values ( Fig 5B–5D ) . Next , we inspected the submitted parameter values to gain further insight into how participants explored the parameter space ( Fig 5F ) . We found that RNA polymerase binding probabilities and metabolic reaction turnover rates had the largest range of submitted values , suggesting that the teams focused on exploring these parameters . We also calculated each parameter’s correlation with the prediction error ( Fig 5G ) to better understand why participants focused on exploring these parameters . We found that the metabolic turnover rate parameters were the most correlated with the prediction error . However , further analysis is needed to understand whether the prediction error was simply correlated with the turnover rate parameters because participants changed these parameters the most significantly , or because the prediction error is most highly sensitive to the turnover rate parameters .
We organized the DREAM8 parameter estimation challenge to develop new parameter estimation techniques for whole-cell models . To mimic the real-life problem of estimating whole-cell model parameters , we constructed a mutant in silico strain by modifying the parameters of a whole-cell model of M . genitalium and asked participants to identify the modified parameter values given the model’s structure and several simulated experimental data sets . We provided participants with the BitMill cloud computing service to simulate the model free of charge and encouraged participants to form teams . The challenge represented a simplified version of the parameter estimation problem faced in real-world whole-cell modeling . Participants were asked to identify a subset ( 2% ) of the model’s parameters , a common problem researchers face when developing a model of a part of a larger system . In addition , participants were given consistent in silico experimental data representing experiments obtained using a single strain with a single experimental condition . In contrast , real whole-cell models must be identified using heterogeneous data originating from multiple organisms , laboratories , and experimental conditions . Participants were also given much more training data than is typically available experimentally . In real-world applications , it is infeasible to comprehensively characterize each perturbation . Typically only a limited amount of data is available for each perturbation . For example , only growth rates are available for each M . genitalium single gene disruption strain . Lastly , the in silico experimental data contained no measurement noise , only the intrinsic stochastic variation present in the model . We established the challenge as a competition rather than as a conventional research project for two reasons . First , we wanted to expand the whole-cell modeling community by providing researchers an opportunity to contribute to the field . Second , many groups have shown that competitions can quickly and inexpensively produce high-quality scientific results [32–34 , 41–43] . The challenge successfully attracted researchers to the emerging field of whole-cell modeling , including researchers from a broad range of scientific disciplines . We hope these new researchers will help advance whole-cell modeling . Ten teams participated in the challenge . Anecdotally , participants reported that free availability of the BitMill cloud computing service was critical to the challenge’s success . Several teams stated that they would not have had sufficient time or resources to set up computing clusters to compete the challenge , and that they would not have participated without the free and user-friendly BitMill service . Overall , BitMill enabled more scientists to participate and enabled those scientists to focus more of their time on the scientific content of the challenge rather than on duplicating efforts to establish computational infrastructures . We therefore believe that shared cloud computing platforms such as BitMill could improve participation and performance in other DREAM challenges and other crowdsourced scientific projects . The participants primarily pursued two families of approaches . Four teams tried to solve the artificial problem of identifying the unknown model parameters using the parameter error metric and derivative-based approaches . These derivative-based approaches can also be effective for real-world parameter estimation of small , deterministic models where gradient calculations are tractable and where good estimates of the true parameter values are available such that the optimization procedure is seeded in the attractor basin of the global optimum . For these reasons , derivative-based approaches alone are not well suited to estimating stochastic , computationally expensive models . For whole-cell models , derivative approaches must be used in combination with other techniques such as surrogate modeling or model reduction . Five other teams tried to solve the real-world problem of identifying the unknown parameters using only the experimental data and the prediction error metric . These teams used a variety of parameter estimation techniques to reduce the prediction error metric , led by Team Whole-Sale Modelers , who developed a novel combination of DE and random forests . Notably , Team Whole-Sale Modelers identified the directions in which the parameters were modified with 80% ( 12 of 15 modified parameters ) accuracy . In addition , a few teams pursued strategies based on reduced physical models . These teams tried to estimate the RNA polymerase promoter binding probabilities from the RNA polymerase ChIP-seq data , use this information to estimate the RNA half-lives from the RNA microarray data , and use the protein expression data , metabolic fluxes , and FBA metabolic submodel to estimate the reaction turnover rates . We decided to provide participants parameter distance information to give participants qualitative feedback on how far their models were to the true parameter values . We did not intend for participants to use this information to solve the challenge . We incorrectly believed that teams would not use this information because this information is not available in real-world biological parameter estimation applications . Unfortunately , we did not learn that participants were using this information to solve the artificial parameter error optimization problem until the last week of the challenge , at which point we felt it was too late to change the structure of the challenge . In hindsight , we should have anticipated that participants would use the parameter error information because the challenge is organized as a competition with the artificial end goal of “winning” rather than the real-world end goal of creating knowledge . Despite the artificial nature of this challenge , it generated valuable new ideas about how to best identify whole-cell models . One team developed a novel combination of DE and random forests , and two teams explored model reduction strategies . Interestingly , none of the teams pursued distributed optimization or automatic differentiation , which have been used in other fields for computationally expensive models . The challenge also generated useful information about parameter identifiability . The challenge highlighted the degeneracy of the parameter error , meaning that multiple parameter sets can produce similar errors due to degeneracies in phenotypic subspaces , and that comprehensive data is required to make the parameters practically identifiable [44–47] . This degeneracy in phenotypic subspaces is consistent with observations of many other biophysical systems [48–55] . Modelers must avoid creating structurally unidentifiable parameters that can never be estimated .
In addition , we learned several valuable lessons about how to best organize challenges . Most importantly , we learned that participants will use all available information . Organizers should never provide information that could be used to side step the challenge . We also learned that it is important to assess the feasibility of the challenge beforehand . This should be achieved by assessing the feasibility theoretically , as well as by asking a small number of colleagues to beta test the challenge before public release . For parameter estimation challenges , this means rigorously assessing the practical identifiability of the unknown parameters using the training data that will be provided to the participants and limiting the challenge to structurally identifiable parameters . Third , we learned that participants will only share their approaches if they believe they can win a prize . This means that organizers should only release performance statistics prior to prize selection if participants have similar performance; otherwise , only participants who perceive they have a chance to win a prize will share their methods , and the community will never be able to learn from other methods that were explored but never shared . Furthermore , to encourage all participants to share their approaches , regardless of their numerical success , organizers should randomly award prizes simply for participating . Lastly , we learned that to maximize participation , organizers must make every effort to minimize the prior knowledge and resources required to participate in the challenge . For computational challenges , one way to minimize the required resources is to provide free , preconfigured computational resources . We believe this is especially important for computationally expensive challenges that require complicated and expensive computing clusters . Furthermore , we found that modeling challenges must provide participants a clear , thorough , and accessible description of the mathematical model and its parameters .
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Whole-cell models promise to enable rational bioengineering by predicting how cells behave . Even for simple bacteria , whole-cell models require thousands of parameters , many of which are poorly characterized or unknown . New approaches are needed to estimate these parameters . We organized the Dialogue for Reverse Engineering Assessments and Methods ( DREAM ) 8 Whole-Cell Parameter Estimation Challenge to develop new approaches for whole-cell model parameter identification . Here we describe the challenge , the best performing methods , new insights into the identifiability of whole-cell models , and several lessons we learned for improving future challenges . Going forward , we believe that collaborative efforts have the potential to produce powerful tools for identifying whole-cell models .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion",
"Lessons",
"Learned"
] |
[] |
2015
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Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models
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Mucosal Th17 cells play an important role in maintaining gut epithelium integrity and thus prevent microbial translocation . Chronic HIV infection is characterized by mucosal Th17 cell depletion , microbial translocation and subsequent immune-activation , which remain elevated despite antiretroviral therapy ( ART ) correlating with increased mortality . However , when Th17 depletion occurs following HIV infection is unknown . We analyzed mucosal Th17 cells in 42 acute HIV infection ( AHI ) subjects ( Fiebig ( F ) stage I-V ) with a median duration of infection of 16 days and the short-term impact of early initiation of ART . Th17 cells were defined as IL-17+ CD4+ T cells and their function was assessed by the co-expression of IL-22 , IL-2 and IFNγ . While intact during FI/II , depletion of mucosal Th17 cell numbers and function was observed during FIII correlating with local and systemic markers of immune-activation . ART initiated at FI/II prevented loss of Th17 cell numbers and function , while initiation at FIII restored Th17 cell numbers but not their polyfunctionality . Furthermore , early initiation of ART in FI/II fully reversed the initially observed mucosal and systemic immune-activation . In contrast , patients treated later during AHI maintained elevated mucosal and systemic CD8+ T-cell activation post initiation of ART . These data support a loss of Th17 cells at early stages of acute HIV infection , and highlight that studies of ART initiation during early AHI should be further explored to assess the underlying mechanism of mucosal Th17 function preservation .
Eradication of HIV infection has not been achieved except under unique circumstances [1] , [2] . Given the limitations of antiretroviral therapy ( ART ) and recent advances in our understanding of HIV persistence with current treatment regimens , there is a growing recognition that a functional cure for HIV infection is both needed and feasible [3] . Despite potent ART , chronic immune activation , inflammation , and immune dysfunction persist , and are likely to have important effects on the size and distribution of the viral reservoir [4] and non-AIDS ( or non-infectious ) inflammatory related disorders [5] . Acute HIV infection ( AHI ) , defined here as the period between detectable HIV RNA viremia and reactive IgG enzyme immunoassay ( EIA ) antibody to HIV proteins [6] , [7] , is marked by peak viremia ( >106 copies/mL ) , the rapid depletion of gastrointestinal CD4+T cells , followed by a deterioration of the mucosal epithelium and increased microbial translocation [8]–[10] , which may not be restored despite prolonged ART [11] , [12] . In this context , the importance of an IL-17-producing subpopulation of CD4+T cells ( Th17 cells ) has been emphasized . Th17 cells are depleted in HIV and pathogenic simian immunodeficiency virus ( SIV ) infection of humans and rhesus macaques , respectively , but are preserved in SIV infection of the natural hosts , sooty mangabeys and African green monkeys [13]–[15] . In addition , Th17 cells are also preserved in HIV-1 infected long-term nonprogressors [16] . Th17 cells are essential for mucosal immunity as they respond to extracellular bacteria and fungi by promoting neutrophil recruitment and produce antimicrobial peptides such as defensin and mucin [17]–[21] . Furthermore , Th17 cells produce IL-22 , which enhances epithelial regeneration and , as a possible consequence of their loss , impaired mucosal restoration and subsequent increased intestinal permeability and microbial translocation may occur [22]–[24] . Lower frequencies of Th17 cells in the sigmoid colon of individuals with chronic HIV infection ( CHI ) correlated with higher plasma lipopolysaccharide ( LPS ) and were linked to persistent immune activation [25] , [26] . Importantly , the level of immune activation in ART-naïve individuals with CHI is the best predictor of HIV disease progression to AIDS [27] . Despite the significant benefits of ART , immune reconstitution in the gut is often incomplete and immune activation may persist [28] , [29] . A recent study by Kim et al . has shown that mucosal Th17 function is altered during HIV infection and can serve as an independent predictor of immune activation . While mucosal Th17 cells were rapidly restored under ART , normalization of Th17 function and local and systemic immune activation was much more delayed , emphasizing the importance of strategies to preserve mucosal Th17 function for potential therapeutic benefit [30] . Studies of Th17 cells during early HIV infection are crucial for understanding the timing and impact on gut epithelial barrier dysfunction and damage , but have been hampered due to difficulties identifying AHI and obtaining relevant tissue from human volunteers . Using real-time pooled nucleic acid testing ( NAT ) and sequential EIA in high risk HIV-seronegative subjects attending voluntary counseling and testing centers ( VCT ) in Bangkok , we have identified a Thai cohort of volunteers with AHI , mostly infected with HIV-1 circulating recombinant form ( CRF ) 01_AE and to a lesser extent with subtype B [31] , [32] willing to undergo sigmoid biopsies . This cohort represents an unprecedented opportunity to evaluate the impact of AHI on Th17 cells , mucosal barrier integrity and local and systemic inflammation during AHI and moreover allows detailed assessment of the benefit of early initiation of ART on the preservation of CD4+T cell populations and mucosal integrity [33] . Utilizing the unique patient population , we provide the first description of qualitative and quantitative mucosal Th17 cell dynamics , and local and systemic immune activation during the earliest stages of HIV infection in human volunteers . We show that ART initiated during early AHI either prevents loss ( Fiebig stage I/II ) [6] or restores ( Fiebig stage III ) mucosal Th17 cells and is consequently associated with normalization of local and systemic immune activation , reversing a hallmark of pathogenic HIV infection . Our study emphasizes the long-term implications of these early events in viral pathogenesis and argues for systematic evaluation of early and aggressive intervention in AHI and evaluation of the underlying mechanism of potential preservation of mucosal Th17 function [34] , [35] .
Between May 2009 and March 2012 , we identified 42 subjects with AHI , staged according to Fiebig ( F ) classification at time of HIV diagnosis [6] who were willing to undergo sigmoid biopsy and phlebotomy ( S1 Table ) . Seventeen subjects were identified by pooled nucleic acid test ( NAT ) ( non-reactive HIV IgM antibody – Fiebig I/II ) and 25 by sequential EIA ( reactive HIV IgM – Fiebig III/IV/V ) [6] . Additionally 10 age- , gender- and risk group-matched HIV-uninfected ( HIV- ) and 5 treatment-naïve subjects with CHI were enrolled to serve as negative and positive controls , respectively . All subjects underwent sigmoid biopsy and phlebotomy . No underlying histopathological findings in the sigmoid colon were observed in our AHI cohort . AHI subjects were mainly young men who have sex with men ( MSM ) ( 83% ) infected with HIV-1 CRF01_AE ( 74% ) with a median time since history of HIV exposure of 16 days ( SD 6 . 6 ) , a median CD4+T cell count of 465 cells/mm3 , a median plasma HIV RNA of 5 . 5 log10 copies/mL and a median sigmoid colon HIV RNA of 2 . 6 log10 copies/mg tissue ( Table 1 ) . The plasma as well as the colonic HIV RNA increased significantly with progression of infection from FI/II , FIII to FIV/V ( plasma HIV RNA: FI/II 4 . 8 log10 copies/ml vs: FIII 6 . 0 log10 copies/ml , p = 0 . 002 , FIV/V 6 . 2 log10 copies/ml , p = 0 . 02; colonic HIV RNA: FI/II 2 . 3 log10 copies/mg tissue vs: FIII 3 . 1 log10 copies/mg tissue , p = 0 . 01 , FIV/V 3 . 3 log10 copies/mg tissue , p = NS ) . The mean time elapsed since initial diagnosis of HIV infection for CHI subjects was 298 days ( SD 154 . 1 ) . The median CD4+T cell count was 515 cells/mm3 ( range 316 , 883 ) and the plasma HIV RNA was 4 . 9 log10 copies/mL ( range 4 . 0 , 5 . 4 ) ( Table 1 ) . Consistent with our previous report [33] , the proportion of CD4+T cells in the sigmoid colon significantly decreased with progression of Fiebig stages from a median frequency of 49 . 8% at FI/II to 35 . 2% at FIII ( p<0 . 001 ) and 37 . 0% at FIV/V ( p = 0 . 009 ) ( Table 2 ) . The same pattern was observed for the proportion of CD4+CCR5+T cells with a decrease from a median frequency of 67 . 3% in FI/II to 35 . 5% in FIII ( p = 0 . 002 ) and 17 . 4% in FIV/V ( p = 0 . 009 ) . The loss of CD4+CCR5+T cells from FI/II to FIII remained significant when comparing the absolute ( abs ) numbers ( CD4+CCR5+: 6 . 9×106 cells/g of tissue in FI/II vs . 1 . 2×106 cells/g of tissue in FIII , p = 0 . 008 and vs . 0 . 8×106 cells/g of tissue in FIV/V , p = 0 . 04 ) . There were no significant differences in the proportion or abs number of CD4+ or CD4+CCR5+T cells between FI and FII . FIII subjects however , showed significant differences when compared to HIV- subjects ( %CD4+p<0 . 001; abs CD4+p = 0 . 007; %CD4+CCR5+p = 0 . 002; abs CD4+CCR5+p = 0 . 002 ) . The observed loss of CD4+CCR5+T cells occurred mainly in the CD27+CD45RO+ central memory ( CM ) CD4+T cells , and to a lesser extent in the CD27-CD45RO+ effector memory ( EM ) CD4+T cells ( CM: FI/II 48 . 5% , FIII 18 . 1% , p = 0 . 004; EM: FI/II 68% , FIII 32 . 2% , p<0 . 001 ) . The absolute numbers of CD8+T cells did not change with progression of Fiebig stages while the frequency increased from 40% in FI/II to 49 . 4% in FIII ( p = 0 . 009 ) and to 54 . 3% in FIV/V ( p = 0 . 007 ) , likely due to losses in CD4+T cells . In peripheral blood , the proportion of CD4+T cells showed a significant decrease from a median frequency of 35% in FI/II to 26% in FIII ( p = 0 . 003 ) and to 21% in FIV/V ( p = 0 . 001 ) . FI/II showed a significant decrease of CD4+T cells frequency compared to HIV- ( 53 . 9% vs . 35% , p<0 . 001 , respectively ) possibly due to redistribution of T-cell subsets . There were no changes seen in the frequency of CD4+CCR5+T cells in peripheral blood . However , a strong correlation was observed between sigmoid colon and peripheral blood for the frequencies of both CD4+T cells and CD4+CCR5+T cells ( r = 0 . 64 , p<0 . 001 and r = 0 . 73 , p<0 . 001 ) . In contrast , the proportion and abs numbers of CD8+T cells in peripheral blood increased with progression from FI/II to FIII ( ( %CD8+p<0 . 001; abs CD8+p = 0 . 004 ) ( Table 2 ) . In order to localize the depletion of CD4+T cells in the sigmoid colon , immuno-histochemistry and quantitative image analysis was performed to determine the percentage area of LP staining for CD4+T cells ( % area LP CD4+ ) . Comparable to the proportion and abs number of CD4+T cells , a significant decrease of the CD4+T cells within the LP was observed ( 0 . 96% area in FI/II to 0 . 31% in FIII ( p<0 . 004 ) to 0 . 15% in FIV/V; p = 0 . 007 ) . In contrast to the overall frequency and abs number of bulk CD4+T cells in the sigmoid colon , the CD4+T cells within the LP decreased between HIV- and FI/II ( % area LP CD4+: 1 . 96 vs . 0 . 96 , p = 0 . 004 , respectively ) ( Fig . 1a ) . During AHI an inverse correlation was observed between the % area LP CD4+ and the colonic HIV RNA ( r = −0 . 49 , p = 0 . 003 , Fig . 1b ) as well as the plasma HIV RNA , which showed a statistically significant , albeit weak correlation ( r = 0 . 36 and p = 0 . 02; Fig . 1c ) . The anatomical location of productively infected HIV RNA+ cells ( Fig . 1d ) was directly related to CD4+T cell populations ( Fig . 1e ) within the gastrointestinal anatomical compartments ( i . e . LP and isolated lymphoid aggregates [LA] ) . Some patients in FI/II , which demonstrated a variable degree of CD4+T cell depletion within the LP , having had HIV RNA+ cells in both the LP and LA , whereas in FIII concomitant with near complete depletion of CD4+T cells from the LP HIV RNA+ cells are absent from the LP and restricted to the LA where abundant CD4+T cell populations persist . The decrease of CD4+ within the LP correlated with the proportion of CD4+ ( r = 0 . 63 p<0 . 001 ) and CD4+CCR5+T cells ( r = 0 . 68 p<0 . 001 ) in the sigmoid colon and with the proportion of CD4+T cells in the peripheral blood ( r = 0 . 60 p<0 . 001 ) . Studies in the pathogenic non-human primate model suggest that mucosal immune dysfunction is attributed to the preferential loss of CD4+ Th17 cells [14] , [15] , [18] . Recent studies have demonstrated the importance of IL-22 expressing CD4+T cells ( Th22 ) contributing to the homeostasis of mucosal epithelial surfaces [19] , [20] . We performed flow cytometric analysis of IL-17- and IL-22-producing CD4+T cells from sigmoid tissue and peripheral blood after stimulation with PMA and ionomycin . Fig . 2 ( a to d ) shows the gating strategy and representative flow cytometry plots for IL-17 and/or IL-22 expression in different Fiebig stages . Due to limited availability of mucosal mononuclear cells ( MMC ) , data are only available for a subset of subjects: HIV- ( 8 ) , FI ( 9 ) , FII ( 1 ) , FIII ( 14 ) , FIV ( 1 ) , FV ( 3 ) and CHI ( 5 ) . IL-17- and IL-22-producing CD4 T cells decreased with progression of Fiebig stages . The proportion of Th17 cells decreased from 12 . 8% in FI/II to 7 . 9% in FIII ( p = 0 . 02 ) and to 2 . 3% in FIV/V ( p = 0 . 001 ) , and further decreased to 0 . 9% in CHI ( p<0 . 001 , Fig . 2e ) . The same trend was seen in Th22 cells: 2 . 9% in FI/II to 1 . 3% in FIII ( p = 0 . 02 ) and to 0 . 4% in FIV/V ( p = NS ) , and in CD4+T cells producing both IL-17 and IL-22 ( FIII 1 . 7% , p = 0 . 02 and FIV/V 0 . 9% , p = 0 . 04 vs . FI/II 3 . 7% , Fig . 2f ) . The proportion of IL-17- and/or IL-22-producing CD4+T-cell subsets in FI/II was comparable to HIV- subjects ( Th17: 12 . 8% vs . 13 . 5% , p = NS; Th22: 4 . 1% vs . 3 . 6% , p = NS; IL-17/IL-22: 4 . 1 vs . 4 . 0 , p = NS , respectively , Fig . 2g ) . There was a significantly lower proportion of Th17 cells in peripheral blood ( HIV-: 0 . 37% , FI/II: 0 . 65% , FIII: 0 . 5% and FIV/V: 0 . 86% , CHI: 0 . 48% ) compared to the sigmoid colon with no quantitative changes observed between Fiebig stages . Th22 cells were not detected in the peripheral blood . In the sigmoid colon , the frequencies of IL-17 , IL22 and IL-17/IL-22-producing CD4+T cells in AHI were correlated with the frequency of bulk CD4+T cells ( r = 0 . 67 , p<0 . 0001; r = 0 . 59 , p = 0 . 001 and r = 0 . 80 , p<0 . 0001 , respectively ) and the % area LP CD4+ ( r = 0 . 71 , p<0 . 0001: r = 0 . 52 , p = 0 . 005 and r = 0 . 64 , p = 0 . 0003 , respectively ) . In addition , the frequencies of IL-17 and IL-17/IL-22-producing CD4+T cells were inversely correlated with colonic HIV RNA ( r = −0 . 58 , p = 0 . 003 and r = −0 . 30 , p = 0 . 03 , respectively ) . A recent study showed that not only the frequency , but also the function of mucosal Th17 cells is altered during HIV infection [30] . Therefore , we assessed the polyfunctionality of Th17 cells by the co-expression of IFN-γ , IL-2 and/or IL-22 using Boolean gating . We observed a dramatic loss of the triple cytokine-producing subset of Th17 cells from FI/II ( 6 . 5% ) to FIII ( 0 . 3% , p = 0 . 02 ) and to FIV/V ( 0 . 7% , p = 0 . 03 ) correlating with the loss of bulk CD4+T cells ( r = 0 . 47 , p = 0 . 01 ) and % area LP CD4+ ( r = 0 . 45 , p = 0 . 02 ) . This population of polyfunctional Th17 cells was entirely depleted in CHI ( Fig . 2h ) . In the context of Th17 depletion and the integrity of the mucosal barrier , several studies have shown that CD4+CD25+FoxP3+ regulatory T cells ( Treg ) exert anti-inflammatory functions and control self-reactive T cells , including Th17 cells [36] , [37] . Therefore the frequency of Treg was measured in the sigmoid colon . No significant difference in the percentage of Treg was observed throughout Fiebig stages compared to HIV- , but as described earlier [38] CHI displayed a significantly higher frequency of Treg compared to HIV- and AHI subjects ( HIV-: 8 . 5% , FI/II: 7 . 5% , FIII: 7 . 7% , FIV/V: 8% vs . CHI: 13 . 1% , p<0 . 001 ) . However , an increase in the frequency of cycling Treg ( Ki67+ ) was observed with progression of Fiebig stages from 5 . 6% in FI/II to 7 . 1% in FIII ( p = NS ) and to 14 . 7% in FIV/V ( p = 0 . 03; S1 Figure ) . An inverse correlation between the proportion of cycling Treg and Th17 cells supports the hypothesis of an early host counter-regulatory response to local/systemic inflammation and immune activation , in part , due to the loss of Th17 cells and disruption of the mucosal epithelium early in AHI ( r = −0 . 66 , p<0 . 001 ) . In order to assess whether the loss of Th17 cells impacts microbial translocation and immune activation , we determined the plasma levels of different biomarkers and assessed the activation of CD4+ and CD8+T cells in the sigmoid colon and the peripheral blood . The proportion of mucosal Th17 cells in AHI subjects showed an inverse correlation with plasma levels of C-reactive protein ( CRP; r = −0 . 42 , p = 0 . 03 ) , Hyaluronic Acid ( HA; r = −0 . 53 , p = 0 . 003 ) , TNFα ( r = −0 . 49 , p = 0 . 03 ) and IP-10 ( r = −0 . 71 , p<0 . 001 ) , indicating that the loss of Th17 cells might contribute to microbial translocation that leads to the observed levels of systemic immune activation . No correlations were seen between the frequency of Th17 cells and biomarkers that indicate intestinal damage ( I-FABP ) , microbial translocation ( LPS and sCD14 ) , and activation of the coagulation cascade ( D-dimer ) ( S2 Figure ) . Next , we determined T-cell activation in the sigmoid colon and the peripheral blood by measuring the frequency of HLA-DR and CD38 co-expression on CD4+ and CD8+T cells ( Table 3 ) . CD4+T-cell activation in the sigmoid colon increased from 1 . 4% in HIV- to 2 . 0% in FI/II ( p = 0 . 03 ) and to 2 . 7% in FIII ( p = 0 . 02 ) , while there was no statistically significant increase seen in the peripheral blood of these patients . CD8+T-cell activation significantly increased with Fiebig stage in the sigmoid colon ( FI/II 4 . 4% vs . FIII 8 . 9% , p = 0 . 003 ) and the peripheral blood ( FI/II 7 . 8% vs . FIII 15% , p = 0 . 004 ) . FI/II subjects had higher CD8+T-cell activation compared to HIV- subjects in the sigmoid colon ( 4 . 4% vs . 1 . 9% , p<0 . 001 , respectively ) and the peripheral blood ( 7 . 8% vs . 3 . 0% , p<0 . 001 , respectively ) . A similar increase was observed for cycling CD8+T cells ( Ki67 positive ) in the sigmoid colon and the peripheral blood from 4 . 3% and 5 . 4% in FI/II to 14 . 6% and 9 . 0% in FIII ( p<0 . 001 and p = 0 . 01 ) , respectively , as well as cycling CD4+T cells in the sigmoid colon ( FI/II: 1 . 8% , FIII: 3 . 0% , p = 0 . 04 ) . Cycling CD4+ and CD8+T cells in the sigmoid colon and the peripheral blood of AHI subjects correlated inversely with the frequency of CD4+T cells in the respective compartments . This correlation could only be observed for activated CD4+T cells in the peripheral blood and activated CD8+T cells in the sigmoid colon . Cycling CD4+ and CD8+T cells in the sigmoid colon and cycling CD8+T cells in the peripheral blood correlated with plasma and colonic HIV RNA while only activated CD8+T cells in the colon correlated with plasma and colonic HIV RNA ( S2 Table ) . The frequency of mucosal Th17 cells correlated inversely with the proportion of activated CD8+T cells in the periphery and the sigmoid colon r = −0 . 43 , p = 0 . 02 and r = −0 . 40 , p = 0 . 03 respectively ) and cycling CD8+T cells ( r = −0 . 51 , p = 0 . 005 and r = −0 . 56 , p = 0 . 001 respectively ) , suggesting that the loss of Th17 cells at the mucosa is associated with local and systemic immune activation early in AHI . Patients identified during early AHI were immediately placed onto ART and these subjects were followed for 6 months to observe the short-term effect of early initiation of ART . Six months post-ART 29 subjects underwent sigmoid biopsy and phlebotomy , of whom 14 were FI/II and 15 were FIII . AHI subjects had an increased median peripheral blood CD4+T cell count at 6 months of 611 cells/mm3 compared to 465 cells/mm3 at pre-ART ( p<0 . 001 ) . All subjects had undetectable plasma HIV RNA and colonic HIV RNA was undetectable in 28/29 subjects . In addition , after 6 months of ART no subjects had any evidence of HIV vRNA+ cells in colonic tissues as measured by in situ hybridization . The proportion of mucosal CD4+ and CD4+ CCR5+T cells remained stable between pre- and post-ART in patients treated at FI/II: 49 . 8% vs . 46 . 5% and 67 . 3% vs . 62 . 5% , respectively ( p = NS ) . Subjects treated at FIII , who had significantly lower frequencies of CD4+ and CD4+CCR5+T cells pre-ART compared to patients in FI/II , showed an increased frequency of CD4+CCR5+T cells ( 35 . 5% pre-ART vs . 54 . 5% post-ART , p = 0 . 02 ) , but not CD4+T cells ( 35 . 2% pre-ART vs . 35 . 9% post-ART , p = NS; Fig . 3a and b ) . Subjects treated at FIII demonstrated a significant decrease in plasma levels of CRP from 1343 pg/ml to 483 pg/ml ( p = 0 . 02 ) and D-dimer from 359 pg/ml to 146 pg/ml ( p<0 . 001 ) from pre- to post-ART , respectively . Subjects treated during FI/II maintained polyfunctional Th17 cells , with no loss of either total Th17 cells or the proportion of triple cytokine-producing Th17 cells post-ART . Those treated during FIII showed a complete restoration of total Th17 cells ( 7 . 9% pre-ART vs . 10 . 2% post-ART , p = 0 . 05 ) ; however short-term ART did not restore the population of triple cytokine-producing Th17 cells which remained at levels comparable to CHI subjects ( Fig . 3c and d ) . Similar observations were made for Th22 cells and cells expressing IL-17 and/or IL-22 . While subjects treated at FI/II maintained frequencies of these cell populations at levels similar to HIV- individuals , those treated at FIII did not recover these cell populations ( Fig . 3e and f ) . Because short-term ART initiated during FIII did not reconstitute mucosal Th17 cell function , but reduced plasma levels of CRP and D-dimer , which are associated with mortality [39] , we also explored the impact of ART initiated early in AHI on local and systemic immune activation ( Fig . 3g and h ) . Pre-ART initiation , there was a higher frequency of activated ( HLA-DR+/CD38+ ) CD8+T cells in FI/II ( p≤0 . 001 ) and FIII subjects ( p≤0 . 0001 ) after 6 months of ART compared to HIV- in both the sigmoid colon and the peripheral blood ( Table 3 ) . This increased activation was more marked in subjects treated in FIII compared to those treated in FI/II . In both the sigmoid colon and the peripheral blood , a decrease of activated CD8+T cells was seen in FI/II and FIII treated subjects . Only those subjects treated in FI/II demonstrated a normalization of CD8+T cell activation levels following short-term ART ( sigmoid colon: 2 . 1% post-ART vs . 4 . 4% pre-ART , p = 0 . 001 vs . 1 . 9% HIV- , p = NS; peripheral blood: 3 . 7% post-ART vs . 7 . 8% pre-ART , p = 0 . 007 vs . 3 . 0% HIV- , p = NS ) . Subjects treated during FIII failed to normalize CD8+T-cell activation and showed significantly higher activation compared to HIV- individuals ( sigmoid colon: 5 . 0% post-ART vs . 8 . 9% pre-ART , p≤0 . 001 vs . 1 . 9% HIV- , p≤0 . 001; peripheral blood: 9 . 0% post-ART vs . 15% pre-ART , p = 0 . 003 vs . 3 . 0% HIV- , p≤0 . 001 , S3 , S4 and S5 Tables ) .
Progressive HIV infection is characterized by a rapid depletion of gastrointestinal CD4+T cells , with a preferential loss of mucosal CD4+ Th17 cells , which play an important role in maintaining intestinal integrity [10] , [14] , [25] . However , defining the timing of these events during the earliest stages of AHI and determining the impact of early acute ART initiation on Th17 cell loss and recovery have not been determined . We described cellular events in the earliest stages of acute HIV-1 infection confirming and extending previous findings from the pathogenic non-human primate ( NHP ) model that suggest early disappearance of mucosal Th17 cells contributes to deterioration of the mucosal barrier and subsequent systemic immune activation [15] , [25] . While CD4+T cells in peripheral blood recover after AHI , reconstitution of mucosal CD4+T cells is only partial under ART and , in the case of Th17 cells , functional restoration is much delayed [30] . We demonstrate here that initiation of ART prior to HIV IgM detection ( Fiebig I/II ) prevented the functional and quantitative loss of mucosal Th17 cells as well as a normalization of local and systemic T-cell activation . Concordant with previous NHP studies [40] , [41] we observed a massive depletion of CD4+ and CD4+CCR5+T cells in the colon within days of HIV-1 infection correlating with the colonic HIV RNA . Initiation of ART in FI/II prevented CD4+T cell loss over the course of the first 6 months of treatment , while initiation of ART in FIII only partially restored the frequency of CD4+CCR5+T cells and did not restore CD4+T cells . This supports the hypothesis that initial damage to the mucosal CD4+ T-cell compartment cannot be fully restored even after long term ART [28] . However , we observed a more drastic CD4+T-cell depletion in the LP , the main effector site in the GI tract [42] , which showed a significant loss of CD4+T cells as early as FI/II stages . The discrepancy between immunohistochemistry and the bulk CD4+T cells assessed by flow cytometry is potentially methodological . There might be a less dramatic loss occurring in the lymphoid aggregates ( LA ) , and the bulk CD4+T cells in the sigmoid digests do not allow discrimination between LA and LP . In addition , LA are more likely to be observed with high numbers of biopsies as collected for flow cytomerty of bulk CD4+T cells ( 20 to 25 pieces ) . Due to small numbers of biopsies dedicated to immunohistochemistry assessment ( 1 to 2 pieces ) , very few to no LA were identified within a given biopsy , which possibly accounts for the more dramatic CD4+T-cell loss seen in the histological assessment . In addition , our findings within the sigmoid colon may not reflect the distribution of CD4+T cells within other portions of the gastrointestinal tract , such as ileum or duodenum [43] . Previous studies have reported that CD4+T-cell depletion is more severe in the duodenum [44] , ileum [45] , [46] , and colon [28] than in the blood of treatment-naïve patients and in the duodenum , compared with the colon and rectum [47] of patients receiving suppressive ART . Mucosal Th17 cells express a wide range of functions compared to those in blood including the production of IL-22 , IL-17A , IL-17F , IL-1 , IL-2 and IL-21 that together induce the expression of defensins and other antibacterial products [48] . In addition they also produce several effector functions such as TNFα and IFNγ to recruit neutrophils and myeloid cells to effector sites by inducing granulocyte macrophage colony-stimulating factor , and are involved in the regeneration of mucosal epithelium [49]-[51] . During acute SIV infection , the frequency of Th17 cells at mucosal sites decreases dramatically and is not restored to normal levels at the chronic phase [18] . The mechanism related to the apparent loss of Th17 cells is not completely understood , but might be due to the fact that these cells are highly activated because of continuous exposure to bacterial antigens . Th17 cells also express CCR5 and α4β7 and therefore might become a preferential target for HIV-1 [52] . A recent study in humans suggests that Th17 cells are partially restored after ART but the recovery of Th17 function was dramatically delayed [30] . The current study examined evolution and function of mucosal Th17 cells and their relationship with microbial translocation and local and systemic immune activation in early AHI . We demonstrated , for the first time in humans , that Th17 cells are already depleted by FIII , while being preserved during FI/II stages compared to HIV- individuals . The events that lead to this rapid loss of Th17 cells in AHI are not well understood . CD4+T-cells loss has previously been linked mainly to apoptosis [53] , [54] . However , recent studies have suggested that pyroptosis , a highly inflammatory form of programmed cell death , in which dying cells release their cytoplasmic contents and inflammatory cytokines into the extracellular space is a potential mechanism of HIV-related CD4+T-cell death [55] . This cell death pathway thus links the two signature events in HIV infection , CD4 T-cell depletion and chronic inflammation , which might explain the high levels of local and systemic immune activation observed in this study . We also observed a depletion of Th22 cells in FIII , a subset of mucosal CD4+T cells that provides innate immune protection against bacterial and fungal infections and promote inflammation and epithelial proliferation and repair by secretion of IL-22 [56]–[58] and Th17 cells co-expressing IL-22 . The frequencies of mucosal Th17 and Th22 cells were inversely correlated with colonic HIV RNA , supporting the relation between the loss of Th17 T cells and viral replication in early AHI [52] . In parallel with the loss of mucosal Th17 cells we also observed a decrease in polyfunctionality ( defined here as IL-17-expressing CD4+T cells that co-express IL-2 , IL-22 and IFNγ ) . Triple cytokine-producing Th17 cells were present during FI/II at levels comparable to HIV- individuals but were depleted as early as FIII and were absent in treatment-naïve CHI subjects . ART initiated during FI/II was able to completely preserve Th17 cell numbers and polyfunctionality as well as Th22 cells , while ART initiation in Fiebig III or later only partially restored the Th17 cell numbers but did not restore polyfunctionality or Th22 cell numbers . These findings highlight that events during very early AHI may have long-term consequences in viral pathogenesis not reflected in peripheral blood and that even though Th17 numbers can be restored under ART , Th17 function remains impaired and , may still mediate microbial translocation and immune activation [25] . During AHI we did not observe an increase in mucosal Treg , which may also contribute to the disruption of the mucosal barrier [59] . In HIV and SIV infection , Treg may decrease chronic immune activation , thereby slowing disease progression [60] , but potentially may also inhibit anti-viral immune responses , thereby accelerating disease progression [36] , [61]–[64] . We hypothesize that these events occur later during HIV infection , as previous studies have shown that non-controllers have significantly higher percentages of Treg in rectal MMCs compared to HIV- individuals or HIV elite controllers [38] . In addition , the frequency of cycling ( Ki67+ ) Treg increased with Fiebig stages , and inversely correlated with the frequency of Th17 cells . NHP studies have shown that cycling Treg increase at later time points following infection and correlate with immune activation in the LP [15] , implying that dysregulation of Treg begins at early AHI and eventually progresses to the potentially harmful increase in Treg seen in CHI [36] , [61]–[64] . The observed decrease in Th17 cells with the progression of Fiebig stages was directly associated with local and systemic immune activation . Thus , the observed loss of Th17 cells might initiate a vicious cycle early in HIV infection by decreasing the host defense to bacteria that may favor breaches in the GI barrier and result in further increase in local as well as systemic immune activation [65] . T-cell immune activation has been linked to AIDS and non-AIDS related morbidity and mortality in untreated HIV infection [66] . In patients receiving ART , higher T-cell activation has been linked to diminished CD4+T-cell recovery [67] , and surrogate markers of cardiovascular disease and increased mortality [68] . In AHI we observed increased local and systemic T-cell activation and cycling with progression of Fiebig stage , compared to HIV- individuals . This seems to be most profound in the CD8+T-cell compartment beginning as early as FI/II . The increase in cycling CD4+ and CD8+T cells in both compartments was directly related to the depletion of CD4+T cells at the respective site , suggesting that during early AHI a similar mechanism induces CD4+ T-cell depletion and increased cycling of CD4+ and CD8+ T cells in blood and sigmoid mucosa . This observation supports earlier findings that there might not be a compartmentalization between these two distinct sites [46] . The loss of Th17 cells correlated inversely with local and systemic T-cell activation and CRP and HA plasma levels , which are increased in treatment-naïve CHI and associated with an increased risk of developing AIDS [69] . More importantly , the local and systemic CD8+T-cell activation was significantly lower in FI/II compared to FIII and normalized after initiation of ART in FI/II to levels comparable to HIV- individuals . Subjects treated in FIII also showed a significant reduction of CD8+ T-cell activation , which did not normalize to that observed in HIV- individuals . We hypothesize that this might be due either to the greater size of the persistent HIV reservoir in FIII compared to FI/II , as cellular reservoirs of latent integrated HIV are established quickly after infection , or to ongoing low replication levels [70] . Previous studies have shown that initiation of ART less than 6 months after HIV infection was able to decrease chronic CD8+ T-cell activation and limit the size of the persistent reservoir [71] , [72] . During long-term ART , residual T-cell activation and inflammation consistently correlate with disease progression , but the ability of early ART to prevent these potentially irreversible outcomes remains unclear [67] , [73] . Initiation of ART within 6 months of infection compared to later reduces the CD8+ T-cell activation , but levels remained elevated compared to HIV- individuals [71] . However , there was a significant decrease of systemic and local CD8+ T-cell activation in FIII at 6 months post-ART initiation . We cannot exclude that those values will normalize after long-term ART , taking into account that there were much higher levels at the time of diagnosis in FIII compared to FI/II . Firstly , while the cohort is unique with regard to the time of diagnosis and specimen sampling , our study has some limitations . The challenge of recruiting subjects early during AHI has resulted in a relatively small sample size and as the mucosal biopsies at each visit were entirely optional , matched consecutive samples were not available from all volunteers . In addition there is an inherent inter-individual variability to mucosal sampling . In addition , the sample size for CHI is low and while this group showed significant differences to AHI subjects , we did not assess differences in comparison to HIV+ individuals that initiated ART in the chronic phase of infection , a clinically more relevant population . Our study cannot define the mechanism leading to the loss of mucosal Th17 cells and their functional capabilities . We speculate that polyfunctional mucosal Th17 cells might be more susceptible to HIV infection due to a higher activation status because of continuous exposure to bacterial antigens [14] and therefore are profoundly depleted with progression of Fiebig stage . In addition , slower HIV clearance in the gut mucosa after ART initiation may hinder or delay functional reconstitution of Th17 cells [74] , which was not observed after short-term ART in patients initiating treatment in FIII . The factors contributing to the depletion and restoration of polyfunctional Th17 cells and their potential role in tissue viral reservoirs will be an important area of further research . In addition , our experimental design focused on the expression of IL-17 , IL-2 , IL-22 and IFN-γ , leaving the likely possibility that HIV also alters production of other cytokines secreted by Th17 cells . Furthermore , despite observing local and systemic immune activation during early AHI , we did not find an association between the loss of Th17 cells and plasma biomarkers of microbial translocation . In vivo studies have shown that microbial translocation results from a series of immunopathological events in the gut mucosa initiated by severe CD4+T cell depletion and damage to the integrity of the intestinal epithelium including enterocyte apoptosis and tight junction disruption [75] . Increased levels of LPS have mainly been reported during later time points in HIV-infection [76] , [77] . We did not observe significant correlations between microbial translocation marker levels and the frequency of mucosal Th17 cells ( S2 Figure ) , and thus , we hypothesize that in early AHI systemic markers of microbial translocation , such as LPS , may be lower in plasma due to the host's functional pathogen clearance mechanisms ( i . e . functional PMNs and macrophages , EndoCAB antibodies , etc . ) . The extent to which the loss of Th17 cells causes dysregulation of mucosal immunity in HIV is still unclear , however Th17 cells are vital in maintaining a healthy mucosa and their loss is clearly detrimental . We show that a dramatic loss of mucosal Th17 and their functional capabilities occurs even earlier during AHI than previously described and that initiation of ART in Fiebig I/II , before this loss occurs , prevented alterations in Th17 numbers and functionality . Moreover , despite a partial reconstitution of Th17 cells numbers under ART initiated in Fiebig III , near the peak of viremia , polyfunctionality was not restored . The association of these events with local and systemic immune activation and its full reversion under very early initiated ART emphasizes the importance of strategies to prevent mucosal Th17 function loss and argues for early and aggressive intervention for therapeutic benefit and a potential functional cure [3] , [4] . Our study provided evidence that identifying and treating AHI subjects is feasible [7] , [72] . Future studies will be necessary to address evolution of mucosal integrity dysfunction during AHI as well as the long-term success of early initiation of ART as our observations are limited to 6 months of therapy .
The RV254/SEARCH 010 study is an ongoing prospective , open-label study in Bangkok , Thailand ( clinicaltrials . gov NCT00796146 ) . Samples from subjects who had VCT for HIV at The Thai Red Cross Anonymous Clinic and at the Silom Community Clinic were screened in real-time by pooled NAT and sequential EIA according to published methods [78] . Thai subjects who met the AHI laboratory criteria for Fiebig stages I to V [6] were enrolled and had clinical and laboratory assessments as previously described , including CD4 , HIV RNA , liver transaminases , creatinine , lipids and urinalysis [33] . Plasma and peripheral blood mononuclear cells ( PBMC ) were cryopreserved . Sampling of gut-associated lymphoid tissue ( GALT ) occurred by sigmoid biopsy as an optional study procedure at baseline and 6 months . Biopsy pieces were either cryopreserved , embedded in paraffin or mucosal mononuclear cells ( MMC ) were isolated . Initiation of ART was voluntary and done as part of the enrollment in an accompanying protocol ( clinicaltrials . gov NCT00796263 ) . Treatment was initiated on average 3 days ( range 0–5 days ) from enrollment . The first 7 subjects included in this analysis were treated with standard doses of tenofovir/emtricitabine/raltegravir/maraviroc while the subsequent subjects were randomized to either this regimen or tenofovir/emtricitabine/efavirenz . Plasma , PBMC and MMC from HIV-uninfected and chronically HIV-infected ( CHI ) Thai volunteers were obtained from another protocol ( clinicaltrials . gov NCT01397669 ) and were subject to the same procedures . The RV254/SEARCH 010 study ( clinicaltrials . gov NCT00796146 ) was approved by the Institutional Review Boards ( IRBs ) of Chulalongkorn University in Thailand and the Walter Reed Army Institute of Research in the United States . The protocol enrolling HIV-uninfected and chronically HIV-infected ( CHI ) Thai volunteers ( clinicaltrials . gov NCT01397669 ) was approved by the Chulalongkorn University IRB . Initiation of ART was voluntary and done as part of the enrollment in an accompanying protocol ( clinicaltrials . gov NCT00796263 ) , approved by Chulalongkorn University IRB . For all studies mentioned above , subjects gave written informed consent . Diagnosis of AHI was performed as described previously [33] . In brief , AHI subjects were enrolled if they were Thai and fulfilled laboratory criteria for Fiebig stages I to V as follows: FI - positive HIV RNA , negative p24 antigen , non-reactive 3rd generation EIA; FII – positive HIV RNA , positive p24 antigen , non-reactive 3rd generation EIA; FIII - positive HIV RNA , positive p24 antigen , reactive 3rd generation EIA , negative western blot; FIV - positive HIV RNA , positive or negative p24 antigen , reactive 3rd generation EIA , indeterminate western blot; FV - positive HIV RNA , positive p24 antigen , reactive 3rd generation EIA , positive western blot except p31 . All subjects had to have a non-reactive EIA by non-IgM sensitive EIA . The corresponding mean cumulative durations from the first detectable HIV RNA are 5 ( FI ) , 10 . 3 ( FII ) , 13 . 5 ( FIII ) , 19 . 1 ( FIV ) and 88 . 6 ( FV ) days [6] . Subjects underwent a routine sigmoidoscopy procedure under moderate conscious sedation . Approximately 30 endoscopic biopsies were randomly collected from the sigmoid colon using Radial Jaw 3 biopsy forceps ( Boston Scientific , Natick , MA , USA ) with 20–25 processed for flow cytometry analysis within 30 min of collection . In groups of five the biopsies were weighed and placed in 500 µl of RPMI media containing 10% human AB serum ( HAB; Gemini Bio-Product , West Sacramento , CA , USA ) , 1% HEPES , 1% L-Glutamine , 0 . 1% Gentamicin ( Invitrogen , Carlsbad , CA , USA ) , 1% Penicillin/Streptomycin and 2 . 5 µg/ml Amphotericin B ( Invitrogen , Carlsbad , CA , USA ) . Samples were then digested using 0 . 5 mg/ml Collagenase II ( Sigma , St . Louis , MO , USA ) . After digestion samples were filtered through a cell strainer , using a syringe with a 16-gauge blunt end needle . This procedure was repeated once or twice in case undigested tissue remained . After being washed twice with RPMI containing 1% HEPES , 1% L-Glutamin , 1% Penicillin/Streptomycin , 0 . 1% Gentamycin and 2 . 5 µg/ml Amphotericin B , MMC were counted and viable cell enumeration was determined using Trypan Blue exclusion and Beckman Coulter AcT5 hematology analyzer ( Beckman Coulter , Fullerton , CA ) . Freshly isolated MMC were used for flow cytometry analysis . Subjects were screened incidental histopathology . For HIV RNA quantification biopsy pieces were collected in phosphate buffered saline ( PBS ) and subsequently stored in 1mL of RNAlater ( Ambion , Foster , CA , USA ) at −80°C . HIV RNA in plasma was measured using Roche Amplicor v 1 . 5 ultrasensitive assay with a lower quantification limit of 50 copies/mL ( Roche Diagnostics , Branchburg , NJ , USA ) . For gut tissue , one to two biopsy pieces frozen in RNAlater ( Ambion , Foster , CA , USA ) were weighed then homogenized in AVL buffer ( QIAamp Viral mini kit Cat No . 52 , 904 , Netherlands ) using a mini mortar and pestle . Extraction was completed per kit instructions . The Siemens Quantiplex HIV-1 3 . 0 assay was used to measure HIV-1 RNA copy number . Results are expressed as copies/mg of tissue . To ensure optimal detection of productively infected cells from HIV-infected subjects from Thailand , we designed a new set of HIV-1 CRF01_A/E lineage specific in situ hybridization riboprobes for these experiments . HIV-1 CRF01_A/E riboprobes were generated by PCR-based cloning of target regions from the full-length infectious molecular clone pCM235 from Thailand ( Accession number AF259954 ) kindly provided by Dr . George Shaw ( University of Pennsylvania ) . Riboprobes were generated targeting Gag ( 1454–1958 ) , Pol ( 3998–4570 ) , Accessory gene Vif/Vpr/Vpu/Tat/Rev/Env ( 5287–5825 ) , Env ( 7836–8403 ) and Nef ( 8822–9122 ) using primers with either phage T3 ( sense ) or T7 ( anti-sense ) promoter sequences cloned upstream of the viral sequence and pooled into a cocktail at equal concentrations . HIV-1 in situ hybridization was performed as previously described [79] . HIV vRNA+ cells are stained blue/black and tissues are counterstained with nuclear fast red . Immunohistochemistry was performed using a biotin-free polymer approach ( Golden Bridge International , Inc . ) on 5-µm tissue sections mounted on glass slides , which were dewaxed and rehydrated with double-distilled H2O . Heat induced epitope retrieval ( HIER ) was performed by heating sections in 0 . 01% citraconic anhydride containing 0 . 05% Tween-20 in a pressure cooker set at 122–125°C for 30 seconds . Slides were incubated with blocking buffer ( TBS with 0 . 05% Tween-20 and 0 . 25% casein ) for 10 minutes and then incubated with mouse anti-CD68 ( 1∶400; clone KP1 , Dako ) , mouse anti-CD163 ( 1∶400; clone 10D6; Novocastra/Leica ) and rabbit monoclonal anti-CD4 ( 1∶200; clone EPR6855; Abcam , Inc . ) diluted in blocking buffer overnight at 4°C . Slides were washed in 1x TBS with 0 . 05% Tween-20 and endogenous peroxidases blocked using 1 . 5% ( v/v ) H2O2 in TBS ( pH 7 . 4 ) for 10 minutes . Slides were incubated with Mouse Polink-1 AP for 10 minutes followed by Rabbit Polink-1 HRP for 30 minutes at room temperature . Sections were first incubated with Impact DAB ( 3 , 3′-diaminobenzidine; Vector Laboratories ) to develop the CD4 , washed and developed with Warp Red ( Biocare Medical , Inc . ) to mask the faint CD4 expressed on APCs allowing for specific identification of CD4+T cells . Slides were washed in ddH2O , counterstained with hematoxylin , mounted in Permount ( Fisher Scientific ) , and scanned at high magnification ( x200 ) using the ScanScope CS System ( Aperio Technologies ) yielding high-resolution data from the entire tissue section . Representative regions of interest ( ROIs; 500 mm2 ) were identified and high-resolution images extracted from these whole-tissue scans . The percent area of the lamina propria that stained for CD4+T cells ( excluding APC CD4 ) were quantified using Photoshop CS5 and Fovea tools . Immunophenotyping was performed on cryopreserved PBMC and freshly isolated MMC from sigmoid colon . Cells were first stained with Aqua Live/Dead dye ( Invitrogen , Eugene , Oregon , USA ) . Subsequently samples were stained with the following antibodies to identify the different cell subsets . Regulatory T cells ( Treg ) were stained with anti-CD3 PE-Cy7 ( Invitrogen , Eugene , Oregon , USA ) , anti-CD4 ECD ( Beckman Coulter , Brea , CA , USA ) , anti-CD8 PerCP-Cy5 . 5 ( BD Bioscience , San Jose , CA , USA ) and anti-CD25 APC-Cy7 ( BD Pharmingen , San Diego , CA , USA ) for 20 min at room temperature . Subsequently cells were washed twice with Permeabilization Buffer provided in the FoxP3 Staining Buffer Set and stained with anti-FoxP3 APC ( eBioscience , San Diego , CA , USA ) and anti-Ki67 FITC ( BD Pharmingen , San Diego , CA , USA ) for 30 min at 4°C . CD4+ and CD8+ memory T cells were defined using anti-CD4-QDot605 , anti-CD3-PE-TexasRed ( Invitrogen , Eugene , Oregon , USA ) , anti-CD8-V450 ( BD Horizon , San Diego , CA , USA ) , anti-CD27-AlexaFluor700 and anti-CD45RO-PE-Cy7 ( BD Pharmingen , San Diego , CA , USA ) for 20 min at room temperature . Subsequently cells were washed with PBS and stained with anti-CCR5-APC-Cy7 ( BD Pharmingen , San Diego , CA , USA ) for 30 min at 37°C . Activation status of CD4+ and CD8+T cells was determined by staining cells using anti-CD3 PE-Cy7 ( Invitrogen , Eugene , Oregon , USA ) , anti-CD4 ECD ( Beckman Coulter , Brea , CA , USA ) , anti-CD8 PerCP-Cy5 . 5 , anti-HLA-DR V450 and anti-CD38 APC ( BD Bioscience , San Jose , CA , USA ) for 20 min at room temperature , subsequently washed with Permeabilization Buffer and stained with anti-Ki67 FITC ( eBioscience , San Diego , CA , USA ) for 30 min at 4°C . Post staining cells were resuspended in 1% Formaldehyde and acquired within 24 hours using a custom built BD LSRII or Fortessa flow cytometer ( BD , San Jose , CA , USA ) and analyzed using FlowJo software version 9 . 6 . 3 or higher ( TreeStar , Ashland , OR , USA ) . At least 80 , 000 live cells were acquired in the lymphocyte gate . In the peripheral blood percentage and absolute numbers of CD4+ and CD8+T cells were determined using the BD Multitest IMK Kit ( BD Bioscience , San Jose , CA , USA ) . Cryopreserved PBMC and freshly isolated MMC were rested over night at 37°C before stimulation for 5 hours with 40 ng/mL of Phorbolmyristate acetate ( PMA ) and 1 µM Ionomycin ( Sigma-Aldrich , St . Louis , MO , USA ) in the presence of 1 µl/mL of Brefeldin A ( BD Bioscience , San Jose , CA , USA ) to prevent cytokine release . As negative control cells remained unstimulated with 1 µl/mL Brefeldin A added for 5 hours of incubation . After two washes with RPMI medium containing 10% heat-inactivated HAB serum , cells were fixed and permeabilized according to the BD Cytofix/Cytoperm protocol . Subsequently cells were stained with the following antibodies: anti-CD3 PE-Cy7 ( Invitrogen , Eugene , Oregon , USA ) , anti-CD4 ECD ( Beckman Coulter , Brea , CA , USA ) , anti-IL-2 FITC , anti-CD8 PerCP-Cy5 . 5 ( BD Bioscience , San Jose , CA , USA ) , anti-IL17A PE ( BD Pharmingen , San Diego , CA , USA ) , anti-IL22 APC ( R&D systems , Minneapolis , MN , USA ) and IFNγ eFlour450 ( eBioscience , San Diego , CA , USA ) for 20 min at 4°C . Samples were acquired and analyzed as described above . Positive responses shown were calculated after unstimulated background was subtracted . Frequencies of IFNγ , IL-2 and IL-17 and/or IL-22 expressing cells is based on the population of CD4+T cells , while the frequency of triple-cytokine producing cells ( IFNγ , IL-2 and IL-22 ) is based on the population of CD4+IL-17+T cells . Absolute numbers of CD4+ and CD8+ T-cell subsets per gram of gut tissue were calculated by multiplying the total viable cell count by percentages obtained from flow cytometry analysis . The total cell count per gram of tissue was calculated by dividing the viable cell count by the tissue weight . This proportion was then multiplied by the percent of cells in the live lymphocyte gate and that number was subsequently multiplied by the percent of CD3+ lymphocytes . The absolute number of colonic CD3+T cells was used in conjunction with the subset percentages to determine the absolute number of each T-cell subset per gram of biopsy tissue . Cytokines and biomarkers were measured using cryopreserved EDTA plasma . Interferon gamma-induced protein ( IP ) -10 was measured by standard ELISA ( Invitrogen , Carlsbad CA ) . D-dimer was measured using an enzyme-linked fluorescent assay on a VIDAS instrument ( bioMerieux Inc . , Durham , North Carolina , USA ) . Hyaluronic acid ( HA ) was measured using a commercially available HA test kits ( Corgenix , Inc , Westminster , Colorado , USA ) . C-Reactive Protein ( CRP ) was measured by electro-chemo-luminescence ( Meso Scale Discovery , Gaithersburg , Maryland , USA ) . Soluble CD14 ( sCD14 ) was measured in duplicates using a commercially available ELISA assay ( R&D Systems , Minneapolis , MN , USA ) , and analyzed according to manufacturer's protocol . TNFα was quantified from citrate plasma in triplicate by custom ELISA array according to the manufacturer's protocol ( Quansys Biosciences , Logan UT ) . Data was captured on the Odyssey infrared imaging system ( Li-Cor Biosciences , Lincoln , NE ) and analyzed using Quansys Q-view Plus software ( Quansys Biosciences ) . Intestinal fatty acid binding protein ( I-FABP ) was measured using a commercially available ELISA assay ( R&D Systems , Minneapolis , MN , USA ) with samples diluted to 10% in sCD14 diluent . Lipopolysaccharide ( LPS ) levels were quantified by first diluting fasting plasma samples , collected in EDTA tubes , to 10% with endotoxin-free water and subsequent heat inactivation of plasma proteins for 15 minutes at 80°C . Measurements of the samples were made with a Limulus Amebocyte Assay ( Lonza Group Ltd . , Switzerland ) . Samples were measured in duplicate , background was subtracted and LPS levels were calculated by first setting the y-intercept for the standard regression line at zero and then by the manufacturer's protocol . Due to low subject numbers in certain Fiebig stages , such as FII ( n = 4 ) , FIV ( n = 1 ) and FV ( n = 3 ) , FI and II and FIV and V were combined into two groups: FI/II and FIV/V , respectively for the analysis . The Mann-Whitney U test was used for between group comparisons and Spearman's rank test was used to evaluate associations . All values reported were median ( interquartile range ) . Statistical tests were 2-sided with p-values <0 . 05 considered statistically significant . Statistical analyses were performed using Prism version 6 . 0b software program ( GraphPad Software , Inc . , La Jolla , CA , USA ) .
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Persistent systemic immune activation is a hallmark of chronic HIV infection and an independent predictor of disease progression . The underlying mechanism is not yet completely understood but thought to be associated with the loss of Th17 cells leading to the disruption of the mucosal barrier and subsequent microbial translocation . However , it remains unclear when these events take place in HIV infection , as the only data available to date are from SIV models . We evaluated the kinetics of Th17 depletion , microbial translocation and subsequent immune activation in early acute HIV infection and the effect of early initiated ART on these events . We discovered that a collapse of Th17 cell number and function , accompanied by local and systemic immune activation , occurs already during acute HIV infection . However , early initiation of ART preserved Th17 number and function and fully reversed any initial HIV-related immune activation . These findings argue for the importance of early events during HIV infection setting the stage for chronic immune activation and for early and aggressive treatment during acute HIV infection .
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2014
|
Initiation of ART during Early Acute HIV Infection Preserves Mucosal Th17 Function and Reverses HIV-Related Immune Activation
|
Spinocerebellar ataxia type-3 ( SCA3 ) is among the most common dominantly inherited ataxias , and is one of nine devastating human neurodegenerative diseases caused by the expansion of a CAG repeat encoding glutamine within the gene . The polyglutamine domain confers toxicity on the protein Ataxin-3 leading to neuronal dysfunction and loss . Although modifiers of polyglutamine toxicity have been identified , little is known concerning how the modifiers function mechanistically to affect toxicity . To reveal insight into spinocerebellar ataxia type-3 , we performed a genetic screen in Drosophila with pathogenic Ataxin-3-induced neurodegeneration and identified 25 modifiers defining 18 genes . Despite a variety of predicted molecular activities , biological analysis indicated that the modifiers affected protein misfolding . Detailed mechanistic studies revealed that some modifiers affected protein accumulation in a manner dependent on the proteasome , whereas others affected autophagy . Select modifiers of Ataxin-3 also affected tau , revealing common pathways between degeneration due to distinct human neurotoxic proteins . These findings provide new insight into molecular pathways of polyQ toxicity , defining novel targets for promoting neuronal survival in human neurodegenerative disease .
Spinocerebellar ataxia type 3 ( SCA3 ) is the most common dominantly inherited ataxia worldwide and is caused by a CAG repeat expansion encoding glutamine within the ATXN3 gene [1 , 2] . The expanded polyglutamine ( polyQ ) is thought to confer toxicity on the protein Ataxin-3 , leading to neural dysfunction and loss [3] . At least nine human diseases , including additional spinocerebellar ataxias and Huntington's disease , share this mechanism . Studies on such pathogenic proteins reveal that the long polyQ domain alters protein conformation , causing an enriched beta sheet structure [4] . Mutant polyQ protein also dynamically associates with chaperones and colocalizes with proteasome subunits , indicating that the protein is misfolded or abnormally folded [5 , 6] . Such accumulation of misfolded protein is a common pathology of human degenerative disorders , including Alzheimer , Parkinson , and prion diseases [7–9] , indicating that these diseases may share molecular and cellular mechanisms . Models for human neurodegenerative diseases in simple systems have provided valuable tools to dissect molecular mechanisms of disease pathology [10–13] . Directed expression of pathogenic human Ataxin-3 in Drosophila recapitulates key features of disease , with late-onset neuronal dysfunction and degeneration accompanied by ubiquitinated inclusions [14 , 15] . Neurotoxicity is more severe with increasing length of the polyQ repeat , similar to the human disease where longer repeats are associated with more severe and earlier onset disease [16 , 17] . In the fly , increased levels of expression of the disease protein leads to more severe degeneration and earlier onset protein accumulation , suggesting that abnormal accumulation of the mutant protein is central to disease and degeneration . A number of modifiers of select polyQ disease proteins have been identified using animal models , including chaperones , transcriptional coregulators , and microRNAs [18–22] . Although these approaches have revealed genes that modulate polyQ toxicity , little is known regarding how the modifiers act biologically to modulate polyQ degeneration . Among polyQ proteins , Ataxin-3 is unique because it has been implicated in ubiquitin pathways , and its normal activity may impinge on protein degradation pathways [23–26] . Truncation of Ataxin-3 to remove the ubiquitin protease domain , or mutation of the ubiquitin protease activity , dramatically enhances toxicity [15] , indicating that the normal activity of Ataxin-3 may be critical in Ataxin-3-induced degeneration . To reveal insight into pathways that modulate Ataxin-3 neurodegeneration , we performed a genetic modifier screen in Drosophila . These studies revealed a range of modifiers that , despite some broadly diverse predicted molecular functions , converge on protein misfolding with a subset mitigating toxicity through proteasome and/or autophagy pathways . These findings underscore the critical role of protein quality control in SCA3 pathogenesis and provide potential new targets toward therapeutics .
To define modifiers that may reveal new insight into mechanisms of human SCA3 disease , we performed an overexpression screen for modifiers of Ataxin-3-induced neurodegeneration in Drosophila . SCA3trQ78 causes late onset progressive degeneration characterized by loss of pigmentation and collapse of the eye ( Figure 1A and 1B ) [14] . We initially screened a subset of 2 , 300 available EP-element insertion lines , each carrying a transposon engineered to direct expression of the downstream gene in the presence of the yeast GAL4 protein [27] . Because reproducibility was variable with this collection , we then performed a screen de novo , selecting for new EP-insertions that modified SCA3trQ78 toxicity . This approach identified 17 suppressors and one enhancer ( Figures 1C–1J and S1 ) , which affected both external and internal retinal degeneration . Plasmid rescue was performed to identify the genes affected . BLAST searches with genomic sequence from the integration sites revealed that the lines bore insertions in the 5′ regulatory region of select genes , and all were in an orientation to direct GAL4-dependent gene expression ( Text S1; Table S1 ) . Northern and reverse transcription PCR analysis confirmed upregulation of the targeted genes; using a variety of tests we confirmed that the modifiers did not appear to affect transcription of the Ataxin-3 transgene or general GAL4-UAS transgene expression ( Figure S2 ) . Both molecular and genetic analyses confirmed that the insertions were single insertions ( see Materials and Methods ) . Reversion analysis proved that the EP-elements were causal in modification . Taken together , these data indicated that the EP modifiers resulted in increased expression of the targeted downstream genes , which modulated SCA3trQ78 toxicity and neurodegeneration . Analysis of the targeted genes revealed that the majority fell into two major classes of chaperones and ubiquitin-pathway components ( Table 1; Figures S1 and S3 ) . The remaining modifiers were placed in a third category of miscellaneous functions . Class 1 ( Figure 1C–1E ) included an Hsp70 family member , Hsp68E407; two Hsp40 genes , DnaJ-1B345 . 2 and mrjE1050; a small heat shock protein αB crystalline CG14207EP1348; and the cochaperone Tpr2EB7-1A . The class 2 ubiquitin-pathway components included: polyubiquitin CR11700EP1384; a ubiquitin-specific protease Ubp64EE213-1A; two ubiquitin ligases , CG8209B3-Sa and Faf E659; and an F-box protein CG11033EP3093 , the only enhancer of the group . Class 3 ( Figure 1F and 1G ) included genes with a variety of predicted molecular functions: the nuclear export protein Embargoed , embE2-1A and embE128-1A; three transcriptional regulators Sin3AB9-E , NFAT ( NFATEP1335 and NFATEP1508 ) , and debra ( dbrEP456 and dbrEP9 ) ; three translational regulators , including four alleles of the lin-41 homologue dappled ( dpldJM120 , dpldJM265 , dpldEP546 , and dpldEP2594 ) , a polyA binding protein orb2B8-S , and insulin growth factor II mRNA binding protein ImpEP1433; and finally a fatty acid oxidation enzyme palmitoyl co-A oxidase CG5009B227 . 2 . This indicated that , although chaperone and ubiquitin-pathway components are major modifier categories , a variety of functional pathways are implicated in Ataxin-3 pathogenesis . The screen selected for modifiers that , upon upregulation , affected toxicity . To determine whether the activity of these genes may normally play a role in SCA3 toxicity , we examined whether reduction in the level of the modifier genes had an effect . To do this , we reduced gene expression by 50% using loss-of-function alleles where available or deficiency lines . Among these , reduction with a deficiency of DnaJ-1 and Ubp64E ( within the same deficiency; reduction of DnaJ-1 has previously been shown to enhance with a dominant-negative construct [16] ) , Trp2 , emb ( with an allele ) , and dbr dramatically enhanced degeneration ( Figure 1K–1O; Table S2 ) . Although the deficiencies reduce the level of a number of genes , these data suggest that the endogenous activity of these genes may normally help to protect against degeneration . To address whether morphological rescue correlated with functional rescue , we determined the ability of select suppressors to restore function in a phototaxis assay . When flies bearing the SCA3trQ78 protein were given a choice between a light and dark chamber , they distributed randomly , indicating that they are functionally blind ( Figure 1P ) . However , when mrjE1050 , which dramatically rescued degeneration , was co-expressed , normal vision was restored . A milder suppressor that anatomically restored less retinal tissue , CG14207EP1348 , restored vision partially . Thus , anatomical rescue correlated with functional rescue . These and other studies indicated that the modifiers not only modulated toxicity of the external eye , but also that of the neuronal cells . To confirm this in another situation , we tested select modifiers for the ability to mitigate polyQ toxicity when directed exclusively to the nervous system with elav-GAL4 ( Figure S4 ) . These studies confirmed that the modifiers mitigated neuronal toxicity of the Ataxin-3 protein . We performed select additional experiments with dpld , for which we obtained many independent EP overexpression alleles . These detailed studies confirmed activity of the EP alleles of dpld with independent UAS-dpld transgenic lines . Further , suppression by dpld was not limited to development but also extended to the adult timeframe ( Figures S2 , S5 , and S6 ) . Because the genes were isolated as modifiers of a truncated Ataxin-3 protein , we tested whether they could mitigate toxicity of full-length Ataxin-3 , which is largely a neuronal toxicity [15] . Modifiers that phenotypically strongly suppressed toxicity of truncated Ataxin-3 also strongly suppressed full-length Ataxin-3 toxicity; however , a number of modifiers that were weak or moderate suppressors of the truncated protein were , in contrast , strong suppressors of full-length Ataxin-3: CG14207EP1348 ( αB crystalline ) , CR11700EP1384 ( polyubiquitin ) , CG8209B3-Sa ( putative ubiquitin ligase ) , and Sin3AB9-E ( Figure S7 and unpublished data ) . Consistent with strong anatomical rescue , CG14207EP1348 also robustly suppressed functional vision defects of full-length Ataxin-3 ( Figure 1Q ) . The enhancer , however , CG11033EP3093 had a minimal effect on toxicity of full-length Ataxin-3 ( unpublished data ) . This indicated that the modifiers varied in strength and selectivity depending upon whether the Ataxin-3 protein was intact or truncated . As truncation may be a feature of SCA3 disease [28–30] , the effectiveness of modifiers against different forms of Ataxin-3 has implications for disease pathogenesis . Previous studies have shown that the molecular chaperone Hsp70 is a potent suppressor of SCA3 toxicity [31] . Therefore , we considered that the class 1 modifiers may function similar to Hsp70 to help cells handle the misfolded disease protein , whereas those of class 2 likely have a role in ubiquitin-dependent pathways that process misfolded proteins . However , class 3 presented a range of potential activities . To address how the modifiers were functioning biologically , we tested whether the modifiers could affect a general protein misfolding phenotype: compromised chaperone activity with a dominant-negative form of Hsp70 ( Hsp70 . K71E ) . This situation results in an eye phenotype that resembles severe polyQ degeneration ( Figure 2A and [32] ) . Thus , modifiers that affected both SCA3 and Hsp70 . K71E toxicity would likely include those whose mode of action was to modulate protein misfolding . Strikingly , we found that most of the suppressors of polyQ toxicity also mitigated the Hsp70 . K71E phenotype , as well as or better than directed expression of Hsp70 itself ( Figure 2; Table 1 ) . Interestingly , DnaJ-1B345 . 2 and Tpr2EB7-1A enhanced this phenotype; we interpreted this to indicate that these genes , which encode proteins thought to act as cochaperones of Hsp70 , may compromise residual Hsp70 in the dominant-negative situation . The chaperone mrjE1050 , although an Hsp40 class chaperone , acted in a manner distinct from DnaJ-1B345 . 2 , as it suppressed rather than enhanced the Hsp70 . K17E phenotype . Moreover , the enhancer of SCA3trQ78 toxicity , CG11033EP3093 , suppressed the misfolding phenotype . Only one modifier did not affect this phenotype , the ubiquitin protease Ubp64EE213-1A; interestingly , normal Ataxin-3 also has ubiquitin protease activity that mitigates its own pathogenicity , and similarly , has no effect on Hsp70 . K71E [15] . We considered that one mechanism by which the modifiers may mitigate the Hsp70 . K71E phenotype is by upregulating Hsp70/Hsc70 chaperones; however , none of the modifiers appeared to act in this way ( Figure 2K and unpublished data ) . These results indicated that the majority of modifiers of SCA3trQ78 toxicity appeared to function biologically by aiding in situations of compromised chaperone activity and/or protein misfolding . The degree of neurodegeneration induced by pathogenic polyQ protein is typically correlated with the level of accumulation of the protein in animals in vivo . We reasoned , therefore , that the modifiers may affect protein levels . We therefore examined protein accumulation by immunohistochemistry and western analysis . Although nuclear inclusions may not be causal in disease [33] , later onset , smaller nuclear inclusions are typically reflective of reduced pathogenicity of the protein in vivo . Hsp70 and Hsp40 have also been shown to increase the solubility of pathogenic polyQ protein by western blots , concomitant with reducing toxicity [16] . We therefore examined protein accumulation using rh1-GAL4 or the full-length Ataxin-3 protein—both situations that allow sensitive analysis of protein accumulation [15 , 22] . In these studies , we limited analysis to the strong and moderate modifiers . Immunohistochemical analysis revealed that select modifiers had striking effects to reduce NIs . These included the class I chaperones Hsp68E407 , DnaJ-1B345 . 2 , mrjE1050 , but did not include αB-crystalline CG14207EP1348 or Tpr2EB7-1A ( Figure 3; Table 1 ) . Of the class 2 ubiquitin-pathway components , polyubiquitin CR11700EP1384 and the ubiquitin protease Ubp64EE213-1A reduced NIs , but the other strong modifiers of this class did not . Of class 3 modifiers tested , all reduced NI except ImpEP1433 ( Figure 3B and 3F ) . We then analyzed solubility of the pathogenic protein by immunoblot . This approach revealed that , although the modifiers had no effect on protein levels at early time points prior to inclusion formation , all suppressors increased the level of monomeric protein over time , thus all increased the solubility properties of the pathogenic protein ( Figure 3E ) . The effect was specific , as co-expression of a control protein ( green fluorescent protein [GFP] ) had no effect ( unpublished data ) . Similarly , the enhancer reduced monomer levels ( unpublished data ) . These findings indicate that the modifiers either affected pathogenic protein accumulation or altered the solubility properties of the toxic protein , concomitant with altering protein pathogenicity . Given that many modifiers mitigated protein accumulation , we asked whether there were interactions with genes of protein degradation pathways . A key pathway thought to modulate the pathogenic polyQ toxicity is the ubiquitin-proteasomal system ( UPS ) [34] . We therefore tested whether suppression by modifiers that lowered protein levels was dependent upon a fully functional proteasome . Proteasome activity can be reduced by a dominant temperature-sensitive mutation in a proteasome protein subunit ( DTS5 ) [35] . In a situation where limiting proteasome activity using this mutation had no effect on SCA3 toxicity on its own , we found that a striking number of modifiers still suppressed polyQ toxicity , thus indicating that they were not sensitive to inhibition of the proteasome by this assay; these included dpld alleles ( Figure 4; Table 1 ) . In contrast , a striking exception was the class 2 ubiquitin-pathway suppressors: all of these modifiers lost the ability to suppress upon proteasome inhibition with the DTS mutation . Autophagy , or lysosome-mediated protein degradation , has also been implicated in polyQ toxicity and cell survival in situations of stress [36 , 37] . We therefore asked whether the modifier genes had an effect on this process . First , we determined whether normal or pathogenic Ataxin-3 itself induced lysosomal accumulation reflective of autophagy , by examining the fat body tissue from larvae , a standard assay for autophagy [38] . Normally , well-fed animals show minimal lysosomal induction , whereas starved animals show a dramatic increase , reflected by the uptake of dye ( Figure 4F and 4G ) . Whereas expression of normal Ataxin-3 ( SCA3Q27 ) had minimal effect , expression of pathogenic Ataxin-3-induced autophagy ( Figure 4J ) . To further address the role of autophagy , we determined whether limiting the activity of autophagy genes affected SCA3 toxicity . Key genes to which RNA interference transgenic lines are available include Atg5 . Whereas reduction of Atg5 activity on its own had little effect , reducing Atg5 in the presence of pathogenic SCA3 protein appeared to enhance toxicity , with increased loss of retinal integrity ( Figure 4L–4N ) . This suggests that , normally , autophagy may mitigate toxicity of the pathogenic protein . Reduction of autophagy also enhanced aggregation of the protein by western immunoblot and enhanced cytoplasmic protein accumulation along photoreceptor axons ( Figure 4O–4Q ) . We then determined whether strong modifier genes also modulated autophagy . We examined two situations: ( 1 ) to determine whether strong modifier genes could induce autophagy in well-fed animals when autophagy is normally minimal; ( 2 ) to determine whether they could block autophagy under starvation , when autophagy is stimulated as a protective mechanism . We tested these as we considered that a modifier may affect Ataxin-3 pathogenicity either by inducing autophagy-mediated lysosomal degradation of the pathogenic protein , or alternatively , by blocking autophagy if autophagy contributes to loss of the cells in response to mutant polyQ protein . These studies revealed that select modifiers induced , whereas others mitigated , autophagy ( Figure 4H , 4I , and 4K ) . Of the class 1 chaperone modifiers , the two Hsp40 genes ( DnaJ-1B345 . 2 and mrj E1050 ) increased autophagy , whereas Hsp70 and the class 3 modifier ImpEP1433 reduced autophagy . Although Dpld showed no effect in these assays , limiting autophagy by reduction of Atg5 , Atg7 , or Atg12 mitigated Dpld suppression , suggesting that its activity was dependent on autophagy ( Figure 4R , 4S , and unpublished data ) . These and other data ( Figures S8 and S9 ) suggested that Dpld may act upstream of autophagy genes to activate autophagy in select situations . We also tested available GFP protein trap lines to examine localization of modifier proteins . Although none of these lines showed GFP immunostaining , one line with a protein trap insertion in Hsc70Cb enhanced SCA3 neuronal toxicity and increased protein accumulation in the neurophil similar to autophagy genes ( Figure S8G–S8K ) . Taken together , these studies indicated that the modifiers , despite broad molecular nature , mitigated situations of protein misfolding; in some cases their activity appeared dependent on the proteasome , whereas others may involve autophagy-based protein clearance or autophagy-related cell loss . The modifiers were selected based on ability to modulate SCA3 degeneration; however , our studies suggested that the modifiers may have broader functions in protein misfolding . We therefore determined whether they could modulate toxicity of tau . Abnormal accumulation of tau in neurofibrillary tangles or mutations in tau are associated with Alzheimer disease and frontotemporal dementia [39] . Tau-induced degeneration is mitigated by the caspase inhibitor P35 and DIAPs , implicating programmed cell death pathways in tau toxicity [40] . Expression of normal ( tau . wt ) or mutant ( tau . R406 ) tau causes toxicity reflected in a reduced and degenerate eye [41] . Co-expression of the class 1 chaperone Tpr2EB7-1A and the Hsc70Cb line class 2 modifier polyubiquitin CR11700EP1384 , and class 3 modifiers dpld JM265 , ImpEP1433 , and CG5009B227 . 2 strikingly suppressed toxicity of tau ( Figure 5; Figure S8 ) . The class 3 modifier embE2-1A enhanced tau ( Figure 5 ) , whereas NFAT EP1335 enhanced tau . wt , but had no effect on mutant tau . R406W ( unpublished data ) . We then examined the ability of the modifiers to affect programmed cell death . Several class 3 modifiers had an effect on hid-induced eye loss: co-expression of embE2-1A and NFATEP1508 enhanced hid , whereas ImpEP1433 and dpldJM265 alleles suppressed hid-induced cell death ( Figure 5 and unpublished data ) . Alleles of those genes that modulated tau and programmed cell death similarly ( emb , dpld , NFAT , and Imp ) may modulate tau toxicity by altering cell death . In contrast , the others ( Tpr2 , polyubiquitin CR11700 , and Hsc70Cb ) likely modulate tau toxicity through other means . Taken together , these data indicate that select modifiers that influence cell survival and protein misfolding may be common to both SCA3 and tau-induced degeneration .
Our screen was designed to identify upregulation modifiers , targeting genes whose increased activity could modulate neuronal toxicity and degeneration of a pathogenic Ataxin-3 protein . This screen identified activities that may become compromised or normally insufficient in the disease protein situation . A number of modifiers appeared dosage sensitive , in that reduction of the genetic region encoding the gene enhanced degeneration . This suggests that , at least for select modifiers , their normal activity is also critical for maintenance of neurons in disease . Because of the severity of the degenerate eye , we may have selected for particularly strong modifiers . Modifiers were also initially selected for the ability to mitigate an external eye degeneration , rather than direct neuronal toxicity . Despite this , all of the modifiers mitigated internal neural degeneration . This indicates that the degree of external eye degeneration is a reasonable predictor of internal neuronal integrity . A number of modifiers were identified multiple times ( dpld , NFAT , emb , and dbr ) , although most were found only once , indicating that the screen is not saturated . Additional screens using different mutagens or different types of screening approaches may reveal additional and different classes of modifiers . Although the modifiers were selected for mitigation of toxicity of a truncated Ataxin-3 protein , nearly all effectively mitigated toxicity of the full-length pathogenic protein . Our previous and other studies suggest that truncation may normally occur in disease to remove the N-terminal ubiquitin protease domain and would dramatically enhance degeneration [15 , 29 , 30] . Therefore , these are efficacious modifiers that mitigate toxicity of various forms of the pathogenic protein . The majority of modifiers were either chaperones or , by sequence and functional tests , modulated ubiquitin-mediated protein quality control pathways . Whereas those that have sequence implications in this pathway might be anticipated , a surprise was that many genes with widely divergent molecular activities were functionally implicated in this process . These data suggest that many genes can modulate situations of protein misfolding , and moreover , suggest that protein misfolding is central to polyQ pathogenesis . It is also possible that , given the normal function of Ataxin-3 in ubiquitin-modulated pathways , the screen selected for modifiers that impinge on normal activities of Ataxin-3 . Among the modifiers , some decreased protein accumulation , whereas others suppressed with no apparent change in Ataxin-3 inclusions . Of those that decreased Ataxin-3 accumulation , some appeared dependent on functional proteasome activity , whereas others stimulated autophagy , or both , implicating various mechanisms that attenuate Ataxin-3 degeneration . Among the modifiers was polyubiquitin , arguing that the level of ubiquitin itself may normally be insufficient to handle the misfolded protein over prolonged periods . This is consistent with the lack of a robust stress response to pathogenic polyQ [42] . In a Caenorhabditis elegans of model of polyQ , Gidalevitz et al . [43] reveal that polyQ expansions can cause temperature-sensitive alleles of various unrelated genes ( paramyosin , dynamin , and ras ) to display the mutant effect at what would normally be permissive conditions . This finding suggests that long polyQ runs can cause a general disruption of the cellular protein-folding environment to affect the temperature-sensitive mutant proteins . Our work used a pathogenic human disease protein Ataxin-3 and revealed that many modifiers that mitigate Ataxin-3 toxicity also modulate general protein misfolding . Taken together , these results suggest that , in the absence of upregulation of the various components needed , the cell may become overwhelmed , triggering deleterious effects [32] . Our detailed analysis of modifiers of Ataxin-3 have revealed a variety of biological processes that can help manage pathogenic polyQ protein , as well as specific genes of interest ( Figure 6A ) . The precise molecular pathways by which those modifiers that are not clearly within protein folding pathways can modulate misfolding to affect neurodegeneration requires further study . We envisage , however , that they function by their predicted molecular mechanisms , but on targets that impinge on cellular protein homeostasis . For example , chromatin modifiers may tweak the expression of a variety of genes in such processes , whereas translational regulators may translationally modify such genes . The finding that a variety of cellular functions are involved would be consistent with RNA interference screens in C . elegans that identified a variety of genes that impinge on cellular protein homeostasis as enhancers of the aggregation of polyQ protein [20] . These findings , along with other studies including those we present here , underscore the critical importance of proper protein homeostasis to most—if not all—cellular functions , such that a variety of genes can influence this process . Among our modifiers , some that might have been expected to act in a similar manner , appeared to have distinct biological effects . The class 2 modifiers showed complete dependence on proper proteasome activity , while other modifiers , including alleles of DnaJ-1 and Imp , appeared to be insensitive to limiting proteasome activity , but rather affected autophagy in stress conditions . Hsp70 suppresses multiple stress conditions including protein misfolding , starvation-induced autophagy , and paraquat-induced oxidative stress [11] , suggesting it may both facilitate UPS activity , but also block autophagy-dependent degeneration . In vivo , these pathways interplay to maintain proper neuronal function in the face of disease , thus further study of the modifiers may allow greater molecular identification of the individual pathways , as well as their integration . Although only select modifiers affected protein accumulation as assayed by immunohistochemistry , all affected the solubility of the disease protein by biochemical analysis . The relationship between protein inclusions and proposed toxic oligomers is still under investigation , but it seems reasonable to suggest that the change in solubility may reflect activity of the modifiers to buffer or alter toxic conformations of the protein . Our efforts to detect toxic oligomers of Ataxin-3 using available antibodies [33 , 44 , 45] are still in progress . We note that , despite gross similarity , SCA3 degeneration is not identical to general misfolding: some suppressors of Ataxin-3 toxicity strikingly enhanced dominant-negative Hsp70 ( upregulation of DnaJ-1 and Tpr2 ) , whereas the enhancer CG11033E3093 suppressed it . The UPS and autophagy pathways function in either degeneration or polyQ protein pathogenicity [46] . Our findings indicate that these pathways at least in part function in the removal or decrease of the toxic protein , thereby reducing degeneration . It is also possible that activities of these pathways may be inhibited during pathogenesis; such inhibition of normal activity would contribute to disease pathology [47–49] . The normal function of Ataxin-3 is in ubiquitin-modulated pathways [23–26]; our data suggest the possibility that Ataxin-3 may also modulate autophagy . Normal physiological levels of autophagy are critical to integrity of neurons , as loss of autophagy causes degeneration associated with ubiquitinated inclusions [50 , 51] . Our studies also reveal that compromise of autophagy pathways strikingly increased cytoplasmic Ataxin-3 accumulations in the neurophil without an obvious change in NIs . This may indicate that autophagy normally modulates cytoplasmic accumulation of the disease protein . Previous studies show that cytoplasmic polyQ protein is very toxic and blocks axonal transport [52] . These findings suggest that perturbations in autophagy may enhance cytoplasmic toxicity further . In addition to regulation of the disease protein level and misfolding , results with select modifiers ( Hsp70 and Imp ) suggest that autophagy may also regulate loss of the cells . Given that our previous findings failed to reveal a clear role of caspase-dependent cell death in SCA3 [22] , autophagy may be a mechanism of cell loss in this situation . Identification of other components of UPS and autophagy pathway will give further insights into how the disease protein is degraded and mechanisms of neuronal loss . Genetic screens have been performed for modifiers of Ataxin-1 [19] and of pure polyQ domains [18] , revealing similar components of protein folding and degradation: DnaJ-1 [18 , 19] and Tpr2 [18] , but also RNA binding proteins and transcription factors . Although components of ubiquitin pathways that modulate Ataxin-1 appear distinct from Ataxin-3 , this may reflect different regulators required for modifying different proteins or lack of saturation of the screens . Similarly several RNA binding proteins identified as modifiers of Ataxin-1 were suggested to reflect Ataxin-1 function as a putative RNA binding protein; our data suggest that it is also possible that these modifiers work more fundamentally to modulate protein solubility or levels . Our other work , however , has revealed a role for microRNAs in modulating neuronal survival in neurodegenerative situations [22 , 53] , which has recently been extended to vertebrate neuronal integrity as well [54] . A C . elegans screen revealed a large number of modifiers of a pure polyQ aggregation phenotype [20] . Several modifiers identified in that screen affect RNA synthesis , processing , and protein synthesis as modifiers of misfolding . It will be important to test these modifiers against various specific disease proteins , as the action of a pure polyQ repeat may be distinct from a pathogenic repeat within a host protein . One reason for global commonality among modifiers of polyQ disease proteins may be that the proteins themselves fall within a common interacting protein network . Recent studies using the yeast two-hybrid approach and proteomic databases reveal a protein interaction network of the ataxia proteins [55] . A surprise was that many different ataxia proteins , including Ataxin-3 , fall within a few interaction steps from one another , suggesting that their common phenotypes may be a reflection of common interactions , which , when perturbed , contribute to disease . In that study [55] , seven proteins were identified as direct interaction partners of Ataxin-3; however none of these appeared in our screen . Another direct binding protein–VCP [56]—was also not identified . However , the human orthologues of the majority of the genetic modifiers we defined fit into the protein interaction network at various points; some are direct interactors of other ataxia disease proteins , and others are one or more steps removed ( Table S3 ) . Alzheimer disease and polyQ diseases are two unrelated human neurodegenerative diseases that cause neuronal degeneration in distinct brain regions [1 , 2 , 57] . Therefore , no overlap was expected between modifiers of these disease proteins in flies; indeed previous studies suggest minimal overlapping genes [58] . Surprisingly , we found that select modifiers of Ataxin-3 suppressed tau-degeneration , including the cochaperone Tpr2 and polyubiquitin ( Figure 6B ) . This finding is consistent with cell culture studies and C . elegans RNA interference screens that implicate chaperones as modifiers of tau degeneration [59 , 60] . Further study of these modifiers , especially those that may be in common among different disease proteins , should provide the foundation for new therapeutic insight .
Fly lines were grown in standard cornmeal molasses agar with dry yeast at 25 °C . Transgenic lines UAS-SCA3trQ78 , UAS-SCAtrQ61 , UAS-SCA3Q78 , and UAS-SCA3Q84 are described [14–16] . General stock lines , deficiency lines , and specific alleles of the EP modifiers were obtained from the Bloomington Drosophila stock center . The emb mutant lines were from C . Samakovlis ( Wenner-Gren Institute Stockholm University , Stockholm , Sweden ) [61] . rh1-GAL4 , gmr-hid , UAS-DTS5 , autophagy inverted repeat lines ( UAS-Atg5_IR and UAS-Atg7_IR ) , tau transgenic lines ( UAS-htau and UAS-htau . R406W ) , UAS-brat , and Hsc70C protein trap line were kindly provided by C . Desplan ( New York University , New York , New York , United States ) , H . Steller ( Rockefeller University , New York , New York , US ) , J . M . Belote ( Syracuse University , Syracuse , New York , US ) , T . Neufeld ( University of Minnesota , Minneapolis , Minnesota , US ) , M . Feany ( Harvard Medical School , Boston , Massachusetts , US ) , R . Wharton ( Duke University Medical Center , Durham , North Carolina , US ) , M . Buszczakl ( Johns Hopkins University , Baltimore , Maryland , US ) , respectively . The dominant-negative Hsp70 line is described [32] . UAS-dpld FLAG , untagged , and gfp fusion lines were generated from cDNA clone LD02463 by subcloning into the pUAST vector [62] . Excision lines for reversion were made by crossing the EP insertions to lines bearing transposase , then screening for loss of the EP element . For the genetic screen , virgin females of the starter line EP55 , bearing an EP insertion on the X chromosome [27] , was crossed to males with transposase . Males were selected and crossed to virgin females that expressed the disease gene ( w/w; gmr-GAL4 UAS-SCA3trQ78 Tft/CyO ) . F1 progeny were screened for males with either suppressed or enhanced eye phenotypes as compared to controls; any modifiers were then outcrossed and balanced . EP insertions were confirmed to be single insertions through outcrossing , as well as plasmid rescue . Epon sections , paraffin sections , and cryosections of adult heads were performed as described [14 , 16] . Primary antibodies for immunostaining were anti-HA primary antibody ( Y-11 , 1:50 , Santa Cruz Biotechnology; and 12CA5 , 1:100 , Roche ) , anti-Myc ( 9E10 , 1:100 , Santa Cruz Biotechnology ) , anti-Hsp70 ( 7FB , 1:1 , 000 , ) [63] , and anti-Gfp ( A6455 , 1:50 , Molecular Probes ) . Secondary antibodies included anti-mouse or anti-rabbit conjugated to Alexa Fluor 594 or 488 ( 1:200 or 1:100 , Molecular Probes ) . Western immunoblots were performed as described [16] . Primary antibodies used were HA-HRP ( 3F10 , 1:500 , Roche ) , rat anti-Hsp70 ( 7FB , 1:2 , 000 ) , and mouse anti-tubulin ( E7 , 1:2 , 000 , Developmental Studies Hybridoma Bank ) , with the secondary goat anti-rat IgG ( 1:2 , 000 , Roche ) and goat anti-mouse IgG ( 1:2 , 000 , Chemicon International ) . To define the sites of EP insertion , plasmid rescue was performed [27] . To confirm single hits in the genome , multiples of clones from each EP line were sequenced with an EP 3′ P-end specific primer ( 5′-CAA TCA TAT CGC TGT CTC ACT CA-3′ ) . The flanking DNA sequence was used to query flybase BLAST to define the nearby gene and exact site of insertion . Upregulation of the genes by the EP element was confirmed by northern analysis , comparing the EP line alone , to wild-type fly controls with the EP line in the presence of a GAL4 driver , using gene-specific probes generated by reverse-transcription PCR using an EP-specific primer and gene-specific primers to the target genes . That the EP insertions were single insertions was independently verified genetically by molecular and phenotypic analysis of lines . The effect of the modifiers on transgene expression levels was examined by real-time PCR ( see Text S1 and Figure S2 ) and by reverse-transcription PCR . For the latter , total RNA was extracted with the RNAeasy kit ( 74104 , QIAGEN ) following manufacturer's instructions . cDNA synthesis was done using SuperScript First Strand Synthesis for reverse-transcription PCR ( 12371–019 , Invitrogen ) . PCR was performed using primers: for the SCA3 transcript , 5′-CTATCAGGACAGAGTTCACAT-3′ ( forward ) and 5′-CAGATAAAGTGTGAAGGTAGC-3′ ( reverse ) ; for the GAL4 transcript , 5′-GTCTTCTATCGAACAAGCATGCGA-3′ ( forward ) and 5′-TGACCTTTGTTACTACTCTCTTCC-3′ ( reverse ) and for rp49 control , 5′-CCAGTCGGATCGATATGCTAA-3′ ( forward ) and 5′-ACCGTTGGGGTTGGTGAG-3′ ( reverse ) . Phototaxis was performed as described [15] . The percentage of flies in the light and dark chambers represent an average of three independent groups of flies . At least 100 flies were tested for each genotype , 20 flies were used in each experiment . Third instar larval fat body tissues were stained with Lyso Tracker Red DND-99 ( L-7528 , Molecular Probes ) and Hoechst ( H3570 , Molecular Probes ) as described [38] . For each genotype , well-fed and starved larvae were used as negative and positive controls for the assay conditions . A score of 4 was given to control larvae grown under starvation condition; autophagy for other genotypes was scored relative to this . Controls ( driver line alone and/or modifier alone , in starvation and well-fed conditions ) were performed for each modifier genes in parallel to the experimental situation . The final autophagy score represents the average of 20 larvae from each genotype .
The accession numbers of the proteins used in the analysis in Figure S5 from the National Center for Biotechnology Information ( NCBI ) ( http://www . ncbi . nlm . nih . gov ) are Dm Dappled ( NM_165533 ) , DmCG15105 ( NM_137546 ) , Dm Brat ( NM_057597 ) , Dm Mei-P26 ( NM_143765 ) , Ce Lin-41 ( NM_060086 ) , Hs Lin-41 ( XM_067369 ) , Hs TRIM2 ( NM_015271 ) , Hs TRIM3 ( NM_006458 ) , and Hs TRIM32 ( NM_012210 ) .
|
Spinocerebellar ataxia type-3 is the most common dominantly inherited movement disorder and is caused by a CAG repeat expansion within the gene ATXN3 , encoding the Ataxin-3 protein . This leads to a protein with an expanded polyglutamine domain , which confers a dominant toxicity on the protein , leading to late onset , progressive neural degeneration in the brain . Although some modifiers of Ataxin-3 toxicity have been defined , little was known about their molecular mechanisms of action . The fruit fly Drosophila recapitulates fundamental aspects of the human disease . Here , we performed a genome-wide screen for new modifiers of Ataxin-3 toxicity using the fly and defined 25 modifiers in 18 genes . The majority of the genes belong to chaperone and ubiquitin proteasome pathways , which modulate protein folding and degradation , but the remaining modifiers have a broad range of predicted molecular functions . Assays in vivo revealed that the biological activity of all modifiers converge on aiding in situations of protein misfolding , despite distinct predicted molecular functions . Select modifiers of Ataxin-3 toxicity also modulated tau toxicity associated with Alzheimer disease . These findings underscore the importance of protein homeostasis pathways to disease and provide the foundation for new therapeutic insight .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"cell",
"biology",
"neurological",
"disorders",
"eukaryotes",
"drosophila",
"neuroscience",
"animals",
"genetics",
"and",
"genomics",
"insects"
] |
2007
|
Genome-Wide Screen for Modifiers of Ataxin-3 Neurodegeneration in Drosophila
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Trypanosoma cruzi , the aetiological agent of Chagas disease possess extensive genetic diversity . This has led to the development of a plethora of molecular typing methods for the identification of both the known major genetic lineages and for more fine scale characterization of different multilocus genotypes within these major lineages . Whole genome sequencing applied to large sample sizes is not currently viable and multilocus enzyme electrophoresis , the previous gold standard for T . cruzi typing , is laborious and time consuming . In the present work , we present an optimized Multilocus Sequence Typing ( MLST ) scheme , based on the combined analysis of two recently proposed MLST approaches . Here , thirteen concatenated gene fragments were applied to a panel of T . cruzi reference strains encompassing all known genetic lineages . Concatenation of 13 fragments allowed assignment of all strains to the predicted Discrete Typing Units ( DTUs ) , or near-clades , with the exception of one strain that was an outlier for TcV , due to apparent loss of heterozygosity in one fragment . Monophyly for all DTUs , along with robust bootstrap support , was restored when this fragment was subsequently excluded from the analysis . All possible combinations of loci were assessed against predefined criteria with the objective of selecting the most appropriate combination of between two and twelve fragments , for an optimized MLST scheme . The optimum combination consisted of 7 loci and discriminated between all reference strains in the panel , with the majority supported by robust bootstrap values . Additionally , a reduced panel of just 4 gene fragments displayed high bootstrap values for DTU assignment and discriminated 21 out of 25 genotypes . We propose that the seven-fragment MLST scheme could be used as a gold standard for T . cruzi typing , against which other typing approaches , particularly single locus approaches or systematic PCR assays based on amplicon size , could be compared .
Trypanosoma cruzi , the protozoan causative agent of Chagas disease , is a monophyletic and genetically heterogeneous taxon , with at least six phylogenetic lineages formally recognised as Discrete Typing Units ( DTUs ) , TcI–TcVI [1] , or near-clades ( clades that are blurred by infrequent inter-lineage genetic recombination , [2] ) . T . cruzi is considered to have a predominantly clonal population structure but with at least some intra-lineage recombination [3] , [4] , [5] , [6] . TcI and TcII are the most genetically distant groups , and the evolutionary origins of TcIII and TcIV remain controversial . Based on sequencing of individual nuclear genes Westenberger et al . [7] suggested that an ancient hybridisation event occurred between TcI and TcII followed by a long period of clonal propagation leading to the extant TcIII and TcIV . Alternatively , de Freitas et al . [8] suggested that TcIII and TcIV have a separate evolutionary ancestry with mitochondrial sequences that are similar to each other but distinct from both TcI and TcII . Recently , Flores-Lopez and Machado [9] proposed that TcIII and TcIV have no hybrid origin . Based on the sequence of 32 genes , they strongly suggested that TcI , TcIII and TcIV are clustered into a major clade that diverged from TcII around 1–2 millions of years ago . Less controversially , it is clear that TcV and TcVI , both overwhelmingly represented in the domestic transmission cycles in the Southern Cone region of South America , are hybrid lineages sharing haplotypes from both TcII and TcIII , with both DTUs retaining the mitochondrial genome of TcIII [8] , [10] . Recent phylogenetic studies suggest that the emergence of the hybrid lineages TcV and TcVI may have occurred within the last 60 , 000 years [11] . Reliable DTU identification and the potential for high resolution investigation of genotypes at the intra DTU level are of great interest for epidemiological , host association , clinical and phylogenetic studies . Historically , a plethora of typing techniques have been applied to T . cruzi . Initial pioneering work applied multilocus enzyme electrophoresis ( MLEE ) techniques [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] revealing the remarkable genetic heterogeneity of this parasite . Subsequently , several PCR-based typing assays have been designed to differentiate the main DTUs [21] , [22] , [23] , [24] and more recently , combinations of PCR-RFLP schemes have been published [25] , [26] , [27] . Some approaches based on DTU characterisation by direct sequential PCR amplifications from blood and tissue samples are also promising , although various sensitivity and reliability issues need to be resolved [28] , [29] , [30] . Microsatellite typing ( MLMT ) has also been applied to population data for fine-scale intra DTU genetic analysis [31] , [32] , [33] . Multilocus sequence typing ( MLST ) , originally developed for bacterial species typing , has now been applied to a wide range of prokaryotic [34] , [35] , [36] , [37] and increasingly eukaryotic microorganisms [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] . The technique typically involves the sequencing and concatenation of six to ten internal fragments of single copy housekeeping genes per strain [49] . Data are often hosted on interactive open access databases such as MLST . net for use in the wider research community . A major advantage of MLST analysis is that sequence data are unambiguous , minimizing interpretative errors . In this context , the MLST approach represents an excellent candidate to become the gold standard for T . cruzi genetic typing with outputs suitable for phylogenetic and epidemiological studies , particularly where large numbers of isolates from varied sources are under study . Recently , two multilocus sequence typing ( MLST ) schemes have been developed in parallel for T . cruzi , each of them based on different gene targets [50] , [51] . Both schemes display a high discriminatory power and are able to clearly differentiate the main T . cruzi DTUs . The current work proposes to resolve the optimum combination of loci across the two schemes to produce a reproducible and robust formalised MLST scheme validated across a shared reference panel of isolates for practical use by the wider T . cruzi research community .
Twenty five cloned reference strains belonging to the six known DTUs were examined ( Table 1 ) . These strains have been widely used as reference strains in many previous studies , and are regularly examined in our laboratory by Multilocus Enzyme Electrophoresis ( MLEE ) . Parasite stocks were cultivated at 28°C in liver infusion tryptose ( LIT ) supplemented with 1% hemin , 10% fetal bovine serum , 100 units/ml of penicillin , and 100 µg/mL of streptomycin or in supplemented RPMI liquid medium . Initially a total of 19 gene fragments were considered , 10 housekeeping genes previously described by Lauthier et al . [50] [Glutathione peroxidase ( GPX ) , 3-Hidroxi-3-metilglutaril-CoA reductase ( HMCOAR ) , Piruvate dehydrogenase component E1 subunit alfa ( PDH ) , Small GTP-binding protein Rab7 ( GTP ) , Serine/treonine-protein phosphatase PP1 ( STPP2 ) , Rho-like GTP binding protein ( RHO1 ) , Glucose-6-phosphate isomerase ( GPI ) , Superoxide dismutase A ( SODA ) , Superoxide dismutase B ( SODB ) and Leucine aminopeptidase ( LAP ) ]; and 9 gene fragments from Yeo et al . [51] [ascorbate-dependent haemoperoxidase ( TcAPX ) , dihydrofolate reductase-thymidylate synthase ( DHFR-TS ) , glutathione-dependent peroxidase II ( TcGPXII ) , mitochondrial peroxidase ( TcMPX ) , trypanothione reductase ( TR ) , RNA-binding protein-19 ( RB19 ) , metacyclin-II ( Met-II ) , metacyclin-III ( Met-III ) and LYT1] . However , 6 of them were discarded based on initial findings [50] , [51] . Although some of the excluded targets were informative , they were not amenable for routine use . More specifically , LYT1 was discarded due to unreliable PCR amplification and sequencing despite multiple attempts at optimization; TR , DHFR-TS and TcAPX were also deemed unsuitable as internal sequencing primers were required; finally , Met-III and TcGPXII were also excluded because generated non-specific PCR products with some isolates . The final 13 gene fragments assessed included 3 fragments described by Yeo et al . [51] and the 10 housekeeping genes previously described by Lauthier et al . [50] . These were: TcMPX , RB19 , Met-II , SODA , SODB , LAP , GPI , GPX , PDH , HMCOAR , RHO1 , GTP and STPP2 . For the 13 loci under study , searches in the CL-Brener and Sylvio X10 genomes ( http://tritrypdb . org/tritrypdb/ ) , using the primer sequences as well as the fragment sequences as query , displayed single matches in all cases . Chromosome location , primer sequences and amplicon size for each target are shown in Table 2 . Nucleotide sequences for all the analysed MLST targets are available from GenBank under the following accession numbers: JN129501-JN129502 , JN129511-JN129518 , JN129523-JN129524 , JN129534-JN129535 , JN129544-JN129551 , JN129556-JN129557 , JN129567-JN129568 , JN129577-JN129584 , JN129589-JN129590 , JN129600-JN129601 , JN129610-JN129617 , JN129622-JN129623 , JN129633-JN129634 , JN129643- JN129650 , JN129655-JN129656 , JN129666-JN129667 , JN129676-JN129683 , JN129688-JN129689 , JN129699-JN129700 , JN129709-JN129716 , JN129721-JN129722 , JN129732-JN129733 , JN129742-JN129749 , JN129754-JN129755 , JN129765-JN129766 , JN129775-JN129782 , JN129787-JN129788 , JN129798-JN129799 , JN129808-JN129815 , JN129820-JN129821 , KF889442-KF889646 . Additionaly , we used T . cruzi marinkellei as outgroup . Sequence data of the selected targets for T . cruzi marinkellei were obtained from TriTrypDB ( http://tritrypdb . org ) , under the following accession Ids: TcMARK_CONTIG_2686 , TcMARK_CONTIG_670 , TcMARK_CONTIG_1404 , Tc_MARK_2068 , Tc_MARK_3409 , Tc_MARK_5695 , Tc_MARK_9874 , Tc_MARK_515 , Tc_MARK_4984 , Tc_MARK_5926 , Tc_MARK_8923 , TcMARK_CONTIG_1818 and Tc_MARK_2666 . PCRs were performed in 50 µl reaction volumes containing 100 ng of DNA , 0 . 2 µM of each primer , 1 U of goTaq DNA polymerase ( Promega ) , 10 µl of buffer ( supplied with the GoTaq polymerase ) and a 50 µM concentration of each deoxynucleoside triphosphate ( Promega ) . Amplification conditions for all targets were: 5 min at 94°C followed by 35 cycles of 94°C for 1 min; 55°C 1 min , and 72°C for 1 min , with a final extension at 72°C for 5 min . Amplified fragments were purified ( QIAquick , Qiagen ) and sequenced in both directions ( ABI PRISM 310 Genetic Analyzer or ABI PRISM 377 DNA Sequencers , Applied Biosystems ) using standard protocols . Primers used for sequencing were identical to those used in PCR amplifications . In order to assess reproducibility , each PCR amplification was performed multiple times and associated sequencing was repeated at least twice . MLST data were analysed with MLSTest software ( http://ipe . unsa . edu . ar/software ) [52] with the objective of identifying the most resolutive and minimum number of targets for unequivocal DTU assignment and potential fine scale characterisation . MLSTest contains a suite of MLST data specific analytical tools . Briefly , single nucleotide polymorphisms ( SNPs ) were identified in all loci in MLSTest alignment viewer . Typing efficiency ( TE ) was calculated using the same software . TE for a determined locus is calculated as the number of identified genotypes divided by the number of polymorphic sites in this locus . Additionally , discriminatory power , defined as the probability that two strains are distinguished when chosen at random from a population of unrelated strains [53] , was determined for each target ( Table 3 ) . Sequence data were concatenated and Neighbour Joining phylogenetic trees were generated by using uncorrected p-distances . Heterozygous sites were handled in the analyses using two different methods . First , a SNP duplication method described by Yeo et al . and Tavanti et al . [51] , [54] was implemented . Briefly , the SNP duplication method involves the elimination of monomorphic sites and duplication of polymorphisms in order to “resolve” the heterozygous sites . As an example , a homozygous variable locus scored as C ( cytosine ) will be modified by CC; while a heterozygous locus , for example Y ( C or T , in accordance with IUPAC nomenclature ) , will be scored as CT . Alternatively , heterozygous SNPs were considered as average states . In more detail , the genetic distance between T and Y ( heterozygosity composed of T and C ) is considered as the mean distance between the T and the possible resolutions of Y ( distance T-T = 0 and distance T-C = 1 , average distance = 0 . 5 , see [53] and MLSTest 1 . 0 manual at http://www . ipe . unsa . edu . ar/software for further details ) . Statistical support was evaluated by 1000 bootstrap replications . Overall phylogenetic incongruence among loci ( by comparison with the concatenated topology ) was assessed by the Incongruence Length Difference Test using the BIO-Neighbour Joining method ( BIONJ-ILD , [55] ) and evaluated by a permutation test with 1 , 000 replications . Briefly , the ILD evaluates whether the observed incongruence among fragments is higher than that expected by random unstructured homoplasy across the different fragments . A statistical significant ILD p value indicates that many sites , in at least one fragment , support a phylogeny that is contradicted by other fragments . In order to localize significant incongruent branches in concatenated data we used the Neighbour Joining based Localized Incongruence Length Difference ( NJ-LILD ) test available in MLSTest . NJ-LILD is a variant of the ILD test that allows localizing incongruence at branch level . All combinations from 2 to 12 fragments were analysed using the scheme optimisation algorithm in MLSTest which identifies the combination of loci producing the maximum number of diploid sequence types ( DSTs ) . Three main sequential criteria were applied to select the optimum combination of loci: firstly , monophyly of DTUs and lineage assignment; secondly , robust bootstrap values for the six major DTUs ( 1000 replications ) ; and thirdly detection of genetic diversity at the intra-DTU level .
All 13 gene fragments were successfully amplified using identical PCR reaction conditions ( see methods ) which generated discrete PCR fragments . PCR amplifications of the 13 targets were applied to an extended panel of 90 isolates obtaining more than 98% of positive PCR and amplifications produced strong amplicons and an absence of non-specific products ( data not shown ) . We obtained amplicons of the expected length for all the assayed targets and for all the examined strains . Amplification for various DNA template concentrations was assayed via serial dilution . No difference in PCR amplifications were obtained when DNA concentrations from 20 to 100 ng were used . A total of 5 , 121 bp of sequence data were analysed for each strain ( Table 2 ) . There were no gaps in sequences . The number of polymorphic sites ( Table 3 ) for each of the different fragments varied from 8 ( STPP2 ) to 40 ( Met-II ) . STTP2 showed the lowest discriminatory power ( describing just 5 different genotypes in the dataset ) . Rb19 was the fragment with the highest discriminatory power identifying 21 distinct genotypes in the dataset . Initially , Neighbor Joining trees were generated from concatenated sequences across the 13 prescreened loci which identified four monophyletic DTUs with robust bootstrap support ( TcI , TcII , TcIII , TcIV , bootstrap >98% ) . TcVI was also monophyletic but with a relatively low support ( Figure 1 ) . Additionally , TcV was paraphyletic with Mncl2 as an outlier . The concatenated 13 fragments differentiated all 25 reference strains in terms of DSTs . We observed that bootstrap values were slightly different between the two methods ( SNP duplication and average states ) as they manage heterozygous sites differently . Values were higher for the SNP duplication method in most branches ( Figure 1 , branch values highlighted in blue ) as a consequence of base duplication , which modifies the alignment and increases the informative sites used for bootstrapping . To avoid the potential for methodologically elevated bootstraps , the average states method was implemented for further analyses . From the selected 13 loci , all possible combinations of 2 to 12 loci were analysed ( 8 , 177 combinations ) by implementing the MLSTest scheme optimisation algorithm . One combination of 7 loci was the best according to the proposed criteria . This combination consisted of Rb19 , TcMPX , HMCOAR , RHO1 , GPI , SODB and LAP discriminating all 25 strains as DSTs , and importantly categorising all separate DTUs as a monophyletic group . DTUs were also well-supported by associated bootstraps values ( TcI , 100; TcII , 100; TcIII , 99 . 8; TcIV , 88 . 2; TcV , 88 . 7; TcVI , 99 . 6 ) as illustrated in Figure 2 . Combinations with higher number of loci ( from 8 to 12 ) did not significantly increased bootstrap values of TcIV and TcV . We assessed whether the outlier for TcV ( Mn cl2 ) and the low bootstrap observed for TcVI ( applied to all 13 fragments ) was due to incongruence among fragments . The thirteen fragment dataset was significantly incongruent ( BIONJ-ILD p-value<0 . 001 ) for at least one partition which was corroborated using NJ-LILD with a permutation test and 500 replications . Significant incongruence ( p-value<0 . 05 after Bonferroni correction ) was detected in the TcV and TcVI nodes . Incongruence was likely due to strains within DTUs TcV and TcVI demonstrating apparent loss of heterozygosis ( LOH ) in the Met-II fragment . Excluding Met-II , the p-value for ILD was not significant ( BIONJ-ILD p-value = 0 . 33 ) , and the bootstrap values for TcV and TcVI exceeded 85% , furthermore tree topology was congruent with expected DTU assignment . Attempts were made to reduce the number of fragments required for DTU assignment while maintaining DST identification . All combinations of 3 and 4 fragments ( 1 , 001 combinations ) from the panel of 13 fragments were analysed as described above . A reduced MLST panel incorporating TcMPX , HMCOAR , RHO1 and GPI ( four loci ) produced the highest bootstrap values for DTU assignment across the DTUs , TcI ( 99 . 9 ) , TcII ( 100 ) , TcIII ( 99 . 5 ) , TcIV ( 86 . 7 ) , TcV ( 100 ) and TcVI ( 96 . 8 ) ( Figure 3 ) , and discriminated 19 of 25 DSTs . Other combinations showed higher discriminatory power but presented with lower bootstrap values ( data not shown ) . The TcMPX locus exhibits an apparent loss of heterozygosity ( LOH ) in the hybrid DTU TcV , retaining the TcII like allele but not the TcIII allele . Therefore DTU assignment using TcMPX alone would not assign a TcV isolate correctly . However concatenation of TcMPX with HMCOAR , RHO1 and GPI allow distinguishing TcV from TcII . Topologies obtained for the 7 and 4 loci combinations ( Figures 2 and 3 , respectively ) were similar to the 13 loci scheme , showing consistently the two major groups ( TcI-TcIII-TcIV and TcII-TcV-TcVI ) supported by high bootstrap values , even when trees were rooted using TcMB7 ( Figure 1 ) . The primary difference between the 13 target concatenated phylogenies and the trees obtained for the 7 and 4 targets was that for the 13 concatenated targets TcV was paraphyletic , showing the Mncl2 strain as an outlier . Regarding inter-DTU relationships , the analysis of the concatenated 13 fragments divided DTUs into two major clusters: one composed by TcI , TcIII and TcIV , with a bootstrap value of 100%; while the remaining group containing TcII , TcV and TcVI was supported by lower bootstrap values ( <70% ) , possibly due to presence of the two hybrid DTUs ( TcV and TcVI ) ( Figure 1 ) . Within clusters , internal topologies were supported with relatively high but variable bootstrap values with 4 , 7 and 13 loci combinations and generally consistent intralineage topologies ( Figures 1 , Figure 2 , Figure 3 ) , although the panel of 25 reference strains would need to be expanded further for assessment of fine scale intralineage associations .
Thirteen gene fragments were assessed in an optimised MLST scheme which is a combination of targets from two recently separately proposed schemes [50] , [51] . Here we evaluated the optimal combination of loci based on three main sequential criteria: first , assignment to the expected DTU; second , to attain robust bootstrap values for the six major DTUs , and third to detect intra-DTU diversity . For the first time we propose an optimised MLST scheme , validated against a panel representing all known lineages , for characterisation of T . cruzi isolates . However , it should be emphasized that this MLST scheme is proposed as a typing method for T . cruzi isolates but not as a typing method to be used directly on biological samples as blood , tissues or Triatomine feces , for which more sensitive and simpler methods are needed . Moreover , we have performed assays with the purpose of determining the limit of detection of each gene fragment on blood and triatomines feces ( data not shown ) and we found that none of these targets are suitable for detecting T . cruzi in the normal concentration found in natural biological samples . As a result of our data analyses , we obtained one combination of 7 loci and one combination of only 4 targets which most closely adhered to the selection criteria described above . It is worth noting that the three used criteria for selecting optimum combination of targets are sequential; it means that there is a hierarchical order of these criteria . In first place , we look for obtaining monophyly for the six DTUs and accurate lineage assignment of each examined strain . In a second place , we look for obtaining robust bootstrap values for each of the six major DTUs . Finally , we expect detecting genetic diversity at the intra-DTU level . In this context , due to the hierarchical order of the criteria of selection of loci , the selected combinations will optimise the number of DSTs but subordinated to the two previous criteria . Theoretically , using these criteria , we could obtain a combination of loci that does not give the maximum number of DST for a determined DTU , because our algorithm previously prioritized obtaining monophyly and strong bootstrap values for the six DTUs . This was the case for the selected 4-loci scheme ( which differentiated 19 from 25 strains ) . In spite of this , the selected 7-loci combination that we propose , allow us to differentiate the 25 examined strains , i . e . the maximum possible number of DSTs . The results illustrate that MLST is a highly discriminatory strain-typing technique . From these data we suggest that the 7 locus scheme provides scope for both lineage assignment and diversity studies , generating robust bootstrap values for distance based phylogenies and that a reduced panel of only four targets is sufficient for assignment to DTU level . For population genetics scale analyses and detailed epidemiological studies a comprehensive larger panel of T . cruzi isolates should be assessed by sequencing the proposed targets . The phylogenetic associations among DTUs TcI , TcII , TcIII and Tc IV are debatable . Split affinities and incongruence have been observed in nuclear phylogenies [7] , [8] , [51] , [56] . One interpretation of phylogenetic incongruence is genetic recombination , although due to the highly plastic nature of the T . cruzi genome other causes are also possible . Mutation rates and gene conversion may create distinct levels of sequence diversity [57] . Here , concatenated phylogenies showed a partition into two main clusters for all gene combinations tested , the first consisting of TcI , TcIII and TcIV ( bootstrap value = 100% ) ; and the second composed of TcII , TcV and TcVI ( bootstrap value <70% ) . The presence of the two known hybrid lineages ( TcV and TcVI ) generated artifactual phylogenetic structuring and excluding these representatives revealed clustering of DTUs TcI , TcIII and TcIV , indicating that TcI has a closer affinity to TcIII than to TcIV . TcII is the most genetically distant group which is in agreement with previous findings [9] , [10] , [51] . In addition , it would be interesting to analyze in the future the new lineage described as TcBat [58] using the MLST scheme proposed here , since it could shed light on the phylogenetical position of this interesting lineage . LOH observed in Met-II and TcMPX gene fragments affecting the hybrid lineages TcV and TcVI has potentially significant consequences for MLST and lineage assignment [51] . Isolates affected retain the TcII like allele and would be misassigned in single locus characterisation . For example , hybrid isolates TcV would be assigned to TcII based on TcMPX sequencing due to apparent LOH . Despite this LOH the TcMPX locus was included in the 4 target scheme to increase bootstrap support in differentiating between TcV from TcVI . Although MLST has been successfully applied to other diploid organisms including Candida albicans , the potential for heterozygous alleles complicates typing schemes . In the present work , two methods to handle heterozygous sites , SNPs duplication and average states algorithms , produced broadly similar results with SNP duplication producing marginally higher bootstraps due to the physical duplication of informative sites . Here we decided to implement the average states methodology to derive genetic distances and phylogenies . Both approaches can be found in the software MLSTest [52] producing results that enable resolution at the DTU level and an associated DP of 1 for the panel tested . A significant advantage of MLST based analysis over sequential PCR based gels is that once generated , sequences can be applied to a range of complementary downstream analyses . For example , the resolution of haplotypes for recombination analysis and investigation of more detailed evolutionary associations can be applied to population sized studies . At present , whole genome sequencing applied to large numbers of isolates is not feasible and microsatellite analysis is often difficult to reproduce precisely across laboratories , unlike MLST which has proven reproducibility both within and between laboratories [59] . However , microsatellites could be more convenient for population genetics studies at a microevolutionary level , due to their high resolution power . A further consideration in the analysis of diploid sequences is differentiating heterozygosity from copy number diversity . Ideally , we should prefer single copy genes for MLST schemes in order to avoid comparisons among paralogous . We performed in silico analyses in order to estimate the copy number of the selected targets on the genomic data of CL-Brener ( TcVI ) and Sylvio X10 ( TcI ) ( http://tritrypdb . org/tritrypdb/ ) . For these analyses , we used as query the primer sequences as well as the complete fragment sequences . These searches displayed just single matches in all cases . Consequently , we propose that all the examined MLST fragments may be considered as single copy genes , at least for typing and clustering . One of the most important aspects in any MLST scheme is to provide targets that consistently produce PCR amplicons requiring minimal cleanup and are suitable for sequencing . Although in the current protocol , we recommend purifying PCR products with a suitable commercial kit ( Quiagen ) , in most cases , this was not necessary and sequencing was performed directly from the PCR product . The exception was TcGPXII , and very occasionally SODA produced nonspecific products , neither of which are included in final recommended panels . Although the two previously published MLST [50] , [51] schemes showed promise in identifying diversity , some of the gene targets were not amenable for routine use . For example , LYT1 was discarded due to unreliable amplification and DHFR-TS due to the need for internal primers . Therefore further optimisation performed here was necessary for practical use . An important criterion for choosing targets was identifying those that used the same primers for both PCR amplification and sequencing to maintain simplicity and reduce costs . Taken together , we propose a MLST scheme validated against a panel representing all of the known lineages of T . cruzi . We propose that a 7 loci MLST scheme could provide the basis for robust DTU assignment and strain diversity studies of new isolates and a reduced 4 loci scheme for lineage assignment . Importantly , the sequence data generated can be utilised for a wide range of downstream analyses , including the resolution of haplotypes for recombination analysis , population genetics analyses , and other statistical approaches to the phyloepidemiological study of T . cruzi . Finally , we propose that the seven-fragment MLST scheme could be used as a gold standard for T . cruzi typing , against which other typing approaches , particularly single locus approaches or systematic PCR assays based on amplicon size , could be compared .
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The single-celled parasite Trypanosoma cruzi occurs in mammals and insect vectors in the Americas . When transmitted to humans it causes Chagas disease ( American trypanosomiasis ) a major public health problem . T . cruzi is genetically diverse and currently split into six groups , known as TcI to TcVI . Multilocus sequence typing ( MLST ) is a method used for studying the population structure and diversity of pathogens and involves sequencing DNA of several different genes and comparing the sequences between isolates . Here , we assess 13 T . cruzi genes and select the best combination for diversity studies . Outputs reveal that a combination of 7 genes can be used for both lineage assignment and high resolution studies of genetic diversity , and a reduced combination of four loci for lineage assignment . Application of MLST for assigning field isolates of T . cruzi to genetic groups and for detailed investigation of diversity provides a valuable approach to understanding the taxonomy , population structure , genetics , ecology and epidemiology of this important human pathogen .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases",
"medicine",
"and",
"health",
"sciences",
"chagas",
"disease",
"parasite",
"evolution",
"biology",
"and",
"life",
"sciences",
"microbiology",
"protozoan",
"infections",
"parasitic",
"diseases",
"parasitology"
] |
2014
|
Optimized Multilocus Sequence Typing (MLST) Scheme for Trypanosoma cruzi
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Parasitic infections are prevalent among pregnant women in sub-Saharan Africa . We investigated whether prenatal exposure to malaria and/or helminths affects the pattern of infant immune responses to standard vaccinations against Haemophilus influenzae ( Hib ) , diphtheria ( DT ) , hepatitis B ( Hep B ) and tetanus toxoid ( TT ) . 450 Kenyan women were tested for malaria , schistosomiasis , lymphatic filariasis ( LF ) , and intestinal helminths during pregnancy . After three standard vaccinations at 6 , 10 and 14 weeks , their newborns were followed biannually to age 36 months and tested for absolute levels of IgG against Hib , DT , Hep B , and TT at each time point . Newborns’ cord blood ( CB ) lymphocyte responses to malaria blood-stage antigens , soluble Schistosoma haematobium worm antigen ( SWAP ) , and filaria antigen ( BMA ) were also assessed . Three immunophenotype categories were compared: i ) tolerant ( those having Plasmodium- , Schistosoma- , or Wuchereria-infected mothers but lacking respective Th1/Th2-type recall responses at birth to malaria antigens , SWAP , or BMA ) ; ii ) sensitized ( those with infected/uninfected mothers and detectable Th1/Th2-type CB recall response to respective parasite antigen ) ; or iii ) unexposed ( no evidence of maternal infection or CB recall response ) . Overall , 78 . 9% of mothers were infected with LF ( 44 . 7% ) , schistosomiasis ( 32 . 4% ) , malaria ( 27 . 6% ) or hookworm ( 33 . 8% ) . Antenatal maternal malaria , LF , and hookworm were independently associated with significantly lower Hib-specific IgG . Presence of multiple maternal infections was associated with lower infant IgG levels against Hib and DT antigens post-vaccination . Post-vaccination IgG levels were also significantly associated with immunophenotype: malaria-tolerized infants had reduced response to DT , whereas filaria-tolerized infants showed reduced response to Hib . There is an impaired ability to develop IgG antibody responses to key protective antigens of Hib and diphtheria in infants of mothers infected with malaria and/or helminths during pregnancy . These findings highlight the importance of control and prevention of parasitic infections among pregnant women .
Vaccine-preventable diseases ( VPD ) continue to kill an estimated one to two million children each year , comprising about 14% of global mortality in children under 5 years of age [1 , 2] . Vaccination studies have repeatedly shown that children in developing nations are less responsive to vaccines than children from developed countries [3–7] . Recent outbreaks of polio in Africa and Syria have also shown that vaccine failures can result in resurgence of vaccine preventable diseases even in countries with high vaccination coverage [8 , 9] . This suggests that vaccine efficacy can be lower in certain settings . That is , failure to respond appropriately to vaccination is likely to be associated with poverty-related conditions , including malnutrition and chronic infection , particularly chronic parasitic infections [10 , 11] . Animal models suggest that nematode infections decrease vaccine efficacy and contribute to risk for infection by vaccine preventable diseases [12 , 13] . Concurrent schistosomiasis infection in humans has a negative impact on vaccination for tetanus [14] and tuberculosis ( BCG vaccination ) [15] . Chronic parasitic infections other than helminths can impair immune responses , resulting in decreased tetanus , Haemophilus influenzae type b ( Hib ) , and typhoid vaccine efficacy in the presence of malaria infection [16 , 17] . The immune consequences of parasitic infections can be reflected in the unborn children of infected mothers . Prenatal exposure to parasitic infections can generate a number of effects on fetal immune responses , and can affect functional response to post-partum vaccination , as we and others have shown for BCG [18–21] . Over the past decade , we have studied the influence of chronic maternal parasitic infections ( lymphatic filariasis , schistosomiasis and malaria ) on immune response in newborns and young children living in Kenya [18–20 , 22–25] . It appears that transplacental trafficking of parasite antigens from mother to fetus occurs on a frequent basis , leading to multivalent T and B cell responses to parasitic infections in the newborn [20 , 26–31] . This fetal priming results in two phenotypes: those that have an enhanced response to the parasite antigen ( “sensitized” , with cord blood lymphocytes ( CBL ) producing IFNγ , IL-2 , IL-4 , and IL-13 to parasite antigen challenge ) and those that have a suppressed response ( “tolerized” , where CBL do not produce parasite antigen-induced IFNγ , IL-2 , IL-4 or IL-13 , but do produce IL-10 to parasite antigen challenge ) [23 , 32] . The goal of the present study is to determine how the individual and combined antenatal parasitic infections , and the resulting sensitization or tolerization of infant immune responses , could influence early childhood responses to standard H . Influenzae type B , diphtheria toxoid , tetanus toxoid , and hepatitis B virus vaccination .
Approval for the study was obtained from the Kenya Medical Research Institute National Ethical Review Committee and from the Institutional Review Board for Human Studies at University Hospitals of Cleveland Case Medical Center . Mothers provided written informed consent for their own participation and that of their infants . Healthy pregnant women and their offspring born at the Msambweni District Hospital on the south coast of Kenya were enrolled in this mother-child cohort study . Mothers underwent a detailed questionnaire that queried their education level , spouse’s occupation , and household income . Women enrolled in the study were given malaria prophylaxis consisting of two single doses of sulfadoxine–pyrimethamine ( SP ) at the beginning of the second and third trimester , respectively , of pregnancy , and a single dose of albendazole ( 400mg ) in accordance with recommendations from the Kenya Ministry of Health . Mothers and children were also examined and tested for parasitic infections at times of any intercurrent acute illnesses during the follow-up period , and treated appropriately . Pregnant women provided venous blood , urine , and stool at their first antenatal clinic visit and again at delivery . For the mother-infant pairs , maternal venous blood , placental intervillous blood , and umbilical cord blood were collected at delivery , as previously described [18] . Infant venous blood , urine and stool samples were collected beginning at 6 mo . of age and every 6 mo . thereafter until age 36 mo . Plasma was stored at -80ºC until antibody assays were performed . Cellular immune response at birth was performed on fresh cells . Infants received standardized immunizations provided by the Ministry of Health following established Kenya National Health Service guidelines . Pentavalent ( diphtheria-tetanus-whole cell pertussis-hepatitis B-Hib ) vaccine was given at 6 , 10 , and 14 weeks , oral trivalent polio was given at birth , 6 , 10 , and 14 weeks and one dose of measles vaccine was given at 9 months . At birth , and at each 6-month follow up visit , length/height , weight , and head circumference were measured . Maternal venous blood , intervillous placental blood , cord blood , and infant venous blood were examined for malaria infection status by light microscopy . DNA was extracted from blood and tested for Plasmodium falciparum ( Pf ) by real time quantitative PCR ( RTQPCR ) [33] . A newborn was considered “exposed” to malaria in utero if one or more of the blood smear preparations or RTQPCR results were positive from antenatal , placental , or cord blood testing . A newborn was considered “not exposed” when both diagnostic tests were negative on all specimens . Stool and urine were also obtained from infants at every 6 month visit and examined for the presence of intestinal helminths and S . haematobium ova as described previously [19 , 20] . Infection status with S . haematobium was also assessed by ELISA detection of SWAP-specific ( soluble worm antigen of S . haematobium ) IgG4 antibodies in plasma samples collected [19 , 20] . Wuchereria bancrofti infection was detected by assay for circulating microfilaria antigen in plasma samples with the use of a commercial Og4C3 antigen detection assay ( TropBioMed , Townsville , Australia ) and also assessed by ELISA detection of BMA-specific ( Brugia malayi antigen ) IgG4 antibodies [19 , 20] . CBMC were isolated from fresh cord blood and were cultured in the presence of malaria antigens for recall responses to malaria as described [18] . For schistosomiasis , SWAP was used as the antigen challenge , and for LF , Brugia malayi antigen , ( BMA ) , was used as the antigen marker for anti-filarial immune response , as this antigen is preserved across species and can be used to detect exposure to W . bancrofti , the filarial worm endemic to Kenya [19 , 20] . All culture supernatants were collected at 72 h and immediately frozen at -80ºC for storage , pending cytokine assays . Quantification of IFNγ , IL-5 and IL-13 was performed on culture supernatants by ELISA and positive response was scored as previously described [18] . Response to vaccination was determined by standard ELISAs for IgG levels against tetanus toxoid ( TT ) , diphtheria toxoid ( DT ) , Hepatitis B virus ( Hep B ) , and Hib ( using its polyribitol phosphate ( PRP ) antigen ) [34–36] . Briefly , ELISA plates were coated with 1 µg/ml of TT ( Massachusetts Biolabs , Cambridge , MA ) ; 0·5 LF/ml ( flocculation unit/ml ) of DT ( diphtheria Antitoxin Human Serum NIBSC code: 00/496 ) ; 1µg/ml of Hep B surface antigen ( adw subtype , Fitzgerald Industries , Concord , MA , USA ) or 1 µg/ml H . influenza type b oligosaccharide-human serum albumin conjugate ( ATCC: NR-12268 ) and incubated overnight at 4ºC . After blocking and washing , diluted plasma samples were added to the plates and incubated for 1 h at room temperature . The plates were washed and alkaline phosphatase-conjugated anti-human IgG ( Jackson ImmunoResearch , Malvern , PA ) was added at 1:1000 for 1 h at 37°C . Substrate orthophenoylenediamine ( o-p-NN ) was added after the final wash . The reaction was stopped by adding 5% EDTA , and absorbance was read at 405 nm with an ELISA reader . For all assays , standard sera obtained from NIBSC ( National Institute of Biological Standards and Control ) were used to create standard curves for determination of the subjects’ anti-antigen IgG concentrations . To assess potential mechanisms of altered vaccine responses in some offspring of women with parasite infection , we hypothesized that prenatal exposure to one or more parasite antigens would generate a tolerogenic immunophenotype , such that upon exposure to vaccines in early childhood , a bystander effect might alter the ability to generate a robust immune response . Children were classified as either sensitized , tolerized , or unexposed to the individual parasite pathogens studied . This classification was based on their mother’s malaria , Schistosoma , and lymphatic filarial infection status and on their detectable Th1/Th2-type cord blood ( CB ) recall responses to each of these respective parasite’s antigens . The tolerant classes were defined as: i ) tolerant to malaria if children had mothers with malaria ( P . falciparum-positive by blood smear and/or PCR ) but lacked detectable Th1/Th2-type CB recall responses to malaria blood stage antigens; ii ) tolerant to Schistosoma if mothers had schistosomiasis , ( SWAP IgG4 and/or S . haematobiumegg positive ) but lacked detectable Th1/Th2-type CB recall responses to SWAP antigens , and iii ) tolerant to LF if mothers had evidence of LF , ( BMA IgG4 and/or Og4C3 antigen positive ) but lacked detectable Th1/Th2-type CB recall responses to BMA antigens . Sensitized infants were defined as sensitized to malaria if cord blood lymphocytes had detectable Th1/Th2-type recall responses to malaria , sensitized to Schistosoma if there was a SWAP-specific recall response in CB , and sensitized to LF if there was a BMA-specific recall response in CB; the mothers may or may not have been tested positive for the respective infection during delivery . SWAP and BMA were used at 25 µg/ml and 10 µg/ml respectively; these concentrations of antigens failed to induce an immune response in North American controls . Unexposed classes were defined as children who had mothers P . falciparum-negative , anti-SWAP-negative , and who also had a lack of CB lymphocyte responses individually to malaria , Schistosoma or LF antigens . Demographic characteristics of participating mothers were summarized using descriptive statistics . Pearson chi-squared test was used to compare maternal infection rates among different demographic groups , including age , education , household income , ethnic group and parity . To investigate the association between maternal infections and infants’ IgG responses to DT , Hep B , Hib and TT during the first 30 months of life , mixed-effects model was used to model infants’ IgG responses over time . A random intercept and slope were included to account for between-subject variance in vaccine response over time . A quadratic function of age time was also included as a fixed effect accounting for the natural curvature of infant vaccine response during infancy and early childhood . To avoid over-fitting models , covariates were selected a priori based on biological knowledge and included characteristics determined at enrollment: maternal age , parity , sex of infant , education and income level of the mothers , and maternal dose of tetanus ( for IgG response to TT only ) , and characteristics collected during the study ( infant parasitic infections at their follow-up visits ) . Due to very low infant infection rates of schistosomiasis , LF and hookworm , only infant malaria infection was included in the model when studying maternal malaria infections . Similar models were used to investigate the effect of sensitization and tolerization on infants’ IgG responses to DT , Hep B , Hib and TT during the first 30 months of life . SAS Version 9 . 2 was used for all analyses . Statistically significant differences were assessed using P < 0∙05 as criterion .
An overview of the enrollment and follow up of participants is presented in Fig . 1 . A total of 510 mother-newborn pairs were recruited between 2006 and 2009; of those , 450 children had informative cord blood testing and returned at least once after delivery , and were included in the analysis . For assessment of post-vaccination antibody responses , study children were followed every 6 months up to 36 months of age . The mean follow up time was 27 months ( SD = 2∙7 months ) . Children were followed on an average of three to four times . Children were retained in the study if they missed one or more follow-ups , 299 , 294 , 246 , 254 and 138 infants were followed at 6 , 12 , 18 , 24 and 30 months respectively . The number of children followed at 36 months of age was small . Therefore , the 36 month outcomes were not included in the analysis . The primary reason for loss to follow-up was permanent emigration from the study area . There was no difference in the average number of maternal infections , age , or parity between the group of mother-infant pairs who dropped out and those who remained in the study . The demographic characteristics of the participating mothers , along with their rates of malaria , schistosomiasis , lymphatic filariasis and soil-transmitted-helminth infections are shown in Table 1 . Mothers’ age showed a significant effect on the prevalence of malaria , S . haematobium , and soil-transmitted helminths ( STH ) . The level of maternal education was inversely related to rates of STH ( P<0 . 0001 ) . Mothers from low-income households had more schistosomiasis than mothers from high-income households ( P = 0∙003 ) . There was no significant difference in infection rates according to mother’s gravidity or ethnicity . Overall , 78·9% of the mothers were infected with one or more of the following infections; P . falciparum malaria , lymphatic filariasis ( LF ) , schistosomiasis and/or intestinal helminths ( Fig . 2 ) . Prevalence of Strongyloides and Ascaris was <2% . Polyparasitism proved common in our cohort: a single infection was detected in 29·6% , two infections in 27·6% , three infections in 15·6% , four infections in 4·2% and five infections in 1·3% of women . To determine whether the presence of individual infection during pregnancy was associated with impaired IgG responses to Hib , DT , Hep B , and TT in infancy and early childhood , children were stratified into groups whose mothers were independently malaria- , schistosomiasis- , LF- , or hookworm-infected , or not infected during pregnancy; other STH , such as Trichuris , Strongyloides were detected in <2% and Ascaris , was detected in <11% of the mothers and therefore were not considered further in the analysis . The presence of pre-natal maternal malaria , LF , and hookworm ( Fig . 3 , rows A , B , and D respectively ) was associated with significantly lower post-vaccination levels of Hib-specific IgG ( for malaria , P = 0•031 , 0•005 , 0•007 , 043 at 6 , 12 , 18 , 24 months of age; for LF , P = 0•007 , 0•03 at 12 , 18 months of age; and for hookworm , P = 0•034 , 0•019 at 12 , 18 months of age , respectively , controlling for maternal age , parity , education , income , and childhood parasitic infections ) . Maternal schistosomiasis alone ( Fig . 3 , row C ) had no effect on vaccine-specific IgG levels . Malaria , LF , schistosomiasis and hookworm had no effect on DT , ( Fig . 3 ) , Hep B and TT ( see Supporting Information , S1 Fig . ) . The presence and the multiplicity of prenatal parasitic infections were significantly associated with reduced IgG responses to vaccine antigens during infancy following pentavalent vaccination given at 6 , 10 , and 14 weeks of age . Compared to no prenatal maternal infection , the presence of single or double maternal infections during pregnancy resulted in significantly lower anti-Hib PRP-specific IgG levels in infants and young children to 2 years of age ( Fig . 4 row A ) ; ( P = 0·04 , <0·0001 , 0·001 , 0·04 with one infection for 6 , 12 , 18 , and 24 month time points , and P = 0·008 , 0·01 , 0·04 with two infections at 12 , 18 , and 24 months of age , respectively ) . Offspring of mothers with 3 or more parasitic infections did not have reduced anti-Hib PRP-IgG ( Fig . 4 , row A ) . Prenatal parasitic infections had little impact on DT-specific IgG levels in infants of mothers with one or two infections , but there was an effect in infants of mothers with three or more infections at 6 and 12 months of age , ( Fig . 4 , row A , P = 0·002 and 0·03 , respectively ) . To better understand the interaction of different parasitic infections in mothers on vaccine responses in their offspring , we examined the response to vaccine antigens in infants of uninfected mothers versus those infected with: i ) malaria alone , ii ) malaria plus one helminth and iii ) malaria plus two or more helminths . As shown in Fig . 4 , row B , response to Hib was significantly lower in infants of mothers with single malaria infection ( P = 0·027 , 0•005 , 0•007 , and 0•046 at 6 , 12 , 18 , and 24 months of age ) ; whereas response to DT was lower in infants of mothers with malaria plus one helminth ( P = 0•001 , 0•003 , and 0•024 at 12 , 18 , and 24 months ) and malaria plus two or more helminths ( P = 0•0001 , 0•005 , 0•037 , and 0•022 at 6 , 12 , 18 and 24 months ) . We also examined the response to vaccines in infants of uninfected mothers versus those infected with: i ) one helminth and no malaria , ii ) two helminths and no malaria , and iii ) three or more helminths and no malaria ( Fig . 4 , row C ) . The Hib-specific IgG levels were lower in infants of mothers with single helminth and no malaria ( P = 0•001 and 0•003 at 12 and 18 months ) and two helminths and no malaria ( P = 0•018 , 0•002 , and 0•011 at 6 , 12 , and 18 months ) . In contrast , there was no significant difference in TT or Hep B-specific post-vaccination IgG levels in infants of mothers with multiple infections as compared to mothers with no infection ( S2 Fig . ) . Children were classified as either sensitized , tolerized , or unexposed to the individual parasite pathogens studied , as described in detail in the methods . With respect to Hib vaccine , classification of children by cord blood response to prenatal malaria had no effect on anti-Hib Ab levels ( Fig . 5 , row A ) , however offspring who developed a tolerogenic response to filarial antigens had significantly reduced anti-Hib responses at 12 , 18 , and 24 months of age ( P = 0·052 , 0·033 , and 0·035 , respectively , Fig . 5 , row B ) . There was no impact of cord blood response to prenatal schistosome exposure on anti-Hib IgG levels ( Fig . 5 , row C ) . By contrast immunophenotype acquired by prenatal exposure to malaria antigens had significant effects of development anti-DT IgG levels following vaccination . Children classified as malaria-tolerant showed significantly reduced DT-specific IgG levels at 12 and 18 months of age , ( P = 0·032 and 0·048 , respectively ) when compared to unexposed infants . There were increased anti-DT IgG levels in malaria-sensitized infants at 6 and 12 months ( P = 0·006 and 0·04 respectively , Fig . 5 , row A ) . To determine if the sensitization in this group of infants was to malaria antigens alone or a T cell response to malaria plus other antigens , we further analyzed the sensitized group looking at the Th1/Th2-type recall responses to malaria alone , malaria+SWAP and malaria+BMA . There was no difference in anti-DT IgG in infants sensitized to malaria alone , but infants sensitized to malaria+SWAP showed a relative increase in DT-specific IgG levels . Children classified as tolerized to LF ( based on Og4C3 or BMA-specific IgG4 positivity of their mothers ) had levels of anti-Hib IgG that were significantly reduced at 12 , 18 , and 24 months of age ( P = 0·052 , 0·033 , and 0·035 , respectively , Fig . 5 , row B ) . There were no significant differences in the levels of IgG response to Hib , DT , Hep B , and TT ( Fig . 5 , as well as S3 Fig . ) among Schistosoma-tolerized or-sensitized infants as compared to the responses of those who were unexposed .
This study shows that there is an impaired ability to develop IgG antibody responses to key protective antigens of Hib and diphtheria among infants of mothers infected with malaria and/or helminths during pregnancy , as compared to infants of uninfected mothers . This association with maternal infection was most pronounced for anti-Hib antibody responses , with some evident effects on diphtheria antibody response , but no measurable effect on acquisition of antibodies to TT and hepatitis B . Independently , antenatal malaria , LF , and hookworm each lowered the IgG response to Hib , but schistosomiasis had no effect . Most pregnant women were infected with more than one of these parasitic infections , yet having multiple infections did not increase the observed impaired levels of antibody responses , suggesting that single species-specific mechanisms may be responsible for this vaccine-response impairment . To examine the possibility that maternal parasitic infections represented only a proxy for other factors that could impact children’s responses to vaccination , we adjusted for the effects of maternal age , parity , occupation , income , and acquisition of childhood parasitic infections . None of these co-factors had a significant effect on the strength of association of maternal infection with antibody responses , indicating there could indeed be a causal association between maternal parasitic infections and impaired vaccine responses . We hypothesized that the effects of prenatal maternal malaria or helminth infections could be based on in utero modification of fetal immune responses by transplacental exposure of the fetus to parasite antigens [29 , 31 , 37–39] . Therefore , the vaccination outcomes among offspring of infected women were further analyzed after classification of the infant study subjects into sensitized and putatively tolerant groups , based on their cord blood responses to identified maternal infections . Tolerance is likely to contribute to the long-term persistence of many intravascular parasitic infections and this phenomenon is associated with impaired or altered fetal immune response by generation of regulatory T cells [40–42] . Not all in utero exposure to maternal helminth infections results in tolerance . Instead , for some newborns , prenatal parasite exposure results in a constant state of anti-parasite immune activation that is characterized by a Th2-dominant cytokine profiles , i . e . , high IgE levels , eosinophilia , and generation of regulatory T cells . Such an immune profile could have an adverse impact on the efficacy of vaccines by limiting Th1-type pathways of immune response to vaccination . By altering the immunologic balance between Th1-type and Th2-type pathways and generation of regulatory T cells , chronic parasitic infections appear to alter the immunologic milieu and could also likely impair or suppress the ‘‘normal’’ responses to vaccines that have been described in parasite-free , developed countries . Our data suggest that the malaria-tolerized group was less likely to respond well to DT , and that the filarial-tolerized group was less likely to respond well to Hib , when compared to unexposed children . By contrast , the schistosomiasis-tolerized group showed no evident effect on the vaccination responses we studied . Interestingly , maternal helminth infections , in the absence of malaria infection , had no effect on antibody responses to DT , TT and hepatitis B , whereas maternal malaria infection with one or more helminth infection resulted in a significant drop in antibody responses to DT , and not to TT or hepatitis B . This impaired response to DT was most pronounced in children who were classified as immune tolerant . The differential impact of maternal infections on immune responses to various vaccines may arise from the type of vaccine and breadth of antibody responses . In contrast to DT , TT , and hepatitis B , Hib is a polysaccharide vaccine conjugated to TT and the antibody responses is directed to polyribosylribitol phosphate , an oligosaccharide with two protective epitopes [43] , and thus a very restricted epitope repertoire . Importantly , a low antibody to PRP correlates with impaired vaccine efficacy ( <0 . 15 ug/ml ) . Even though most children with impaired PRP antibody response had anti-PRP above the protective threshold , lower initial steady-state anti-PRP IgG antibody response could lead to a reduced long-term immunological memory [44] . DT and TT are complex antigen mixtures containing many bacterial pathogen-associated molecular patterns ( PAMPs ) that may elicit a more pro-inflammatory cytokines that may circumvent any antenatal acquired immunoregulatory response . In addition , antibody responses measured to these crude antigenic mixtures may mask a tolerogenic effect to certain epitopes . An epitope-specific tolerogenic responses has been observed to the malaria surface protein 1 ( MSP1 ) in offspring of mice infected with malaria [45] . Results from the current study support the hypothesis that antenatal single and multiple parasitic infections can imprint effects on fetal immunity , and thereby affect certain infant vaccine responses in the first months after birth . Both animal and human studies indicate that parasitic infections can have an effect on long-term responses to vaccination . Chronic concurrent parasitic infections have proven to have harmful immune effects including decreased tetanus , Hib , and typhoid vaccine responses in the presence of active malaria infection in humans [14 , 46] . By contrast , studies have shown no effect of maternal infection with T . cruzior congenital Chagas disease on responses to BCG , hepatitis B , diphtheria , or tetanus vaccination in the neonatal period [47] . A recent randomized , placebo-controlled trial did not find an effect for antenatal anti-helminthic treatment ( of low intensity infections ) on infant responses to tetanus , BCG , or measles vaccinations , although a small effect was noted for tetanus response when the analysis was focused only on the subgroup of mothers known to be infected [48] . In our study , we observed lower IgG responses to Hib and DT , but not to tetanus . This may be because the pregnant mothers were immunized with tetanus vaccine according to Ministry of Health guidelines , and the maternal antibodies during pregnancy and later infant responses were not affected by antenatal malaria or helminth infection . In past studies , active schistosomiasis in humans has been shown to have a negative impact on responses to tetanus and BCG vaccination [14] . However , we did not observe an effect of maternal schistosomiasis alone on tetanus , Hep B , DT , or Hib responses . There were strengths and limitations in the present study . In our study , the detection of malaria , schistosomiasis and LF infections utilized very sensitive and specific experimental detection assays to classify infection status . By avoiding the sole use of stool and urine examinations , which are less sensitive and could miss low level infections , our results provide realistic estimates of STH and S . haematobium infections . Conversely the reliance on serological assay to detect most of the Schistosoma infections , in particular , may have overestimated ongoing infections or detected such light infections that they were unlikely to have significantly exposed the fetus to parasite antigens . Our long-term , prospective follow-up of a large cohort of mother-infant pairs allowed for better definition of the time-related pattern of vaccine response at the individual level . The outcomes of the present study are just based on the antibody responses to Hib , DT , TT , and Hep B vaccine antigens . Other vaccines are given in the study site , and analysis of these will be included in future studies . The children were vaccinated for Hib , DT , TT , and Hep B at 6 , 10 and 14 weeks , but vaccine response was not measured until 6 months of age , so the children’s immediate responses to vaccination are not known . It is possible that children who were exposed to natural infection during the observation intervals may have had varied vaccine responses and that we may have missed differences between our groups in their post-primary and post-secondary responses . Similarly , an in-depth assessment of maternal and child nutrition was not done in this study , and aspects of macro- and micro-nutrient intake may have had confounding effects on infection and response to vaccines [49] . Finally , our prenatal infection data include only maternal malaria and helminth infections at the first antenatal visit and at the time of delivery; mothers may have been exposed to these and other infections during pregnancy without detection by the study’s testing . Despite these limitations , we feel that this study offers an accurate documentation of the effect of antenatal parasitic infection on IgG antibody response to vaccine-preventable disease antigens . Given the complexity of parasitic impact on fetal immunity , our results suggest that antenatal malaria , LF , schistosomiasis , and hookworm variously affect the levels of post-vaccination protection against Hib and diphtheria among infants between 6–30 months of age . While the observed lower levels of antibody response may not obviate the individual protective effect of vaccination , these lower levels of immune response could contribute to persistent pathogen carriage , leading to continuing disease transmission among affected communities . Many of the effects we found were parasite-specific and vaccine-dependent , suggesting that parasitic effects can differ depending on the type and the intensity of infections . To explore the duration of the observed effects , we are in the process of re-examining our study cohort , now aged 5–8 years old , and testing their immune responses to new vaccine antigens . Overall , the present findings reinforce the importance of control and prevention of parasitic infections in pregnant women . They also suggest that if vaccine efficacy is to be ensured in disease-endemic developing countries , eradication of chronic helminthic infections may be imperative to the overall success of global vaccination efforts .
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Parasitic infections are prevalent among pregnant women in sub-Saharan Africa . Prenatal exposure to parasitic infections can generate several potential effects on fetal immune responses and affect functional antibody generation during subsequent vaccination . There is a paucity of data on the detrimental effects of chronic parasitic infections during pregnancy on the response to vaccine from birth to childhood . This paper highlights the overwhelming presence of helminth infection and malaria in pregnant women in rural Kenya . The study shows that the presence of single and multiple antenatal parasitic infections is associated with impaired infant IgG levels against Haemophilus influenzae ( Hib ) and diphtheria ( DT ) antigens post-vaccination from birth to 30 months of age . This study found that the response to DT was reduced in malaria-tolerized infants , and the response to Hib was impaired in filarial-tolerized infants; by contrast , the Schistosoma-tolerized group showed no effect . Deworming campaigns must be directed towards pregnant mothers , infants , and young children to improve response to vaccination .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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Effect of Antenatal Parasitic Infections on Anti-vaccine IgG Levels in Children: A Prospective Birth Cohort Study in Kenya
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Meiotic recombination and chromosome synapsis between homologous chromosomes are essential for proper chromosome segregation at the first meiotic division . While recombination and synapsis , as well as checkpoints that monitor these two events , take place in the context of a prophase I-specific axial chromosome structure , it remains unclear how chromosome axis components contribute to these processes . We show here that many protein components of the meiotic chromosome axis , including SYCP2 , SYCP3 , HORMAD1 , HORMAD2 , SMC3 , STAG3 , and REC8 , become post-translationally modified by phosphorylation during the prophase I stage . We found that HORMAD1 and SMC3 are phosphorylated at a consensus site for the ATM/ATR checkpoint kinase and that the phosphorylated forms of HORMAD1 and SMC3 localize preferentially to unsynapsed chromosomal regions where synapsis has not yet occurred , but not to synapsed or desynapsed regions . We investigated the genetic requirements for the phosphorylation events and revealed that the phosphorylation levels of HORMAD1 , HORMAD2 , and SMC3 are dramatically reduced in the absence of initiation of meiotic recombination , whereas BRCA1 and SYCP3 are required for normal levels of phosphorylation of HORMAD1 and HORMAD2 , but not of SMC3 . Interestingly , reduced HORMAD1 and HORMAD2 phosphorylation is associated with impaired targeting of the MSUC ( meiotic silencing of unsynapsed chromatin ) machinery to unsynapsed chromosomes , suggesting that these post-translational events contribute to the regulation of the synapsis surveillance system . We propose that modifications of chromosome axis components serve as signals that facilitate chromosomal events including recombination , checkpoint control , transcription , and synapsis regulation .
Meiosis is a special type of cell division that gives rise to haploid gametes required for sexual reproduction . To halve the chromosome number , two successive chromosome segregation events follow a single round of DNA replication . At the first stage of meiosis , the leptotene stage of prophase I , recombination is initiated between homologous chromosomes ( homologs ) by programmed DNA double-strand breaks ( DSBs ) formed by the SPO11 protein [1] . Recombination is , in some organisms including mice , required for synapsis of homologs [2] , [3] . At the zygotene stage of prophase I , homologs come into close proximity and the synaptonemal complex ( SC ) assembles between the aligned homologs [4] , [5] . At the pachytene stage of prophase I , the homologs become fully synapsed by the SCs and repair of a subset of DSBs results in crossover recombination . At the diplotene stage of prophase I , the SCs are disassembled and the homologs undergo desynapsis , now attached to each other only at crossover sites . The physical connections between the homologs , called chiasmata , are essential for correct segregation of the homologs at the anaphase stage of meiosis I [6] . Thus , processes that transform the nature of meiotic chromosomes , such as recombination and synapsis , are executed in a coordinated manner during prophase I . The integrity of the recombination process and chromosome synapsis during prophase I is monitored by cellular surveillance systems [7] . Checkpoint kinases such as ATM ( ataxia telangiectasia mutated ) and ATR ( ATM and Rad3-related ) play key roles in the meiotic surveillance systems in many organisms , including mice . In budding yeast , Mec1 and Tel1 , the yeast orthologs of ATR and ATM , respectively , are activated by Spo11-generated DSBs to regulate the pachytene checkpoint that monitors recombination and synapsis [7] . In mammals , deficiencies in recombination or synapsis give rise to meiotic arrest or cell death at the late zygotene or pachytene stage of prophase I [8]–[11] . This checkpoint-like phenomenon is thought to be controlled by MSUC ( meiotic silencing of unsynapsed chromatin ) , a surveillance system that monitors synapsis and causes gene silencing [10] . In MSUC , ATR is recruited to unsynapsed chromosomal regions together with ATR activators , such as BRCA1 and TOPBP1 , and induces phosphorylation of histone H2AX ( γH2AX ) in those regions [10] . This post-translational signal triggers chromatin alterations , leading to transcriptional silencing [10] , [12] . The MSUC machinery is proposed to control meiotic progression , by silencing gene expression on the XY chromosomes in male germ cells and by a yet-to-be-determined mechanism in female germ cells [10] , [13]–[16] . In contrast to ATR , ATM is dispensable for meiotic surveillance systems including MSUC in mouse meiosis , while it is required for completion and regulation of meiotic recombination [8] , [9] , [17] . Meiotic recombination and synapsis take place in the context of a prophase I-specific chromosome structure . Chromosomes consisting of two sister chromatids are organized in linear arrays of chromatin loops whose bases are attached to the chromosome axis [4] . The chromosome axis is associated to a single axial chromosome core composed of cohesin complex proteins and cohesin regulators , which promote sister chromatid cohesion and include meiosis-specific cohesin subunits , such as REC8 , RAD21L , SMC1β and STAG3 , as well as the canonical cohesin subunits ( SMC3 , SA1/2 , SMC1α and RAD21 ) and cohesin-associated proteins ( WAPL and PDS5B ) [18]–[23] . The cohesin core serves as a scaffold for the assembly of the axial element ( AE ) of the SC , a protein structure that promotes additional chromosome axis organization . SYCP2 and SYCP3 are major components of the mammalian AE and are essential for its formation [24] , [25] . Proteins harboring a HORMA ( Hop1 , Rev7 and Mad2 ) domain represent a third group of chromosome axis proteins in eukaryotes and include the mammalian HORMA domain-containing proteins , HORMAD1 and HORMAD2 . In contrast to cohesin complex proteins and AE proteins , HORMAD1 and HORMAD2 bind preferentially to chromosome axes where homologs are not synapsed , such as axes prior to synapsis ( unsynapsed ) and axes where the SC has disassembled after completion of synapsis ( desynapsed ) [26] , [27] . We have monitored here the phosphorylation status of individual chromosome axis proteins in mouse spermatocytes during prophase I , to better understand the relationship between axis morphogenesis and axis-associated chromosomal events . We report that chromosome axis proteins , such as cohesin complex proteins , AE proteins and HORMA domain-containing proteins , are phosphorylated in a spatially and temporally distinct manner during mammalian meiosis . We suggest that the observed dynamic changes in the phosphorylation pattern of chromosome axis proteins serve as signals that integrate the recruitment of regulatory proteins with the facilitation of chromosomal events that take place on meiotic chromosomes .
To examine the phosphorylation status of meiotic chromosome axis components , we performed immunoblotting experiments following SDS-gel separation of mouse testis nuclear extracts . The nuclear extracts were also treated with phosphatase to identify phosphorylated proteins by changes in their gel mobility ( Figure 1A and Figure S1 ) . Phosphatase-sensitive protein bands were detected for SYCP2 , SYCP3 , STAG3 , REC8 , HORMAD1 and HORMAD2 ( Figure 1A , black and gray arrowheads ) , whereas no obvious mobility shifts were seen for SMC3 and SMC1β ( Figure 1A ) . Thus , most of the chromosome axis proteins that we analyzed are phosphorylated . To examine whether the phosphorylated forms of these proteins are bound to chromosomes , we fractionated testis nuclear extracts . Testis nuclear extracts were treated with detergents containing Triton X-100 , and then fractionated by centrifugation into a pellet ( insoluble fraction ) including chromosome-associated proteins and a supernatant ( soluble fraction ) containing nucleoplasmic proteins . SYCP2 was found to be highly enriched in the insoluble fraction , whereas the other chromosome axis proteins were found in both fractions ( Figure 1B ) . We found that the phosphorylated forms of HORMAD1 , HORMAD2 , STAG3 and REC8 were preferentially bound to the chromosome , by comparing the gel mobility of the protein bands in the insoluble fraction to those in the nuclear extracts . In contrast , the phosphorylated forms of SYCP3 appeared at similar levels in both fractions . Thus , chromosome axis proteins bound to chromosomes are frequently phosphorylated . We next analyzed the timing with which phosphorylation of chromosome axis proteins takes place . We used testis nuclear extracts of juvenile mice , in which a synchronous first wave of spermatogenesis occurs . As shown in Figure 1C , the phosphorylated forms of SYCP2 , STAG3 , REC8 and HORMAD1 were detected as early as 11 or 12 days postpartum ( dpp ) , corresponding to the leptotene and early zygotene stages of prophase I . For HORMAD2 , a phosphorylated form of this protein was first seen at 12 dpp ( Figure 1C , gray arrowhead ) . In addition , a second phosphorylated form appeared at 17 dpp , corresponding to the late pachytene stage ( Figure 1C , black arrowhead ) , suggesting that phosphorylation of HORMAD2 occurs in a temporally-regulated manner . To gain insights into the nature of the kinases responsible for the observed phosphorylation events targeting chromosome axis proteins , we used an anti-pS/T-Q antibody that recognizes a phosphorylated serine or threonine followed by a glutamine residue , a consensus target sequence for ATM /ATR ( S/T-Q motif ) . Testis nuclear extracts were subjected to immunoprecipitation with the anti-pS/T-Q antibody , and the immunoprecipitates were probed for chromosome axis proteins by immunoblotting . We detected strong protein bands representing SYCP2 , HORMAD1 and HORMAD2 in the immunoprecipitates , suggesting that phosphorylation of these proteins occurs at an S/T-Q motif ( Figure 1D ) . We also detected a relatively strong signal for SMC3 in the immunoprecipitates ( Figure 1D ) , implying that this chromosome axis protein is also phosphorylated at an S/T-Q motif despite the absence of a detectable shift in gel mobility ( Figure 1A ) . We saw little or no signal in the anti-pS/T-Q immunoprecipitates for STAG3 , SMC1β , REC8 and SYCP3 ( Figure 1D ) , suggesting that these proteins are phosphorylated at other motifs than the S/T-Q motif . Altogether , our results suggest that multiple kinases with different motif-specificity contribute to phosphorylation of chromosome axis proteins . We next investigated the phosphorylation events that target HORMAD1 and HORMAD2 in more detail . Immunoprecipitates of the anti-pS/T-Q antibody were examined using gel conditions that provided better resolution than that seen in Figure 1D , identifying one band strongly labeled by the anti-HORMAD1 antibody ( Figure 2A , black arrowhead ) and two bands labeled by the anti-HORMAD2 antibody ( Figure 2A , black and gray arrowheads ) . The enrichment of the slowest-migrating phosphorylated form of HORMAD1 ( Figure 2A , black arrowhead ) suggests that two phosphorylated forms of HORMAD1 exist , one that is phosphorylated primarily at a non-S/T-Q site and one that is phosphorylated at multiple sites containing an S/T-Q site . In contrast , the observation that both phosphorylated forms of HORMAD2 were enriched in the anti-pS/T-Q immunoprecipitates ( Figure 2A , black and gray arrowheads ) suggests that the both forms of HORMAD2 are phosphorylated at an S/T-Q site ( s ) . Mouse HORMAD1 and HORMAD2 contain several S/T-Q motifs , including the Ser375-Gln376 motif in the C-terminal region of HORMAD1 that is highly conserved in vertebrate HORMAD1 proteins ( data not shown ) . Based on this information , we generated a peptide antibody against the Ser375-phosphorylated form of HORMAD1 ( anti-pS375 ) . Immunoprecipitation and immunoblotting experiments using the anti-pS375 antibody showed that HORMAD1 is phosphorylated at Ser375 in testis nuclear extracts ( Figure 2B and 2C ) . To examine the chromosomal localization of the Ser375-phosphorylated form of HORMAD1 , nuclear spreads of mouse testicular cells were immunostained using the anti-pS375 antibody . The Ser375-phosphorylated form of HORMAD1 was first detectable as series of small foci along the chromosome axes in leptotene spermatocytes , temporally coinciding with loading of HORMAD1 onto the entire chromosome axis , as labeled by the regular anti-HORMAD1 antibody ( Figure 2D ) . The Ser375-phosphorylated form of HORMAD1 appeared as discontinuous lines composed of small foci on HORMAD1-labelled unsynapsed chromosome axes during zygotene ( Figure 2D and 2E ) . In pachytene and diplotene spermatocytes , the Ser375-phosphorylated form of HORMAD1 overlapped with HORMAD1 at unsynapsed chromosome axes of the XY chromosomes ( Figure 2D ) . Strikingly , whereas the anti-HORMAD1 antibody also labeled desynapsed chromosomal regions that appear at the diplotene stage [26] , [27] , the anti-pS375 antibody did not ( Figure 2D ) . To confirm this staining pattern , we examined the localization of the Ser375-phosphorylated form of HORMAD1 in oocytes during prophase I . We observed that the anti-pS375 antibody labeled series of foci along unsynapsed chromosomal regions in these cells , but notably did not label synapsed or desynapsed regions of chromosomes ( Figure S2A ) . Depletion of HORMAD1 from the synapsed chromosome axes requires the TRIP13 AAA-ATPase [27] . We therefore examined the chromosomal distribution of the Ser375-phosphorylated form of HORMAD1 in a Trip13 mutant . We observed that the anti-pS375 antibody , in contrast to the situation in wild-type spermatocytes , also labeled discontinuous lines along the chromosome axes of synapsed autosomes in the mutant spermatocytes ( 89/100 pachytene cells ) ( Figure 2F ) . Taken together , our data show that HORMAD1 is phosphorylated at Ser375 , that the Ser375-phosphorylated form of HORMAD1 is restricted to unsynapsed chromosomes in wild-type meiocytes and that TRIP13 facilitates the depletion of the Ser375-phosphorylated form of HORMAD1 from synapsed chromosomes . We detected SMC3 in the anti-pS/T-Q immunoprecipitates of testis nuclear extracts ( Figure 1D ) . SMC3 is known to be phosphorylated in mammalian cells at an S/T-Q motif , the Ser1083-Gln1084 motif , in response to DNA damage [28] . Indeed , immunoprecipitation of SMC3 from testis nuclear extracts followed by immunoblotting using a selective antibody for the Ser1083-phosphorylated form of SMC3 ( anti-pS1083 ) identified a protein band in a phosphatase-sensitive manner ( Figure 3A ) . Since SMC3 is expressed in both mitotic and meiotic cells , we addressed whether phosphorylation of SMC3 at Ser1083 occurs in the context of the meiotic chromosome axis . Indeed , we found several meiosis-specific cohesin components and AE proteins to be co-immunoprecipitated from testis nuclear extracts with the Ser1083-phosphorylated form of SMC3 ( Figure 3B ) . In addition , the anti-pS1083 signal increased when the first wave of spermatogenesis reached the leptotene stage ( Figure S3A ) . Next , nuclear spreads of mouse spermatocytes and oocytes were immunostained using the anti-pS1083 antibody ( Figure 3C , Figures S3B and S2B ) . The Ser1083-phosphorylated form of SMC3 was first detectable as foci on chromosome axes in leptotene cells ( Figure 3C , Figures S3B and S2B ) . The Ser1083-phosphorylated form of SMC3 was present on both synapsed and unsynapsed chromosomal regions at early zygotene ( Figure 3C ) , whereas the signal intensity increased preferentially at unsynapsed chromosomal regions during late zygotene ( Figure 3C , Figures S3B and S2B ) . In pachytene and diplotene spermatocytes , the Ser1083-phosphorylated form of SMC3 accumulated on the XY chromosomes ( Figure 3C and Figure S3B ) . Thus , the Ser1083-phosphorylated form of SMC3 is preferentially associated with unsynapsed chromosomes . We have identified a set of phosphorylation events that target HORMAD1 and SMC3 localized at unsynapsed chromosomal regions and shown that they are phosphorylated at an S/T-Q motif , a known motif for ATM/ATR kinases . We therefore investigated the role of these kinases in phosphorylation of chromosome axis proteins . Nuclear extracts were prepared from the testes of Atm−/− mice and the occurrence of the phosphorylated forms of chromosome axis proteins in the insoluble fraction was analyzed . We found that SYCP2 , STAG3 , REC8 and HORMAD1 are phosphorylated in the Atm−/− testis nuclear extracts ( Figure 4A ) . We also detected the Ser375-phosphorylated form of HORMAD1 and the Ser1083-phosphorylated form of SMC3 in the Atm−/− testis extracts ( Figure 4B and 4C ) , as well as in the Atm−/− spermatocytes ( Figure S4 ) . We observed a reduced intensity of the slowest-migrating form of HORMAD2 ( Figure 4A , black arrowhead ) . However , since this phosphorylated form of HORMAD2 occurs at the late pachytene stage ( Figure 1C ) , the reduced intensity of this band in the Atm−/− testis extracts is most likely due to the observed loss of germ cells that takes place at the pachytene stage in Atm−/− male mice [8] , [29] . Therefore , we conclude that ATM is dispensable for phosphorylation of chromosome axis proteins prior to the pachytene stage . ATR is localized to unsynapsed chromosomal axes [10] . We found that the distribution of ATR is similar to that of the Ser375-phosphorylated form of HORMAD1 from late zygotene to diplotene ( Figure S5 ) . To examine if ATR phosphorylates chromosome axis proteins during prophase I , we took advantage of the fact that BRCA1 is required for a subset of ATM/ATR-dependent phosphorylation events [30] and that BRCA1 facilitates the proper distribution of ATR at unsynapsed chromosomal regions during prophase I in meiocytes [13] , [31] . We prepared nuclear extracts from testes of Brca1Δ11/Δ11 Trp53+/− males , which express a mutated BRCA1 protein that lacks a protein domain encoded by exon 11 . The mutated BRCA1 protein fails to correctly distribute recombination proteins to repair sites and ATR to unsynapsed chromosomal regions in spermatocytes [13] , [31] , [32] . Immunoblotting experiments of the insoluble fraction prepared from the mutant testis nuclear extracts identified the phosphorylated forms of SYCP2 , STAG3 and REC8 , as well as the Ser1083-phosphorylated form of SMC3 ( Figure 4D and 4F ) . In contrast , the intensities of the bands representing the slowest-migrating form of HORMAD1 ( Figure 4D , black arrowhead ) , the Ser375-phosphorylated form of HORMAD1 ( Figure 4E ) and the two slow-migrating forms of HORMAD2 ( Figure 4D , black and gray arrowheads ) were partially decreased in this mutant . By immunostaining of the mutant pachytene spermatocytes , the Ser375-phosphorylated form of HORMAD1 was detected as discontinuous lines on unsynapsed axes of the XY chromosomes ( 50/50 pachytene cells ) ( Figure 4G ) . These findings suggest that the bulk of HORMAD1 phosphorylation is independent of ATR recruited to unsynapsed axes by the MSUC pathway and that BRCA1-regulated ATR may be required for efficient activation or maintenance of phosphorylation of HORMAD1 and HORMAD2 at the unsynapsed chromosome axis . To explore the relationship between phosphorylation of chromosome axis proteins and meiotic recombination , we examined the phosphorylation status of chromosome axis proteins in Spo11−/− testicular cells . SPO11-induced DSBs are required for the initiation of meiotic recombination . The phosphorylated forms of SYCP2 , STAG3 and REC8 were detected in the insoluble fraction of testis nuclear extracts prepared from Spo11−/− mice , showing that Spo11 is dispensable for phosphorylation of these proteins ( Figure 5A ) . In contrast , the slowest-migrating form of HORMAD1 ( Figure 5A , black arrowhead ) and the two slow-migrating forms of HORMAD2 ( Figure 5A , black and gray arrowheads ) were not observed in the Spo11−/− mutant . Furthermore , a considerably reduced signal was seen for the anti-pS375 antibody for HORMAD1 ( Figure 5B ) and the anti-pS1083 antibody for SMC3 ( Figure 5C ) in Spo11−/− mutant testes . We also analyzed the phosphorylation status of HORMAD1 and SMC3 by immunostaining Spo11−/− spermatocytes . Most of the chromosomes in Spo11−/− spermatocytes remain unsynapsed due to lack of recombination , as visualized by intense HORMAD1 labeling on unsynapsed axes in these cells ( Figure 5D and 5E ) . Importantly , the Ser375-phosphorylated form of HORMAD1 and the Ser1083-phosphorylated form of SMC3 were hardly detected on the axis of unsynapsed chromosomal regions ( Figure 5D and 5E ) . Thus , SPO11 is critically important for phosphorylation of HORMAD1 at Ser375 , SMC3 at Ser1083 and HORMAD2 . To address the relationship between the phosphorylation of axis proteins and chromosome axis organization , we examined the phosphorylation status of chromosome axis proteins in three SC-deficient mutants , Sycp1−/− , Tex12−/− and Sycp3−/− , as well as in a mutant deficient for a cohesin complex protein , Smc1β−/− . SYCP1 and TEX12 are components of the central region of the SC , a structure that is essential for chromosome synapsis [33] , [34] , whereas SMC1β is a meiosis-specific cohesin subunit that contributes to chromosome organization and synapsis [35] . As shown in Figure 6A , the phosphorylated forms of SYCP2 , STAG3 , REC8 and HORMAD1 were detected in the insoluble fraction of Sycp1−/− , Tex12−/− and Smc1β−/− testis nuclear extracts . The intensity of the slowest-migrating band of REC8 was increased in the Sycp1−/− and Tex12−/− mutants ( Figure 6A , black arrowhead ) . We also detected the Ser375-phosphorylated form of HORMAD1 in Sycp1−/− , Tex12−/− and Smc1β−/− spermatocytes ( Figure S6 ) . The slowest-migrating form of HORMAD2 that appears at the late pachytene stage was not detected in these three mutants ( Figure 6A , black arrowhead ) , most likely explained by the fact that spermatogenesis arrests at late zygotene or early pachytene in these three mutants [33]–[35] . Taken together , SYCP1 , TEX12 and SMC1β are dispensable for phosphorylation of chromosome axis proteins prior to the pachytene stage . In the Sycp3 mutant , which does not form AEs and displays synapsis defects [24] , [36] , the immunoblotting signal of SYCP2 could not be detected in the insoluble fraction of testis nuclear extracts ( Figure 6A ) , consistent with the fact that SYCP2 is not loaded onto the chromosome axis in this mutant [37] . Importantly , the slowest-migrating form of HORMAD1 ( Figure 6A , black arrowhead ) and the two slow-migrating forms of HORMAD2 ( Figure 6A , black and gray arrowheads ) were reduced in the absence of SYCP3 . We confirmed the reduced level of HORMAD1 phosphorylation in the Sycp3 mutant by immunoblotting ( Figure 6B ) and immunostaining ( Figure 6D and Figure S7A ) using the anti-pS375 antibody . In contrast , the Ser1083-phosphorylated form of SMC3 was detectable in the Sycp3 mutant in both assays ( Figure 6C , 6E and Figure S7C ) . These results show that SYCP3 is required for efficient phosphorylation of HORMAD1 at Ser375 and HORMAD2 . It was recently reported that HORMAD1 is required for loading the MSUC machinery , including ATR and γH2AX , onto the chromosome [16] , [38] . To find out if phosphorylation of HORMAD1 and HORMAD2 has a role in chromosome targeting of the MSUC machinery , we analyzed the distribution of γH2AX and ATR in Sycp3−/− spermatocytes , in which phosphorylation of HORMAD1 and HORMAD2 is impaired ( Figure 6 ) . In wild-type spermatocytes , SPO11-formed DSBs at the leptotene stage trigger a first wave of γH2AX mediated by ATM , a γH2AX signal that starts to fade away at the early zygotene stage [8] , [17] . Subsequently , a second wave of γH2AX emerges during the zygotene stage , phosphorylation of H2AX now mediated by ATR as part of the MSUC pathway that targets unsynapsed chromosomes [17] . Thus , unsynapsed chromosomal regions in wild-type spermatocytes , whose axes are marked by HORMAD1 , are labeled with γH2AX during the zygotene stage ( Figure 7A and 7D ) and also the unsynapsed AEs of the XY chromosomes at the pachytene and diplotene stages ( data not shown ) [39] . Sycp3−/− spermatocytes are eliminated at a late zygotene stage or an early pachytene stage , and as consequence of this , many chromosomes in the mutant cells remain partially unsynapsed [24] , [40] . In Sycp3−/− spermatocytes , the first wave of γH2AX at the leptotene stage took place as seen in wild-type spermatocytes , and the γH2AX signal began to disappear at the early zygotene stage ( Figure 7B , top panels ) . Importantly , in Sycp3−/− cells at a late stage of zygotene , γH2AX failed to become localized to the unsynapsed chromosomes as seen in wild-type zygotene cells . Instead , the γH2AX signal in the mutant cells was localized to restricted domains ( Figure 7B , middle panels ) or displayed a pseudo-sex-body-like staining pattern ( Figure 7B , bottom panels ) . The pseudo-sex body is a chromosomal domain seen in Spo11−/− zygotene-like spermatocytes ( Figure 7C ) , within which the MSUC machinery accumulates [14] , [17] , [39] . As seen in Spo11−/− spermatocytes [14] , ATR accumulated in the pseudo-sex-body-like domain in Sycp3−/− spermatocytes ( Figure 7E ) . These results show that ATR and γH2AX fail to correctly accumulate at unsynapsed chromosomal regions in Sycp3−/− spermatocytes , as well as in Spo11−/− spermatocytes . To exclude the possibility that mislocalization of γH2AX and ATR in the Sycp3−/− and Spo11−/− mutants is due to a synapsis defect in these mutants , we also examined the distribution of these markers in Sycp1−/− and Tex12−/− spermatocytes . In these mutant spermatocytes , where meiosis does not proceed beyond the pachytene stage , γH2AX and ATR were observed on the entire length of unsynapsed chromosomes ( Figure 7F and 7G ) and did not display a pseudo-sex-body-like staining pattern . These results show that the distribution of the MSUC machinery and phosphorylation of HORMAD1 and HORMAD2 are normal despite absence of synapsis . Our results reveal that SYCP3 and SPO11 contribute both to phosphorylation of HORMAD1 and HORMAD2 and to the process through which ATR becomes correctly distributed to unsynapsed chromosomes . Possibly , it is the phosphorylated forms of HORMAD1 and HORMAD2 that mediate the distribution of the MSUC machinery among unsynapsed chromosomes .
We show here that a large number of chromosome axis proteins are phosphorylated during the prophase I stage of mouse meiosis . This includes HORMA domain-containing proteins ( HORMAD1 and HORMAD2 ) and components of the cohesin complex ( SMC3 , STAG3 and REC8 ) and the AE ( SYCP2 and SYCP3 ) , similar to what has been shown previously for some individual mammalian chromosome axis proteins [26] , . Chromosome axis proteins are intimately involved in several critical meiotic processes including sister chromatid cohesion , chromosome organization , recombination , synapsis and checkpoint control [16] , [24] , [35] , [38] , [45]–[47] . What is the role of the post-translational modifications added to the chromosome axis proteins ? They could promote dissociation of proteins from the chromosome axis , in analogy with the displacement of the cohesin complex that occurs in response to phosphorylation at the prophase stage of mitosis [48] . We consider this explanation unlikely however , as phosphorylation of chromosome axis proteins during meiosis starts at an early stage of prophase I , not coinciding with their displacement from the chromosome axis . Phosphorylation of chromosome axis proteins could act more directly to promote different meiotic processes . Supporting this , phosphorylation of the yeast HORMA-domain containing protein , Hop1 in S . cerevisiae , is required for the prevention of inter-sister recombination and the pachytene checkpoint [49] , while elimination of phosphorylation sites within Rec8 in S . cerevisiae causes defects in recombination and synapsis during prophase I [50] . To gain more insight into the functional consequences of the phosphorylation of various chromosome axis proteins during meiosis , we have focused on the role of the phosphorylation events that target SMC3 , HORMAD1 and HORMAD2 . In mouse spermatocytes , SMC3 localizes to the meiotic chromosome axis irrespective of the status of chromosome synapsis ( Figure S3B ) [51] . We found that the Ser1083-phosphorylated form of SMC3 is preferentially associated with unsynapsed chromosomal regions but not with synapsed or desynapsed regions from late zygotene to diplotene , similar to the Ser375-phosphorylated form of HORMAD1 . Phosphorylation of SMC3 at Ser1083 depends on SPO11 but is not affected in the absence of full-length BRCA1 and SYCP3 , indicating that SMC3 is regulated differently from HORMAD1 and HORMAD2 . Moreover , the Ser1083-phosphorylated form of SMC3 was detected on both synapsed and desynapsed chromosomes during early zygotene , in contrast to the Ser375-phosphorylated form of HORMAD1 , which is not detected in synapsed regions . Probably , TRIP13-mediated displacement of HORMAD1 from synapsed chromosome axes enables more strictly regulated localization of HORMAD1 phosphorylation in unsynapsed chromosomal regions . The cohesin complex is one of the important factors in DNA damage response pathways [52] . SMC1α and SMC3 are phosphorylated at S/T-Q motifs by ATM/ATR and these phosphorylation events are crucial for the DNA damage checkpoint at the intra-S phase of mitosis [28] . As in mitotic cells , SMC3 may be phosphorylated primarily in response to DSBs that are introduced by SPO11 ( Figure 8A , arrow 4 ) . Since DSBs are processed and repaired by recombination on the chromosome axis , SMC3 phosphorylation may reflect the progression of this process and be involved in DNA damage repair or checkpoints as in mitotic cells . The Ser1083-phosphorylated form of SMC3 is also detected at the diplotene stage on the XY chromosomes where DSBs are repaired . This phosphorylation suggests that SMC3 is additionally phosphorylated at unsynapsed regions by ATR in a manner similar to H2AX in the MSUC pathway ( Figure 8A , arrow 8 ) . To summarize , SMC3 may change the modification status according to the progression of recombination and synapsis . HORMAD1 has multiple phosphorylation sites , including Ser375 and a non-S/T-Q site , which are differently regulated . HORMAD1 is associated with unsynapsed and desynapsed chromosome axes [26] , [27] , but the Ser375-phosphorylated form of HORMAD1 is restricted to unsynapsed chromosomes . Collectively , our results show that HORMAD1 is phosphorylated at a non-S/T-Q site in the nucleoplasm , as well as on the chromosome , and that HORMAD1 is further phosphorylated at Ser375 on unsynapsed chromosomes in a SPO11-dependent manner . HORMAD2 also has multiple phosphorylation sites . One phosphorylated form of HORMAD2 contains phosphorylation possibly at an S/T-Q site , which is regulated in a manner temporally and genetically similar to phosphorylation of HORMAD1 at Ser375 . The other phosphorylated form of HORMAD2 is temporally regulated to take place at the late pachytene stage . Considering the localization of HORMAD2 at the unsynapsed chromosome axis during the leptotene to pachytene stages [27] , we infer that HORMAD2 is primarily phosphorylated on unsynapsed chromosomes probably at an S/T-Q site similarly to Ser375 of HORMAD1 and that additional phosphorylation might occur on the XY chromosomes at the late pachytene stage . ATR is recruited to unsynapsed chromosomal regions , to which HORMAD1 and HORMAD2 are localized , and phosphorylates histone H2AX , leading to MSUC [10] . Recent studies using Hormad1-deficient mice revealed that HORMAD1 has multiple functions , one of which is to load ATR onto the chromosome [16] , [38] . We found here that phosphorylation of HORMAD1 at Ser375 and that of HORMAD2 are reduced in Spo11−/− , Brca1Δ11/Δ11 and Sycp3−/− spermatocytes . Intriguingly , the three mutants exhibit a similar defect in which ATR and γH2AX fail to localize to unsynapsed chromosomal regions and instead assemble at aberrant nuclear sites ( Figure 7 ) [31] . This phenotypic similarity leads us to propose a model in which phosphorylation of HORMAD1 and HORMAD2 is required for the distribution of ATR at unsynapsed chromosomal regions ( Figure 8A ) . HORMAD1 is primarily required for the loading of ATR irrespective of its phosphorylation state , because pseudo-sex body is formed in the Spo11 mutant in a HORMAD1-dependent manner [16] . Therefore , HORMAD1/2 phosphorylation is dispensable for the loading of ATR , but may regulate its distribution on the prophase I chromosome . It is possible that ATR tends to aggregate at certain domains on chromosomes , as seen in the pseudo-sex body formation . Phosphorylation of HORMAD1/2 may increase the affinity of HORMAD1/2 for ATR or ATR activators , leading to the anchoring of the ATR activity at entire unsynapsed chromosomes , against this tendency . This model explains why γH2AX is localized to the unsynapsed XY chromosomes but not to the desynapsed autosomes at the diplotene stage [39] , despite the presence of HORMAD1/2 at both unsynapsed and desynapsed chromosomes . Phosphorylation-based regulation of checkpoint proteins is also known for other HORMA domain-containing proteins , such as yeast Hop1 in the pachytene checkpoint [49] and mammalian MAD2 in the spindle checkpoint [53] . Thus , phosphorylation of HORMAD1/2 may regulate phosphorylation-dependent protein-protein interactions to recruit or anchor proteins involved in synapsis surveillance processes to unsynapsed chromosomes . HORMAD1/2 phosphorylation may also recruit proteins that promote SC formation , since synapsis is defective in Hormad1-deficient mice [16] , [38] . In addition , phosphorylation of HORMAD1/2 possibly regulates inter-homolog partner choice in meiotic recombination like yeast Hop1 , because this regulation appears to be impaired in the Sycp3 mutant [54] . The presence or absence of HORMAD1 and HORMAD2 can distinguish whether homologs are not synapsed ( unsynapsed and desynapsed ) or synapsed , respectively [26] , [27] . We show here that the presence or absence of the Ser375-phosphorylated form of HORMAD1 and the Ser1083-phosphorylated form of SMC3 can distinguish whether homologs that are not synapsed are unsynapsed or desynapsed , respectively . These findings prompt us to propose that modification status and composition of proteins that constitute the chromosome axis can label chromosomal regions according to the meiotic stage or progression of chromosomal events ( Figure 8B ) . In support of this , a cohesin subunit , RAD21L , is replaced by another subunit , RAD21 , in response to completion of recombination at the late pachytene stage [19] , [20] . Our proposal is analogous to current models of how histone modifications and variations , which label certain chromatin regions according to DNA damage status and transcriptional activity , contribute to the recruitment of proteins involved in DNA repair , DNA-damage checkpoints and transcriptional regulation . Similarly , combinations of modifications and compositions of chromosome axis components may serve as landmarks for recruitment of proteins involved in recombination , SC formation and checkpoint control . Our findings shed light on regulations of meiotic chromosomal events through phosphorylation of chromosome axis components . In addition to phosphorylation , other modifications of axis components may mark certain chromosomal regions to regulate meiotic events . Indeed , in yeast , SUMOylation of AE protein ( s ) regulates recombination and synapsis [55] . Further identification of modification sites and modification enzymes will provide more insights into regulation through the axis marks .
Wild-type C57BL/6 and mutant mice were used in accordance with regulations provided by the animal ethics committee of Karolinska Institutet . The Trip13 [56] , Atm [29] , Brca1 [57] , Spo11 [2] , Sycp3 [24] , Smc1β [35] , Sycp1 [33] and Tex12 [34] mutants were reported previously . To generate a phospho-specific antibody for Ser375 of HORMAD1 ( pS375 ) , rabbits were immunized with a Ser375-phosphorylated peptide corresponding to amino acids 372–382 of mouse HORMAD1 . The anti-pS375 antisera were passed through a column conjugated with the non-phosphorylated peptide to remove fractions cross-reacting with non-phosphorylated HORMAD1 . The flow-through fractions were then subjected to affinity-purification using the phosphorylated peptide . The purified antibody was further passed through a column conjugated with the non-phosphorylated peptide . The flow-through fractions were collected and concentrated by ultrafiltration ( Amicon , Millipore ) . The following antibodies were also used: guinea pig anti-SYCP2 , anti-SMC1β , anti-STAG3 , anti-REC8 and anti-SYCP1 antibodies [58]; guinea pig anti-HORMAD1antibody [26]; rabbit anti-HORMAD1 antibody ( 13917-1-AP ) from Proteintech Group; rabbit anti-pS/T-Q antibody ( #2851 ) from Cell Signaling Technology; rabbit anti-pS1083 antibodies ( A300-480A and IHC-00070 ) from Bethyl Laboratories; mouse and rabbit anti-γH2AX antibodies ( #05-636 and #07-164 ) from Millipore; mouse anti-SYCP3 ( sc-74569 ) , rabbit anti-HORMAD2 ( sc-82192 ) , goat anti-SMC3 ( sc-8198 ) and goat anti-ATR ( sc-1887 ) antibodies from Santa Cruz Biotechnology; rabbit anti-SMC3 ( ab9263 ) and rabbit anti-SYCP1 ( ab15090 ) antibodies from Abcam; mouse anti-SYCP1 antibody ( a gift from C . Heyting ) . Testis nuclear extracts were prepared as described previously [26] . For phosphatase treatment , nuclear extracts were incubated with λ-phosphatase ( New England Biolabs ) in the presence or absence of phosphatase inhibitor cocktail ( Merck ) for 90 min at 30°C . For fractionation of nuclear extracts , testes were homogenized in a buffer containing 0 . 32 M Sucrose , 10 mM HEPES pH 7 . 4 , 1 mM phenylmethylsulfonyl fluoride ( PMSF ) and the complete protease inhibitor cocktail ( Roche ) . After centrifugation at 1000 g , the pellet was suspended in a buffer containing 25 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 5 mM EDTA , 1% Triton X-100 , 0 . 5% Na-deoxycholate , 0 . 1% SDS and protease inhibitors . After centrifugation at 16000 g , the supernatant was collected as a soluble fraction . The pellet was resuspended in the same buffer . After sonication and centrifugation at 16000 g , the supernatant was recovered as an insoluble fraction . Proteins were separated on a 5 , 8 , or 10% polyacrylamide gel to detect slow-migrating forms or on a 4–12% NuPAGE Bis-Tris gel ( Invitrogen ) , and were subsequently transferred onto an Immobilon-P membrane ( Millipore ) . Immunoprecipitation was performed as described previously [26] . For preparation of nuclear spreads , a drying-down technique [59] was used . Indirect immunofluorescence analysis was performed using previously described antibodies [26] . We also used the following primary antibodies and dilutions: anti-pS375 , 1∶100; anti-pS1083 , 1∶100; anti-γH2AX , 1∶400; anti-ATR , 1∶50 . Slides were viewed at room temperature using Leica DMRA2 and DMRXA microscopes . Images were captured with a Hamamatsu digital charge-coupled device camera C4742-95 and processed with Openlab 3 . 1 . 4 software ( Improvision ) and Adobe Photoshop .
|
Meiosis is a specialized cell division to generate haploid sperm and eggs . For accurate segregation of homologous chromosomes during the first meiotic division , chromosome synapsis and recombination should be properly established between them during the prophase I stage . Chromosome synapsis and recombination proceed in the context of the meiotic chromosome axis . While studies using knockout mouse models have revealed that chromosome axis components play roles in multiple chromosomal events during mammalian meiosis , it remains to be elucidated how they contribute to the processes . Here , we show that many mammalian meiotic chromosome axis proteins are phosphorylated in a spatially and temporally distinct manner during the prophase I stage . Especially , phosphorylation of HORMAD1 and SMC3 was observed preferentially in chromosomal regions where synapsis has not occurred . Moreover , phosphorylation of HORMAD1 and HORMAD2 was reduced in mutant testicular cells that were defective in recombination initiation or chromosome axis organization . Additionally , the mutant spermatocytes failed to correctly distribute checkpoint proteins that coordinate chromosome synapsis with gene expression and meiotic progression . Thus , it is suggested that phosphorylation of chromosome axis proteins serves as integrative axis marks for the status of events that take place on meiotic chromosomes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"biology",
"molecular",
"cell",
"biology",
"genetics",
"and",
"genomics"
] |
2012
|
Phosphorylation of Chromosome Core Components May Serve as Axis Marks for the Status of Chromosomal Events during Mammalian Meiosis
|
Fasciolosis caused by Fasciola gigantica is a neglected tropical disease but a constraint on the growth and productivity of cattle , buffaloes and sheep in the tropical countries of Asia and Africa . Resistance to commonly used anthelmintics in Fasciola has increased the need to search for alternative therapeutic targets . RNA interference is the current tool of choice in the search for such targets in Fasciola . The susceptibility of juvenile Fasciola hepatica to double stranded ( ds ) RNA induced RNAi has been established but in F . gigantica a single preliminary report on RNAi induced mRNA transcript knockdown is available . Here we optimized conditions for RNAi in the liver fluke F . gigantica targeting six genes including superoxide dismutase ( SOD ) , σ class of glutathione-s-transferase ( GST ) , cathepsin ( Cat ) L1-D , Cat B1 , Cat B2 and Cat B3 that showed robust transcriptional silencing of the targets following exposure of the newly excysted juveniles ( NEJs ) to long ( 170–223 nt ) dsRNA . Knockdown was shown to be concentration dependent with significant mRNA transcript suppression occurring at 5 ng / μl that showed further suppression with the increase in the dsRNA concentration . The dsRNA induced persistent silencing of the mRNA transcript of SOD and σGST up to 15 days of observation . Delivery of the long dsRNA and siRNA to the newly excysted juveniles by soaking method was found to be efficient by tracking the uptake and diffusion of Cy3 labelled siRNA and long dsRNA in the flukes . Off-target effects of dsRNA trigger on some of the non-target genes were detected in the present investigation on RNAi in F . gigantica . The dsRNA induced superoxide dismutase protein suppression while impact of RNAi on other target proteins was not studied . There is no in vitro culture system for prolonged survival of the F . gigantica and in the present study in vitro maintenance of the NEJs is reported for a period of 3 weeks . The present study is the first attempt on optimization of RNAi protocols in F . gigantica where long dsRNA allowed for an efficient and persistent gene silencing , opening prospects for functional validation of putative vaccine and therapeutic targets in this neglected parasite .
Fasciola gigantica ( tropical liver fluke ) and F . hepatica ( temperate liver fluke ) are the two causative agents of fasciolosis in livestock and are economically important veterinary parasites due to the substantial production and monetary losses that these parasites cause to the livestock industry . Fasciolosis caused by F . gigantica is a neglected tropical disease but a constraint on the growth and productivity of cattle , buffaloes and sheep in the tropical countries of Asia and Africa [1 , 2] . In addition to its adverse impact on livestock economy , fasciolosis is an emerging zoonosis particularly in the South American countries like Bolivia , Peru , Equador and in rural areas of central Africa and northern Asia [3 , 4 , 5] . In the Indian sub-continent F . gigantica is the causative agent of fasciolosis in livestock . Control measures rely mainly on anthelmintic drugs but in recent years reports of resistance to commonly used anthelmintics in Fasciola have emerged thereby increasing the need for alternative therapeutic targets [6 , 7 , 8] . The advent of new sequencing technologies facilitated the knowledge of the genomes and transcriptomes of trematodes [9 , 10 , 11 , 12]; providing sources for seeking novel drug and vaccine targets . Whereas genome sequence information is ever expanding , studies on the genes coding for proteins of unknown function are limited . RNA interference is the method of choice for gene function analysis since classical tools of functional genomics are not available in most of these parasites [13 , 14] . The complex developmental cycle , robust tegument and inability to maintain the worms for extended periods in vitro have slowed down the deployment of techniques involved in gene function analysis in parasitic flatworms [15 , 16] . Development of RNAi-based gene silencing methods is pivotal for the effective exploitation of the increasing database resources in Fasciola . Fasciola is increasingly the focus for transcriptome and genome analyses [12 , 17 , 18 , 19 , 20 , 21] but RNAi and other functional genomics tools have not been widely adopted by the liver fluke research community . In parasitic flatworms most progress has been reported for schistosomes [22 , 23 , 24] that has demonstrated the utility of RNAi in functional genomics in trematodes . These studies have also indicated that genes are not equally affected; off-target effects can occur and developmental stages display different susceptibility to interference [25] . Successful gene silencing by RNAi reported in Opisthorchis viverrini , F . hepatica and Clonorchis sinensis [26 , 27 , 28] will provide framework for utilization of this technique to investigate the function of unexplored genes that in turn might be the targets for vaccine or drug development . However , in F . gigantica research on vaccine development or drug discovery has been hindered in the absence of use of reverse genetics tools including RNAi . Parasites express antioxidant enzymes including superoxide dismutases ( SODs ) , glutathione-S-transferase , glutathione peroxidase , catalase and peroxiredoxins that would suppress oxidative killing by the host effector cells . Superoxide dismutase a metallo-enzyme , the main superoxide radical scavenger , protects cells from the oxidant mediated damage . The identification of the Cu/Zn-superoxide dismutase in F . gigantica [29] may suggest that antioxidative response protects the parasite against neutrophil , macrophage or dendritic cell derived reactive oxygen species but precise role of the SOD enzyme expressed in Fasciola species in defense against superoxide mediated killing of the parasite is not known . Glutathione-s-transferase ( GST ) belongs to a family of enzymes that are involved in the cellular detoxification process . The GSTs of helminths act as immune defense proteins and have significant activity with lipid peroxidation-derived carbonyls and also have the potential to neutralize exogenously derived toxins such as anthelmintics [30] . GSTs are considered a promising vaccine candidate against Fasciola species [27 , 31] . Cysteine proteases are essential for acquiring nutrients and enabling the parasite to migrate from the intestine and through the liver [32 , 33] in immunomodulation process [34 , 35] and host immunoglobulin cleavage [36] . In order to understand the diverse functions performed by these classes of proteins in F . gigantica , their functional analyses using RNAi are essentially required . Therefore , we focused here on the optimization of RNAi platform in juveniles of the liver fluke F . gigantica using the above genes as targets for its establishment as a viable tool for investigating the gene function in this parasite .
Fasciola gigantica metacercariae were harvested on polythene strips from naturally infected Lymnaea auricularia collected from the rural ponds and hatched into newly excysted juveniles ( NEJs ) in vitro [37] . Briefly , metacercariae were treated with 1% pepsin in 0 . 4% HCl prepared in sterile distilled water and incubated at 37°C for 45–60 min . The metacercariae were washed with several changes of sterile distilled water to remove the outer cyst wall debris and were further incubated for 2–3 h at 37°C in 10 ml excystment solution of 20 mM sodium dithionite ( sodium hypodisulphite ) , 1 . 5% ( w/v ) NaHCO3 , 0 . 8% ( w/v ) NaCl , 0 . 2% ( w/v ) taurocholic acid and 0 . 5% ( v/v ) conc . HCl in 50 ml centrifuge tube with its cap sealed firmly with parafilm ( reagents used in the NEJ hatching were procured from Sigma Chemicals , USA ) . The cysts were subsequently washed in sterile distilled water and resuspended in serum free RPMI-1640 medium , supplemented with 50 μg /ml of gentamicin and incubated at 37°C overnight for their hatching . The NEJs that hatched from the metacercariae were filtered through a sterile nylon mesh of ~100 μm pore size in RPMI-1640 medium at 37°C for 60 min and cultured in complete RPMI-1640 medium . Species identification of the NEJs for F . gigantica was done by PCR amplification and sequencing of ITS-2 and 28S rDNA markers [38] . Three commercially available culture media RPMI-1640 ( Hyclone , USA ) , DMEM ( Hyclone , USA ) and DME / F12 ( 1:1 ) ( Hyclone , USA ) were tested for supporting the in vitro survival and growth of the juveniles . Growth media were supplemented with the final concentrations of foetal bovine serum ( 10% ) ( Hyclone , USA ) / chicken serum ( 50% ) ( Himedia , India ) , glucose ( 2% ) and HEPES ( 25 mM ) for enhancing their efficacy in extending the survival of the juveniles . Appropriate doses of antibiotics ( 1x streptomycin-penicillin ) and amphotericin B ( 1 μg / ml ) were added and parasite culture was maintained at 37°C in 5% CO2 atmosphere . Total RNA was extracted from the juveniles using the RNAqueous Micro kit ( Ambion , USA ) with the mini extraction protocol as per the manufacturer's instructions . Briefly , 100–150 NEJs treated for RNAi or as untreated controls were used for RNA isolation . Total RNA isolated with the kit was digested with DNase and quantified by Nano-drop spectrophotometer ( Thermo Scientific , USA ) . Equal amounts of total RNA ( 300 ng ) from all experimental groups were used for cDNA synthesis using oligo-dT primer and M-MLV reverse transcriptase enzyme ( MBI Fermentas , USA ) . Six target genes of the fluke F . gigantica including superoxide dismutase ( SOD ) , σ class of glutathione-s-transferase ( GST ) , cathepsin ( Cat ) L1-D , Cat B1 , Cat B2 and Cat B3 were selected for RNAi . The cDNA synthesized from the total RNA was subjected to PCR amplification of full length open reading frame of F . gigantica SOD ( accession no: GU906887 ) , σGST ( accession no: DQ974116 ) , cathepsin L1-D ( accession no: AF239266 ) , Cat B1 ( accession no: ( AY227673 ) , Cat B2 ( accession no: ( AY227674 ) and Cat B3 ( accession no: AY227673 ) using N and C terminal primer sequences specific to the respective genes . The PCR products representing each of the target genes , except for Cat B1 and B3 , were cloned in pDRIVE TA-cloning vector and the respective dsRNA trigger was generated by PCR amplification of the short sequences of these clones . However , the PCR templates for the generation of dsRNA trigger for Cat B1 and Cat B3 were amplified from the cDNA directly using a single set of primers for both the targets . The SOD PCR product ( 441bp ) was sub-cloned in prokaryotic expression vector pPROEXHT-b . The histidine tagged fusion protein was expressed in Escherichia coli BL 21 ( DE3 ) by inducing the recombinant protein expression with 1mM IPTG at 37°C for 7–8 h . The recombinant protein was purified under denaturing conditions by Ni-NTA affinity chromatography following standard purification protocols . Two New Zealand white rabbits , weighing ~1kg each , were immunized with recombinant SOD protein at 100 μg ( each dose ) in Freund’s complete and incomplete adjuvant , respectively at 2 week intervals . Each animal received one immunization with the antigen in Freund’s complete adjuvant and two boosters with incomplete adjuvant . Rabbits were bled after 2nd booster and titre of the antibodies was determined in ELISA . The newly excysted juveniles ( n = ~500 ) from the dsRNA treated and untreated control groups , respectively were manually homogenized with a sterile micropestle in 200 μl of phosphate buffered saline ( PBS ) , pH 7 . 2 in a round-bottom 2 ml microcentrifuge tube under exposure to liquid nitrogen and sonicated at 5 micron amplitude x 5 cycles of 15 sec each over ice in three biological replicates . A cocktail of protease inhibitors ( 1x , Sigma Aldrich ) was added to each tube and the protein content in each group was quantified by Lowry method [39] and equal quantities of protein ( 50 μg ) from dsRNA treated and untreated groups were loaded in the wells of SDS-polyacrylamide gel in each experimental repeat . The proteins were resolved in 15% SDS-PAGE using tris-glycine buffer pH 8 . 6 and transferred to nitrocellulose membrane in tris-glycine buffer with 20% methanol at 100 mA for 1h . The blots were washed in PBS pH 7 . 2 post-transfer and blocked with 5% skimmed milk in PBS for 1 h at 37°C . Membranes were probed at 37°C for 1 h in the blocking buffer containing two primary antisera ( one raised against FgSOD target protein in rabbit and other raised against FgGST in rabbit as loading control normalizer ) at 1:200 dilution . Following 5x5 min washes in PBS-Tween 20 , membranes were reacted with goat anti-rabbit IgG-HRP conjugated secondary antibodies ( Sigma Chemicals , USA ) diluted 1:800 in blocking buffer . The blot was developed with 3 , 3'-diaminobenzedene ( 1 mg / ml , W/V ) ( Sigma-Aldrich USA ) in PBS , pH 7 . 2 and 30% hydrogen peroxide ( 1 μl / ml ) as per the standard protocols . Membranes were then dried , scanned and band intensities quantified by densitometry using GelQuant . NET software provided by biochemlabsolutions . com . The relative protein quantification was done by normalization of the band intensity of the protein of interest to the intensity of the loading control band from the same sample and finally figure expressed as a percentage relative to the untreated control ( 100% ) sample . Statistical analysis was performed using ANOVA . RNAi experiments were conducted on the newly excysted juvenile stage of the parasite . RNAi triggers used for silencing the targeted genes in the F . gigantica NEJs comprised of long double stranded ( ds ) RNA molecules ( 170–223 nt ) that were generated by T7 RNA polymerase-driven transcription of single RNA strands from the target-specific PCR generated templates tailored with T7 RNA polymerase promoter sequence 5'-TAATACGACTCACTATAGGG-3' . The cDNAs coding for full length open reading frame of the SOD , σGST , Cat L1-D , Cat B1 , Cat B2 and Cat B3 were PCR amplified using primer sequences specific to N- and C-termini of respective genes . These PCR products were used as template for the generation of dsRNA molecules specific to these targets . Short target sequences of SOD cDNA spanning 151–320 nucleotides ( 170 bp ) and σGST from 57–279 nucleotides ( 223 bp ) were PCR amplified for the generation of dsRNA triggers . The nucleotide sequence from 301–475 ( 175 bp ) of Cat L1-D was PCR amplified for the generation of Cat L1-D dsRNA . The Cat B1 and Cat B3 dsRNAs were generated by PCR amplification of 370–572 nucleotides ( 203 bp ) of Cat B1 and Cat B3 cDNA using a single set of forward and reverse primers designed on the short conserved sequences of these genes . Likewise , Cat B2 specific dsRNA was generated by PCR amplification of the target sequence of 351–572 nucleotides ( 222 bp ) ( S1 Fig ) . Each target sequence was PCR amplified using gene specific primers incorporated with T7 promoter sequence at 5' end of the sense and anti-sense primer ( Table 1 ) . Plasmodium falciparum knob associated histidine rich protein ( PfKAHRP ) gene ( accession no: X92413 ) was used as negative control ( Pfcont ) as PfKAHRP gene lacked significant sequence similarity with Fasciola nucleotide sequence ( accession nos: LN771073; LN771075; LN771081 ) . The PfKAHRP gene fragment was PCR amplified with forward and reverse primers ( Table 1 ) and dsRNA was generated as described for the above target genes . All the PCR amplicons were sequenced for their authenticity . In vitro transcription of the dsRNA trigger molecules against six target genes was carried out using commercially available in vitro transcription kit ( TranscriptAid T7 High Yield Transcription Kit , Thermo Scientific , USA ) . The transcription reaction mixture was incubated at 37ºC for 2 . 5 h for synthesis of the dsRNA as per the instructions of the manufacturer . The dsRNA was quantified by Nano-drop spectrophotometer and analysed for the presence of discrete , correct sized product on a non-denaturing 1 . 5% ( w/v ) agarose gel . The dsRNA that gave a correct sized band and 260/280 of 1 . 8 was used in RNAi experiments . The purified dsRNA was stored at -80ºC for further use . Out of the six dsRNAs generated against the six target genes , the SOD specific dsRNA was labelled with Cy3 dye . Labelling of the SOD dsRNA was carried out using SilencersiRNAi Labelling Kit ( Ambion , USA ) with a protocol optimized to siRNA as per the manufacturer's instructions . The GAPDH siRNA ( 25 nt ) ( commercially synthesized ) was also labelled with Cy3 dye following the procedure used for the labelling of long dsRNA . The Cy3 labelled GAPDH siRNA and SOD long dsRNA were used as reporter RNA for determining the comparative efficacy of the uptake of long dsRNA and siRNA molecules by the newly excysted juveniles . The Cy3 labelled dsRNA and siRNA were delivered to the NEJs by soaking method and the NEJs were maintained in vitro for a period of 24–72 h . The flukes were washed with several changes of PBS , pH 7 . 2 and were observed under UV fluorescence microscope ( Olympus UTV 63XC Fluorescent Microscope , Japan ) over a period of 24 to 72 h for the incorporation of the long dsRNA and siRNA in the fluke tissue . Delivery of the RNAi trigger molecules to newly excysted juveniles was attempted by soaking and electroporation to determine the efficacy of each method in the delivery of dsRNA molecules to the parasite tissue . F . gigantica NEJs were soaked in solutions of long dsRNA molecules dissolved to defined concentrations of 5 ng / μl to 150 ng / μl in RPMI-1640 in 24 well culture plates . Soaks were handled in each well alongside the untreated ( with no dsRNA ) controls and each assay was carried out in three replicates . The NEJs were initially exposed to a defined concentration of dsRNA for 24 h in 1 ml RPMI-1640 medium that was diluted to 2 ml with fresh RPMI-1640 medium for next 48 h , thus allowing for the exposure of the NEJs to dsRNA for a period of 72 h . However , for studies on persistence of RNAi in the fluke , the NEJs were exposed to dsRNA in RPMI-1640 medium for 48 h and the medium was replaced with fresh RPMI-1640 medium without dsRNA . Thereafter , the juveniles were maintained in this medium for the next 13 days of study without dsRNA , with change of medium every 48 h . For each experiment ~150 NEJs were used per soak . The NEJs were maintained aseptically in RPMI-1640 at 37ºC in 5% CO2 atmosphere . Worms were visually assessed for aberrant motility or morphological changes during the culture period . At the end of each soak experiment , worms were processed for total RNA extraction . The dsRNA molecules were also delivered to the NEJs by electroporation . Square wave electroporation was carried out in BTX Electro Square Porator ( BTX Square Wave Electroporation system ECM830 BTX , USA ) . The flukes were electroporated with different concentrations of dsRNA . The NEJs were maintained in RMI-1640 medium at 37ºC in 5% CO2 incubator for 24 h post-electroporation for measuring the mRNA transcript knockdown . Five sets of primers were synthesized for quantitative real-time PCR ( qRT-PCR ) amplification of six target genes post-RNAi treatment ( Table 2 ) . Primers specific to F . hepatica GAPDH ( accession no: AY005475 ) were used for the amplification of the GAPDH sequence in F . gigantica due to high identity between the relevant orthologues . Real-time PCR was carried out in Applied Bio Systems 7500 v 2 . 3 Stepone plus and Applied Bio Systems 7500 v 2 . 0 . 6 fast Real-time PCR System . The primers for real-time PCR analysis were designed outside of the target sequences of the dsRNA trigger to avoid non-specific amplification of reverse transcribed dsRNA . Reactions were performed in triplicate with initial incubation of the reaction mixture at 50°C for 2 min , followed by 15 min exposure at 94°C and cycling conditions of 94°C , 15 sec; 60 oC , 30 sec and 72°C , 30 sec ( 40 cycles ) using Maxima SYBR Green / ROX qPCR Master Mix Kit ( Thermo Scientific , USA ) . Fluorescence was detected during the extension step and melt curve analysis was performed after PCR cycling for the presence of a single peak for each expected amplicon . The relative transcript levels were analyzed by the 2−ΔΔCt method [40] using F . gigantica GAPDH as the internal reference gene for normalization . The differences of Ct for the target and reference genes were calculated ( ΔCt = Ct ( target gene ) –Ct ( endogenous control ) for each condition and normalized by subtracting the values obtained for treated and non-treated samples ( ΔΔCt = ΔCt ( dsRNA treated worms ) – ΔCt ( untreated control worms ) . Results are presented as the mean ± standard error ( SE ) of the unit value of 2−ΔΔCt from three independent experiments . Statistical analysis was performed by paired Student’s t-test on ΔCt values , with SPSS 20 ( IBM ) software; with P values of ≤0 . 05 considered significant . Experiments were repeated ≥3 times . Significant differences between dsRNA treated and untreated control worms are indicated ( * , P<0 . 05; ** , P<0 . 01; *** , P<0 . 001 ) .
Three commercially available culture media including RPMI-1640 , DMEM and DME/F12 ( 1:1 ) supplemented with varying concentrations of foetal bovine serum ( 5–10 . 0% ) , glucose ( 1–3 . 0% ) and HEPES ( 5 mM to 25 mM ) were tested for their ability to sustain a prolonged culture of the fluke NEJs . Of the three culture media tested RPMI-1640 showed extended survival of the parasite . RPMI-1640 supplemented with 25mM HEPES supported survival and growth of the NEJs for more number of days and resisted the pH change of the culture medium . The NEJs ( ≥80 . 0% ) survived for three weeks in RPMI-1640 medium supplemented with 10% foetal bovine serum and 2% glucose with 25mM HEPES . However , in the 4th week of culture a rapid decline in the viability of the flukes was detected and all the flukes died by 28–30 days of culture ( Fig 1 ) . Interestingly , in three experiments ≤ 40% the juveniles survived up to 6 weeks of in vitro culture with no morphological changes in the tegument . The flukes survived for 10–12 days of culture in the RPMI-1640 medium supplemented with 25% and 50% concentrations of chicken serum , respectively . The worm viability was assessed as a visual measure of worm motility and morphology; non-motile worms with a visually disrupted tegument considered dead . Target specific dsRNA was in vitro transcribed from each of the target PCR template and delivered as RNAi trigger for silencing of specific genes in the NEJs of the fluke either by soaking or by electroporation . Square wave electroporation was carried out in the NEJs ( n = 100 ) at 125V , 30 milli-seconds in 2 mm gap cuvettes in 100 μl of RPMI-1640 with SOD specific dsRNA at 5 and 10 ng / μl concentration , respectively . The control group of NEJs received the same pulse rate but no dsRNA . Another group of NEJs ( n = 100 ) was exposed to the same concentrations of dsRNA as a soak control in the soaking method . The results showed a mean knockdown of the mRNA transcript of 71 . 0% at 5 ng /μl dsRNA and 77 . 0% at 10 ng /μl dsRNA concentration in the electroporated groups of NEJs that was comparable to the mean mRNA transcript knockdown of 67 . 0% and 71 . 0% , respectively in the NEJs treated with dsRNA by soaking method ( Fig not given ) . Soaking method being simple and less technically demanding was used as a standard protocol for dsRNA delivery to silence a range of virulence gene targets . The comparative efficacy of the uptake of long dsRNA and siRNA by the NEJs was analyzed under UV fluorescence microscope using Cy3 labelled long dsRNA and siRNA molecules . The exposure of the NEJs to SOD specific Cy3 labelled long dsRNA ( 210 nt ) and Cy3 labelled GAPDH siRNA ( 25 nt ) in two respective groups indicated that uptake of both long dsRNA and siRNA was through the gut of the fluke that diffused through the adjacent parenchyma of the NEJs . The Cy3 labelled dsRNA and siRNA diffused in the parenchyma adjacent to the gut in 24 to 72 h . There seemed no difference in the efficacy of the uptake and diffusion of long dsRNA and siRNA molecules in the tissues of the fluke as visualized by Cy3 dye fluorescence in the microscope ( Fig 2 ) . Thereafter , all experiments on RNAi were performed with long dsRNA only . Protein suppression after the robust mRNA transcript knockdown of the above target genes by RNAi was studied in SOD protein only . The NEJs were exposed to the concentration of 50 ng / μl of the SOD specific dsRNA for an initial period of 72 h and in vitro maintained in RPMI-1640 medium without dsRNA for a further period of 6 days before being analyzed for the protein suppression . Western blot carried out with anti-FgSOD antibodies and subsequent densitometric analysis detected significant suppression of the SOD protein ( 56 . 4 ±5 . 8 , P<0 . 05 , n = 3 ) in the group of NEJs treated with SOD specific dsRNA ( Fig 6 ) . Suppression of other target proteins on account of RNAi was not studied in the present work .
The current study has optimized the RNAi protocols in F . gigantica where long dsRNA was delivered to the NEJs by simple soaking method that allowed for an efficient and persistent gene silencing up to 15 days of observation , opening prospects for functional validation of putative vaccine and therapeutic targets . However , off-target effects of the dsRNA induced silencing of the specific genes is a concern for in vivo studies on RNAi in the parasite . Also , silencing of the proteins coded by the genes targeted in the present study for their phenotypic effects in the parasite needs to be studied .
|
RNA interference ( RNAi ) is a powerful method for selectively silencing genes for the validation of potential targets for drug and vaccine development . The susceptibility of juvenile Fasciola hepatica to double stranded ( ds ) RNA induced RNAi has been established but in F . gigantica a single report of a preliminary study on knockdown of a single gene transcript exists . In the absence of other tools of reverse genetics , RNAi occupies a centre stage in the validation of gene functions in Fasciola species . This study focuses on F . gigantica , an economically important veterinary parasite with a zoonotic potential . Here in this study , we optimized a set of simple methods for triggering RNAi in the F . gigantica juvenile liver fluke , which shows that a robust transcriptional suppression can be readily achieved across all targets tested and with protein suppression confirmed in one of the targets . These studies also highlight the need for developing an in vitro maintenance system for the fluke for validation of the RNAi protocols . These findings are important for researchers aiming to employ RNAi in investigations of liver fluke biology and target validation .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"invertebrates",
"rna",
"interference",
"helminths",
"gene",
"regulation",
"enzymes",
"messenger",
"rna",
"enzymology",
"dismutases",
"animals",
"organisms",
"trematodes",
"epigenetics",
"molecular",
"biology",
"techniques",
"research",
"and",
"analysis",
"methods",
"small",
"interfering",
"rnas",
"specimen",
"preparation",
"and",
"treatment",
"artificial",
"gene",
"amplification",
"and",
"extension",
"genetic",
"interference",
"proteins",
"gene",
"expression",
"fasciola",
"flatworms",
"molecular",
"biology",
"biochemistry",
"rna",
"superoxide",
"dismutase",
"mechanical",
"treatment",
"of",
"specimens",
"double",
"stranded",
"rna",
"eukaryota",
"specimen",
"disruption",
"nucleic",
"acids",
"genetics",
"biology",
"and",
"life",
"sciences",
"non-coding",
"rna",
"electroporation",
"polymerase",
"chain",
"reaction"
] |
2017
|
RNA interference in Fasciola gigantica: Establishing and optimization of experimental RNAi in the newly excysted juveniles of the fluke
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The quantity of mRNA transcripts in a cell is determined by a complex interplay of cooperative and counteracting biological processes . Independent Component Analysis ( ICA ) is one of a few number of unsupervised algorithms that have been applied to microarray gene expression data in an attempt to understand phenotype differences in terms of changes in the activation/inhibition patterns of biological pathways . While the ICA model has been shown to outperform other linear representations of the data such as Principal Components Analysis ( PCA ) , a validation using explicit pathway and regulatory element information has not yet been performed . We apply a range of popular ICA algorithms to six of the largest microarray cancer datasets and use pathway-knowledge and regulatory-element databases for validation . We show that ICA outperforms PCA and clustering-based methods in that ICA components map closer to known cancer-related pathways , regulatory modules , and cancer phenotypes . Furthermore , we identify cancer signalling and oncogenic pathways and regulatory modules that play a prominent role in breast cancer and relate the differential activation patterns of these to breast cancer phenotypes . Importantly , we find novel associations linking immune response and epithelial–mesenchymal transition pathways with estrogen receptor status and histological grade , respectively . In addition , we find associations linking the activity levels of biological pathways and transcription factors ( NF1 and NFAT ) with clinical outcome in breast cancer . ICA provides a framework for a more biologically relevant interpretation of genomewide transcriptomic data . Adopting ICA as the analysis tool of choice will help understand the phenotype–pathway relationship and thus help elucidate the molecular taxonomy of heterogeneous cancers and of other complex genetic diseases .
Microarray technology is enabling genetic diseases like cancer to be studied in unprecedented detail , at both transcriptomic and genomic levels . A significant challenge that needs to be overcome to further our understanding of the relation between the quantitative transcriptome of a sample/cell and its phenotype is to unravel the complex mechanism that gives rise to the measured mRNA levels . The amount of a given mRNA transcript in a normal sample/cell is determined by a whole range of biological processes , some of which ( e . g . , transcription repression and degradation ) act to reduce this number , while others ( e . g . , transcription factor induction ) act to increase it . Therefore , it is natural to model the level of a given mRNA transcript as the net sum of a complex superposition of cooperating and counteracting biological processes , and , furthermore , to assume that disease is caused by aberrations in the activation patterns of these biological processes that upset the delicate balance between expression and repression in otherwise healthy tissue . Many distinct biological mechanisms that underlie the aberrations observed in human cancer have been identified , most notably copy-number changes [1] and epigenetic changes [2] , yet it is the effect that these changes have downstream on the functional pathways that ultimately dictates whether these changes are pathological or not . While several studies have recently characterised the altered functional pathways and transcriptional regulatory programs in human cancer , they have done so either by interrogating the expression data directly with previously characterised pathways , regulatory modules [3–6] , and functionally related gene lists [7] , or by interrogating derived “supervised” lists of genes for enrichment of biological function [8] . Hence , these studies have not attempted to infer the altered biological processes , which putatively map to alterations of known functional pathways and transcriptional regulatory programs . Thus , an unsupervised method that first infers the underlying altered biological processes and then relates these to aberrations in pathways or regulatory module activity levels is desirable . A necessary property of such an algorithm is that it allows “gene-sharing , ” so that a specific gene can be part of multiple distinct pathways . In this regard , it is worth noting that popular approaches for analysing transcriptomic data , such as hierarchical or k-means clustering , do not allow for genes to be shared by multiple biological processes , since they place a gene in a single cluster [9] , and so they are not tailored to the problem of inferring altered pathways . Algorithms that allow genes to be part of multiple processes/clusters have also been extensively applied [10–12] . Among these , Singular Value Decomposition ( SVD ) or Principal Components Analysis ( PCA ) provides a linear representation of the data in terms of components that are linearly uncorrelated [12] . While this linear decorrelation of the data covariance matrix can uncover interesting biological information , it is also clear that it fails to map the components into independent biological processes , since there is no requirement for PCA components to be statistically independent . Mapping the data to independent biological processes , whereby independence is measured using a statistical criterion , should provide a more realistic representation of the data , since it explicitly recognises how the data was generated in the first place . This assumption , which is to be tested a posteriori , underlies the application of Independent Component Analysis ( ICA ) to gene expression data [13 , 14] . Specifically , ICA decomposes the expression data matrix X into a number of “components” ( k = 1 , 2 , . . K ) , each of which is characterised by an activation pattern over genes ( Sk ) and another over samples ( Ak ) ( Figure 1 and Materials and Methods ) , in such a way that the gene activation patterns ( S1 , S2 , . . . , SK ) are as statistically independent as possible while also minimising the residual “error” matrix E ( in the above , ⊗ denotes the Kronecker tensor product ) . It is worth noting that while ICA also provides a linear decomposition of the data matrix , the requirement of statistical independence implies that the data covariance matrix is decorrelated in a non-linear fashion , in contrast to PCA where the decorrelation is performed linearly . Many studies have shown the value of ICA in the gene expression context as a dimensional reduction and gene-functional discovery tool [15–20] and also as a potential tool for classification and diagnosis [21 , 22] . To validate the ICA model , most of these studies used the Gene Ontology ( GO ) framework [23] . However , GO does not provide the best framework in which to evaluate the ICA paradigm , since many genes with the same GO term annotation may not be part of the same biological pathway or may not be under the control of the same regulatory motif , and vice versa . In fact , to date no study has evaluated the ICA paradigm in the explicit context of biological pathways and regulatory modules . In this work we apply various popular ICA algorithms to six of the largest available microarray cancer datasets . We focus on breast cancer for two reasons . First , for this type of cancer many large patient cohorts that have been profiled with microarrays are available . Second , breast cancer is a highly heterogeneous disease and hence it provides a more challenging ( and hence suitable ) arena in which to compare and evaluate different methodologies . We also use two large microarray datasets from two other cancer types to show that our results are valid more generally . The aim of our work is 2-fold . First , to test the ICA paradigm by showing that it significantly outperforms both a gene-sharing method that does not use the statistical independence criterion ( PCA ) and a traditional ( “non–gene-sharing” ) clustering method ( k-means ) . We achieve this by using a pathway and regulatory module–based framework for validation . The second aim is to find the most frequently altered pathways and regulatory modules in human breast cancer and to explore their relationship to breast cancer phenotypes .
The main modelling hypothesis underlying the application of ICA to gene expression data is that the expression level of a gene is determined by a linear superposition of biological processes , some of which try to express it , while other contending processes try to suppress it ( Figure 1 ) . It is assumed that these biological processes correspond to activation or inhibition of single pathways or sets of highly correlated pathways , and that each of these pathways only affects a relatively small percentage of all genes . Because of the statistical independence assumption inherent in the ICA inference process , we would expect the identified independent components to map more closely to known pathways than an alternative linear decomposition method , like PCA , that does not use the statistical independence criterion . Similarly , we would expect ICA components to map closer to pathways than clusters derived from popular clustering algorithms such as k-means or hierarchical clustering . To test the modeling hypothesis of ICA for expression data , we first asked how well the inferred components mapped to known pathways , as curated in the MSigDB pathway database [24] ( Materials and Methods , Table S1 ) . This strategy was initially applied to a total of six breast cancer microarray datasets ( “Perou” [25] , “JRH-1” [26] , “Vijver” [27] , “Wang” [28] , “Naderi” [29] , “JRH-2” [30] ) , summarised in Table 1 , and for four different implementations of the ICA algorithm ( “fastICA” , “JointDiag” , “KernelICA” , and “Radical” ) [31–34] as well as for ordinary PCA and two versions of k-means clustering ( PCA-KM and MVG-KM ) ( Materials and Methods and Protocol S1 ) . For each of the ICA algorithms and PCA , we inferred ten components and selected the genes based on their weights in the corresponding column of the source matrix S ( Materials and Methods ) . The average number of genes selected per component ranged from 50 to 200 depending on the cohort ( Table S2 ) . For the two k-means clustering algorithms , ten gene clusters were inferred on subsets of most variable genes to ensure that the average number of genes per cluster was similar to that of the PCA and ICA components . This step was necessary to ensure an objective comparison of the different algorithms . In what follows we also use the term component to denote clusters . To evaluate how close the inferred components of a given algorithm in a particular cohort mapped to existing pathways , we defined a pathway enrichment index , PEI , as follows . For each component i and pathway p , we first evaluated the significance of enrichment of genes in that pathway in the selected feature set of the component by using the hypergeometric test ( see Materials and Methods ) . This yielded for each component i and pathway p a p-value Pip . Correction for multiple testing was done using the Benjamini-Hochberg procedure to obtain an estimate for the false discovery rate ( FDR ) . A component i was then declared enriched for a pathway p if the Benjamini-Hochberg corrected p-value was less than 0 . 05 . Hence , we would expect approximately 5% of significant tests to be false positives . Finally , we counted the number of pathways enriched in at least one component and defined the PEI as the corresponding fraction of enriched pathways . The PEI for each of the seven methods ( “PCA” , “MVG-KM” , “PCA-KM” , “fastICA” , “JointDiag” , “KernelICA” , “Radical” , and “PCA” ) and the four largest breast cancer sets ( “Vijver” , “Wang” , “Naderi” , “JRH-2” ) are shown in Figure 2A ( the results for all six breast cancer cohorts are presented in Figure S1 ) . This showed that across the four major cohorts the PEI was higher for ICA algorithms when compared with PCA and the clustering-based methods . Interestingly , for the two largest cohorts ( “Vijver” and “Wang” ) , the degree of improvement in the PEI of ICA over PCA , MVG-KM , and PCA-KM was highest . In contrast , for the smaller cohorts ( e . g . , “Perou” and “JRH-1” ) , the degree of improvement of ICA over PCA or KM was less marked . Hence , since we found that cohort size had a significant impact on the inferred components , we restricted all subsequent analysis to the four major breast cancer cohorts . It is also noteworthy that when comparing the various ICA algorithms with each other we didn't observe any appreciable difference in their respective PEI . To investigate this further , we next compared the algorithms on the subset of nine cancer-signalling pathways from the curated resource NETPATH ( http://www . netpath . org ) and five oncogenic pathways [35] . These are pathways that are frequently altered in cancer and hence we would expect many of these to be captured by the ICA algorithm . Thus , for each method and study we counted the number of pathways that were enriched in any of the components ( Figure 2B ) . This showed that in the three largest breast cancer studies ( “Vijver” , “Wang” , and “Naderi” ) , PCA and the KM-methods captured the least number of pathways . In the two largest cohorts ( “Vijver” and “Wang” ) , for example , the “RADICAL” ICA algorithm captured ten and six of the 14 pathways , while PCA captured eight and two pathways , respectively . As a further validation that ICA outperforms PCA , we investigated the relation of the derived components with regulatory modules . Specifically , we tested the selected gene sets from each component for enrichment of genes with common regulatory motifs in their promoters and 3′ UTRs [36] . Under the ICA paradigm we would expect genes that are under the common regulatory control of a transcription factor to appear in the same ICA component . Thus , for each breast cancer cohort and method we counted the number of regulatory motifs whose associated genes were overrepresented in components ( Figure 2C ) , using as before the hypergeometric test to test for significant enrichment ( Materials and Methods ) . This showed that PCA performed worst out of all algorithms . In two cohorts ( “Wang” and “Naderi” ) , none of the PCA components was associated with any of the 173 distinct regulatory motifs . In contrast , the components derived by ICA algorithms were consistently associated with regulatory motifs . Interestingly , the improvement of ICA over KM-based methods was less marked with only study ( “Wang” ) showing a substantial improvement ( Figure 2C ) . The results above show that ICA provided a more biologically meaningful decomposition of breast cancer expression data than PCA or KM-based methods . This suggested to us that similar results would hold in other types of cancer . To check this , we analysed two additional large microarray datasets , one profiling 221 lymphomas [37] ( “Hummel” ) and another profiling 132 gastric cancers [38] ( “Chen” ) ( see Table 1 ) . The same analysis on these two additional datasets confirmed that the PEI was higher for ICA when compared with PCA or KM-clustering methods ( Figure 2A ) , and that ICA components also mapped closer to known regulatory motifs ( Figure 2C ) . To investigate the robustness of the algorithms , we next compared the ability of the algorithms to identify pathways and regulatory modules that were differentially activated independent of the breast cancer cohort used . Two important observations that were independent of the ICA algorithm and cohort used could be derived from the heatmaps of differential activation of pathways and regulatory modules ( Figures S2–S5 ) . First , ICA identified many more pathways that were consistently differentially activated across all four breast cancer cohorts ( Figure 3A ) . This further confirmed that the associations between components and pathways as picked out by ICA were more robust and consistent between cohorts than those identified through PCA , MVG-KM , or PCA-KM . Among the pathways that were found to map most frequently and consistently to components were those related to estrogen signalling as well as to other important breast cancer–signalling pathways such as the EGFR1 and TGF-β pathways ( Figures 3B and S2–S5 ) . We also found cell-adhesion , immune-response , cell-cycle , and metabolic pathways to be commonly differentially activated across the cohorts . While breast cancer studies have found study-specific gene clusters associated with cell-cycle , estrogen-response , cell-adhesion , and immune-response functions , our results show that expression variation across breast tumours can be understood in terms of single pathways ( i . e . , a fixed common set of genes for all studies ) that relate to these biological functions . Second , we also observed that ICA outperformed PCA , MVG-KM , and PCA-KM in identifying regulatory modules that were consistently differentially activated across cohorts ( Figure 3C ) . Specifically , the KernelICA algorithm identified the regulatory modules TATA , AACTTT , NFAT , IRF , and NF1 , while MVG-KM only picked out TATA , with PCA and PCA-KM failing to capture any regulatory module . Among the motifs with regulatory gene modules that were most frequently captured by independent components , we found several with important general ( e . g . , TATA ) and specific transcription factors ( e . g . , NF1 and ETS2 ) ( Figures 3D and S2–S5 ) . We next asked whether components mapping into the various pathways/modules were associated with breast cancer phenotypes . Specifically , we considered three categorical phenotypes: estrogen receptor ( ER ) status ( 0 , 1 ) , histological grade ( 1 , 2 , 3 ) , and outcome ( 0 , 1 ) . To evaluate statistical significance of any association between a component k and phenotype , we considered the distribution of weights from the corresponding row of the mixing matrix , i . e . , Ak ( Materials and Methods ) , across the different categories . We used the Wilcoxon rank-sum test for the two binary phenotypes and the Kruskal-Wallis test for histological grade . Because of the clustering nature of the MVG-KM and PCA-KM algorithms , in these two cases we first applied k-means over the genes in the cluster to partition the samples into two groups and subsequently used Fisher's exact test to determine whether the phenotype distribution across the two groups was significantly different from random or not . This revealed a complex pattern of significant associations with several components differentiating breast tumours according to ER status and histological grade ( Figures S2–S5 ) . It is notable that in all cohorts ICA components associating with clinical outcome were also found , while PCA generally did not . Another feature was the fact that more and stronger phenotype associations were uncovered by using ICA as compared with PCA . On the other hand , MVG-KM performed as well as ICA in mapping to ER , grade , and outcome phenotypes . Since we characterised each component in terms of the differential activation pattern of cancer-related pathways and regulatory modules , for those components associated with a phenotype we were able to link the corresponding pathways and regulatory motifs with the phenotype ( Figure 4 ) . This led to several well-known but also novel observations . First , as expected , ICA components that were strongly associated with ER status were frequently mapped to the estrogen signalling pathway . Second , ICA components that mapped to the CR ( cancer related ) cell-cycle pathway [39] were frequently associated with either grade or outcome . The association between cell-cycle genes and grade or outcome is well-known [26 , 30 , 40] , and our finding further shows that an independently characterised cell-cycle pathway associates with these clinical variables across multiple studies . Third , we observed that pathways relating to immune response functions and the classical complement pathway were frequently correlated with ER status , grade , and , although less frequently , with clinical outcome . For example , we found in each of the four major breast cancer cohorts an ICA component that mapped to the CR immune response pathway [39] , and which was consistently overactivated in ER− relative to ER+ tumours ( Figure 5A and Table 2 ) . We note that the same set of genes , when viewed over the measured expression matrix also separated the samples according to ER status ( Figure 5B and Table 2 ) . Fourth , in all studies where grade information was available , an ICA component mapping to either matrix-metalloproteinases ( MMP ) or the cell-adhesion pathway was found to be associated with histological grade . In three studies ( “Wang” , “Vijver” , and “Naderi” ) , the MMP pathway was also found to be associated with outcome . Another interesting pathway we found to be associated with histological grade was an epithelial–mesenchymal transition ( EMT ) signalling pathway characterised in [41] . Specifically , ICA revealed a component driving upregulation of genes involved in EMT in poorly differentiated tumours relative to low-grade tumours across the three studies where grade information was available ( Figure 6A and Table 3 ) . When the same set of genes defining the EMT pathway was viewed over the measured expression matrix , their pathway coherence was less evident , although the association with grade was still revealed by k-means clustering ( Figure 6B and Table 3 ) . The parallel analysis for regulatory motifs and breast cancer phenotypes provided direct links between the associated transcription factors and clinical variables ( Figure 4B ) . Strikingly , we found that the interferon regulatory factor ( IRF ) showed the strongest associations with both the ER and grade phenotypes . The regulatory module associated with the TATA box was also frequently associated with ER , grade , and outcome . Interestingly , we found differential activation of the regulatory modules associated with the neurofibromin-1 ( NF1 ) , NFAT , and ETS2 transcription factors to be associated with clinical outcome , which is significant in view of the results of several recent studies linking these transcription factors with the metastatic and cell-growth properties of breast cancer cells [42–46] . It is important to point out that ICA facilitated the identification of many of the biological associations in comparison with PCA , MVG-KM , and PCA-KM ( Figure 7 ) . Thus , for example , we can see that the association between immune response and ER status was found in all cohorts by any one of the four ICA algorithms , whereas PCA and the KM methods were generally not as robust ( Figure 7A ) . A similar observation could be made for the associations between the EMT pathway and grade , and that of the IRF module and ER status ( Figure 7B and 7C ) . For the case of NF1 and clinical outcome , this association was not identified by PCA or the KM-based methods ( Figure 7D ) . Finally , we verified that in many cases the identified associations were independent , in the sense that the component ( s ) or genes linking a pathway with a phenotype could be different from the one ( s ) linking another pathway with the same phenotype . For example , we noted that this was the case for the associations of the cell-adhesion and estrogen-signalling pathways with grade ( see Figures S2 and S4 ) . Similarly , the associations of the immune response pathway and IRF module with ER status ( Figure 7A and 7C ) could not be attributed to a common gene subset selection , since the pathway and module gene sets shared no genes in common . Networks are a useful tool for graphically representing relational structures between many layers of organisation . In our application , we sought to construct a network of associations , linking breast cancer phenotypes , pathways , and regulatory modules with each other as the nodes in the network . To represent only the most salient and robust features , we focused attention on those pathways and regulatory modules with most phenotypic associations ( Figure 4 ) and on those associations that were most consistently predicted across cohorts . Thus , we constructed an average network over the networks for each study by defining a link between any two nodes in the network if there were at least three studies in which there was a link between the two nodes , as predicted by ICA ( Figure 8 ) ( KernelICA was used but the other ICA algorithms gave similar networks ) . This revealed a complex network of associations between transcription factors , pathways , and breast cancer phenotypes . Strengthening the association of immune response with ER status further , we found triangular relationships involving the NF-κβ , ETS2 , and IRF transcription factors ( Figure 8A ) , which is plausible in view of their role in regulating immune response pathways [47–49] . The corresponding network for clinical outcome showed that apart from the cell-cycle and estrogen-signalling pathways , only the EGFR1 and TGF-β pathways were consistently associated with outcome ( Figure 8B ) .
In our view , it is most natural to analyse gene expression data in the context of a generative model , however approximate this model is to the true underlying mechanism that gives rise to the measured expression levels . ICA provides such a generative model since it explicitly recognises how the data was generated in the first place . By comparing ICA with PCA and clustering-based methods , we have shown that a more realistic representation of the data is obtained by allowing “gene-sharing” and using the statistical independence criterion ( non-linear decorrelation ) in the inference process ( ICA ) , as opposed to not allowing gene-sharing ( MVG-KM , PCA-KM ) and only using a linear decorrelation criterion ( PCA ) . We showed this on a total of six cancer microarray datasets , using existing pathway knowledge and gene regulatory module databases for evaluation . Specifically , we found that ICA components mapped closer to cancer-related pathways as well as to gene modules that are under the control of a common regulatory motif . It is worth pointing out though that the improvement of ICA over KM methods was less marked in the case of regulatory motifs , as we would expect , since a clustering method is partially tailored to finding co-regulatory structure . Importantly , when comparing the results across cohorts , we found that ICA algorithms were much more robust than PCA or KM-based methods , in the sense that pathways that were found to be differentially activated through ICA in one cohort were also consistently differentially activated in the other cohorts . A similar observation could also be made for the regulatory motifs and their regulatees . For example , using PCA or PCA-KM , no regulatory module was found to be differentially activated across all four major breast cancer studies , while the ICA algorithms found an average of four modules . The most likely explanation for the relatively smaller number of regulatory modules found in common across the four studies , as compared with pathways , is that many regulatory modules important to breast cancer have yet to be elucidated . Of note , we also performed the enrichment analysis of the independent components for chromosomal bands ( using the MSigDB database ) , which confirmed that the independent components were not capturing transcriptional programs localised to specific chromosomal regions . Instead , we believe that the inferred independent components encapsulate “net” transcriptional programs that act globally and downstream of the epigenetic and genetic modifications underlying cancer . We also found that ICA components were associated more often with known breast cancer phenotypes , including clinical outcome , and that these associations were also much stronger for ICA than for PCA . While this result is to be expected , since ICA components map closer to pathways that have been characterised using phenotypic information , one should also bear in mind that these pathways were derived from independent experiments; hence , the stronger associations between components , pathways , and phenotypes as revealed by ICA provides a validation , not only of the algorithm itself , but also of the characterised pathways . Another important observation was the presence of multiple components showing an association with a particular pathway , regulatory module , or phenotype . This suggests that a significant proportion of pathways are part of multiple biological processes . Alternatively , the presence of multiple components enriched for a given pathway may reflect distinct gene subset selection , which in turn suggests that the pathways in MSigDB and NETPATH may need to be refined further . In the context of phenotypes , the presence of multiple components correlating with ER status , grade , or outcome , is suggestive of tumour heterogeneity , since , more often than not , the differential distribution of the phenotype across samples is dependent on the precise component . Hence , the fingerprint patterns of pathway activation derived from ICA could potentially form the basis for further clinically relevant definitions of breast cancer subtypes . In an exploratory analysis , ICA revealed many interesting associations between pathways and phenotypes that can form the basis for future investigations . While all methods were able to identify the expected relationships of the estrogen-signalling pathway with ER status and cell-cycle pathway with histological grade , ICA clearly outperformed PCA and KM-clustering in identifying many other biologically relevant associations ( Figure 7 ) . For example , ICA consistently found an expression mode involving immune response pathways that was upregulated in ER− versus ER+ tumours . Thus , while the relation between immune response and ER status is still poorly understood [50] , our results clearly point at an important link between the immune response and estrogen signalling in breast cancer , which needs to be explored further . ICA also revealed interesting associations of the EMT-signalling , cell-adhesion , and MMP pathways with histological grade and clinical outcome . Specifically , we found a component upregulating EMT genes in high-grade versus low-grade tumours , and which was statistically significant in three major cohorts . The association between the activity level of the cell-adhesion and MMP pathways with clinical outcome as revealed by ICA is also noteworthy given that supervised approaches tend to only find genes related to cell-cycle pathways , as these are the strongest predictors of grade and outcome . While the association of cell-adhesion genes with outcome has been noted before in breast cancer [29] and to a lesser extent in gastric cancer [51] , here we show that this result holds for a specific pathway and across several breast cancer cohorts . ICA , in contrast to PCA and KM-clustering , also identified interesting associations between transcription factor modules and phenotypes ( Figure 7 ) . For instance , it found strong associations between the IRF and ER status and between NF1 and clinical outcome , as well as an association between NFAT and outcome ( Figure 4 ) . These associations are plausible given that changes in NFAT have been shown to alter the metastatic and growth properties of breast cancer cells [42–44] , and given the important role NF1 and IRF play in breast cancer generally [52–56] . It could be argued that both IR- and cell-adhesion pathways are differentially activated across tumours merely as a result of lymphocytic or stromal contamination , respectively . However , microarray studies profiling breast cancer cell lines ( BCL ) have shown that genes associated with IR- and cell-adhesion functions are also differentially regulated across cell lines [25 , 57] . In particular , it was shown that genes related to cell-adhesion functions were overexpressed in ER− compared with ER+ cell-lines [57] . While the study in [57] did not explicitly mention the differential expression of immune response genes , we verified , by applying ICA to this set of only 31 breast cancer cell lines ( BCL ) , that an independent component enriched for immune response genes was present and that it correlated with the ER status of the cell lines ( Figure S6 ) . This provided further validation of the link between differential regulation of immune response pathways with the ER status of breast cancer cells , while also simultaneously confirming that the differential regulation of these genes across the tumour set is not necessarily related to varying degrees of lymphocytic infiltration . Generally , we found that genes selected in the same independent component showed a relatively strong co-expression pattern ( Figure 5B ) . It follows that ICA components can often be given a biological interpretation similar to that of clusters inferred through , say , hierarchical or k-means clustering . To illustrate this with another example , we considered the case of estrogen signalling and ER status . This showed that clustering over the genes selected in an IC that was associated with estrogen signalling and ER status yielded similar heatmaps for the measured expression matrix and the IC submatrix , and , furthermore , for both heatmaps the association with the phenotype was evident ( Figure S7 ) . On the other hand , ICA also found “non-trivial” associations , such as the association of the EMT pathway with grade ( Figure 6A ) , where the functional relationship of the genes in the same pathway was not as evident from the gene expression matrix ( Figure 6B ) . Given that genes are shared by multiple pathways , the functional relationship of the genes may indeed not manifest itself as a strong co-expression pattern . Thus , it would appear that ICA , through the statistical independence criterion , which effectively uses non-linear correlation measures ( as opposed to mere linear co-expression ) to determine common functionality , is able to capture non-trivial functional relationships of genes in a common pathway , in spite of the fact that these genes may not exhibit strong co-expression . In summary , this work is the first to our knowledge to validate the ICA paradigm using a framework based on existing pathway-knowledge and regulatory-module databases . Moreover , it confirms the added value of ICA over PCA and clustering-based methods in identifying novel associations of known pathways and regulatory modules with breast cancer phenotypes . Our results also indicate that larger datasets may be required before a more complete understanding of the ICA model in the gene expression context can be obtained , as well as to understand to what degree ICA can help in defining a more clinically relevant molecular taxonomy of breast cancer .
To test the ICA model , we first generated a comprehensive list of pathways , most of which are known to be directly or indirectly involved in cancer biology . To compile this list , we used the Molecular Signatures Database MSigDB [24] , which included 522 distinct pathways curated from the literature and from other databases such as KEGG ( http://www . genome . jp/kegg/ ) and CGAP ( http://cgap . nci . nih . gov/ ) . We augmented this list with known oncogenic pathways recently derived in [35] and cancer-signalling pathways from NETPATH ( http://www . netpath . org ) , yielding a total of 536 pathways . Not all of these pathways had sufficient representation across the six major studies . Specifically , out of these 536 pathways , 277 had at least five genes represented on each of the six microarray platforms ( probes on specific microarrays were also filtered based on quality , which explains why there wasn't a higher percentage of pathway gene lists with sufficient representation ) . The full list of pathways used are summarised in Table S1 in terms of their representation on each of the arrays . We used the sequence-derived regulatory motifs in human promoters and 3′ UTRs from [36] . For each such motif we defined the associated regulatory gene module as the set of genes having this motif in their promoters or 3′ UTR , as provided in MSigDB [24] . The selected feature sets of the inferred components were tested for enrichment of regulatory modules , which provided us with putative links between components and the transcription factors that bind to these motifs . Briefly , we review the ICA model [58] as used in this work . Let Xgs denote the normalised data matrix of expression values where g = 1 , . . . , n denotes the genes and s = 1 , . . . , N denotes the samples . We assume further that X has been normalised so that the mean of each column of X is zero . Then ICA ( or PCA ) produces an approximate decomposition of the matrix X into the product of two matrices S ( the “source” matrix ) and A ( the “mixing” matrix ) : where K ≤ min{n , N} is the number of components to be computed . When K is strictly smaller than min{n , N} , it is in general impossible to pick S and A such that the error matrix vanishes . Therefore , the algorithms aim at making E as small as possible , usually in the least squares sense . This condition on E still leaves much leeway to select the matrices S and A . PCA consists of identifying an orthonormal matrix S ( i . e . , for all k ≠ k′ , and for all k ) and an orthogonal matrix A ( i . e . , for all k ≠ k′ ) so that the data covariance matrix is diagonalised . In comparison , most ICA algorithms start with a preprocessing step , in which the means of the columns of X are set to zero , followed by a PCA . Thus , as with PCA itself , this first requires an orthonormal matrix S′ and an orthogonal matrix A′ such that X = S′A′ + E′ . It should be noted that orthonormality of S′ implies a sample covariance between the columns of S′ that equals zero . The ICA step per se amounts to then finding a transformation W of S′ , such that the columns of S are “as independent as possible” . Most ICA methods consider that the zero covariance property of S′ is compatible with this goal , hence they preserve this property in S′ by restricting W to the set of K × K orthogonal transformations . The ICA algorithms , thus , search for an orthogonal matrix W that maximises the statistical independence of the columns of S′ . The mixing matrix finally equals and the error E is identical to E′ . A quantitative measure of independence between measurements of random variables , in this case the columns of S′ , is provided by a contrast function . The only requirement on the contrast function is that it goes with probability one to a prescribed extremum ( usually zero ) if and only if the random variables are statistically independent and as the number of measurements n goes to infinity . This leaves many possibilities for the contrast function , leading to a variety of ICA algorithms , which may also differ in the numerical algorithm used for the optimisation procedure . Here , we considered four different ICA algorithms , which are described in more detail in Protocol S1: the JADE ( or “JointDiag” ) algorithm [59] , the “FastICA” algorithm [31] , the “KernelICA” algorithm [32] , and the “RADICAL” algorithm [33] . The estimation of the number of sources in ICA is a hard outstanding problem . While approaches to estimating the number of sources exist , for example , the Bayesian Information Criterion ( BIC ) in a maximum likelihood framework [34] or using the evidence bound in a variational Bayesian approach [60–62] , we decided to infer the same number of components for each algorithm . There are two reasons for this . First , because of the still relatively small sample sizes of microarray experiments , estimating the correct number of components is difficult . It has therefore been conventional to use a fixed number of components [15 , 16] . Second , since the aim with our work was to provide a comparison between the PCA-derived components and those derived from ICA algorithms , using the same number of components for each algorithm facilitated such a comparison . For each component that is inferred , ICA and PCA yield a corresponding list of genes and signed weights . The ICA model is based on the premise that ICA modes selectively pick out a small percentage of genes ( ∼1% ) that are strongly activated or repressed in response to the deregulation of a particular pathway , while the great majority of genes are unaffected . Mathematically , the distribution of inferred weights must be non-gaussian , and in the gene expression context they must be supergaussian ( or leptokurtic ) , since most of the genes in a mode belong to a gaussian component centred at zero . Thus , to find the genes that are differentially activated , it is conventional to set a threshold , typically two or three standard deviations from the mean , and to pick out those genes whose absolute weights exceed this threshold . Although a more elegant method for determining an appropriate threshold , and which is based on measuring the deviation from normality of the weight distributions , is available [20] , this method is not applicable to PCA components where deviation from normality is not a requirement . Hence , since the main aim was to provide an objective comparison of ICA with PCA , we decided to use the threshold method as this method would yield approximately the same number of features per component for PCA and ICA . To focus on the pathways that dominate an ICA mode , we used the more stringent threshold of 3 sigma on either side from the zero mean , which picks out the 0 . 2% of genes in the tails of the signed weight distributions . Robustness of our results to the choice of threshold was evaluated by considering less stringent thresholds of 2 and 2 . 5 sigma . Thus , for each inferred ICA mode or principal component , we obtained a list of selected features and associated signed weights . This resulted in a mean number of approximately 160 features ( 3 sigma threshold ) selected per component , although this number varied significantly depending on study . Importantly though , while ICA algorithms did generally capture more features per component than PCA ( as we would expect since ICA algorithms seek supergaussian components ) , the difference in selected feature numbers was not significant ( Table S2 ) . To provide an objective comparison of ICA/PCA with clustering methods , the clustering step was preceded by a feature selection step which ensured that all methods selected an approximately equal number of genes . This feature selection step was performed in two different ways . For a given cohort , genes were first ranked according to their expression variance across samples . In the most-variable-genes ( MVG ) method , the top 15% variable genes were then selected . In the second method , we used all the distinct genes selected through PCA using the 3 sigma threshold . Since this number is less than the total number ( i . e . , not distinct ) of features selected from the PCA components , the remaining distinct genes were selected from the ranked MVG list . Having selected the features via one of the above methods , clustering was then performed using a robust version of k-means clustering , known as partitioning around medoids [63] , where k was set to 10 in order to match the number of components inferred by ICA and PCA . Thus , PCA-KM selected the same number of total features as PCA and approximately the same number as ICA , while the threshold of 15% was chosen to ensure that MVG-KM did not select less total number of features than ICA or PCA ( Table S2 ) . For the genes selected in a ICA or PCA component or for the genes in a given cluster derived from either MVG-KM or PCA-KM , enrichment analysis evaluates whether there is statistically significant enrichment of genes from a given pathway or regulatory module . For a given study s and inference method m , let i denote a given inferred component ( or cluster ) and p a pathway ( or regulatory module ) . In what follows , we also use “component” to refer to the clusters of the KM-algorithms , and also use “pathway” to refer to the regulatory modules . Let NS denote the number of genes on the array of data set s , and nsp denote the number of genes from pathway p on that same array . Similarly , let dsmi denote the number of genes selected in component i , and tsmi the number of genes from pathway p among the selected dsmi features . Then , under the null hypothesis , where the selected genes are chosen randomly , the number tsmi follows a hypergeometric distribution . Specifically , the probability distribution is and a p-value can be readily computed as P ( t > tsmi ) . Note that Vandermonde's identity implies that the probability distribution is correctly normalised . Thus , for a given study and method , we can compute a p-value for each component-pathway pair that evaluates how enriched the component is in terms of genes from that particular pathway . To correct for multiple testing , we used the Benjamini-Hochberg procedure [64] and called a component–pathway pair association significant if the p-value was less than a threshold determined by setting the false discovery rate ( FDR ) equal to 0 . 05 .
|
The amount of a given transcript or protein in a cell is determined by a balance of expression and repression in a complex network of biological processes . This delicate balance is compromised in complex genetic diseases such as cancer by alterations in the activation patterns of functionally important biological processes known as pathways . Over the last years , a large number of microarray experiments profiling the expression levels of more than 20 , 000 human genes in hundreds of tumor samples have shown that most cancer types are heterogeneous diseases , each characterized by many different expression subtypes . The biological and clinical goal is to explain the observed tumor and clinical heterogeneity in terms of specific patterns of altered pathways . The bioinformatic challenge is therefore to devise mathematical tools that explicitly attempt to infer these altered pathways . To this end , we applied a signal processing tool in a meta-analysis of breast cancer , encompassing more than 800 tumor specimens derived from four different patient cohorts , and showed that this algorithm significantly outperforms popular standard bioinformatics tools in identifying altered pathways underlying breast cancer . These results show that the same tool could be applied to other complex human genetic diseases to better elucidate the underlying altered pathways .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"oncology",
"homo",
"(human)",
"genetics",
"and",
"genomics",
"computational",
"biology"
] |
2007
|
Elucidating the Altered Transcriptional Programs in Breast Cancer using Independent Component Analysis
|
Intracellular replication within specialized vacuoles and cell-to-cell spread in the tissue are essential for the virulence of Salmonella enterica . By observing infection dynamics at the single-cell level in vivo , we have discovered that the Salmonella pathogenicity island 2 ( SPI-2 ) type 3 secretory system ( T3SS ) is dispensable for growth to high intracellular densities . This challenges the concept that intracellular replication absolutely requires proteins delivered by SPI-2 T3SS , which has been derived largely by inference from in vitro cell experiments and from unrefined measurement of net growth in mouse organs . Furthermore , we infer from our data that the SPI-2 T3SS mediates exit from infected cells , with consequent formation of new infection foci resulting in bacterial spread in the tissues . This suggests a new role for SPI-2 in vivo as a mediator of bacterial spread in the body . In addition , we demonstrate that very similar net growth rates of attenuated salmonellae in organs can be derived from very different underlying intracellular growth dynamics .
Infection of a host by a bacterium is a dynamic process that can be measured and analyzed at different scales . Many experimental systems for understanding infection measure the overall increase or decrease in numbers of bacteria in different organs in the host over time . Often , fine-structure dynamics of the interactions between bacteria and host cells are measured using ex vivo or in vitro systems , and these results are used to infer mechanisms that explain net patterns of survival in vivo . Most of these inferences have not been tested experimentally in whole-animal models of infection . It is clear that similar overall net survival patterns in organs might be derived from very different underlying processes . For example , an apparent net lack of growth could arise when a bacterium grows very slowly or not at all in an organ over a period of time , or it could arise from a bacterium replicating quickly but also being killed at a similar rate , giving the appearance of a net lack of growth . Unraveling these complex underlying infection dynamics is important for a full understanding of the host-pathogen interaction , and is crucially important if intervention and prevention strategies are to be improved and applied to maximum effect . An ideal model system with which to study these fundamental dynamic mechanisms in vivo is provided by invasive Salmonella enterica serovar Typhimurium infections of mice . In this system the bacteria live within spleen and liver phagocytes [1] and replicate inside a specialized , membrane-bound vacuole: the Salmonella-containing vacuole ( SCV ) . Wild-type salmonellae in susceptible mice grow rapidly in the organs , at a net rate approximating to a ten-fold increase per day . On the other hand , live attenuated vaccine strains containing mutations in defined genes show extremely slow or no net growth , and then are cleared from the organs , in the process stimulating protective immunity . Of particular interest here are those vaccine strains that are mutated in the same genes as new vaccine candidates being tested in the field for protection of humans against typhoid fever caused by S . Typhi . Prime examples are mutants that lack the type 3 secretion system ( T3SS ) encoded by Salmonella Pathogenicity Island 2 ( SPI-2 ) [2] . In S . enterica , as in other bacterial species , T3SSs have evolved to deliver proteins from the bacterium into the host cell [3] . SPI-2 T3SS is required for replication of S . Typhimurium in some cell lines in vitro [4]–[7] , and a reasonable assumption from this is that SPI-2 is also required for intracellular replication of salmonellae in the host animal [5] , [6] , [8] . This idea is supported by the fact that mutants lacking SPI-2 survive in the livers and spleens of infected animals , but show limited or no net growth in numbers [5] , [8] , [9] . Previously , we have used multi-color fluorescence microscopy ( MCFM ) to image host cells in histological sections of infected mouse organs , counting the number of bacteria per cell and the distribution of infected cells throughout infected organs . This is a direct way of observing fine-structure infection dynamics cell-by-cell , and it reveals the intracellular bacterial growth dynamics that underpin the net dynamics observable at the whole organ level . We found that when wild-type salmonellae are replicating rapidly in the organs , the number of bacteria per infected phagocyte is unexpectedly low and remains low [10]–[13] . Consequently , as the infection progresses and viable bacterial numbers per organ increase , the bacteria must undergo only a few replication cycles before they escape from the originally infected cells and disperse to infect new cells , where new infection foci emerge and the cycle repeats itself [10]–[15] . We used this more sophisticated understanding of the dynamics of growth of salmonellae in murine organs to address the hypothesis that SPI-2 is required for intracellular growth of the bacteria in vivo . Based on our previous studies , we predicted that a mutant lacking the SPI-2 T3SS would be present in the organs at the very low intracellular densities typical of slowly dividing strains [10] , [11] . However , we show that SPI-2 T3SS mutants can replicate to high intracellular densities in the spleens and livers of infected animals , but appear less able to leave infected cells than wild-type salmonellae , restricting bacterial dispersal through the tissues and dramatically reducing the formation of new foci of infection in the tissues .
Initially we studied a mutant of S . Typhimurium strain NCTC 12023 ( S12023 ) lacking the ssaV gene [16] , which is unable to assemble the SPI-2 secretory machinery [17] ( Figure 1A ) . The overall net growth of this mutant in infected organs is dramatically reduced compared with the wild-type parent , as determined by colony counts on organ homogenates [6] , [7] . The wild-type exhibited the expected infection dynamics at the cellular level , with low numbers of bacteria per cell at 72 h post infection ( p . i . ) ( Figure 1B ) , despite rapid net growth per organ , this being entirely consistent with our previous findings [10]–[13] . On the other hand , most unexpectedly , at 72 h p . i . the ssaV mutant was present at high numbers of bacteria per cell ( Figure 1C ) and was much less dispersed throughout the tissue than the wild-type bacteria . This was very surprising . We expected it to be distributed mostly as one bacterium per cell . To test whether this observation was generally true and not limited to the bacterial strain used , we repeated the analysis using ssaV mutants in two other strains ( SL1344 and C5 ) , and observed similar results , with , on average , high numbers of ssaV mutant bacteria per cell ( data not shown ) . To investigate whether these observations were the result of the ssaV mutation specifically , or whether this pattern was a general phenomenon , we studied a different SPI-2 mutant lacking sseB ( SseB forms part of the SPI-2 T3SS translocon ) ( Figure 1A ) . S12023 sseB [5] and S12023 ssaV behaved similarly in terms of slow-to-negligible net growth per organ in vivo , apparently paradoxically high intracellular densities ( Figure 1D ) , and relative lack of dispersal throughout the tissues . Complementation of sseB using plasmid psseB [5] resulted in the intracellular densities and intra-organ dispersal pattern returning to wild-type ( Figure 1E ) . To ensure that the SPI-2 T3SS mutants being observed were indeed intracellular and not simply aggregated around the outside of the cell , we used confocal microscopy , staining for markers for phagocyte cell membranes . Three-dimensional reconstruction of these images showed that the bacteria were completely enclosed within phagocyte membranes , and that they are therefore intracellular ( data not shown ) . Thus mutants lacking SPI-2 T3SS can grow to high numbers in some infected cells in vivo , despite having a low net growth pattern per organ . Given that these results were very unexpected , we proceeded with a deep and rigorous quantification of intracellular bacterial densities in the tissues of mice infected with wild-type S12023 , S12023 sseB , or the complemented S12023 sseB ( psseB ) . We therefore compared , at 72h p . i . , the overall net levels of viable bacteria per liver or spleen ( Figure 1F ) with underlying bacterial loads within CD18+ phagocytes ( Figure 1G ) . Because of the markedly different net growth rates per organ of the wild-type and mutant bacteria , different initial doses of bacteria were used to ensure that the total number of bacteria per organ per strain would be similar at 72 h p . i . . To assess formally the intracellular bacterial distributions between the strains , proportional odds ratios ( ORs ) and 95% credible intervals were generated from a Bayesian ordinal regression model ( for more details see Supporting Information – Protocol S1 ) . At 72 h p . i . the intracellular bacterial distributions of the wild-type bacteria and the complemented mutant were both as expected , based on our previous work [10]–[13] , with intracellular densities heavily skewed towards low numbers ( Figure 1G , Figure S1 and Table S1 ) . Conversely , despite its low net growth rate per organ ( Figure 1F ) , there is clear evidence that S12023 sseB-infected phagocytes are more likely to have higher intracellular bacterial densities than the wild-type or complemented strains ( Figure 1G , Figure S1 and Table S1 ) . Our data support the hypothesis that SPI-2 T3SS mutant bacteria can multiply inside phagocytes . To account for the low overall net bacterial growth per organ , this replication may occur in fewer cells , may be slower or may initiate at wild-type rate and then slow down or stop as bacterial numbers per cell become high ( the latter density-dependent scenario being predicted in our previous mathematical models [11] ) . The higher intracellular densities combined with our initial observations that SPI-2 T3SS mutant bacteria are less dispersed in the tissues were suggestive of a role for SPI-2 T3SSs as a mediator of bacterial escape from infected cells , dispersion through infected tissues and increase in the number of infection foci . To investigate this , we quantified the number of infected cells per field-of-view throughout the tissues at 72 h p . i . ( Figures 2A–D ) . The sseB mutant was substantially less dispersed throughout the tissue than the wild-type or complemented bacteria ( Figures 2A–D , Figure S2 and Table S2 ) . The wild-type bacteria and the complemented mutant had formed many more infection foci than the S12023 sseB mutant ( Figures 2A–D , Figure S2 and Table S2 ) despite there being similar total bacterial numbers in the tissues at this time ( Figure 1F ) . There was a small decrease in the number of infection foci between 0 . 5 h p . i . and 72 h p . i . in animals infected with the S12023 sseB mutant further confirming the impaired dispersion of this strain ( Figure 2E , Figure S2 and Table S2 ) . As further corroboration of our data we generated a mutant lacking spiC , which is essential for SPI-2 T3SS protein secretion and effector translocation by interacting with SsaM and SsaL ( Figure 1A ) , themselves encoded within the spi-2 locus [18]–[21] . Some studies have proposed that SpiC may be exported by the SPI-2 T3SS into the host cell cytosol [22] , where it interacts with host proteins , TassC [23] and Hook3 [24] , which are implicated in cellular trafficking and the activation of signal transduction pathways [25]–[28] . We found that a mutant lacking spiC gave a similar phenotype to the sseB and ssaV mutants in terms of intracellular bacterial densities and tissue dispersion ( Figure S3 and Table S2 ) . In addition , we found that an ssaM mutant had similar intracellular bacterial densities to a spiC mutant ( data not shown ) . The SPI-2 T3SS mutants ( ssaV , sseB , spiC , ssaM ) are therefore present in far fewer infected cells per organ but at higher densities per cell than the wild-type . The accumulation of SPI-2 T3SS mutant bacteria inside cells is most likely explained by the intracellular replication of an infecting bacterium , but all other possible explanations had to be explored before this unexpected mechanism could be supported . The first possibility tested was that the high intracellular densities seen with the SPI-2 T3SS mutants depended on phagocytes taking up clumps of bacteria . Groups of mice were infected with the same dose of the wild-type or the mutant bacteria . At 0 . 5 h p . i . , when there is no evidence of any discernible bacterial growth or death [14] , most of the bacteria within CD18+ phagocytes were present as a single bacterium per cell with negligible differences in the intracellular bacterial distributions between any of the strains ( Figure 3A , Figure S1 and Table S1 ) . This is consistent with individual resident macrophages taking up single bacterial cells from the blood and eliminates the clumping hypothesis . We proceeded with a rigorous quantification of intracellular bacterial densities in the tissues of mice infected with S12023 sseB , and discovered that he proportion of host cells containing large numbers of intracellular bacteria increased with time during an infection ( Figure 3B , Figure S4 and Table S1 ) . In addition , the number of bacteria in the heavily infected cells increased with time , with some of the heavily infected cells containing ∼100 bacteria at 72 h p . i . ( data not shown ) . This data is consistent with the bacteria growing inside the cell . Alternatively , although extremely unlikely , high intracellular numbers could be reached by phagocytic cells moving around the organ and taking up bacteria on the way , a mechanism illustrated by analogy to the arcade game “Pac-Man” . To test the “Pac-Man” idea , we performed simultaneous infections where we inoculated two Salmonella sseB mutants ( expressing different LPS O antigens enabling the mutants to be differentially visualized by immunostaining in tissue sections ) into the same animal . During the course of the infection the two strains remained segregated to different phagocytes and infection foci ( Figure 3C ) , indicating that the high numbers of bacteria in each cell are not the result of a “Pac-Man” mechanism . The most likely explanation for the high intracellular numbers of SPI-2 T3SS mutants is that there is replication and clonal expansion of an individual bacterium , and that therefore SPI-2 is not an absolute requirement for intra-macrophage replication of salmonellae . To see whether these surprising intracellular bacterial growth dynamics underpin all attenuation of salmonellae in mice , we analyzed two other well-known and extensively described attenuated mutants . S12023 aroA ( blocked in aromatic compound biosynthesis ) and S12023 purA ( deficient in purine metabolism ) have very similar net growth characteristics to S12023 sseB ( Figure 4A ) . Wild-type S12023 delivered at a similar dose to that used for the mutants exhibited low numbers of intracellular bacteria in livers and spleens throughout the infection ( Figure 4B–D ) , despite exhibiting rapid net growth per organ . At 48 h p . i . these mice were very close to death , with large numbers of extracellular bacteria and necrotic organs; however , those cells that remained infected still had an intracellular density lower than that observed in heavily infected cells from mice infected with the S12023 sseB mutant at the same time point ( Figure 4E ) . At each time point during the infection , the intracellular bacterial loads of the S12023 aroA and S12023 purA bacteria were heavily skewed towards low intracellular densities ( Figure 4B–F , Figure S5 and Tables S1 ) . Thus , similar net growth rates of salmonellae in organs can be derived from very different underlying intracellular growth dynamics ( e . g . aroA mutant and purA mutant vs sseB mutant ) . We also generated double mutants , namely S12023 sseB aroA and S12023 sseB purA . These exhibited an increasing number of cells containing large numbers of intracellular bacteria during the infection ( Figure 4B–F , Figure S5 and Table S1 ) . Thus , the intracellular bacterial loads of the double mutants followed the sseB mutant pattern . We then examined the distribution of infected cells in the tissues for the aroA and purA mutants and observed a small increase in the number of infected cells per field-of-view between 0 . 5 to 72 h p . i , for both S12023 aroA and S12023 purA , whereas for S12023 sseB aroA and S12023 sseB purA there was a small decrease in the number of infected cells per field-of-view over the same period of infection ( Figures 4G and 4H , Figure S6 and Table S2 ) . In other words the dispersion of the double mutants followed the sseB mutant pattern . Finally , we co-infected mice with an sseB mutant and an aroA mutant simultaneously and at the same dose ( Figure S7A ) . The intracellular bacterial loads of the different bacteria were the same as those seen when the two strains are injected individually , with very low intracellular densities observed for the aroA mutant and the characteristically higher densities for the sseB mutant ( Figure S7B ) . Overall the data show that very similar net growth kinetics can be the result of very different intracellular infection dynamics . Salmonellae induce host cell death during infection of cell cultures by several different mechanisms [29] . The exact contribution of each of these mechanisms to in vivo infection is far from clear . In vitro studies have demonstrated the involvement of SPI-2 T3SS effectors mediating cytotoxicity , namely SseL [30] and SpvB [31] , [32] . Recently a model has been proposed , which suggests that sseL and spv genes promote host cell apoptosis , enabling the bacteria to be taken up by other cells resulting in further intracellular replication [29] , [30] . We considered whether the reduced spread and increased number of SPI-2 mutant bacteria inside host cells could be due to the loss of SPI-2 T3SS-dependent cytotoxicity . We generated single mutants in sseL , spvB and an sseL spvB double mutant , and looked at intracellular bacterial numbers in the livers and spleens of C57BL/6 mice infected with these strains ( data not shown ) . With none of these mutants did we observe the heavily infected cells characteristic of the SPI-2 T3SS mutants , suggesting that the loss of secretion of SseL and/or SpvB in the SPI-2 mutant is not responsible for the increased number of SPI-2 mutant bacteria inside host cells . In mammalian species , reactive oxygen radicals produced by the NADPH oxidase ( Phox ) are a major innate host defense mechanism against engulfed pathogens , including Salmonella [33] , [34] . The dynamic consequences of the interplay between Salmonella SPI-2 and the NADPH oxidase at the single cell level in vivo are uncertain . gp91phox−/− mice lacking oxidase activity are fully susceptible to infection with SPI-2 mutants [33] and , in macrophages , a role has been ascribed to SPI-2 T3SS in the inhibition of the recruitment of the NADPH oxidase to the phagosome [35]–[37] . Recent studies have instead suggested a model in which Salmonella resistance relies on a range of detoxifying enzymes to cope with Phox-mediated oxidative stress [38] . To explore the cellular dynamics that underpin the interplay between SPI-2 and Phox in Salmonella , we infected C57BL/6 and gp91phox−/− mice with S12023 wild-type and its sseB mutant ( Figure 5A ) . As expected , the net growth rates of both the wild-type and the mutant were greater in the gp91phox−/− mice than in wild-type C57BL/6 mice confirming previous observations that SPI-2 mutants can grow rapidly in the tissues in the absence of a functional NADPH oxidase [33] . Next we observed the intracellular bacterial densities of S12023 sseB in gp91phox−/− mice at the 48 h p . i . time point , and compared them to our earlier data using the same bacterial mutant in C57BL/6 mice . Unexpectedly , we found that the sseB mutant in the gp91phox−/− mice was present at low numbers of bacteria per cell ( Figure 5B , Figure S8 and Table S1 ) , characteristic of wild-type Salmonella in a C57BL/6 mouse . Thus , in the absence of a functional NADPH oxidase the sseB mutant grew faster in the tissues , but did not accumulate within phagocytes . Then we quantified the number of infected cells per field-of-view for the sseB mutant in C57BL/6 and gp91phox−/− mice throughout spleen tissues at 48 h p . i . ( Figure 5C , Figure S9 and Table S2 ) . The sseB mutant in the gp91phox−/− mice had formed many more infection foci than the sseB mutant in C57BL/6 mice despite there being similar total bacterial numbers in the tissues at this time ( Figure 5A ) . Taken together , these results suggest that in the absence of an active NADPH oxidase , SPI-2 T3SS becomes dispensable for the spread of Salmonella in the tissues as shown by increased numbers of infection foci and low intracellular densities of an sseB mutant in the gp91phox−/− mice . Conversely , when an active NADPH oxidase is present a SPI-2 T3SS mutant grows inside cells to high intracellular densities but appears to be impaired in tissue spread and formation of new infection foci . The fact that an sseB mutant can escape from cells in the gp91phox−/− mice also indicates that SPI-2 independent mechanisms can mediate escape from cells and that these as yet unidentified mechanisms/effectors are normally under the inhibitory effects of the NADPH oxidase in the absence of SPI-2 . These results suggest a new interplay between SPI-2 T3SS and innate immunity in the dynamics of within-host bacterial growth and spread . Where NADPH-mediated mechanisms prevent bacterial escape from cells and at the same time exert bactericidal/bacteriostatic activity on the intracellular bacteria . However , we cannot rule out the possibility that the dynamics in the gp91phox−/− mice are an artifact of infection of the mutant mice with the mutant bacteria and may be proceeding by mechanisms different to natural murine infection .
We have shown that intracellular Salmonella infection dynamics in vivo are dramatically altered by deletion of the SPI-2 T3SS in an unexpected way . Wild-type bacteria are present in low numbers per cell on average , and the rapid net growth of these bacteria in tissues coincides with escape from the intracellular environment and dispersal to new cells where new infection foci are established . On the other hand , mutants lacking SPI-2 T3SS seem unable to escape from the infected cell and by inference are therefore unable to disperse through the tissues . What is more , the SPI-2 T3SS mutants are able to grow to high numbers within the intracellular environment , which is not what has been reported as happening in many studies of infected macrophages in vitro . The observation that two variants of the Salmonella sseB mutant , which can be labelled either red or green , segregate to different infected cells upon simultaneous infection into the same animal provides strong evidence that in systemic Salmonella infections each infected cell and each multicellular infection focus is the product of the clonal expansion of a single founder bacterium . This also excludes the possibility that the mutant bacteria have accumulated intracellularly by phagocytosis . Therefore , even if a proportion of the bacteria were dead or dormant at the time of observation the high intracellular bacterial numbers observed in animals infected with the sseB mutants show that these mutants can and do grow to high numbers per cell as a result of intracellular division . Our results call into question the usefulness of in vitro systems for studying the natural history and dynamics of Salmonella infection of cells when these methods are used in isolation and not validated against what actually happens in the infected animal . Another important observation from our study is that very similar net growth rates in organs can be derived from very different underlying intracellular growth dynamics . For example , slow growth of salmonellae per organ , typical of bacteria that are currently being trialed in humans as attenuated vaccine strains , can be generated by highly dispersed infections with low numbers of bacteria per cell , as is the case for aroA mutant bacteria , or by relatively non-dispersed infections with high numbers of bacteria per cell , as we have shown here is the case for SPI-2 mutants . That the phagocyte oxidase in involved in the restraint of spread of the salmonellae is a new concept , and suggests that this important innate immunity mechanism hampers bacterial escape from cells and at the same time ( our previous work [33]–[35] ) exerts antimicrobial functions on the intracellular bacteria . Very high quality science has been published , concerning the dynamics of infectious disease spread through communities of people or animals , but much less work has been done to understand infectious disease dynamics within the host . Many conclusions about how infectious agents work are based on experiments in isolated monocultures of cells or in somewhat crude experiments in whole animals , where gross read-outs are used to try to capture what is a complex underlying process . Understanding this complex process at a more detailed level in whole animals is the next major challenge for infectious disease biologists , and is required if intervention strategies to prevent and cure infectious diseases are to be improved and targeted effectively .
All animals were handled in strict accordance with good animal practice as defined by the relevant international ( Directive of the European Parliament and of the Council on the protection of animals used for scientific purposes , Brussels 543/5 ) and local ( Department of Veterinary Medicine , University of Cambridge ) animal welfare guidelines . All animal work was approved by the ethical review committee of the University of Cambridge and was licensed by the UK Government Home Office under the Animals ( Scientific Procedures ) Act 1986 . We used Salmonella enterica serovar Typhimurium strain NCTC S12023 ( wild-type ) ( identical to ATCC 14028s ) and mutant strains S12023 ssaV [39] , S12023 sseB [5] , S12023 sseB ( psseB ) [5] , S12023 sseL [30] , S12023 aroA ( this study ) , S12023 purA ( this study ) , S12023 sseB aroA ( this study ) , S12023 sseB purA ( this study ) , S12023 spiC ( this study ) , S12023 ssaM ( this study ) , S12023 spvB ( this study ) and S12023 sseL spvB ( this study ) . We used S . Typhimurium C5 , a highly virulent strain with an intravenous ( i . v . ) LD50 of <10 CFU in innately susceptible mice [40] and mutant strain C5 ssaV ( this study ) . We used S . Typhimurium SL5559 and SL5560 which are sister transductants of S . Typhimurium C5 that differ only in O antigen type allowing their differential identification after immunostaining [41] , and mutant strains SL5559 aroA ( this study ) , SL5559 sseB ( this study ) and SL5560 sseB ( this study ) . We used S . Typhimurium SL1344 , a virulent wild-type strain which has an LD50 by the i . v . route of <20 CFU for innately susceptible mice [42] , and mutant strain SL1344 ssaV ( this study ) . Preparation of electrocompetent Escherichia coli and S . enterica cells and transformations were performed as previously described [43] . Bacteria were grown on Luria-Bertani ( LB ) medium . Media were supplemented with the appropriate antibiotic for selection ( ampicillin 100 µg/ml , kanamycin 50 µg/ml or chloramphenicol 10 µg/ml ) . In vitro growth rates of Salmonella strains in LB broth were determined by both optical density and viable count . Standard methods were used for molecular cloning [44] . Chromosomal and plasmid DNA purifications , and routine DNA modifications including restriction endonuclease digestion of DNA , modifications of DNA and ligations were carried out using commercial kits and supplies according to the manufacturers' instructions ( QIAGEN , Crawley , UK; Promega , Southampton , UK; Invitrogen , Paisley , UK; Roche , Lewes , UK; New England Biolabs , Hitchin , UK ) . DNA concentration and purity were measured using a Nanodrop ND-1000 spectrophotometer . PCR primers were designed using Primer3 ( http://frodo . wi . mit . edu/ ) and purchased from Sigma ( Sigma-Genosys , UK ) . PCRs were performed in 25 µl reaction volumes in 0 . 2 ml Eppendorf tubes in a Perkin Elmer Gene Amp 2400 thermal cycler . Reactions contained 200 µM dNTPs , 2 mM Mg2+ , 0 . 01 volumes of Proof Start DNA polymerase ( QIAgen; 2 . 5 U µl−1 ) , 0 . 1 volumes polymerase buffer ( 10× ) , 1 µM forward and reverse primers and template DNA ( ∼50 ng plasmid DNA or ∼100 ng chromosomal DNA ) . Thermal cycler conditions were 94°C for 10 min , then 35 cycles of 94°C for 1 min , 55°C for 1 min and 72°C for 1 min , followed by a final extension at 72°C for 10 min . Mutants were generated using a modification of the ET-cloning procedure [45] , [46] as previously described [47] . PCR was used to amplify the chloramphenicol resistance cassette from pACYC184 [48] or the kanamycin resistance cassette from pACYC177 [48] with 5′ and 3′ 60 bp homology arms complementary to the flanking regions of the gene to be deleted . Approx 1 µg of linear PCR product was used for integration onto the chromosome using a modification of the Lambda Red method [49] , as previously detailed [14] . Transformants were selected by plating onto media containing chloramphenicol or kanamycin . Screening for loss of the pBADλred helper plasmid was essentially as previously described [50] , using MAST ID Intralactam circles ( MAST Diagnostics , Bootle Merseyside , UK ) to screen for the absence of beta-lactamase in bacterial colonies . ( Further details of mutant constructions are provided in Supporting Information – Protocol S1 ) . Sex- and aged-matched 9–12 week old C57BL/6 mice ( Harlan Olac Ltd ) and gp91phox−/− mice ( bred at the Wellcome Trust Sanger Institute , Hinxton , Cambridge , United Kingdom ) were infected by intravenous ( i . v . ) injection of bacterial suspensions in a volume of 0 . 2 ml . Bacterial cultures were grown from single colonies in 10 ml LB broth incubated overnight without shaking at 37°C , then diluted in phosphate buffered saline ( PBS ) to the appropriate concentration for inoculation . Inocula were enumerated by plating dilutions onto LB agar plates . Mice were killed by cervical dislocation and the livers and spleens were aseptically removed and homogenized in sterile water using a Colworth Stomacher 80 . The resulting homogenate was diluted in a 10-fold series in PBS and LB agar pour plates were used to enumerate viable bacteria . Half of each organ was fixed overnight in 4% paraformaldehyde diluted in PBS , washed for 90 min in three changes of PBS and then immersed in 20% sucrose in PBS for 16 h at 4°C before being embedded in Optimal Cutting Temperature ( OCT ) ( Raymond A Lamb Ltd , Eastbourne , U . K . ) in cryomoulds ( Park Scientific , Northampton , U . K . ) . Samples were frozen and stored at −80°C . 30 µm sections were cut , blocked and permeabilized for 10 min in a solution containing 10% normal goat serum and 0 . 02% Saponin in PBS ( Sigma , Poole , UK ) . Subsequently sections were incubated with primary antibodies in permeabilizing solution , washed in PBS then incubated with secondary antibodies and observed using a fluorescence microscope ( Leica DM6000B ) , or a confocal laser scanning microscope ( Leica TCS SP5 ) . Primary antibodies used in this study were: a 1∶1000 dilution ( Figures 1B , 1C , 1D , 1E , 2A , 2B , 2C , 3A , 3B , 4B , 4C , 4D , 4E , 4F , 4G , 4H , 5A , 5B , 5C , S3B and S3C ) , a 1∶100 dilution ( Figures 3B and S7B ) of rat anti-mouse CD18+ monoclonal antibody ( clone M18/2 , BD Pharmingen ) ; a 1∶500 dilution ( Figures 1B , 1C , 1D , 1E , 1G , 2A , 2B , 2C , 3A , 3B , 4B , 4C , 4D , 4E , 4F , 4G , 4H , 5B , 5C , S3B and S3C ) of rabbit anti-LPS O4 agglutinating antiserum ( Remel Europe Ltd ) ; a 1∶500 dilution ( Figures 3C and S7B ) of rabbit anti-LPS O9 agglutinating serum ( Remel Europe Ltd ) . Secondary antibodies used in this study were: a 1∶100 dilution of Alexa Fluor 568-conjugated goat anti-rat antibody ( Invitrogen-Molecular Probes , U . K . ) and a 1∶1000 dilution of Alexa Fluor 488-conjugated goat anti-rabbit antibody ( Invitrogen-Molecular Probes , U . K . ) ( Figures 1B , 1C , 1D , 1E , 1G , 2A , 2B , 2C , 3A , 3B , 4B , 4C , 4D , 4E , 4F , 4G , 4H , 5B , 5C , S3B and S3C ) . All sections were mounted onto Vectabond-treated glass slides ( Vector Laboratories Ltd . ) using Vectashield containing DAPI ( Vector Laboratories Ltd . ) for fluorescence microscopy and Fluoromount-G ( SouthernBiotech ) for confocal microscopy . Intracellular bacterial distributions were counted by eye , from tissue obtained from multiple mice per group , as indicated in each Figure legend . All data analysis was produced using the open-source R statistical language [51] . The MCMC routines were written in C and C++ utilizing the GNU GSL library [52] . The R package “coda” [53] , was used to read in and summarize the output from the MCMC runs . Color palettes in the plots were obtained from the “RColorBrewer” package [54] . Data in the tables are given to 2 significant figures . ( Further modeling detail is provided in Supporting Information – Protocol S1 ) .
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High quality science has been published concerning the dynamics of infectious disease spread through communities of people or animals , but less work has been done to understand infectious disease dynamics within the host . Many conclusions about how infectious agents work are based on experiments in isolated monocultures of cells or in somewhat crude experiments in whole animals . Understanding this complex process in whole animals is the next major challenge for infectious disease biologists , and is required if intervention strategies to prevent and cure infectious diseases are to be improved and targeted effectively . Bacteria of the species Salmonella enterica are a threat to public health , causing a wide range of life-threatening diseases in humans and animals world-wide . In vitro cell experiments and inference from measuring net growth kinetics in mouse organs suggest that intracellular replication of S . enterica requires proteins delivered by the Salmonella pathogenicity island 2 ( SPI-2 ) type 3 secretion system ( T3SS ) and that mutants in SPI-2 cannot replicate efficiently intracellularly . However , by observing directly infection dynamics at the single-cell level in vivo , we show that SPI-2 T3SS mutants can replicate to high intracellular densities in phagocytes in the organs of infected animals , but appear unable to leave infected cells .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"infectious",
"diseases",
"molecular",
"cell",
"biology",
"mathematics",
"statistics",
"genetics",
"immunology",
"biology",
"genomics",
"microbiology",
"population",
"biology",
"genetics",
"and",
"genomics"
] |
2012
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Attenuated Salmonella Typhimurium Lacking the Pathogenicity Island-2 Type 3 Secretion System Grow to High Bacterial Numbers inside Phagocytes in Mice
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Natural killer ( NK ) cells produce interferon ( IFN ) -γ and thus have been suggested to promote type I immunity during bacterial infections . Yet , Listeria monocytogenes ( Lm ) and some other pathogens encode proteins that cause increased NK cell activation . Here , we show that stimulation of NK cell activation increases susceptibility during Lm infection despite and independent from robust NK cell production of IFNγ . The increased susceptibility correlated with IL-10 production by responding NK cells . NK cells produced IL-10 as their IFNγ production waned and the Lm virulence protein p60 promoted induction of IL-10 production by mouse and human NK cells . NK cells consequently exerted regulatory effects to suppress accumulation and activation of inflammatory myeloid cells . Our results reveal new dimensions of the role played by NK cells during Lm infection and demonstrate the ability of this bacterial pathogen to exploit the induction of regulatory NK cell activity to increase host susceptibility .
Immune defense against diverse pathogens requires timely recruitment of monocytes to sites of infection and activation of their antimicrobial functions [1] . IFNγ promotes antimicrobial activation of myeloid cells and is required for innate resistance to numerous pathogenic bacteria , including Lm [2 , 3] . Lm is an intracellular pathogen that causes severe and life-threatening infections primarily in elderly , pregnant , and immune compromised individuals [2–4] . In murine models of infection , Lm elicits a robust innate immune response that is characterized by IFNγ production by activated NK cells [5 , 6] . Memory-phenotype T cells can also serve as an early source of IFNγ following Lm and other bacterial infections [7 , 8] . NK cells protect against several viral infections and mediate anti-tumor immune responses in animal models and human patients [9 , 10] . Thus , NK cells have also often been assumed to confer protection during bacterial infections . However , there is a paucity of experimental evidence supporting a protective role for NK cells during in vivo antibacterial immune responses . Moreover , it has been puzzling that an Lm expressed virulence protein , p60 , promotes NK cell activation and IFNγ production during infection [5 , 11] . NK cells were the first described innate lymphoid cell ( ILC ) population [12] . Activation of NK cell effector functions is regulated by germ line-encoded activating and inhibitory receptors [9] . Inhibitory NK cell receptors recognize host MHC or MHC-like molecules . Activating receptors recognize diverse stress-induced host proteins and , in some cases , microbe-encoded proteins [10] . Cytokines produced in response to infections also regulate NK cell activity . During Lm infection , the p60 protein appears to stimulate NK cell activation indirectly by promoting cytokine secretion from dendritic cells [11] . An abrupt increase in cytokines or activating receptor ligands or an encounter with target cells that have lost expression of ligands for inhibitory receptors licenses NK cells for cytolytic activity and secretion of IFNγ [9] . Some older studies provided evidence that depletion of NK1 . 1+ cells ( which include both NK cells and NKT cells ) increases host resistance to Lm infection [13 , 14] . The contributions of NK versus NKT cells to this phenotype and the mechanism for how these cells limit host resistance to Lm have not been described . With appropriate stimulation , human and mouse NK cells have been observed to produce the immune regulatory cytokine IL-10 [15 , 16] . During Lm infection IL-10 has been shown to suppress both innate and adaptive immune responses and increases host susceptibility [17] . It is not known whether NK cells might be a crucial source of the IL-10 mediating these suppressive effects . However , one study provided evidence to suggest NK cells might produce IL-10 during Lm and Toxoplasma infections [18] . Additionally , IL-10 production by NK cells was shown to impair immunity during infection with the parasite Leishmania [19] . NK cells also have been shown to limit T cell responses during infections by MCMV and LCMV [20 , 21] . It is not known if or how NK cells affect adaptive immunity during Lm infection in wildtype mice . However , mice with a point mutation in NKp46 demonstrated hyper-activation of NK cells that correlated with reduced T cell responses to Lm-expressed ovalbumin [22] . These prior studies raised the hypothesis that NK cells responding to Lm infection might suppress host resistance through the production of IL-10 , thus providing a rationale for Lm to express a protein that promotes NK cell activation . Here , we used the murine model of systemic Lm infection to investigate how activation of NK cells and NK cell production of IFNγ impacts host susceptibility to this bacterial pathogen . Our results confirmed that NK cell activation exerts pro-bacterial effects . These effects were independent from IFNγ production and , in fact , NK cell IFNγ had no discernable effect on host resistance . Rather , we found that NK cells responding to Lm infection rapidly switched from IFNγ production to the secretion of IL-10 . The secreted p60 virulence protein was sufficient to drive IL-10 production by mouse and human NK cells . IFNγ signaling in the NK cells dampened their IL-10 production . IL-10 producing NK cells were sufficient to dampen resistance to Lm infection and this regulatory activity was selectively associated with the suppression of inflammatory myeloid cell recruitment and activation . These data demonstrate the ability of a bacterial pathogen to exploit NK cell activation for selective suppression of innate immune responses during establishment of infection .
To investigate the impact of NK cells during bacterial infection , mice were depleted of NK1 . 1+ cells by a single injection of purified monoclonal Ab ( αNK1 . 1 ) at 24 h prior to i . v . infection with 104 live Lm ( ~0 . 5 LD50 ) . This protocol eliminated splenic CD3-NK1 . 1+NKp46+ NK cells from the time of infection ( 0 h post infection; hpi ) through 96 hpi ( S1A Fig ) . At 96 hpi , Lm burdens in the depleted mice were observed to be 10–100 fold lower than in mice treated with an isotype control Ab ( IgG2a ) ( Fig 1A ) . NK1 . 1 cell depletion was also effective at reducing bacterial burdens following low dose Lm infection ( S1B Fig ) , and prolonged survival of mice infected with ~2 . 5 LD50 ( Fig 1B ) . To address whether depletion of NK1 . 1+ NKT cells contributed to these effects , NKT cell-deficient B6 . cd1d-/- mice were infected with Lm . Unlike αNK1 . 1 treatment , the absence of NKT cells in B6 . cd1d-/- mice had no significant effect on Lm burdens ( Fig 1C ) . Antisera specific for the ganglioside asialo-GM1 ( αGM1 ) also depletes NK cells but does not deplete conventional NKT cells [23] . Lm burdens in B6 mice treated with αGM1 before infection were identical to those in mice treated with αNK1 . 1 ( Fig 1D ) . Depleting just the subset of NK cells expressing Ly49C/I also significantly reduced Lm burdens , though not to the extent as seen with αNK1 . 1 ( S1C Fig ) . However , treatment of mice with a non-depleting monoclonal Ab that binds the NK cell surface markers NKG2A/C/E ( αNKG2 ) did not impact Lm burdens ( Fig 1D ) . These data indicated that removing NKT cells had no effect on Lm burdens whereas depletion of NK cells or a subset of NK cells dramatically suppressed bacterial survival and growth in host tissues . We conclude that the presence of NK cells acts to increase host susceptibility to Lm and that the protective effects of αNK1 . 1 treatment are due to depletion of NK cells . NK cells were the largest population staining positive for intracellular IFNγ at 24 hpi ( S1D and S1E Fig ) , which corresponded to the peak of their IFNγ production as determined by intracellular staining ( Fig 1E ) . Serum IFNγ concentrations were reduced significantly in mice depleted of NK1 . 1+ cells , particularly at 24 hpi ( Fig 1F ) . T cells also stained positive for intracellular IFNγ and were likely the source of residual serum IFNγ in the depleted mice ( S1D and S1F Fig ) . We have previously shown that production of type I interferon down-regulates IFNGR , reducing host resistance to Lm [24] . To evaluate whether the effects of αNK1 . 1 treatment were dependent on type I interferon or IFNγ signaling , we evaluated Lm burdens in mice lacking expression of the type I interferon receptor ( B6 . ifnar1-/- ) or the IFNγ receptor ( B6 . ifngr1-/- ) . Despite the extreme differences in susceptibility of these mouse strains to Lm , depletion of NK1 . 1+ cells was protective in both ( Fig 1G and 1H ) . We also found that NK cell depletion did not impact Lm burdens until 72 hpi ( Fig 1I ) , well after the peak of IFNγ production by NK cells ( Fig 1E ) . Finally , when early NK cell IFNγ production was allowed to occur prior to αNK1 . 1 treatment , NK1 . 1+ cell depletion remained highly effective at reducing Lm burdens ( Fig 1J ) . These results indicated that in mice with an intact T cell compartment NK cell production of IFNγ has no discernable impact on host resistance or susceptibility to Lm , arguing the pro-bacterial effects of NK cells are not due to hyper-production of IFNγ . NK cells were previously shown to have the capacity to produce IL-10 at late stages of infections by the parasites Leishmania donovani and Toxoplasma gondii and viruses such as murine cytomegalovirus ( MCMV ) [18 , 19 , 25] . In one of these studies , IL-10-gfp+ NK cells were also observed at 4 dpi with Lm in Vert-X IL-10 GFP-reporter mouse [18] . We thus speculated that NK cells might be a source of IL-10 during Lm infection . Consistent with this hypothesis , we observed elevated serum IL-10 concentrations by 72–96 hpi in control but not αNK1 . 1-treated mice ( Fig 2A ) . To more directly assess the potential for IL-10 production by NK cells , we evaluated gfp staining in NK cells from tiger IL-10 GFP-reporter mice infected with Lm [26] . In tiger IL-10 GFP-reporter mice the 3’UTR of IL-10 remains unaltered , unlike Vert-X and other IL-10 GFP-reporter mouse strains that contain a mRNA-stabilizing sequence [27] . This preserves post-transcriptional regulation of the labile il10 transcripts and thus permits more reliable detection of IL-10-producing cells over time without ex-vivo re-stimulation . At 72 hpi , gfp staining was selectively increased in NK cells from spleens , livers , and blood of Lm-infected reporter mice versus uninfected reporter mice or Lm-infected control B6 mice ( Fig 2B and 2C ) . IL-10-gfp reporter expression was not observed at 24 hpi ( Fig 2C ) . Intracellular staining for IFNγ confirmed the differing kinetics of IFNγ and IL-10-gfp production and demonstrated that only a small population of NK cells produced both cytokines ( Fig 2D and S2 Fig ) . Thus , the timing of NK cell IL-10-gfp reporter activity correlated well with NK cell-dependent increases in serum IL-10 ( Fig 2A ) and Lm burdens ( see Fig 1I ) . These data further suggested that most NK cells responding to Lm infection are committed to either IFNγ or IL-10 production at these time points during the infection . We previously reported that the N-terminal LysM domain ( LysM1 ) of the secreted Lm virulence protein p60 indirectly promotes NK cell IFNγ secretion during systemic infection and in cell culture studies . This protein domain stimulates DC production of cytokines including IL-12 and IL-18 that together with cell-contact stimulate NK cell IFNγ production [5 , 11] . Given the protective effects of IFNγ , it has not been clear how the pathogen might benefit from stimulating these responses . We thus asked whether Lm expression of p60 might also promote NK cell IL-10 secretion . Consistent with this hypothesis , serum IL-10 was significantly reduced in mice infected with an Lm strain deficient in p60 ( Δp60; Fig 3A ) . However , the Δp60 strain is also attenuated in vivo , though this does not impair Lm infection of cultured macrophages or DCs [11 , 28] . To more directly investigate whether p60 expression permits Lm to stimulate NK cell IL-10 production we used a co-culture system consisting of bone marrow-derived DC ( BMDC ) and purified splenic NK cells [29] . Use of IL-10-deficient BMDC ensured any IL-10 in these cultures was derived from NK cells . BMDC were infected with wt Lm or the Δp60 strain . At 1hpi the BMDC were washed and media containing gentamicin was added . Purified splenic NK cells were added at 2 hpi ( Fig 3B ) . Under these conditions , any IFNγ produced in the cultures is dependent on NK cells ( [11 , 29] ) . Consistent with our prior findings , BMDCs infected with Δp60 Lm elicited significantly less NK cell-dependent IFNγ than those infected with wt Lm ( Fig 3C ) . As shown above , NK cell IFNγ production during systemic Lm infection peaks at 24 hpi ( Fig 1E and 1F ) , while IL-10 production peaks later ( Fig 2A ) . Consistent with these results , IL-10 was not detected in culture supernatants at 24 hpi , but was reproducibly detected by 72 hpi ( Fig 3D ) . As seen in serum , IL-10 concentrations in culture supernatants were also significantly reduced following infection with Δp60 Lm . These data suggested Lm expression of p60 stimulates DC to promote serial NK cell secretion of IFNγ and IL-10 . To specifically investigate whether the region of p60 protein that stimulates NK cell IFNγ secretion also promotes secretion of IL-10 , BMDC were primed with TLR agonists and stimulated with a recombinant p60 fragment ( L1S ) that contains the LysM1 domain ( Fig 3E ) . As previously reported [11] , IFNγ secretion was observed selectively in 24 h co-cultures where BMDC and NK cells were stimulated with a priming agent ( LPS ) and L1S protein ( Fig 3F ) . L1S also induced NK cell IL-10 secretion , but again this was selectively observed in the 72 h cultures ( Fig 3G ) . As for NK cell production of IFNγ , IL-10 production required stimulation with both TLR agonist and L1S protein . Thus , our data suggested L1S stimulates primed BMDCs to promote NK cell activation for sequential IFNγ and IL-10 production . To determine whether human NK populations were also responsive to L1S , DCs were cultured 7 days from healthy donor PBMCs , then stimulated with a priming agent ( pI:C ) ± L1S and purified autologous NK cells as in Fig 3E . The results using human cells paralleled those above . Co-cultures with cells from 4/4 donors produced IFNγ at 24 h ( Fig 3H ) and IL-10 at 72 h ( Fig 3I ) in response to the L1S treatment . Thus , the L1S fragment of the Lm p60 protein is necessary and sufficient to stimulate the ability of primed DCs to promote early IFNγ and delayed IL-10 secretion from both murine and human NK cells . Because IL-10 production by both mouse and human NK cell cultures was delayed relative to IFNγ production we considered whether production of IFNγ might inhibit NK cell IL-10 secretion . Subsequent to L1S or control stimulation , recombinant IFNγ was added to the co-cultures . NK cell IL-10 secretion was significantly reduced in the cultures treated with IFNγ ( Fig 3J ) . To confirm these effects were due to IFNγ signaling in the NK cells , we established co-cultures using B6 . il10-/- BMDC and NK cells purified from spleens of B6 . ifngr1-/- mice . The IFNGR-deficient NK cells produced 4–5 fold more IL-10 than wt B6 NK cells ( Fig 3K ) . Secreted IL-10 was also detected earlier in the cultures with IFNGR-deficient NK cells ( Fig 3L ) . These results suggest that early IFNγ production may contribute to the observed delay in IL-10 production by the responding NK cells . To further investigate the relationship between pro-bacterial effects of NK cells and their production of IL-10 , NK cell depletion was performed in IL-10-deficient ( B6 . il10-/- ) mice . Analysis of the infected B6 . il10-/- animals revealed that liver bacterial burdens were comparable to those seen in B6 mice depleted of NK cells prior to infection ( Fig 4A ) . NK cell depletion failed to further reduce Lm burdens in the B6 . il10-/- mice . Thus , NK cell depletion and IL-10 deficiency had similar and non-additive effects on susceptibility to Lm , suggesting production of IL-10 by NK cells might be responsible for the increased host susceptibility . To further test this , we performed adoptive transfer experiments . CD45 . 2+ or CD45 . 1+ B6 . il10-/- recipients were infected with Lm then respectively transferred with NK cells from the spleens of naïve wt CD45 . 1 ( B6 . ptprca ) or IL-10 deficient CD45 . 2 ( B6 . il10-/- ) mice ( Fig 4B ) . At 96 hpi ( 72 h after transfer ) small populations of donor CD45 . 1+ wt and CD45 . 2+ IL-10 deficient NK cells could be detected in spleens of the B6 . il10-/- CD45 . 2+ and CD45 . 1+ recipients , respectively ( Fig 4C ) , demonstrating persistence of the transferred cells . The detection of IL-10 protein in lysates of splenocytes from the B6 . il10-/- recipients of wt , but not IL-10-deficient , NK cells indicated the transferred cells were activated to produce IL-10 ( Fig 4D ) . This IL-10 production in the presence of wt NK cells was also associated with 10–100 fold increases in Lm burdens in livers and spleens of the B6 . il10-/- mice ( Fig 4E ) . To establish whether IFNγ production by the NK cells might mediate pro- or anti-bacterial effects in the recipient mice , groups of B6 . il10-/- mice received NK cells from the spleens of IFNγ deficient ( GKO , B6 . ifng-/- ) mice . The GKO NK cells produced IL-10 , as measured in spleen lysates ( Fig 4D ) , and GKO NK cells sufficed to increase Lm burdens ( Fig 4E ) . There was no significant difference in burdens of mice receiving wt or GKO NK cells and both NK cell types increased burdens in the il10-/- mice to a level near that seen in wt mice infected with Lm ( compare Fig 4A and 4E ) . Experiments using donor NK cells labeled with CFSE further confirmed that the take of GKO , WT , and il10-/- NK cells was similar in the il10-/- recipients ( S3A Fig ) . Thus , the ability of NK cells to produce IL-10 is a crucial factor governing Lm survival and replication during systemic infection and these pro-bacterial effects are independent from NK cell production of IFNγ . IL-10 is known to suppress M1-type myeloid cell activation as well as the production of IFNγ by activated T cells [15] . Activated myeloid and T cells are both important for mediating resistance to Lm infection [2 , 3] . Thus , we asked whether NK cell IL-10 production might suppress myeloid or T cell responses during Lm infection . A Ly6C+CD11b+ inflammatory myeloid cell population was observed to accumulate by 3–4 dpi with Lm infection in both control and NK cell-depleted mice ( Fig 5A , top ) . In both cases , the accumulating cells were a mixture of Ly6G+ neutrophils and Ly6G-CD11cl° inflammatory monocytes ( Fig 5A , bottom ) . However , we observed significantly more of these inflammatory cells in spleens ( Fig 5B ) and livers ( Fig 5C ) of the mice lacking NK cells . Increased numbers of Ly6C+CD11b+ cells were also seen in spleens of Lm-infected il10-/- mice and these numbers were reduced when mice received NK cells capable of producing IL-10 during the experiments shown in Fig 4 ( S3B Fig ) . These results suggest NK cell IL-10 suppresses the accumulation of these inflammatory myeloid cell populations at sites of bacterial infection . Both Ly6G- inflammatory monocytes and Ly6G+ granulocytes can ingest and kill bacteria to mediate protection against Lm [30 , 31] . Thus , blunting the accumulation of these inflammatory cell populations might itself promote bacterial infection . NK cell IL-10 also appeared to suppress the activation of these recruited myeloid cells , as we observed increased serum concentrations of IL-12p70 ( a product of activated myeloid cells ) in mice depleted of NK cells ( Fig 5D ) . Further , in vivo depletion of NK cells before Lm infection led to an increased production of reactive oxygen species ( ROS ) by adherent splenocytes cultured from spleens of infected mice as measured by increased fluorescence of the ROS-sensitive dye , DCF ( Fig 5E ) . These data together suggest regulatory NK cell activity suppresses accumulation and activation of inflammatory myeloid cells and thus their ability to ingest and kill Lm or infected cells . In contrast to the effects on myeloid cell responses , depleting NK cells did not notably affect the activation of T cells specific for Lm-expressed antigens when measured at 7 dpi or following secondary Lm challenge ( S4 Fig ) . Here , mice were treated with control and αNK1 . 1 antibodies before immunization with an ovalbumin expressing Lm strain ( Lm-OVA ) . NK cell depletion did not change the number of Lm- or OVA- peptide responsive CD4+ or CD8+ T cells that stained positive for intracellular IFNγ+ 7 d later ( S4A–S4C Fig ) . We also failed to observe altered expansion of T cells in response to secondary Lm challenge and the NK cell-depleted mice developed protective immunity ( S4D and S4E Fig ) . These results suggest that the increased bacterial burdens associated with NK cell regulatory activity are primarily due to suppression of inflammatory myeloid cell , but not T cell , mediated immune responses .
Lm infection elicits a potent NK cell response , but whether and how NK cells might impact host resistance to Lm and other bacteria has remained poorly understood . One notion often stated in the literature is that NK cells protect against bacterial infections through their production of IFNγ . Certainly , IFNγ is crucial for resistance to Lm [32] . However , as shown here , NK cell IFNγ production peaks and wanes rapidly . T cells were also a significant early source of IFNγ production in our studies , and memory-phenotype T cells are known to be capable of antigen-independent IFNγ production that can mediate protection against Lm [7] . Memory-phenotype T cells transferred into IFNγ-/- mice conferred significantly more protection than transferred NK cells , though the latter conferred a low degree of protection in the absence of any other source of IFNγ [33] . Thus , IFNγ production by T cells appears to obviate IFNγ production by NK cells . Consistent with this interpretation , depleting NK cells in wildtype mice did not increase Lm burdens . Instead , we confirmed several older reports showing that depletion of NK1 . 1+ cells reduces severity of systemic infections by this and other bacteria [14 , 34 , 35] . The older studies did not discriminate the effects of NK versus NKT cells , but we showed here that the absence of NKT cells had no effect on Lm burdens during systemic infection . These results support the conclusion that NK cells are not a crucial source of early IFNγ production and that NK cells instead mediate pro-bacterial effects . Our data further demonstrate the mechanism responsible for these pro-bacterial effects . We found that NK cells responding to Lm produce IL-10 and this production is necessary and sufficient to increase Lm burdens . Further , the p60 virulence protein of Lm drives NK cell activation and IL-10 production . NK cells consequently suppressed innate myeloid cell responses . These findings together suggest Lm promotes NK cell activation to exploit their regulatory effects on antibacterial myeloid cell responses . Despite the evidence that NK cell activation has deleterious effects during systemic Lm infection we are aware of a few seemingly contradictory reports . In studies where Lm was introduced into the footpad of mice , NK cell depletion was shown to modestly increase bacterial burdens in the draining lymph nodes [36 , 37] . Lm does not normally infect the host through the skin and little is known about the sequence of immune responses in this model . Perhaps footpad-inoculated Lm is unable to exploit NK cell IL-10 production , for example due to differences in the pattern or kinetics of inflammatory cell recruitment . Another example where NK cell deficiency was reported to increase susceptibility was in mice doubly deficient for the common gamma chain ( γc ) and Rag2 [38] . The γc is important for cellular responses to several cytokines , including IL-2 , 4 , 7 , 9 , 15 and 21 . IL-15 signaling is particularly important for development and survival of NK and memory CD8+ T cells [39 , 40] . The increased susceptibility in the γc-/- x rag2-/- mice might thus be interpreted to indicate a protective role for NK cells . However , mice singly deficient for γc or Rag2 did not demonstrate increased susceptibility to Lm infection [38] . Thus , the reported susceptibility of mice doubly deficient for Rag2 and γc is not simply a result of NK cell deficiency . It was suggested that NK cells may be a key source of protective IFNγ in the absence of T cells [38] . It is also notable that recent studies indicate that Rag protein expression in NK cells modulates their survival and functional activity [41] . Regardless , it has since been shown that IL-15-/- mice exhibit increased resistance to systemic Lm infection [42] . It has been previously postulated that NK cells might exacerbate Lm infection through overproducing IFNγ [14] . Three lines of evidence from our studies argue against this model: ( 1 ) We showed that mice lacking IFNGR1 expression were still protected by depletion of NK1 . 1+ cells despite their increased susceptibility overall . ( 2 ) Depletion of NK cells was protective when initiated subsequent to peak NK cell IFNγ production . ( 3 ) Transfer of GKO NK cells were as effective as wt NK cells at increasing Lm burdens in IL-10 deficient recipients . However , Jablonska and colleagues recently argued that NK cell secretion of IFNγ contributes to pro-bacterial effects based on the finding that mice treated with a low dose of anti-IFNγ antibody had heightened resistance [43] . These apparently discrepant results could be explained by antibody stabilization of IFNγ to enhance its signaling , as has been shown to occur when cytokines such as IL-2 and IL-15 are complexed with antibodies or soluble receptor subunits [44 , 45] . We thus interpret the available data as evidence that NK cells exert pro-bacterial effects independent from their IFNγ production . We instead found that the key mechanism by which NK cells increase susceptibility to Lm is through production of IL-10 . How does NK cell-derived IL-10 suppress resistance to bacterial infection ? It is well established that mice entirely deficient for IL-10 are highly resistant to Lm infection . This resistance is associated with increased innate and adaptive immune responses [17] . Consequently , we evaluated the effects of NK cell depletion on both T and myeloid cell responses to Lm infection . We failed to observe any effect of NK cells on the T cell response to Lm infection , suggesting that NK cell IL-10 is not responsible for the previously observed suppression of T cell responses . Consistent with this result , CD4+ T cells were recently shown to be a crucial source of IL-10 that regulates memory CD8+ T cell responses during LCMV infection [46] . In contrast , our findings here indicated that NK cell IL-10 suppresses both accumulation and activation of myeloid cells . Coincident with the timing of NK cell IL-10 production in control Lm infected mice ( 3–4 dpi ) , we found that depleting NK cells increased inflammatory monocyte and neutrophil accumulation in spleens and livers , increased serum IL-12p70 , and increased ROS production in cultures of adherent splenocytes . Activated myeloid cells are the primary source of IL-12 , and its production is known to be suppressed by IL-10 [47 , 48] . ROS production is also a correlate of M1-type activation , is suppressed by IL-10 , and correlates with macrophage and neutrophil bactericidal activity [49 , 50] . Further , IL-10 blockade is known to increase macrophage bactericidal activity against Lm [49 , 51 , 52] . Production of IL-12 or ROS may not themselves mediate reduced Lm burdens in NK cell-depleted mice , but certainly indicate enhanced myeloid cell activation . Accumulation and activation of myeloid cells is crucial for immune resistance to infections by Lm and many other intracellular pathogens [1] . Hence , the impairment of these processes likely accounts for the ability of NK cell IL-10 to increase susceptibility during Lm , and possibly other , bacterial infections . Presumably , this dampening of inflammatory responses benefits the host in other settings , such as in the context of inflammatory diseases . Lm infection is not unique in its ability to stimulate IL-10 secretion by NK cells . NK cell IL-10 production was previously observed at late stages of chronic infection by Leishmania donovanni [19] , and during infections by Toxoplasma gondii [18] , and murine cytomegalovirus ( MCMV ) [53] . During T . gondii infection , NK cell activation is regulated by cytokine ( IL-12 ) production and this IL-12 is driven by ligation of TLR11 and 12 by the parasite profilin protein [54] . IL-12 production during T . gondii [18] induces NK cell expression of the aryl hydrocarbon receptor ( Ahr ) transcription factor to drive il10 transcription [55] . IL-12 also drives il10 transcription in NK cells during chronic infection by the parasite L . donovanii and IL-10 producing NK cells were shown to increase parasite numbers in this model [19] . However , it is not yet clear whether a specific L . donovanii protein drives the NK cell response to this infection . NK cell IL-10 production during MCMV infection is largely seen in the Ly49H+ NK cell subset [25] . Ly49H is an activating receptor that responds to the virus-encoded M157 protein [56 , 57] . Work here showed that the Lm p60 protein was important for promoting regulatory NK cell activity . Our prior work found that Lm expression of the p60 protein increases both bacterial replication in host tissues and NK cell production of IFNγ early after systemic infection [5] . Recombinant p60 protein was subsequently shown to stimulate production of IL-18 by primed BMDC to stimulate cell contact-dependent NK cell production of IFNγ [11 , 58] . In the present paper Lm expression of p60 increased NK cell secretion of IL-10 in vivo and in BMDC/NK cell co-cultures . Stimulation of primed murine BMDCs or human PBMC-derived DCs with a recombinant fragment of p60 protein ( L1S ) likewise sufficed to trigger NK cell secretion of IL-10 . These data suggest Lm uses p60 to actively promote NK cell IL-10 secretion . Together with the prior work in other pathogen models , these data with p60 further suggest that the presence of NK cell stimulating proteins might be a marker for pathogens that have evolved to exploit NK cell regulatory activity . The kinetics of NK cell IL-10 secretion was found here to occur only after reductions in NK cell IFNγ secretion , both during systemic infection and in cell co-cultures infected with Lm or stimulated with recombinant L1S protein . Similar delays were observed in other models where NK cell IL-10 production occurs [18 , 19 , 53] . For example , during Leishmania donovani infection NK cell IFNγ production is stimulated in an IL-12-dependent manner from 24 hr infected mice , followed by NK cell IL-10 production from 21 day infected mice [19] . Similarly , during MCMV infection IL-2 and IL-12 drove NK cell IFNγ as well as subsequent IL-10 production in culture ex vivo [53] . These studies suggest that the switch from IFNγ to IL-10 production is a consequence of NK cell activation in multiple infections . However , we do not believe this switch is a “hard-wired” response to activation given previous reports showing certain cytokine stimulation protocols which induce IFNγ production by human NK cells fail to also trigger IL-10 secretion [59 , 60] . Recent work from Biron and colleagues suggested that during MCMV infection this delay reflects a requirement for NK cell proliferation to open the il10 locus to transcriptional machinery [53] . Proliferation might similarly contribute to IL-10 production by NK cells during Lm or other infections , though this remains to be determined . Defining the mechanistic basis for sequential production of IFNγ and IL-10 was not the purpose of our studies , but we did observe that IFNγ acts to suppress NK cell IL-10 secretion . Further , we found that NK cells deficient in IFNγ signaling secreted increased quantities of IL-10 . These results suggest early NK cell IFNγ production may be important for suppressing or delaying NK cell IL-10 secretion . In T cells an initial pro-inflammatory response is necessary for switching from IFNγ to IL-10 production [61–63] . However , we found that IFNγ-deficient NK cells remained capable of producing IL-10 and were as effective as wt NK cells at increasing susceptibility in IL-10-/- mice . Thus , cell-intrinsic IFNγ production does not appear to be an essential stimulus for NK cell IL-10 secretion . What additional factor ( s ) might contribute to driving NK cell transitioning from IFNγ to IL-10 production during Listeria and other infections remains to be determined . The impact of NK cell regulatory activity on human health and disease is not yet known . However , human NK cells were previously shown to produce IL-10 [59] , and we showed that Lm p60 could drive IL-10 secretion by human NK cells . Thus , NK cell IL-10 production may well impact human susceptibility to infections and other diseases , including Lm infection . Severe Lm infections primarily occur in elderly and pregnant individuals . Ageing is associated with an increase of circulating NK cells in humans [64] , and NK cells are a major cell population in the placenta of pregnant humans and animals [65] . Perhaps then , the increased prevalence of NK cells and their IL-10 production is an important factor governing the susceptibility of these populations . Pregnant individuals also have increased susceptibility to infections by T . gondii , Cytomegalovirus , and Leishmania [66] . Depletion of NK cells or more selective approaches to suppress their acquisition of regulatory activity could thus prove useful in some clinical settings . Improved understanding of how Lm p60 and other pathogen factors induce pro-inflammatory and regulatory NK cell responses will be an important step in defining the potential benefits , risks , and feasibility of manipulating NK cells in the context of infectious , autoimmune , and cancerous diseases .
Female mice were used at 8–12 weeks of age . C57BL/6J , B6 . il10-/- , B6 . ptprca , B6 . ifngr1-/- , B6 . ifnar1-/- , B6 . ifng-/- ( GKO ) and B6 . IL-10-gfp ( tiger ) mice were purchased from Jackson Labs . B6 . cd1d-/- mice were from Dr . Laurent Gapin ( Univ . Colorado ) . Mice were maintained in the National Jewish Health Biological Resource Center and University of Colorado Office of Laboratory Animal Resources . Mice were treated i . p . with PBS or PBS containing 100 μg of purified Ab or 100 μl of rabbit antisera to the ganglioside asialoGM1 ( α-GM1 , Wako USA ) . Endotoxin free IgG2a control ( C1 . 18 ) and αNK1 . 1 ( PK136 ) Abs were purchased ( Bio X Cell ) . Anti-Ly49C/I ( clone 5E6 ) and anti-NKG2A/C/E ( 20D5 ) Abs were purified from hybridoma supernatants using protein A affinity chromatography . Unless otherwise stated , Abs were given in a single dose at 24 h before infection . Depletions were confirmed by flow cytometry . L . monocytogenes ( Lm; strain 10403s ) , congenic p60-deficient [28] , and OVA-expressing [67] Lm ( OVA-Lm ) were thawed from frozen stocks and diluted for growth to log phase in brain-heart infusion or tryptic soy broth ( MP Biomedicals ) , then diluted in PBS and given to mice i . v . in the lateral tail vein . Unless stated otherwise , mice received a single sublethal dose of 104 CFU . Lm was given at 4000 CFU for infection in B6 . ifngr1-/- mice , and a lethal dose of 5 x 104 CFU for analysis of survival ± NK cell depletion . OVA-Lm was given at 5000 CFU for immunizations . Challenges used a lethal dose of Lm-OVA ( 105 CFU ) . For CFU counts , organs were harvested into 0 . 02% Nonidet P-40 , homogenized for 1 min with a tissue homogenizer ( IKA Works , Inc . ) and serial dilutions were plated on BHI or TSB agar plates . Spleens were harvested into media containing penicillin/streptomycin at 100 U/ml then transferred to a solution of 1 mg/ml type 4 collagenase ( Worthington ) in HBSS plus cations ( Invitrogen ) . After a 30 min incubation at 37°C , spleens were disrupted by passage through a 70 μm cell strainer and the resulting single cell suspensions treated with RBC lysis Buffer ( 0 . 15 M NH4Cl , 10mM KHCO3 , 0 . 1 mM Na2EDTA , pH 7 . 4 ) for 2 min . Prior to intracellular staining , splenocytes were incubated 3–4 h in RP10 media ( RPMI 1640 , 10% FBS , 1% L-glutamine , 1% Sodium Pyruvate , 1% Penicillin , 1% Streptomycin and 0 . 1% β-ME ) containing Brefeldin A ( GolgiPlug; BD Biosciences ) . No additional ex vivo stimulation was included for NK cell analyses . For T cells , 1 μM concentrations of synthetic OVA257–264 ( SIINFEKL ) or LLO190–201 ( NEKYAQAYPNVS ) peptides were included during the incubation . Blood cells were harvested into HBSS plus cations and heparin ( Sigma ) . Cells were treated twice with RBC lysis Buffer for 1 min . Liver cells were harvested and treated with collagenase in the same manner as spleen cells . Following passage through a 70 μm cell strainer , cells were re-suspended in 40% Percoll in HBSS minus cations . The 40% Percoll was underlayed with 60% Percoll , and cells were collected from gradient following centrifugation . RPMI with 5% FBS was added to cells to pellet , and cells were treated with RBC lysis Buffer for 1 min . Anti-CD16/32 ( 2 . 4G2 hybridoma supernatant ) was added to block Fc receptors prior to staining , which used FACS buffer ( 1% BSA , 0 . 01% NaN3 , PBS ) containing fluorophore-labeled antibodies purchased from eBioScience or BioLegend . Staining antibodies included anti- CD3 ( clone 145 2C11 ) , CD4 ( clone RM-4-5 ) , CD8 ( clone 53–6 ) , CD11b ( M1/70 ) , CD11c ( N418 ) , CD27 ( clone LG . 7F9 ) , CD45 . 1 ( clone A20 ) , IFNγ ( clone XMG1 . 2 ) , Ly6C ( HK1 . 4 ) , Ly6G ( 1A8 ) , NK1 . 1 ( clone PK136 ) , and NKp46 ( clone 29A1 . 4 ) . After surface staining , cells were fixed in 2–4% paraformaldehyde for direct analysis with or without saponin treatment for intracellular staining . To amplify IL-10-gfp signal [53] , fixed and permeabilized cells were stained with a rabbit monoclonal anti-GFP followed by goat anti-rabbit IgG Alexa Fluor 488 ( both from Life Technologies ) . At least 100 , 000 data events per sample were collected using an LSRII ( BD Biosciences ) . FlowJo software ( Treestar ) was used for analysis of flow data . Bone marrow-derived DC ( BMDC ) were cultured from B6 . il10-/- mice and infected with Lm or stimulated with recombinant L1S protein purified as previously described [11 , 29] . Briefly , bone marrow was cultured 6d in GM-CSF and 3 x 105 BMDC ( >90% CD11c+ ) per well were cultured overnight in 24 well plates . For infections , log phase wt or Δp60 Lm were added at a multiplicity of one bacterium per BMDC . One h later , cells were washed and gentamycin was added at 10 μg/mL . For L1S stimulation , BMDC were activated 1 h by treatment with 20 μg/ml poly I:C ( Invivogen , San Diego , CA ) or 10 ng/ml LPS ( L8274 Sigma-Aldrich , St . Louis , MO ) and purified L1S protein was added to the BMDC at 30 μg/ml . For IFNγ treatment , 50 ng/mL IFNγ ( Life Technologies ) was added at 1 hr post L1S stimulation . Purified splenic NK cells were negatively sorted using the EasySep Mouse NK cell Enrichment Kit ( 19755 Stemcell Technologies ) and added to cultures 2 h after Lm or L1S treatments at a ratio of . 1:1 ( NK cells:BMDC ) . Purified NK cell populations were >80% NK1 . 1+CD3- . To culture human DCs , adherent PBMCs were obtained from unrelated donors and grown in RPMI 1640 ( Invitrogen ) supplemented with 10% human AB serum ( Innovative Research , Novi , MI ) , 0 . 01M HEPES , 0 . 02mg/ml gentamicin , 200 IU/ml IL-4 ( eBioscience ) , and 100 IU/ml GM-CSF ( R&D Systems , Minneapolis , MN ) . After 6 days , DC were plated at 105 cells/well in 96 well plates , primed , and stimulated with L1S and polyI:C as above . Purified human NK cells were negatively sorted from PBMCs using the EasySep Human NK cell Enrichment Kit ( 19015 Stemcell Technologies ) . Supernatants were harvested for analysis of IFNγ and IL-10 at 24 or 72 h cytokines using commercial ELISAs ( BD Biosciences ) . Blood was collected by cardiac puncture . Serum from clotted blood was collected and frozen prior to analysis using commercial ELISAs for IFNγ , IL-12p70 , or IL-10 ( BD Biosciences ) . Equal numbers of splenocytes were homogenized in 1 mL of 0 . 02% Nonidet P-40 with protease inhibitor cocktail ( ThermoFisher ) using a dounce for measurement of IL-10 in homogenates . NK cells were purified from spleens of naïve B6 . ptrpca , B6 . il10-/- , and B6 . ifng-/- ( GKO ) mice ( >80% purity ) using the EasySep Negative Selection Mouse NK Cell Enrichment Kit ( Stemcell Technologies ) . Each recipient received 1 . 5–2 x 106 live NK cells i . v . at 24 hpi . Single cell splenocyte suspensions were prepared as above and 106 cells were added per well to a 96 well round bottom suspension plate ( Greiner ) followed by centrifugation at 500 x g for 5 min . Pelleted splenocytes were resuspended in DPBS ( Gibco ) containing 10μM 2' , 7'-dichlorodihydrofluorescein diacetate ( DCF; ThermoFisher ) and 1% DMSO . After a 30 min incubation at 37°C , spenocytes were washed twice in DPBS and resuspended at 100 μl/well in phenol red-free DMEM ( Gibco ) then transferred to 96 well white bottom Nunc F96 plates ( ThermoFisher ) . Plates were incubated in a 37°C Biotek Synergy HT plate reader and fluorescence read at 15 min intervals using a 528/20 emission filter and Gen5 software . Graphing and statistical analyses used Prism ( GraphPad Software ) . Statistical tests included t-tests , analysis of variance ( ANOVA ) , and linear regression with Pearson correlation . P<0 . 05 was considered significant . The Animal Care and Use Committees for National Jewish Health ( Protocol# AS2682-08-16 ) and the University of Colorado School of Medicine ( Protocol# 105614 ( 05 ) 1E ) approved all studies . These protocols adhere to standards of the United States Public Health Service and Department of Agriculture .
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Natural killer ( NK ) cells are an innate immune cell population known to promote antiviral immunity through cytolysis and production of cytokines . Yet , some pathogens encode proteins that cause increased NK cell activation . Here , using a model of systemic infection by the bacterial pathogen Listeria monocytogenes ( Lm ) , we show that NK cell activation increases host susceptibility . Activated NK cells increased bacterial burdens in infected tissues despite their early production of the pro-inflammatory cytokine IFNγ . We found that the ability of NK cells to exacerbate infection was independent from their production of IFNγ and instead due to subsequent production of the anti-inflammatory cytokine IL-10 . A single bacterial protein , p60 , was sufficient to elicit NK cell production of both early IFNγ and delayed IL-10 . IL-10-production by NK cells has been shown to occur in other systems , but our studies are first to show how this “regulatory” response impacts the course of a bacterial infection . We found that IL-10 producing NK cells suppress accumulation and activation of inflammatory myeloid cells . Our studies suggest that the exploitation of NK cell regulatory activity provides selective pressure for the evolution of pathogen proteins that promote NK cell activation .
|
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2016
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Bacterial Manipulation of NK Cell Regulatory Activity Increases Susceptibility to Listeria monocytogenes Infection
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Kaposi’s sarcoma associated herpesvirus ( KSHV ) , like all herpesviruses maintains lifelong persistence with its host genome in latently infected cells with only a small fraction of cells showing signatures of productive lytic replication . Modulation of cellular signaling pathways by KSHV-encoded latent antigens , and microRNAs , as well as some level of spontaneous reactivation are important requirements for establishment of viral-associated diseases . Hypoxia , a prominent characteristic of the microenvironment of cancers , can exert specific effects on cell cycle control , and DNA replication through HIF1α-dependent pathways . Furthermore , hypoxia can induce lytic replication of KSHV . The mechanism by which KSHV-encoded RNAs and antigens regulate cellular and viral replication in the hypoxic microenvironment has yet to be fully elucidated . We investigated replication-associated events in the isogenic background of KSHV positive and negative cells grown under normoxic or hypoxic conditions and discovered an indispensable role of KSHV for sustained cellular and viral replication , through protection of critical components of the replication machinery from degradation at different stages of the process . These include proteins involved in origin recognition , pre-initiation , initiation and elongation of replicating genomes . Our results demonstrate that KSHV-encoded LANA inhibits hypoxia-mediated degradation of these proteins to sustain continued replication of both host and KSHV DNA . The present study provides a new dimension to our understanding of the role of KSHV in survival and growth of viral infected cells growing under hypoxic conditions and suggests potential new strategies for targeted treatment of KSHV-associated cancer .
Kaposi Sarcoma associated herpesvirus ( KSHV ) or Human herpesvirus 8 ( HHV8 ) infects human endothelial cells and B-lymphocytes and is strongly associated with Kaposi sarcoma ( KS ) , Pleural Effusion Lymphoma ( PEL ) and Multicentric Castleman’s Disease ( MCD ) [1–4] . Like other herpesviruses , KSHV maintains the viral genome as extra-chromosomal episomes in latently infected cells with only a limited number of KSHV-encoded genes expressed [5–7] . Upon successful infection and establishment of latency , cellular transformation by KSHV relies upon its ability to degrade tumor suppressors or activating pro-oncogenic factors [8–11] , though immune competency of infected individual plays a critical role in pathogenesis of KSHV infection [12] . KSHV-encoded latency associated nuclear antigen ( LANA ) is the major factor responsible for maintaining latency as well as tethering the viral episomal DNA to host chromatin [5 , 13–15] . LANA binds directly to the terminal repeats , which contains the minimal replication unit through its carboxy-terminus while binding to cellular chromatin through its amino-terminus [16 , 17] . For persistent replication of the KSHV genome , LANA also recruits the clamp loader proliferating cell nuclear antigen ( PCNA ) to the KSHV genome [18] . Epigenetic reprogramming of the KSHV genome is another key requirement for maintaining latent infection and escaping from host immune response by switching off expression of the majority of the genes [19 , 20] . Recent studies demonstrated genome wide changes in methylation patterns , as well as histone modifications throughout the steps of infection for establishment of latency or lytic reactivation post-infection [21–23] . In normoxia , the KSHV genome replicates once per cell cycle to maintain the gross copy number , and its replication is dependent on the host cellular machinery . The inhibition of KSHV replication through Geminin , an inhibitor of Cdt1 and mammalian replication confirmed the involvement of host regulatory factors in latent replication of KSHV[17] . Additionally , expression of Cdt1 , rescued the replication ability of plasmids containing the KSHV minimal replicator element [17] . LANA is involved in recruiting the DNA clamp loader PCNA to mediate efficient replication and persistence of KSHV [18] . We have also previously identified and characterized another latent origin , which supports replication of plasmids ex-vivo without LANA expression in trans and prompted our investigation using single molecule analysis of replicated DNA ( SMARD ) [24 , 25] . The study resulted in identification of multiple replication initiation sites within the entire KSHV genome [25] . Chromatin immuno-precipitation assays performed using anti-origin recognition complex 2 ( ORC2 ) , and LANA antibodies from nuclear extracts of cells containing plasmids RE-LBS1/2 , RE-LBS1 , LBS1 , or RE showed an association of ORC2 with the RE region[17] . Similarly , other host trans factors like MCMs was shown to be associated with the replication initiation complex [25] . Hypoxia and the hypoxia inducible factor HIF1α play a critical role in pathogenesis of KSHV by modulating expression of critical KSHV-encoded genes , as well as stabilizing several KSHV-encoded proteins . Although only a few hypoxia responsive elements ( HREs ) within promoters of KSHV-encoded genes have been validated , there are hundreds of uncharacterized HREs present across KSHV genome . The most important HREs characterized within the promoters of KSHV genes are those within the regulatory regions of LANA , the reactivation and transcriptional activator ( RTA ) , and the viral G-Protein coupled receptor ( vGPCR ) [26–28] . LANA is involved in maintenance of KSHV latency , and it promotes tumorigenic properties through either activation of oncogenic pathways or repression of apoptotic pathways [14 , 29] . RTA is involved in transcriptional activation of KSHV-encoded genes and lytic replication of the KSHV genome [30 , 31] . The direct involvement of hypoxia in KSHV lytic replication have been demonstrated by a number of studies which showed that HIF1α facilitated KSHV-encoded RTA-mediated reactivation by binding to LANA to upregulate RTA expression [27] . Hypoxia is also reported to enhance the viral reactivation potential of the well-known reactivating compound 12-O-tetraecanoylphorbol-13-acetate [32] . Furthermore , the role of hypoxia in maintenance of latency is also crucial , where promoters of the key latent gene cluster coding for LANA , vFLIP and vCyclin harbor hypoxia responsive elements regulated by HIF1α [28] . Hypoxia-dependent expression of vGPCR is well known for modulating expression of several metabolic genes through ROS dependent epigenetic modifications [26] . It is also important to note that vGPCR up-regulated expression of HIF1α through activation of the MAPK kinase signaling pathway through the targeting of P38 [33] . This HIF1α-vGPCR positive feedback mechanism may explain in part the elevated levels of HIF1α in KSHV infected cells . The elevated levels of HIF1α in KSHV-infected cells was shown to modulate several pathways essential for cell proliferation , apoptosis , angiogenesis and metabolic reprogramming [34] . Hypoxia is a detrimental stress to aerobic cells and a consequence of restricted blood supply in the context of in-vivo conditions [35] . Cessation of cell cycle progression , and DNA replication are the main adaptive response of cells to minimize their energy and macromolecular demands [36 , 37] . Additionally , transcription factors specifically stabilized in hypoxia ( Hypoxia inducible factors; HIFs ) regulate transcription of a number of genes responsible for reprogramming cell metabolism and promote survival [38 , 39] . Stabilized HIF1α is also recognized as a negative regulator of cell division and replication through its non-transcriptional associated functions [40–42] . Furthermore , HIF1α knock-down abrogates hypoxia-mediated cell cycle arrest and promotes DNA replication in the subsequent synthesis phase [42] . It is well established that KSHV infection promotes HIF1α stabilization , and paradoxically further exposure of KSHV positive cells to hypoxia induces lytic replication . [27 , 32] . The differential character of replication of the KSHV genome under hypoxic conditions begs the exploration of how KSHV manipulates the replication machinery to promote latent replication under hypoxia , a non-permissive and unfavorable condition . In this study we now show that KSHV not only allows an efficient transition to S-phase by stabilizing CyclinE/CDK2 , but also protects critical replication-associated proteins involved in origin recognition , initiation and elongation from hypoxia-dependent degradation . In addition , KSHV-encoded LANA in conjunction with host-encoded HIF1α is necessary for efficient replication in the hypoxic microenvironment .
Cellular adaptive response towards hypoxia includes cessation of cell cycle progression and DNA replication to minimize energy demand and to ensure cell survival . In contrast to normal cells , the KSHV genome in infected cells is known to undergo reactivation and lytic replication [27 , 32] . Interestingly , both the latency associated nuclear antigen ( LANA ) and replication and transcriptional activator ( RTA ) , are up-regulated under hypoxic conditions . Where the former promotes cellular proliferation and oncogenesis , and the latter is the essential mediator of lytic replication[14 , 30 , 43] . BJAB-KSHV cells were used to investigate the role of KSHV in modulating cellular proliferation and DNA replication events under hypoxic conditions as compared with KSHV negative BJAB cells . The two cells lines were checked for their isogenic background by short tandem repeat ( STR ) profiling and after thawing a similar passage number of cells was used for the experiment [26] . As expression of transcript or protein levels of HIF1α does not represent a good marker of long-term hypoxic induction [44] , PDK1 levels ( a transcriptional target of HIF1α ) was used to demonstrate induction of hypoxia ( Fig 1A ) . Cell cycle analysis of BJAB and BJAB-KSHV cells grown in hypoxia for different time periods clearly indicated that the presence of KSHV can facilitate the G1/S transition under hypoxic conditions ( Fig 1B and 1C and S1 Fig ) . Hypoxia induces arrest of G1/S transition [42] , and bypassing this arrest is essential for entry and subsequent DNA replication and cellular proliferation . Therefore , we hypothesized that KSHV can manipulate the cellular machinery to bypass this arrest and promote DNA replication . To investigate this , we checked the status of Cyclins ( Cyclin D1 & Cyclin E ) and Cyclin dependent kinase ( Cdk2 ) , associated with G1/S transition ( Fig 1D ) [45] . We first investigated the status of Cyclin D1 , Cyclin E and Cdk2 at the transcript level in BJAB and BJAB-KSHV cells growing under normoxic or hypoxic conditions at various time points ( Fig 1E , 1F and 1G and S2 Fig ) . The results suggested that hypoxia exerts a similar effect on the transcription expression of these genes . Briefly , an almost 50% reduction in expression of Cyclin D1 and Cyclin E was observed at 24 hours of hypoxia treatment in both BJAB and BJAB-KSHV cells . Similarly , a 75% reduction in expression of Cyclin E was observed at 36 hours of hypoxia treatment in both BJAB and BJAB-KSHV cells ( Fig 1E and 1F and S2 Fig ) . Interestingly , the expression of Cdk2 in these cells was observed to be independent of hypoxia with no significant differences seen in expression at any time periods ( Fig 1G and S2 Fig ) . We , therefore , hypothesized that KSHV may affect the expression of these factors at the protein level . Based on the transcript analysis of the expression , we choose to analyze the expression of these proteins after 36 hours of hypoxic treatment . Cells were grown in normoxic or hypoxic conditions for 36 hours followed by analysis by Western blot to detect the differences in the protein levels . Interestingly , we observed that levels of both Cyclin D1 and Cyclin E as well as Cdk2 were significantly reduced in KSHV negative BJAB cells ( Fig 1H ) . The presence of KSHV in BJAB-KSHV cells had a protective effect on these proteins from hypoxia-associated degradation ( Fig 1H , compare lane 2 and 4 ) . Furthermore , to corroborate these results , we performed the same experiment in HEK293T and HEK293T-BAC16-KSHV cells . The protection of Cyclins and Cdk2 protein levels in KSHV positive HEK293T-BAC16-KSHV compared to HEK293T confirmed that KSHV was able to block hypoxia-dependent degradation to allows S-phase entry and subsequent DNA replication ( Fig 1I , compare lane 2 and 4 ) . Origin recognition by origin recognition complex ( ORCs ) proteins and formation of pre-initiation complex are the initial event of DNA replication[46] . Therefore , we investigated whether the presence of KSHV can differentially modulate origin recognition and pre-initiation steps under hypoxic conditions . A schematic showing the comprehensive list of proteins involved in origin recognition and pre-initiation of DNA replication are shown ( Fig 2A ) . We investigated the levels of origin recognition complex proteins ( ORC1-6 ) at both transcript and protein levels in BJAB and BJAB-KSHV cells growing under normoxic or hypoxic conditions . Additionally , levels of cell division cycle 6 ( CDC6; essential component for assembly of pre-replication complex ) , Cdt1 ( replication licensing protein essential for loading of minichromosomal maintenance proteins , MCMs ) , and a representative of MCMs ( MCM3 ) were also measured at the transcript and protein levels ( Fig 2A–2F ) . Among the ORCs , ORC1 and ORC4 appeared to be transcriptionally stable at the both 24 and 36 hours of hypoxic treatment ( 1%O2 ) with only a marginal down-regulation in both the BJAB and BJAB-KSHV cells ( Fig 2B and S3A–S3D Fig ) . The expression of ORC2 , ORC3 and ORC5 showed changes in expression profiles at transcript levels in both the BJAB and BJAB-KSHV cells and at both time points ( 24 and 36 hours ) . Briefly , ORC2 showed a down-regulation by nearly 20% in both BJAB and BJAB-KSHV at the end of 24 hours of hypoxic treatment . ORC2 expression was further down-regulated by nearly 50% at 36 hours of hypoxic treatment ( Fig 2B ) . The expression of ORC3 at the transcript level showed an intermediate effect where the fold change difference was not significantly different in both BJAB and BJAB-KSHV cells at the end of 24 hours of hypoxic treatment . However , at 36 hours , expression of ORC3 was down regulated at nearly 50% in both BJAB and BJAB-KSHV cells ( S3B Fig ) . Among the ORCs investigated , ORC5 showed the most drastic effects of hypoxia , where the fold change of ORC5 transcripts was observed to be nearly 50% down regulated at the end of 24 hours of hypoxic treatment , and by the end of 36 hours of hypoxic treatment , almost an 80% decrease was observed in both BJAB and BJAB-KSHV cells ( S3D Fig ) . MCM3 , critical for origin recognition was also dramatically down-regulated by greater than 80% in 24 hrs of hypoxia treatment and over 90% by 36 hrs ( Fig 2E ) . The effects in BJAB-KSHV was similar but not substantially greater than in BJAB alone ( Fig 2E ) . We hypothesized that similar to cyclins and CDK2 , KSHV may influence the expression of ORCs and MCM3 at the protein level . Western blot analyses were also performed to monitor these proteins using lysates from BJAB and BJAB-KSHV cells grown under normoxic or hypoxic conditions for 36 hours . Results clearly suggested that the presence of KSHV had a protective effect on these proteins from hypoxia-mediated degradation ( Fig 2F and S3E Fig ) . We further corroborated these results in another KSHV positive cell lines , HEK293T-BAC16-KSHV cells compared to HEK293T cells . Similarly , protection of these proteins in HEK293T-BAC16-KSHV cells grown under hypoxic conditions confirmed that KSHV infection played a role in origin recognition by providing a level of protection for the origin recognition proteins from hypoxia-mediated degradation ( S3F Fig ) . Results showing KSHV-mediated protection of cell cycle , and DNA replication associated proteins from hypoxia-dependent degradation stimulated further investigation of key proteins involved in initiation of DNA replication . We investigated the levels of cell division cycle 45 ( CDC45; essential for the loading of DNA polymerase 1 alpha on replication complex ) [47] , and DNA polymerase 1 alpha ( DNA pol 1α; the rate limiting DNA polymerase with primase activity ) [48] at the transcript , and protein levels in BJAB and BJAB-KSHV cells grown under normoxic or hypoxic conditions . Real-time expression analysis of these genes showed that the presence of KSHV did not provide a dramatic differential at the transcript level for CDC45 in normoxic or hypoxic conditions , but it up-regulated expression of DNAPol1 alpha to approximately 1 . 6-fold in normoxic condition ( Fig 3A and 3B ) . Furthermore , at the 36 hour time point , hypoxia induced a similar level of down-regulation of transcripts by approximately 50–75% for the transcripts of both CDC45 and DNA pol 1α in BJAB and BJAB-KSHV cells grown under hypoxic conditions ( Fig 3A and 3B ) . Similar to the expression of genes involved in origin recognition and pe-initiation , we hypothesized that KSHV may influence the levels of these factors at the level of post-translation . Western blot analyses were performed to monitor CDC45 and DNA Pol 1α proteins in BJAB and BJAB-KSHV cells grown in normoxic or hypoxic conditions for 36 hours . The results indicated a clear protection of these replication proteins from hypoxia-mediated degradation by the presence of KSHV ( Fig 3C compare lane 2 and 4 ) . We further examined the role of KSHV as a major contributor to the inhibition of hypoxia-mediated degradation in HEK293T-BAC16-KSHV cells when compared to HEK293T cells ( Fig 3D ) . Similarly , the levels of these proteins in HEK293T-BAC16-KSHV cells grown under hypoxic conditions were enhanced and supported a role for KSHV in origin recognition through protection of origin recognition proteins from hypoxia-mediated degradation . To further rule out any possible role due to differences in the source of BJAB/BJAB-KSHV or HEK293T/ HEK293T-BAC16-KSHV cells or their passage numbers , we infected PBMCs with KSHV generated from HEK293T-BAC16-KSHV . We infected PBMCs with purified KSHV at a multiplicity of infection equal to 10 . Initially , the infected cells were grown under normoxic conditions for 24 hours to allow for the expression of KSHV-encoded genes in the infected cells . KSHV infection of PBMCs was confirmed by GFP signals ( for infection with KSHV generated from HEK293T-BAC16-KSHV ) ( Fig 4A ) . The mock control or infected PBMCs were then incubated under hypoxic conditions for another 24 hours . The induction of hypoxia was confirmed by western blot against PDK1 . The status of the proteins examined above were monitored by western blot . Similar to the results seen in BJAB or HEK293T cells , a significant decrease in the level of all the proteins investigated ( CCNE , CDK2 , ORC2 , MCM3 , CDC6 , Cdt1 , CDC45 and DNAPol1A ) was observed ( Fig 4C ) . As expected , KSHV infected PBMCs grown under hypoxic conditions was able to rescue these proteins from hypoxia-mediated degradation ( Fig 4C ) . These results strongly supported a role for KSHV in the rescue of DNA replication-associated proteins from hypoxia-mediated degradation . In another approach , levels of these proteins in KSHV negative BL41 were compared with KSHV positive BCBL1 cells . The cell lines were confirmed for the absence or presence of KSHV by immune staining against LANA protein ( Fig 4B ) . The cells were grown under hypoxic conditions followed by analysis of PDK1 levels for the confirmation of induction of hypoxia . The comparative analysis of replication associated proteins further confirmed the protection potential of KSHV for replication associated proteins from hypoxia-mediated degradation ( Fig 4D ) . Latently infected KSHV positive cells predominantly express only a limited set of KSHV-encoded proteins . Furthermore , hypoxia is well known to induce expression of other KSHV-encoded genes . The latency associated nuclear antigen ( LANA ) , replication and transcriptional activator ( RTA ) , viral G-Protein coupled receptor ( vGPCR ) and viral cyclin ( vCyclin ) , are well-established antigens being expressed either in latency or during hypoxic conditions [26–28] . We hypothesized that one , or a combination of these proteins will be responsible for rescuing the DNA replication-associated proteins from hypoxia-mediated degradation . To identify which of the KSHV-encoded protein was responsible for rescuing DNA replication-associated proteins from hypoxia-mediated degradation , we individually expressed these proteins in HEK293T cells along with mock transfection as control . Transfected cells were allowed to grow under normoxic or hypoxic conditions . The expression of KSHV-encoded antigens was confirmed using western blots with antibodies against the epitope tag fused to these proteins ( Fig 5A–5D , top panel ) . The induction of hypoxia was confirmed by western blot analysis of PDK1 ( Fig 5A–5D , second panel at the top ) . Interestingly , analysis of the levels of proteins involved in DNA replication revealed that ectopic expression of KSHV-encoded LANA efficiently rescued these proteins from hypoxia-mediated degradation . Representative blots for the proteins that clearly showed that degradation occurred in hypoxia are shown ( Fig 5A ) . Importantly , the expression of other KSHV-encoded antigens such as RTA , vGPCR or vCyclin showed little or no ability to protect these proteins from hypoxia-mediated degradation ( Fig 5B–5D ) . We then wanted to investigate the mechanism of how KSHV-encoded LANA protected these proteins from hypoxia-mediated degradation . KSHV-encoded LANA can interact with a number of replication-associated proteins such as ORC2 and MCM3 under normoxic conditions[49] . Therefore , we hypothesized that LANA may interact with these proteins to block their degradation in hypoxia by interfering with their ubiquitination . To demonstrate their interaction , FLAG tagged LANA was expressed in HEK293T cells ( Fig 5E ) . Cells were grown under hypoxic conditions followed by immuno-precipitation assays using anti-FLAG antibodies . The results indicated that LANA associated with the select set of replication-associated proteins when grown under hypoxic conditions ( Fig 5F ) . Further validation of these associations in cellular complexes was examined using immuno-fluorescence assays taking ORC2 and MCM3 as representative proteins . The results showed that LANA and ORC2 or LANA and MCM3 co-localized under hypoxic conditions which corroborated their association in cellular replication compartments ( Fig 5G and 5H ) . To further support the role of LANA in protecting DNA replication-associated proteins from hypoxia-mediated degradation , we investigated whether knock down of KSHV-encoded LANA in KSHV infected cells resulted in a loss of protection potential due to the presence of KSHV . The lentivirus-mediated knock down of KSHV-encoded LANA in BC3 cells was previously described[50] . BC3-ShControl or BC3-ShLANA cells were monitored for GFP fluorescence as well as the down-regulated expression of LANA at the protein levels ( Fig 6A and 6B , top panel ) . BC3-ShControl or BC3-ShLANA cells were incubated under normoxic or hypoxic conditions for 36 hours . This was followed by investigation of the levels of a representative set of proteins involved in DNA replication . Western blot analysis for the proteins in these cells confirmed that LANA was a crucial viral antigen required for inhibition of degradation of these proteins under hypoxic conditions ( Fig 6B ) . Briefly , the levels of CCNE , CDK2 , ORC2 , MCM3 , CDC6 , Cdt1 , CDC45 and DNAPol1A were investigated . As expected , BC3-ShControl cells showed almost similar levels of these proteins independent of whether grown under normoxic or hypoxic conditions ( Fig 6B; lane 1 compared to lane 2 ) . However , BC3-ShLANA cells grown under hypoxic conditions showed a relatively lower level of these proteins under hypoxic conditions when compared to normoxic conditions ( Fig 6B , lane 2 compared to lane 4 ) . Further , the role of LANA knock-down was validated by single molecule analysis of replicated DNA ( SMARD ) . Analysis for replicated vs non-replicated KSHV was performed by pulsing the BC3-ShControl or BC3-ShLANA cells , and visualizing KSHV DNA using KSHV specific probes while replicated DNA was visualized by immuno-staining against IdU/CldU ( Fig 6C–6E ) . A significantly low level of replication was observed in BC3-ShLANA cells compared to BC3-ShControl cells grown under hypoxic conditions ( see lower compartment , Fig 6E ) . Even under the normoxic conditions , knock down of LANA also had a negative effect on KSHV replication , but significantly less compared to hypoxic conditions ( Fig 6D ) . These experiments clearly showed that the differences seen in the stabilization of replication associated proteins in hypoxic conditions was mainly at the protein level and not at the transcript level . We further showed that , that this occurred through inhibition of the ubiquitin-mediated proteosomal degradation system which is targeted by KSHV-encoded LANA . Cells grown in hypoxic condition with medium containing proteosomal inhibitor MG132 was compared with cells grown under normoxic or hypoxic conditions . The results strongly suggested that the presence of MG132 had a protective effect on these proteins from hypoxia-mediated degradation ( S4A Fig ) . Also , a representative protein CDC6 was used to confirm role of LANA in inhibition of proteosomal degradation in hypoxic conditions . Cells expressing mock or LANA were grown under hypoxic conditions ( with or without MG132 ) followed by immuno-precipitation and western blot with ubiquitin antibody . The results showed that presence of LANA significantly reduces ubiquitination under hypoxic conditions ( S4B Fig , compare lane 2 and 4 ) and suggested that LANA is likely inhibiting the activity of one of the cellular E3-ubiquitin ligase . HIF1α is a major cellular regulator which plays a critical role in KSHV-mediated oncogenesis and is known to interact at the transcriptional and post-transcriptional levels with KSHV factors to promote the cancer phenotype . Also , HIF1α is required for upregulation of LANA during growth of KSHV positive cells in hypoxia [28] . We wanted to investigate the role of HIF1α in hypoxia-mediated degradation of DNA replication-associated proteins in KSHV negative background or their protection in KSHV-positive background . To study the role of HIF1α , we transduced both BJAB and BJAB-KSHV cells with plasmid vectors containing ShControl or ShHIF1α . Knock down of HIF1α was confirmed by real-time PCR by monitoring the HIF1α transcripts in these cells grown under normoxic or hypoxic conditions ( Fig 7A ) . The effect of HIF1α knock down was further confirmed by investigating expression of P4HA1 , a known target of HIF1α after these cells were grown under normoxic or hypoxic conditions ( Fig 7B ) . As expected , the fold change expression of P4HA1 in both BJAB and BJAB-KSHV cells transfected with a ShHIF1α construct was significantly less when compared to the cells containing the ShControl construct under similar conditions ( Fig 7B ) . Upon , confirmation of HIF1α knock down in both BJAB and BJAB-KSHV cells , analysis of the levels of DNA replication- associated proteins was analyzed in cells grown under normoxic or hypoxic conditions . As expected , we observed an almost complete loss of all proteins investigated in BJAB cells transfected with either ShControl or ShHIF1α constructs . Notably , BJAB-KSHV cells showed rescue of these proteins in hypoxia when transfected with ShControl plasmid . Interestingly , knock down of HIF1α in BJAB-KSHV cells showed that the ability of KSHV to protect these proteins from hypoxia-mediated degradation was severely compromised ( Fig 7C ) . These results suggested that HIF1α contributes to the effects of KSHV-encoded LANA-mediated rescue of the replication-associated proteins from hypoxia-mediated degradation . The observation of HIF1α knock down dependent degradation of replication associated proteins in hypoxia were further validated in naturally infected KSHV positive BC3 cells . Generation and characterization of BC3-ShControl and BC3-ShHIF1α were described earlier[26] . BC3-ShControl and BC3-ShHIF1α cells were grown under normoxic or hypoxic conditions followed by investigation of the replication-associated proteins . As expected , the BC3-ShControl cells showed protection from degradation for all the studied proteins in hypoxia while BC3-ShHIF1α cells were unable to protect these proteins under hypoxic conditions ( Fig 7D ) . The results clearly suggest that HIF1α dependent transactivation of LANA is required for rescue of these proteins from degradation in hypoxic conditions as LANA levels were substantially reduced in the BC3 shHIF1α cells . Finally , we investigated whether KSHV reactivation was induced in the hypoxic conditions . We incubated BJAB-KSHV as well as naturally infected BC3 cells under normoxic and hypoxic conditions and estimated the relative yield of KSHV in the extracellular medium . The yield was compared with the cells grown in normoxic conditions for the maximum time period used for hypoxic induction . As expected , the results clearly showed that hypoxia induced viral reactivation ( S4C Fig ) .
Oncogenic herpesviridae signatures are frequently found in blood and tissue samples of the world’s population with high representations in cancer patients [51 , 52] . Pathogenesis due to herpesviridae infection is also a consequence of multi-factorial events , which depends on immune status , as well as genetic heterogeneity of infected individuals [12 , 53 , 54] . KSHV , a large double stranded DNA containing virus was identified in the late 20th century and its infection correlated strongly with the incidences of KS , PEL , MCD and KSHV inflammatory cytokine syndrome ( KICS ) [3 , 55 , 56] . During latent infection , epigenetic modification of the KSHV genome allows expression of only a limited number of KSHV encoded genes such as LANA , vFLIP , vCyclin and certain viral interferon regulatory factors [7 , 22 , 57] . LANA is a necessary factor for tethering of KSHV episomes to the host genome and also functions as a master regulator of latency [14 , 58] . LANA can support latent replication through binding to the terminal repeats , as well as supporting hypoxia-mediated lytic replication by cooperating with HIF1α to up-regulate expression of the RTA[27] . Several viral gene products , including the KSHV LANA , vGPCR and vIRF-3 proteins , have been shown to influence HIF1α to function directly through protein-protein interaction or indirectly by enhancing transcriptional or post-transcriptional events [59–61] . LANA augmented HIF-1α stabilization by degrading VHL in the EC5S ubiquitin complex [9] , and HIF-1α protein levels is higher in KSHV-positive PEL lines when compared to KSHV-negative cells . Additionally , expression studies showed that HIF-1α is enhanced by LANA , and that LANA stimulated the nuclear accumulation of HIF-1α[62 , 63] . Notably , expression of vGPCR into NIH 3T3 mouse fibroblast cells resulted in activation of MEK and p38 signaling cascades , leading to direct phosphorylation of HIF-1α , and thus subsequent increases in HIF-1α transcriptional activity [33] . Furthermore , another latent nuclear antigen-2 ( LANA-2 ) gene of KSHV , more commonly known as viral interferon regulatory factor 3 ( vIRF-3 ) , was implicated in the stabilization of HIF-1α , in addition to its oncogenic role of p53 inhibition [64] . Under normoxic conditions , vIRF-3 binds to the bHLH domain of HIF-1α and inhibits the breakdown of HIF-1α , which does not have an impact on its dimerization capability , but further enhanced the nuclear localization and transcriptional activity of HIF-1α [64] . The well characterized KSHV genetic loci influenced by hypoxia include open reading frames for LANA , RTA and vGPCR [26–28] . KSHV-encoded vGPCR is a constitutively active homolog of cellular GPCR and a bona-fide oncogene[65] . It can activate expression of HIF1α and in turn acts on MAP kinase pathways to enhance tumor development and angiogenesis [33] . The KSHV-encoded LANA can interact directly with HIF1α to activate expression of KSHV-encoded reactivation and transcriptional activator RTA [27] . We have recently observed that KSHV-encoded vGPCR is itself under the control of HIF1α , which activates its expression through its action at HREs within the vGPCR promoter to upregulate its expression [26] . Critically , hypoxia-dependent activation of vGPCR can potentially reprogram the metabolism of infected cells globally through generation of reactive oxygen species as well as targeting expression of DNA methyl transferases . In fact , hypoxia can work globally on the KSHV genome to modulate transcription of KSHV-encoded genes [26] . Hypoxia , in general exerts an arrest of cell cycle and DNA replication through activation of HIF1α , ATM , p53 and p21 dependent pathways as well as suppression of Myc dependent transcriptional cascade [41 , 42 , 66] . Furthermore , stabilized HIF1α binds efficiently with minichromosomal maintenance proteins ( MCMs ) to keep them inactive and keep the replication machinery in a dormant stage as a direct inhibition of DNA replication during hypoxic conditions [67] . Despite these negative regulations , KSHV infected cells bypass the G1/S transition to enter S-phase and allows productive replication ( reactivation ) of KSHV through yet undefined mechanisms [27 , 32] . In this study , we investigated how KSHV manipulated hypoxia-mediated inhibition of DNA replication to drive replication when grown in this non-permissive and non-favorable condition . As the infection-based experiments using purified KSHV pose restrictions due to low and variable infection rates , as well as epigenetic reprogramming of the KSHV genome after entering the cells which lead to variable expression of KSHV-encoded genes , we compared the differential between BJAB and BJAB-KSHV cells , or HEK293T and HEK293T-BAC16-KSHV cells . Though , these cells are not naturally infected by KSHV , the presence of the complete KSHV genome confers to these cells the characteristics of latently infected cells . Comparative studies between these cells grown under normoxic or hypoxic conditions allowed for observation of the role of KSHV in protection of essential proteins required for G1/S transition ( through stabilized Cyclins/CDK2 ) , origin recognition ( ORC1-5 ) , Pre-initiation , initiation and elongation associated proteins ( for example MCM , CDCs , and DNAPol1A ) . These results were replicated in PBMCs after infection with purified KSHV further supporting the role of KSHV in protecting cell cycle and DNA replication-associated proteins from hypoxia-mediated degradation . Interestingly , these differences were mainly observed at the level of proteins and not at the levels of transcript . The effect of hypoxia at the levels of transcript was similar in both KSHV negative and positive conditions suggesting that the stabilization was at the post translational stage . The indispensable role of KSHV-encoded LANA in protecting these proteins from hypoxia-mediated degradation added a new function to the list of activities related to this multi-functional bonafide oncoprotein . The role was further confirmed by monitoring levels of ubiquitination of replication associated proteins in LANA expressing cells grown under hypoxic condition , which was significantly less compared to mock expressing cells grown under similar conditions . Importantly , the role of HIF1α in stabilization of these proteins provides additional clues as to why KSHV induced expression of this protein in infected cells . Combining the knowledge of HIF1α-mediated activation of KSHV-encoded antigens , and feedback regulation of HIF1α-vGPCR-HIF1α for sustained high levels of HIF1α as well as upregulation of LANA/RTA by HIF1α provides a more comprehensive strategy employed by KSHV to maintain continuous replication in hypoxia . It also provides additional information as to the mechanism by which KSHV is reactivated in non-permissive and non-favorable hypoxic conditions ( Fig 8 ) . Though , the slight stabilization of CDK2 protein under hypoxic conditions due to expression of RTA or vGPCR remain unclear , it is a matter for further investigation if these antigens do contribute by playing a role in transcriptional activation or stabilization through other strategies . A number of questions remain unexplored in this study such as the regulatory proteins involved , mainly ubiquitin ligases that are likely targeted by LANA to mediate the stabilization of replication-associated proteins . LANA was shown to form complexes with replication-associated proteins in the replication compartments to regulate their activities by enhancing their stability in hypoxia , which also requires HIF1α activities . Also , the domain of LANA responsible for protecting cell cycle and replication-associated proteins from hypoxia dependent degradation would also be important to identify . Another factor associated with hypoxia , and responsible for G1/S arrest or replication stress is the shortage of energy in the form ATP , where molecular oxygen is an essential component for generation of energy through oxidative phosphorylation[68] . Shortage of ATP in hypoxic conditions pose a direct mechanism for termination of replication elongation . Identifying the mechanism by which these cells are able to manage this energy deficit for sustained replication in hypoxic condition would be another interesting topic to explore . Further , studies are ongoing to identify the different E3-ubiquitin ligases targeted by LANA in hypoxia to provide a more comprehensive picture of the molecular mechanism and to design targeted therapeutic strategies for intervention against KSHV-associated pathologies . Further , as sustained transcription of viral and host genes are also a pre-requisite for proper productive replication and maturation of virus upon reactivation , it would be interesting to investigate the role of KSHV infection on transcriptional stabilization under hypoxic conditions . Additionally , a comparative analysis of epigenetic reprogramming of KSHV genome under hypoxic conditions , which leads to release of repressors of lytic replication would be other area of exploration . These studies further elucidate the mechanism through which modulation of viral and host physiology under hypoxic conditions is regulated by KSHV .
Peripheral blood mononuclear cells ( PBMCs ) from undefined and healthy donors were obtained from the Human Immunology Core ( HIC ) of University of Pennsylvania . The Core maintains approved protocols of Institutional Review Board ( IRB ) in which a Declaration of Helsinki protocols were followed , and each donor/patient gave written , informed consent . KSHV-negative BJAB cells were obtained from Elliot Kieff ( Harvard Medical School , Boston , MA ) and BJAB cells stably transfected with KSHV ( BJAB-KSHV ) were obtained from Michael Lagunoff ( University of Washington , Seattle , WA ) . KSHV-positive body cavity lymphoma-derived BC3 and BCBL1 cells were obtained from the American type culture collection ( ATCC ) ( Manassas , VA ) . BC3-ShControl and BC3-ShHIF1α cells were generated by lentivirus mediated transduction as described earlier [26] . BJAB , BJAB-KSHV , BC3 , BC3-ShControl , BC3-ShHIF1α and BCBL1 cells were maintained in RPMI medium . Human Embryonic Kidney cell line ( HEK293T ) was obtained from Jon Aster ( Brigham and Women’s Hospital , Boston , MA ) . HEK293T and HEK293-BAC16-KSHV cells were maintained in DMEM medium containing 7% bovine growth serum ( BGS ) and appropriate antibiotics at 37°C and 5% CO2 . BC3-ShControl , BC3-ShHIF1α stable cells were maintained in selection media with puromycin ( 2μg/ml ) . HEK293T-BAC16-KSHV cells were also maintained in selection with hygromycin ( 100μg/ml ) . ShControl , ShHIF1α , pBS-puroA ( KSHV terminal repeats ) , pBS-puroH ( 6kb KSHV genomic fragment; co-ordinate 26937–33194 ) , pBS-puro-GA5 ( 10kb KSHV genomic fragment; co-ordinate 36883–47193 ) and Supercos1-GB22 ( 15kb KSHV genomic fragment; co-ordinate 85820–100784 ) , pA3F-LANA and pEF-RTA plasmids were described in earlier publications [25] . Generation and maintenance of BC3ShControl , ShHIF1α and ShLANA cells was described earlier [26] . pLVX-ACGFP-vFLIP , pLVX-ACGFP-vCyclin and pLVX-ACGFP-vGPCR constructs were generated by PCR amplification and ligated into Xho1/Hind III , EcoR1/BamH1 and EcoR1/Apa1 site , respectively . For vFLIP and vCyclin , cDNA from KSHV positive BC3 cells was used as template while for vGPCR , pCEFL-vGPCR construct ( a gift from Enrique A . Mesri; University of Miami Miller School of Medicine , Miami , FL ) was used as template for PCR amplification . For Hypoxic induction , cells were grown in 1%O2 at 37°C for the indicated time periods . MG132 was procured from Sigma Aldrich ( St . Louis , MO ) and was used at a final concentration of 5μM . RNA was isolated by standard phenol chloroform extraction . cDNA was synthesized from 2μg of RNA using superscript cDNA synthesis kit ( Applied Biosystem Inc . , Foster city , CA ) according to manufacturer protocol . The sequence of primers used in the study is provided in the S1 Table . KSHV reactivation and purification was performed according to standard protocol described earlier [26] . To isolate KSHV virion DNA , KSHV virions were resuspended and lysed in 200μl of HMW buffer ( 10 mM Tris , 150 mM NaCl , 1 mM EDTA , 0 . 5% SDS and 0 . 5 mg/ml proteinase K . The virion DNA was further extracted using standard phenol chloroform extraction . Copy number calculation from purified KSHV preparation was performed using standard method . KSHV infection was performed at the multiplicity of infection equivalent to 10 in the presence of 20 μg/ml polybrene as described earlier[26] . The number of extracellular KSHV from the cell culture medium was estimated by real-time PCR through the standard curve method . In brief , equal number of cells in equal volume of cell culture medium were grown in normoxic or hypoxic conditions . Viral reactivation was measured by calculating viral copy number in extracellular culture medium . Viral particles from culture medium were concentrated through centrifugation followed by DNA isolation . DNA pellet were dissolved in an equal volume of water . The standard curve was generated using dilutions of plasmid vector containing KSHV genomic region ( 15kb KSHV genomic fragment; genome co-ordinates 85820–100784 ) . The sequence of primers used for real-time PCR is given in S1 Table . Unit volume of DNA preparation from extracellular cell culture medium of individual samples were used to estimate the KSHV copy number using primers specific to the cloned KSHV DNA fragment . Protein lysates were separated on 10% polyacrylamide gel followed by wet transfer to nitrocellulose membrane . Skimmed milk ( 5% ) was used for blocking at room temperature for 1 hour with gentle shaking . Primary antibody against CDK2 , Cyclin D1 , Cyclin E , PDK1 , ORC1 , ORC2 , ORC3 , ORC4 , ORC5 , ORC6 , MCM3 , GFP , Ubiquitin and GAPDH ( Santa Cruz Inc . , Dallas , TX ) , Myc tag and LANA ( purified ascites ) , DNAPOL1A and Cdt1 ( Novus Inc . , Centennial , CO ) , CDC6 and CDC45 ( Cell Signaling Technology Inc . Danvers , MA ) , FLAG ( Sigma Aldrich Inc . , St . Louis , MO ) were incubated overnight at 4°C with gentle shaking followed by washing with TBST . Probing with IR conjugated secondary antibody was performed at room temperature for 1 hour followed by washing with TBST . Membranes were scanned on an Odyssey scanner for detection of signals . A complete list and details of antibodies used are provided in S2 Table . For confocal microscopy , 25 , 000 cells were semi-dried on 8-well glass slides followed by fixation in 4% paraformaldehyde . Combined permeabilization and blocking was performed in 1XPBS containing 0 . 3% Triton X-100 and 5% goat serum followed by washing ( 5 minutes each ) with 1X PBS . Anti-LANA antibody ( 1:200 dilution ) was diluted in 1X PBS containing 1% BSA and 0 . 3% Triton X-100 and was incubated overnight at 4°C . Slides were washed with 1X PBS followed by incubation with Alexa 448 conjugated anti-mouse secondary antibody . DAPI staining was performed for 15 minutes at room temperature followed by washing and mounting . Images were captured by confocal microscope . Whole cell lysates were pre-cleared with Protein Agarose A/G beads and incubated overnight with indicated antibodies with gentle shaking . This was followed by antibody incubation , and Protein Agarose A/G beads were used to collect the immune complexes . The beads were washed 3 times with 1X PBS and resuspended in 60 μl 2X SDS loading dye . The immuno-precipitated complexes were run against 5% input sample lysate . Pulse field gel electrophoresis ( PFGE ) and southern blot were described earlier [25] . Briefly , cells were pulsed with Chlorouridine ( CldU , 10 μM ) in cell culture medium for 4 hours and harvested by centrifugation . Cells were further resuspended in fresh medium containing Iodouridine ( IdU , 10 μM ) and again pulsed for 4 hours . At the end of pulsing , cells were pelleted , washed with 1XPBS and resuspended in 0 . 5ml 1XPBS . An equal volume of cell suspension and 1% InCert agarose were equilibrated at 45°C before mixing and molding in the cast for the preparation of cell embedded agarose plugs . The agarose plugs were digested with Proteinase K and followed by washing in 1XTE buffer pH8 with buffer changes after each 24 hours . Final washing of plugs were done in 1XTE pH8 containing 1mM PMSF . Pme1 was used to digest and linearize KSHV episomal DNA . Cell embedded plugs were fitted with agarose gel casting tray in 0 . 7% low melt agarose . DNA was separated by Pulse field gel electrophoresis for 36 hours on BioRad Chef DRII system ( Bio-Rad Inc . Hercules , CA ) . Post PFGE , Pme1 digested DNA in agarose gel was depurinated and the gel was rinsed with double distilled water followed by denaturation . The gel was then rinsed with double distilled water followed by neutralization and further rinsed with distilled water and equilibrated in SSPE before alkaline transfer on Nylon membrane . Transferred DNA was subjected to UV cross-linking at 1400 Joules and prehybridization was performed at 42°C in prehybridization buffer . Hybridization probes were prepared by random priming method and hybridization was performed at 49°C in prehybridization buffer devoid of salmon sperm DNA . Membrane was washed with low stringency buffer followed by high stringency buffer . Membranes were wrapped in Saran Wrap and exposed to sensitive plates followed by imaging using a Phosphorimager . Cells were harvested by centrifugation at 1000 rpm for 5 minutes and resuspended in 300μl 1X PBS . 700μL ice cold absolute ethanol ( 70% final ) was added drop wise to the cell while gentle vortexing to avoid clumping of cells . Cells were fixed at 4°C on a rotating shaker for 30 minutes followed by washing in PBS . Cells were resuspended in 200μl 1X PBS and incubated with RNase A for 1 hour at 37°C . Following this , 250μl 1X PBS and 50μl Propidium Iodide ( 1mg/mL ) was added to the cells , mixed and stained for 30 minutes at room temperature . The cells were washed with 1XPBS and resuspended then analyzed on FACS Calibur ( Becton Dickinson Inc . , San Jose , CA , USA ) . The acquired data were analyzed using FlowJo software ( TreeStar Inc . , San Carlos , CA , USA ) .
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Hypoxia induces cell cycle arrest and DNA replication to minimize energy and macromolecular demands on the ATP stores of cells in this microenvironment . A select set of proteins functions as transcriptional activators in hypoxia . However , transcriptional and translational pathways are negatively regulated in response to hypoxia . This preserves ATP until the cell encounters more favorable conditions . In contrast , the genome of cancer cells replicates spontaneously under hypoxic conditions , and KSHV undergoes enhanced lytic replication . This unique feature by which KSHV genome is reactivated to induce lytic replication is important to elucidate the molecular mechanism by which cells can bypass hypoxia-mediated arrest of DNA replication in cancer cells . Here we provide data which shows that KSHV can manipulate the DNA replication machinery to support replication in hypoxia . We observed that KSHV can stabilize proteins involved in the pre-initiation , initiation and elongation steps of DNA replication . Specifically , KSHV-encoded LANA was responsible for this stabilization , and maintenance of endogenous HIF1α levels was required for stabilization of these proteins in hypoxia . Expression of LANA in KSHV negative cells confers protection of these replication proteins from hypoxia-dependent degradation , and knock-down of LANA or HIF1α showed a dramatic reduction in KSHV-dependent stabilization of replication-associated proteins in hypoxia . These data suggest a role for KSHV-encoded LANA in replication of infected cells , and provides a mechanism for sustained replication of both cellular and viral DNA in hypoxia .
|
[
"Abstract",
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"Discussion",
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"methods"
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2019
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KSHV-encoded LANA protects the cellular replication machinery from hypoxia induced degradation
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Chlamydia trachomatis is responsible for trachoma , the primary cause of preventable blindness worldwide . Plans to eradicate trachoma using the World Health Organization's SAFE program ( Surgery , Antibiotics , Facial Cleanliness and Environment Improvement ) have resulted in recurrence of infection and disease following cessation of treatment in many endemic countries , suggesting the need for a vaccine to control infection and trachomatous disease . Vaccine development requires , in part , knowledge of the mucosal host immune responses in both healthy and trachomatous conjuctivae—an area of research that remains insufficiently studied . We characterized 25 secreted cytokines and chemokines from the conjunctival mucosa of individuals residing in a trachoma endemic region of Nepal using Luminex X100 multiplexing technology . Immunomodulating effects of concurrent C . trachomatis infection were also examined . We found that proinflammatory cytokines IL-1β ( r = 0 . 259 , P = 0 . 001 ) and TNFα ( r = 0 . 168 , P<0 . 05 ) were significantly associated with trachomatous disease and concurrent C . trachomatis infection compared with age and sex matched controls from the same region who did not have trachoma . In support of these findings , anti-inflammatory cytokine IL-1 receptor antagonist ( IL-1Ra ) was negatively associated with chronic scarring trachoma ( r = −0 . 249 , P = 0 . 001 ) . Additional cytokines ( Th1 , IL-12p40 [r = −0 . 212 , P<0 . 01] , and Th2 , IL-4 and IL-13 [r = −0 . 165 and −0 . 189 , respectively , P<0 . 05 for both] ) were negatively associated with chronic scarring trachoma , suggesting a protective role . Conversely , a pathogenic role for the Th3/Tr1 cytokine IL-10 ( r = 0 . 180 , P<0 . 05 ) was evident with increased levels for all trachoma grades . New risk factors for chronic scarring trachoma included IL-6 and IL-15 ( r = 0 . 259 and 0 . 292 , respectively , P<0 . 005 for both ) with increased levels for concurrent C . trachomatis infections ( r = 0 . 206 , P<0 . 05 , and r = 0 . 304 , P<0 . 005 , respectively ) . Chemokine protein levels for CCL11 ( Eotaxin ) , CXCL8 ( IL-8 ) , CXCL9 ( MIG ) , and CCL2 ( MCP-1 ) were elevated in chronic scarring trachoma compared with age and sex matched controls ( P<0 . 05 , for all ) . Our quantitative detection of previously uncharacterized and partially characterized cytokines , a soluble cytokine receptor , and chemokines for each trachoma grade and associations with C . trachomatis infections provide , to date , the most comprehensive immunologic evaluation of trachoma . These findings highlight novel pathologic and protective factors involved in trachomatous disease , which will aid in designing immunomodulating therapeutics and a vaccine .
Trachoma , the leading global cause of preventable blindness , has plagued populations for thousands of years [1] . There are over 360 million trachoma cases of whom ∼6 million are blind [2] , [3] . Limited access to clean water and inadequate sanitary conditions provides ideal conditions for the persistence of this endemic disease caused by the obligate intracellular bacterium Chlamydia trachomatis . Additionally , C . trachomatis is the leading bacterial cause of sexually transmitted diseases ( STD ) throughout the world [4] , causing chronic conditions such as arthritis [5] , infertility and ectopic pregnancy [6] . The economics and health burden from trachoma alone claims billions of dollars in productivity loss in already poverty stricken countries [7] . Recognizing this , the World Health Organization ( WHO ) proposed the SAFE program ( Surgery , Antibiotics , Facial cleanliness and Environmental improvement ) in 2001 with the goal of eradicating blinding trachoma by the year 2020 . In trachoma endemic villages , disease progression is associated with repeated C . trachomatis infections of the conjunctivae , which can eventually lead to scarring and blindness from trachomatous trichiasis ( TT; ≥1 in-turned eyelash ) [1] . Recently , we and others have demonstrated that the S and A components of the SAFE program results in recurrent C . trachomatis infections and disease in endemic countries 6–24 months following cessation of treatment [8] , [9] , [10] , [11] , [12] , [13] . Additionally , the difficulty in implementing and sustaining the F and E programs in resource poor countries suggests that a vaccine is the most effective method of control . Designing efficacious vaccine candidates requires , in part , characterization of host inflammatory risk factors versus protective immunological components that are associated with the different grades of trachoma . These include follicular trachomatous inflammation ( TF ) ; intense trachomatous inflammation ( TI ) ; trachomatous conjunctival scarring ( TS ) , trachomatous trichiasis ( TT ) and corneal opacity ( CO ) [14] . A limited number of animal models and human studies have begun to elucidate the host immune response in trachoma . The use of Giemsa stains and immunohistochemistry has characterized active trachoma ( TF , TI or both ) in young children as an epithelial and stromal infiltrate containing neutrophils [15] , macrophages , dendritic cells , and B and T lymphocytes [16] . Physiologic characterization has been obtained from non-human primate models where trachoma-induced immunopathology consisted of conjunctival follicles containing B cells coexisting with a rich T lymphocyte population in the perifollicular area [17] , [18] . These T lymphocytes were associated with macrophages , and epithelial and stromal cells . The quantity and size of trachoma-related follicles have been shown to directly correlate with the frequency of repeated C . trachomatis infections in the non-human primate model [17] . Characterization of intense inflammation during early stages of trachomatous disease exhibits elevated transcriptional levels of the proinflammatory cytokines TNFα and IL-1β , and amplified expression in the presence of C . trachomatis infection in human populations [19] , [20] , [21] . These studies also linked elevated Th1 cytokines IL-12 and IFNγ mRNA levels in conjunctival samples with TI cases [19] , [20] , [21] , [22] and diminished levels in the presence of scarring [19] . No mRNA expression was detected for the Th2 cytokines IL-4 and IL-5 for any trachoma grade [19] , [21] . However , elevated IL-10 mRNA levels were apparent in TF and TI cases who were also infected with C . trachomatis [21] , [22] . Together these data suggest Th1 polarization as protective and IL-10 as a risk factor for exacerbating disease progression . As trachomatous scarring develops , there is a diminished lymphocytic proliferative response to C . trachomatis elementary bodies ( EBs ) in both cynologus monkeys [23] and humans [24] , [25] . Significantly elevated levels of T cytotoxic/suppressor cells compared to helper T lymphocytes have been shown in monkeys with chronic ocular infections [17] , [23] , [26] . Similarly , this immunosuppressive polarized environment was mirrored in infected TS cases where conjunctival IFNγ , TNFα , and IL-12 mRNA expression levels were decreased compared to levels among TF/TI cases , although IL-1β and TGFβ1 levels were consistently elevated for all grades of trachoma [19] . However , these studies have not examined the post-transcriptional and –translational regulation of these cytokines and their respective protein levels , making it difficult to assess their relevance to trachoma [27] , [28] , [29] , [30] . To date , only one study has evaluated protein levels in which TS cases were shown to have significantly elevated TNFα levels compared with controls [31] . While the above data have started to elucidate our understanding of the immune responses in trachoma , the lack of cytokine protein data prevent us from determining the immunological modulators associated with successive disease severity and identifying protective versus pathologic risk factors . Furthermore , resolving infection and disease relies on localizing and attracting effective leukocyte populations to the infected site . Chemokines are a group of small molecular weight proteins ( 8–12 kDa ) responsible for such a task . However , there have been no studies of chemokines and their association with trachoma . To begin to address these deficiencies , we quantitatively analyzed 25 secreted conjunctival mucosal cytokine and chemokine proteins in a trachoma endemic Nepali population . We detected previously uncharacterized and partially characterized cytokines , a soluble cytokine receptor , and chemokines for each grade of trachoma , and the effect of C . trachomatis infections on their production . Our data provide the most comprehensive immunological evaluation associated with trachoma to date .
This cross-sectional study included 208 individuals residing in a trachoma endemic region of Southwestern Nepal that were age and sex matched post hoc using a stratified sampling method . Samples from mutually exclusive trachoma grades ( TF/TI , TS , TT , and Normal ) were placed into separate strata by age ( 5 year spread ) and sex . Samples were randomly selected ( using a table of random numbers ) from each age and gender stratum for each trachoma grade and matched with randomly selected samples from each age and gender stratum for normals . Verbal informed consent was obtained from all study subjects following the institutional review board approval by Children's Hospital and Research Center at Oakland , CA , and the Nepal Netra Jhoti Shang . Verbal consent was documented on an information sheet by the team member who consented each individual who agreed to be a study subject . Grading of the upper tarsus of each study subject was performed according to a modified grading scale previously described by WHO [14] . Briefly , the grades were: no evidence of trachoma characterized by ≤4 follicles on the lower 2/3 of the upper tarsal conjunctiva ( Normal denoted as T0 ) , follicular and/or intense trachomatous inflammation ( TF/TI ) , trachomatous conjunctival scarring ( TS ) , trachomatous trichiasis ( TT ) and TT with inflammation ( TT/TI ) . An agreement between two of three independent readers ( authors DD and RK , and Dr . Tracey Hessel ) on final grading was made for each patient . Conjunctival mucosal secretions were obtained by applying a sterile Weck-cel sponge ( Medtronic Inc . , Minneapolis , MN ) to the inner canthus of each eye and allowing the swab to reach saturation . Samples were placed in a sterile eppendorf tube . A Dacron swab ( Remel Inc . , Lenexa , KS ) was used to sample the upper tarsal and lower conjunctivae of each eye and was placed in M4-RT media ( Micro Test Inc . , Lilburn , GA ) immediately following collection . All swabs were kept on ice for no longer than eight hours until transfer to liquid nitrogen tanks and then to Children's Hospital Oakland Research Institute , Oakland , CA and stored at −80°C until analyzed . DNA was isolated from a M4-RT media containing the conjunctival swabs as previously described [32] following manufacturer's instructions . Samples were defined as positive at an OD450 nm>0 . 8 and negative at an OD450 nm<0 . 2 [32] . Equivocal samples were defined as those that fell within an OD450 nm of 0 . 2 to 0 . 8 , and were further evaluated using an in-house validation PCR test to assess the presence or absence of chlamydiae as we have described previously [10] . Briefly , DNA from equivocal and negative and positive control samples were amplified using primers flanking the ompA gene . The presence of a 1200 bp band corresponding to the positive C . trachomatis DNA control , while the negative control was negative , defined an equivocal sample as positive for C . trachomatis . Mucosal sponges were thawed on ice , and 80 µl of resuspension fluid [50 mM Tris , 0 . 15 M NaCl , 10 mM CaCl2 , serine and cysteine protease inhibitors ( Protease Inhibitor Cocktail tablets , Roche Diagnostics , Mannheim , Germany ) at pH 7 . 5] was applied to each sponge . Fluid was extracted , and insoluble protein separated via centrifugation at 10 , 000×g for 10 min at 4°C . Samples were applied to a Human Cytokine/Chemokine 25-plex [IL-1β , IL-2 , IL-4 , IL-5 , IL-6 , IL-7 , IL-8 ( CXCL8 ) , IL-10 , IL-12p40 , IL-13 , IL-15 , IL-17 , IFNα , IFNγ , TNFα , GM-CSF , CCL2 ( MCP-1 ) , CCL3 ( MIP-1α ) , CCL4 ( MIP-1β ) , CCL11 ( Eotaxin ) , CXCL10 ( IP-10 ) , CXCL9 ( MIG ) , CCL5 ( RANTES ) , IL-1Ra , and IL-2R] 96 well plate assay ( Biosource International , Inc . , Camarillo , CA ) following manufacturer's instructions . Briefly , 25-Plex beads were vortexed and sonicated to disperse aggregates , and washed using a vacuum manifold not exceeding 5 psi . Subsequently , 25-Plex beads were incubated with 50 µl of sample or standards for 2 h on an orbital shaker at 500 rpm . Wells were aspirated and washed as described above through the vacuum manifold . Biotinylated detector antibodies were added and incubated on the orbital shaker for 1 h with subsequent washes . After the addition of Streptavidin-RPE , the plates were analyzed using a Luminex X100 instrument ( Luminex Technologies , Inc ) to determine the quantities of each protein . Calibration was performed before each run , and all 25 cytokines/chemokines were gated ( 9050–12050 ) to eliminate bead aggregates and debris associated with the samples . Luminex analysis was set to 50 µl/sample and 80 events/bead . Standard curves were assessed from duplicates consisting of all 25 cytokines/chemokines using a five parameter logistic modeling system . Contiguous dilutions ( 8 three-fold followed by 3 two-fold dilutions ) were applied to the standards , and resuspension fluid was used to determine background . Sensitivity levels were determined for each cytokine/chemokine as the lowest dilution , two standard deviations above background . Sensitivity levels were as follows: TNFα ( 0 . 2 pg/ml ) , IL-1β ( 1 . 9 pg/ml ) , IL-1Ra ( 14–31560 pg/ml ) , IL-6 ( 2 . 6 pg/ml ) , IL-4 ( 1 . 0 pg/ml ) , IL-5 ( 0 . 8 pg/ml ) , IL-13 ( 2 . 3 pg/ml ) , IL-12p40 ( 2 . 2 pg/ml ) , IFNγ ( 0 . 9 pg/ml ) , IL-10 ( 0 . 3 pg/ml ) , IL-2 ( 0 . 9 pg/ml ) , IL-2R ( 4 . 5 pg/ml ) , IL-15 ( 2 . 1 pg/ml ) , IFNα ( 4 . 2 pg/ml ) , IL-7 ( 16 . 2 pg/ml ) , IL-17 ( 5 . 6 pg/ml ) , Eotaxin ( 0 . 3 pg/ml ) , GM-CSF ( 2 . 7 pg/ml ) , IL-8 ( 2 . 7 pg/ml ) , MCP-1 ( 12 . 0 pg/ml ) , MIG ( 9 . 9 pg/ml ) , IP-10 ( 4 . 0 pg/ml ) , MIP-1α ( 4 . 3 pg/ml ) , MIP-1β ( 5 . 5 pg/ml ) and RANTES ( 8 . 9 pg/ml ) . We used a stratified sampling method to divide the population into mutually exclusive groups based on disease grade ( TF/TI vs T0 , TS vs T0 , etc ) as described above . Cytokine and chemokine data , comparing particular disease grades versus age and sex matched controls , were assessed for normality by Shapiro-Wilk W test to determine the appropriate statistical tests . Two-sample Wilcoxon rank-sum ( Mann-Whitney ) test was performed when p values were <0 . 05 from the Shapiro-Wilk test . Student t test with equal variance was used when p values were >0 . 05 ( Figures 1–4 ) . Significance for the association between cytokine/chemokine production and different trachoma grades was determined using multiple logistic regression and adjusting for age and C . trachomatis infection for the TF/TI cases . Statistical analysis of immuno-modulating effects by C . trachomatis for trachoma cases or controls was determined using frequency of cytokine or chemokine detection as the dependent variable adjusting for age . We used Spearman's rank test to quantify the association between individual cytokines/chemokines and different trachoma grades and infection . All statistics were performed using Stata 9 . 0 software ( Stata Corp , College Station , TX ) .
Our Nepali study population comprised individuals with TF/TI , TS , TT , and TT/TI who were age and sex matched with identical numbers of individuals without disease ( T0 ) from the same community ( Table 1 ) . We assessed the prevalence of C . trachomatis infection with different grades of trachoma . Table 1 shows that there was an inverse association between C . trachomatis infection and age [P = 0 . 003; OR = 0 . 97 ( 0 . 95–0 . 99 ) ] while infection was directly associated with TF/TI cases [ ( P = 0 . 017; OR = 4 . 4 ( 1 . 30–14 . 92 ) ] . The presence of chronic trachoma defined as any grade demonstrating scarring ( TS , TT and TT/TI ) was associated with age [P<0 . 001; OR = 1 . 20 ( 1 . 12–1 . 30 ) ] . We found significantly elevated protein concentrations of the pro-inflammatory cytokine TNFα for TF/TI cases compared to age and sex matched controls ( T0 ) ( Figure 1A , P = 0 . 003 ) but not for the pro-inflammatory cytokine IL-1β . For the pleiotropic cytokine IL-6 , which has been linked to acute and chronic inflammation [33] , we found a greater percentage of TF/TI cases ( 50% ) that produced IL-6 compared with controls ( 25% ) ( Table S1 ) . A previous study suggested a protective effect with elevated Th1 , IL-12 and IFNγ , mRNA expression for cases with TF/TI , but not for those with scarring [19] . At the protein level , we did not find the same association for TF/TI cases . For Th2 cytokines , we detected IL-4 and IL13 protein levels in over 60% of TF/TI cases compared to controls . However , minimal differences in protein concentrations were observed between the two groups ( data not shown ) . The anti-inflammatory and Th2 cytokine IL-10 , which is also referred to as a Th3/T regulatory cytokine 1 ( Tr1 ) , was significantly associated with TF/TI cases ( Figure 1B , P = 0 . 005 ) where the mean represented a three-fold higher concentration compared with controls . We found no significant elevation in any of the IL-2 family of cytokines and receptors ( IL-2 , IL-7 , IL-15 , and IL-2R ) for the TF/TI cases ( Table S1 and data not shown ) . We found minimal differences in conjunctival mucosal chemokine protein levels among TF/TI cases except for the CXC family where significantly elevated levels of IL-8 and MCP-1 were found among TF/TI cases compared to controls ( Figure 1C; P<0 . 05 and P<0 . 005 , respectively ) . These chemokines are primarily associated with attracting and activating neutrophils , monocytes and T lymphocytes [34] , which have all been linked to the immunopathology associated with TF/TI [15] , [16] . Cases with scarring had significantly elevated levels of the proinflammatory cytokines TNFα and IL-1β compared to controls ( Figure 2A , P = 0 . 001 and P<0 . 05 , respectively ) . Levels of IL-6 were barely detected among these cases . However , there was an overall greater frequency of detectable IL-6 protein levels among TS cases ( Table S1 , 17% TS vs 0% T0 ) , which was similar to the TF/TI cases mentioned above . The Th2 cytokine IL-13 was decreased in TS cases compared with controls ( Table S1 , P<0 . 005 ) , while IL-12p40 was elevated ( Figure 2B , P<0 . 05 ) , suggesting a slight polarization towards the Th1 phenotype . Elevation in the Th3/Tr1 cytokine IL-10 protein levels for TS cases , although not quite significant ( Figure 2B , P = 0 . 051 ) , suggested immuno-regulatory patterns coexisting with Th1 . A distinguishing finding for TS cases was the significant protein elevation of an IL-2 family cytokine , IL-15 ( Figure 2C , P<0 . 05 ) . This cytokine is produced by a plethora of cell types , including epithelial , monocytic and dendritic cells , and is involved in the activation of effector memory T lymphocytes [35] . Significantly elevated levels of Th1 CC chemokines , MIP-1α and MIP-1β ( Figure 2D , P<0 . 01 and P<0 . 001 respectively ) , and Th2 chemokines , MCP-1 and Eotaxin ( Figure 2D , P<0 . 01 ) were found for the TS cases compared with controls . Conjunctival mucosal secretions demonstrated minimal differences at the protein level among proinflammatory and Th1 , Th2 and Th3 cytokines for TT cases ( data not shown ) . However , IL-6 protein levels were significantly elevated among cases with TT compared to controls ( Figure 3A , P<0 . 01 ) as in the other trachoma grades . For the IL-2 family of cytokines , minimal differences were observed except for significantly elevated concentrations of IL-15 ( Figure 3B , P<0 . 05 ) , which further suggests that this cytokine is a risk factor for chronic trachoma . For chemokines , TT cases had increased MIG protein levels compared with controls ( Figure 3C , P = 0 . 05 ) , which were not observed in other trachoma grades . To our knowledge , the inflammatory response in TT/TI cases has been characterized only once for a limited number of cytokine mRNA expression levels [19] . In our study , the most novel finding was the significant reduction in IL-1Ra protein for cases with TT/TI ( Figure 4A , P<0 . 001 ) compared to controls , further supporting a chronic inflammatory environment for this grade . This is the first time an IL-1β antagonist has been characterized in trachomatous disease . In agreement with an inflammatory environment , significantly higher protein levels of IL-1β were also evident in TT/TI cases compared to controls ( Figure 4A , P<0 . 05 ) . For Th1 , Th2 and Th3 cytokines , TT/TI cases had significantly reduced protein levels for the Th1 cytokine IL-12p40 ( Figure 4B , P<0 . 001 ) and for the Th2 cytokine IL-4 ( Figure 4B , P<0 . 05 ) . There was a trend for elevated protein levels for the Th3/Tr1 cytokine IL-10 , similar to what was observed in most grades of trachoma ( Table S1 and data not shown ) . Among the IL-2 family , the TT/TI cases had elevated levels of IL-2 and IL-15 compared to controls ( Figure 4C , P<0 . 05 ) . In agreement with the decreased Th1 response , CC chemokines MIP-1α and MIP-1β were significantly lower for TT/TI cases compared to controls ( Table S1 , P<0 . 05 for both ) . However , CXC chemokines , IL-8 and MCP-1 , remain elevated ( Figure 4D , P<0 . 05 ) . Figure 5A shows that TF/TI cases had a higher frequency of TNFα production compared to all other grades , although the difference was only significant between TF/TI and TT/TI ( P<0 . 05 ) , likely due to small patient numbers for TS and TT . A reverse trend was apparent for IL-1β , which was supported by the significant reduction in its soluble antagonist receptor ( IL-1Ra ) concentrations in the TT/TI cases compared with TF/TI and TS cases ( data not shown; P<0 . 05 and P<0 . 005 respectively ) . Cytokines involved in Th1 and Th2 phenotypes showed a consistent reduction for all grades ( TF/TI to TT/TI ) ( Figure 5B ) with TF/TI and TS cases demonstrating more frequent IL-4 production over TT/TI and TT cases ( Figure 5B , P<0 . 01 , respectively ) . This correlated with decreased Th2 , IL-13 , and Th1 , IFNγ and IL-12p40 , cytokines for TT/TI cases ( Figure 5B; P<0 . 05; IL-12p40: OR = 0 . 80 ( 0 . 66–0 . 97 ) , P<0 . 05 TT/TI versus TS ) . Among the IL-2 family of cytokines and receptor , decreased IL-2R levels were evident in TT/TI cases compared with TF/TI cases , although these differences were not significant ( P = 0 . 082 , data not shown ) . However , these data correlated with elevated IL-2 production in TT/TI compared to TF/TI cases ( Table S1 ) , further supporting IL-2 as a risk factor for the development of TT/TI . A significant reduction in IP-10 levels was found for TT/TI cases compared with TF/TI cases ( OR = 0 . 9980 ( 0 . 9961–0 . 9998 ) , P<0 . 05 ) . Though not significant , a reverse trend was found for MCP-1 levels . The effects of chronic trachoma ( TS , TT and TT/TI ) on inflammatory components were elucidated by grouping these grades and comparing them against their age and sex matched controls . Inflammatory patterns for cases with chronic trachoma compared with controls are shown in Table 2 and Table S2; significantly elevated TNFα , IL-1β and IL-6 concentrations and diminished concentrations of the anti-inflammatory cytokine IL-1Ra were found in chronic trachoma cases compared with controls . In characterizing host Th1 , Th2 and Th3 cytokine levels , IL-4 , IL-13 and IL-12p40 were negatively associated with chronic trachoma cases compared with controls ( Table 2 ) . However , elevated IL-10 levels were associated with chronic cases ( Table 2 ) , further supporting an association with the Th3/Tr1 phenotype and the role of IL-10 as a risk factor for chronic trachoma . Members of the IL-2 cytokine family , IL-2 and IL-15 , were also significantly associated with chronic trachoma cases compared with controls ( Table 2 ) . Among the chemokine family , chronic trachoma was significantly associated with elevated levels of Eotaxin , IL-8 , MCP-1 and MIG compared with controls ( Table 2 ) . Table 3 shows the frequency of cytokine , chemokine and cytokine receptor protein concentrations comparing C . trachomatis infected and uninfected patients for trachoma cases ( all grades ) and for controls . TNFα and IL-6 were significantly associated with C . trachomatis infections for chronic trachoma cases ( Table 3 and S3 ) with similar patterns for IL-1β , though not significant ( Table S3 and S4 ) . This phenotype was not evident among controls ( Table 3 ) . Minimal differences were observed for Th1 and Th2 cytokines in the presence or absence of C . trachomatis infections ( Table S3 and S4 ) . However , elevated IL-10 levels persisted in the presence of C . trachomatis infections regardless of trachoma grade ( Table 3 ) . Additionally , C . trachomatis infections appeared to effect the IL-2 family of cytokines with a significant association of elevated IL-2 concentrations with controls ( Table 3 ) ; elevated IL-15 was associated with trachoma ( Table 3 and Table S3 ) . In the chemokine family , C . trachomatis infection was associated with increased levels of Th1-associated chemokines MIP-1β and RANTES ( Table 3 ) . Independent of trachoma grade , MIG was significantly associated with infection ( OR = 2 . 61 ( 1 . 25 to 5 . 45 ) , P = 0 . 011 ) .
While there is some important research on cytokine mRNA expression in association with trachoma , our fundamental knowledge of the host immune response has been limited by a lack of quantitative protein data , especially for those cytokines and chemokines that are post-transcriptionally and –translationally modified , in association with each grade of trachoma . The goal of this study was to validate previous gene expression findings and to characterize novel inflammatory cytokine and chemokine protein concentrations in mucosal conjunctival secretions and how each may influence inflammation for the different grades of trachomatous disease . Additionally , we further characterized immuno-regulatory effects induced by concurrent C . trachomatis infection . Our Nepali study population showed a significant inverse association of C . trachomatis infection with age and a direct association with TF/TI cases agreeing with our previous findings and those of others [19] , [22] , [32] . Proinflammatory cytokines have previously been associated with acute and chronic trachoma [36] , [37] . We and others [19] , [20] found elevated TNFα levels among TI and chronic trachoma cases and also higher levels when both C . trachomatis infection and trachoma were present . Additionally , in our study , elevated IL-1β levels were significantly associated with chronic trachoma cases but not with infection . Previous studies have demonstrated elevated IL-1β mRNA levels during both disease and infection [19] , [20] . These differences may be due to post-translational modifications of IL-1β , thus supporting both findings but suggesting the need for quantitative protein studies for confirmation . The previous associations of these cytokines with the induction of scarring-associated proteins , matrix metalloproteins ( MMPs ) and collagen [19] , [20] , [21] , suggest plausible mechanisms for the development and progression of chronic trachoma . This chronic inflammatory phenotype is further supported in our study by the significantly decreased levels of the anti-inflammatory cytokine IL-1Ra . Recently , Hvid et al . characterized the role of IL-1Ra and IL-1α in a human fallopian tube organ culture model [38] . C . trachomatis infection resulted in destruction of ciliated and secretory cells within the tubes . However , when co-cultured with IL-1Ra , tissue destruction was minimal while IL-1α exacerbated disease pathology . In trachoma , the prolonged production of TNFα and IL-β together with a reduction in IL-1Ra inhibitory pathways may promote the development of scarring and progression to TT . The pleiotropic cytokine IL-6 has been associated with chronic trachoma [33] . One previous study evaluated IL-6 gene expression in trachoma but the findings were inconclusive due to detectable mRNA levels in only two patients [19] . We found elevated IL-6 production for all grades of trachoma . Additionally , we demonstrated for the first time a significant association of C . trachomatis infection with elevated IL-6 protein levels in both inflammatory and chronic trachoma . Our findings are supported by a previous study demonstrating elevated IL-6 production in the fallopian tubes of macaques after repeated infection with C . trachomatis [39] . Murine studies , however , have provided conflicting data . One study showed an increased bacterial burden and mortality associated with pulmonary infection with the mouse pneumonitis strain ( MoPn; now referred to as Chlamydia muridarum ) in IL-6 -/- KO mice [40] . In the mouse genital tract model , however , there was an absence of any pathological or bacterial complications when IL-6 -/- KO mice were infected with C . muridarum [41] . Darville et al . , found that TLR2 -/- KO mice exhibited decreased levels of IL-6 and oviduct pathology during C . muridarum -genital tract infection [42] , further supporting IL-6 as a risk factor for chronic trachoma . Considering the obligate intracellular nature of C . trachomatis and previous studies , host cell-mediated immunity ( Th1 ) appears to be critical in eliciting protection against Chlamydia-associated diseases . Transcriptional studies have associated elevated Th1 cytokines , IL-12 and IFNγ , mRNA expression with C . trachomatis infection for cases with TF/TI [19] , [21] but decreased levels with scarring [19] . Wang et al . demonstrated elevated endocervical IL-12 protein concentrations in adolescents prior to resolution of their C . trachomatis infection [43] , further supporting a role for IL-12 in host defense against infection . In our study , we found minimal differences in IL-12p40 concentrations for TF/TI compared with controls but significantly decreased mucosal levels associated with scarring . Additionally , TT/TI cases had lower levels for both Th1 cytokines , IFNγ and IL-12p40 , compared to TF/TI . Together , these data suggest that Th1 cytokines are protective immunologic factors against disease progression . While Th2 cytokines are known to be associated with humoral mediated immunity , previous studies have been inconclusive due to undetectable transcriptional levels of these cytokines [19] , [21] . In our study , the Th2 cytokines , IL-4 and IL-13 , displayed similar patterns to the Th1 cytokines , suggesting that both Th1 and Th2 cytokines may be protective factors against chronic sequelae . The negative associations of Th1 and Th2 cytokines with chronic trachoma suggest a potential role for Th3/Tr1 . The previous categorization of IL-10 as an anti-inflammatory Th2 cytokine has recently been extended given the documented association with T-regulatory ( Tregs ) cells [44] . Kinjyo et al . characterized IL-10 as a Th3/Tr1 cytokine , showing the over production of IL-10 and TGFβ in the absence of a Th2 polarizing gene , SOC3 , the suppressor of cytokine signaling [45] . Recently , Faal et al . found elevated levels of a T cell regulatory gene , forkhead box 3 ( FOXP3 ) , during active trachoma ( TF/TI ) [22] . This was associated with elevated IL-10 and indoleamine-2 , 3-dioxygenase ( IDO ) mRNA expression levels . IDO levels have been associated with immune tolerance and regulatory pathways [46] , which have lead to outgrowth of secondary pneumococcal infections [47] . Additionally , Mark et al . demonstrated diminished FOXP3 and IL-10 levels with early clearing of C . trachomatis infection in a murine model [48] . In our study , IL-10 was overproduced during all grades of trachoma and with C . trachomatis infection . These findings are supported by previous IL-10 studies where elevated mRNA expression was similarly associated with trachoma and infection [20] , [21] , [22] . In the genital tract , elevated levels of IL-10 have been found in infertile women with documented C . trachomatis infections [49] and in macaques that were repeatedly infected with C . trachomatis [39] . These collective data support the association of IL-10 with a Th3/Tr1 phenotype and suggest that IL-10 may be a major risk factor for chronic trachoma associated with C . trachomatis infection . In contrast , Hvid et al . demonstrated reduced ex vivo fallopian tube pathology with C . trachomatis infection in the presence of excess IL-10 , suggesting a protective effect [38] at least during the early stages of infection in the female genital tract . Further investigations are needed to clearly define the role of IL-10 for each trachoma grade . The involvement of T lymphocytes in the perifollicular area of conjunctival follicles suggests an active involvement of the IL-2 family of cytokines , IL-2 , IL-2R , and IL-15 , in trachoma . Our IL-2 findings showed an association with chronic trachoma and also agree with a previous study that found an association of IL-2 mRNA expression , although at low levels , with TI and C . trachomatis infection [19] . In this study , we demonstrated higher concentrations of IL-15 in cases with scarring and in cases with concurrent C . trachomatis infection . IL-15 shares the IL-2Rβ and IL-2Rγ receptors with IL-2 , and , therefore , has overlapping bioactivity , which includes the stimulation of proliferation of activated T cells and natural killer ( NK ) cells [50] , [51] . Similar trends in elevated IL-15 protein have been found in the lymph nodes of chronically infected HIV patients [52] . In a murine model , IL-15 KO mice have been shown to have significantly diminished acute and chronic colitis compared to wild-type mice [53] , further suggesting that IL-15 production is required to sustain chronic infections . Previous studies have also shown elevated IL-15 mRNA expression in patients with C . trachomatis-induced arthritis compared to healthy controls [54] . Immunopathology studies in cynologus monkeys have demonstrated elevated ratios of CD8+ to CD4+ T cell populations in C . trachomatis infected naïve compared to orally immunized monkeys [55] , suggesting that an over abundance of CD8+ T cells are a risk factor for disease . Recently , IL-15 was linked to extended CD8+ memory T lymphocyte survival rates in mice [56] . IL-15 has also been associated with Treg proliferation [57] . These data support IL-2 and IL-15 as risk factors for chronic trachoma , especially with concurrent C . trachomatis infection , which may be associated with abundant CD8+ T lymphocyte populations . Cellular infiltration resulting in typical trachomatous follicles suggests a major role for chemokines in the progression of disease . However , to our knowledge , there has been no characterization of these proteins in trachoma . Most chlamydial studies that have evaluated chemokines focused on murine and human genital tract infections . In our study , the significant elevation of CXC chemokine IL-8 protein levels for all trachoma grades agrees with previous findings where an abundance of neutrophil populations were present in conjunctival swabs from individuals with trachoma [15] . A chlamydicidal role for neutrophils within the conjunctiva has been demonstrated in one in vitro study by Yong et al . [58] . However , further studies are needed to characterize these findings in vivo . Our findings for IL-8 are also supported by a murine study where the murine form of IL-8 , MIP-2 , had prolonged production along with neutrophil infiltration and pathology in the genital tract of BALB/c and C3H/HeN mice but not in the more resistant C57BL/6 mouse strain [59] . We found pronounced protein levels for MCP-1 for all grades of trachoma , but lower MIP-1α production for TT/TI cases compared to controls . In addition , there was a trend for elevated MCP-1 in TT/TI compared to TS and TF/TI cases , further associating MCP-1 with chronic grades of disease . These findings are supported by a murine study that found elevated MIP-1α and decreased MCP-1 levels among C57BL/6 mice that had a shorter course of infection [60] , suggesting that these patterns of chemokine production are protective . Another murine study exhibited elevated levels of Th1 associated chemokines , RANTES , IP-10 and MIG , with chronic , upper genital tract infections compared to lower genital tract infections for C . muridarum [61] . Our study also demonstrated elevated MIG production among chronic trachoma cases with elevated RANTES levels among those who were also infected with C . trachomatis . In conclusion , we characterized the secreted cytokines , chemokines , and cytokine receptor associated with immunopathology for each grade of trachoma and determined the immunomodulating effects of concurrent C . trachomatis infection . Our studies are in agreement with others who demonstrated Th1 cytokines as protective and Th3/Tr1 cytokine IL-10 as a possible risk factor for chronic trachoma . Additionally , we reconfirmed previous gene expression studies linking elevated proinflammatory cytokines IL-1β and TNFα with C . trachomatis infections , and IL-1β as a strong risk factor for chronic trachoma . Our findings expanded on the IL-1β linkage by demonstrating for the first time , to our knowledge , inverse levels of its antagonist , IL-1Ra , in association with TT , suggesting that IL-Ra is a protective factor against chronic sequelae . We also identified two new risk factors , IL-6 and IL-15 , which were associated with chronic trachoma with significantly elevated levels evident with concurrent C . trachomatis infection . Currently , we are elucidating signal transduction pathways affiliated with these cytokines and chemokines during C . trachomatis infections and their possible inter- and intra-cellular roles during disease . In addition , future vaccine design will likely need to take into consideration the immune responses we have characterized and to ensure a vaccine has the desired protective outcome .
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Trachoma , a disease of antiquity dating back to the 16th century B . C . E . , predominates among developing countries , where it remains the primary cause of preventable blindness worldwide . In trachoma , recurrent Chlamydia trachomatis bacterial infections during childhood are thought to result in inflammation and subsequent conjunctival scarring that can progress to trichiasis ( TT; chronic trachoma; inversion of ≥1 eyelash that touches the globe of the eye ) . The trachomatous follicular grade ( TF; active disease ) is a self-limiting disease , suggesting the coexistence of protective inflammatory proteins . The trachomatous inflammatory grade ( TI; active disease ) is more likely to progress to trachomatous scarring ( TS; chronic trachoma ) . To date , there are only a handful of studies that have examined the immune response in trachoma , and these were primarily based on gene expression . Characterizing quantified conjunctival mucosal immune differences for secreted proteins among individuals with no , active , and chronic trachoma may identify protein biomarkers associated with protection versus disease , which would greatly aid our understanding of the immunopathogenesis of trachoma . In this study , we characterized 25 cytokine and chemokine proteins for all trachoma grades . We identified eight cytokines and chemokines as risk factors for chronic trachoma and four as protective . Together , these findings further characterize the immunopathologic responses involved during trachoma , which will likely aid in the design of a vaccine and immunomodulating therapeutics for trachoma .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"immunology/cellular",
"microbiology",
"and",
"pathogenesis",
"immunology/immunomodulation",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"infectious",
"diseases/bacterial",
"infections",
"immunology/immunity",
"to",
"infections"
] |
2008
|
Role of Secreted Conjunctival Mucosal Cytokine and Chemokine Proteins in Different Stages of Trachomatous Disease
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We previously established an 80 kb haplotype upstream of TNFSF4 as a susceptibility locus in the autoimmune disease SLE . SLE-associated alleles at this locus are associated with inflammatory disorders , including atherosclerosis and ischaemic stroke . In Europeans , the TNFSF4 causal variants have remained elusive due to strong linkage disequilibrium exhibited by alleles spanning the region . Using a trans-ancestral approach to fine-map the locus , utilising 17 , 900 SLE and control subjects including Amerindian/Hispanics ( 1348 cases , 717 controls ) , African-Americans ( AA ) ( 1529 , 2048 ) and better powered cohorts of Europeans and East Asians , we find strong association of risk alleles in all ethnicities; the AA association replicates in African-American Gullah ( 152 , 122 ) . The best evidence of association comes from two adjacent markers: rs2205960-T ( P = 1 . 71×10−34 , OR = 1 . 43[1 . 26–1 . 60] ) and rs1234317-T ( P = 1 . 16×10−28 , OR = 1 . 38[1 . 24–1 . 54] ) . Inference of fine-scale recombination rates for all populations tested finds the 80 kb risk and non-risk haplotypes in all except African-Americans . In this population the decay of recombination equates to an 11 kb risk haplotype , anchored in the 5′ region proximal to TNFSF4 and tagged by rs2205960-T after 1000 Genomes phase 1 ( v3 ) imputation . Conditional regression analyses delineate the 5′ risk signal to rs2205960-T and the independent non-risk signal to rs1234314-C . Our case-only and SLE-control cohorts demonstrate robust association of rs2205960-T with autoantibody production . The rs2205960-T is predicted to form part of a decameric motif which binds NF-κBp65 with increased affinity compared to rs2205960-G . ChIP-seq data also indicate NF-κB interaction with the DNA sequence at this position in LCL cells . Our research suggests association of rs2205960-T with SLE across multiple groups and an independent non-risk signal at rs1234314-C . rs2205960-T is associated with autoantibody production and lymphopenia . Our data confirm a global signal at TNFSF4 and a role for the expressed product at multiple stages of lymphocyte dysregulation during SLE pathogenesis . We confirm the validity of trans-ancestral mapping in a complex trait .
Tumour Necrosis Factor Superfamily ( TNFS ) members control wide-ranging facets of immunity when they interact with their complimentary TNF Receptors [1] . One of these , TNFSF4 ( OX40L ) , uniquely binds its receptor , monomeric TNFRSF4 ( OX40 ) , on T lymphocytes to strongly activate NF-κB [2] . Several lines of evidence published over the last 15 years suggest the TNFSF4–TNFRSF4 interaction is required for the induction of anti-tumour immunity , allergy and autoimmunity [3]–[6] but also inhibits generation of adaptive T regulatory ( TR1 ) cells [7] . The outcome is not limited to human disease; blockade of the TNFSF4-TNFRS4 interaction has ameliorative effects in animal models of T cell pathologies [8] including allergic and autoimmune manifestations [9] . Genetic variation at TNFSF4 has been associated with the autoimmune disease systemic lupus erythematosus ( SLE ) , and other inflammatory conditions including atherosclerosis and ischaemic stroke . SLE is the prototypic multi-system autoimmune disorder . High-affinity , pathogenic IgG autoantibodies to an array of nuclear antigens are a hallmark of pathogenesis and characterise the global perturbation of the immune system in SLE . Variation in at least 25 genetic loci with modest effect sizes are thought to explain the genetic component of SLE [10] . The strong genetic basis to disease is well-established and has been strengthened by the advent of GWAS , which has corroborated the association of immunologically relevant loci with SLE [11]–[13] . We have previously shown single nucleotide polymorphisms ( SNPs ) in the 5′ TNFSF4 region to be associated with lupus in European families and a case-control cohort [4] . The increased association of 5′ risk alleles with disease has been replicated in East Asian populations [14] , [15] , highlighting the genetic similarities at this locus in these ancestrally distinct populations . Multiple SLE risk-associated TNFSF4 variants are also associated with systemic sclerosis [16] , primary Sjögren's syndrome [17] and myocardial infarction [18] , [19] . A major obstacle in the identification of disease-specific causal variants at TNFSF4 in the European and East Asian SLE cohorts has been the strong linkage disequilibrium ( R2>0 . 8 ) exhibited by genotyped TNFSF4 alleles , which has resulted in a high frequency extended haplotype associated with risk of disease instead of delineating causal variations at the locus [4] . It is probable that migration out of Africa involved many founder effects and bottlenecks to increase haplotype length in East Asian and European populations [20] . Hispanic and African-American populations are disproportionately affected by SLE [21] and health disparities in these groups show onset at a younger age [22] . Hispanic and African-American populations have genomes which reflect recent admixture on ancient substructures [16] . Hispanic cohorts have rich diversity of source ancestry with Southern European , Amerindian and West African contribution to the inherited genome and the forced diaspora of Africans to the Americas also resulted in gene flow and two-way admixture between previously reproductively isolated West African and European ancestral populations [23] . African populations today tend to have shorter haplotypes because they usually have ancestors who have experienced more recombination events without population bottlenecks or founder effects in emigrant populations [24] . Common shorter haplotypes are often subdivisions of the larger haplotypes found in non-Africans and so can be correlated to these [25] . In admixed populations , the genetic component attributable to the West African ancestral population would equate to a faster decay of LD . The breakdown of LD is therefore greater in African-Americans , because the major component ( 80% or more ) of their genome is West-African , compared to Hispanics who have component estimates between 4–11% [23] , [26] . We infer a fine-scale map of the recombination rate and location of hotspots within each entire population and in subgroups of interest . We have used principal components ( PC ) -based strategies to adjust for major ancestry before performing a high-resolution association study which utilises typed and probabilistic genotypes to map the TNFSF4 locus . By surveying common variants up to 1000 Genomes Phase 1 ( v3 ) , we aim to identify common causal variation at TNFSF4 associated with SLE . Cross-comparison of associated risk haplotypes across four populations focuses these analyses . The AA association replicates in a smaller cohort of AA-Gullah [27] . Our haplotype analyses find informative recombinants in African-Americans and Europeans to resolve genetic variants associated with SLE at this locus . These data are used to perform six case-control association studies and a trans-ancestral mapping experiment using in excess of 17900 subjects . We attempt to define causal variation at TNFSF4 in SLE susceptibility . In a complementary strategy we perform association analysis using TNFSF4 alleles and lupus phenotypes . We explore the mechanism by which TNFSF4 influences perturbation of the immune process in inflammatory disease . Finally , we interrogate risk alleles in terms of their influence on transcription factor binding using a bioinformatics approach . The research presented uses trans-ancestral mapping to inform this complex trait .
The European sex-averaged and female-only recombination maps generated by deCODE ( http://www . decode . com/addendum/ ) , are based on 15 , 257 and 8 , 850 directly observed recombinations , respectively . These maps have a resolution effective down to 10 kb and comparing them to the HapMap 3 and 1000 Genomes population-averaged maps [30] , [31] , we found differences at the TNFSF4 locus . Thus , we estimated background recombination rates in AA , East Asians , Europeans and Hispanics using a Bayesian composite-likelihood method . The inclusion of a hotspot model allowed sampling of hotspots from the Markov chain and inference of mean posterior hotspot densities from a threshold upwards of 0 . 25 , giving a detection power of 50% and a false-discovery rate of 4% [32] . In Asians , Europeans and Hispanics the bulk of the recombination occurs in less than 5% of sequence ( Figure 1 and Figure 2 ) . An exception to this pattern is found in the African-American cohort , with increased recombination rate and higher density and proportion of hotspots across the locus ( Figure 1 , Figure 2 ) . In all populations , peak recombination is at the 5′ boundary of the TNFSF4 gene and approximately 120 kb into the 5′ region . A difference in African-Americans is that recombination extends 30 kb from the TNFSF4 gene boundary into the 5′ region , whilst there is negligible recombination in this section in the other populations ( Figure 1 ) and this is compatible with increased complexity of the genomic region in African-Americans . The association data presented are for markers after imputation using the 1000 Genomes phase I integrated variant set v3 ( March 2012 , NCBI build37 ) [30] . The TNFSF4 locus is well established in SLE therefore we have presented uncorrected nominal p-values for variants . In East Asians , Europeans and Hispanics many strong associations ( Pu 10−8<10−16 ) at TNFSF4 are detected . Multiple susceptibility alleles in the TNFSF4 5′ region are overrepresented in SLE cases ( Table 2 , Figure 2 ) . In terms of single markers , best evidence of association with disease in Europeans is observed with rs2205960-T , 10 kb 5′ from the TNFSF4 gene ( P = 5 . 61×10−15 , OR = 1 . 34 ( 95%CI 1 . 25–1 . 44 ) ) . The T allele of rs2205960 also has strongest association with Hispanic SLE ( P = 1 . 7×10−10 , OR = 1 . 65 ( 95% 1 . 42–1 . 91 ) ) . In Europeans , an additional 15 SNPs reach genome-wide significance ( P<5×10−8 ) most of these risk alleles also reach this level of significance in the East Asian and Hispanic cohorts ( Table 2 ) . Several 5′ risk alleles associated with disease in East Asians , Europeans and Hispanics are also associated in African-Americans and the 5′ association replicates in a small cohort of AA-Gullah ( Supplementary Table S1 ) , underpinning this gene as a global SLE susceptibility gene . In African-Americans , the best evidence for the 5′ SNP association with disease are from rs1234317-T ( P = 2 . 28×10−5 , OR = 1 . 4 ( 95%CI 1 . 25–1 . 56 ) ) and rs2205960-T ( P = 7 . 2×10−5 , OR = 1 . 48 ( 95%CI 1 . 22–1 . 67 ) ) and rs1234314 –G ( P = 3 . 11×10−5 , OR = 1 . 22 ( 95%CI 1 . 13–1 . 32 ) ) . There is a trend for under-representation of the minor alleles of rs1234314-C , rs1234315-C , rs844642-G , rs844644-A , rs2795288-T and rs844654-A in SLE cases resulting in a flipped OR for these variants ( Table 2 ) . Examining the genetic association between SNPs within the TNFSF4 gene and SLE we identify association of rs1234313-G , within intron1 , with SLE in Asians ( P = 4 . 37×10−8 , OR = 1 . 38 ( 95%CI 1 . 32–1 . 44 ) ) , and Europeans ( P = 1 . 11×10−5 , OR = 1 . 15 ( 95% CI 1 . 11–1 . 27 ) . In both cohorts rs1234313-G is partitioned from other associated SNPs by a recombination hotspot at the TNFSF4- 5′ boundary . However , correlation coefficient R2 values between this marker and risk-associated 5′ variants suggest strong correlation . We identify under representation of rs10798265-A in African-American SLE ( P = 9 . 24×10−5 , 0 . 84 ( 95%CI 0 . 78–0 . 9 ) ) . There is suggestion of additional modest association signals ( P<10−4 ) from a series of SNPs located at the TNFSF4- 3′UTR boundary in the same cohort . Imputation gave 257 common ( >1% MAF ) bi-allelic indels at the TNFSF4 locus , mostly neutral . The indels were included in the same imputation analysis and subject to the same QC as the SNPs and probabilistic genotypes incorporated into our association analyses . We identify a deletion at rs200818062 [-/G] to be associated with SLE in all groups tested . This indel is located 22 . 4 kb from the start site of the common transcript ( Transcript 1 ) of TNFSF4 and is in strong LD with ( R2>0 . 8 ) rs1234317 and rs2205960 . We used a logistic regression model fitted with an interaction term ( effect ) in the R statistical package to investigate cross-study heterogeneity . P-values for individual associated SNPs were generated using a likelihood-ratio test . We found no evidence of heterogeneity for the key risk- haplotype-tagging common variants which span the locus . Our null hypothesis - that all studies were evaluating the same effect size- held true for key variants associated with risk of SLE . We combined the association data for variants across the 5′ TNFSF4 region in East Asians , Europeans , Hispanics and African-Americans , to more powerfully estimate the true effect size ( Table 3 ) . The average effect size across all datasets was computed using inverse variance weighting of each study . We find the 5′ association of TNFSF4 with SLE greatly reinforced . rs2205960-T , the most associated allele in Europeans and Hispanics , ( P = 1 . 71×10−34 , OR = 1 . 43[1 . 26–1 . 60] ) , and rs1234317-T ( P = 1 . 16×10−28 , OR = 1 . 38[1 . 24–1 . 54] ) have the strongest combined association with disease ( Table 3 ) . These adjacent markers are separated by 3 kb of chromosome 1 . Allele frequencies for rs2205960 for 1000 Genomes populations are presented in supplementary data . As expected , our 5′ association data suggest pairwise LD between markers is weakest in African Americans and strongest in Asians . In order to establish whether the signals identified by our trans-ancestral fine-mapping experiment represent causal variants , independent risk factors , or if we have simply found surrogate markers strongly correlated with causal variants , we conditioned the association data from each population with the marker which represented the best evidence of association . In all populations , rs2205960-T , a risk-haplotype tag SNP with high p-value and effect size ( Table 3 , Figure 3 ) is associated with SLE; a similar trend is illustrated by the adjacent marker rs1234317-T . Conditioning on rs1234317 or rs2205960 we find the signal at rs1234317 is lost after conditioning for rs2205960 , and this is consistent for across populations ( Table 4 ) . If the reverse analysis is performed and we condition on the presence of rs1234317 , there is residual association at rs2205960 in Asians , Europeans and Hispanics ( P = 9×10−4AS , P<10−4EU , P = 0 . 015Hi ) . In all apart from the AA group , conditioning on rs1234317 or rs2205960 leaves a residual signal at rs1234314 . We included rs2205960 , rs1234317 and rs1234314 in these analyses . We find conditioning on the presence of rs1234317 or rs2205960 , association of intron 1 markers tested for all groups is lost , confirming these as secondary to 5′ risk associations ( Table 2 ) . We also conditioned the meta-analysis association data on rs2205960 and found residual association at rs1234314 ( P = 3 . 81×10−7 ) , located at the TNFSF4-5′ boundary . The reverse analysis found increased residual association at rs2205960 ( P = 4 . 12×10−14 ) . These data suggest two independent signals with increased association and effect at rs2205960 compared to rs1234314 in SLE . Conditioning the meta-data on both rs1234314 and rs2205960 removed association at TNFSF4 ( Table 3 ) . In genotype-based analyses , the models that best fits the 5′ association of TNFSF4 with SLE are the additive/dominant models . Haplotypes significantly associated with risk of disease were identified for each population . To better visualise the breakdown of LD of associated haplotypes , we constructed bifurcation diagrams from phased genotypes for each cohort tested ( Figure 4 , risk ) . The plots illustrate the breakdown of linkage disequilibrium ( LD ) at increasing distances in both directions from rs1234314 , the most proximal genotyped SNP located at the TNFSF4 gene-5′ boundary and which is used as the core variant in the figure ( labelled , circular core from which haplotype branches ) . The location of rs1234317 and rs2205960 , best-associated in the meta-analysis , are also marked onto the diagram . The thickness of the line in each plot corresponds to the number of samples with the haplotype , branches indicate breakdown of LD . For the risk haplotype , the lines are most robust in East Asians ( Figure 4 , risk ) , followed by Hispanics and Europeans , and least robust in African-Americans . We find branch junctions depicting breakdown of LD on the risk haplotype to be coincident with the section of the TNFSF4 locus encompassing rs1234317 and rs2205960 . The non-risk haplotype retains its thickness with distance from the core in the AA group , indicating long-range homozygosity ( Figure 4 , non-risk ) . Contrasting the recombination rate in risk and non-risk haplotype homozygotes finds increased recombination in the risk individuals ( Supplementary Figure S3 ) , supporting these bifurcation data . Significantly associated haplotypes are found in each population ( Table 5 ) . Low recombination and similar location of hotspots at the TNFSF4-5′ boundary in East Asians , Europeans , and Hispanics allow for the construction of near-identical haplotype blocks including risk and non-risk haplotypes ( designated TNFSF4OR>1 and TNFSF4OR<1 , respectively ) ( Figure 3 ) which extend approx . 80 kb into the TNFSF4 5′ region . Multiple associated risk alleles uniquely tag TNFSF4OR>1 , the risk haplotype , which is overrepresented in SLE individuals in each population , whilst TNFSF4OR<1 is the most frequent haplotype for all cohorts tested but underrepresented in SLE individuals . The risk haplotype found in East Asians , Europeans and Hispanics is fragmented in the African-American cohort; the most associated risk haplotype is 11 kb ( P = 2 . 12×10−5 , OR = 1 . 52 ) . This haplotype block extends from rs1234317 to the bi-allelic indel rs200818062 . Only one allele uniquely tags this haplotype , rs2205960-T , the associated alleles of rs1234317-T and rs200818062- are also found on a completely neutral haplotype . This haplotype block is separated from the adjacent distal block by R2 = 0 . 33 . Haplotype association data for TNFSF4OR>1 and TNFSF4OR<1 are presented in Table 5 . In Asians , Europeans and Hispanics , the non-risk haplotype is tagged by rs1234314-C , rs1234315-C , rs844642-G , rs844644-A , rs2795288-T and rs844654-A . These variants have a flipped OR ( Table 2 ) and there is residual signal at these variants after conditioning on risk-associated variants . In African-Americans , there is a signal at rs1234314-C . Conditioning our meta-analysis data on rs2205960-T there is residual association at each of these variants and the OR for the minor allele is flipped . The best-associated variant after conditioning on the risk signal is rs1234314-C . This variant is associated in all groups tested and resides at the TNFSF4-5′ boundary . Conditioning on rs1234314 and rs2205960 removes association at TNFSF4 . In all groups , we conditioned upon the presence of TNFSF4OR>1 and found residual association of TNFSF4OR<1 . Reversing the analysis by conditioning on presence of TNFSF4OR<1 also finds residual association of TNFSF4OR>1 . These analyses demonstrate that the observed signals in the TNFSF4 promoter region independently confer risk and protection against SLE . We found haplotypes in the European and AA cohorts which are tagged by the risk allele rs1234317-T but the non-risk allele rs2205960-G and not associated with disease . In African-Americans , the alleles of rs1234317-T and rs200818062- are found on a neutral haplotype , not associated with SLE . This haplotype block is separated from the adjacent distal block by a correlation coefficient value R2 = 0 . 33 . These data support our conditional regression data which indicate rs2205960-T as the driver of the risk association . Given TNFSF4 surface expression on a range of cell types which control immune functionality , one might expect TNFSF4 alleles to be associated with disease manifestations of SLE . Median ( IQR ) age at diagnosis , autoantibody production and renal disease were examined within SLE cases and against controls in each ancestral group . American College of Rheumatology ( ACR ) classification criteria [33] were additionally examined in East Asians , Europeans and Hispanics . Phenotypic subsets of SLE cases are less heterogenous than SLE per se and so may enrich for risk variants with increased effect size or prove informative for causal mechanism . Clinical characteristics of SLE individuals sorted by population are presented with case-only and phenotype-control association results ( Supplementary Table S2 ) . Case-only analysis reveals association of TNFSF4 risk variants with autoantibody production in African-American , European and Hispanic SLE cohorts: Evidence of association of rs2205960-T with Anti-Sm autoantibodies in African-American cases ( P = 5 . 1×10−3 , OR = 1 . 57 ( 95% CI 1 . 14–2 . 16 ) is reinforced by testing this variant against controls ( P = 6 . 67×10−7 , OR = 1 . 91 ( 1 . 47–2 . 47 ) ) . We find this marker also segregates with Anti-Sm autoantibodies in European case-only and phenotype-control analyses . In Europeans the adjacent variant rs1234317-T is associated with Anti-Ro autoantibodies ( P = 9 . 5×10−4 , OR = 1 . 31 ( 95% CI 1 . 12–1 . 54 ) and this is reinforced against controls ( P = 9 . 5×10−8 , OR = 1 . 52 ( 1 . 3–1 . 76 ) ) . In African-Americans analyses of 5′ variants against controls improves the significance of risk-haplotype-tagging variants with Anti-dsDNA autoantibodies ( rs1234317-T , Pu = 5 . 36×10−6 , OR = 1 . 68 ( 95%CI 1 . 34–2 . 1 . ) ) We find a transancestral trend of underrepresentation of TNFSF4 intron 1 alleles with autoantibody production ( Hispanic P = 1 . 7×10−4 , OR = 0 . 52 ( 95% CI 0 . 36–0 . 73 ) , European P = 2 . 5×10−3 , OR = 0 . 81 ( 0 . 7–0 . 93 ) and East Asian P = 3 . 6×10−2 , OR = 0 . 7 ( 95% CI 0 . 5–0 . 98 ) ) . Conditional regression analysis of the best-associated marker in each population removes all evidence of association . Examination within cases also reveals association of distal 5′ TNFSF4 alleles with age of onset ( IQR ) across all cohorts examined apart from East Asians ( Supplementary Table S2 ) . We classified the first and last quartile of age of onset into early and late onset in the analysis . Underrepresentation of distal 5′ TNFSF4 alleles in lupus individuals with early age of onset is found in AA ( P = 9×10−4 , OR = 0 . 69 ( 95% CI 0 . 56–0 . 86 ) ) , European ( P = 1 . 43×10−3 , OR = 0 . 78 ( 0 . 68–0 . 91 ) ) and Hispanic ( P = 1 . 43×10−3 , OR = 0 . 57 ( 95% CI 0 . 41–0 . 81 ) ) populations . Inverse square meta-analysis finds the marker with best evidence of association with this phenotype ( rs844654-A , P = 8 . 7×10−6 , Z score 4 . 45 ) , 60 Kb from the TNFSF4 gene-5′ boundary . To gain further insight into the transcriptional regulation of the TNFSF4 gene we analysed the 5′ ends of four putative TNFSF4 transcripts from the activated B lymphocytes of a European individual . We evaluated the mRNA predictions for TNFSF4 because the Gencode mRNA predictor annotates three TNFSF4 splice variants , whilst Aceview , which has increased sensitivity for the cDNA-supported transcriptome , annotates four mRNA splice variants [34] . To position our association data accurately against the TNFSF4 gene , we generated the 5′ ends of transcripts by 5′ RACE-PCR and found multiple transcripts which differ in their first exon usage ( Figure 5 ) including a transcript for what is likely to be a soluble form of TNFSF4 ( Figure 5 ) , this transcript maps identically to a transcript found in the Ensembl and UCSC genome browsers , but is yet to be found translated . We have anchored our association data to the most abundant transcript ( Transcript A , Figure 5 ) sequenced . Expression profiling of common TNFSF4 variants was carried out in a cis eQTL study in LCL samples from 777 female TwinsUK participants [35] . Association of RNA expression with >2×106 SNPs was tested by two-step mixed model–based score test [35] . To characterize likely independent regulatory effects , the identified cis eQTLs were mapped to recombination hotspot intervals . For each gene , the most significant SNP per hotspot interval was selected , and LD filtering performed . The top-cis-eQTL in the LD bin , for the probe located at TNFSF4 ( ILMN_2089875 ) , was rs2205960 ( P = 3 . 75×10−4 ) . We examined the interaction of individual transcription factors ( TFs ) and other proteins with the DNA sequence at rs2205960 . A decameric DNA sequence including the rs2205960 variant at the 8th position was predicted to bind to the NF-κB p65 protein ( RELA ) with high confidence . We investigated changing rs2205960 allele , from the minor ( T ) to major ( G ) allele and its impact on binding affinity of the motif for the target protein , p65 . Using SELEX binding data and position weight matrix ( PWM ) profiles stored in the Jaspar core database [36] , we found the DNA sequence with rs2205960-T at the 8th nucleotide position had a binding affinity of approximately 90% for NF-κB p65 ( Figure 6 ) . Altering the allele to rs2205960-G decreased the binding affinity for NF-κB p65 by over 10% , but highlighted degeneracy of the motif ( Figure 6b ) . Binding of NF-κB at rs2205960 has been confirmed by genomewide ChIP-seq experiments in EBV - B cell lines as part of the ENCODE project ( Figure 6c ) [37] . These ChIP-seq data indicate that signal intensity for NF-κB at rs2205960 in a heterozygous ( G/T ) cell-line ( GM12878 ) is double that for a non-risk homozygote ( G/G ) cell line ( GM06990 ) . We further examined the sequence encompassing rs1234314 for transcription factor binding . According to our conditional analysis , rs1234314 is the best-associated variant after conditioning on the risk-association . Furthermore this variant tags the non-risk haplotype . Scanning the data held in the Ensembl genome browser revealed rs1234314 to be part of a 400 bp segment which has repressed/low activity in LCL cells but with no such activity in a T cell line . The UCSC genome browser predicts rs1234314 to be located within a region associated with the H3K27Ac chromatin signature which is associated with active enhancers . Interrogating the sequence at rs1234314 with PWM binding data in the Jaspar core database gave no clear pattern of binding of either allele to the motif of a regulatory element . Examining the sequence with rs1234317-T against PWM binding data stored in the Jaspar Core database finds it completes a TATATT-binding motif and this motif is disrupted in the presence of rs1234317-C . The ENCODE project does not highlight binding of a TBP protein at this variant . Genome-wide ChIP-seq data from the ENCODE project has data for LCLs which carry the T allele of rs1234317 . For LCLs carrying the risk ( T ) allele , there are currently no regulatory features annotated at this position .
We present a trans-ancestral fine-mapping association study of TNFSF4 in SLE . We have genotyped haplotype-tagging and proxy variants and major ancestry informative markers in 6 populations , including admixed groups , across 200 kb of 1q25 encompassing the TNFSF4 gene , and 5′ and 3′ regions . We also present a fine mapping association analysis of TNFSF4 SNPs in African-American SLE . Association testing of TNFSF4 variants revealed strong association of 5′ variants with disease in all cohorts ( Tables 2–5 ) establishing it as a global lupus susceptibility gene . Resolution of the association signal was aided by increased recombination in the AA group ( Figure 1 ) , and by increased power from the large numbers in our European cohort . Maximal power was achieved testing with a genetic model concordant with the major underlying mode of inheritance of the 5′ TNFSF4 region in SLE , which is additive . Our study would suggest trans-ancestral mapping as a useful tool where linkage disequilibrium is an obstacle . Testing most of the common polymorphisms at the locus allowed identification of additional candidate variants that might underlie association at TNFSF4 . As expected , most high-frequency SNP probabilistic genotypes included in this study are present in dbSNP; especially in the TNFSF4 gene itself . Prior to QC filtering , the African-American population contributed the largest number of probabilistic genotypes at SNP loci . Although our ability to impute bi-allelic indels accurately from the 1000 Genomes Project resource is limited by FDR , it still increased power to detect association signals at a majority of common small indel sites accurately . In excess of 50% of the indels in the imputation scaffold were novel in all groups . We mention the bi-allelic deletion , rs200818062 , which is in LD with our best-associated variant , rs2205960 , and which is associated with SLE in all cohorts tested . Our AA and European data suggest this risk-associated deletion is found on a neutral haplotype which is not associated with disease . After QC filtering of imputed variants in these populations , our data suggests no new imputed variant better explained the risk signal than the typed SNP rs2205960-T . A key limitation of this study is TNFSF4 imputation may have missed common variations located in the distal 5′ TNFSF4 region which could be causal . Accurate characterisation of variants remains challenging in low-complexity regions including the LINE element found in the distal 5′ section of this locus . As a result , variants in this region are systematically underrepresented in genetic association studies . Furthermore , an association signal may reside in the fraction of SNPs which have a lower imputation performance and were omitted using our info threshold of 0 . 7 . This fraction is likely to include rare variants which are too infrequent to be imputed with confidence but which might have a large effect on risk . However , our data suggest the true causal variants are likely to be common ( >5% frequency ) and located in the proximal section of the 5′ region . The standard error of the beta coefficients for most imputed variants included in later analyses reflect high imputation certainty . Mapping the alleles uniquely tagging the risk haplotype in each cohort has established the boundaries of risk and non-risk haplotypes in East Asians , Hispanics , and African-Americans and validated the haplotype boundaries previously defined in Europeans [4] . We avoided spurious associations through poor matching of cases with controls by the removal of outlying individuals ( Supplementary Figures S1 and S2 ) and tested the association of risk alleles across all groups in this study . Comparing recombination patterns in African-American individuals homozygous for the risk and non-risk haplotypes finds increased recombination in the risk-haplotype . Our results provide evidence for global association of rs2205960-T with SLE and assessment of the contribution of rs2205960-T to disease risk by conditional regression suggests that this allele drives the 5′ TNFSF4 association in African-Americans , Europeans and Hispanics . Increased decay of 5′ LD at TNFSF4 in AAs anchor the associated haplotype to the proximal 5′ region of TNFSF4 . Examining the LD structure at TNFSF4 in African-Americans and Europeans validates our association data: Neutral haplotypes in these populations , recombinant between rs1234317 , rs2205960 and rs200818062- , support our conditional regression results . Association testing within the Anti-Smith autoantibody-positive AA lupus subgroup strengthens the association P value and effect size of rs2205960-T and this trend replicates in Europeans ( Table S2 ) . Curated and non-redundant profiles of SELEX binding experiments , stored in the JASPAR core database [36] , suggest rs2205960-T would form the 8th nucleotide of a decameric motif with high binding affinity for NF-κB p65 ( Figure 6 ) . Altering the 8th nucleotide of the decamer to rs2205960-G reduces the binding affinity of this sequence for this NF-κB protein by approximately 10% , according to these data . ChIP-seq data generated for two Hapmap lymphoblastoid cell lines confirm binding of NF-κB at this location . ENCODE ChIP-seq data also suggest binding of the transcription factors BCL11a , MEF2a and BATF at rs2205960 , albeit with lower signal intensity compared to NF-κB . These data suggest the genomic region encompassing rs2205960-T to have strong regulatory potential . These data were generated for the ENCODE project [37] , and establish that a positive signal for NF-κB binding is found at rs2205960 but not rs1234317 . A signal is found in both cell lines and there is increased signal intensity in the risk/non-risk heterozygote compared to the non-risk homozygote cell line . These data suggest a mechanism by which rs2205960-T could increase gene expression , which may underlie the SLE risk . Our data suggest a putative role for TNFSF4 in autoantibody generation , further clarifying the role of this gene in lupus pathogenesis . Correlation of rs1234317-T with the presence of anti-Ro autoantibodies in European cases is strengthened against controls . The Genomatix SNP analysis web tool predicts rs1234317-T to destroy the DNA binding site for the transcriptional repressor E4BP4 , a transcription factor with a role in the survival of early B cell progenitors [38] . The DNA sequence encompassing either the C or T allele of rs1234317 was investigated for binding to this transcription factor using the curated set of binding profiles stored in the Jaspar core database . We could not confirm binding of the sequence with either allele to the E4BP4 repressor with these data . However , the T-allele of rs1234317 completes a TATATT consensus sequence for the TATA-Binding Protein ( TBP ) . External sources of regulatory data stored in Ensembl and UCSC do not validate the binding of TBP or other members of the transcription initiation complex . The genomewide ChIP-seq data from the ENCODE project has data for LCLs which carry the T allele of rs1234317 associated with SLE risk . We would expect enrichment for TFs such as TBP , or marks of open chromatin , but there are currently no data for LCLs carrying the risk ( T ) allele . However binding of this factor is associated with transcription initiation and so this variant merits further investigation in Rho- autoantibody-positive subsets of SLE individuals . Association of rs2205950-T with African-American lupus concurs with data published previously by our group establishing a 5′ TNFSF4 association with SLE in Northern Europeans [4] . The risk-associated variants rs2205960-T and rs1234317-T are strongly associated in the Minnesota cohort consistent with our results in four racial groups . In this previous study LD was a major obstacle in delineation of causal variation . Crucially we find association testing using a very large number of Europeans and the admixed AA group allow delineation of the signal through conditional analyses and the presence of neutral recombinant haplotypes . The African-American data presented does not validate data presented by Delgado-Vega and colleagues [39] , suggesting rs12039904-T and rs1234317-T to explain the entire haplotypic effect at TNFSF4 with SLE . A possible explanation for the modest association of rs12039904-T in our African-American cohort is that it is monomorphic in West African populations such as the Yoruba from Ibadan , Nigeria . Our data find rs12039904-T a borderline rare allele in African-Americans and we find nominal allelic association of rs12039904-T with disease , conditional regression analyses of rs2205960 results in absence of an association signal at rs12039904 in all groups . Sanchez and colleagues use TNFSF4 rs2205960 and single markers at 15 other lupus susceptibility loci to illustrate correlation of Amerindian ancestry with increased frequency of lupus risk alleles [40] . Delineation of rs2205960-T in the context of LD with adjacent markers isn't the aim of the Sanchez study , as a single SNP is typed at each locus . They find aggregation of deleterious alleles in Amerindian SLE individuals which are complemented by the increased effect sizes we find for associated TNFSF4 variants in Amerindians and Hispanics in this study . In Asians , Europeans and Hispanics , the non-risk haplotype is tagged by rs1234314-C , rs1234315-C , rs844642-G , rs844644-A , rs2795288-T and rs844654-A . In African-Americans , there is a signal at rs1234314-C and a weaker signal at rs844654-A . Conditioning our meta-analysis data on rs2205960-T , the variant which is best-associated with risk in this study , there is residual association at each of these variants and the OR for the minor allele is flipped . The best-associated variant after conditioning on the risk signal is rs1234314-C . This variant is associated in all groups tested and resides at the TNFSF4-5′ boundary . Conditioning on rs1234314 and rs2205960 removes association at TNFSF4 . In summary , the data presented establish TNFSF4 as a global susceptibility gene in SLE . We have replicated and refined the 5′ association with disease and anchored risk and non-risk signals to the proximal TNFSF4 promoter region through our efforts in African-Americans , and in Europeans by virtue of increased power in this large cohort . Recombination at the locus in African-Americans , and the conditional regression strategies employed , focus the 5′ TNFSF4 association with disease to rs2205960-T . This variant uniquely tags the risk- haplotype in African-Americans and is strongly associated with disease in all groups tested . We find this marker segregates with autoantibody subsets in African-Americans , European and Amerindian/Hispanic groups . Furthermore , ChIP-Seq and bioinformatic data suggest that rs2205960-T sits within DNA that binds NF-κBp65 ( RelA ) . This suggests that the risk allele would convey greater responsiveness of TNFSF4 expression to an NK-κB stimulus . Collectively , these data confirm cross-ancestral TNFSF4 association with SLE and suggest trans-ancestral mapping a useful strategy in complex traits .
European samples held as part of the UK SLE and control collection held at Kings College London ( KCL ) were approved by 06/MRE02/009; additional AA samples from the CASSLE group were held at the University of Alabama at Birmingham ( UAB ) and approved by the UAB IRB . This study included over 17 , 900 SLE and control individuals of self-reported European , African-American ( AA ) , AA-Gullah , East Asian and Hispanic/Amerindian ancestry . All cases fulfilled four or more of the 1997 ACR revised criteria for the classification of SLE and provided written informed consent . Samples were collected from multiple sites with Institutional Review Board ( IRB ) permission and processed at the Oklahoma Medical Research Foundation ( OMRF ) under guidance from the OMRF IRB . Clinical data on SLE manifestations in all subjects were obtained from medical record review performed at individual institutions , collected and processed at the OMRF , with additional phenotypic information from KCL , MUSC and UAB . Genotyping was performed in two independent experiments on the Illumina iSelect platform at OMRF for combinations of haplotype tag SNPs and proxy variants capturing all common haplotypes , this meant we did not type all markers in all groups , marker selection was dictated by TNFSF4 locus architecture and included SNPs found to be associated in our previous European association study [4] and Hapmap phase 3 populations [31] . In all , 125 different SNPs in a 200 Kb region ( chromosome 1 , 171 , 400 , 000–171 , 600 , 000 NCBI build 36 . 3 ) encompassing the TNFSF4 gene and 5′ region were genotyped . Population stratification bias and effects due to admixture were addressed by genotyping 347 genome-wide SNPs as used by Halder and colleagues [29] to correct for major ancestry . 20 Additional 1q25-specific ancestry markers were genotyped to correct for two-way admixture between Europeans and AAs . Within each population , Eigenstrat was used for principal components ( PC ) analysis and global ancestry estimates were additionally inferred by a combined Bayesian and sampling-theory approach ( Admixmap ) . We spiked the African-American population with Yoruba , Tuscan and Northern/Western European Hapmap III individuals to cross-compare two-way admixed AAs with their founder populations ( Supplementary Figure S1 ) . Markers with less than 90% genotyping efficiency were excluded from the analysis . Hardy-Weinberg Equilibrium ( HWE ) was assessed in control samples of each cohort . We included markers which deviated up to a P>0 . 01 threshold for HWE . We also included markers which had an acceptable HWE p-value in three of the four cohorts , if associated with SLE in multiple populations . Following filtering for duplicates , first-degree relatives , HWE , missingness and major ancestry , the non-imputed dataset comprised 111 TNFSF4 SNPs and 294 AIMs and 17900 samples prior to imputation ( Table 1 ) . Imputation of the genomic region from 173 , 112 , 930 to 173 , 349 , 886 ( NCBI build 37 ) on chromosome 1q25 . 1 was performed using IMPUTE2 . 2 and the phased haplotypes from the 1000Genomes phase-1 integrated_v3 dataset [31] . Genotypes from our UK-Canadian GWAS ( unpublished ) were used as a second reference for the imputation of the European cohort ( Table 1 ) . Our aim was to fill missing gaps in the genotyping data and impute common markers ( >1% MAF ) missing between datasets to examine association at TNFSF4 and to better inform the structure of common haplotypes across the populations . We estimated concordance between imputed and true genotypes and Imputed SNPs were included in downstream analysis if SNP info scores exceeded 0 . 7 and a HWE>0 . 01 . These criteria successfully filtered out all but the best-imputed SNPs . We used FASTPHASE to phase 6272 unrelated control chromosomes ( 1568 from each population ) , randomly chosen after QC filtering . Rhomap from the LDhat2 . 0 package [32] was used to estimate population scale recombination rates in the presence of hotspots using pre-computed maximum likelihood tables in the analysis . Using the approach of Auton and colleagues , Rhomap was run for a total of 1 , 100 , 000 iterations including a burn-in of 100 , 000 iterations , the chain was sampled every 100 iterations after the burn-in . Each simulation incorporated 196 chromosomes meaning a total of 8 simulations were completed per group and the mean average recombination calculated between each pair of markers at the TNFSF4 locus . Simulations were executed in their entirety on 3 separate occasions to ensure there were no irregularities . The data did not change if we increased the parameters used . These analyses were then extended to infer recombination in phased chromosomes from African-American risk and non-risk homozygote individuals ( Supplementary Figure S3 ) . After QC filtering , single marker association and conditional data were generated using a case-control format and the continuous covariate function in SNPtestv2 under the additive model . We used a frequentist statistical paradigm and a probabilistic method for treating genotype uncertainty . Odds ratios ( OR ) with 95% confidence intervals ( 95% CI ) were calculated using the beta statistic and 95% confidence intervals the SE . Data are represented as nominal uncorrected p-values . We used a logistic regression model fitted with an interaction term ( effect ) in the R statistical package to investigate cross-study heterogeneity . P-values for individual associated SNPs were generated using the likelihood-ratio test . We found no evidence of cross-study heterogeneity for the key haplotype-tagging common variants which span the locus . These were rs1234314 , rs1234317 , rs2205960 , rs12039904 , and rs10912580 . We have presented the results of a fixed-effects meta- . results for East Asians , Europeans and Hispanics and African-Americans to more powerfully estimate the true effect size ( Table 3 ) . The effect size across all datasets was computed using inverse variance weighting of each study . By using the Long Range Haplotype ( LRH ) test to look for common alleles with long-range linkage disequilibrium ( LD ) , we were able to represent the breakdown of the risk haplotype , TNFSF4risk . TNFSF4risk was anchored by rs1234314 in all groups , a marker conveniently positioned at the boundary of the TNFSF4 gene and 5′ region . Haplotype bifurcation diagrams were generated in the program Sweep . Two SNPs which show best evidence of association after meta-analysis , rs1234317 and rs2205960 , are marked on the scale of each bifurcation plot . Haplotypes in the TNFSF4 gene and 5′ region were generated using Haploview 4 . 2 using the custom algorithm , based on the R2 measure of linkage disequilibrium ( LD ) . Markers and haplotypes with frequencies greater than 4% were included in the analyses . Haplotypes were anchored using tag SNP genotype data and boundaries were inferred using recombination data . SLE case-control association and step-wise conditional regression data for each haplotype was generated in PLINK , as were OR ( 95% CI ) and the association is represented by nominal uncorrected p values . Individuals with early age of SLE onset were classified using interquartile range and analysed using case–only format and case-control formats in SNPTest . Presence/absence of leukopenia and lymphopenia , anti-La , anti-Ro and anti-Sm autoantibody subsets , which are associated with SLE , together with renal disease , were analysed using both case-only and phenotype-control formats . Linear regression data of the most associated marker for each phenotype in each population was generated . Peripheral blood mononuclear cells ( PBMC ) were isolated from 40 ml whole blood from a European individual using the ACCUSPIN System-Histopaque ( Sigma-Aldrich ) . B lymphocytes , expressers of TNFSF4 , were negatively selected using the Dynabeads Untouched Human B Cell kit ( Invitrogen ) . Cell purity was assessed by FACS analysis of CD19-FITC-conjugated B cells and these were 97% pure . The cells were stimulated with 25 µg/ml anti-IgM- ( Fab′ ) 2 , 0 . 1 µg CD40L and 0 . 1 µg enhancer of CD40L to upregulate TNFSF4 . Upregulation of cell-surface TNFSF4 was assessed by FACS . Total RNA was isolated using the TRIzol ( Sigma ) method from 5×106 B lymphocytes . 5′ ends of TNFSF4 transcripts were generated by the SMARTer RACE cDNA Amplification Kit ( Clontech ) . Primer3 was used to design gene specific primers suitable for four alternative splicing variants predicted by the Aceview alternative splicing modelling tool [34] . During PCR a universal primer was added to the 5′ end of the cDNA . In combination with each transcript specific primer , cDNA was amplified up to the 5′ end as dictated by transcript sequence and in a positive control . In order to identify clones relevant for the TNFSF4 manuscript , we undertook colony hybridisation with a 32P-labelled probe specific for the 5′ region of TNFSF4 cDNA . Following colony selection , we cloned individual PCR products using the TOPO TA Cloning Kit ( Invitrogen ) in order to identify individual transcript isoforms . Bacterial cultures were mini-prepped as per manufactures instructions ( QIAprep Spin Miniprep Kit , Qiagen ) . Samples were digested with EcoRI and different sized fragments sequenced and Blasted against transcript sequences . Genome-wide expression profiles stored in the Multiple Tissue Human Expression Resource ( MuTHER ) were available for download at http://www . muther . ac . uk/Data . html . Transcription factors ( TFs ) which are predicted to interact with DNA at the risk-associated TNFSF4 variants identified as part of this study were investigated in a sequence-specific manner . We analysed DNA-binding patterns at these locations using curated , non-redundant matrix profiles stored in the Jaspar core database [35] . In a complementary approach , putative risk loci were investigated using profiles derived from whole-genome ChIP-seq experiments on lymphoblastoid cell lines generated for the ENCODE project and stored in the Ensembl ( http://www . ensembl . org/Homo_sapiens/encode . html ) , UCSC databases ( http://genome . ucsc . edu/ENCODE/ ) and 1000genomes variant call format files downloaded from http://www . 1000genomes . org/ . In accordance with the Department of Health's Research Governance Framework for Health and Social Care , the research project titled ‘Genetics susceptibility of systemic lupus erythematosus ( SLE ) ’ has received favourable approval from an ethics committee and approval from the Department of Research and Development prior to commencement . Ethics number: 06/MRE02/009 Sponsor: Wellcome Trust Funder: King's College London End date: 31/03/2016 R&D Approval Date: 05/08/2011 Chief Investigator: Professor Timothy Vyse
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Systemic lupus erythematosus ( SLE/lupus ) is a complex disease in which the body's immune cells cause inflammation in one or more systems to cause the associated morbidity . Hormones , the environment and genes are all causal contributors to SLE and over the past several years the genetic component of SLE has been firmly established . Several genes which are regulators of the immune system are associated with disease risk . We have established one of these , the tumour-necrosis family superfamily member 4 ( TNFSF4 ) gene , as a lupus susceptibility gene in Northern Europeans . A major obstacle in pinpointing the marker ( s ) at TNFSF4 which best explain the risk of SLE has been the strong correlation ( linkage disequilibrium , LD ) between adjacent markers across the TNFSF4 region in this population . To address this , we have typed polymorphisms in several populations in addition to the European groups . The mixed ancestry of these populations gives a different LD pattern than that found in Europeans , presenting a method of pinpointing the section of the TNFSF4 region which results in SLE susceptibility . The Non-European populations have allowed identification of a polymorphism likely to regulate expression of TNFSF4 to increase susceptibility to SLE .
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2013
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Trans-Ancestral Studies Fine Map the SLE-Susceptibility Locus TNFSF4
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Using a large , passive , clinic-based surveillance program in Iquitos , Peru , we characterized the prevalence of rickettsial infections among undifferentiated febrile cases and obtained evidence of pathogen transmission in potential domestic reservoir contacts and their ectoparasites . Blood specimens from humans and animals were assayed for spotted fever group rickettsiae ( SFGR ) and typhus group rickettsiae ( TGR ) by ELISA and/or PCR; ectoparasites were screened by PCR . Logistic regression was used to determine associations between patient history , demographic characteristics of participants and symptoms , clinical findings and outcome of rickettsial infection . Of the 2 , 054 enrolled participants , almost 2% showed evidence of seroconversion or a 4-fold rise in antibody titers specific for rickettsiae between acute and convalescent blood samples . Of 190 fleas ( Ctenocephalides felis ) and 60 ticks ( Rhipicephalus sanguineus ) tested , 185 ( 97 . 4% ) and 3 ( 5% ) , respectively , were positive for Rickettsia spp . Candidatus Rickettsia asemboensis was identified in 100% and 33% of the fleas and ticks tested , respectively . Collectively , our serologic data indicates that human pathogenic SFGR are present in the Peruvian Amazon and pose a significant risk of infection to individuals exposed to wild , domestic and peri-domestic animals and their ectoparasites .
Rickettsiae and rickettsia-like organisms are a diverse group of obligate intracellular bacteria within the order Rickettsiales that include members of the genera Rickettsia , Orientia , Ehrlichia , Anaplasma , Neorickettsia and Wolbachia . Based on whole-genome analysis , species within the genus Rickettsia are divided into four geno-groups: 1 ) spotted fever group rickettsiae ( SFGR ) , which comprises R . rickettsii , R . conorii and others; 2 ) typhus group rickettsiae ( TGR ) with R . prowazekii and R . typhi; 3 ) an ancestral group with the non-pathogenic members R . bellii and R . canadensis; and 4 ) a transitional group that harbours the disparate species R . akari , R . australis and R . felis . This latter group is often still included within the SFGR due to antigenic relatedness . Not all of the known Rickettsia species are pathogenic to humans . Outbreaks caused by some Rickettsia species are occasionally associated with enzootic vectors , such as mosquitoes , fleas , mites and ticks [1–7] . Rickettsial diseases are neglected , potentially severe , but easily treatable and preventable . Due to overlapping symptoms , it is often not possible to distinguish them from dengue ( and other arbovirus infections ) , leptospirosis , typhoid fever , malaria , or enterovirus infection . Overall , the distribution of rickettsial pathogens is poorly characterized in Peru with the exception of R . parkeri , a pathogen identified in Amblyomma maculatum ticks from northwestern Peru [8] and Candidatus Rickettsia andeanea [9] , a novel rickettsial species detected during a febrile disease outbreak investigation in the town of Sapillica in northern Peru . Previous data from the Amazon Basin of Peru suggested that rickettsial agents represent potential causes of fever [10] and that seroprevalence in domestic animals was high [11]; however , there remains a paucity of information on risk factors , vectors and circulating rickettsial species . Rickettsial infections remain underrecognized and underreported due to a lack of awareness and limited access to diagnostics , many of which are often suboptimal . Current research efforts are directed both towards defining the epidemiology of these diseases and development of improved diagnostics . The objectives of our study were: ( 1 ) to determine the proportion of human febrile illnesses that were associated with rickettsial infections and ( 2 ) to identify vectors and reservoirs of rickettsial pathogens in the Peruvian Amazon .
This study was conducted in Iquitos , which is located in the Amazon forest ( 73 . 2’W longitude , 3 . 7°S latitude , 120 m above sea level ) in the Department of Loreto , in the northeastern region of Peru . The city is populated by approximately 400 , 000 people and is accessible only by air or river [12] . Participants included in the study lived in neighborhoods in urban , peri-urban or rural Iquitos . This project was nested in an ongoing surveillance study ( protocol # NMRCD . 2010 . 0010 ) , which was approved by the Naval Medical Research Unit , No 6 ( NAMRU-6 ) Institutional Review Board ( Lima , Peru ) in compliance with all U . S . federal regulations governing the protection of human subjects . In addition , the study protocol was reviewed and approved by health authorities in Peru ( Instituto Nacional de Salud ( INS ) , Direccion General de Epidemiologia ( DGE ) and Dirección Regional de Salud Loreto ( DIRESA-LORETO ) ) . Written informed consent was obtained from patients 18 years of age and older . For patients younger than 18 years , written informed consent was obtained from a parent or legal guardian . Additionally , written assent was obtained from patients between 8 and 17 years of age . Animal handling and ectoparasite collection was approved and performed in accordance with the NAMRU-6 Animal Care and Use Committee ( NAMRU-6 Protocol number 13–5 ) . The experiments reported herein were conducted in compliance with with the Animal Welfare Act and in accordance with the principles set forth in the “Guide for the Care and Use of Laboratory Animals , ” Institute of Laboratory Animals Resources , National Research Council , National Academy Press , 1996 . Written informed consent was obtained from all animal owners before specimen collection . The proportion of human febrile illness associated with rickettsial infection was determined by testing human samples obtained through an ongoing clinic-based passive febrile surveillance study . The febrile surveillance study offered enrollment if the individual presented to one of 12 health facilities ( 3 hospitals and 9 outpatient clinics ) distributed across 4 districts of Iquitos; 2 were military health facilities and the rest were Ministry of Health hospitals and clinics . Febrile patients fulfilled the inclusion criteria of the surveillance study if their fever ( axillary temperature ≥ 37 . 5°C ) duration was ≤ 5 days and they were 5 years or older . A serum sample was collected at the time of enrollment ( acute ) and a convalescent serum sample was obtained 10–30 days later . These samples have already been used to investigate other pathogens ( mainly dengue virus and other arboviruses ) ; the results and testing methods are described in detail elsewhere [13 , 14] . The objective of identifying vectors and reservoirs of rickettsial pathogens was approached by a prospective sentinel-case driven , case-control design that was nested in the ongoing surveillance study for human febrile disease described above . We visited households of human participants whose laboratory results indicated recent rickettsial infection . We asked residents if they had contact with domestic animals such as dogs , cats , pet birds , and livestock ( pigs , poultry and guinea pigs ) . If so , they were invited to participate in the study after a brief explanation of the study objectives and procedures . After obtaining permission from the owner of the domestic animals , we obtained blood samples and removed ectoparasites including ( but not limited to ) fleas , ticks and lice . The location , name and description of the animal and owner’s address were collected for each animal . In order to obtain a control-group of animals and ectoparasites , we randomly selected another household at a distance of greater than 500 m but under 2 km away from the sentinel household . Controls were processed as described for households with confirmed rickettsial disease . The ratio of rickettsial and non-rickettsial households was 1:1 . Additionally , all military bases in and around Iquitos were visited and the same procedure was repeated there . Depending on the size of the animal , we collected 1–3 ml of blood using EDTA tubes . Tubes were immediately stored on ice and transported to NAMRU-6‘s Iquitos field laboratory within 3 hours . Samples were centrifuged and plasma was separated and stored at -80°C until further testing . The residual blood cells were placed in a separate vial and immediately stored at -80°C . Ectoparasites were collected from domestic animals using combs and tweezers and placed in vials that were dry , plastic , and covered . Fleas , ticks and lice from each animal were placed in separate vials ( maximum 30 specimens per vial ) . The vials were stored on ice until transport to the Iquitos field laboratory , where fleas , ticks and lice were taxonomically identified and stored at -80°C until shipment on dry ice to Lima , Peru , for DNA extraction and molecular analysis . Fleas , ticks and lice were identified according to the entomological keys of Aragao and Fonseca ( 1961 ) , Graham and Price ( 1997 ) , and Johnson ( 1957 ) [15–17] . Ticks , fleas and lice were pooled by species and individual host animal . Prior to laboratory testing , ectoparasites were rinsed using distillated water , placed on a sterile petri dish and divided into 2 pieces using sterile surgical blades . A new blade was used for each ectoparasite . Ticks and lice were divided longitudinally , whereas fleas were cut horizontally , dividing upper from lower body part . Half of each ectoparasite was immediately frozen and stored , and the other half was used for DNA extraction . DNA was extracted from human and animal whole blood samples using QIAmp DNA mini kits ( QIAGEN , Valencia , CA ) following the manufacturer’s instructions into a final elution volume of 100 μl and stored at -80°C . Ectoparasite halves were extracted individually by mechanical disruption using 100 μl of PrepMan Ultra Sample Preparation Reagent ( Applied Biosystems , Waltham , MA ) and a Kontes Pellet Pestle ( Thermo Fisher Scientific , Waltham , MA ) . After grinding , individual samples were heated to 95°C for 10 minutes using a heat block . To clarify them , samples were centrifuged at room temperature , for 5 minutes at top speed ( 13 , 000 rpm ) using a table top Eppendorf centrifuge ( Hamburg , Germany ) . Cleared supernatants were transferred to clean tubes and stored at -20°C until further processing . Human and animal whole blood samples were individually screened using a qPCR assay targeting the Rickettsia genus-specific 17-kD gene ( Rick17b ) that has been previously described [24] . Positive ( plasmid ) and negative ( no template control—water ) controls were used in all the assays . To optimize the efficiency of workflows for ectoparasites , individually extracted samples were pooled by host in groups of 5 samples or less per pool . Pools were initially screened using Rick17b [24] . Samples in positive pools were further tested individually using the same approach . Individual positive samples were then tested using a nested PCR assay that also targeted the 17-kD gene but that can differentiate between SFGR and TGR [3] . Positives from this screen were then tested using two additional qPCR assays that targeted variable regions of the ompB gene: R . felis group ( RfelG ) and the Ca . Rickettsia asemboensis genotype ( Rasemb ) [3] . Statistical analysis was performed using Stata 12 ( StatCorp . , College Station , TX , USA ) . Data were double entered and crosschecked . Means with corresponding standard deviations ( SD ) or medians and interquartile ranges are presented for normally and non-normally distributed variables , respectively , to account for the sampling design . Comparisons across groups for categorical variables were done with Chi-square test or Fisher's exact test if an expected cell count was less than five . Continuous variables were analyzed with Student’s t-test . The association between seroconversion ( recent active infection ) and independent variables was determined using logistic regression . To evaluate strength of association , odds ratios and their 95% confidence intervals were calculated . Multivariate logistic regression was performed with seroconversion as the outcome , using substantive knowledge to guide variable selection . All variables that were associated with an outcome at a significance level of p<0 . 10 in the univariate analysis were included in the initial model . The significance level for removal from the model was set at p = 0 . 06 and that for addition to the model at p = 0 . 05 . Strength of association was determined by estimating the odds ratio and the 95% confidence intervals ( CI ) . Logistic regression models were constructed with the dichotomous dependent variable SFGR or TGR seroconversion ( recent active infection ) to evaluate risk factors for infection . For those with evidence of active infection , symptoms and clinical findings as originally reported in the participant questionnaires were evaluated . We tested: age ( three age categories ) , sex , occupation ( 4 categories: students , home-based , high-risk exposure , other ) , and potential animal contact ( Table 1 ) . The occupation groups were formed using the information on the participant questionnaire when available . “Home-based occupation” contains housewives , retired and unemployed individuals . The “High-risk exposure occupation” group contains active military duty ( majority ) , local law enforcement and occupations outside of town such as hunting , fishing , farming and logging . The group of “others” contains various job activities , such as self-employed individuals , occupation in health establishments , office , construction or merchandising . The following independent variables were evaluated additionally as risk factors for acquiring an infection: trip outside of the city during 15 days prior to presentation at health care facility and contact with febrile individual 15 days prior to presentation . Symptoms , clinical findings and information on the course and outcome of disease ( hospitalization , length of stay , duration of illness , disease evolution at follow-up visit ) were also analyzed . The different bleeding manifestations ( epistaxis , oral mucosal bleeding , hematochezia , hematuria and hematemesis ) were collapsed to “any form of bleeding” due to the small sample size among seroconverters . The group of participants with evidence of co-infection with rickettsial agents and another pathogen were further analyzed separately ( S1 Table ) .
Between January 2013 and February 2014 , 2 , 562 participants were enrolled in the main study . Of them , 2 , 054 had paired ( acute and convalescent ) samples and therefore they became our study population . The median age of this population was 23 years and ranged from 5 to 82 years . The population was evenly distributed by gender , and nearly 20% were military . Of the 2 , 054 participants tested , 786 ( 37 . 4% ) were infected with dengue virus when tested by PCR and/or isolation confirmed by indirect immunofluoresence assay . Other arboviruses were detected by PCR and/or isolation in 27 of cases ( 1 . 3% ) ( 3 Mayaro virus , 1 Group C orthobunyavirus ( only isolation ) , 1 Maguari virus , 22 Venezuelan equine encephalitis virus ) . When tested for TGR IgG no seroconversions or 4-fold rise of titer were detected . Thirty-eight ( 1 . 85% ) of all febrile participants seroconverted or had a 4-fold or greater rise in titer of SFGR IgG between their acute and convalescent samples . These were classified as active rickettsial infections at the time of acute sample collection . Of these , 13 ( 34 . 2% ) were identified in a military hospital , 12 ( 31 . 6% ) were on active military duty , with 9 living permanently in camp . The active rickettsial infection cases were identified from 11 out of 12 study sites across and around the city . Univariate logistic regression analysis indicated that people 21–35 years of age , being a student , and having a high risk occupation were risk factors for acute rickettsial infection ( Table 1 ) . Median age and sex did not differ between groups . Univariate logistic regression analysis of symptoms , clinical findings and course of febrile illness suggested that SFGR infection was associated with longer duration and persistence of illness at the follow-up visit ( Tables 2 and 3 ) . None of the analyzed symptoms and clinical findings were clearly associated with rickettsial infection . Only photophobia showed an association in univariate analysis , which did not remain statistically significant in multivariate analysis . In the multivariate logistic regression model , home-based occupations as well as high-risk occupations were associated with SFGR seroconversion ( Table 4 ) . The longer duration of illness remained significantly associated in multivariate analysis . Of the 38 participants with serologic evidence of active rickettsial infection , 14 ( 36 . 8% ) and 2 ( 5 . 2% ) of the participants were co-infected with dengue virus and Venezuelan Equine Encephalitis virus , both diagnosed by PCR ( S1 Table ) . The remaining 22 did not have serologic or molecular evidence of co-infection with an arbovirus . We also tested the 38 acute samples of the seroconverters by PCR targeting the 17-kD antigen , but none tested positive . Domestic animals were sampled from 15 households of the 38 human participants with evidence of active rickettsial infection , that were eligible for a household visit . The remaining 23 participants and their households were not included for the following reasons: inaccessibility of the dwelling ( 10 . 5% ) , no animal contact ( 30 . 4% ) , participant had been transferred away from military camp ( 39 . 1% ) , refused participation ( 4 . 3% ) , could not be located ( 4 . 3% ) , and pet had died since febrile episode ( 4 . 3% ) . Overall , we visited 30 households ( 15 sentinel and 15 control households ) and 5 military camps . During these visits , a total of 51 dogs , 9 cats and 14 other animals ( 1 pet bird , 1 duck , 4 goats and 8 chickens ) were sampled . We identified anti-SFGR IgG antibodies in only 3 animal samples: 1 cat ( titer of 400 ) and 1 chicken ( titer of 1600 ) from separate military camps; and 1 chicken from a sentinel household ( titer of 400 ) . All 51 animals tested were negative for the 17-kD antigen gene targeted by PCR . Of a total of 284 ectoparasites collected ( Table 5 ) : 190 were fleas from dogs and cats ( all of which belonged to the species Ctenocephalides felis ) ; 34 were lice from poultry and dogs ( 3 belonged to the species Goniocotes gallinae , 1 to Menopon gallinae and the rest to Menacanthus stramineus ) ; and 60 were ticks from dogs ( all of which belonged to the species Rhipicephalus sanguineus ) . All samples were tested for the presence of rickettsia using a variety of PCR assays . Initially , all 284 samples were tested using Rick17b qPCR assay . Of these , 188 ( 185 fleas and 3 ticks ) were positive and 96 were negative . This result was confirmed using an additional 17KDa nested PCR assay that distinguished between the SFGR and TGR . With this assay , we determined that all 188 positives belonged to the SFGR . Further testing ( RfelG R . felis-genogroup specific qPCR assay and ompB fragment ( 2484-bp ) sequencing ) allowed us to determine that the 188 positives contained Rickettsia felis-like organisms , but none were positive for Rickettsia felis . Out of these 188 positive samples 79 ( 76 fleas and 3 ticks ) were further tested with the recently described Ca . Rickettsia asemboensis species-specific Rasemb qPCR assay and all 76 fleas as well as 1 of 3 ticks were positively identified as Ca . R . asemboensis . The two ticks negative for the Rasemb qPCR assay contained R . felis-like organisms not R . felis or Ca . R . asemboensis .
We demonstrated that almost 2% of individuals presenting with a febrile episode in Iquitos had evidence of recent active SFGR infection when serologic testing was performed during a 14-month period . TGR infection did not seem to play an important role in causing human infection in this area though 1 . 0% of febrile patients had evidence of a previous infection with TGR . This proportion of febrile cases due to SFGR infection is very similar to previous studies [25] . However , in that previous study the sample size was much smaller . In particular , our study is the first that systematically analyzed febrile samples for rickettsial infection in the area of Iquitos , Peru . We showed that among study participants with evidence of rickettsial infection , the presentation of disease was non-specific and symptoms or clinical findings did not help guide diagnosis . This is in agreement with previous reports where rickettsial infection presented as acute febrile illness with poor clinical predictors [26 , 27] . Further detailed comparison of febrile patients with SFGR infection to those without SFGR did not reveal any helpful differences [28 , 29] and diagnosis was mostly made retrospectively . This underlines the inability to rely on clinical presentation and rapid reliable diagnostic methods to identify cases . In our study we defined severe disease by respiratory distress , circulatory collapse , multiorgan failure , loss of consciousness , fluid accumulation or shock; having any of these manifestations was rare among febrile patients ( 3 patients ) and was not identified among patients with rickettsial infection . But 21% of patients with rickettsial infection were hospitalized with a mean duration of 3 . 1 days ( range 1 to 8 days ) . During this study we did not record if the patients received antibiotic treatment during the hospitalization or if a rickettsial infection was suspected in this context by the treating physician . But it is not uncommon in this setting for the patients to receive doxycycline as other diseases considered in the differential diagnosis such as leptospirosis are also endemic in this study area . During the study follow-up convalescent visit , significantly more patients with rickettsial disease reported persistent symptoms than those with another febrile disease . This impact of morbidity was also demonstrated by significantly longer duration of illness reported for rickettsial infection compared to the other febrile patients . According to the analysis of patient data , we demonstrated that acute rickettsial infection was strongly associated with home-based occupations ( including housewives , retired and unemployed individuals ) but also with rural and out of town ( high-risk ) exposure occupations , while other professions were not associated with the disease . The majority of those working in high-risk exposure professions were military personnel , and most of them lived permanently on base and were men . All military individuals affected in our study worked extensively outdoors . The protective effect of being a student in the univariate analysis is probably confounded by age and thus appropriately fell out of our final multivariate model . Sixteen of our participants with evidence of active rickettsial infection presented with a co-infection with an arbovirus . This is an important finding , as rickettsial infection can be treated with antibiotics ( i . e . , doxycycline ) ; however , it would not be uncommon for a physician in a dengue endemic area to stop looking for additional infections in someone with presumed or confirmed dengue . The high percentage of co-infections among those with rickettsial infection could raise concerns about the specificity of our in house ELISA assay; however , the observation that out of 786 dengue virus positive samples ( by PCR and/or isolation ) , only 14 showed seroconversion to SFG argues against this being a non-specific reaction . Also , co-infection causing rickettsial disease plus another illness ( such as dengue , malaria or other bacterial diseases ) has been well-demonstrated [29 , 30] . Unfortunately , our study could not determine which pathogen—arbovirus ( mainly dengue virus ) , rickettsial agent , or both—was responsible for the observed clinical symptoms . In a separate analysis of the co-infection subgroup , we observed statistically significant features distinguishing co-infections from either mono-infected arboviral or monoinfected rickettsial infections ( S1 Table ) , but the very low number of co-infections precludes drawing definitive conclusions . The substantial proportion of co-infections may represent the similarity of epidemiological risk factors for the different infections . We were unable to confirm the diagnosis of rickettsial infection in our study with specific molecular diagnostic assays . This is not surprising considering the low sensitivity when performed in blood samples [22] . PCR , culture or immunohistochemical identification using biopsies of skin lesions ( rash or eschar ) would be desirable and is reported to have much higher sensitivity [22 , 31 , 32] but is not available in many limited-resource settings and was not part of our study procedure . This underlines the fact that empiric anti-rickettsial treatment should often be based on the clinicoepidemiologic diagnosis due to the retrospective nature of the currently available serologic tools . Surprisingly , reported contact with animals was not associated with active rickettsial infection . In accordance , our evaluation of domestic animals did not reveal that they played a major role as hosts for rickettsial pathogens of the SFGR . This finding contrasts with the results of a previous study in this area where almost 60% of all dogs tested were found to be positive for SFGR antibodies [11] . Besides differences in sample size and location of animals tested , the different serological methods used could explain some of the differences observed . Unfortunately , we could not detect rickettsial pathogens by PCR in any of the animal samples . This could possibly support the conclusion that domestic animals do not seem to be an important reservoir for rickettsiae; however , sensitivity with animal blood samples is known to be low and varies with the molecular methods used [33] , as is the case with human specimens [22] . Another limitation of this study was the fact that we did not sample rodents . In our data , home-based occupations were a clear risk factor for contracting the infection , which indicates exposure to a reservoir in and around the house . At the same time , reporting a high-risk profession , which mainly represents living on a military camp was also associated with rickettsial infection . Both occupation groups could have exposure to rodents . By analyzing the ectoparasites collected from domestic animals , we demonstrated that almost all of the fleas were positive for Ca . R . asemboensis , which belongs to the SFGR . However , the known rickettsial flea-borne pathogens , R . typhi and R . felis were not detected . Ca . R . asemboensis was originally isolated from fleas from Kenya [34] and has recently been reported in Ecuador [35] . The high prevalence of Ca . R . asemboensis in fleas known to bite humans and to transmit agents that cause human illness gives rise to suspect this agent could be transmitted to humans . However , in a similar situation in Kisumu , Kenya , where high prevalence of Ca . R . asemboensis was found , only R . felis DNA was found in febrile patients blood [20] . At this time , this agent’s involvement as a cause of human disease cannot be ruled out . The involved flea species ( C . felis ) is known to bite a variety of animals , including rodents . Collecting rodent samples in and around human housing would be an important next step in order to investigate the presence of both the SFGR and TGR infections . In conclusion , almost 2% of all undifferentiated febrile illnesses in Iquitos , the major Peruvian city in the Amazon basin , had an active rickettsial infection based on serology . Similar to other past reports , we could not identify features that would distinguish rickettsial diseases from other endemic diseases , and thus permit implementation of appropriate treatment . We demonstrated that home-based occupations , as well as high-risk occupations outside of town , were risk factors for rickettsial infection . This implies that exposure to domestic , as well as non-domestic animals could be important . In those individuals with undifferentiated fever , clinicians should be aware of the possibility of co-infection with rickettsial pathogens , even in confirmed arboviral cases as our study demonstrated . This has direct consequences for management and treatment and can potentially impact morbidity and time missed from work . Although hospitalization rate and severity of disease did not differ between rickettsial disease and other febrile illnesses , the association with prolonged duration of illness can have an important impact on morbidity and health care cost . Evaluation of specimens from domestic animals and their ectoparasites revealed a high percentage of fleas infected with Ca . R . asemboensis . Human pathogenicity of Ca R . asemboensis and its main reservoir remains to be determined .
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Rickettsial infection remains relatively unexplored in South America compared to other regions of the world . For most regions of Peru ( including the Amazon Basin ) , nothing more than broad serological characterization is available about circulating rickettsiae . Even less is known about the animal reservoirs and insect vectors involved in disease transmission . With this study we aimed to better characterize the circulating species of Rickettsia in humans in the Amazon Basin , as well as investigate their domestic animal reservoir and arthropod vectors . Out of 2054 fever patients enrolled we identified 38 individuals with serologic evidence for acute rickettsial infection . Their homes were visited in order to draw blood samples and collect ectoparasites from their domestic animals . Serology and molecular methods were used to test the animal blood samples as well as the ectoparasites . The information collected contributes to the understanding of the transmission dynamics of rickettsial diseases in Iquitos and leads to a better understanding of the exposure risk to rickettsial infection and it will guide approaches for prevention .
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2016
|
Rickettsial Disease in the Peruvian Amazon Basin
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In recent times , stochastic treatments of gene regulatory processes have appeared in the literature in which a cell exposed to a signaling molecule in its environment triggers the synthesis of a specific protein through a network of intracellular reactions . The stochastic nature of this process leads to a distribution of protein levels in a population of cells as determined by a Fokker-Planck equation . Often instability occurs as a consequence of two ( stable ) steady state protein levels , one at the low end representing the “off” state , and the other at the high end representing the “on” state for a given concentration of the signaling molecule within a suitable range . A consequence of such bistability has been the appearance of bimodal distributions indicating two different populations , one in the “off” state and the other in the “on” state . The bimodal distribution can come about from stochastic analysis of a single cell . However , the concerted action of the population altering the extracellular concentration in the environment of individual cells and hence their behavior can only be accomplished by an appropriate population balance model which accounts for the reciprocal effects of interaction between the population and its environment . In this study , we show how to formulate a population balance model in which stochastic gene expression in individual cells is incorporated . Interestingly , the simulation of the model shows that bistability is neither sufficient nor necessary for bimodal distributions in a population . The original notion of linking bistability with bimodal distribution from single cell stochastic model is therefore only a special consequence of a population balance model .
In the study of cell populations , with vastly improved flow cytometry , access to multivariate distribution measures of cell populations has advanced considerably , calling for a concomitant application of theory sensitive to population heterogeneity . In this regard , the population balance framework of Fredrickson et al . [1] has provided the requisite modeling machinery for the same . While this recognition generally exists in the literature , the modeling of gene regulatory processes has been at the single cell level based on it being viewed as an “average” cell . Since gene regulatory processes typically involve a small number of molecules , the reaction network is stochastic in its dynamics , a feature that is included in the single cell analysis . A further issue of importance , that of bistability , occurs when two levels of gene expression , one high and referred to as “on , ” and the other low and referred to as “off” exist for a given concentration of the signaling molecule . This issue is very much a part of the stochastic modeling of the single cell [2] , [3] . Several kinds of stochastic models have been developed; two of them that have been broadly used are the Stochastic Simulation Algorithm ( SSA ) [4] , [5] , and the Fokker-Planck equation or Stochastic Differential Equations ( SDE ) [6]–[8] . The Stochastic model certainly cures the drawback of the deterministic model which describes only the averaged behavior on large populations without realizing the fluctuating behaviors in different cells . Bistability has been studied extensively through experiments , theoretical analysis , and numerical simulations [2] , [3] , [9]–[11] . A bistable system is characterized by the existence of two stable steady states . The modes relating to two stable steady states appear as a bimodal distribution of the population . The coexistence of bistability and bimodal distribution has been shown in many publications [2] , [3] , [9] , [12]–[14] . However , almost all of the modeling works on stochastic gene regulation relate to processes at the single-cell level . The outcome of numerous simulated trajectories of single cell behavior has been interpreted as population behavior . A cell is assumed to act totally independently of other cells without regard to the fact that the signaling environment is continuously altered by the concerted action of all members of the population . That no interaction between other cells has been taken into consideration in these models could indeed lead to serious bias . The drawback of the single cell model may be overcome by applying the Population Balance approach [15] . A detailed general framework of the application of population balances to microbial populations was developed by Fredrickson et al . [1] . However , the population balance model ( PBM ) in the cited work and many others that followed in the literature are based on deterministic behavior of the particulate entities . Ramkrishna [15] shows how the PBM can accommodate random particulate behavior described by stochastic differential equations . In this study , we demonstrate formulating a stochastic gene regulation incorporating PBM , which is capable of tracing time evolution of the behavior of the entire cell population . A system of pheromone-induced conjugative plasmid transfer [16] contributing to the dissemination of antibiotic resistance and the virulence of Enterococcus faecalis infections [17] , [18] has been simulated in this study as an example of the critical difference between stochastic gene regulation incorporating PBM and single-cell stochastic model . It is our objective in this paper to formulate population balance models with stochastic gene expression in single cells . Further , we explore circumstances under which bimodal distributions are observed in protein distributions; in particular we investigate the generally prevailing view in the literature that bistability and bimodality of protein distribution occur concurrently [2] , [3] , [9] , [12]–[14] . An exception to this view appears in the work of Karmakar and Bose [19] , [20] , who showed that bimodal distributions can arise without bistability when the reaction time of the downstream gene regulation is short relative to the time required for change of DNA conformation . Other similar publications can also be found in literature [21]–[25] . While these cited works show bimodal distributions without bistability , it must be understood that their conclusions are based on mechanistic differences in the behavior of isolated single cells . In this study , we approach the issue of the relationship between bistability and bimodality from a rational viewpoint; i . e . , to examine the nature of protein distribution from cells with and without bistability within the framework of population balances . Thus , circumstances will be investigated for Figure 1A , in which bimodal distributions can arise without bistability , and for Figure 1B , in which unimodal distributions can arise even when bistability exists .
The gene regulatory network for pCF10 based conjugation system is shown in Figure 2A . Under natural circumstances , pCF10 deficient recipient cells release a pheromone called cCF10 into the extracellular environment , whereas pCF10 carrying donor cells release an inhibitor molecule , iCF10 into the environment [26] . Both iCF10 and cCF10 are transported into the donor cells to interact with pCF10 DNA favoring off vs on state respectively . A pair of divergent genes prgQ and prgX present on pCF10 DNA regulates the genetic switch controlling onset of conjugation . The transcription of prgQ gene results in the formation of QPRE; QPRE gives rise to two kinds of RNAs known as QL RNA and Qs RNA . In the opposite direction , the prgX encodes the PrgX repressor and a non-coding antisense RNA called Anti-Q which may bind to QPRE [27] , [28] . A QPRE bound with Anti-Q leads to shorter Qs RNA . On the other hand , the final product of free QPRE is longer QL RNA . Under off conditions iCF10 bound PrgX tetramer represses prgQ; small amounts of QPRE are nearly all bound by overwhelming Anti-Q and result in QS , which is incapable of inducing conjugation , predominantly expressed . In the on state iCF10 bound PrgX tetramer is replaced by cCF10 bound PrgX dimer which relieves repression of prgQ , thus causing expression of a longer QL transcript from prgQ gene [29] . The QL RNA consequently results in expression of PrgB protein , an indicator for the onset of conjugation [30] . The deterministic ( average ) equations based on mass-action kinetics that represent the gene regulatory network in Figure 2A are represented below with its associated nomenclature listed in Table 1 . ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) Eq . ( 5 ) , representing the mass balance of PrgB , treats the production rate as a sigmoid function of QL [31] . A variation considering the production rate of PrgB as a linear function of QL is contained in the modified differential equation ( 6 ) below . ( 6 ) The differential equation which remains is that expressing the mass balance of intracellular pheromone concentration given by ( 7 ) In Eqs . ( 1 ) to ( 3 ) , , represents DNA in repressed configuration . The above equations are based on mechanisms already published in the literature [28] , [30] , [32]–[34] . The parameter values for the simulations , adopted from those used for a similar reaction system [35] , are summarized in Table 2 . RNA species QS , QL and Anti-Q are described in Eqs . ( 1 ) , ( 2 ) and ( 3 ) , considering rate of production , degradation and dilution due to growth . The transcription rate of RNA species is modeled by a non-linear function of iCF10 , cCF10 and Anti-Q to take into consideration effects of Transcriptional Interference and Antisense interaction , and are discussed in greater detail elsewhere [36] . Transport of both iCF10 and cCF10 into the donor cell is modeled as a first order reaction ( Eqs . ( 4 ) and ( 7 ) ) . Eq . ( 5 ) considers the production rate of PrgB as a sigmoid function [31] with respect to QL and has been used to simulate Figures 3B and 4 . Instead of sigmoid function , for reducing computational burden , Eq . ( 6 ) assumes that the expression of PrgB is linear in QL and has been used to simulate Figures 3A , Figure 5 , Figure 6 and Text S1 . The trends simulated by this deterministic model are consistent with experimental observations [17] ( refer to Text S1 ) . While the above notation for concentration serves to remind the reader of the reaction species to which it belongs , it is not convenient for their compact representation in the upcoming equations of population balance . Therefore , the intracellular concentrations are renamed as shown in Eq . ( 8 ) . ( 8 ) Note that the extracellular concentration variable and are spared from inclusion in the vector . Eq . ( 9 ) provides an explanation of the different symbols in the foregoing differential Eqs . ( 1 ) – ( 7 ) . ( 9 ) where , Further , the differential equation for the mass balance of remains to be identified ( is modeled as add-in with constant concentration ) . Towards this end , we define as the number of inhibitor molecules in the extracellular space . Assuming that the fraction of extracellular volume to total volume as constant , we identify Eq . ( 10 ) for as ( 10 ) where is the volume per cell . The first term on the right hand side of Eq . ( 10 ) represents the number of inhibitor molecules exiting the cell per unit time , the second their uptake rate by cells , and the third their degradation in the extracellular volume . For each cell , the uptake rate depends on the extracellular inhibitor concentration , so that the total uptake rate is proportional to the product of number of cells and the extracellular inhibitor concentration . Note that is not a constant because the uptake of inhibitor occurs by active transport and its rate depends on PrgZ protein [37] . Assuming that the uptake rate is proportional to number of the PrgZ protein and that the latter is proportional to the volume of the cell , we rewrite which yields as a reasonable constant . Next , we define as the volume fraction of extracellular volume so that would be volume fraction of the cell , from which we have where is the total volume . Substituting this into Eq . ( 10 ) , using renamed variables , we obtain the differential equation for as ( 11 ) The last term on the right hand side can also be represented as , a dilution term resulting from assuming that the volume fraction of cells remains constant as 0 . 5 so that an increase of cell volume by growth also results in an increase of extracellular volume to the same extent . Note that the above assumption about the extent of the extracellular volume has no influence on our conclusion . In this regard the reader is referred to the toy example where volume is modeled as constant . The effort of applying PBM on analyzing cell behaviors can be traced back to mid-twentieth century [1] , [38] . More recently , the number of publications applying PBM has notably increased on analyzing complex cellular behavior ( e . g . Mantzaris [39] ) . Thus the behavior of an entire culture of microorganisms can be simulated by PBM in the form of a multivariate population distribution . A generic formulation of PBM is presented by Ramkrishna [15] . This formulation distinguishes a vector of internal coordinates and a vector of external coordinates ; the former represents different quantities associated with the cell and the latter denotes the position vector of the cell . Cells with the same coordinates are viewed as indistinguishable . The dynamics associated with the intracellular variables through cellular processes ( including gene regulation ) can be described by a rate vector containing the deterministic reaction rates in terms of internal coordinates . The vector includes quantities such as cell mass , various intracellular components associated with gene expression , and so on . The vector is a vector of extracellular variables influencing the intracellular processes; which may include concentrations of nutrient , signaling molecules , inhibitor and so on . The motion of cells with respect to a fixed coordinate frame may be written as Eq . ( 13 ) , where the vector describes the velocity of the cell which may be caused by the mixing of cells ( for instance , in a planktonic growth situation ) or zero when imbedded in a biofilm without motion . ( 12 ) ( 13 ) where “ ” means “given ” . The notation is used to recognize the influence of extracellular variables , on intracellular processes . We use to denote the actual number density and denote its expectation by number density [40] , for a given set of internal coordinates x , position coordinates r at a certain time . The term density here refers to number of cells per unit volume of space of internal coordinates , as well as that of external coordinates , . The existence of this density in physical space must be recognized even when there is no explicit dependence of cell numbers with position . The population balance equation is a state-specific balance due to various processes such as by conjugation , cell division , and so on . It may be written as ( 14 ) The function in Eq . ( 14 ) represents the net ( number ) rate of production of cells of state at a particular location and time . Note in particular that , although represented as a simple function of its arguments , acquires its dependence on them through being a functional of the number density . Eq . ( 14 ) is coupled with a conservation equation written for , for which we define as the rate at which a cell of state “consumes” or “contributes” to the extracellular variables in . The extracellular reaction rate is described by . Noting that is a function of position and time , the conservation equation for is described in Eq . ( 15 ) where is the total flux of the various components in including convective as well as diffusive transport . ( 15 ) The population balance model is defined by Eqs . ( 14 ) and ( 15 ) , properly supplemented by initial and boundary conditions . We next incorporate the intracellular stochastic behavior of gene regulation into the population balance model described in Eqs . ( 14 ) and ( 15 ) . The formulation of population balance equations has been presented by Ramkrishna [15] when the internal state is a stochastic process described by the stochastic differential equation ( 16 ) where is the term that determines the magnitude of stochastic fluctuations ( refer to Gillespie's Chemical Langevin equation [41] ) of signal transduction reactions on the associated intracellular variables; , a vector , represents the increment of a standard Wiener process ( during the time interval ) . We further note that the SDE are based on Ito formulation . Also , its equivalent Fokker-Planck equation can be written as Eq . ( 17 ) . ( 17 ) where “ : ” represent double dot product so can also be written as . The population balance equation with position coordinate can be written as Eq . ( 18 ) ( 18 ) which is the number balance of cells of state accounting for stochastic changes in internal coordinates as defined by the Ito SDE , Eq . ( 16 ) . The derivation of this equation is available in Ramkrishna [15] . Next , we consider the cells to be completely ( uniformly ) mixed so that spatial coordinates may be eliminated . The resulting population balance equation is given by ( 19 ) Eq . ( 19 ) is coupled to a version of Eq . ( 15 ) in environmental variables modified to account for a well-mixed system given by ( 20 ) where is the expected rate of consumption of extracellular variables by cells of state . Note that is stochastic in view of the single cell behavior being stochastic so that the cumulative contribution from a large collection of cells to the environment is deterministic given by . Eqs . ( 19 ) and ( 20 ) must be considered with initial and boundary conditions . Note that number density function , , satisfies “natural boundary conditions” ( i . e . , vanishing of the function and its gradient at infinity ) . With this background , we are in a position to consider the population balance model for the system of interest , viz . , conjugation of plasmid pCF10 system . Since the conjugative response can be influenced by the number of copies of pCF10 plasmid , we include plasmid copy number as a discrete internal coordinate for the cell in view of its effect on cell dynamics as the number of plasmids in each cell becomes important . As a result , the number density Eq . ( 3 ) is further embellished with a discrete variable representing copy number . We define the expected number density of cells , with internal state and plasmid copy number , where varies between and . Kinetics of gene expression is denoted as to account for the effect of plasmid copy number . Cell division rate , denoted by , is assumed to be a constant . A more complicated situation of cell growth , which is not taken into consideration in this study , including the dependence of on intracellular stochastic state x can be found in Tanase-Nicola's work [42] . However , this cited work suffers from neglecting interaction between cells through population effects on the environment . The random partitioning of plasmid between daughter cells is denoted by where is the copy number of the dividing parent and is that of the daughter cell . Plasmids replication is assumed to occur instantaneously prior to cell division . The population balance equation with stochastic intracellular behavior for this case can be written as ( 21 ) Eq . ( 21 ) represents a number balance of cells of state with copy number . The first term on the left hand side represents the rate at which such cells accumulate , the second denotes the net flux by “convective” transport ( in internal coordinate space ) , while the first term on the right hand side represents the mean fluctuation due to random effects , the second the loss of cells by division and the last term the gain of cells of copy number by division of other cells . The factor 2 accounts for doubling of the population by binary division . The intracellular variables of parent and daughter cells are considered to be same . The extracellular components include only pheromone and inhibitor . We do not concern ourselves with the resistance transfer process in this paper as our focus is only on the expression of the protein PrgB in the donor cells . We enunciate three further model assumptions: ( i ) the population density is maintained constant in the growing population by appropriate dilution; in other words , the system volume is allowed to expand suitably . This assumption is only made to provide for a true steady state in the protein level distribution . A similar assumption is also made in the single cell analysis . ( ii ) constant extracellular pheromone concentration , which implies that we need only consider the component for the dynamics of the vector , and ( iii ) that inhibitor is produced and secreted directly into the environment through intracellular reaction as described by Eq . ( 11 ) . The mass balance of extracellular inhibitor is adopted from Eq . ( 20 ) acknowledging any copy number dependence of the rate of consumption of extracellular variables by cells . A macroscopic mass balance for the extracellular variables , based on assumption of perfect mixing , is given by . ( 22 ) In Eq . ( 22 ) , we have used the renamed concentration variables and in place of and respectively . It is also worth noting that the dilution term on the second term on the right hand side comes about from the assumption that the system volume is allowed to expand to keep the population density constant . The motivation for this assumption , as pointed out earlier , is the attainment of a true steady state in the population with respect to protein levels as in the single cell analysis . A schematic illustration of PBM described by Eqs . ( 21 ) and ( 22 ) is shown in Figure 2B . We restrict ourselves in this work to the case where plasmid copy number of the daughter cells is the same as that of the parent cell . In other words , if a cell of copy number divides , just prior to division , the plasmids double in number and are equally shared by the two daughter cells . Of course more general cases are admissible in the model framework which can account for plasmid replication and uneven distribution among daughter cells . The specific assumptions in this work will , however , suffice for demonstration of population balance modeling . The even partitioning of plasmids among daughter cells in a population of uniform copy number distribution is described in Eq . ( 23 ) . ( 23 ) Using Eq . ( 23 ) converts the population balance Eq . ( 21 ) into Eq . ( 24 ) shown below with , which yields an average copy number of 5 close to experimental observation . ( 24 ) The of Eq . ( 24 ) is identified belowwhere and the is exactly the same as which is identified in Eq . ( 9 ) . The reason for using index is to clearly state that PBM account for population heterogeneity of plasmid copy number . Note that Eq . ( 24 ) should be coupled with Eq . ( 22 ) . The overall expected number density , denoted , can be obtained in Eq . ( 25 ) ( 25 ) The difficulty involved in solving PBM with stochastic intracellular behaviors comes from the natural boundary condition ( i . e . , vanishing of the function and its gradient at infinity ) . To handle this problem , we transform the population balance equation ( 24 ) into a Fokker-Planck equation using the transformation shown in Eq . ( 13 ) to obtain Eq . ( 14 ) . The integral over coordinates is unity because represents the initial number density of cells with copy number . ( 26 ) ( 27 ) Eq . ( 27 ) is a Fokker-Planck type differential equation whose solution represents the probability distribution for the stochastic process defined by the Ito SDE ( refer to Eq . ( 16 ) ) . Thus the sample-pathwise solution to Ito SDE will provide an alternative route to calculate expectations of all quantities associated with the stochastic process , including the quantity , for substitution into Eq . ( 22 ) . We solved Ito SDE relating to Eq . ( 27 ) by using the Euler algorithm [43] . The computation proceeds in a stepwise manner for each discrete interval to keep abreast of environmental variables .
Although bistability is featured by Eqs . ( 1 ) – ( 10 ) for the range of parameter values shown in Table 2 ( Figure 3A ) , bistability can be ( analytically ) excluded by forcing parameter , the degradation rate of QL mRNA , equal to , the degradation rate of Qs mRNA , regardless of the values of other parameters ( Figure 3B ) . The detailed derivation is shown in the Text S1 . The parameters used in this simulation are shown in Table 2 , except ( 1/s ) . Stochasticity is restricted to protein alone to reduce computational time . Forty-five thousand cells are used . The outcome is shown in Figure 4 . Results of stationary distribution responding to different pheromone concentration are shown in Figure 4A and 4B . At zero and low pheromone concentration , all cells are at off mode . With increasing pheromone concentration , the cell population gradually migrates from mode of low PrgB , viewed as off state , to mode of high PrgB , viewed as on state . Finally , when the pheromone concentration exceeds 30 nM , all cells stay at on state . The transcription rate of various RNA species is directly proportional to the plasmid copy number . The difference between cells of various plasmid copy numbers causes population heterogeneity resulting in a distribution across the population . The results of dynamic behavior are shown in Figure 4C . This simulation is done for extracellular pheromone concentration equal to 10 nM . The initial distribution is obtained by simulating cells with no pheromone added . Population from initial unimodal distribution finally develops into a bimodal distribution . Figure 4 demonstrates that bistability is not necessary for a bimodal distribution and that the bimodal distribution arises directly out of population heterogeneity . If there is no plasmid copy number distribution , the population balance model can then be written as: ( 28 ) and indicate Eq . ( 28 ) describing system with plasmid copy number equal to 5 . With the solution method described in Methods we convert the population balance Eq . ( 28 ) into ( 29 ) Notice that above equation should be coupled with environmental equation of extracellular inhibitor Eq . ( 22 ) . The single cell stochastic model may be written as ( 30 ) Where index indicates both intracellular and extracellular variables are involved . For reasons that have already been elucidated earlier , Eqs . ( 29 ) and ( 30 ) are not the same . In Eq . ( 29 ) , the vector is different from the vector in Eq . ( 30 ) because the latter also includes the extracellular inhibitor as a stochastic variable . The outcome of the simulation is shown in Figures 5A and 5B . While the outcome of Eq . ( 30 ) shows a bimodal distribution that of Eq . ( 29 ) shows a unimodal distribution , thus indicating the strong impact of the population on the behavior of individual cells . In order to exclude the possibility that Figure 5B was a result of insufficient simulation time to develop into a bimodal distribution , a simulation was conducted with PBM using initial distribution as a bimodal distribution calculated from the single cell Fokker-Planck equation . The simulation outcome , Figure 6 , shows a result consistent with Figure 5B . A bimodal distribution under the influence of the population effect finally merges into one mode . The purpose of this example , shown in Figure 7 , is to elucidate the key elements of the more complicated model of the pCF10 System . To simplify the discussion , we use symbols to denote not only the molecular species but their concentrations . The precursor of the signal molecule , denoted as , needs membrane protein , , to mature into intracellular signal molecule , . Two kinds of gene , xp gene and xi gene , encode product and inhibitor , respectively . As the signal molecule dominates , the transcription rate of xp gene is high and that of xi gene is low . On the other hand , inhibitor favors xi gene instead of xp gene by consuming signal molecule . Further , defining the intracellular concentration of inhibitor as , the concentration of product as , the extracellular concentration of inhibitor as , and letting be the volume per cell , we formulate the mass balance equations for the single cell as ( 31 ) ( 32 ) ( 33 ) ( 34 ) where and describe how DNA configurations change generation rate and are defined asand the values of parameters are identified in Table 3 . Note that is greater than one because the signal molecule favors the transcription of xp gene and is less than one as the signal molecule prevents the transcription of xi gene . Eqs . ( 32 ) and ( 34 ) , together with and as defined above , imply that binding of intracellular inhibitor to signal molecule is an irreversible reaction with suitably large rates so that only the signal molecule or inhibitor dominates the system . The toy example is composed of seven reactions with the system at constant volume and the volume of cells negligible compared to that of the system . For intracellular variables , describes the generation rate of intracellular signal molecule with which is assumed to be controlled by a certain gene and maintained constant in each cell; is the generation rate of product where is a basic rate multiplied by a “configuration factor” , ; describes the uptake rate of inhibitor where is the extracellular inhibitor concentration; , and are degradation terms and is cell growth rate . For extracellular inhibitor , the generation term includes a basic transcription rate , , multiplied by a “configuration factor” , . Because extracellular inhibitor is the accumulated result from all the cells , Eq . ( 34 ) further accounts for number density , . Note that although steady state exists for deterministic equation , there is no true steady state for stochastic model or stochastic gene regulation incorporating PBM . However , the effect from the increment of is small enough to consider the system as in pseudo steady state ( refer to Figure 8 and 9 ) . The PBM with stochastic intracellular behaviors of this system is shown in Eq . ( 35 ) with environmental equation , Eq . ( 36 ) . Following the method introduced in the section “The solution method for PBM” , Eq . ( 35 ) can be transferred into Eq . ( 37 ) . ( 35 ) ( 36 ) ( 37 ) where and is the initial number density of cells . Each of the terms in Eq . ( 37 ) are identified belowWith the parameter values shown in Table 3 , the deterministic Eq . ( 31 ) – ( 34 ) are featured with bistability and the outcome of single cell stochastic model , Eq . ( 38 ) , shows corresponding bimodal distribution , Figure 8A . However , the prediction of PBM shows unimodal distribution , Figure 8B . The same as mentioned in pCF10 system , such qualitative difference is raised from the interaction between cells . ( 38 ) Each of the terms in Eq . ( 38 ) are identified below . where is the system volume . Next we demonstrate bimodal distribution from no bistability . Note that the bistability can be excluded by forced equal to one . In other words , when , there is no bistability . Instead of , we assign for each subpopulation and each of them starts with the same biomass concentration . The simulation outcome is shown in Figure 9 . The bimodal distribution comes from population heterogeneity .
In this paper , we have investigated how population effect changes the behaviors of a culture of cells and demonstrated that the single cell approach does not account for the effects of population heterogeneity and is therefore at risk of producing erroneous results . For this application , we demonstrated that bistability is neither necessary nor sufficient for bimodal distribution . In incorporating stochastic effects , we have relied on a continuous description of the intracellular variables by SDE . As the stochasticity arises from the randomness of chemical transformations of a small number of reacting molecules , the variables are essentially discrete . The SSA uses the chemical Master equation which is based on discrete variables , whereas the SDE approach has found various justifications in the literature . For example , van Kampen [8] uses system size expansion to obtain continuous descriptions of the stochastic variables . Although the continuous description is known to be appropriate for relatively larger number of molecules , publications exist in the literature that demonstrate the usefulness of continuous description of discrete variables for as low as even ten particles [43] . Estimates of the expressed protein level in the system of interest here range in the thousands in the on state and roughly in the range 14–35 particles per cell in the off state . Arguments for these estimates are included in the Text S1 . Hence the adoption of SDE may be regarded as appropriate for this application . In addition , the analysis of populations involves several cells of small variations about a given state so that intracellular behavior averaged among them qualifies for the SDE approach even more than in an isolated single cell . In the section of resolving bistability versus bimodal distribution , if cells act independently from each other , bimodal distribution can be observed . However , cells change distribution from bimodal to unimodal due to population effect . In general , planktonic cells diffuse freely in the culture and such an isolated situation is hardly reached , but for a cell immobilized by extracellular matrix , such as biofilms [44] , an isolated situation may be possible [45] . This study simulates the response of E . faecalis donor cells , harboring plasmid pCF10 , to pheromone concentration . At low concentrations of pheromone as found in the natural situation [46] , [47] , for a perfectly mixed system all cells are predicted to be at off-state as shown in Figure 5B . However , for unmixed systems , non-uniformity of inhibitor concentration can lead to some cells being at the off-state , others at the on-state which together make up a bimodal distribution for the integrated population . This provides a possible mechanism for the observation of bimodal behaviors under naturally occurring conditions such as biofilms involved in dissemination of antibiotic resistance as has been shown in recent work ( Cook 2010 , unpublished work ) . Our effort in this paper has been to show that a cell in a population can behave in a significantly different manner as its environment is altered by the concerted action of other cells . A natural follow-up to this paper is the modeling of the transfer of drug resistance accounting for the presence of donor and recipient cells in different environments for which population balances will indeed provide the proper framework .
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Traditionally cells in a population have been assumed to behave identically by using deterministic mathematical equations describing average cell behavior , thus ignoring its inherent randomness . A single cell stochastic model has therefore evolved in the literature to overcome this drawback . However , this single cell perspective does not account for interaction between the cell population and its environment . Since stochastic behavior leads to each cell acting differently , the cumulative impact of individual cells on their environment and consequent influence of the latter on each cell could constitute a behavior at variance . Thus in nature , cells are constantly under the influence of a highly dynamic environment which in turn is influenced by the dynamics of the cell population . A typical single cell stochastic model ignores such an interaction between the population and its environment , and uses probability distribution of a single cell to represent the entire population , which may lead to inappropriate predictions . In this study , we propose a population balance model coupled with stochastic gene regulation to demonstrate the behavior of a population in which its interactive behavior with its environment is considered . Our simulation results show that bistability is neither sufficient nor necessary for bimodal distributions in a population .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"engineering"
] |
2011
|
Bistability versus Bimodal Distributions in Gene Regulatory Processes from Population Balance
|
Human adenovirus ( HAdV ) infection of the human eye , in particular serotypes 8 , 19 and 37 , induces the formation of corneal subepithelial leukocytic infiltrates . Using a unique mouse model of adenovirus keratitis , we studied the role of various virus-associated molecular patterns in subsequent innate immune responses of resident corneal cells to HAdV-37 infection . We found that neither viral DNA , viral gene expression , or viral replication was necessary for the development of keratitis . In contrast , empty viral capsid induced keratitis and a chemokine profile similar to intact virus . Transfected viral DNA did not induce leukocyte infiltration despite CCL2 expression similar to levels in virus infected corneas . Mice without toll-like receptor 9 ( Tlr9 ) signaling developed clinical keratitis upon HAdV-37 infection similar to wild type mice , although the absolute numbers of activated monocytes in the cornea were less in Tlr9−/− mice . Virus induced leukocytic infiltrates and chemokine expression in mouse cornea could be blocked by treatment with a peptide containing arginine glycine aspartic acid ( RGD ) . These results demonstrate that adenovirus infection of the cornea induces chemokine expression and subsequent infiltration by leukocytes principally through RGD contact between viral capsid and the host cell , possibly through direct interaction between the viral capsid penton base and host cell integrins .
Human adenoviruses ( HAdV ) are major mucosal pathogens of the ocular , respiratory , and gastrointestinal tracts [1] . HAdV are also a source of disseminated infections involving multiple organs in immunocompromised patients [2] , [3] . Epidemic keratoconjunctivitis ( EKC ) is a highly contagious infection of the eye caused principally by serotypes HAdV-8 , 19 , and 37 . Multifocal leukocytic infiltration of the subepithelial corneal stroma is the sine qua non of EKC [4] , and is associated with prolonged discomfort and poor vision . In experimental studies , infection of human keratocytes with adenoviruses results in expression of chemokines before the onset of viral gene expression [5] , [6] , [7] , suggesting that innate immune responses can occur independently of the effects of viral gene products or viral replication . Such observations are consistent with data from studies of innate immune responses to vectors used in adenovirus based gene therapy [8] , [9] , [10] , [11] , [12] , [13] . Activation of the innate immune system by microbes involves stimulation of a range of host molecular pattern recognition receptors ( PRRs ) that sense the unique molecular patterns present on pathogens [14] . These molecular patterns are typically distinct ligands present on the pathogens' surface or their nucleic acid . It was recently demonstrated that HAdV nucleic acids play an important role in cytokine expression after infection in vitro [9] , [11] , [15] , [16] . Genomic adenoviral DNA activates multiple PRRs including Tlr9 , a transmembrane protein present in the endocytic vesicles of cells that signals through the MyD88 pathway upon interaction with phosphodiester 2′ deoxyribose sugar backbone [17] or unmethylated CpG motifs of DNA [18] . Adenoviral DNA may also activate DNA-dependent activator of interferon-regulatory factors ( DAI ) present in the cytosol [19] . DAI is Tlr-independent [20] , [21] , and distinct from known sensors of double stranded RNA , retinoid-inducible gene 1 ( RIG1 ) and melanoma differentiation-associated gene 5 ( MDA5 ) . The DAI pathway mediates type 1 interferon and chemokine expression through interferon regulatory factor 3 ( IRF3 ) , inhibitor of IκB kinase epsilon ( IκBkε ) and Tank binding kinase 1 ( Tbk1 ) [19] , [20] , [21] . Intracytoplasmic HAdV DNA in peritoneal macrophages also induces expression of cytokines through cryopyrin/NALP3 and ASC which are components of the inflammasome [16] . Cytokine responses to adenoviral molecular patterns appear to be cytokine , cell , and molecular pattern specific . In murine peritoneal macrophages [15] and bone marrow derived macrophages [22] , IL-6 expression upon adenovirus infection was mediated by Tlr9 . Adenovirus infection of both human [11] and murine [9] plasmacytoid dendritic cells resulted in Tlr9-MyD88-dependent type 1 interferon expression . In murine conventional dendritic cells [9] , and bone marrow macrophages [15] type 1 interferon expression induced by intact adenovirus or naked adenoviral DNA was Tlr-independent and relied on DNA sensors in the cytosol rather than in the endosome . Similarly , in adenovirus infection of murine splenic cells , type I interferon expression occurred independently of known Tlr molecules , cytosolic sensors , and IRF3 , but required viral endosomal escape within the host cell [23] . Adenoviral capsid components bind to primary and secondary host cell receptors to mediate viral entry and transport . Capsid elements may also serve as virus-associated molecular patterns to activate an innate immune response . The coxsackie –adenovirus receptor ( CAR ) is a primary receptor used by many HAdV [24] . CAR interaction with a recombinant HAdV-5 fiber protein has been shown to activate signaling pathways in vitro and results in the expression of IL-6 [25] . After binding to CAR , Arg-Gly-Asp ( RGD ) motifs located in the penton base of adenoviruses , including HAdV-37 , interact with cellular integrins , including αvβ1 , αvβ3 , αvβ5 , α5β1 , and αMβ2 , leading to the internalization of HAdV via clathrin-coated pits [26] , [27] and activate intracellular signaling pathways resulting in chemokine expression [8] , [28] . Adenoviral empty capsids – devoid of DNA – have been shown to induce chemokine expression in vitro [29] , [30] , presumably through interactions with cellular integrins . In vivo , interaction of adenovirus with splenic macrophages triggered IL-1α activation in integrin ( β3 ) dependent fashion [31] . However , the response to adenovirus-associated molecular patterns has not been studied in the cornea , an important site of adenovirus infection [4] . The human cornea is a specialized avascular tissue forming the outermost part of the visual axis , and is divided anatomically into epithelial , stromal , and endothelial layers . The stromal layer contains predominantly extracellular matrix , with a highly organized interconnected network of fibroblast-like cells , the keratocytes [32] . A lesser number of resident bone-marrow derived cells with dendritic cell markers and macrophages also populate the corneal stroma [33] , [34] , [35] . The precise arrangement of collagen fibrils and other extracellular matrix components in the corneal stroma is an important determinant of corneal transparency . [36] . Stromal cells are highly responsive to pathogenic or mechanical insult , to which they produce prodigious quantities of chemokines [37] , [38] . Therefore , the corneal stroma is highly endowed with resources for innate and adaptive immune responses against ocular pathogens . Given the tissue architecture and ease of observation of the corneal stroma , the mouse cornea is an excellent model to study the interactions of specific viral molecular patterns with tissue stromal cells in vivo . In this study , we show that viral capsid is a sufficient molecular pattern for the development of clinical keratitis in a mouse adenovirus keratitis model [39] . Furthermore , virus induced leukocytic infiltrates and chemokine expression in mouse cornea could be blocked by treatment with a peptide containing RGD , while viral DNA , viral gene expression , and viral replication were not essential to the development of keratitis . Viral DNA differentially stimulated IL-6 and CCL2 through Tlr9 and cytoplasmic DNA sensors , respectively , but by itself , viral DNA was insufficient to induce keratitis . Therefore , chemokine expression and cellular infiltration in adenovirus keratitis is predominantly an outcome of the interaction between viral capsid and the host cell .
To determine if corneal leukocytic infiltrates can be induced in the absence of viral gene expression and replication , we utilized intact , heat-inactivated , and UV-inactivated HAdV-37 . Heating of the adenovirus damages its protein capsid structure , rendering it incapable of interacting with its cellular receptor , and thus from entering the host cell or triggering downstream signaling mechanisms . UV-exposure of virus damages its DNA , allowing receptor interaction , viral entry , and passage of viral DNA into the nucleus , but prevents subsequent transcription and viral replication . We tested the capacity of UV and heat treated virus to be internalized by host cells using Cy3-labeling of the virus and confocal microscopy , after validation of heat and UV-inactivation by real-time PCR for viral gene expression . When analyzed by real-time PCR , intact adenovirus showed robust transcription of its early gene E1A10S at 4 hours post-infection ( hpi ) in human A549 cells . In comparison , heat- and UV-inactivated adenovirus showed minimal expression at the same time-point ( Fig . 1A ) . In vivo analysis by confocal microscopy showed that Cy3-labeled heat-inactivated virus was unable to enter corneal stromal cells at 1 hpi . In contrast , intact and UV-inactivated virus were perinuclear in location at the same time-point ( Fig . 1B ) . To determine if adenoviral gene expression was essential for the development of keratitis in vivo , C57BL/6J mouse corneas were injected with virus free buffer , 105 TCID of intact HAdV-37 , or equivalent quantities of heat- or UV-inactivated HAdV-37 . Clinical examination of infected eyes showed corneal opacity developing by 1 day post-infection ( dpi ) in intact and UV-inactivated virus injected corneas ( data not shown ) . This opacity peaked at 4 dpi ( Fig . 1C ) . In contrast , buffer ( mock infected control ) and heat-inactivated virus injected corneas did not develop corneal opacity up to 4 dpi . Histopathology demonstrated corneal stromal edema and leukocyte infiltration at 4 dpi in intact and UV-inactivated virus infected corneas ( Fig . 1D ) . Buffer or heat-inactivated virus injection did not cause appreciable leukocyte infiltration . Next , we applied flow cytometry to characterize the leukocyte phenotypes in the corneal stroma of infected animals . At 4 dpi , the numbers of infiltrating cells that were Gr1+F4/80- ( polymorphonuclear neutrophils ) and Gr1+F4/80+ ( inflammatory monocytes ) [40] were similar ( p> . 05 ) in intact virus and UV-inactivated virus injected corneas . However , both groups had significantly higher number of Gr1+F4/80− and Gr1+F4/80+ cells when compared to corneas injected with either buffer or heat-inactivated virus ( p< . 05 ) ( Fig . 2A and B ) . The phenotypes and proportion of the leukocytes after injection of intact virus and UV-inactivated virus did not differ . Levels of myeloperoxidase ( MPO ) , a surrogate indicator for the extent of neutrophil infiltration , were also significantly higher at 24 hpi in intact and UV-inactivated virus injected corneas than after injection of buffer or heat-inactivated virus ( Fig . 2C ) . CXCL1 and CCL2 have been shown to be expressed in adenovirus infection and are paradigm chemokines responsible for neutrophil and monocyte chemotaxis , respectively [41] , [42] . IL-6 was shown to be expressed early in the mouse model of adenoviral pneumonia [43] . We next tested for the expression of CXCL1 , CCL2 and Interleukin-6 ( IL-6 ) at 16 hpi . Levels of all the three cytokines were elevated in intact virus and UV-inactivated virus injected corneas as compared to the values from buffer- or heat-inactivated virus injected corneas ( p< . 05 ) ( Fig . 2D–F ) . CXCL1 and CCL2 expression after infection with UV-inactivated virus was not statistically different from that with intact virus . However , intact virus induced IL-6 expression to a greater degree than UV-inactivated virus ( p< . 05 ) ( Fig . 2F ) . Tlr9 is a pathogen-associated molecular pattern receptor present in intracellular endocytic vesicles , and is activated by the presence of unmethylated CpG motifs and the phosphodiester sugar DNA backbone [17] , [18] . Tlr9 is the critical toll-like receptor for cellular recognition of nucleic acid in DNA viruses [44] , [45] , [46] . Tlr9 is also expressed in the murine cornea and has been implicated in the pathogenesis of experimental viral and bacterial keratitis [47] , [48] . Because UV-inactivated virus induced keratitis to a similar degree as that induced by intact virus ( Figs . 1 and 2 ) , the possibility remained that viral CpG motifs and DNA in UV-inactivated virus might be activating Tlr9 in corneal cells . To test this possibility , we infected wild type and Tlr9−/− corneas with HAdV-37 . Development and progression of corneal opacities in wild type and Tlr9−/− mice appeared similar at 1 ( data not shown ) and at 4 dpi ( Fig . 3A ) . Buffer injection did not result in corneal opacity in either mouse group . Histology of virus infected corneas at 4 dpi demonstrated a similar pattern of stromal infiltration by leukocytes in wild type and Tlr9−/− mice ( Fig . 3B ) . By flow cytometry at 1 dpi , both Tlr9−/− and wild type mice infected with virus showed a similar degree of infiltration with Gr1+F4/80− and Gr1+F4/80+ cells ( p> . 05 ) ( Fig . 3C ) . However , at 4 dpi , the Gr1+F4/80+ cells were significantly less in Tlr9−/− mice as compared to wild type mice ( p< . 05 ) ( Fig . 3D ) , suggesting that Tlr9 might play a role in the sustained infiltration of monocytes into the adenovirus infected cornea . The levels of CXCL1 and CCL2 protein also appeared comparable in wild type and Tlr9−/− corneas infected with adenovirus ( Fig . 3E and F ) . However , IL-6 levels were significantly less in Tlr9−/− corneas ( p< . 05 ) ( Fig . 3G ) , suggesting a role for Tlr9 in IL-6 induction by adenovirus , as seen previously [22] . Buffer injection did not cause significant upregulation of cytokines in either group of mice , and viral replication was absent in Tlr9−/− corneas ( data not shown ) , as was previously shown in corneas of wild type mice [39] . Recently , it was demonstrated that DNA in the cytoplasm of mammalian cells can initiate Tlr-independent innate immune responses [16] , [20] , [21] . In light of our results in Tlr9−/− mice , we sought to test the hypothesis that viral DNA might be stimulating inflammation by another pathway . First , to confirm our ability to deliver DNA to corneal cells , we injected enhanced green fluorescent protein ( eGFP ) expressing plasmid vector into mouse corneas . Robust expression of eGFP was seen at 1 dpi and was similar to expression of eGFP by a HAdV-5 vector ( Fig . 4A ) . At 1 dpi , transfection efficiency of plasmid , as measured by flow cytometry , was comparable to that of HAdV-5 vector ( Fig . 4B ) . To analyze the development of keratitis after transfection of adenoviral DNA , corneas were transfected with Ava1 digested HAdV-37 genomic DNA in two different concentrations , the first equal to the amount of DNA contained in 105 TCID of intact HAdV-37 ( 90 ng ) , and the second , a roughly 5 ½ -fold higher concentration ( 500 ng ) . This enzyme was chosen because it digests the DNA into small enough fragments to be efficiently taken up by transfected cells , with resulting fragments still of sufficient size to activate cytosolic DNA sensors [20] . Transfection of viral DNA did not induce corneal opacity up to 4 dpi ( Fig . 4C ) . Histopathology also did not show appreciable leukocytic infiltration in DNA transfected corneas at 4 dpi ( Fig . 4D ) . In addition , we analyzed the response to DNA transfection by flow cytometry . Infiltrating Gr1+F4/80− and Gr1+F4/80+ cells were significantly higher in intact HAdV-37 infected corneas when compared to DNA injected and mock injected corneas ( Fig . 5A ) . As previously mentioned , naked DNA has been shown to initiate expression of chemokines and cytokines [16] , [20] , [21] . Intact virus induced significant upregulation of IL-6 , CXCL1 , and CXCL2 at 16 hpi when compared to transfected DNA ( p< . 05 ) ( Fig . 5B–D , respectively ) . In contrast , CCL2 levels were significantly higher in both intact virus and transfected DNA treated corneas as compared to mock infection ( Fig . 5E ) . These data suggest that CCL2 expression in adenovirus infection may be dependent upon the presence of viral DNA , but independent of Tlr9 . Human adenovirus has been shown to induce expression of CXCL8 within minutes of infection in human cells via activation of intracellular signaling [5] , [49] , [50] , suggesting that interactions between viral capsid and host cellular receptor ( s ) may be mediating cell signaling and the downstream expression of chemokines . However , the ability of empty capsids to mediate chemokine expression has not been tested in vivo . We confirmed the purity of our empty capsid preparation by silver staining , which showed the absence of adenoviral core proteins V or VII in empty capsid preparations after polyacrylamide gel electrophoresis ( Fig . 6A ) , and by real-time PCR , which showed no genomic DNA in empty capsid ( data not shown ) . We further confirmed the competence of our empty capsid preparations by the entry of Cy3-labeled capsid into human corneal fibroblasts in vitro at 1 hpi ( Fig . 6B ) . Endotoxin was not detectable in our empty capsid preparations ( data not shown ) and cannot be responsible for the observed inflammatory response to empty capsid in vivo . Empty capsid at a protein concentration similar to 105 TCID of intact HAdV-37 did not induce any visible corneal opacity up to 4 dpi ( data not shown ) . Because empty HAdV capsids are known to be structurally unstable and therefore may be less robust ligands [29] , we also tested a more concentrated capsid preparation . When empty capsid was concentrated 5-fold and injected , corneal opacities developed by 4 dpi in all mice ( Fig . 6C ) . Similarly , histopathological examination at 4 dpi showed infiltration of leukocytes and formation of characteristic subepithelial infiltrates in the corneal stroma of mice injected with concentrated empty capsid ( Fig . 6D ) . Flow cytometry at 4 dpi demonstrated lower levels of Gr1+F4/80− and Gr1+F4/80+ cells after concentrated empty capsid injection than with intact virus ( p< . 05 ) ( Fig . 7A ) . However , leukocyte infiltration and MPO levels were significantly higher in concentrated empty capsid infection as compared to mock injected corneas ( p< . 05 ) ( Fig . 7A and B , respectively ) . Empty capsid injections also induced expression of CXCL1 and CCL2 , but not IL-6 ( Fig . 7C–E , respectively ) , suggesting differential regulation of these cytokines by capsid proteins . Most HAdV use integrins αvβ3 or αvβ5 as an entry receptor through interaction with an Arg-Gly-Asp ( RGD ) sequence in the virion penton base protein [26] . β3 integrin was recently shown critical to IL-1 signaling in the adenovirus infected mouse spleen [31] . We next utilized a 15-mer synthetic peptide encompassing an RGD motif and a control peptide with RGD replaced with the amino acids Lys-Gly-Glu ( KGE ) to study the role of corneal integrins in viral capsid induced inflammation . We first confirmed that the RGD-containing peptide prevented corneal cell adhesion to plastic tissue culture plates; adhesion was also restricted by EDTA ( data not shown ) . The KGE-containing peptide exerted no effect on cell adhesion . We co-injected RGD or KGE containing peptides with HAdV-37 or virus free buffer into the corneas of wild-type mice . Clinical examination of infected eyes showed corneal opacity at 1 dpi in KGE plus virus ( KGE+V ) injected corneas ( Fig . 8A ) , peaking at 4 dpi ( Fig . 8B ) . In contrast , neither RGD plus buffer ( RGD+M ) , KGE plus buffer ( KGE+M ) , or RGD plus virus ( RGD+V ) injected corneas developed corneal opacities ( Fig . 8B ) . Histopathology demonstrated corneal stromal edema and leukocyte infiltration with formation of subepithelial infiltrates at 4 dpi only in KGE+V infected corneas ( Fig . 8C ) . When co-injected with virus , RGD , but not KGE , also decreased expression of IL-6 , CXCL1 , CXCL2 and CCL2 ( p< . 05 ) ( Fig . 8D–G , respectively ) although RGD+V injected corneas also had more CXCL1 than controls ( p< . 05 ) ( Fig . 8E ) , suggesting that RGD inhibition of cytokine induction was incomplete . These data suggest that RGD blocks a critical step in adenovirus induced chemokine expression and subsequent leukocyte infiltration .
Pathogen-associated molecular patterns ( PAMP ) are unique molecular ligands on or within microbes that induce activation of innate immunity through specific receptors on or within target cells . Except for a recent manuscript demonstrating the importance of the RGD-β3 integrin interaction in splenic macrophages [31] , and a previous study demonstrating reduced innate immune responses in NALP3−/−mice [16] , little is known about PAMPs in adenovirus infections in vivo . The purpose of our study was to elucidate adenovirus-associated molecular patterns and their specific role in innate immune responses in a living animal , using a defined disease model [39] , [51] . Liu and Muruve [28] previously showed that adenoviral vectors activate innate immune responses in the liver independently of viral gene expression or viral replication . Similarly , in our keratitis model , we demonstrate that a UV-inactivated ( transcriptionally inactive ) adenovirus can initiate an innate immune response in the cornea . The degree of corneal opacity , cytokine expression , and cellular infiltration was similar to that induced by intact virus . These data are consistent with a murine model of adenoviral pneumonia , in which viral replication was not essential for pneumonitis [43] , and suggest that activation of specific innate immune responses by adenovirus do not require viral gene expression or viral replication . Several recent studies have shown the importance of viral CpG motifs and viral DNA in the initiation of innate immune responses against adenovirus infection in vitro . Type 1 interferon expression upon adenovirus infection of plasmacytoid dendritic cells was dependent upon the Tlr9 pathway [9] , [11] . In contrast , conventional dendritic cells were activated by adenoviruses independent of Tlr signaling [9] . Blocking Tlr9 attenuated innate immune responses after intravenous administration of helper dependent adenovirus vectors in mice [22] . In our studies , we demonstrated keratitis in Tlr9−/− mice similar to that in wild type mice . Furthermore , expression of CXCL1 and CCL2 was comparable in Tlr9−/− and wild type mice . However , we did show significantly less IL-6 expression in Tlr9−/− as compared to wild type mice . Similarly , expression of IL-6 after intravenous administration of adenovirus or adenovirus infection of bone marrow macrophages was dependent on Tlr9 and Myd88 , respectively [15] , [22] . In our model , the infiltration of Gr1+F4/80+ inflammatory monocytes at 4 dpi was also reduced in Tlr9−/− mice compared to wild type . These data suggest that viral genomic DNA may differentially simulate cytokine expression and play a role in mononuclear cell infiltration , but is not essential to the development of keratitis . The murine corneal stroma contains cells of various lineages , including macrophages and bone marrow derived antigen presenting cells , in addition to the fibroblast-like keratocytes [33] , [34] . Whether different resident corneal cells disparately produce cytokines in response to Tlr9 activation is not presently known . Plasmacytoid dendritic cells have not been demonstrated to date in the murine or human cornea , and in preliminary experiments , we were unable to show interaction of HAdV-37 with corneal stromal macrophages ( Zhu and coworkers , unpublished data ) . Cytoplasmic sensors of DNA have also been implicated in innate immune responses to DNA transfection of mammalian cells [16] , [19] , [20] , [21] . Hence , we wished to investigate the role of viral genomic DNA in adenoviral keratitis . We injected adenoviral genomic DNA in an amount equivalent to , or 5-fold greater than , that contained in 105 TCID of intact virus . Injection of either of these concentrations failed to induce clinical keratitis , but did induce expression of CCL2 and a modest mononuclear cell infiltrate . Similarly , adenoviral DNA transfected in both macrophages and lung fibroblasts induced expression of CCL2 via an interferon regulatory factor 3 mediated pathway [15] . These results again suggest differential control of cytokine expression by different PAMP receptors , possibly due to the diversity of cell types within the corneal stroma . The lack of keratitis upon viral DNA transfection was not due to differences in uptake between intact virus and naked DNA , as the efficiency of transfection and transduction was equivalent . Taken together , the experiments with Tlr9−/− mice and DNA transfections confirm that viral genomic DNA contributes to cytokine expression and infiltration of mononuclear cells , but is not sufficient to induce clinical keratitis . Because viral DNA and CpG motifs were not sufficient for the development of keratitis , we next examined viral capsid as a PAMP . We prepared HAdV-37 empty capsid , lacking the central nucleoprotein core of intact virus . Adenoviral empty capsid has been shown to be somewhat unstable [29] , and free fibers in the preparation may prevent capsid – receptor interactions and reduce binding and downstream signaling [52] . Despite these limitations , empty capsid induced clinical keratitis , chemokine expression , and infiltration of both neutrophils and monocytes . These data suggest that viral capsid is a major virus-associated molecular pattern for adenovirus keratitis . Interestingly , IL-6 expression was not induced by empty viral capsid , but was dependent on Tlr9 . In addition , the clinical keratitis and leukocyte infiltration due to empty capsid were less than that induced by intact virus . Reduced innate immune responses to capsid might be due to the unstable structure of empty viral particles and a less efficient interaction with host cell viral receptors [29] , [52] . Alternatively , the complete innate immune response to adenovirus in the cornea might require the combined effects of both viral capsid and viral DNA . We further showed that a peptide containing RGD , as in the HAdV-37 penton base [53] inhibited leukocyte infiltration associated with virus infection , whereas an otherwise identical molecule except for the RGD did not . Treatment with the RGD containing peptide also deceased cytokine expression in adenovirus infected corneas . Taken together , these data indicate that RGD sequence within the adenovirus penton base is critically important not only for internalization [54] , but also for inflammation , possibly through interaction with β3 integrin [31] . The mechanism of viral capsid interaction with the target host cell varies greatly between different tissue and cell types . For example , adenovirus vectors have been shown to bind neutrophils via Fc receptors and complement receptor 1 [55] . The transduction of liver cells by intravenously administered adenovirus vectors was facilitated by clotting factors [12] , [13] . In other and diverse cell types , adenovirus infection proceeds via an integrin-dependent mechanism [54] . Stromal cells in the cornea express αv and β3 integrins ( unpublished data , Chintakuntlawar and Chodosh ) . In human corneal epithelial cell culture , growth of cells on vitronectin , a ligand for αvβ3 , enhanced replication of HAdV-19 [56] . Our laboratory also previously demonstrated in human corneal fibroblasts infected with HAdV-19 , that CCL2 and CXCL8 expression was mediated by intracellular signaling activated by viral binding [5] , [6] . However , the exact nature of adenovirus interaction with corneal stromal cells in vivo is unknown and remains to be studied . It is important to determine the primary initiating event in the innate immune response to any pathogen . In this study , we show that viral capsid is an essential PAMP for the induction of adenovirus keratitis in the mouse model . Keratitis was not dependent upon viral gene expression , viral replication , or the presence of viral DNA . Further studies will be necessary to delineate the cell types responsible for specific responses to adenovirus in the cornea .
All animals were treated according to the Association for Research in Vision and Ophthalmology ( ARVO ) statement for the use of animals in ophthalmic and vision research and all experimental protocols were approved by the Institutional Animal Care and Use Committee at the University of Oklahoma Health Sciences Center , and the Animal Care Committee of the Massachusetts Eye and Ear Infirmary . Eight to 12-week-old wild type female C57BL/6J mice were purchased from Jackson Laboratories ( Bar Harbor , ME ) . Tlr9−/− mice on C57BL/6J background were kind gift from Dr . Paul Kincade ( Oklahoma Medical research Foundation , Oklahoma City ) and Dr . Shizuo Akira ( Osaka University , Osaka , Japan ) . Human lung carcinoma cell line A549 was obtained from American Type Culture Collection ( ATCC , Manassas , VA ) . Cells were maintained in Dulbecco's modified Eagle's medium containing 10% heat-inactivated fetal bovine serum . Human adenovirus 37 ( HAdV-37 ) was obtained from ATCC and purified by cesium chloride gradient . UV-inactivation of the virus was done by irradiating the virus on ice with UV light of wavelength 254 nm at a distance of 15 cm for 20 minutes . Heat-inactivation of the virus was done by incubating in a water bath at 56°C for 30 minutes . Empty capsids were prepared by harvesting the upper band in cesium chloride gradient purification , followed by overnight centrifugation at 38000× g on a continuous cesium chloride gradient followed by overnight dialysis against the dialysis buffer ( 10 mM Tris , 80mM NaCl , 2mM MgCl2 , and 10% glycerol ) . Empty capsids were concentrated five-fold using centrifugal filter units ( Millipore , Billerica , MA ) and concentrations were measured by bicinchoninic acid protein assay ( Pierce , Rockford , IL ) . Virus was titered in triplicate using A549 cells . Mice were anesthetized by intramuscular injection of ketamine ( 85 mg/kg ) and xylazine ( 14 mg/kg ) . Anesthetic drops ( 0 . 5% proparacaine hydrochloride , Alcon , Fort Worth , TX ) were applied topically to each eye before injections . One microliter of virus free dialysis buffer , HAdV-37 ( 105 TCID [tissue culture infective dose] ) , UV-inactivated HAdV-37 , heat-inactivated HAdV-37 , empty HAdV-37 capsid or Ava1 digested HAdV-37 DNA was injected in the center of corneal stroma with a glass micropipette needle fitted with a gas-powered microinjection system ( MDI , South Plainfield , NJ ) under an ophthalmic surgical microscope ( Carl Zeiss Meditec , Inc . , Thornwood , NY ) . At indicated time-points after injection , mice were euthanatized using CO2 inhalation and corneas were dissected and processed for further analysis . Synthetic 15-mer peptides were obtained from GenScript Corporation ( Piscataway , NJ ) and were reported to be >90% pure by the manufacturer . The sequence of wild type penton base peptide including RGD was PPKRRGDLAVLFAKV , and the negative control peptide , which had KGE in place of RGD , was PPKRKGELAVLFAKV . The peptides were dissolved in water , diluted in phosphate-buffered saline ( PBS ) , and 0 . 5 µl RGD ( 2 mM ) or KGE ( 2 mM ) containing peptide was mixed with 0 . 5 µl HAdV-37 ( 2×105 TCID ) or virus free dialysis buffer and injected in the corneal stroma of wild type mice as described above . Mouse corneas were removed at indicated time-points . Intact , UV-inactivated and heat-inactivated HAdV-37 were used to infect human A549 cells . Four hpi total RNA was isolated by single step isolation method using TRIzol ( Invitrogen , Eugene , OR ) according to the manufacturer's instructions . Following DNase treatment ( Ambion , Austin , TX ) , 2 µg of total RNA was used to synthesize cDNA using reverse transcriptase ( Superscript II , Invitrogen ) . A total of 2 µL of cDNA obtained by reverse transcription was used for amplification in a final volume of 20 µL containing 10 µL of 2× SYBR green master mixes ( Applied Biosystems [ABI] , Foster City , CA ) and 250 nM of specific forward and reverse primers . RNA concentrations of samples were normalized using quantification of GAPDH mRNA as the internal control . E1A10S primers were as follows , forward 5′ GGAGGTAGATGCCCATGATGA 3′ and reverse 5′ GTTGGCTATGTCAGCCTGAAGA 3′ . GAPDH primers were as follows , forward 5′ GACAATGAATACGGCTACAGCAACAGG 3′ and reverse 5′ GTTGGGATAGGGCCTCTCTTGCTCA 3′ . Quantitative real-time PCR amplification and analysis was performed as described previously [39] . Mouse corneas were removed at indicated time-points ( n = 3/time-point/group ) and flash frozen in liquid nitrogen . Corneas were then homogenized in 400 µL of PBS with 1 mM phenylmethylsulfonyl fluoride ( PMSF ) , 1 µg/mL aprotinin , and 10 µg/mL leupeptin ( Sigma-Aldrich , St . Louis , MO ) . The lysates were centrifuged at 10 , 000× g for 10 minutes at 4°C , and the supernatants were used for ELISA . Mouse CXCL1 ( KC ) , CXCL2 ( MIP-2 ) , CCL2 ( MCP-1 ) , IL-6 ( all from R&D Systems , Minneapolis , MN ) and myeloperoxidase ( Cell Sciences , Canton , OH ) protein detection was performed with commercially available sandwich ELISA kits with capture and detection antibodies , according to the manufacturer's instructions . Each sample and standard was analyzed in duplicate . The plates were read on a microplate reader ( Molecular Devices , Sunnyvale , CA ) and analyzed ( SOFTmax software; Molecular Devices ) . Injected mouse corneas were removed , rinsed in PBS , and fixed with 10% neutral buffered formalin for 24 hours at room temperature . After paraffin embedding , whole eyes were cut into 5-µm-thick sections , mounted on positively charged slides and air dried overnight . After deparaffinization and rehydration , slides were stained with hematoxylin and eosin . Slides were coverslipped using a synthetic resin , and photographed ( Axiovert 135; Carl Zeiss Meditec , Inc . ) , using a 40× objective . Intact HAdV-37 , and empty capsids were conjugated with Cy3 dye ( GE Healthcare , Piscataway , NJ ) as per Leopold and co-workers [57] . One milligram of Cy3 dye was reconstituted in 1 mL of 0 . 1 M sodium bicarbonate ( pH 9 . 3 ) . Labeling was performed by conjugating Cy3 dye to virus at a concentration approximately equal to 1012 Ad particles/mL , where reconstituted Cy3 dye was 20% of the final solution . The mixture was allowed to incubate for 30 minutes in the dark with gentle mixing every 10 minutes , followed by overnight dialysis to remove the excess Cy3 dye . Adenoviral DNA was isolated from purified HAdV-37 by phenol chloroform extraction . Adenoviral DNA was further digested with restriction enzyme Ava1 ( Promega , Madison , WI ) and purified by phenol chloroform extraction , followed by ethanol precipitation . After careful washing , DNA was suspended in nuclease-free water and stored at −20°C . In vivo transfection of the mouse corneal stroma was done according to the method described by Mohan et al . [58] . DNA was mixed with 100 nm of DOPE ( dioleoyl phosphatidyl ethanol amine ) and 100 nm of DDAB ( dimethyl dioctadecyl ammonium bromide ) cationic lipids in phosphate buffered saline and incubated on ice for 1 hour before injections . One microliter of mixture containing 90 or 500 ng of adenoviral genomic DNA or transfection reagent alone was injected in the mouse cornea . Mice were euthanized at indicated time-points and corneas dissected for further analysis . Similarly , to measure the transfection efficiency of DNA injection , a plasmid vector ( EGFP-C1 , Clontech , Mountain View , CA ) or HAdV-5 vector expressing eGFP were injected as described above . The mice corneas were dissected at 1 day post-injection for confocal microscopy and flow cytometry . Cells and whole corneas ( cut radially to flatten them ) were fixed with 4% paraformaldehyde for 30 minutes at 25°C , and coverslipped using mounting medium containing DAPI ( 4 , 6-diamidino-2-phenylindole; Vectashield; Vector Laboratories , Burlingame , CA ) . Samples were scanned with confocal laser scanning microscope ( IX81-FV500; Olympus , Melville , NY ) . Whole corneas were scanned in the z-axis with a step size of 1–2 µm . The microscope system software ( FluoView; Olympus ) was used for analysis . Corneas were dissected from mouse eyes at the indicated time-points following infection . The corneas were cut into small ( 1–2 mm segments ) pieces and digested with 1 mg/ml collagenase type I ( Sigma Chemical Co . , St . Louis , MO ) for 2 hours triturating the sample every 15 minutes . Single cell suspensions were washed twice ( 300× g , 5 min/wash ) in PBS and then incubated on ice for 15 min with 2 µl anti-mouse Fc block ( BD Pharmingen , San Diego , CA ) in a total volume of 100 µl PBS-1% BSA . Following the incubation , the cells were centrifuged ( 300× g , 5 min ) and resuspended in 5% normal rat serum ( Jackson Immuno Research Inc . , West Grove , PA ) for an additional 15 min on ice . Cells were then triple labeled with 6 µl containing 2 µl FITC-conjugated anti-mouse F480 ( CI:A-3 ) , 2 µl phycoerythrin-Cy5-conjugated anti-CD45 ( clone 30-F11 ) , and 2 µl PE-conjugated anti-mouse Gr1 ( RB68C5 ) and incubated in the dark on ice for 30 min . Following the incubation period , the cells were washed 3 times with PBS-1% BSA ( 300× g , 5 min/wash ) and resuspended in PBS containing 1% paraformaldehyde . After overnight fixation at 4°C in the dark , cells were pelleted ( 300× g , 5 min/wash ) and resuspended in PBS-1% BSA . Immediately before analysis , CountBright absolute counting beads ( Invitrogen ) were added ( 21600 beads/sample ) . Cell suspensions were gated on CD45high labeled cells , and the percentage of each cell type were determined at this gate setting . A second gate was established to count the number of beads that passed through during the run ( 300 sec ) . The absolute number of cells per cornea were determined by calculating the number of beads counted in 300 seconds/21600× number of cells in the CD45high-gated sample . For in vivo DNA transfection efficiency experiments corneas were dissected at 1 day pi and incubated in 10 mM EDTA in phosphate buffered saline for 30 minutes at 37°C . Epithelial sheet was stripped from underlying stroma using smooth forceps . Corneal stroma was digested and washed as described above and stained with propidium iodide to exclude dead cells . GFP positive cells were counted as percentage of total cells to measure the efficiency of transfection . Real-time PCR , ELISA and flow cytometry experiments were each performed at least three times . Mean of observations from three experiments were compared by ANOVA with the Scheffé multiple comparison test using statistical analysis software ( SAS institute Inc . Cary , NC ) . Statistical significance was set at α = . 05 .
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Adenoviruses are nonenveloped DNA viruses that infect mucosal tissues , causing a wide array of diseases . Adenovirus infection of the cornea induces inflammation in the form of multifocal leukocytic infiltrates . Although studied extensively in tissue culture models , how adenoviruses induce inflammation in the living host is not well characterized in the cornea or elsewhere . Using a unique mouse model , we studied the role of viral components in the cornea , to determine which viral part ( s ) induce an innate immune response . We found that neither viral DNA or viral gene expression was necessary for the development of inflammation . In contrast , viral capsid , the protein coat of the virus , induced inflammation similar to intact virus . Mice lacking the toll-like receptor 9 ( Tlr9 ) molecule , which acts as a pathogen DNA-sensing molecule within the cell , developed clinical inflammation upon adenovirus infection similar to wild type mice . Virus associated inflammation in the mouse cornea could be blocked by treatment with a peptide containing components of the adenoviral capsid . Adenovirus infection of the cornea induces inflammation principally through contact between the viral capsid and the host cell . Our study provides new insights into how the innate immune system in the eye responds to a clinically important viral pathogen .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"virology/host",
"antiviral",
"responses",
"virology/animal",
"models",
"of",
"infection",
"virology"
] |
2010
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Viral Capsid Is a Pathogen-Associated Molecular Pattern in Adenovirus Keratitis
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The most common cause of the neurodegenerative diseases amyotrophic lateral sclerosis and frontotemporal dementia is a hexanucleotide repeat expansion in C9orf72 . Here we report a study of the C9orf72 protein by examining the consequences of loss of C9orf72 functions . Deletion of one or both alleles of the C9orf72 gene in mice causes age-dependent lethality phenotypes . We demonstrate that C9orf72 regulates nutrient sensing as the loss of C9orf72 decreases phosphorylation of the mTOR substrate S6K1 . The transcription factor EB ( TFEB ) , a master regulator of lysosomal and autophagy genes , which is negatively regulated by mTOR , is substantially up-regulated in C9orf72 loss-of-function animal and cellular models . Consistent with reduced mTOR activity and increased TFEB levels , loss of C9orf72 enhances autophagic flux , suggesting that C9orf72 is a negative regulator of autophagy . We identified a protein complex consisting of C9orf72 and SMCR8 , both of which are homologous to DENN-like proteins . The depletion of C9orf72 or SMCR8 leads to significant down-regulation of each other’s protein level . Loss of SMCR8 alters mTOR signaling and autophagy . These results demonstrate that the C9orf72-SMCR8 protein complex functions in the regulation of metabolism and provide evidence that loss of C9orf72 function may contribute to the pathogenesis of relevant diseases .
Amyotrophic lateral sclerosis ( ALS ) is a fatal neurodegenerative disease characterized by the progressive degeneration of motor neurons . Frontotemporal dementia ( FTD ) is the second most common type of dementia in people younger than 65 and is characterized by degeneration of the frontal and temporal lobes of the brain . A hexanucleotide repeat expansion ( HRE ) , ( GGGGCC ) n , in the promoter or intron of the uncharacterized gene , chromosome 9 open reading frame 72 ( C9orf72 ) , has been found to be the most common cause of both ALS and FTD [1 , 2] and has been linked to a number of other neurological disorders . How the C9orf72 HRE leads to neurodegeneration remains to be determined , although both gain-of-toxicity and loss-of-function mechanisms have been proposed . The gain-of-toxicity mechanisms involve both RNA and protein products generated from the expanded hexanucleotide repeats . For example , RNAs containing the expanded repeats can interfere with the functions of specific RNA-binding proteins [3–5] , and toxic repeat polypeptides can be generated through repeat-associated non-ATG-dependent translation [6–10] . However , the HRE could be pathogenic through loss-of-function mechanisms when the expression of the C9orf72 gene is disrupted . Multiple studies have demonstrated that C9orf72 RNA and protein levels are reduced in patient cells and brains [11–15] . Although partial knockdown of C9orf72 in the brain or its neural-specific deletion does not affect survival in mice [16 , 17] , loss of C9orf72 orthologs in zebrafish and C . elegans has deleterious effects [18 , 19] . Studies of these loss-of-function mechanisms are hampered by a lack of knowledge about the physiological function of the C9orf72 protein . Bioinformatic analysis suggested that C9orf72 is a DENN-like protein [20 , 21] , which is a family of proteins that regulate small GTPases and membrane trafficking . DENN domain-containing proteins have also been implicated in autophagy and in the mammalian target of rapamycin ( mTOR ) signaling pathways [22] . Although a recent study has reported that C9orf72 regulates autophagy and endosomal trafficking [23] , the function of the C9orf72 protein remains largely unknown . Here we report the findings in mice and human cells that loss of C9orf72 inhibits mTOR signaling and leads to a profound upregulation of transcription factor EB ( TFEB ) and enhanced autophagy flux . We further show that C9orf72 interacts with another DENN-like protein Smith-Magenis syndrome chromosome region candidate 8 ( SMCR8 ) , which also regulates mTOR signaling and autophagy . The results suggest that a deficiency in the function of C9orf72 may contribute to the pathogenesis of relevant neurodegenerative diseases .
To study the physiological functions of C9orf72 in mammals , we generated a knockout ( KO ) mouse model lacking the protein . Human C9orf72 has one orthologous gene in the mouse , 3110043O21Rik , which is located on chromosome 4 . For convenience , we refer to the mouse gene as C9orf72 hereafter . The mouse C9orf72 gene is predicted to produce seven transcripts , three of which are protein-coding , as compared to the human C9orf72 gene , which produces three transcripts and two protein isoforms . The mouse C9orf72 proteins share 98% identity with their human C9orf72 counterparts ( S1 Fig ) . We generated C9orf72 KO mice by using a mouse embryonic stem ( ES ) cell line that contains a heterozygous allele of a 7754 base pair deletion in the C9orf72 gene . This deletion results in the removal of exons 2–6 and is predicted to produce nonfunctional truncated protein products from all three protein-coding transcripts of the mouse C9orf72 gene ( Fig 1A ) . We further removed the neomycin cassette by crossing the C9orf72 KO male mice carrying the original targeted allele with SOX2-Cre transgenic females ( Fig 1A ) . Western blotting of brain homogenates from C9orf72 wild-type and KO littermates , using an antibody predicted to detect all mouse C9orf72 isoforms , showed a protein band at 55 kDa ( corresponding to mouse isoform 1 ) , not present in the C9orf72-/- samples ( Fig 1B ) , confirming that our KO mice lack C9orf72 in brain . We were unable to detect the other two mouse C9orf72 isoforms , suggesting that mouse isoform 1 is the major isoform in the mouse brain . The homozygous C9orf72 KO mice showed a decrease in survival compared with littermates , with more than 50% dead in 600 days ( Fig 1C ) . This decrease in survival was also observed in heterozygous C9orf72+/- animals to a lesser degree with only about 20% dead in 600 days . Both C9orf72 homozygous and heterozygous knockout mice developed normally before exhibiting rapidly progressive lethargy before death . The stage of lethargy could last for days up to a month . At the end stage , the animals showed a lack of excitability or response to external stimuli ( S1 Movie ) . In post-mortem examination , consistent with recent reports of immune dysregulation in C9orf72 knockout mice [24–27] , we observed splenomegaly in the C9orf72-/- mice . The spleen was generally increased in length from ~3/4 inches to 1–1 . 25 inches . In addition , we frequently observed potential tumors in the thymus or in the regions of the abdomen . There was no obvious neuronal cell death in brain or spinal cord , but functional deficits of the nervous system could not be excluded . The exact cause of death for these C9orf72 knockout mice remains to be determined . Although we observed no obvious neuronal defects in C9orf72 KO mice , it is possible that C9orf72 has functions in the nervous system in response to stresses . Thus , we asked if mTOR signaling is altered when C9orf72 is absent , since mTOR signaling is a central signaling pathway that senses the stresses related to nutrient availability , oxygen , and energy levels [28] . Also , DENN-like proteins have been implicated in mTOR signaling and nutrient sensing [29–31] and C9orf72 contains DENN domains . We monitored mTOR activity by assessing the phosphorylation of its downstream target ribosomal protein S6 kinase B1 ( S6K1 ) . Cells were starved for amino acids for 50 minutes before amino acids were added back to induce the phosphorylation of S6K1 . Interestingly , knockdown of C9orf72 in HEK293T cells resulted in a decrease in the phosphorylation of S6K1 within 10 to 20 minutes after addition of amino acids , as compared with control cells transfected with scrambled control shRNAs ( Fig 2A and 2B ) . These results suggest that the loss of C9orf72 decreases mTOR activation after amino acid stimulation . To study the molecular defect in the complete absence of C9orf72 protein , we generated mouse embryonic fibroblasts ( MEFs ) from C9orf72 wild-type and KO littermates . And we assessed the phosphorylation of S6K1 in the C9orf72-/- MEF lines . Phosphorylation of S6K1 was decreased in C9orf72-/- MEF lines compared with lines derived from wild-type littermates ( Fig 2C ) , suggesting that mTOR activation after amino acid stimulation is diminished in the absence of C9orf72 . Subsequently , we asked whether the observed reduction of mTOR activation in the absence of C9orf72 impacts the function of TFEB , a transcription factor that is a master regulator of lysosome biogenesis and autophagy-related genes , and a substrate of mTOR [32] . In an autoregulatory loop , nuclear translocation of TFEB leads to increased expression of itself . Phosphorylation of TFEB by mTOR prevents its translocation to the nucleus and causes down-regulation of TFEB . We transfected GFP-TFEB into HEK293T cells and observed that knockdown of C9orf72 resulted in a significant increase in GFP-TFEB levels ( Fig 3A and 3B ) , consistent with the decrease in mTOR activity . Moreover , imaging analysis indicated that nuclear localization of GFP-TFEB was significantly increased upon knockdown of C9orf72 as compared with cells treated with control shRNAs ( Fig 3C and 3D ) . Western blotting of the nuclear and cytoplasmic fractions further confirmed that GFP-TFEB was enriched in the nucleus upon knockdown of C9orf72 ( Fig 3E ) . Next , we validated these results in C9orf72-/- and wild-type MEF cells . Consistently , the complete absence of C9orf72 led to a significant increase in the nucleus to cytoplasm ratio of GFP-TFEB signals ( Fig 3H and 3F ) . Furthermore , consistent with the notion that TFEB promotes the biogenesis and activity of lysosomes [32] , we observed a significant increase in the number of LysoTracker-stained acidic vesicles in the C9orf72-/- MEF cells , confirming functional consequences on lysosomes of enhanced nuclear TFEB ( Fig 3H and 3G ) . We then questioned whether our results held true in vivo . Analysis of brain homogenates by western blotting from all examined C9orf72 KO mice showed a dramatic increase in endogenous TFEB levels compared with wild-type controls ( Fig 3I ) , consistent with our results in tissue culture . We next asked whether downstream targets of TFEB were also increased by loss of C9orf72 . Indeed , western blot analysis of lysosome-associated membrane glycoprotein 1 ( LAMP1 ) , which is a transcriptional target of TFEB [33] , indicated that LAMP1 was profoundly increased in the C9orf72 KO mouse brains ( Fig 3I ) . Another related lysosomal protein LAMP2 was also markedly increased in the absence of C9orf72 in the KO mouse brains . Taken together , these results suggest that , consistent with the inhibition of mTOR signaling , loss of C9orf72 increases TFEB activity . Since we observed a function of C9orf72 in mTOR signaling and mTOR is known to negatively regulate autophagy , we assessed the levels of the autophagy marker LC3 by immunoblotting in these cells . During autophagy , LC3I is processed to LC3II via lipidation , which allows for insertion of the LC3 protein into the autophagosome membrane . Our results show a significant increase of LC3I in C9orf72-/- MEFs when compared with wild-type MEFs , indicating that basal autophagy is altered in these cells ( S2A and S2B Fig ) . To test the role of C9orf72 in neurally differentiated cells , we generated embryonic stem cells from C9orf72 KO mice and littermate controls and differentiated them into the motor neuron precursors that further grew into mature motor neurons ( ~40% of the culture ) plus astrocytes and oligodendrocytes . We assessed the level of LC3 by immunoblotting and found that the C9orf72-/- cells enriched with motor neurons showed a substantial accumulation in LC3I ( S2C Fig ) , in line with what was observed in C9orf72-/- MEFs . The decreases in LC3II/LC3I ratio observed in our western blots can indicate a defect in lipidation or an increase in degradation via the lysosome . To distinguish between these two possibilities , we assessed LC3 levels after nutrient deprivation-induced autophagy in the absence and presence of the lysosomal inhibitor Bafilomycin in C9orf72-/- and wild-type MEF cells . We found that the Bafilomycin-induced accumulation of LC3II was significantly enhanced in C9orf72-/- MEFs compared with wild-type MEFs ( Fig 4A and 4B ) , indicative of an enhanced autophagic flux in C9orf72-depleted cells . To further examine the status of autophagic flux , we analyzed the numbers of LC3-positive autophagic vesicles and the colocalization between LC3 vesicles and Rab7 , a late endosome-/lysosome-associated GTPase that marks mature autophagolysosomes [34–36] ( Fig 4C–4F ) . Quantification of LC3-positive vesicles in the absence and presence of Bafilomycin , demonstrates that , consistent with western blot results , the number of LC3-positive autophagic vesicles was significantly increased in C9orf72-/- MEFs ( Fig 4D ) . An autophagic flux index , defined as the difference in the volumes of LC3-positive vesicles before and after Bafilomycin treatment , was quantified , further confirming the increased autophagic flux capacity in C9orf72-/- MEFs ( Fig 4E ) . Similarly , we observed an increase in the colocalization of LC3-positive autophagic vesicles with Rab7-positive vesicles ( Fig 4F ) , confirming enhanced autophagolysosome formation . In addition to induced autophagy , we also examined basal autophagic flux under fully supplemented nutrient conditions in the absence of C9orf72 ( S3 Fig ) . Despite of relatively low level of signals , the LC3/Rab7 vesicle colocalization assay indicated a trend that there were more LC3-positive vesicles and more colocalized LC3/Rab7 vesicles in Bafilomycin-treated C9orf72-/- MEFs than in wild-type control cells ( S3A Fig ) . This result is consistent with the western analysis of LC3 protein levels , in which LC3II accumulated robustly in Bafilomycin-treated C9orf72-/- MEFs ( S3B Fig ) . In addition to MEF cells , we observed a similar result in HEK293T cells for autophagic flux after knockdown of C9orf72 . Under nutrient deprivation , in cells treated with C9orf72 shRNA , despite a decrease in LC3II/LC3I ratio before Bafilomycin treatment , lysosomal inhibition induced a robust accumulation of LC3II ( S3C Fig ) , suggesting that the total autophagic flux was increased . Taken together , the observed increases in autophagic activity are consistent with the impairment of mTOR signaling and the profound increase of TFEB as results of loss of C9orf72 . Of note , our results do not rule out the possibility that C9orf72 functions in other aspects of autophagy . For example , we examined the activity of ATG4B , which catalyzes the cleavage of proLC3 to produce LC3I and also removes LC3II from the autophagosome membrane after it fuses with the lysosome ( S4A Fig ) . Knockdown of C9orf72 in HEK293T cells resulted in a significant decrease in the signal of an ATG4B activity luciferase reporter when compared with control cells ( S4B Fig ) , suggesting that ATG4B activity is impaired under basal conditions . However , no change was detected in ATG4B protein levels by western blotting upon knockdown of C9orf72 ( S4C Fig and S4D Fig ) , indicating that the reduction in ATG4B activity was not due to a decrease in its protein level . The unchanged level of ATG4B protein could presumably make it readily available to support the enhanced autophagic flux observed in nutrient deprivation-treated C9orf72 deficient cells . We next investigated whether the absence of C9orf72 alters the markers of autophagy in vivo . Since mTOR signaling senses nutrient stresses and autophagy induction is a natural response to nutrient stresses through mTOR , we asked whether the absence of C9orf72 affects the autophagic response under these stress conditions . We applied amino acid withdrawal by feeding mice a low-protein diet that is well-tolerated in young animals [37] . Beginning at 4 months of age , gender-matched wild-type and C9orf72 KO littermates were fed either normal or amino acid-deficient chow for four weeks before tissues were harvested for analysis ( Fig 5A ) . We first examined autophagy in the brain of C9orf72 KO mice . Because of the low levels of LC3 conversion during starvation in the brain [38] , we assessed the levels of the autophagy marker protein p62 . Western blotting of brain homogenates showed a slight decrease in p62 levels in C9orf72 KO mice when compared with wild-type littermates , a defect that became more pronounced when the mice were on the low-protein diet ( Fig 5C and 5D ) . The decrease of p62 was not due to change in its solubility since no insoluble p62 was detected in western analysis or histological examinations ( Fig 5B ) . The lack of accumulation of p62 in the brains of C9orf72 KO mice suggests an increased autophagy activity . Consistent with the results in the mouse brain , we also observed a decrease in p62 levels in C9orf72-/- MEF lines compared with wild-type cells ( S5A Fig and S5B Fig ) . We next examined the liver , a common tissue type used to study autophagy , harvested from the wild-type and C9orf72 KO littermates , for changes in LC3 . We observed a decrease in the level of LC3II protein or relative increase of LC3I protein in C9orf72-/- livers relative to the wild-type controls under the low protein diet condition ( S5C Fig and S5D Fig ) . To gain molecular insight into the function of C9orf72 , we performed a quantitative proteomic screen for protein interactors of the C9orf72 protein using stable isotope labeling by amino acids in cell culture ( SILAC ) mass spectrometry ( Fig 6A ) . Human C9orf72 Isoform A with a C-terminal Flag tag was expressed in HEK293T cells metabolically labeled with 13C , 15N L-Arginine and L-Lysine and immunoprecipitated using Flag-tag beads . A parallel immunoprecipitation was performed using unlabeled mock-transfected cells as a control to identify proteins that bound to the Flag-tagged beads alone . The resulting immunoprecipitates were pooled and analyzed via mass spectrometry to identify proteins that were enriched by the C9orf72 bait . We identified SMCR8 as the top C9orf72 interactor since it had the highest SILAC ratio or enrichment ( S6A and S6B Fig and S1 Table ) . Notably , SMCR8 , although uncharacterized , is also a DENN-like protein [20 , 21] . We validated this interaction by co-immunoprecipitation , with Flag-tagged C9orf72 pulling down endogenous SMCR8 in HEK293T cells ( Fig 6B ) . Conversely , reciprocal immunoprecipitation experiments demonstrated that an anti-SMCR8 antibody pulled down Flag-tagged C9orf72 , confirming their interaction ( Fig 6C ) . The interaction was further validated by co-immunoprecipitation of co-expressed Flag-SMCR8 and C9orf72-V5 proteins ( S6C Fig ) . Consistently , GFP-tagged C9orf72 and mCherry-tagged SMCR8 both localized to the nucleus and the cytoplasm in HEK293T cells ( S6D Fig ) . Since we identified SMCR8 as the most abundant protein interactor of C9orf72 , we asked whether C9orf72 influences the level of SMCR8 protein . While examining the brain lysates from the C9orf72 KO mice , we observed a dramatic reduction in the level of SMCR8 protein . Although present in wild-type brains , SMCR8 was not detected in C9orf72-/- brain homogenates by western blotting ( Fig 6D ) . Examination of SMCR8 transcripts by qPCR showed no reduction in its mRNA levels , supporting that C9orf72 influences SMCR8 at the protein level ( S7A Fig ) . Notably , WD repeat-containing protein 41 ( WDR41 ) , another protein identified in our proteomic screen ( S1 Table ) and recently confirmed to be an interactor of the C9orf72/SMCR8 complex [28 , 29] , was not decreased in C9orf72-/- brain samples ( S7C Fig ) . In addition , overexpression of C9orf72 in HEK293T cells increases SMCR8 , suggesting that C9orf72 regulates SMCR8 protein levels ( Fig 6F and 6H ) . To further study the function of SMCR8 , we obtained a CRISPR/Cas-9 generated SMCR8 KO cell line . This cell line contains a frameshift mutation in the first exon of SMCR8 resulting in the loss of the full-length protein product ( S8A–S8C Fig ) . Since we observed that C9orf72 regulates SMCR8 protein levels , we asked whether SMCR8 reciprocally influences the levels of C9orf72 . By examining the lysates from the SMCR8 KO cells by western blotting , we observed a dramatic reduction in the level of C9orf72 protein ( Fig 6E ) . We observed the same effect on the C9orf72 protein when we treated HEK293T cells with validated SMCR8 shRNA compared with control cells transfected with a scrambled shRNA control ( S8D and S8E Fig ) . Examination of C9orf72 transcripts in SMCR8 KO cells by qPCR showed an increase in its mRNA levels ( S7B Fig ) , suggesting that the loss of SMCR8 decreased the C9orf72 protein level not by reducing its RNAs . Next we studied how SMCR8 regulates C9ORF72 protein levels . We first asked whether the regulation occurs due to changes in protein stability or turnover . Since the level of C9ORF72 was too low to allow for chase experiments to probe their turnover in the SMCR8 KO cells , we overexpressed C-terminal-V5 tagged C9orf72 and N-terminal-mCherry tagged SMCR8 , or an mCherry only control , into HEK293T cells and studied their protein levels . Compared with the mCherry control , the expression of mCherry-SMCR8 substantially increased the level of C9orf72-V5 ( Fig 6G ) . Importantly , under a 12-hr chase condition after treatment of the cells with the translation inhibitor cycloheximide , mCherry-SMCR8 dramatically stabilized the co-expressed C9orf72-V5 as compared with the mCherry control ( Fig 6G ) . We also confirmed that the C9orf72-V5 protein was degraded through both proteosomal and lysosomal pathways , since inhibition of proteasomal degradation by MG132 treatment or inhibition of lysosomal degradation by Bafilomycin treatment stabilized the C9orf72-V5 protein ( S7E Fig ) . These data indicate that C9orf72 and SMCR8 form a stable cognate protein complex that protects C9orf72 from degradation . Given the connections between C9orf72 and SMCR8 , we asked whether loss of SMCR8 plays a role in mTOR signaling similar to that of C9orf72 . In accordance with the results from C9orf72-/- MEF cells , knockout of SMCR8 led to a similar defect . In SMCR8 KO HAP1 cells , the phosphorylation of S6K1 after amino acid treatment was significantly decreased when compared with control cells ( Fig 7A and 7B ) . Next , we investigated if loss of SMCR8 also affected autophagy . First , we examined LC3 levels after shRNA-mediated knockdown of SMCR8 in HEK293T cells by immunoblotting . As observed in C9orf72-/- MEFs and C9orf72 shRNA treated HEK293T cells , knockdown of SMCR8 led to a decrease in the ratio of LC3II to LC3I , when compared with cells treated with scrambled shRNA ( Fig 7C and 7D ) . Additionally , Bafilomycin treatment of the cells under starvation showed a similar accumulation of LC3II with the SMCR8 knockdown as that of the control cells ( Fig 7E ) . Thus , the autophagic flux appears to be intact in the absence of SMCR8 in this cell line .
In the present study , we have identified a function of C9orf72 in regulating mTOR signaling and autophagy . Loss of C9orf72 leads to deficiency in the phosphorylation of S6K1 and increase of TFEB protein levels and nuclear activity , demonstrating a regulatory role of C9orf72 in the mTOR signaling pathway upstream of autophagy . We identified the major interacting partner of C9orf72 protein as SMCR8 . The most structurally homologous proteins to SMCR8 and C9orf72 in the human proteome are folliculin ( FLCN ) and folliculin-interacting proteins ( FNIP1 or 2 ) , respectively [20 , 21] . Like SMCR8 and C9orf72 , FNIP and FLCN are DENN domain-containing proteins [20 , 21] that interact with each other in a protein complex [29] , that have also been shown to regulate autophagy and mTOR signaling [30 , 31] . Since the FNIP and FLCN complex was shown to function as either GAP or GEF for the Rag GTPases in the mTORC1 pathway , we speculate that the C9orf72-SMCR8 complex may function in a similar fashion in autophagy and mTOR signaling . Our results demonstrate that loss of C9orf72 can alter the dynamics of autophagy . We observed a relative increase in LC3I levels upon loss of C9orf72 ( S2 Fig ) , in consistence with a recent report for LC3 levels in C9orf72 KO mouse liver and spleen tissues [39] , which we interpret as an increase in autophagosome turnover instead of a decrease in LC3II formation . In support of this model , we did not observe a decrease in LC3II levels after Bafilomycin treatment under full nutrient conditions , suggesting that the formation of LC3II is intact ( S3 Fig ) . Moreover , we observed increased autophagic flux in response to nutrient deprivation in C9orf72-/- cells ( Fig 4 ) . Consistent with our model of increased autophagic flux , we observed a loss of mTOR activity after loss of C9orf72 , which is classically associated with increases in the autophagic pathway . In support of our finding , a recent study showed decreased mTOR signaling in C9orf72-depleted HeLa cells [40] . Importantly , we observed a substantial increase of TFEB and its lysosomal targets in C9orf72 knockout mice ( Fig 3 ) . As a master regulator of lysosome biogenesis , TFEB is known to promote cellular lysosomal capacity and autophagy [32] . Consistent with our findings , we also observed a decrease in levels of the autophagy receptor p62 in brain tissues from C9orf72 KO mice and observed a similar decrease in the C9orf72 KO MEFs . Interestingly , it was recently reported that loss of the SMCR8 homologue folliculin similarly results in decreased mTOR signaling and a TFEB-mediated enhancement of the lysosomal compartment [31] . There have been recent reports describing C9orf72’s functions in autophagy [39 , 41–43] , including a decrease in autophagy initiation as a result of knockdown of C9orf72 [41 , 42] . These observations are not necessarily mutually exclusive to our present study . C9orf72 might play a multifunctional role in different steps of the autophagic pathways . While C9orf72 may influence the function of the FIP200/ULK1 autophagy initiation complex [41 , 42] , it could also regulate mTOR signaling and TFEB and thus promote autophagic flux , as observed in the present study . Furthermore , the manifestation of the phenotypes could be influenced by the dynamic nature and condition-dependent activity levels of autophagy pathways . Due to the reduced state of mTOR signaling in C9orf72-depleted cells , the increased autophagic flux of these cells could be more readily revealed under nutrient deprivation conditions , as employed in the present study . Notably , the autophagy receptor p62 is both a substrate of autophagy and a transcriptional target of TFEB [44] , therefore it is subject to opposing regulation by upregulation of TFEB . Taken together , our study provides evidence that long-term loss of C9orf72 leads to physiological changes that are characterized by reduced mTOR activity , in consistence with increased TFEB signaling leading to enhanced cellular lysosomal capacity and autophagic flux . Since multiple studies have reported that the hexanucleotide repeat expansion led to reduced expression of C9orf72 mRNAs and proteins in patient cells and brains [11–15] , the defects associated with loss of C9orf72 protein function could contribute to the pathogenesis of relevant neurodegenerative diseases . Several studies have reported that neither mice lacking C9orf72 protein nor those expressing the human C9orf72 gene containing the HRE mutation exhibited major neuronal loss [17 , 45–47] , with the exception of one study reporting neurodegeneration in transgenic mice expressing HRE-containing C9orf72 [48] . Our observation that C9orf72 ablation changes LC3 levels in motor neuron cultures suggests that loss of C9orf72 might affect neuronal functions . Autophagy and nutrient sensing are essential for neuronal health and their alteration is an increasingly recognized feature in aging-related neurodegenerative diseases [49 , 50] . Of note , several autophagy-related genes , including p62 , optineurin , and TBK1 , have been linked to ALS [51–53] . Proteinaceous inclusions positive for p62 are a pathologic feature in brains from patients carrying the C9orf72 HRE mutation [54] . Taken together , our findings suggest that C9orf72 protein has a function in the metabolic processes of the cell and reduction in its function may contribute to related age-dependent neurodegenerative diseases .
The animal protocol ( MO15M165 ) was approved by the Johns Hopkins Animal Care and Use Committee following the National Research Council’s guide to the Care and Use of Laboratory Animals . C9orf72 cDNA ( HsCD00398737 ) was obtained from Arizona State University and SMCR8 cDNA ( HsCD00347993 ) from Harvard Plasmid Repositories . The C9orf72 constructs were generated using the Gateway cloning system ( ThermoFisher , Waltham , MA ) with a C-terminal 3xFlag or V5 tag . The SMCR8 constructs were generated with an N-terminal Flag or mCherry tag using Gateway or classical cloning methods , respectively . All shRNAs were cloned into the pRFP-C-RS vector ( Origene ) , which was modified to remove the RFP coding sequence via digestion with MluI and BglII followed by blunting and religation . The following shRNA sequences were used: 5’ctgtgttacctcctgaccagtcagattga 3’ ( SMCR8 ) ; 5’cttccacagacagaacttagtttctacct 3’ ( C9orf72 ) . The autophagy luciferase assay plasmids were kindly provided by Brian Seed ( Harvard ) and the normalization plasmid pCMV-SEAP was from Addgene ( 24595 , Alan Cochrane , University of Toronto ) . GFP-TFEB was obtained from Addgene ( 38119 , Shawn Ferguson , Yale University ) . GFP-TFEB used for MEF experiments was described before [55] . For GFP-LC3 , human LC3 was cloned into pEGFP-C1 . RFP-Rab7 was generated from EGFP-Rab7 ( a kind gift from Bo van Deurs at University of Copenhagen ) by exchanging EGFP into RFP . Mouse ES cell lines containing a heterozygous allele of 3110043O21Riktm1 . 1 ( KOMP ) Mbp were obtained from the KOMP repository . The ES cells with a strain background of C57BL/6N-Atm1Brd were microinjected into blastocysts , and the germline-transmitted allele was maintained on the C57BL/6 background . Male mice bearing the original targeting allele were crossed with SOX2-Cre recombinase transgenic female mice ( Jackson Laboratory , 008454 ) to remove the LoxP-flanked neomycin selection cassette . The resulting allele was bred to heterozygotes and homozygotes that were used in this study . The genotyping primers were the following: gaatggagatcggagcacttatgg ( wild-type , forward ) , gccttagtaactaagcttgctgccc ( wild-type , reverse ) , gcacaagctatgttcatttgg ( KO , forward ) , gactaacagaagaacccgttgtg ( KO , reverse ) . For the low-protein diet assay , 16 week old , gender-matched littermates were fed a low-protein diet ( Test Diet 5767 , 5% protein ) or standard chow for 4 weeks prior to tissue collection . Mouse tissue was lysed in modified RIPA buffer ( 50 mM Tris pH 6 . 8 , 150 mM NaCl , 0 . 5% SDS , 0 . 5% Sarkosyl , 0 . 5% NP40 , 20 mM EDTA , Roche protease inhibitors ) using a Dounce homogenizer , sonicated , and used for further analysis . For the survival analysis , Kaplan-Meyer curves were generated using GraphPad Prism software . All cells were maintained in DMEM supplemented with 10% FBS unless otherwise noted . The SMCR8 knockout HAP1 cells ( HZGHC003606c011 ) were created at Horizon Genomics ( Vienna , Austria ) by using CRISPR/Cas9 and maintained in IMDM supplemented with 10% FBS . All cell lines were cultured in 95% O2/5% CO2 . Cell lines were transfected using Lipofectamine 2000 ( ThermoFisher ) according to the manufacturer’s instructions . Mouse embryonic fibroblasts were isolated from Day 13 embryos by trypsin digestion and their genotypes confirmed by PCR . The lines were immortalized by transfecting cells with the SV40-T antigen-expressing plasmid pSG5 Large T using Lipofectamine 2000 . The cells were passaged at least 5x to ensure the homogeneity of the cell population before use in experiments . To isolate embryonic stem cells , 14-week old C9orf72 heterozygous females were treated with Pregnant Mare Serum Gonadotropin via intraperitoneal injection followed by injection 24 hours later with human chorionic gonadotrophin to induce superovulation prior to mating with C9orf72 heterozygous males . Embryos were collected 48 hours after the second injection at the transgenic core facility at Johns Hopkins University and the genotypes confirmed by PCR . Wild type and C9ORF72-/- ES cells were cultured on 0 . 1% gelatin coated plates in 2i media consisting of half of DMEM/F12 and half of Neurobasal media containing N2-supplement ( ThermoFisher Scientific 17502048 ) , B-27 supplement ( ThermoFisher Scientific 17504044 ) , 0 . 05% BSA ( ThermoFisher Scientific 15260037 ) , 50 units Penicillin-Streptomycin , 1 μM PD03259010 ( Stemgent 04–0006 ) , 3 μM CHIR99021 ( Stemgent 04–0004 ) , 2 mM Glutamine , 150 μM Monothioglycerol ( Sigma M6145 ) and 1 , 000 U/ml LIF . Motorneuron differentiation protocol was modified from a previously reported induction protocol using retinoic acid and Smoothened agonist ( SAG , Millipore ) [56] . Briefly , 1 X 106 ES cells were harvested by dissociating with 0 . 05% trypsin-EDTA ( ThermoFisher ) and cultured in suspension condition in DFK5 media ( DMEM/F12 based media containing 5% knockout serum replacement , 1 x insulin transferrin selenium ( ThermoFisher ) , 50 μM nonessential amino acids , 100 μM β-mercaptoehanol , 5 μM thymidine , 15 μM adenosine , 15 μM cytosine , 15 μM guanosine and 15 μM uridine ) for 48 hours . After two days , the resulting embryonic bodies were treated with 2 μM retinoic acid and 600 nM of SAG in fresh DFK5 media and cultured another 4 days . Media was replaced every two days . For experiments , 1 . 5 x 106 cells were plated on each well of laminin-coated 6 well plates in DFK5 media containing 5 ng/mL glial-derived neurotrophic factor ( GDNF; Peprotech ) , 5 ng/mL brain-derived neurotrophic factor ( BDNF; Peprotech ) , 5 ng/mL neurotrophin-3 ( NT-3; Peprotech ) for 24 hours . After 24 hours , media were changed with DFKNB media consisting of half of DFK5 media and half of Neurobasal media with B27 , 5 ng/mL GDNF , 5 ng/mL of BDNF and 5 ng/mL of NT-3 . All cells were starved using Earles’s balanced salt solution ( EBSS; Sigma ) for 50 min . Amino acid stimulation was applied by treating cells with essential amino acids ( Gibco ) and non-essential amino acids ( Quality Biologicals ) in EBSS . Amino acids were diluted to match DMEM concentrations . Cells were treated in EBSS plus amino acids for 10–20 min prior to lysate collection . Lysates were processed as described above except that Phospho-stop inhibitor tablets ( Roche ) were added to the lysis buffer . Wild type or C9orf72-/- MEF cells were transfected with GFP-LC3 and RFP-Rab7 , and cells were treated with DMEM containing 10% FBS ( full medium; FM ) or EBSS ( nutrient deprivation; ND ) , in the presence or absence of the lysosomal inhibitor Bafilomycin A1 ( Baf ) for 3 hours , before being fixed with 4% paraformaldehyde . High resolution images were acquired using a Z sweep function , permitting acquisition of total cellular fluorescence using a DeltaVision Elite microscope ( GE Healthcare ) with 60× PlanApo NA 1 . 4 Oil objective lens ( Olympus ) and images were deconvolvd using SoftWoRx software . Subsequently , individual cells were manually segmented; LC3-positve vesicles in the green channel and Rab7-positive vesicles in the red channel . Using a boolean function , the overlap between these segmented images was used to generate a third mask corresponding to co-localized LC3 and Rab7 vesicles ( Yellow image ) which are autolysosomes . Single vesicle areas were calculated from LC3 , Rab7 and co-localization masks , and mean values for each experiment were normalized to ND or FM , as indicated . HEK293T cells were grown on class coverslips and transfected with the indicated constructs as described above . Images were captured using an SP8 confocal microscope ( Leica ) and processed using ImageJ software . For GFP-TFEB imaging in HEK293T cells , live cells were imaged while maintained in phenol red free DMEM containing 10% FBS . For LysoTrackerBlue staining , 50nM of LysoTrackerBlue was added into the media for an hour and the media was changed before imaging . Cells were collected in modified RIPA lysis buffer ( 50 mM Tris pH 6 . 8 , 150 mM NaCl , 0 . 5% SDS , 0 . 5% Sarkosyl , 0 . 5% NP40 , 20 mM EDTA , Roche protease inhibitors ) and sonicated using a Diagenode Bioruptor for 15 min ( high setting , 30 sec pulse , 3x 5 min ) and the resulting lysates were centrifuged at 16 , 000 x g for 10 min at 4°C . For mTOR assays , cells were collected in HEPES lysis buffer ( 40 mM HEPES pH 7 . 4 , 2 mM EDTA , 1% Triton , Roche protease and PhosphoStop inhibitors ) and centrifuged at 16 , 000 x g for 10 min at 4°C . Protein concentrations were determined using the bicinchonic acid assay ( ThermoFisher ) . For GFP-TFEB nuclear import analysis , cells were fractionated using Subcellular Fractionation Kit for Cultured Cells ( ThermoFisher ) following the manufacturer’s protocols . Then the cytoplasmic and membrane proteins were combined as the cytosolic fraction and the nuclear soluble and chromatin bound proteins were combined as the nuclear fraction . PARP was used as a nuclear marker and Caspase 3 as a cytoplasmic marker . For autophagic flux determination , wild type and C9orf72-/- MEFs were subjected to fresh fully supplemented medium or nutrient deprivation ( EBSS ) for 3 hours . Antibodies used were: mouse anti-Flag , rabbit anti-SMCR8 ( Sigma ) , mouse anti-GFP , rabbit anti-GAPDH ( ThermoFisher ) , rabbit anti-C9orf72 , mouse anti-actin ( Santa Cruz ) , rabbit anti-p62 , rabbit anti p-70S6K , rabbit anti p-p70S6K , rabbit anti-PARP , rabbit anti-Caspase 3 , rabbit anti-DYKDDDDK ( Cell signaling ) , rabbit anti-V5 ( Novus ) , mouse anti-V5 ( Invitrogen ) , mouse anti-TFEB ( Mybiosources ) , mouse anti-Lamp1 ( Hybridoma Bank; #H4A3-s ) , mouse anti-Lamp2 ( Hybridoma Bank , #H4B4-s ) , and rabbit anti-LC3B ( Abcam ) . HEK293T cells were incubated in heavy ( 13C6 , 15N4 L-Arginine , 13C6 , 15N2 L-Lysine ) or light ( 12C6 , 14N4 L-Arginine , 12C6 , 14N2 L-Lysine ) DMEM and verified for near-completion of labeling by mass spectrometry . Heavy isotope-labeled cells were transfected with C9orf72-Flag and light isotope-labeled cells were mock transfected with Lipofectamine . After immunoprecipitation with Flag-tag beads , the resulting immunoprecipitates were pooled , concentrated , separated via SDS-PAGE , and subjected to trypsin in-gel digestion . The digested samples were subjected to LC-MS/MS analysis on an Orbitrap Elite mass spectrometer coupled with Easy nLC II liquid chromatography system . The mass spectrometry data were analyzed using the Proteome Discoverer 1 . 4 software suite against human Refseq 59 protein database . A 1% peptide-spectrum-match and peptide-level false discovery rate was applied for data analysis . Cells were lysed in IP buffer ( 50 mM Tris pH 7 . 4 , 150 mM NaCl , 1% Triton , Roche protease inhibitors ) , incubated for 30 min on ice and centrifuged at 16 , 000 x g prior to immunoprecipitation . For Flag immunoprecipitations , the resulting supernatants were added to Flag-conjugated beads ( Sigma , St . Louis , MO ) and incubated for 2 h at 4°C with gentle rotation . The beads were washed 5x with IP buffer and the immunoprecipitates eluted by incubating the beads with SDS-PAGE loading dye for 5 min at 95° C . For SILAC analysis , immunoprecipitates were eluted using Flag peptide ( Sigma ) at 5μg/μl . For SMCR8 immunoprecipitation , anti-SMCR8 antibody ( Abcam ) was incubated with protein A Sepharose beads ( BioRad , Hercules , CA ) and incubated at room temperature for 2 h and the beads treated as described above . The ATG4B-dependent processing of LC3 in autophagy was quantified with a Gaussia luciferase release assay [57 , 58] . ATG4B-induced proteolytic cleavage of an actin-anchored LC3-luciferase fusion protein ( Act-LC3-Gluc ) releases the Gluc fragment and enables its secretion into the cell medium . Cells were transfected with shRNA or scrambled control and Act-LC3-Gluc or control Act-Gluc plasmid together with the secreted alkaline phosphatase normalization control , CMV-SEAP . Cell medium ( 150 μl ) was withdrawn 48–72 h after transfection and the luciferase and SEAP in the medium were analyzed by using Gaussia luciferase assay kit ( New Englabnd Biolabs ) and the Phospha-light SEAP reporter system ( ThermoFisher ) using a microplate reader ( Synergy H1 , Bio-Tek ) . Gender matched four month old mice were intracardially perfused with ice-cold 4% paraformaldehyde . Brains were removed and post-fixed and equilibrated with 30% sucrose . Sections were prepared using Cryostar NX70 ( ThermoScientific ) . Sections were washed with PBST three times to permeabilize cells , pre-incubated with 10% anti-goat serum for an hour at RT , incubated with an anti-p62 antibody ( Cell Signaling ) for overnight at 4°C , and then incubated with Alexa488-congugated secondary antibody after washing three times with PBS . Images were obtained using an SP8 confocal microscope ( Leica ) after samples were washed three times with PBS and mounted with Vectashield . Total RNA was isolated from cells with the RNeasy Plus Mini kit and cDNAs were synthesized with the QuantiTect reverse transcription kit ( Qiagen ) . Primers for quantitative RT-qPCR were from PrimerBank unless otherwise noted ( S2 Table ) . RT-qPCRs were performed on a BioRad thermal cycler with iQ SYBER Green PCR mix ( BioRad ) . All quantitation and statistical tests were performed using ImageJ and GraphPad Prism software ( Version 6 . 0 ) . The p-values for all experiments were obtained using Student’s t tests unless indicated otherwise .
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C9orf72 is one of many uncharacterized genes in the human genome . The presence of repeated nucleotides in the non-coding region of the C9orf72 gene ( GGGGCC ) has been linked to the neurodegenerative diseases Amyotrophic Lateral Sclerosis ( ALS ) and Frontotemporal dementia ( FTD ) . However , how the presence of these repeats in the gene leads to neurodegeneration is unknown . One possible explanation is that the repeats lead to a reduced expression of the C9orf72 gene and loss of function of the C9orf72 protein . Although C9orf72 is well-conserved among multi-cellular organisms , its protein function remains to be determined . In this study , we demonstrated that loss of C9orf72 reduces mTOR signaling and enhances autophagy . mTOR signaling and autophagy are important for the cellular maintenance of metabolic balances , especially under stress conditions . C9orf72 protein exists in a complex with another DENN-like protein , SMCR8 , which also regulates mTOR signaling and autophagy . We generated mice lacking C9orf72 , which died prematurely and showed dramatic upregulation of TFEB , a crucial transcriptional regulator of autophagy and lysosomal genes , that integrates mTOR activity state and autophagic capacity . We propose that C9orf72 function is important for metabolic control and its deficiency can contribute to the development of neurodegenerative diseases .
|
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"and",
"Methods"
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2016
|
Loss of C9orf72 Enhances Autophagic Activity via Deregulated mTOR and TFEB Signaling
|
C-to-U editing is an important event in post-transcriptional RNA processing , which converts a specific cytidine ( C ) -to-uridine ( U ) in transcripts of mitochondria and plastids . Typically , the pentatricopeptide repeat ( PPR ) protein , which specifies the target C residue by binding to its upstream sequence , is involved in the editing of one or a few sites . Here we report a novel PPR-DYW protein EMP21 that is associated with editing of 81 sites in maize . EMP21 is localized in mitochondria and loss of the EMP21 function severely inhibits the embryogenesis and endosperm development in maize . From a scan of 35 mitochondrial transcripts produced by the Emp21 loss-of-function mutant , the C-to-U editing was found to be abolished at five sites ( nad7-77 , atp1-1292 , atp8-437 , nad3-275 and rps4-870 ) , while reduced at 76 sites in 21 transcripts . In most cases , the failure to editing resulted in the translation of an incorrect residue . In consequence , the mutant became deficient with respect to the assembly and activity of mitochondrial complexes I and V . As six of the decreased editing sites in emp21 overlap with the affected editing sites in emp5-1 , and the editing efficiency at rpl16-458 showed a substantial reduction in the emp21-1 emp5-4 double mutant compared with the emp21-1 and emp5-4 single mutants , we explored their interaction . A yeast two hybrid assay suggested that EMP21 does not interact with EMP5 , but both EMP21 and EMP5 interact with ZmMORF8 . Together , these results indicate that EMP21 is a novel PPR-DYW protein required for the editing of ~17% of mitochondrial target Cs , and the editing process may involve an interaction between EMP21 and ZmMORF8 ( and probably other proteins ) .
Plant mitochondrion possesses its own genome which retains ~5% genes from its prokaryotic ancestor . These genes encode proteins , ribosomal RNAs and transfer RNAs for oxidative phosphorylation , and protein translation . Plant mitochondria have acquired characteristic and complex RNA metabolism in the process of co-evolution with nucleus , including RNA cytidine ( C ) -to-uridine ( U ) editing , the splicing of introns , the maturation of transcript ends , RNA stabilization and RNA translation [1 , 2] . Numerous eukaryote-specific factors have been found to play vital roles in these processes . The pentatricopeptide repeat ( PPR ) proteins , which exceed 400 in many species , are one large family of these factors [3–6] . PPR proteins feature a tandem array of ~35-amino-acid repeat motifs [7] , classified into P-class and PLS-class [4] . The P-class proteins harbor bona fide P-motifs with 35 amino acids , while the PLS-class ones comprise a mixture of P , L , and S motifs , where L motifs are 35–36 amino acids and S motifs are 31 amino acids [4 , 8] . Moreover , the PLS-class PPR proteins usually carry an E and/or DYW domain at their C-terminus . Many PPR proteins have been identified as being needed for the effective conversion of cytidine to uridine in both the chloroplast and the mitochondrion ( reviewed in S3 Table ) , but the majority of them are responsible for editing at just one or a few sites . RNA C-to-U editing is widespread in land plant organelles . Many angiosperms have more than 300 editing sites in mitochondria [9–12] , and 20–40 editing sites in plastids [13–17] . RNA editing often restores evolutionarily conserved amino acids , generates start/stop codons , promotes intron splicing , and increases the efficiency of transfer RNA processing [18–24] . The process of cytidine to uridine conversion involves deamination [25 , 26] , and the specific cytidine deaminase ( CDA ) has not been identified until recently [27] . This report found that two DYW domains containing the cytidine deaminase-like ( CDAs-like ) zinc binding signature residues ( HxE ( x ) nCxxC ) at the PpPPR56 and PpPPR65 C-terminus , respectively , function as cytidine deaminase in the C-to-U editing process . Most DYW domains in higher plant also harbor the CDAs-like signature residues , which are irreplaceable for RNA editing [28–30] . DYW1 is responsible for the editing of ndhD-1 site in Arabidopsis [31] . Mutation of the CDAs-like signature residues in DYW1 significantly decreases the zinc ion binding capacity , and abolishes the editing at ndhD-1 site [28] . Thus , DYW domains in higher plant probably have the same function as these domains in Physcomitrella patens . In addition to the PPR proteins , C-to-U editing involves proteins from diverse families including multiple organelle RNA editing factors/RNA-editing factor interacting proteins ( MORFs/RIPs ) , organelle RRM proteins ( ORRMs ) , organelle zinc-finger 1 ( OZ1 ) , protoporphyrinogen oxidase 1 ( PPO1 ) , hydroxymethylbilane synthase ( HEMC ) , tetratricopeptide repeat protein ( WTG1 ) and chloroplast RNA helicase ( ISE2 ) . PLS-class PPRs dictate specificity by recognizing the approximately 5–20 nucleotides upstream of the editing sites [32–36] . MORFs/RIPs containing a conserved MORF/RIP motif are required for the editing of most sites in organelles . MORFs/RIPs have been reported to form hetero- or homo-dimer [37–39] , and selectively interact with PPRs [40 , 41] . Four of ORRMs , ORRM2 , ORRM3 , ORRM4 , and ORRM5 , are required for the editing in mitochondria [42–44] , and ORRM1 and ORRM6 function in the editing in plastids [45 , 46] . ORRMs can interact with RIPs/MORFs , and form hetero- or homo-dimer [42 , 43 , 46] . In Arabidopsis plastids , OZ1 , a RanBP2-type zinc finger motif-containing protein , is responsible for editing at 30 sites , interacting with ORRM1 and RARE1 , but not with any of the MORFs [47] . PPO1 , a key enzyme for tetrapyrrole metabolism , was shown to play a surprising role in plastid RNA editing and interact with plastidial RIPs/MORFs , but not with PPRs [48] . HEMC , encoded a porphobilinogen deaminase , is associated with the RNA editing in plastids . AtECB2 ( a PPR-DYW protein ) directly interacts with HEMC , which in turn interacts with MORF8/RIP1 [49] . In chloroplast , the tetratricopeptide repeat protein WTG1 has been shown to involve the editing of at least two genes; it associates with both RIP1/MORF8 and MORF9 [50] . In addition , CP31 , OCP3 and ISE2 influence the efficiency of cytidine to uridine conversion in plastids [51–54] . Recently , an active editing complex containing PPRs , RIP2 , RIP9 , RIP1 , OZ1 , ORRM1 , and ISE2 was isolated from maize chloroplasts [55] , lending convincing support for the hypothesis that the RNA C-to-U editing in plant organelles is carried out by an editosome . Many PPR-DYW proteins have been reported to be required for the C-to-U editing in chloroplasts and mitochondria . But most of these proteins are responsible for the editing of just one or a few sites . Here we report a novel PPR-DYW protein EMP21 that functions in the editing of 81 sites in mitochondria . These editing events are crucial to the mitochondrial function and seed development in maize . Furthermore , we provide evidence that EMP21 may exert its function by interacting with ZmMORF8 ( and probably other proteins ) .
The Mu insertion mutant ( emp21-1 ) was isolated from the UniformMu mutagenic population [56] . The selfed progenies of emp21-1 heterozygotes segregated about 1/4 empty pericarp ( emp ) kernels ( wild type: emp = 883:296 = 2 . 98:1 , Fig 1A ) , indicating a nuclear and recessive mutation . The mutant kernels sampled at 12 days after pollination ( DAP ) were smaller than the wild type ones , and harbored a very much tiny embryo and a small transparent endosperm while the wild type kernels developed all structures ( Fig 1B , 1C , 1E and 1F ) . At physiological maturity , the mutant kernels appeared shrivelled ( Fig 1A and 1G ) . Hence , we named the mutant as empty pericarp 21 ( emp21 ) . Inspection of sectioned tissue confirmed that both embryogenesis and endosperm development were defective in the mutants ( Fig 2D–2F ) . While 12 DAP wild type embryos harbored a visible leaf primordium , a shoot apical meristem and a root apical meristem ( Fig 2A ) , emp21-1 embryos at this stage had only just reached the transition stage ( Fig 2D ) . By 16 DAP , wild type embryos had entered the late embryogenesis stage ( Fig 2B and 2C ) , but the emp21-1 embryos remained at the transition stage and their endosperms were arrested at the cellularization stage ( Fig 2E and 2F ) . Thus , loss of the Emp21 function severely arrests embryogenesis and endosperm development in maize . The mutation proved to be embryo-lethal as all attempts to rescue them through in vitro culture failed . A high throughput Mu-seq analysis [57] was used to identify the gene compromised in the emp21-1 mutant . A Mu insertion at +280 bp from the translation start codon in GRMZM5G849971 was identified to be linked with the mutant ( Figs 3A and S1A ) . A linkage analysis based on 46 segregants showed that the mutant phenotype is tightly linked with the Mu insertion ( S1B Fig ) . To confirm that GRMZM5G849971 is the causal gene for the emp21-1 phenotype , an independent insertion mutant was isolated from the UniformMu population . In this case , the Mu element is inserted at +608 bp from the GRMZM5G849971 translation start codon , designated emp21-2 ( Fig 3A ) . The selfed progenies of emp21-2 heterozygotes segregated emp kernels similar to emp21-1 ( S2A Fig ) . Reciprocal crosses between emp21-1 and emp21-2 heterozygotes produced approximately 25% mutant kernels ( S2B and S2C Fig ) . Thus , the emp21 phenotype is caused by the mutation of GRMZM5G849971 . Wild type Emp21 transcripts could not be detected in either the emp21-1 or emp21-2 kernels ( Fig 3B ) , suggesting that both alleles are likely null . Emp21 encodes a canonical DYW-subclass PPR protein , consisting of 647 amino acid residues ( Fig 4A ) . Based on the redefined PPR motifs [8] , EMP21 possesses 11 PPR motifs , E1 , E2 and DYW motifs ( Figs 4A , S3 and S4 ) . The DYW-motif in EMP21 contains the conserved CDAs-like signature residues ( HxE ( x ) nCxxC ) ( S4 and S5 Figs ) . A phylogenetic analysis revealed extensive conservation in the sequence across both mono- and dicotyledonous species ( Fig 4B ) . The EMP21 orthologs in sorghum bicolor ( SbEMP21 , SORBI_3001G151300 ) , Triticum aestivum ( TaEMP21 , unnamed protein product ) and Oryza sativa ( OsEMP21 , Os03g0816600 ) share 91 . 2% , 79 . 7% and 73 . 3% sequence identity with the maize EMP21 , respectively ( S4 Fig ) . No target peptide was predicted for EMP21 according to the TargetP ( http://www . cbs . dtu . dk/services/TargetP ) and Predotar algorithms ( https://urgi . versailles . inra . fr/predotar/predotar . html ) . To localize EMP21 , the full-length EMP21 ( without stop codon ) was fused with the green fluoresent protein ( GFP ) and transformed Arabidopsis . Fifteen transgenic lines were generated , but none of them showed GFP signal . We suspected that EMP21-GFP may be too large to be efficiently expressed , or over-expression of the full-length protein may be detrimental to the cell . Then , the N-terminal region containing all the PPR motifs and E1 , E2 motifs of EMP21 was fused with GFP and transformed Arabidopsis . Twenty transgenic lines were isolated and all showed GFP signals . The GFP signals were found in punctated dots that merged with the mitochondria which were stained by the MitoTracker Red ( Fig 4C ) . No GFP signal was detected in chloroplasts or other compartments in the cell ( Fig 4C ) , indicating that EMP21 is exclusively targeted to mitochondria . Quantitative real-time PCR ( qRT-PCR ) assay indicated that Emp21 is ubiquitously transcribed throughout the maize plant , with a relatively high level of expression observed in root , stem and pollen , and low expression in leaf , tassel and developing seeds ( Fig 3C ) . Thus , Emp21 is not a seed specific gene , but rather a constitutive gene that may have an essential role throughout plant growth and development . Because emp21 is embryo-lethal , these impacts cannot be assessed . The maize mitochondrial genome is predicted to harbor 35 protein-encoding genes including 22 genes of electron transport chain , 11 ribosomal protein genes , one maturase gene ( matR ) , and one transporter gene ( mttB ) [58] . The Arabidopsis and rice mitochondrial transcripts harbor over 600 and 490 editing sites [9 , 12 , 59] , whereas the maize editing sites in mitochondrial transcripts were only analyzed by direct sequencing of the RT-PCR amplified transcripts [60] . We used the strand- and transcript-specific RNA-seq ( STS-PCRseq ) method to analyze the editing sites in these 35 mitochondrial transcripts [59] . Based on a total of 600 Mb sequence data , 493 C-to-U editing sites were identified in these transcripts in maize ( Table 1 , S1 and S2 Dataset ) . Among those sites , 12 sites are edited 100% , 72 sites 99–100% , 170 sites 90–99% , 154 sites 50–89% , and 85 sites less than 50% ( S3 Dataset ) . Most of these editing events cause alteration of the encoded amino acids ( S3 Dataset ) . Since most of the known DYW-subclass PPRs function in the RNA C-to-U editing ( reviewed in S3 Table ) , the STS-PCRseq method and direct sequencing of the RT-PCR amplified transcripts [18] were used to assess the editing profiles of the 35 mitochondrial protein-coding genes between emp21 and wild type . These results revealed that the editing is completely abolished at the nad7-77 , atp1-1292 , atp8-437 , nad3-275 and rps4-870 sites in both the emp21-1 and emp21-2 mutants ( Fig 5A and S2 Dataset ) . The first three sites are fully edited in wild type , whereas the last two sites are edited at a 15% and 21% level in wild type , respectively . In addition , the editing at a further 76 sites , distributed in 21 transcripts ( nad2 , -3 , -4 , -6 , -9 , cob , cox3 , atp8 , rps1 , -3 , -4 , -7 , -12 , -12-ct , -13 , rpl16 , ccmB , -FC , -FN , matR and mttb ) , was substantially reduced in the mutants ( Table 1 , S6 Fig and S2 Dataset ) , and the editing of 22 sites in 11 transcripts ( nad1 , -2 , -4 , -5 , -7 , cob , cox2 , rps2A , -3 , ccmFC , and -FN ) ( Table 1 , Figs 5C and S7 and S2 Dataset ) was substantially increased in the emp21 mutants in comparison with the wild type . Increased editing has been reported in several mutants , such as emp5 , emp7 in maize , and mef8 , orrm5 in Arabidopsis [18 , 44 , 61 , 62] . However , decreased editing in such a large number of sites for a typical PPR-DYW gene mutant has not been reported previously . Based on codes defined by the combinatorial residues at residue 6 of one PPR repeat and residue 1’ of the next PPR repeat [63–65] , the EMP21 PPR motifs were largely aligned with the sequence upstream of nad7-77 , atp1-1292 and atp8-437 , but less aligned with those of nad3-275 , rps4-870 and the other 76 sites where editing was compromised in the absence of EMP21 ( S1 Table ) . It is possible that EMP21 recognizes nad7-77 , atp1-1292 and atp8-437 by direct binding to the sequences , but through other means on the other 78 edited sites . Deficient editing at most of the sites requiring EMP21 resulted in a change in the encoded amino acid residues , for example Leu26 to Ser26 in Nad7 , Leu431 to Pro431 in Atp1 , and Leu146 to Ser146/Pro146 in Atp8 ( Fig 5A ) . A comparison of both the gDNA and cDNA sequences of the orthologs of nad7 , atp1 and atp8 suggested that Leu26 in Nad7 and Leu431 in Atp1 are conserved in both lower and higher plants ( Fig 6A and 6B ) , while the amino acid residues at Atp8-146 encoded by atp8-437 have diverged markedly ( Fig 6C ) . The amino acid residue at Atp8-146 is a Leu in Zea mays , Triticum aestivum , Glycine max , Beta vulgaris , Nicotiana tabacum , Physcomitrella patens , and Marchantia polymorpha , but a Pro in Vitis vinifera and Val in Arabidopsis thaliana and Brassica napus ( Fig 6C ) . The defective editing in emp21 occurs in the genes encoding the subunits of four mitochondrial respiratory chain complexes ( complex I , III , IV , and V ) . The impact of the Emp21 mutation on the assembly and function of the mitochondrial respiratory chain was investigated through the use of Blue Native-PAGE ( BN-PAGE ) . The abundance of complex I in emp21-1 was greatly reduced , while that of supercomplex I+III2 was below the level of detection ( Fig 7A ) . An in-gel staining assay for NADH dehydrogenase activity gave a consistent result ( Fig 7B ) . Similarly , assays targeting F1Fo-ATPase hydrolysis activity and assembly showed that neither F1Fo-ATPase nor the free F’ and F1 moieties were formed in the mutant ( Fig 7C and 7D ) , indicating that the assembly and activity of complex V were both compromised by the loss of Emp21 function . In contrast , the abundance of complex III was markedly increased in the mutant ( Fig 7E ) . The outcome of a series of Western blot experiments was that the abundance in the mutant of Nad9 ( complex I ) was greatly reduced , that of Atp1 ( complex V ) was barely detectable , that of Cox2 ( complex IV ) was unaffected and that of Cytc1 ( complex III ) was greatly increased ( Fig 7F ) . Thus , the loss of EMP21 function clearly impaired the assembly and function of mitochondrial complexes I and V . An up-regulation on complex III was probably the result of the regulatory mechanism of the complex gene expression . The block of the cytochrome pathway of the respiratory chain often leads to enhanced alternative pathway [24 , 61 , 66 , 67] . Three ZmAOX genes ( ZmAOX1 , ZmAOX2 and ZmAOX3 ) were found in the maize genome [68] . Both RT-PCR and qRT-PCR assays indicated that the abundance of ZmAOX2 and ZmAOX3 transcripts was much higher in the emp21-1 mutant than in wild type ( S8A and S8B Fig ) , while , consistently , the measured abundance of AOX protein was increased ( Fig 7F ) . Together , these results indicate that EMP21 is crucial for the biogenesis and activity of complexes I and V in maize mitochondria . EMP5 is found to be required for the editing of 10 sites in maize mitochondrial transcripts [18] , and six of these sites overlap with those of EMP21 ( Figs 5B and 8 and S2 Dataset ) . The C-to-U editing of rpl16-458 was less effective in the emp21-1 mutant than in wild type ( Fig 8 and S2 Dataset ) , while it is abolished in the emp5-1 mutant [18] . In addition , the editing of nad9-190 , nad9-356 , cox3-245 , cox3-257 , and rps12-71 sites was reduced in both emp21 and emp5-1 ( Fig 8 and S2 Dataset ) [18] . In the emp5-4 allele , mutational Emp5-4 encodes a truncated EMP5 protein lacking the E+ and DYW domains . Most of the editing events affected by EMP5 show similar editing levels in emp5-4 and wild type , except the editing of rpl16-458 which is decreased compared with wild type [18] , promoting the idea that the EMP5-4 mutant protein without the DYW domain may interact with another PPR-DYW protein to facilitate editing . To explore the genetic relationship between Emp21 and Emp5 , we generated double mutants from the cross Emp21/emp21-1 x Emp5/emp5-4 . The emp5-4/emp5-4 emp21-1/Emp21 plants were identified by PCR in F2 ( Fig 9B ) . Kernels in these double mutant selfed ears exhibited 1:2 . 8 segregation ratio of emp to normal kernels ( Fig 9C ) , where the normal kernels proved to be emp5-4 single mutants and the empty pericarp ones are emp5-4 emp21-1 double mutants ( Fig 9C and 9D ) . An analysis of the editing profile at the six shared sites in the double and single mutants using both STS-PCRseq and direct sequencing , showed that only 35% of the rpl16-458 sites were edited in the emp5-4 emp21-1 double mutant , as against 73% in the emp5-4 single mutant and 80% in the emp21-1 single mutant ( Figs 9E and S9 and S2 and S4 Dataset ) . The editing efficiency of nad9-190 , -356 , cox3-245 , -257 , and rps12-71 sites in the emp5-4 emp21-1 double mutant was similar to that in the emp21-1 single mutant ( Fig 9E ) . Because the editing of rpl16-458 site is completely dependent on the presence of EMP5 , these results suggested that a portion ( ~30% ) of the rpl16-458 sites are edited by EMP21 and EMP5 jointly . To determine whether EMP21 directly interacts with EMP5 , a yeast two hybrid ( Y2H ) assay was conducted . The yeast cells containing BD-EMP21/AD-EMP5 set or BD-EMP5/AD-EMP21 set did not grow on the SD/-Trp-Leu-His-Ade dropout plates ( Fig 10A ) , suggesting that these two proteins may not interact in the yeast . MORFs/RIPs are responsible for the editing at most of the sites in the mitochondrial and plastidial transcripts in Arabidopsis [37 , 40 , 59] . Functions of the maize MORFs/RIPs are not identified . We analyzed the 81 EMP21 edited sites with respect to the sites edited by MORFs/RIPs in Arabidopsis . Interestingly , 44 of the 81 sites edited by EMP21 in maize do not need editing in Arabidopsis as these sites are mostly “Ts” ( S2 Table ) . Among the rest 37 edited sites , 34 require the editing function of MORF8 in Arabidopsis ( S2 Table ) . Eight editing sites mediated by MORF8 overlap with those mediated by EMP5 ( S2 Table ) . A BLAST search identified six putative mitochondrion-targeted MORF orthologs in maize , named ZmMORF1 ( GRMZM2G003765 ) , ZmMORF3 ( GRMZM2G054537 ) , ZmMORF4 ( GRMZM2G139441 ) , ZmMORF5 ( GRMZM2G383540 ) , ZmMORF6 ( GRMZM5G808811 ) and ZmMORF8 ( GRMZM2G169384 ) . The overlaps promoted us to explore the relationship among EMP21 , EMP5 and six ZmMORFs . The results of Y2H assays indicated that both BD-EMP21 and BD-EMP5 are able to interact with AD-ZmMORF8 , but not with other five ZmMORFs ( Figs 10A , S10 and S11 ) . However , the reciprocal mating pairs did not grow on the SD/-Trp-Leu-His-Ade dropout plates ( Fig 10A ) . It is possible that BD-ZmMORF8 or AD-EMP21/AD-EMP5 cannot be properly expressed in yeast . Deletion of the MORF domain in ZmMORF8C182 abolished the interaction with EMP5 and EMP21 ( Figs 10A and S12 ) . ZmMORF4 displayed auto-activation when fused to the BD domain , hence tested in AD-ZmMORF4 ( S10 Fig ) . The implied direct interactions between ZmMORF8 and both EMP21 and EMP5 were further verified using a bimolecular fluorescence complementation ( BIFC ) assay . After co-expressing the N-terminal YFP fusion of ZmMORF8 and C-terminal YFP fusion with either EMP21 or EMP5 in Arabidopsis protoplasts , we observed the punctated dot YFP signals merged with the mitochondria which were stained by the MitoTracker Red ( Fig 10B ) . No signal was generated in protoplasts co-expressing a fusion between the YFP N terminus and a truncated version of ZmMORF8ΔMORF with deleted MORF motif , and C-terminal YFP fusion with EMP21 or EMP5 ( Figs 10B and S12 ) . When the truncated EMP21 and EMP5 ( either only the PPR motifs , or the PPR+E motifs , or the E+DYW motifs ) were tested , a weak interaction was observed between ZmMORF8 and both the PPR motifs and the PPR+E motifs , while the E+DYW motifs failed to interact ( S11 Fig ) . These results suggest that EMP21 and EMP5 function in the editing at some sites by interacting with ZmMORF8 and this interaction depends mainly on the PPR motifs of these two PPRs .
Experimental evidence has shown that abolishing editing in mitochondrial genes can disturb mitochondrial functionality and thereby inhibit the development of the maize kernel [18 , 24 , 67 , 69 , 70] . For example , an analysis based on the behavior of mutants has demonstrated that the rpl16-458 site requires EMP5 ( a DYW-subclass PPR protein ) to perform the editing needed to support normal kernel development [18] . Similarly , in the emp9 mutant , editing at ccmB-43 is abolished , resulting in the translation of a Pro rather than a Ser residue; this single residue change is sufficient to disrupt the assembly of complex III and results in kernel abortion [24] . Meanwhile , the effect of mutating Emp18 , which encodes a mitochondrial PPR-DYW protein involved in editing the atp6-635 site , is to convert a Leu to Pro in Atp6 , a subunit of F1Fo-ATPase; the alteration disrupts the α-helix of subunit a , leading to the disassembly and reduced activity of complex V , finally resulting in embryo lethality and a failure in endosperm development [69] . In the present emp21 mutants , the non-editing of atp1-1292 resulted in the translated Atp1 protein carrying a Pro rather than a Leu at position 431 ( Fig 5A ) . Atp1 is the α-subunit in F1-factor of complex V ( F1Fo-ATPase ) , a multimeric enzyme ( α3β3γδϵ ) in mitochondrial respiratory chain [71] . Based on the structure of its ortholog [72] , Leu431 lies within the conserved α-helix , so the failure to correct this residue probably disrupts the α-helix , so likely compromising the assembly of complex V ( Fig 7C and 7D ) . The emp21 mutation also abolished editing at atp8-437 and reduced its effectiveness at atp8-436 ( Fig 5A ) . As this residue is located in the non-conserved C terminal region of Atp8 ( Fig 6C ) , this argues a possibility that the mutated forms of Atp8 have a ( moderate ) impact on complex V . Considering the conservation of the editing site at atp1-1292 , as well as the lack of complex V assembly and activity , the editing deficiency of atp1-1292 probably causes the defective complex V and arrested embryogenesis and endosperm development in emp21 . In addition to its influence over editing at atp1-1292 and atp8-437 , the loss-of-function of Emp21 also abolished the editing at nad7-77 and nad3-275 , as well as resulting in a reduction in the effectiveness of editing at one site in nad2 , 16 in nad3 , one in nad4 , six in nad6 and two in nad9 ( Figs 5A and S6 and S2 Dataset ) –these genes all encode subunits of mitochondrial complex I . In the mutant , the effect at nad7-77 resulted in a change from the wild type residue at position 26 ( Leu ) to Ser ( Fig 5A ) . The Leu26 residue is widely conserved across both higher and lower plants ( Fig 6A ) . In the porcine accessory subunit NADH dehydrogenase iron-sulfur protein 2 , which shares 69 . 6% identity with Nad7 [73] , the Leu26 residue is located in the highly conserved AHGVLR linker between two β-sheets . A Leu26 to Ser26 change probably disrupts the protein stability , as suggested by the behavior of maize dek36 mutants . An E+-subgroup PPR DEK36 being responsible for nad7-383 editing , converts Ser to Leu located in a highly conserved VGALT linker between two α-helixes in Nad7 . Mutation of DEK36 dramatically impairs the stability of Nad7 and activity of complex I [60] . Defective editing at multiple sites in the nad3 transcript may similarly have contributed to the observed inhibition of complex I assembly and activity noted in the emp21 mutant . Such as , the editing at nad3-247 ( nad3-250 in Arabidopsis ) is severely decreased in emp21 ( S6 Fig and S2 Dataset ) . Defective editing at nad3-250 has been implicated as strongly impairing the complex I activity in the Arabidopsis slg1 mutant [74] . The effectiveness of editing at a further 50 sites , scattered across 17 transcripts , was reduced in emp21 ( S6 Fig and S2 Dataset ) , but this seems unlikely to have contribution to mitochondrial dysfunction , since complex III assembly was enhanced in the emp21-1 mutant ( Fig 7E ) , and the abundance of other respiratory chain proteins ( notably Cox2 ) was indistinguishable from that present in wild type ( Fig 7F ) . The conclusion is that the compromised kernel development induced in the emp21 mutants is likely attributable to a failure to convert cytidine to uridine at atp1-1292 and nad7-77 , in conjunction with a reduced level of conversion at multiple nad3 sites . Many PLS-class PPRs have been identified as factors involved in the C-to-U editing in mitochondria and plastids; roughly half belong to the DYW-subclass ( S3 Table ) . Most of these proteins each target only a small number of sites for editing , the exceptions being DYW2 [75 , 76] , NUWA [75] , EMP21 ( this study ) and MEF8 [62] which target , respectively , 392 , 223 , 81 and 38 sites . Both DYW2 and MEF8 are atypical DYW-subclass proteins lacking a canonical E domain and harbor only five PPR repeats which are thought not sufficient to confer a tight specificity on the substrates [62 , 75] . And DYW2 functions in both plastids and mitochondria . NUWA is a P-class of PPR protein lacking the DYW and E domain which is usually not found to have the editing function . NUWA is also targeted to mitochondria and plastids [75 , 76] . In this context , EMP21 is novel among these proteins in which it is a canonical PPR-DYW protein possessing conserved E and DYW domains and eleven PPR-motifs ( Fig 4A ) . The requirement of EMP21 and the above three other PPR proteins required for the editing of such a large number of sites provides certain clues to the editing machinery in plant organelles . PPR proteins are thought to bind to the upstream sequences of the target Cs by one PPR-repeat one nucleotide manner based on the amino acid at the 6 and 1’ position of the PPR repeats [63–65] . Such binding has been verified in several reports [33 , 77] . In consistent with this binding codes , we found a good agreement between the EMP21 repeats and the upstream sequences of nad7-77 , atp1-1292 and atp8-437 ( S1 Table ) . EMP21 is essential to the editing of those three sites . However , the upstream sequences of other 78 editing sites are not aligned well with the EMP21 repeats ( S1 Table ) . We considered the possibility that the defective editing in the emp21 mutant at these 78 sites represents a secondary effect caused by compromised mitochondrial function , but this explanation is not supported by the behavior of other mutants . One such example is the smk1 mutant which features a severely reduced assembly and activity of complex I and abnormal mitochondria . However , SMK1 only functions in the editing of nad7-836 , and no other editing sites are affected in the smk1 mutant [67] . A second example relates to EMP18 in maize; when Emp18 is disabled , editing is only compromised at two sites [69] . More generally , it is well established that defective editing of mitochondrial transcripts is not an inevitable consequence of mitochondrial dysfunction [24 , 66 , 70 , 78] . Thus , the dysfunctional mitochondria cannot result in the decreased editing of these sites in emp21 . An alternative possibility is that these 78 sites are not specified by EMP21 , but rather by other PLS-class PPRs that exist in an editosome where the DYW domain of EMP21 containing the conserved CDAs-like signature residues ( HxE ( x ) nCxxC ) ( S5 Fig ) provides the deaminase activity . This hypothesis is supported by the finding that DYW domains are the cytidine deaminase operating on RNA editing [27] . It is also in agreement with the finding that roughly half of the editing sites recognition cannot be explained by the one PPR-repeat one nucleotide codes in the PPR-DYW and PPR-E proteins [65] . Accumulating evidence points to the likelihood that editing is carried out by large ribonucleoprotein complexes composed of a variety of PLS-PPRs , MORFs/RIPs , ORRMs , OZ1 , certain P-subclass PPRs and other proteins in flowering plant [55 , 75 , 76 , 79] . The DYW2 protein has been proposed to be recruited to specific sites by E+-subclass PPRs , where it provides the necessary deaminase activity; meanwhile NUWA supports the interaction between the E+-subclass PPRs and DYW2 [75 , 76] . In addition , an in vivo pull-down assay has demonstrated that MORF1 connects with DYW2 and NUWA [80] . These are clear evidence that editing involves a large complex that mainly serve to recruit functional DYW domains by ( multiple ) protein-protein interaction . We have uncovered that EMP21 and EMP5 are required for the editing of six overlapping sites in mitochondria ( Figs 5B and 8 ) [18] . The emp5-4 allele , which shows reduced editing of rpl16-458 , may be able to encode a truncated product lacking the DYW domain but retaining the E domain . It is proposed that this truncated protein may still possess the editing function by recruiting other PPR-DYW proteins [18] . The emp5-4 emp21-1 double mutant displayed substantially reduced editing efficiency at rpl16-458 ( Figs 9E and S9 ) : the editing efficiency at this site in the single mutants was approximately 73% ( emp5-4 ) and 80% ( emp21-1 ) , falling to 35% in the double mutant . Both the Y2H and BIFC assay confirmed that EMP5 and EMP21 interacted with ZmMORF8 ( Fig 10A and 10B ) . Since EMP5 is essential for the editing at rpl16-458 site [18] , it is possible that EMP5 specifies the rpl16-458 site and recruits either ZmMORF8 and/or EMP21 ( or possibly other PPRs ) to enable the editing process . This provides a reasonable explanation that loss of the DYW domain of EMP5 can be partially complemented by EMP21 and the C-to-U editing is carried out by protein complexes . The Y2H assay implied that EMP5 did not directly interact with EMP21 ( Fig 10A ) . As reported elsewhere , the two PPR-E+ proteins CLB19 and SLO2 showed either no , or at best a weak direct interaction with DYW2 , while a P-type PPR NUWA , detected as PPR- E+-interacting partner , bridges and stabilizes the interaction between PPR-E+ and the DYW protein [75 , 76] . Thus , it is possible that an as yet unidentified P-type PPR protein ( or perhaps some other editing factor ( s ) ) are needed to support an interaction between EMP5 and EMP21 . The loss-of-function of Emp21 caused an increase in the editing at 22 sites in 11 transcripts ( Figs 5C and S7 and S2 Dataset ) . This phenomenon has also been reported in the Arabidopsis dyw2 , mef8 and reme1 mutants , as well as the maize emp5 mutant [18 , 62 , 75 , 76 , 81] . Absence of DYW2 , an atypical PPR protein , decreased the editing efficiency of over 300 sites while increased the editing of over 90 sites [75] . Null mutation of MEF8 , another atypical PPR protein , exhibited reduced editing at 38 sites and increased editing at 24 sites [62] . REME1 is a typical PPR-DYW , and its absence decreased in the editing extent of two sites and increased in the editing extent of two sites [81] . In maize , the loss of the functional EMP5 ( a typical DYW-type PPR ) resulted in a decrease at 10 sites , along with an enhancement in editing effectiveness at 5 sites [18] . It appears that in these mutants the more sites decreased in editing is associated with the more sites increased in editing , and that the increase in editing has site-specificity . For example , the editing efficiency at the mttB-552 and nad2-558 sites is reduced in reme1 [81] , whereas in dyw2 , the editing efficiency is reduced at the mttB-552 site , but enhanced at the nad2-558 site [75 , 76] . As enhanced editing results from increased expression of editing factors ( in editosome ) mostly encoded by the nuclear genes , signal transduction is expected to be involved between the nucleus and mitochondrion . Dysfunction of mitochondria may trigger this signal transduction from mitochondria to nucleus which selectively up-regulate the expression of genes with function in mitochondria . Which signals and how these genes are regulated remain to be elucidated . This provides a possibility that the impaired processes in emp21 mitochondria enhance the expression of certain editing factors , which results in increased editing at certain sites . Another possibility is that absence of one editing factor leads to increased formation of other editing complexes . This possibility lays on an assumption that components in editosomes are highly dynamic and in equilibrium . It is equally possible that some PPR-DYW proteins play an inhibitory role on mitochondrial editing [62] . This hypothesis is supported by the phenomenon that approximately 75% of the sites with increased editing efficiency in the mef8 mutant returned to almost normal level when complemented by a mutated MEF8 ( HxE→HxA in DYW domain ) [62] . The nature of this inhibition remains a question , but this hypothesis is consistent with the notion that components of editosome are highly dynamic and in equilibrium , as the mutated MEF8 can still be incorporated in the complexes .
The emp21 alleles which render nearly isogenic W22 background ( 99 . 6% ) were isolated from the UniformMu mutagenic population [56] . Maize ( Zea mays ) was grown in the experimental field at Shandong University in Jinan , Shandong province under natural conditions . Wild type and transgenic Arabidopsis were grown at 22°C with 16 h light and 8 h dark in culture room . Wild type and mutant kernels were harvested at multiple developmental stages ( 12 and 16 DAP ) from selfed ears in the emp21 heterozygous plants . The kernels were cut along longitudinal axis , and the slices containing embryo and endosperm were fixed in 4% paraformaldehyde at 4°C for 24 h . After dehydration in an ethanol gradient series ( 50 , 70 , 85 , 95 , and 100% ethanol ) , the materials were cleared with xylene and infiltrated by paraffin wax . And then , the samples were embedded in paraffin wax and sectioned at 10 μm thickness by using the Leica 2035 Biocut . The sections were stained with Johansen’s Safranin O and observed with ZEISS microscope . Genomic DNA was isolated by a urea-phenol-chloroform-based method [82] . 0 . 1 g fresh leaf tissues were broken by bead grinding and resuspended with 500 μl of DNA extraction buffer ( 7 M urea , 0 . 3 M NaCl , 50 mM Tris-HCl , 24 mM EDTA , and 1% sarkosine , pH 8 . 0 ) . After mixing with chlorofrom-isoamyl alcohol ( 25:24:1 ) , the mixture was gently shaked for 30 min at room temperature , and then separated by centrifugation at 14000 rpm for 15 min . The supernatant was transferred into a new 1 . 5 ml tube and mixed with 0 . 1 volume of 3 M sodium acetate ( PH 5 . 2 ) and 380 μl isopropanol . DNA was pelleted at 14000 rpm for 15 min , washed with 70% ethanol two times , and dissolved in TE buffer ( 10 mM Tris-HCl , 1 mM EDTA , pH 8 . 0 ) . The Mutator ( Mu ) insertion flanking sequences were identified by Mu-seq strategy as described previously [57] . To investigate the localization of EMP21 , the full-length ( without stop codon ) and the truncated gene fragments encoding 511 amino acids peptide at N-terminus were amplified from W22 genomic DNA and cloned into pENTR/D-TOPO ( ThermoFisher Scientific , http://www . thermofisher . com ) . And then , pGWB5-Emp21 or pGWB5-Emp21N511 vectors , which express EMP21-GFP and EMP21N511-GFP fusion protein , respectively , were constructed by Gateway site-specific recombination . These vectors were transformed into Agrobacterium tumefaciens strain EHA105 . The strains carrying pGWB5-Emp21 and pGWB5-Emp21N511 vectors were used to transform Arabidopsis Columbia-0 by the floral-dip [83] . The transgenic plants were screened in MS medium containing hygromycin and identified by PCR using primers GFP-R and EMP21-F2 . The protoplasts were isolated from transgenic plants using described method [18] , and detected by ZEISS LSM 880 confocal microscope . The mitochondria were stained by MitoTracker Red ( ThermoFisher Scientific ) . Total RNA was extracted from wild type and emp21 embryo and endosperm at 12 DAP using the TRIzol reagent ( ThermoFisher Scientific , www . thermofisher . com ) and was treated with DNase I ( New England Biolabs , www . neb . sg ) to remove any contaminating genomic DNA . Single-stranded cDNA was generated from the RNA via a reverse transcription reaction primed with random hexamers , using a Transcriptor First Strand cDNA Synthesis kit ( ThermoFisher Scientific ) . Quantitative real-time polymerase chain reaction ( qRT-PCR ) was carried using LightCycler 96 ( Roche Diagnostics ) . The relative gene expression value was calculated with the 2^ ( -ΔΔCt ) fomular . The expression level of ZmActin ( GRMZM2G126010 ) served as the reference to normalize the target gene expression . And each experiment was replicated three times . The primers used by RT-PCR and qRT-PCR were shown in S4 Table . The STS-PCRseq [59] method was applied to characterize RNA editing in the maize kernel . Embryo and endosperm tissue from kernels sampled at 12 DAP was prepared from both wild type ( WT ) and emp21 kernels ( WT-1 and emp21-1 , WT-2 and emp21-2 ) set by plants heterozygous for the respective mutant allele . The 35 targeted mitochondrial genes were PCR-amplified from the cDNA templates obtained as described above ( primers given in S4 Table ) . The RT-PCR amplicons obtained from each template were mixed in an equimolar ratio and sheared by sonication . Sequencing libraries were generated using a NEB Next Ultra DNA . Library Prep kit for Illumina ( New England Biolabs ) following the manufacturer’s protocol and index codes were added in order to allow sequences to be attributable to each sample . The quality of each library was assessed using a Bioanalyzer 2100 system device ( Agilent ) . The four resulting DNA libraries were sequenced using a Hiseq Xten-PE150 instrument . Read quality control , read trimming and alignment were performed following the SNP-calling method given in [84] . The threshold for declaring a difference in editing effectiveness was defined as: ( T/ ( T+C ) % in emp21-T/ ( T+C ) % in WT ) had to be ≤-10% ( decrease of editing in emp21 ) or ≥10% ( increase of editing in emp21 ) for all the four pairwise comparisons ( emp21-1 vs . WT-1 , emp21-1 vs . WT-2 , emp21-2 vs . WT-1 , emp21-2 vs . WT-2 ) . This method was modified from the previously reported [62] . The same method was used to detect the extent of editing at six sites in the emp5-4 mutant and the emp5-4 emp21-1 double mutant . Embryo and endosperm samples were dissected from wild type and emp21 kernels at 12 DAP sampled from three independent ears . An RNA editing analysis was conducted from these samples by directly sequencing the RT-PCR amplicons , as described in [18] . The necessary cDNA was obtained as described above and subjected to a series of RT-PCRs directed at the full set of 35 mitochondrial genes ( primers given in S4 Table ) . And each experiment was replicated three times . Mitochondria were isolated from embryo and endosperm of emp21-1 and wild type at 12 DAP . The Blue native polyacrylamide gel electrophoresis ( BN-PAGE ) and in-gel complex I activity analyses were performed as previous report [85] . The complex V activity assay was performed following the description by Wittig et al [86] . Mitochondrial proteins extracted from embryo and endosperm at 12 DAP were separated by BN-PAGE and/or SDS-PAGE . And then proteins were transferred to the nitrocellulose membrane . Proteins were detected by using specific antibodies as described previously [66] . The Y2H assays were performed as described previously by Glass et al . 2015 [41] . Briefly , the fragments ( without the targeted peptide ) of Emp21 , Emp5 , and ZmMORFs and truncated fragments of Emp5 , Emp21 and ZmMORF8 ( ZmMORF8C182 , S12 Fig ) were recombined into either the GAL4 activation domain plasmids ( pGADT7 ) or the GAL4 binding domain plasmids ( pGBKT7 ) using restriction enzyme ligation ( Clontech Laboratories ) . Both plasmids were then co-transformed into yeast strain Y2H Gold . Protein–protein interactions were determined by measuring in SD/-Trp-Leu-His-Ade dropout ( QDO ) and SD/-Trp-Leu-His-Ade dropout + x-α-gal ( QDO+ x-α-gal ) plates for 6 days at 30°C . To investigate the interaction among ZmMORF8 , EMP21 and EMP5 , plasmids containing N- and C-terminal fusions of YFP were co-transformed into Arabidopsis protoplasts as previously described [87] . The ZmMORF8ΔMORF ( S12 Fig ) which was deleted MORF box was cloned by fusion PCR using primers ZmMORF8-F14/F19 and ZmMORF8-R14/R19 ( S4 Table ) . The protoplasts were observed using ZEISS LSM 880 after incubating under dark for 24–30 h . The mitochondria were stained by MitoTracker Red ( ThermoFisher Scientific ) .
|
Plant mitochondrial transcripts harbor hundreds of cytidine ( C ) that need to be converted to uridine ( U ) prior to translation . Typically , PPR proteins are recruited to specify the target Cs by binding to the upstream sequences of the edited site , and a given PPR is responsible for the editing of just one or a few sites , except atypical PPR-DYW protein DYW2 and MEF8 , and P-class PPR NUWA . Here , we identified a canonical PPR-DYW protein EMP21 that is required for the editing of 81 mitochondrial target Cs . The editing is essential for mitochondrial complex assembly , and embryo and endosperm development in maize . In addition , the genetic assay showed that a portion ( ~30% ) of the rpl16-458 sites are edited by EMP21 and EMP5 jointly . EMP21 and EMP5 directly interact with ZmMORF8 . These findings revealed that the canonical PPR-DYW protein EMP21 plays a major role in the editing of a large number of sites in mitochondria , possibly by binding directly to the upstream sequences of certain edited sites or by recruiting other editing factors to the edited sites .
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2019
|
Empty Pericarp21 encodes a novel PPR-DYW protein that is required for mitochondrial RNA editing at multiple sites, complexes I and V biogenesis, and seed development in maize
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There are few animal models of dengue infection , especially in immunocompetent mice . Here , we describe alterations found in adult immunocompetent mice inoculated with an adapted Dengue virus ( DENV-3 ) strain . Infection of mice with the adapted DENV-3 caused inoculum-dependent lethality that was preceded by several hematological and biochemical changes and increased virus dissemination , features consistent with severe disease manifestation in humans . IFN-γ expression increased after DENV-3 infection of WT mice and this was preceded by increase in expression of IL-12 and IL-18 . In DENV-3-inoculated IFN-γ−/− mice , there was enhanced lethality , which was preceded by severe disease manifestation and virus replication . Lack of IFN-γ production was associated with diminished NO-synthase 2 ( NOS2 ) expression and higher susceptibility of NOS2−/− mice to DENV-3 infection . Therefore , mechanisms of protection to DENV-3 infection rely on IFN-γ-NOS2-NO-dependent control of viral replication and of disease severity , a pathway showed to be relevant for resistance to DENV infection in other experimental and clinical settings . Thus , the model of DENV-3 infection in immunocompetent mice described here represents a significant advance in animal models of severe dengue disease and may provide an important tool to the elucidation of immunopathogenesis of disease and of protective mechanisms associated with infection .
Dengue viruses ( DENV ) are the most prevalent mosquito-borne RNA viruses worldwide , classified serologically into four antigenically distinct types ( DENV-1–4 ) . They are transmitted to humans by the mosquitoes Aedes aegypti and Aedes albopictus [1]–[3] . According to the World Health Organization ( WHO ) a total of 500 , 000 cases of dengue hemorrhagic fever ( DHF ) occur annually , and 20 , 000 deaths are estimated to happen every year [4]–[5] . The hallmark of severe dengue infection is a transient increase in vascular permeability , characterized by hemorrhagic manifestations , thrombocytopenia , and hemoconcentration , resulting in plasma leakage , which is believed to be immune mediated [4] , [6]–[7] . Furthermore , deranged liver function is very common in patients with dengue infection and is generally manifested by the elevation of transaminase levels representing reactive hepatitis [8]–[10] . Treatment of dengue fever ( DF ) and of the severe forms of dengue infection is largely supportive [7] , [11] . The pathogenesis of DENV infection remains poorly understood and involves a complex interplay of viral and host factors [1] , [3] , [6] , [12]–[15] . The lack of a suitable animal model that emulate dengue disease , specially the severe forms ( DHF/DSS ) , has hindered progress in many areas of dengue research , including pathogenesis , immunity , drug development and vaccine design and testing [7] , [16] . Several studies in mice and humans have noted higher levels of viremia in severe dengue disease , which supports the assertion that increased viral replication is associated with more severe disease manifestation [17]–[21] . However , we and other groups have also demonstrated that inflammatory response , characterized by cytokine storm , also plays a fundamental role in dengue pathogenesis [7] , [22]–[29] . Most of these studies were first characterized in a model of dengue infection using a mouse-adapted DENV-2 strain that mimics several clinical parameters seen in human disease , without affecting the CNS [22] , [24]–[28] . In addition to characterizing mechanisms associated with pathogenesis , the DENV-2 model showed to be adequate to study pathways important for host resistance to infection . In this regard , we have recently demonstrated that IFN-γ production depends on IL-12 and IL-18 combined action and mediated host resistance to DENV-2 infection in a nitric oxide-dependent manner [30] . The development of animal models of all 4 DENV serotypes is extremely necessary and may help to determine: ( i ) whether different pathogenetic mechanisms operate in the different serotypes , ( ii ) the consequence of sequential infection and ( iii ) the efficacy of drugs and vaccine candidates [11] , [31] . In this regard , in the present study we characterize a novel model of DENV-3 infection in immunocompetent adult mice , using the same strategy previously used for DENV-2 model . After inoculation of the adapted DENV-3 strain , we observed the occurrence of the major clinical manifestations of severe dengue infection , characterized by inoculum-dependent lethality that was preceded by significant clinical and biochemical alterations such as thrombocytopenia , hemoconcentration , plasma extravasation , liver damage with elevated AST/ALT levels in serum and massive cytokine production . Moreover , DENV-3 was detected in spleen and liver and viremia was detected from the fifth day of infection . There was also enhanced expression of NS3 in liver and NS1 concentration in plasma . The development of animal models for the four DENV serotypes will also allow to determine whether mechanisms of protection to infection are similar or not among the different serotypes . Hence , using this novel DENV-3 model , we demonstrate that the IFN-γ-induced Nitric Oxide production , found to be essential for host resistance to DENV-2 infection [30] plays a major role in host protection to DENV-3 infection . Mice deficient for IFN-γ and for NOS2 are markedly susceptible to DENV-3 infection , with elevated lethality rates , more severe disease and increased viral load after infection . Therefore , we describe a novel model of DENV-3 infection in immunocompetent mice that emulates many of the manifestations seen in human disease . The present model may provide an important tool to study host–virus interactions and mechanisms mediating protection or those associated with severe disease manifestation .
This study was carried out in strict accordance with the Brazilian Government's ethical and animal experiments regulations . The experimental protocol was approved by the Committee on the Ethics of Animal Experiments of the Universidade Federal de Minas Gerais ( CETEA/UFMG , Permit Protocol Number 113/09 ) . All surgery was performed under ketamine/xylazine anesthesia and all efforts were made to minimize animal suffering . Mice deficient for IFN-γ and NOS-2 were obtained from The Jackson Laboratory and were bred and maintained at the Gnotobiology and Immunology Laboratory of Instituto de Ciências Biológicas . Mice deficient for IL-12p40 were kindly provided by Dr . J . Magran through Dr . L . V . Rizzo ( Instituto de Ciências Biomédicas ( ICB ) , University of São Paulo , São Paulo , Brazil ) and were bred and maintained at the Gnotobiology and Immunology Laboratory of Instituto de Ciências Biológicas . Mice deficient for IL-18 [32] and IFNGR1 were kindly provided by Dr . F . Q . Cunha and were bred and maintained at the Gnotobiology and Immunology Laboratory of Instituto de Ciências Biológicas . All mice were on C57BL/6J genetic background ( back-crossed at least 10 times ) and wild-type control C57BL/6J ( WT ) mice were used . For experiments , 7–10 weeks old mice were kept under specific pathogen–free conditions , in filtered-cages with autoclaved food and water available ad libitum . Adult BALB/c mice ( 7–10 weeks ) were also used . During DENV-3 virus adaptation process , newborn and BALB/c mice of different ages ( 1-4 weeks old ) were maintained at the same conditions described above . A clinical isolate of Dengue virus type 3 ( DENV-3 ) , genotype I , was used ( access number JN697379 ) . All work with the infectious virus was performed in a BSL-2 facility of the Laboratório de Interação Microrganismo-Hospedeiro - ICB – UFMG . Dengue virus 3 ( DENV-3 ) was adapted similarly as previously described [22] . Briefly , the virus had undergone two passages intracerebrally ( ICR ) in suckling mice . The brains of the moribund mice were harvested for preparing 10% ( w/v ) mice brain suspension in modified Eagle's medium ( MEM ) . The death of the suckling mice was observed on day 5 after cerebral infection . After that , 10 sequential passages through BALB/c mice of different ages ( 1–4 weeks old ) by intraperitoneal ( i . p . ) injection were performed . Two sequential passages were carried out for each age group of BALB/c mice . After each passage , the brains of the moribund mice were harvested for preparing 10% brain suspension and then used for the next passage . The last passage of DENV-3 obtained from the brain of 4-week-old BALB/c mice was used for five sequential passages in the brain of suckling mice ICR and was collected to produce stocks . Ten percent brain suspension served as virus stock and was stored at −70°C . In addition , virus stocks were produced from infected mosquito C6/36 cells , in vitro . To calculate virus titer , plaque assays were conducted in LLC-MK2 cells as described below . Viral titer of stock was 5 , 8×106 PFU/mL of cell supernatant . Suspension from brain of non-infected mice was prepared in a similar way and was used as control in all experiments . In some experiments , the suspension of the adapted DENV-3 virus was UV-irradiated ( exposure of virus stock for 15 min to a UV lamp producing irradiation predominantly at 365 nm ) or heat inactivated ( 56°C for 1 h ) before inoculation of mice . For infection experiments , the virus-containing brain suspensions were diluted in endotoxin-free PBS ( 3 . 2 mM Na2HPO4 , 0 . 5 mM KH2PO4 , 1 . 3 mM KCl , 135 mM NaCl ) and injected i . p . into mice . For the evaluation of lethality , mice were inoculated i . p . and lethality rates evaluated every 12 h for 14 days . The various other parameters were evaluated at 3 , 5 and 7 days or daily after i . p . inoculation of the virus . In all experiments using genetically deficient mice , relevant WT controls were performed in parallel . Non-infected animals were inoculated with brain suspension from non-infected suckling-mice diluted in a similar manner . In the experiments involving genetically deficient mice , the NI group represents the pooled results obtained from the analysis of deficient mice and WT non-infected mice . Results were pooled for ease presentation . In some experiments IL-18 was neutralized by daily i . p . injection of 1mg/kg of recombinant human IL-18BP per animal ( hIL-18 bp ) , starting 1 hour after DENV-3 inoculation and lasting until day 6 after virus inoculation . The dose was chosen based in a previous study of [33] . Control animals received the vehicle saline alone . The hIL-18 bp isoform was a kind gift of Dr . Amanda Proudfoot from Merck-Serono Pharmaceuticals ( Geneve , Switzerland ) . In other experiments , mice were pretreated i . p with 100 µL anti-DENV-3 polyclonal antiserum or control serum , 60 min before inoculation of the adapted DENV-3 . The anti-DENV serum utilized was kindly given by Dr . Ricardo Galler from Departamento de Bioquímica e Biologia Molecular do Instituto Oswaldo Cruz-Fiocruz , RJ , Brazil [34] . Serum was obtained from Rhesus monkeys ( macaca mullata ) inoculated subcutaneously on the anterior surface of the left forearm with 0 , 5 ml of the viral suspension containing 105 PFU of the DENV-3 H87 ( 13 dpi ) [34] . Murine bone marrow cells were isolated from femurs and were differentiated into myeloid DCs after culturing ( change on days 3 , 6 , and 8 ) at 2×106 cells/ml for 10 days in RPMI supplemented with 10% FCS and 4% J558L cell-conditioned medium as a source of GM-CSF as described [35] . DCs were plated in 96-well microculture plates ( at 2×105 cells/well in DMEM supplemented with 2 mM L-glutamine and 2×10−5 M 2-ME ) and for infection , cells were incubated with 50 µL of the brain suspension containing DENV-3 at a MOI of 0 , 05 PFU/cell in the presence or not of IFN-γ ( 100 U/ml ) . Negative controls were stimulated with sterile brain suspensions submitted to the same procedures of the DENV-3 containing brain homogenate . For positive controls , cells were stimulated with TLR4 agonist LPS ( Escherichia coli , serotype O111∶B4 , Sigma-Aldrich , at 100 ng/ml ) . Cell supernatants were harvested after 72 h of stimulation for nitrite measurements by Griess reagents . Cell-free culture medium was obtained by centrifugation and assayed for nitrite content , determined by the Griess method [36] . For this assay , 0 . 1 ml of culture medium or serum was mixed with 0 . 1 ml of Griess reagent in a multiwell plate , and the absorbance at 550 nm read 10 min later . The NO2− concentration was determined by reference to a NaNO2 standard curve ( 1 to 200 µM ) . Diaminofluorescein diacetate ( DAF-2DA ) , a non-fluorescent cell permeable dye , was used . For NO estimation , esplenocytes of non-infected and DENV-3- infected mice were isolated ( 106 cells/well ) and incubated with 10 µM , DAF-2DA for 30 min at 37°C and fluorescence was determined in a fluorometer ( Synergy 2 , BIOTEK , USA ) at excitation wave length 488 nm and measuring emission at 515 nm . Data were expressed as mean ± SEM of fold increase of fluorescence over stained-esplenocytes of NI mice . Mice were assayed for viral titers in blood , brain , spleen and liver . Blood samples ( 50 µL ) were collected in heparinized tubes , diluted in 450 µL of endotoxin-free PBS ( 3 . 2 mM Na2HPO4 , 0 . 5 mM KH2PO4 , 1 . 3 mM KCl , 135 mM NaCl ) and stored at −70°C . For virus recovery in brain , spleen and liver , the organs were collected aseptically in different time points and stored at −70°C until assayed for DENV-3 virus . Tissue samples were weighed , grounded by using a pestle and mortar and prepared as 10% ( w/v ) homogenates in minimal essential medium ( MEM ) without fetal bovine serum ( FBS ) . Viral load in supernatants of tissue homogenates and blood samples were assessed by direct plaque assay using LLC-MK2 cells as described [24] , [27] . In Brief , LLC-MK2 cells were seed in 6 well plates and grown to confluence . 24 hours later , cell layers were incubated with serially diluted 0 . 4 mL of virus samples for 1 and half hour and overlaid with 1 , 5% methylcellulose +199 medium , 3% FBS . Plates were incubated for 7 days at 37°C , fixed in 10% formaldehyde , and stained with 1% crystal violet in water for 30 min . Plaques were counted by eye . The results were measured as plaque forming units ( PFU ) per gram of tissue weight or per mL of blood . The limit of detection of the assay was 100 PFU/g of tissue , or per mL . Plaque purification was performed as previously described [37] . Briefly , LLC-MK2 cells were seeded in a 6-wells plate and grown to 80% confluence . Then , cells were infected with different inoculums of the brain adapted DENV-3 , overlaid with agarose 0 , 5% prepared in DMEM 5% FBS . Culture was incubated at 37°C , 5% CO2 for 7 days and single plaques were picked for expansion in LLC-MK2 cells . Nine clones were obtained and were titrated in LLC-MK2 cells for further in vivo evaluation . Blood was obtained from the cava vein in heparin-containing syringes at the indicated times under ketamin and xylazine anesthesia ( 150 mg/Kg and 10 mg/Kg , respectively ) . The final concentration of heparin was 50 u/ml . Platelets were counted in a Neubauer chamber . Briefly , 10 ul of solution ( amonium oxalate 1% and blood in a dilution of 1∶100 ) were placed in the chamber and platelets were visualized in a Nikon XP-1000 microscope , magnification of 400× , using phase contrast . Results are presented as number of platelets per µl of blood . For the determination of the hematocrit , a sample of blood was collected into heparinized capillary tubes ( Perfecta ) and centrifuged for 10 min in a Hematocrit centrifuge ( Fanem , São Paulo , Brazil ) . Dengue virus NS1 antigen was measured in individual serum samples ( 1∶3 dillution ) , using a commercially ELISA available kit ( BIO-RAD Platelia ™ Dengue NS1 AG ) . The optical density reading obtained with a spectrophotometer set at 450 nm was proportional to the amount of NS1 antigen present in the sample . Results are expressed as absorbance at 450 nm . The concentration of cytokines ( TNF-α , IFN-γ , IL-6 , IL-12p40 and IL-18 ) in serum or tissue samples was measured using commercially available antibodies and according to the procedures supplied by the manufacturer ( R&D Systems , Minneapolis , except for IL-18 , manufactured by BD Pharmingen ) . Results are expressed as pg/mL or pg/100 mg of tissue . The detection limit of the ELISA assays was in the range of 4–8 pg/ml . The extravasation of Evans blue dye into the tissue was used as an index of increased vascular permeability , as previously described [38] . Briefly , Evans blue ( 20 mg kg−1 ) was administered i . v . ( 1 ml kg−1 ) via an eye vein 30 min prior to mice sacrifice . After that , one lobe of liver and the left lung were cut and allowed to dry in a Petri dish for 24 h at 37 . C . The right ventricle was flushed with 10 ml of phosphate-buffered saline ( PBS ) to wash the intravascular Evans blue in the lungs . The left lung was then excised and used for Evans blue extraction . The dry weight of the tissue was calculated and Evans blue extracted using 1 ml of formamide ( 24 h at room temperature ) . The amount of Evans blue in the tissue was obtained by comparing the extracted absorbance with that of a standard Evans blue curve read at 620 nm in an ELISA plate reader . Results are presented as the amount of Evans blue per mg per 100 mg of tissue . The transaminases Aspartate aminotransferase ( AST ) and Alanine aminotransferase ( ALT ) activity were measured in individual serum samples , using a commercially colorimetric available kit ( Bioclin , Quibasa , Belo Horizonte , Brazil ) . Results are expressed as the mean mean ± SEM of transaminase concentration in U/dL of plasma . Body weight ( BW ) and systolic blood pressure ( SBP ) were measured in uninfected and infected mice on days 0 , 3 , 4 , 5 , 6 and 7 after infection . All animals were habituated to the blood pressure measurement device for 7 days . SBP was determined with tail-cuff plethysmography method in unanesthetized mice , as previously described [39] . All data are expressed as mean ± SEM . Changes in SBP from baseline are expressed as absolute values as well as areas under the BP curves . Hypernociception was assessed as described by Sachs et al , 2010 [40] . Briefly , mice were placed in acrylic cages with a wire grid floor 15–30 min before testing for environmental adaptation . In these experiments , an electronic pressure-meter was used . It consists of a hand-held force transducer fitted with a polypropylene tip ( INSIGTH Instruments , Ribeirão Preto , SP , Brazil ) [41] . A standard large tip ( 0 . 5 mm2 ) was applied in the hind paw of the DENV-3 infected mice or it respective controls and an increasing perpendicular force was applied to the central area of the plantar surface of the hind paw to induce the flexion of the knee joint , followed by paw withdraw . After the flexion-elicited withdrawal threshold , the intensity of the pressure was automatically recorded . The value for the response was obtained by averaging three measurements . Animals were tested daily after inoculation . Results are expressed as Δ withdrawal threshold ( g ) calculated by subtracting zero-time mean measurements from the time interval mean measurements . For typing of adapted-DENV-3 virus , RNA was extracted with QIAMP viral RNA kit ( Qiagen , Hilden , Germany ) from the adapted-DENV-3 after different passages in C6/36 mosquito cells and of clone 4 obtained from the adapted DENV-3 . The non-adapted DENV-3 was also used as control . First-strand cDNA synthesis for subsequent PCR assays was performed with approximately 400 ng of total RNA and random primer C118A ( PROMEGA , Madison ) . A PCR assays was performed with specific primer combinations D1/TS3 ( DENV-3 ) , previously described by [42] . PCR products were run on a 1 . 5% agarose gel stained with ethidium bromide . For evaluation of NOS2 mRNA expression , spleens were removed 3 , 5 and 7 days after DENV-3 inoculation into mice . Total RNA was isolated from tissues by using a QIAGen RNEasy RNA Isolation Kit . The RNA obtained was resuspended in diethyl pyrocarbonate treated water and stocked at −70°C until use . Real-time RT-PCR was performed on a StepOne sequence-detection system ( Applied Biosystems ) by using SYBR Green PCR Master Mix ( Applied Biosystems ) after a reverse transcription reaction of 2 µg of RNA by using M-MLV reverse transcriptase ( Promega ) . The relative level of gene expression was determined by the comparative threshold cycle method as described by the manufacturer , whereby data for each sample were normalized by 18S ribosomal RNA and expressed as a fold change compared with non-infected controls or medium cultivated cells . The following primer pairs were used: 18S ribosomal RNA , 5′-CGTTCCACCAACTAAGAACG-3′ ( forward ) and 5′-CTCAACACGGGAAACCTC AC-3′ ( reverse ) ; and nos2 , 5′- AGCACTTTGGGTGACCACCAGGA-3′ ( forward ) and 5′- AGCTAAGTATTAGAGCGGCGGCA -3′ ( reverse ) . Spleen cells were evaluated ex vivo for extracellular molecular expression patterns and for intracellular cytokine expression patterns . Briefly , spleens were removed from infected mice on day 7 after infection and cells were isolated , and immediately stained for surface markers , fixed with 2% formaldehyde and then permeabilized with a solution of saponin and stained for 30 min at room temperature , using anti-IFN-γ monoclonal antibodies directly conjugated with FITC . Preparations were then analyzed using a FACScan ( Becton Dickinson ) , gating on a total lymphocyte/monocyte population . The antibodies used for the staining were rat immunoglobulin control ( s ) , anti-CD4-PE , anti-CD8-PE , anti-NK1 . 1-PE , anti-CD3-biotin and anti-IFN-γ-FITC ( all from Biolegend Inc ) . For detection of CD3 staining , cells were incubated with streptavidin conjugated to PE-Cy5 fluorochrome ( Serotec Inc ) for 30 min at 4°C before fixing . Spleen cells were analyzed for their intracellular cytokine expression patterns and frequencies using the software Flow Jo 7 . 2 ( Tree Star Inc ) . The frequency of positive cells was analyzed using a gate that included lymphocytes , large blast lymphocytes and monocytes/macrophages . Limits for the quadrant markers were always set based on negative populations and isotype controls . Liver samples from adult euthanized mice were obtained at the indicated time points . Afterwards , they were immediately fixed in 10% buffered formalin for 24 hours and embedded in paraffin . Tissue sections ( 4 µm thicknesses ) were stained with hematoxylin and eosin ( H&E ) and evaluated under a microscope Axioskop 40 ( Carl Zeiss , Göttingen , Germany ) adapted to a digital camera ( PowerShot A620 , Canon , Tokyo , Japan ) . Histopathology score was performed according to a set of custom designed criteria modified from [43] evaluating hepatocyte swelling , degeneration , necrosis and hemorrhage , added to a five-points score ( 0 , absent; 1 , minimal; 2 , slight; 3 , moderate; 4 , marked; and 5 , severe ) in each analysis . For easy interpretation , the overall score was taken into account and all the parameters totalized 20 points . A total of two sections for each animal were examined and results were plotted as the media of damage values in each mouse . For immunohistochemistry , sections were treated with 3% H2O2 diluted in Tris-buffered saline ( TBS ) ( pH 7 . 4 ) for 30 minutes . For antigen retrieval , tissue sections were immersed in citrate buffer ( pH 6 . 0 ) for 20 minutes at 95°C . For NOS2 detection the slides were then incubated with the rabbit polyclonal anti-NOS2 ( N-20 , sc-651 , Santa Cruz Biotechnology , Santa Cruz , CA ) diluted 1∶100; at 4°C overnight in a humidified chamber . For detection and quantification of DENV-3 infected cells an anti-DENV NS3 MAb E1D8 or an isotype control was used in a dilution of 1∶350 for liver and 1∶100 for brain; at 4°C overnight in a humidified chamber . After incubation , tissue sections were washed with TBS and treated with a labeled streptavidin-biotin kit EnVision® + Dual Link System-HRP ( Dako ) . Sections were then rinsed in PBS with 3 , 3′-diaminobenzidine tetrahydrochloride ( K3468 , Dako ) for 5 minutes and stained with Mayer's hematoxylin . For quantification of NOS-2+ cells or NS3+ cells , cells counts were performed in 10 alternate microscopic high-power fields ( ×400 ) for each sample ( 4–5 mice per group ) . It was counted the number of positive hepatocytes , kupffer cells and inflammatory cells in each field . Areas of necrosis and hemorrhage were excluded from the analysis . The distribution of NS3 was assessed throughout the brain on at least two different brain coronal sections . Liver intravital microscopy was performed as previously described [44] . Briefly , mice were anesthetized as describe previously . Mice were placed in a right lateral position on an adjustable microscope stage . A lateral abdominal incision along the costal margin to the midaxillary line was made to exteriorize the liver , and all exposed tissues were moistened with saline-soaked gauze to prevent dehydration . The liver was placed on a stage for an upright microscope and the liver surface was then covered with a coverslip to hold the organ in position . The liver was visualized using intravital multiphoton and confocal microscopy system based on a modified Olympus confocal microscope ( FV300 ) in an up- right configuration ( BX51 Microscope ) . The images presented were obtained using the confocal laser at 488 nm using a 10/0 . 30 UplanFLN objective . Cells were fluorescently labeled by rhodamine 6G ( 0 , 05%; i . v . ) to assess hepatocyte size by measuring the longest cell axis of 20–30 cells/field ( Image J , NIH , USA ) . Sinusoids were labeled by i . v . injection of phycoerythrin-anti PECAM-1 ( 0 , 5 µg/mice ; PE anti-CD31 , clone 390 ; Ebioscience , USA ) and the percentage of perfused sinusoids was assessed by digital quantification of the area fraction stained by the antibody ( Image J , NIH , USA ) . Results are shown as means ± S . E . M . Percent inhibition was calculated by subtracting the background values obtained in non-infected animals . Differences were compared by using analysis of variance ( ANOVA ) followed by Student-Newman-Keuls post-hoc analysis . Differences between lethality curves were calculated using Log rank test ( Graph Prism Software 4 . 0 ) . Changes in SBP from baseline are expressed as absolute values as well as areas under the BP curves . Results with a P<0 . 05 were considered significant .
Infection of adult C57BL/6j ( Figure 1A ) or BALB/c ( Figure S1A ) mice with an adapted strain of DENV-3 induced an inoculum-dependent lethality that was usually observed around the 7th or 6th days after inoculation of DENV-3 , respectively . Next , we performed series of experiments to characterize the disease caused by the adapted DENV-3 in both mice strains . In all experiments , control mice were inoculated with brain suspension which caused no clinical or biochemical alterations in comparison with non-inoculated mice ( data not shown ) . Infection kinetic studies were carried out with an inoculum of 10LD50 and 1LD50 for C57BL/6j and BALB/c mice strains , respectively . Inoculi were equivalent to 1000 and 100 PFU , respectively , as verified by plaque assay in LLC-MK2 cells . Experiments were conducted till day 7 , the peak of infection , as there was significant lethality in WT mice after this period ( Figures 1A and S1A ) . Lethality of both strains of adult mice infected with DENV-3 was preceded by significant changes in clinical and biochemical parameters as shown in Figures 1 , 2 and S1 . There was marked weight loss , beginning at day 4 after infection , reaching about 20% on day 7 after DENV-3 inoculation ( Figure 1B and S1B ) . There was also significant hypernociception in response to mechanical stimulation , an index of pain in experimental animals , lasting from day 3 until day 7 post-infection ( Figure 1C and S1C ) . In addition , DENV-3 infection induced significant hematological alterations . Thrombocytopenia was observed as early as 3 days after infection and platelets counts were around 50% of normal at day 7 ( Figure 1D and S1D , right panels ) . The hematocrit , a marker of hemoconcentration , was elevated from day 3 and increased to greater than 50% by day 7 ( Fig . 1D and S1D , left panel ) . In addition to hemoconcentration , there was marked plasma extravasation in target organs , as assessed by increase in concentration of Evans blue dye in liver and lungs , respectively ( Fig . 1E ) . These findings were accompanied by changes in hemodynamic parameters , showed by reduction in systolic blood pressure , more sharply on day 7 p . i . Hence , at this time point , there was a striking 40 mmHg fall in systolic blood pressure in DENV-3-infected mice ( Figure 1F ) . The concentration of liver enzymes in serum ( Aspartate aminotransferase [AST] and Alanine aminotranferease [ALT] ) were elevated after DENV-3 infection . There was an increase of AST and ALT of approximately 10 and 30 times , respectively , at day 7 after infection in both strains ( Figure 1G and S1E ) . Evaluation of the liver microvasculature by intravital confocal microscopy revealed a significant increase in hepatocyte diameter at day 7 after DENV-3 inoculation as compared to non-infected mice ( Figure S2A ) . There was a decrease of sinusoidal perfusion which paralleled the increase in hepatocyte diameter ( Figure S2B ) . The levels of IL-6 , TNF-α , IFN-γ , IL-12/23p40 and IL-18 were evaluated in spleen or serum of infected mice . Overall , there was a good correlation between levels of cytokines in serum and spleen and the severity of disease ( Figure 1H-L ) . IL-6 levels were elevated at 5 and 7 dpi in spleen and 7 dpi in serum after DENV-3 inoculation ( Figure 1H ) . Levels of TNF-α rose rapidly from day 3 in spleen and serum of infected mice , peaking on day 7 ( Figure 1I ) . Nevertheless , IFN-γ peaked on day 5 , and still remained elevated at day 7 in spleen and serum of DENV-3 infected mice as compared to NI group ( Figure 1J ) . There were detectable levels of both IL-12/23p40 ( Figure 1L , left panel ) and IL-18 ( Figure 1L , right panel ) cytokines in the spleen of WT mice already on day 5 of infection . IL-18 levels reduced to basal values at the 7th day of infection , while IL-12/23p40 remained above background levels at this time point ( Figure 1L right and left panels , respectively ) . Of note , neither limb paralysis nor any other sign of CNS inflammation ( eg . levels of TNF-α and IL-6 in brain ) were noticed after peripheral inoculation of DENV-3 into adult mice ( data not shown ) . Therefore , in summary , we show that immunocompetent adult mice infected systemically with an adapted strain of DENV-3 virus presented several clinical and pathological systemic features that resemble severe dengue disease in humans . After inoculation , the virus was detected from day 3 in the spleen ( Figure 2A and S3A ) , from day 5 in liver ( Figure 2B and S3B ) , and there was significant viremia from day 5 p . i . ( Figure 2C and S3C ) in C57BL/6j and BALB/c mice , respectively . Viral load escalated further at day 7 in all tissue aforementioned ( Figure 2A–C and S3A–C ) . In addition to high viremia , serum levels of dengue virus NS1 antigen was increased on days 5 and 7 after DENV-3 infection ( Figure 2D ) . The presence of DENV-3 in the liver tissue of infected mice was also investigated by immunohistochemistry assay using an anti-dengue NS3 antibody . As expected , negative controls did not present any positive reaction ( Figure 2E ) . On the other hand , we found NS3-positive staining in liver of DENV-3 inoculated mice ( Figure 2E ) , demonstrating active viral replication of the virus in this target organ . The histoquantitative analyses revealed elevated number of cells expressing virus antigens on day 7 after DENV-3 inoculation . Of the total number of NS3-positive cells , 84% were hepatocytes , whereas there were also 8% kupffer cells and 7% inflammatory cells , as assessed morphologically . These results suggested that DENV-3 replicates in such cells , mainly in hepatocytes , which are present in the same areas of tissue damage ( Figure 2E ) . Corroborating this data , marked hepatic injury was found in DENV-3 infected mice at day 7 p . i . ( Figure 2F ) . Histopathological analyses revealed intense multifocal to coalescing areas of hemorrhagic necrosis ( Figure 2F ) . Overall , all infected mice exhibited inflammatory infiltrates composed of neutrophils , macrophages and lymphocytes around blood vessels ( portal and central veins ) and scattered throughout the parenchyma . Moderate to intense hepatocyte swelling and degeneration were also detected . The total score in DENV-3 infected group was 13 . 6±4 . 2 points , in a total of 20 points , demonstrating a significant degree of liver injury , possible directly associated with the higher viral replication in this organ . DENV-3 obtained from C6/36 cell culture supernatant induced disease that was very similar to the disease induced by viral stocks prepared from brain suspension ( Figure 2G-H and S3D ) . UV irradiation or heat inactivation of the virus prevented lethality and any form of clinical manifestation of C57BL/6j mice in vivo ( Figure 2G and data not shown ) . Figure S4A demonstrates typing of adapted-DENV-3 after several passages in mosquito C6/36 cells . Moreover , treatment of C57BL/6j mice with an anti-DENV-3 polyclonal antiserum obtained from DENV-3-infected monkeys reduced the mortality rate of adapted-DENV-3 infected mice to approximately 50% ( Figure 2H ) . Plaque purification technique was performed to isolate DENV-3 clones and test the capacity of these clones to induce severe disease . We obtained nine DENV-3 clones that were expanded in LLC-MK2 cells and tested in vivo . These clones were designated clones 1 to 9 . BALB/c mice that were infected by the DENV-3 clones 1 , 2 or 5–9 did not present any form of clinical manifestation and therefore 100% survived to inoculation ( Figure S3D ) . However approximately 25% of mice infected with DENV-3 clone 3 progressed to death ( Figure S3D ) . In addition , all mice infected with DENV-3 clone 4 , BALB/c ( Figure S3D ) or C57BL/6j ( Figure S5A ) strains , presented severe clinical manifestation of disease showed by enhanced mortality rate ( Figure S3D and S5A ) , viremia , thrombocytopenia and hemoconcentration ( Figure S5B–D ) , similarly to mice infected with adapted-DENV-3 grown in C6/36 or brain-derived DENV-3 adapted virus . Figure S4B demonstrates typing of adapted DENV-3 ( clone-4 ) after passage in LLCMK-2 cells . In addition , we have performed subsequent rounds of plaque purification of the adapted Clone 4 of DENV-3 and we have found that injection of a clone from the clone in adult mice induced disease that was similar to the disease seen in mice infected with the original clone ( hematocrit: NI: 38 . 8±0 . 9%; DENV-3 ( 7th dpi ) : 45±1 . 3% , p = 0 . 014; platelet counts: NI: 891±22×103/µL of blood; DENV-3 ( 7th dpi ) : 570±31×103/µL of blood , p<0 . 001 ) To evaluate the presence of virus in the CNS , we measured number of DENV-3 plaques after systemic or CNS inoculation in adult and weaning mice . Systemic injection of the virus in adult mice resulted in detection of the virus in spleen ( Figure 2A and S6A ) but no detection of virus in the brain ( Figure S6B ) . In contrast , systemic inoculation of the virus in weaning mice resulted in significant detection of the virus in the brain ( Figure S6B ) . However , the viral load in the CNS was much higher when the virus was inoculated directly into the brain ( Figure S6B ) . Immunohistochemistry analysis concurred with the findings above and revealed that NS3 staining was only detect in newborn mice and specially after direct injection into the brain ( Figure S6C ) . These data corroborate with the absence of neurologic symptoms during disease , as well as with the lack of cytokine production in brain tissue , as discussed above . Therefore , the adapted DENV3 strain still maintains its neurotropism in newborn mice which is lost as mice ages and probably correlates with the development of the blood brain barrier . As shown in Figure 1J , during the time course of DENV-3 infection , there was an increase in levels of IFN-γ in serum and spleen from the 5th day of infection that was maintained at the 7th day p . i . ( Figure 1J , right and left panels , respectively ) . In addition , IFN-γ levels in the liver of adapted-DENV-3 mice were also increased from day 5 , reaching higher values on day 7 ( NI = 36±11 pg/100 mg of tissue; 3 d = 64±6 pg/100 mg of tissue; 5 d = 109±11 pg/100 mg of tissue; 7 d = 216±31 pg/100 mg of tissue; n = 6 , p<0 . 05 ) . FACS analysis of esplenocytes isolated from DENV-3 infected mice revealed IFN-γ staining in about 12% of total cells on 7th day after inoculation ( Figure 3A ) . There was an increase in expression of IFN-γ in CD4+ T cells , CD8+ T cells , CD3−NK1 . 1+ NK cells , and CD3+NK1 . 1+ NKT cells . Significantly , over 46% of CD4+ T cells , 36% of CD8+ T cells and 36% of CD3−NK1 . 1+ NK cells and 35% of CD3+NK1 . 1+ NKT cells were IFN-γ+ at this period in comparison with cells of non-infected mice ( Figure 3A ) . To investigate in vivo the role played by IFN-γ during DENV-3 infection , wild type ( WT ) and IFN-γ deficient ( IFN-γ−/− ) mice were inoculated with 1LD50 of adapted DENV-3 and mortality rate and disease parameters were evaluated . After infection , 100% of IFN-γ−/− mice were dead before day 9 of infection , while less than 30% of WT mice had succumbed to infection after 14 days of inoculation of DENV-3 ( Figure 3B ) . In fact , approximately 75% of IFN-γ−/− mice were already dead at day 6 after infection ( Figure 3B ) , which led us to perform the subsequent analysis on day 5 after infection . The early lethality of IFN-γ−/− mice was associated with increased DENV-3 replication . As early as the 3rd day of DENV-3 inoculation , viremia in IFN-γ−/− mice was detectable ( WT: not detectable; IFN-γ−/−: 2 . 9×103 PFU/mL of blood , n = 4 , p = 0 , 03 ) . Viremia was almost 2log higher in IFN-γ−/− mice in comparison to WT mice at 5 days after infection ( Figure 3C ) . Indeed , at this time point , there was marked increase of viral load in spleen ( Figure 3D , left panel ) and liver ( Figure 3D , right panel ) of infected IFN-γ−/− mice . Moreover , NS3+ staining in liver was strikingly higher in infected IFN-γ−/− mice when compared with their WT littermates ( Figures 3E ) . Again , the hepatocytes were the predominant cell stained for NS3 protein , representing almost 90% of positive cells . Of interest , the virus could not be detected in the brain of WT and IFN-γ−/− infected mice ( data not shown ) . In addition to greater lethality rates and to enhanced viral replication , IFN-γ−/− mice presented more severe manifestation of disease after infection ( Figure 3F–J ) . Hypernociception initiated earlier and remained increased at all time points evaluated ( Figure 3F ) . IFN-γ−/− mice also had more marked thrombocytopenia ( Figure 3G , right panel ) , significant greater increase in hematocrit values ( Figure 3G , left panel ) , and more drastic reduction in systolic blood pressure ( Figure 3H ) than infected WT mice . IFN-γ−/− mice showed greater liver injury after DENV-3 infection , as demonstrated by increased AST activity in plasma ( Figure 3I ) . There were marked histopathological alterations in liver of IFN-γ−/− mice which were more intense than those of WT mice ( Figure 3J ) but similar to those described in WT mice infected with a higher inoculum ( 10LD50 ) ( Figure 2F ) . It is noteworthy that the inoculum used ( 1LD50 ) was capable of causing only mild disease in WT mice . Therefore , the data shown here demonstrate that IFN-γ is produced early during infection and plays an important role in mediating host resistance to DENV-3 infection . One of the well known effector mechanisms induced by IFN-γ after viral infections is enhancement of NOS2 expression in phagocytes . To assess the participation of this pathway in host response to dengue infection , we evaluated the kinetics of NOS2 expression and NO production after DENV-3 infection . As shown in Figure 4A , there was an increase in NOS2 mRNA expression in spleen starting on day 3 after DENV-3 inoculation and rising rapidly on days 5 and 7 post infection . In accordance with these data , WT infected mice showed increased NOS2-positive staining in liver from day 5 after infection , peaking at day 7 after infection ( Figure 4B ) , virtually only in infiltrating leukocytes . In addition , there was elevation in DAF staining of esplenocytes isolated from DENV-3-infected mice , showing increased production of NO in spleen on day 7 post-inoculation ( Figure 4C ) . Consistently with the ability of IFN-γ to induce NOS2 mediated NO production , NOS2 mRNA expression in spleen was markedly decreased in IFN-γ−/− mice ( Figure 4D ) . Similarly , immunohistochemistry analysis revealed that NOS2 positive-cells were almost absent in liver on day 5 post-infection ( Figure 4E ) . Furthermore , there was no production of NO by dendritic cells infected with DENV-3 in vitro ( Figure S7 ) . However , treatment of WT bone marrow derived dendritic cells with IFN-γ prior to DENV-3 infection resulted in production of significant amounts of NO , an effect that was absent in IFNGRI−/− cells ( Figure S7 ) . These data suggest that NOS2-mediated NO production during DENV-3 infection is controlled by IFN-γ . To assess the role played by NOS2-induced NO during DENV infection , NOS2−/− mice and their WT littermates were inoculated with 1LD50 of adapted DENV-3 and lethality rate and disease parameters were evaluated . As shown in Figure 5A , NOS2−/− mice were markedly susceptible to DENV infection . While all knockout animals were dead by the 10th day of infection , only 20% of WT mice had succumbed to infection after 14 days of inoculation of DENV-3 . High viremia has been detected on day 5 after DENV-3 infection in NOS2−/− mice ( WT:7 . 8×103 PFU/mL; NOS-2−/−: 8×105 PFU/mL of blood , n = 6 , p = 0 . 01 ) . Viremia in NOS-2−/− was also higher in comparison to WT littermates at day 7 post-DENV-3 inoculation ( Figure 5B ) . Viral load in spleen ( Figure 5C ) , and liver ( Figure 5D ) were also significantly higher in NOS2−/− than in WT mice at day 7 post-DENV-3-inoculation . In addition , there was also increase in number of NS3-positive cells in liver in comparison with WT infected controls ( Figures 5E ) . Importantly , NOS2−/− mice showed significant mechanical hypernociception ( Figure 5F ) , on days 6 and 7 after DENV-3 inoculation , in comparison with WT infected mice . NOS2−/− also showed greater thrombocytopenia , intense hemoconcentration ( Figure 5G right and left panels , respectively ) and marked reduction in systolic blood pressure ( Figure 5H ) in comparison with WT infected mice . Finally , AST activity in serum was more intense in knockout mice in comparison to DENV-3 infected WT controls ( Figure 5I ) . Similarly to the situation found in infected IFN-γ−/− mice , important histopathological alterations were found in liver of NOS2−/− mice after DENV-3 inoculation ( Figure 5J ) . Histopathological analysis revealed greater disease scores in the NOS2−/− group than in WT mice ( Figure 5J ) . Of note , all alterations seen in NOS2−/−-infected mice were not due to a reduction in IFN-γ production after DENV-3 infection ( for example , at day 7 in serum: NI = not-detectable; WT = 3530±317 pg/mL of serum; NOS2−/− = 2968±619 pg/mL of serum , n = 6 , p = 0 , 44 ) . After DENV-3 inoculation , there were detectable levels of both IL-12/23p40 and IL-18 cytokines in the spleen of WT mice already on day 5 of infection ( Figure 1L ) . The early production of these cytokine is consistent with their possible role in the induction of IFN-γ during DENV-3 infection . As seen in the DENV-2 mouse model [30] , there was drastic reduction in production of IFN-γ after DENV-3 infection in IL-12p40−/− mice , which are deficient for both IL-12 and IL-23 cytokine production , and also in IL-18 binding protein ( IL-18 bp ) treated-WT mice ( Figure S8A ) or IL-18−/− mice ( NI = not-detectable; WT = 4435±562 pg/100 mg of spleen; IL-18−/− = 2373±552 pg/100 mg of spleen , n = 6 , p = 0 , 029 ) . Interestingly , combined depletion of both cytokines resulted in total abrogation of IFN-γ staining in spleen cells after DENV-3 infection ( Figure S8A ) . In accordance with these data , IL-12p40−/− mice , IL-18−/− mice or IL-12p40−/− mice treated with IL-18 bp ( IL12p40−/−+IL18 bp ) were more susceptible to DENV-3 infection ( Figure S8B ) . While only 20% of WT mice were dead at the end of 14 days after infection , all knockout mice had succumbed to infection until day 9 of DENV-3 inoculation . Significantly , earlier deaths were accompanied by elevation in viral loads in blood of IL-12p40−/− , IL-18−/− or IL-12p40−/− treated with IL18 bp mice ( Figure S8C ) . Moreover , combined cytokine depletion resulted in intense hemoconcentration ( Figure S8D , left panel ) , which was greater than in the other infected groups . Thrombocytopenia was also increased but there was no difference between the infected groups ( Figure S8D , right panel ) .
Several important questions in dengue immunopathogenesis are difficult to address without adequate animal models of infection and disease . In the present study , we described a novel model of DENV-3 infection in adult immunocompetent mice which mimics the major manifestations of severe dengue infection in humans . We demonstrated that the inoculation of the mouse-adapted DENV-3 strain by a peripheral route induced an inoculum-dependent lethality preceded by severe disease development in adult immunocompetent mice . The major alterations found during disease development were: 1 ) Lethality preceded by development of hemoconcentration , thrombocytopenia , elevated transaminase levels associated with important liver injury , marked body weight loss and reduction in systolic blood pressure; 2 ) Increased levels of cytokines , including IFN-γ , IL-6 , TNF-α , Il-12/23p40 and IL-18; 3 ) Increased viral load in spleen , liver and blood , virus NS1 antigen serum levels and NS3-staining in hepatocytes of infected mice . All these findings indicate that the mouse model described here will add to existing models [7] , [16] , [45]–[46] and together they may aid in the study of the immunopathogenesis of dengue disease . The difficulty in developing a mouse model for DENV is largely the result of the inability of human clinical isolates to replicate well in mice [7] , [16] , [45] , [47] , [48] . Previously , our group has developed an experimental model of infection with an adapted DENV-2 strain that mimics several clinical parameters seen in human disease . This model has allowed the study of some mechanisms mediating protection or those associated with the development of severe disease [22] , [24]–[28] , [30] . One important key point facing dengue researchers is how a viral strain or serotype variation and infection sequence affects the conditions for immune protection and enhancement [48] . In this context , it becomes extremely necessary the establishment of primary and secondary mouse models with all DENV serotypes to verify if this mechanisms of protection and disease share similarities or differences in the face of different context of infections , and also to test the efficacy of possible vaccine candidates and antiviral compounds . Accordingly , using the same strategy described for the DENV-2 model [22] we generated an adapted strain of DENV-3 by several intracerebral ( ICR ) passages of a non-adapted human DENV-3 ( genotype I ) into weaning and progressively older BALB/c mice . Interestingly , this same non-adapted human strain was able to induce meningoencephalitis and behavioral changes that preceded lethality in adult C57BL/6 mice when inoculated by i . c route , however without causing any systemic clinical manifestation in these mice [49] . Importantly , the infection of immunocompetent mice with this new adapted-DENV-3 strain showed many similarities with the disease found in the DENV-2 infection model described before [22] , [24] and also with the disease seen in humans . We believe that the characterization of these two DENV strains and the establishment of primary and secondary mouse models with all DENV serotypes will aid in evaluation of mechanisms of protection and of disease during distinct context of infections , as well as to test the efficacy of possible vaccine candidates and antiviral compounds . Meanwhile , our studies demonstrated that after adaptation process , the adapted DENV-3 strain acquired the ability to induce systemic severe disease in adult immunocompentent mice ( BALB/c or C57BL/6 strains ) when inoculated by a peripheral route , without affecting the CNS , resembling the major manifestations seen in infected humans . It is important to note that the virus kept its ability to replicate in the CNS when given directly in the brain . Previous studies have also shown that the adaptation process is necessary for an efficient infection and occurrence of disease symptoms . Tesh and colls ( 2001 ) [50] showed that sequential series of liver-to-liver passages of YFVs in hamsters are necessary to lead the generation of more virulent strains . In addition , Shresta and colleagues ( 2004 ) [51] have generated a novel virulent DENV-2 strain , D2S10 , by alternately passages between mosquito cells and non-neuronal tissues of mice . Using AG129 mice ( that lack IFN type I and II receptors ) , they have demonstrated that D2S10 strain was more virulent than the parental strain , PL046 , causing a lethal but nonparalytic disease . Sequence comparisons between D2S10 and the parental strain ( PL046 ) revealed amino acid change difference in a conserved region of E gene , suggesting a role for these particular residues in determining viral virulence and pathogenesis in vivo [51] . Besides this , a full molecular analysis is necessary to identify the viral determinants responsible for this strong phenotype of dengue disease in the present model , but this is beyond the scope of the present study . Hallmark features of human DHF/DSS are vascular leakage , higher viral burden , elevated levels of serum cytokines , hypotension and occurrence of thrombocytopenia [52]–[53] . Hence , all these features were observed in the present study , demonstrating that the present mouse model mimics severe dengue disease in humans . We , therefore , suggest that this model of dengue infection may be useful for the study of the pathophysiology of severe dengue disease . Epidemiological observations demonstrated that only a very small percentage of infections results in severe disease ( DHF/DSS ) , represented as a tip of the pyramid and that this incidence varies significantly between primary and secondary DENV infections . It has been documented that a secondary DENV infection is the single and the most important risk factor for severe dengue disease manifestation , although , severe disease during primary infections is also reported [54] , [55]–[59] . It has been hypothesized that subneutralizing levels of antibodies facilitate the entry of viral particles in permissive cells , enhancing viral loads , and exacerbating disease manifestation during secondary infection [60] . Experimental DENV models support this hypothesis and suggest that disease severity is directly associated with enhanced viral replication during infection [45]–[46] . In the present experimental model , lethality hematological and other pathological alterations in infected mice was dependent on the size of the inoculum and were observed in mice presenting elevated viral loads . Of note , infected IFN-γ-deficient and NOS2-deficient mice presented heightened viral replication , in parallel with elevated hematocrits , thrombocytopenia , and liver injury . Thus , although our studies do not mimic the human situation of 2 sequential infections with distinct viral serotypes , these results mimic up to the extent in which we demonstrated that disease in this model is inoculum-dependent , what bears relevance to human disease . In addition to the features above , both clinical and experimental observations suggest that there is important liver involvement during dengue infection [61]–[63] . For example , elevated serum transaminase levels during dengue infection is common and is usually correlated with disease severity [8] , [27] , [43] , [64]–[66] . In accordance , AST and ALT transaminase levels were elevated in the present study mainly on day 7 of infection , correlating with the peak of disease and hepatic damage in the present model . Further , intense necro-hemorrhagic hepatitis , hepatocellular swelling and steatosis associated with vascular damage , which is characteristic of dengue-induced hepatitis in human liver [64] were observed in liver of mice inoculated with a lethal inoculum of adapted-DENV3 on day 7 post-infection . Intravital confocal microscopy of the liver microvasculature revealed a significant increase in hepatocyte diameter and severe reduction in sinusoidal perfusion . In this sense , liver failure may be caused by reduction of sinusoidal perfusion ( directly promoting tissue isquemia ) and also by diffuse hepatocyte necrosis caused by DENV infection and replication and/or by products derived from inflammatory leukocytes . Our data on the liver injury showed many similarities to those demonstrated by Paes et al ( 2005 , 2009 ) [43] , [65] , using a non-adapted DENV-2 in BALB/c mice and even to histopathological findings from DF and DHF postmortem tissue specimens [66]–[68] . Of note , we showed the presence of virus ( or NS3 protein ) in liver , mainly in hepatocytes . All these findings indicate the liver as an important target organ of DENV infection and replication , suggesting an important association between virus replication and hepatic damage as demonstrated by other studies [43] , [45]–[46] , [65] , [67] . Its well knows that the IFN system is essential in the context of DENV infection [69]–[70] . Recently , we have demonstrated that optimal IFN-γ production during DENV-2 infection is controlled by the cytokines IL-12 and IL-18 . Moreover , we showed that one of the mechanisms triggered by IFN-γ during host response to DENV-2 infection is the production of nitric oxide , an important virustatic metabolite . In this sense , to validate this novel mouse model of DENV-3 infection and to verify whether this pathway is also involved in response against different serotypes , we investigates the role of IFN-γ in the context of infection with the adapted-DENV-3 strain . In the present experimental model , IFN-γ is produced early ( day five of infection ) in infected-WT mice and the absence of IFN-γ action was associated with earlier lethality , more severe disease and higher viral loads even during infection with sublethal inoculums . These findings are in agreement with Shresta and coworkers ( 2004 ) [51] that demonstrated the importance of IFN-γ and type I IFNs in restricting viral replication and eliminating virus after primary DENV-2 infection . The correlation between increased IFN-γ production and higher survival rates in DHF patients also supports this idea [70] . Importantly , Gunther and colleagues ( 2011 ) [64] have demonstrated in a human challenge model of DENV infection that only sustained IFN-γ production was associated with protection against fever and viremia during the acute phase of illness [71] . In our studies , enhanced viral replication in IFN-γ-deficient mice was associated with more severe disease manifestation , as shown by enhanced hematological alterations and hepatic damage . These data strongly suggest that in the absence of IFN-γ , there are intense and uncontrolled viral replication , that lead to severe disease manifestation and lethality , already in early times of infection . Accordingly , previous studies have shown that IFN-γ likely contributes to viral clearance through several mechanisms , including direct inhibition of viral replication [72] . Of note , quite similar to that seen in the DENV-2 model [30] , the combined action of IL-12 and IL-18 is also necessary for optimal IFN-γ production and control of DENV-3 infection . Production of reactive nitrogen intermediates via increase in NOS2 expression is among the main IFN-γ-induced pathways involved in control of infections [73] . In fact , it has been shown that NOS2 expression is increased after DENV infection and that its expression in PBMCs of DF patients . This increase in NOS2 expression was found to correlate with the late acute phase of disease and preceded the clearance of DENV from monocytes [74] . NO production was also associated with less severe disease manifestation disease in humans [75] . Finally , NO is able to inhibit DENV replication in vitro [76]–[77] . In the present study , NOS2 expression is increased during adapted DENV-3 infection in different targets organs of infection and this expression was controlled by IFN-γ . Nitric oxide production was also observed in esplenocytes of DENV-3 infected mice ( ex vivo ) and in DENV-infected DCs stimulated by IFN-γ in vitro . Of note , NOS2−/− mice had elevated lethality , more severe disease manifestation and increased viral loads , even in the presence of high levels of IFN-γ . Thus , these data demonstrated here show that NOS2-mediated NO production after primary DENV-3 infection also seems to be an important pathway involved in control of DENV-3 replication and disease evolution . These data are quite similar with the results found in the DENV-2 infection model , suggesting that this mechanism is conserved protective pathway in host response to both DENV-2 and DENV-3 serotypes . These findings support that strategies aiming to potentiate IFN-γ-induced NO production could be useful during the control of primary infection by Dengue virus . In summary , we report a model of DENV-3 infection in immunocompetent mice and describe the clinical , immunopathological and virological features induced by inoculation of the virus . These features clearly resemble the manifestations of severe dengue disease in humans . We have also demonstrated the crucial role of IFN-γ and NOS2-derived NO in host resistance to DENV infection , a protective pathway involved in resistance to other DENV strains . Therefore , this model represents a significant advance in animal models of severe dengue disease and may contribute to the elucidation of the immunopathogenesis of disease and of protective mechanisms associated to infection . In addition , the model may be a relevant tool for vaccine and drug development .
|
Dengue is a mosquito-borne disease caused by one of four serotypes of Dengue virus ( DENV-1-4 ) . Dengue has escalated in geographic distribution and disease severity to become the most common arboviral infection of humans . There are no vaccines or specific therapies for dengue and the treatment is supportive . Immunopathogenesis of dengue disease is also poorly understood , in part , due to of the absence of proper animal models of infection . Here , we describe the phenotype of infection of immunocompetent mice with an adapted DENV-3 strain . Infection caused an inoculum-dependent lethality that was preceded by significant clinical , virological and biochemical changes resembling the severe manifestations of human infection . In addition , we demonstrate that IFN-γ production is essential for the host to deal with DENV-3 infection in a manner similar to that demonstrated previously for DENV-2 . Hence , reduced IFN-γ production during DENV-3 infection was associated with diminished NOS2 expression and Nitric oxide production . Mice deficient for each of these molecules presented more severe disease manifestation and increased viral replication . Therefore , we describe a model of DENV-3 infection in immunocompetent mice that proves to be an interesting tool to study host–virus interactions and mechanisms mediating protection or those associated with severe disease manifestation .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"cytokines",
"emerging",
"viral",
"diseases",
"immunity",
"to",
"infections",
"immunology",
"microbiology",
"host-pathogen",
"interaction",
"mechanisms",
"of",
"resistance",
"and",
"susceptibility",
"emerging",
"infectious",
"diseases",
"animal",
"models",
"of",
"infection",
"inflammation",
"biology",
"immune",
"response",
"immune",
"system",
"immunity",
"virology"
] |
2012
|
A Model of DENV-3 Infection That Recapitulates Severe Disease and Highlights the Importance of IFN-γ in Host Resistance to Infection
|
An outbreak of dengue fever ( DF ) occurred in Guangdong Province , China in 2013 with the highest number of cases observed within the preceding ten years . DF cases were clustered in the Pearl River Delta economic zone ( PRD ) in Guangdong Province , which accounted for 99 . 6% of all cases in Guangdong province in 2013 . The main vector in PRD was Aedes albopictus . We investigated the socioeconomic and environmental factors at the township level and explored how the independent variables jointly affect the DF epidemic in the PRD . Six factors associated with the incidence of DF were identified in this project , representing the urbanization , poverty , accessibility and vegetation , and were considered to be core contributors to the occurrence of DF from the perspective of the social economy and the environment . Analyses were performed with Generalized Additive Models ( GAM ) to fit parametric and non-parametric functions to the relationships between the response and predictors . We used a spline-smooth technique and plotted the predicted against the observed co-variable value . The distribution of DF cases was over-dispersed and fit the negative binomial function better . The effects of all six socioeconomic and environmental variables were found to be significant at the 0 . 001 level and the model explained 45 . 1% of the deviance by DF incidence . There was a higher risk of DF infection among people living at the prefectural boundary or in the urban areas than among those living in other areas in the PRD . The relative risk of living at the prefectural boundary was higher than that of living in the urban areas . The associations between the DF cases and population density , GDP per capita , road density , and NDVI were nonlinear . In general , higher “road density” or lower “GDP per capita” were considered to be consistent risk factors . Moreover , higher or lower values of “population density” and “NDVI” could result in an increase in DF cases . In this study , we presented an effect analysis of socioeconomic and environmental factors on DF occurrence at the smallest administrative unit ( township level ) for the first time in China . GAM was used to effectively detect the nonlinear impact of the predictors on the outcome . The results showed that the relative importance of different risk factors may vary across the PRD . This work improves our understanding of the differences and effects of socioeconomic and environmental factors on DF and supports effectively targeted prevention and control measures .
Dengue fever ( DF ) is an infectious epidemic disease that is principally transmitted by the vector Aedes albopictus and Aedes aegypti . [1] Dengue virus infection in humans is often asymptomatic but can lead to different clinical manifestations . [2] In the past 50 years , the incidence of DF has increased 30-fold with increasing geographic expansion into new countries . [3] In tropical and subtropical regions around the world , the disease causes great concern . The rapid spread of DF has been attributed to a combination of urbanization , globalization and a lack of effective mosquito control . [4] Presently , there are no effective vaccines or specific therapies to stop the rapid worldwide spread of DF . [5] The heterogeneity of DF incidence in time and space is related to many risk factors , such as geography , environment and socioeconomic status . Some studies have mapped the spatial and temporal clustering patterns of DF cases . [6 , 7] Other studies have analyzed the association between these patterns and relevant factors ( e . g . , precipitation , humidity , and temperature ) . [8 , 9] Moreover , some studies have emphasized the relationship between socioeconomic factors and DF . [10] In mainland China , DF cases have been reported every year since 1997 , particularly in the Guangdong province . [11 , 12] Between 2001 and 2010 , a cluster of DF cases in the Guangdong province occurred in the Pearl River Delta economic zone ( PRD ) . [13 , 14] The main vector in PRD was Aedes albopictus based on the routine sentinel vector surveillance . Although these studies have helped us understand the mechanism of DF epidemics on a large scale in mainland China , the main socioeconomic and environmental factors in the PRD area ( in addition to climate factors on a smaller scale ) have not been identified . The relative importance of different risk factors may vary across countries and regions . Due to the low level of diversity in the climatic conditions in the PRD of China , socioeconomic and environmental factors may largely contribute to the spatial heterogeneity of the DF incidence in this area . There was a great increase in the incidence of DF in the Guangdong Province in 2013 . The number of DF cases that year was the highest yet of the previous 10 years and exceeded the total number of cases over the previous 6 years . The DF cases in PRD accounted for 99 . 6% of the total number of DF cases in the Guangdong Province in 2013 . The objective of this paper was to perform an assessment of the socioeconomic and environmental impacts on DF cases between different neighborhoods in the PRD of China in 2013 at the smallest administrative unit—the township level ( the hierarchal levels of Chinese administrative region are National , Province , Prefecture , County and Town ) . This is the first study to identify the relationship between these factors and DF cases on a small scale in mainland China .
The PRD in the Guangdong province is located at the Pearl River estuary , where the river enters the South China Sea . The province comprises seven cities ( Guangzhou , Shenzhen , Dongguan , Foshan , Zhongshan , Zhuhai and Jiangmen ) and parts of Huizhou and Zhaoqing ( see Fig 1 ) . It has been the most economically dynamic region of the Chinese mainland since the launch of China's reform program in 1979 . It is one of the most densely urbanized regions in the world and one of the main hubs of China's economic growth . In our study , the PRD included only the first seven cities , with 402 streets and towns ( excluding four island-towns ) , where the DF cases frequently occurred . Data on the DF cases were obtained from the China Information System for Disease Control and Prevention , which was developed by the Chinese Center for Disease Control and Prevention ( China CDC ) in 2004 . The targeted DF cases in our study included clinically diagnosed ( based on clinical manifestations and epidemiologic exposure history ) or laboratory-confirmed cases ( “clinically diagnosed cases presenting with any of the following lab test results relating to DF: a 4-fold increase in specific IgG antibody titer , positive on a PCR test or viral isolation and identification test” ) . [13] The date of DF onset was in 2013 and the permanent residential addresses of the diagnosed individuals were within the PRD . Analyses were performed with Generalized Additive Models ( GAM ) to fit parametric and non-parametric functions to the relationships between the response and predictors . GAM is a statistical model that extends the generalized linear models to include nonparametric smoothing terms . [15] To evaluate the possible non-linearity of the socioeconomic and environmental effect on the dengue cases , we used a spline-smooth technique and plotted the predicted against the observed co-variable values . We specified the expected number of DF cases as follows: log ( case ) =β0+β1 ( boundary ) +β2 ( urban&rural ) +s ( pop_density ) +s ( GDP_per_capita ) +s ( road_density ) +s ( NDVI ) where s ( ) is the spline smooth , non-parametric function . The “street/town at the prefectural boundary” and “urban & rural” variables were binary , which fit with the parameter function . “Population density , ” “GDP per capita , ” “road density” and “NDVI” were nonlinear and fit with the spline smooth technique using specific degrees of freedom ( df ) for each smoothing , depending on visual inspections of the estimated curves for a range of choices of smoothing ( if there are more df , do not add an obvious structure to the curves , and smaller df change the shape of the curve , and the ‘optimal’ df have been reached ) . [16] All statistical analyses were performed using the statistical software R 3 . 0 . 3 , [17] , with the mgcv library . [18]
DF cases in Guangdong province principally cluster in the PRD . Fig 3 describes the monthly DF incidence rate in the PRD and the Guangdong Province , China from 2004 to 2013 . The epidemic peaks were similar in the Guangdong province and the PRD . The highest monthly incidence was observed in October 2013 , at 3 per 100 , 000 in the PRD , which was 2 . 2-fold higher than the incidence in the Guangdong province . The incidence of DF in 2013 in the PRD was approximately 6 per 100 , 000 , which was much higher than the national incidence in 2013 ( 0 . 34 per 100 , 000 ) . A total 2889 DF cases were clinically diagnosed or laboratory confirmed in the PRD in 2013 . Fig 4 shows the geographic distribution of targeted DF incidence at the township level in the PRD . The color strip from green to red indicates the increasing levels of DF incidence . The cases were highly clustered in the middle of the PRD . No DF cases occurred in nearly half of the streets/towns . According to the criteria for assessing the goodness of fit for the dengue cases ( Pearsonχ2 /df close to 1 ) , the distribution of dengue cases was over-dispersed ( Poisson Pearsonχ2 /df = 108 . 67 ) and fit the negative binomial function ( Pearsonχ2 /df = 2 . 60 ) better . Thus , the log link function for a negative binomial distribution response was selected in our study . The method for negative binomial response data is used to decide which terms to include in the model . The AIC ( Akaike Information Criterion ) /UBRE ( Un-Biased Risk Estimator ) scores for the models were compared with and without the term . The value yielding the lowest AIC/UBRE was selected in the study . Finally , all six factors were entered into the model . We specified an offset for the predictor and modeled the counts as negative binomial variables with the logarithm of the population as the offset variable . Tables 1–3 show the estimates of the negative binomial GAM for DF cases per street/town in the PRD and the diagnosis information of the model . The effects of all socioeconomic and environmental variables were found to be significant at the 0 . 001 level . This specification of the model explained 45 . 1% of the deviance of the DF incidence . The DF incidence showed a significant positive association with “boundary” and “urban & rural , ” indicating that there is a higher risk of DF for people living at the prefectural boundary or in urban areas than for those living in other places in the PRD . The associations between DF cases and population density , GDP per capita , road density , and NDVI were nonlinear , according to the edf value ( effective degrees of freedom of the smooth function terms ) . The partial contributions of six covariates to the conditional probability of DF cases with confidence bands are shown in Fig 5 . Fig 5A shows the parabolic curvature of the population density and reveals that areas with 30 , 000–40 , 000 people per square kilometer are under lower risk than areas with higher or lower population densities . Fig 5B shows small fluctuations around the zero response of DF to GDP per capita below 400 , 000 CNY ( 65 , 000 USD ) , a modest , stable response above this threshold , followed by a rapid decline response when GDP per capita rises above 600 , 000 CNY ( 97 , 000 USD ) . Thus , the higher GDP per capita , the lower the risk of DF occurrence . Fig 5C depicts a positive relationship between the risk of DF and road density with little further benefit of road density between 0 . 4 and 0 . 7 . This result indicates that high area accessibility can increase the risk of DF . The risk of DF declines gradually with rising NDVI ( Fig 5D ) , showing a curve trough at approximately 0 . 65 , after which the response increases . Fig 5E and 5F show that the relative risk of living at the prefectural boundary is higher than that of living in urban areas .
In this study we presented an effect analysis of socioeconomic and environmental variables on DF occurrence in a small scale of the PRD of China , 2013 . Furthermore , we used the GAM statistical method that integrates parametric and non-parametric terms . GAM is specifically designed to analyze data when the impact of the predictors on the outcome is nonlinear . The results of this study demonstrate the power of GAM to reveal meaningful curvatures in exploratory analyses . Generally , temperature and precipitation were regarded to be the main contributors to the occurrence of DF . [5 , 19 , 20] , but they were not included in the model because we compared the temperature and precipitation values within the PRD in 2013 and found little differences in space . We focused on the DF cases in 2013 because there was a large outbreak in the PRD that year . The local , quick spread of DF in 2013 was associated more with socioeconomic and local environmental diversity instead of climate factors , which might increase the accuracy of the model . Risk factors were selected based on the following rationale . Urbanization: DF transmission has been reported in both rural and urban areas . However , urban environments are characterized by many factors , such as a higher population , poor hygiene , poor housing conditions and less environmental management . [5 , 21 , 22] Rapid urbanization with large populations living in peri-urban slums provides attractive features for the Aedes mosquito and promotes DF transmission . [23] . Since we did not have access to a single indicator to reflect urbanization , we used “street/town at the prefectural boundary” , “street and town” and “population density” to indicate urbanization in China from different perspectives . Poverty: Several studies have linked poverty or relative poverty to DF . Poorer areas are characterized by factors that may favor higher DF transmission . [24] The GDP per capita is often considered to be an indicator of a country's standard of living . Accessibility: DF virus introduction and reintroduction are usually caused by individual human movements . [25] The movement of infected humans results in different patterns of spatial distribution . Therefore , human movement is one of the key elements that promotes the spread of DF , particularly in areas with more broad highways and interconnected roads . Environment: A close association between the local climate , vegetation and breeding mosquitos is usually identified . Indeed , vegetation can be the index indicator that “provides resting or feeding sites for mosquitoes or can serve as a proxy for the presence of breeding sites . ”[26] . Many socio-environmental indicators related to DF occurrence occur on a small scale , such as water storage , the frequency of garbage collection , and the type of sewage disposal , which suggests precarious housing conditions and poor access to public services . [27] Six macroscopic socioeconomic and environmental factors were adopted for our model and found to be statistically significant . Although the vector’s proliferation is not directly related to these six factors , they indirectly reflect the urbanization , poverty , accessibility and environment and need to be valued by public health practitioners . “Urban areas , ” “higher road density” and “lower GDP per capita” are considered to be the consistent risk factors in the PRD in the results and are consistent with the findings in previous studies . [5 , 28 , 29] “Population density” and “NDVI” are relatively complicated in that higher or lower values are both dangerous . This finding verifies that the relative importance of different risk factors may vary across the PRD . For example , high DF incidence in areas with lower population density may be caused by the higher NDVI , which provides a microenvironment for the proliferation of mosquitoes . “Street/town at the prefectural boundary” in PRD was identified in this paper as one of the risk factors . In China , “Street/town on the prefectural boundary” can roughly be regarded to be peri-urban with less management from both prefectures because most of the streets/towns on the prefectural boundary are far away from the prefectural centers . These peri-urban areas can easily cause poor hygiene and indirectly promote the vector cluster . Usually , poor hygiene and crowded populations can contribute together to increase the number of DF cases . This finding was proved in our model . Therefore , the identification of the socioeconomic and environmental factors and the partial trend shown in the model in this paper provide meaningful clues for epidemic assessment and local interventions to counteract risk factors . For instance , cases are associated with low population density and vegetated areas would suggest that environmental hygiene measures would be ineffective and targeted Aedes albopictus eradication in vegetated areas will work . High population density areas especially at the prefectural boundary and urban areas may respond to "clean up" campaigns in advance . Focusing more resources on particularly vulnerable areas of the city can aid in strengthening people’s awareness of defense . It can also facilitate timely response strategies during a DF outbreak by pointing to areas that are more vulnerable , with the objective of minimizing the epidemic speed of the virus . However , generalization of the results and causal inference are difficult because this was an ecological study and the study is subject to ecological bias . [30] The differences in vector density , preventive strategies , educational scope , and interventions applied in each epidemic area were not considered . These factors may be diverse , resulting in the heterogeneous distribution of DF cases . Thus , the study may contain errors that are inherent in reported case data and in the use of these data sources for epidemiological studies . We did periodically report data quality investigations and found some differences in the underreporting rate between each area . Because the investigational areas are representative of only the provincial level and cannot be used to adjust the cases in our study area , future targeted data quality investigations in the PRD should be conducted . Although the DF cases in this study occurred in 2013 , some of the data on the influential factors were collected in different years , including the GDP per capita in 2010 , road density in 2011 and the NDVI in 2011 . However , the lack of data in the corresponding year will have little impact on the results because the proportion of these three indicators in the PRD may change little within 2–3 years . Concurvity ( the nonparametric analogue of multicollinearity ) may be present in the data , leading to significance tests with an inflated type 1 error . [31] The values of the estimated indices of concurvity for four nonparametric terms in this study ranged from 0 . 15 to 0 . 73 . In conclusion , a complex relationship between socioeconomic environmental factors and DF occurrence was found in the PRD of China . Additive models offer flexible modeling tools for regression problems . Understanding the role of the socioeconomic and environmental factors in DF occurrence has important implications for the planning and implementation of effective public health prevention and control measures . [32] Targeted strategic planning could focus on vulnerable populations and the higher risk areas , which are consistently affected each year in the future .
|
Dengue fever is an infectious disease transmitted by mosquitoes . It is a major public health problem in tropical and subtropical regions around the world . Dengue fever is of great interest in the Pearl River Delta economic zone ( PRD ) of Guangdong province , China because the outbreak in 2013 was the largest in the previous 10 years . Due to the low degree of diversity in the climatic conditions in the PRD , socioeconomic and environmental factors may be the major contributing factors . The objective of this paper was to perform an assessment and detect the socioeconomic and environmental impact on cases at the smallest administrative unit ( the township level ) . Six factors were identified in this work , representing urbanization , poverty , accessibility and vegetation . The effects of all these factors were found to be significant . The results showed that the relative importance of different risk factors may vary across the PRD . The higher risk areas and vulnerable populations identified in this paper will provide guidance for public health practitioners to create targeted , strategic plans and implement effective public health prevention and control measures .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
The Effects of Socioeconomic and Environmental Factors on the Incidence of Dengue Fever in the Pearl River Delta, China, 2013
|
Despite recent progress in understanding the molecular basis of Vibrio cholerae pathogenesis , there is relatively little knowledge of the factors that determine the variability in human susceptibility to V . cholerae infection . We performed an observational study of a cohort of household contacts of cholera patients in Bangladesh , and compared the baseline characteristics of household members who went on to develop culture-positive V . cholerae infection with individuals who did not develop infection . Although the vibriocidal antibody is the only previously described immunologic marker associated with protection from V . cholerae infection , we found that levels of serum IgA specific to three V . cholerae antigens—the B subunit of cholera toxin , lipopolysaccharide , and TcpA , the major component of the toxin–co-regulated pilus—also predicted protection in household contacts of patients infected with V . cholerae O1 , the current predominant cause of cholera . Circulating IgA antibodies to TcpA were also associated with protection from V . cholerae O139 infection . In contrast , there was no association between serum IgG antibodies specific to these three antigens and protection from infection with either serogroup . We also found evidence that host genetic characteristics and serum retinol levels modify susceptibility to V . cholerae infection . Our observation that levels of serum IgA ( but not serum IgG ) directed at certain V . cholerae antigens are associated with protection from infection underscores the need to better understand anti–V . cholerae immunity at the mucosal surface . Furthermore , our data suggest that susceptibility to V . cholerae infection is determined by a combination of immunologic , nutritional , and genetic characteristics; additional factors that influence susceptibility to cholera remain unidentified .
Vibrio cholerae causes a spectrum of infection in humans ranging from asymptomatic colonization to severe secretory diarrhea . V . cholerae is differentiated serologically by the O antigen of its lipopolysaccharide ( LPS ) ; the vast majority of human cholera is caused by the O1 and O139 serogroups . The O1 serogroup of V . cholerae is classified into two biotypes ( classical and El Tor ) , and two major serotypes ( Inaba and Ogawa ) [1] . In the 1960s , the V . cholerae O1 El Tor biotype emerged as a major cause of cholera , ultimately replacing the classical biotype . In 1992 , the V . cholerae O139 serogroup first appeared , and after briefly predominating in South Asia , now persists in this region , but at much lower levels than V . cholerae O1 El Tor . Although absent from the view of most resource-rich nations , the global impact of V . cholerae infection remains enormous . Cholera is vastly underreported , but it is estimated that there are over one million cases of cholera annually , with more than 100 , 000 deaths [2] . According to the WHO , there is an urgent need for cholera vaccines that provide durable protection , particularly among children less than 5 years of age in developing countries [2] . One limitation to the development of effective vaccination programs for cholera is that the factors that determine human susceptibility to V . cholerae remain poorly defined . Natural infection with V . cholerae O1 induces adaptive immune responses that are protective against subsequent disease . Volunteer studies in non-endemic settings have demonstrated that infection with classical biotype V . cholerae O1 provides 100% protection from subsequent challenge with a classical biotype strain , while infection with the El Tor biotype of V . cholerae O1 provides 90% protection from subsequent challenge with an El Tor strain . This protection lasts at least 3 years in volunteer studies [3] . In an endemic area , an initial episode of El Tor cholera reduces the risk of a second clinically apparent infection by 90% over the next several years [4] . The best-studied correlate of immunity to V . cholerae is the serum vibriocidal antibody , a complement-fixing bacteriocidal antibody . Seroepidemiologic studies in endemic areas have shown that vibriocidal antibody titers increase with age and correlate with protection from cholera [5] , [6] . However , there is no threshold vibriocidal antibody titer above which complete protection from infection is achieved , and the vibriocidal antibody may be a surrogate marker for an undetermined protective immune response at the mucosal surface [7] . Although a major component of the vibriocidal antibody is directed against serotype specific LPS , levels of serum LPS-specific immunoglobulin G ( IgG ) antibody have not been found to correlate with protection from cholera in humans [5] . A portion of the vibriciodal antibody may also be directed against V . cholerae outer membrane proteins [8] . Antitoxin immunity is primarily directed at the B subunit of cholera toxin ( CTB ) ; however serum IgG-antibodies to CTB have not been found to correlate with protection from cholera [5] , and toxin-based vaccines confer only transient protection [9] . Robust mucosal and systemic humoral responses to TcpA , the major subunit of the toxin-coregulated pilus ( TCP ) , a type IV pilus that is required for intestinal colonization , have recently been demonstrated in patients with cholera [10] , but it is unknown whether these responses are associated with protection from disease . In addition to adaptive immune responses , innate host characteristics may also influence the outcome of an individual's exposure to V . cholerae . Multiple case-control studies in cholera endemic areas have demonstrated that individuals with blood group O are at increased risk of hospitalization with cholera , and it has been hypothesized that V . cholerae infection may have selected for the low prevalence of the O blood group in the Ganges Delta region , a historic and current global epicenter of cholera [11] . Other genetic or innate immune factors may also influence susceptibility to cholera . For example , studies of duodenal biopsies obtained from patients with cholera demonstrate recruitment of neutrophils to the intestinal epithelium during acute infection [12] , and also reveal increased expression of broad classes of innate immune effectors , including lactoferrin and other antibacterial proteins [13] , suggesting further potential sources of genetic variability that might contribute to susceptibility to cholera . The nutritional status of the host may also affect susceptibility to diarrheal diseases such as cholera , by influencing both innate and adaptive immunity . The micronutrients zinc and vitamin A play central roles in mucosal immunity , and when given in community-based supplementation programs , may reduce the incidence and morbidity of diarrheal diseases [14] . Although zinc supplementation in children has been found to modify adaptive immune responses to V . cholerae [15] , [16] , there are no prior studies demonstrating a specific association between baseline nutritional characteristics and the risk of V . cholerae infection in individuals . Here , we present the results of a prospective , observational study aimed at identifying host factors that are associated with V . cholerae infection within households in a cholera-endemic setting . Our results may be relevant to the design and evaluation of more effective cholera vaccines for use in resource-poor areas of the world .
The hospital at the Clinical Research and Service Centre ( CRSC ) of the International Centre for Diarrhoeal Disease Research , Bangladesh ( ICDDR , B ) provides care for more than 100 , 000 patients annually , including approximately 10 , 000 to 20 , 000 with cholera , the majority of whom are residents of Dhaka city . Index cases presenting to the hospital with severe acute watery diarrhea were eligible for inclusion in this study if their stool cultures were subsequently positive for V . cholerae , if they were older than 6 months , and if they were without significant co-morbid conditions . Upon presentation of the index case , a field team discussed enrollment with household contacts of the index case , and consenting household contacts were then enrolled into the study immediately upon culture confirmation of the index case ( within 24 hours of presentation of the index case ) . Household contacts were defined as individuals who shared the same cooking pot for three or more days . Blood specimens for ABO typing , baseline vibriocidal titers , and baseline anti-LPS , anti-CTB and anti-TcpA antibody levels were immediately collected from consenting household contacts upon enrollment . The field team visited household contacts on each of the next six days , and again on days 14 and 21 . During these visits , contacts were questioned about diarrheal symptoms , and rectal swabs were obtained for V . cholerae culture . Follow-up blood samples for vibriocidal antibody titers were obtained from contacts on study days 7 and 21 . All patients designated as having completed follow-up had serum successfully obtained at baseline and day 21 , and 98% of the interim field visits resulted in collection of clinical data and rectal swabs . Household contacts were excluded from the analysis if they did not complete 21 days of follow-up . Household contacts were also independently excluded from the analysis of their baseline immunologic characteristics if they had symptoms of diarrhea during the week preceding enrollment , if they had a positive rectal swab culture for V . cholerae on enrollment into the study , or if they developed infection with V . cholerae of a serogroup or serotype that did not correspond to that of the index case . Informed consent for participation in this research was obtained from participants or their guardians . The human experimentation guidelines of the U . S . Department of Health and Human Services were followed in the conduct of this research . Approval for this human study was obtained from the Institutional Review Board of the Massachusetts General Hospital and the Research and the Ethical Review Committees of the ICDDR , B . All index cases of cholera were confirmed by culturing stool for V . cholerae on taurocholate-tellurite-gelatin agar ( TTGA ) . After overnight incubation of plates , serological confirmation of suspected V . cholerae colonies was carried out by slide agglutination [17] , [18] . Rectal swab specimens from household contacts were collected in Cary-Blair transport media for subsequent plating on TTGA and colony identification as above . V . cholerae was the only pathogen for which microbiologic screening was carried out during the 21 day follow-up period . Vibriocidal antibody assays were performed with methodology previously described , using guinea pig complement and the homologous serogroup/type of V . cholerae O1 El Tor Ogawa ( strain 25049 ) , V . cholerae O1 El Tor Inaba ( strain T-19479 ) , or V . cholerae O139 ( strain 4260B ) [19] . Heat-inactivated serum was diluted 5-fold , and serial 2 fold dilutions were assayed , with the vibriocidal titer defined as the reciprocal of the highest serum dilution resulting in greater than 50% reduction of the O . D . 600 when compared to control wells without serum . Positive and negative control sera from infected and non-infected individuals were used to ensure consistency across plates . The concentrations of complement and bacteria have been separately optimized for determining the vibriocidal antibody responses to V . cholerae O1 and V . cholerae O139 [19] . Serum and fecal antibodies specific to CTB , LPS , and TcpA were measured by kinetic ELISAs using methods described previously [10] , [20] . 96-well microtiter plates were coated with either purified LPS ( 250 ng/well ) , sequentially with GM1 ganglioside ( 100 ng/well ) followed by recombinant CTB ( 50 ng/well ) ( gifts of A . M . Svennerholm ) , or with recombinant TcpA ( 150 ng/well ) [10] . Serogroup/type-specific LPS was derived from the same strains used in the vibriocidal assay by hot-phenol extraction , followed by proteinase , DNAse and RNAse treatment . Plates were incubated with diluted patient sera ( 1∶50 for LPS ELISA , 1∶100 for TCP , and 1∶200 for CTB ) , washed , and horseradish peroxidase-conjugated secondary antibodies to either human IgG or IgA were applied ( Jackson Laboratories , Bar Harbor , Maine ) . Plates were developed using 0 . 1% ortho-phenylene diamine ( Sigma , St . Louis , Missouri ) in 0 . 1 M sodium citrate buffer with 0 . 1% hydrogen peroxide , and optical densities ( OD ) were read kinetically at 450 nm for 5 minutes at 19-s intervals . ELISA data were normalized across plates using control serum derived from previously infected patients . Antigen specific-IgA in fecal extracts were expressed as a fraction of total IgA in fecal extracts , which was determined by ELISA using an IgA standard ( 1 mg/ml ) derived from human colostrum , as described previously [20] . Serum retinol levels were assayed by high-performance liquid chromatography , and serum zinc levels were assayed by atomic absorption spectrophotometer [16] . Among household contacts , definite V . cholerae infection was defined as a positive rectal swab culture for V . cholerae during the 21 days of follow-up . Possible infection was defined as a four-fold or greater change in the serum vibriocidal antibody titer and/or the development of diarrheal symptoms in the absence of detection of V . cholerae in serial rectal swab cultures . The absence of infection was defined as no positive culture for V . cholerae , no significant changes in vibriocidal antibody titer , and no symptoms of diarrhea . Symptomatic V . cholerae infection was defined as a positive culture that occurred in association with diarrheal symptoms ( 3 or more loose stools per day ) within 72 hours of developing the positive culture , and asymptomatic infection was defined as a positive culture in the absence of diarrheal symptoms . Analyses were performed using Stata version 9 . 0 ( Stata Corporation , Inc . , College Station , Texas ) , and SAS version 9 . 1 ( SAS Institute Inc , Cary , North Carolina ) . Characteristics of the definitely infected household contacts were compared with characteristics of the contacts with no evidence of infection with logistic regression using generalized estimating equations , with an exchangeable correlation matrix , and the reported odds ratios and p values were adjusted for clustering based on household [21] . A multivariate analysis of baseline immunologic characteristics , including age , vibriocidal antibody , and serum anti-CTB , LPS , and TCP IgG and IgA antibodies , was performed with a logistic regression model clustered by household using generalized estimating equations , with the final model determined based on forward selection with a predetermined cutoff criteria of p≤0 . 05 for inclusion in the model . Odds ratios ( OR ) are reported in the text and tables with 95% confidence intervals ( CI ) , and all reported p values are two-tailed .
A total of 1077 household contacts of 396 index patients with cholera were enrolled in the study between January 2001 and May 2006 . 944 contacts completed 21 days of observation . Of the household contacts that completed follow-up , there were 782 contacts of 304 index patients with cholera due to V . cholerae O1 , and 162 contacts of 57 index patients with V . cholerae O139 . Outcomes were defined by whether the household contacts developed diarrhea , had a four-fold change in vibriocidal antibody titer , or had a positive rectal swab for V . cholerae and are shown in Table 1 . Of the 944 contacts that completed the observation period , 202 developed definite V . cholerae infection , defined as a positive rectal swab culture ( 21% ) , and of those , 127 ( 62% ) developed diarrhea within 72 hours of their positive culture . As described above , an additional subset of 229 of the 944 household contacts that completed the 21 days of observation were excluded from the analysis of baseline immunologic characteristics; this included 206 contacts who had diarrhea the week prior to enrollment or a positive rectal swab upon enrollment , and 23 contacts who developed infection with a V . cholerae serotype/group that did not match the index case . None of the household contacts required hospitalization or intravenous hydration , although symptomatic individuals were treated promptly with antibiotics and home oral rehydration therapy as needed . Among household contacts of index cases infected with V . cholerae O1 , age was inversely related to the probability of developing infection ( p<0 . 001; Figure 1 ) . In contrast , no significant association between age and the probability of infection with V . cholerae O139 was found ( p = 0 . 6 ) . The household contacts with the highest risk of V . cholerae O1 infection were young children . In children 5 years of age or younger , the odds of developing infection with V . cholerae O1 were 2 . 7 times that of older individuals ( P<0 . 001 , 95% CI 1 . 61–4 . 49 ) . Children 5 years or younger also had a higher likelihood of developing symptomatic illness if infected with either serogroup ( OR = 2 . 7 , P = 0 . 03 , 95% CI 1 . 10–7 . 43 ) . No association between gender and susceptibility to infection with V . cholerae of either serogroup was found . We assessed the association between a number of baseline immunologic markers and risk of V . cholerae infection in this cohort of household contacts . Associations between serologic markers and the risk of infection are listed in Table 2 . As described previously , baseline vibriocidal antibody titer predicted protection from subsequent infection with V . cholerae O1 ( OR 0 . 82 for each doubling of titer , 95% CI 0 . 74–0 . 92 ) but not from V . cholerae O139 ( OR 0 . 97 , 95% CI 0 . 82–1 . 15 ) [7] . However , the baseline vibriocidal titer did not predict the risk of developing symptomatic illness among those who were infected with V . cholerae O1 ( OR 0 . 90 , 95% CI 0 . 75–1 . 07 ) . A novel finding in our study was that V . cholerae antigen-specific serum IgA levels predicted an individual's susceptibility to V . cholerae infection . As with the vibriocidal antibody , higher serum levels of LPS-specific IgA were associated with protection from infection with V . cholerae O1 , but not with V . cholerae O139 . CTB-specific serum IgA levels were associated with protection from infection with both the O1 and O139 serogroups ( the ORs were equivalent ) , but this finding only reached statistical significance for V . cholerae O1 infection . TcpA-specific IgA levels were associated with significant protection against both O1 and O139 serogroups . In contrast with serum IgA results , there were no associations between serum LPS , CTB , or TcpA-specific IgG and outcomes in household contacts . In a smaller subset of our cohort of household contacts , we also evaluated CTB and LPS- specific IgA levels in fecal extracts . This analysis included 282 contacts that completed 21 days of follow-up prior to June 2002 . Fecal levels of antigen specific IgA were not significantly correlated with serum IgA levels to the same antigen . Levels of fecal CTB IgA were not significantly associated with the risk of infection with V . cholerae , or with the risk of diarrhea in infected individuals . However , higher levels of LPS-specific antibodies in feces at baseline were significantly associated with a lower likelihood of developing symptomatic disease in individuals infected with V . cholerae O1 ( OR 0 . 58 per two-fold increase , p = 0 . 027 , 95% CI 0 . 36–0 . 94 ) . To explore potential confounding between immunologic markers that were associated with protection from infection with V . cholerae O1 in the univariate analysis , we performed a stepwise , multivariate logistic regression analysis using generalized estimating equations . In the final model derived , three variables – age , baseline vibriocidal titer , and baseline serum CTB-IgA titer – were significant independent predictors of susceptibility to infection with V . cholerae O1 ( Table 3 ) . Serum anti-TcpA and anti-LPS IgA antibodies were more highly correlated with the vibriocidal antibody ( Spearman's rank correlation coefficient , ρ = 0 . 20 , P = 0 . 004 , and ρ = 0 . 35 , P<0 . 0001 , respectively for TcpA and LPS ) and than were anti-CTB-IgA antibodies ( ρ = 0 . 06 , P = 0 . 23 ) , and anti-TcpA and anti-LPS IgA antibody levels were not predictive of susceptibility to V . cholerae O1 independently of the vibriocidal antibody titer . Because baseline TCP-IgA was the only significant predictor of susceptibility to V . cholerae O139 and there were a smaller number of observations , a separate multivariate assessment was not feasible for the O139 serogroup . We evaluated the association between anthropometric markers of nutritional status and susceptibility to infection with V . cholerae in children younger than 5 years of age . Height- and weight-for-age were not significantly associated with likelihood of V . cholerae infection or symptomatic disease in young children exposed in the household ( Table 4 ) . To explore the relationship between micronutrient levels and susceptibility to cholera , we also evaluated baseline zinc and retinol levels in a subset of 278 household contacts ( including a total of 55 retinol and 129 zinc deficient individuals ) . Our evaluation was restricted to contacts of patients with V . cholerae O1 . Zinc deficiency ( defined at serum zinc ≤70 µg/dL [22] ) was not significantly associated with likelihood of infection with V . cholerae O1 or with development of diarrhea . Retinol deficiency ( defined as serum retinol ≤20 µg/dL [22] ) was associated with a higher risk of infection with V . cholerae O1 ( Table 4 ) . Furthermore , retinol deficiency was associated with a higher likelihood of developing symptomatic disease if infected: all 9 of the retinol deficient individuals infected with V . cholerae O1 developed symptomatic disease , while only 14 of the 27 retinol replete infected individuals developed symptomatic infection ( P = 0 . 05 ) . Consistent with what was previously described in a earlier subset of the current cohort [23] , we found that individuals with blood group O were less likely to become infected with V . cholerae O1 than non-blood group O individuals ( OR 0 . 54 , 95% CI 0 . 35–0 . 83 , Table 4 ) , but if infected , had greater than twice the odds of developing symptomatic infection ( OR 2 . 13 , P = 0 . 035 , 95% CI 1 . 05–4 . 33 ) . There was no significant difference in the susceptibility of individuals with blood group O to infection with V . cholerae O139 , although individuals with blood group O were more likely to develop symptomatic disease if infected with either serogroup . Because we hypothesized that additional host genetic factors might contribute to susceptibility to cholera , we collected pedigree data on a subset of the households enrolled in the study ( beginning in 2003 ) . A total of 259 household contacts that completed 21 days of follow-up were classified based on relatedness to the index case . The analysis included 197 first-degree relatives ( i . e . siblings , parents or children ) of the index case , and 62 non-first degree relatives . Among this population , individuals who were first-degree relatives of the index case had significantly greater odds of being infected with V . cholerae compared to non-related or less closely related contacts in the same household ( OR [crude] 2 . 90 , P = 0 . 03 , 95% CI 1 . 12–7 . 52 , Table 4 ) . This finding was independent of blood group phenotype and age in a multivariate analysis ( OR [adjusted] 2 . 91 , P = 0 . 03 , 95% CI 1 . 11–7 . 61 ) . Household contacts of index patients infected with V . cholerae O139 were more likely to become infected than contacts of patients with V . cholerae O1 ( OR 1 . 67 , p = 0 . 015 , 95% CI 1 . 10–2 . 52 ) . There was no difference in the likelihood of infection between contacts of index patients infected with Inaba ( N = 466 ) versus the Ogawa ( N = 366 ) serotypes of V . cholerae O1 ( OR 0 . 95 , p = 0 . 86 , 95%% CI 0 . 60–1 . 53 ) . In addition to intrinsic host and microbiologic characteristics , other environmental factors might influence the outcome of exposure to V . cholerae within a household . We found that attack rates were markedly higher in individuals living in households that included more than one infected individual ( OR 5 . 50 , P<0 . 001 , 95% CI 3 . 18–9 . 51 ) . This finding was independent of baseline vibriocidal antibody titers of household contacts , suggesting that variability in risk of infection in households may be due to a common environmental risk and/or common genetic factors influencing household susceptibility .
An improved understanding of factors that influence host susceptibility to cholera may aid in the development and implementation of an effective vaccination program . In this study , we identified novel immunologic markers that predict protection from V . cholerae infection in a population in Bangladesh , as well as other host characteristics that modify susceptibility . The vibriocidal antibody is the only previously described marker of immunity to cholera and is routinely utilized in pilot studies of vaccine efficacy . Here , we show that levels of serum IgA specific to three V . cholerae antigens – CTB , LPS and TcpA – also predict protection among household contacts of patients infected with V . cholerae O1 El Tor , the current predominant cause of cholera . Interestingly , levels of serum IgG directed against these same antigens did not predict protection , possibly because serum IgA levels better reflect protective immune responses at the intestinal mucosal surface where secretory IgA ( sIgA ) is the predominant immunoglobulin . We also examined baseline fecal levels of V . cholerae antigen specific IgA , although there was no significant correlation of these with baseline serum IgA antibodies to the same antigen . Antigen-specific fecal IgA levels did not correlate with protection from cholera , except for a mild effect of fecal IgA specific to LPS . This might reflect proteolysis of IgA in fecal samples , such that baseline fecal IgA measurement may not adequately represent an accurate level of the response to V . cholerae antigens at the mucosal level . Among the antigens evaluated , responses to TcpA appear to be unique in that they are associated with protection from both the O1 and O139 serogroups . Expression of tcpA mRNA is highly up-regulated in V . cholerae during human infection [24] , yet it remains uncertain to what extent this antigen is present in current killed and live-oral cholera vaccines . A systematic comparison of TcpA responses in naturally-infected individuals compared to vaccine recipients would be important to address this question . Our data suggest that inclusion of TcpA as a vaccine component may be useful in boosting protective immunity across both serogroups . Studies in the rabbit ileal loop model of cholera suggest that anti-toxin and anti-LPS immune responses are synergistic in the prevention of fluid accumulation [25] . In concordance with this observation , our multivariate analysis showed that both anti-toxin ( anti-CTB ) and anti-bacterial ( vibriocidal antibody ) responses were independent predictors of susceptibility to cholera . This finding is also consistent with the results of clinical trials of the whole cell-B subunit vaccine , which suggest a role for anti-CTB responses in conferring additional short term protection compared with whole cell vaccine alone [9] . Our multivariate model also indicated that increasing age predicted immunity from cholera , independent of both anti-CTB IgA and vibriocidal antibody levels , suggesting that additional components of protective immunity remain unidentified . In addition to adaptive immune responses , we identified other host and environmental characteristics that affected the risk of developing cholera in our study population . Although an association between blood group phenotype and severity of cholera has previously been recognized , we observed an additional familial segregation of susceptibility to cholera within households . In particular , first-degree relatives of cholera patients were significantly more likely to develop infection with V . cholerae than less closely related members living in the same household , and this was independent of blood group . Such increased susceptibility might be related to closer contact or shared behaviors between first degree relatives , or could reflect additional genetic components of susceptibility to cholera . We found direct evidence to support the importance of nutrition in susceptibility to infection with V . cholerae . Increasing levels of retinol , but not zinc , were associated with decreased susceptibility to both infection and symptomatic disease . This observation underscores the importance of retinol supplementation in young children in developing countries where cholera is endemic . Our study has a number of limitations . Although we demonstrated that cholera antigen-specific serum IgA levels correlate with protection from infection with V . cholerae , it is possible that these immunologic markers , like the vibriocidal antibody , are surrogates for protective immune responses localized at the mucosal surface . Thus , the inability to measure intestinal levels of sIgA using non-invasive techniques remains a major limitation and necessitates the discovery of better proxy measurements of mucosal immunity . It is also possible that a portion of household contacts in our study were infected prior to onset of symptoms of the recognized household index case . We accounted for this by excluding from assessment of baseline immunologic characteristics those contacts reporting diarrhea in the week prior to enrollment or having a positive rectal swab upon entry into the study . Taken together , our data suggest that immunologic , genetic , and nutritional characteristics of individuals all contribute to human susceptibility to infection with V . cholerae in a household . Although it is hypothesized that immune responses at the mucosal surface are the primary mediators of protection from V . cholerae , the specific antigens to which these responses are directed have not been identified . Our observation that levels of serum IgA ( but not serum IgG ) directed at certain V . cholerae antigens are associated with protection from infection underscores the need to better understand anti-V . cholerae immunity at the mucosal surface . Such understanding would be critical in designing and evaluating improved vaccines against V . cholerae .
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Vibrio cholerae is the bacterium that causes cholera , a severe form of diarrhea that leads to rapid and potentially fatal dehydration when the infection is not treated promptly . Cholera remains an important cause of diarrhea globally , and V . cholerae continues to cause major epidemics in the most vulnerable populations . Although there have been recent discoveries about how the bacterium adapts to the human intestine and causes diarrhea , there is little understanding of why some people are protected from infection with V . cholerae . This article describes several factors that are associated with the risk of developing V . cholerae infection among people living in the same household with a patient with severe cholera who are at high risk of contracting the infection . One of the findings is that IgA antibodies , a type of antibody associated with immunity at mucosal surfaces such as the intestine , that target several components of the bacteria are associated with immunity to V . cholerae infection . This article also describes genetic and nutritional factors that additionally influence susceptibility to V . cholerae infection .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/bacterial",
"infections",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"microbiology/immunity",
"to",
"infections"
] |
2008
|
Susceptibility to Vibrio cholerae Infection in a Cohort of Household Contacts of Patients with Cholera in Bangladesh
|
Increasing the durability of crop resistance to plant pathogens is one of the key goals of virulence management . Despite the recognition of the importance of demographic and environmental stochasticity on the dynamics of an epidemic , their effects on the evolution of the pathogen and durability of resistance has not received attention . We formulated a stochastic epidemiological model , based on the Kramer-Moyal expansion of the Master Equation , to investigate how random fluctuations affect the dynamics of an epidemic and how these effects feed through to the evolution of the pathogen and durability of resistance . We focused on two hypotheses: firstly , a previous deterministic model has suggested that the effect of cropping ratio ( the proportion of land area occupied by the resistant crop ) on the durability of crop resistance is negligible . Increasing the cropping ratio increases the area of uninfected host , but the resistance is more rapidly broken; these two effects counteract each other . We tested the hypothesis that similar counteracting effects would occur when we take account of demographic stochasticity , but found that the durability does depend on the cropping ratio . Secondly , we tested whether a superimposed external source of stochasticity ( for example due to environmental variation or to intermittent fungicide application ) interacts with the intrinsic demographic fluctuations and how such interaction affects the durability of resistance . We show that in the pathosystem considered here , in general large stochastic fluctuations in epidemics enhance extinction of the pathogen . This is more likely to occur at large cropping ratios and for particular frequencies of the periodic external perturbation ( stochastic resonance ) . The results suggest possible disease control practises by exploiting the natural sources of stochasticity .
There is increasing social pressure to integrate science , policy and regulation in order to assess and minimize the risks associated with agricultural practices . Major risks and uncertainties persist whereby pests and pathogens rapidly overcome disease control methods using resistant cultivars and fungicides . Although disease resistant genes have been successfully used for disease management , many crop geneticists and plant breeders view resistance genes as a limited and potentially non-renewable resource , whereby once a pathogen has evolved to overcome the resistance , the resistance genes have permanently lost their value . Thus one of the key goals of virulence management is to increase the durability of crop resistance , a concept that has been extensively discussed in the literature , but which is still difficult to measure and predict [1]–[7] . Johnson [2] was perhaps the first to provide a definition of durable resistance , i . e . a resistance that remains effective over a prolonged period of widespread use under conditions conducive to the disease . However , such definition , although conceptually simple , does not provide an objective procedure for measuring and predicting the durability of crop resistance ( see e . g . the discussion in [3] ) . In particular , the beguilingly simple concepts of ‘remaining effective’ , ‘prolonged period’ and ‘widespread use’ are subject to a range of interpretations . Durability of resistance is also confounded with the inherent variability exemplified by a wide range of plant pathogens . As pointed out by Leach et al . [3] , although many resistance genes have been identified in plant germplasm , identifying the factors that render the resistance ‘effective’ is still a challenging task . One exception is , perhaps , the polygenic vs monogenic paradigm , according to which resistance due to the additive action of many genes ( also known approximately in the literature as polygenic , quantitative , horizontal resistance , see e . g . [8] , [9] ) is expected to be more durable than resistance due to the action of a single gene ( also referred to as monogenic , qualitative , vertical resistance [1] , [4]–[10] ) . However , even this generally accepted consensus has been challenged by several authors showing that erosion of polygenic resistance may be important and relatively rapid [2] , [11]–[19] and presenting evidence of durable resistance due to the action of a single gene [2] , [18] . This raises the key question why resistance , especially monogenic resistance , can be so ephemeral and subjected to the well known boom-and-bust cycles [20] . The review of Leach et al . [3] focuses on this issue and supports the hypothesis that the inherent quality and durability of a plant resistance gene is a direct function of the amount of fitness penalty imposed on the pathogen to overcome that resistance gene . Despite this important clarification , the mechanism regulating the durability of resistance is expected to be more complex than the simple molecular changes alone in pathogen adaptation and any associated fitness cost . The population sizes of the pathogen and host also matter . McDonald and Linde [20] provide a conceptual overview of the evolutionary forces that drive the evolution of plant pathogens ( mutation , genetic drift , gene flow , reproduction/mating system , and selection ) emphasising the role of population size . A large population is likely to have greater gene diversity than a smaller population and it can influence the so-called random genetic drift ( i . e . the change in the frequency of alleles in a randomly chosen subset of a population ) that typically occurs when a subset of population survives a catastrophic event that dramatically reduces the population size ( a bottleneck ) , or due to external immigration of a small , random subset of a pathogen into a new host . In addition , the durability of resistance may be affected by the landscape composition ( i . e . host variety frequencies ) [21] and can be increased by using spatially heterogeneous mixtures of different cultivars with similar agronomic traits , but differing in resistance genes [5] , [17] , [22] . All these studies suggest that any theoretical approach to analyse the evolution of pathogen and durability cannot focus on gene frequency alone ( the proportion of the pathogen population carrying a particular allele of a gene ) and the density of the host and the pathogen ought to be included . To this end van den Bosch and Gilligan [23] explicitly linked population dynamics and population genetics to investigate the durability of resistance . They introduced new concepts to measure durable crop resistance and analysed these using deterministic models . They identified three measures , the expected time until a virulent genotype invades following release of a resistant crop cultivar , the time until a virulent genotype takes over the pathogen population and the additional number of uninfected host growth days , , effected by the growth of the resistant cultivar ( see Figure 1 ) . For each measure they examined the effect of cropping ratio ( the proportion of resistant cultivar grown in a landscape ) on the durability of resistance . Here we focus on because of its practical usefulness in measuring durability of resistance and its applicability to pesticides , antibiotics and drug resistance ( see also [24] , [25] ) . To a first approximation can be identified with the additional crop yield gained during the deployment of a resistant gene [26] . Thus in the light of Johnson's [2] definition , the resistance is considered to ‘remain effective’ until its use ( deployment period ) continues to produce additional crop yield; as the frequency of the virulent strain increases , the contribution to the additional crop decreases to zero . In this case the resistance is considered broken and the resistant and susceptible cultivars are no longer distinguishable from each other . Thus any further deployment of the resistance gene in a resistant cultivar has no effect . A key message from the work of van den Bosch and Gilligan [23] , also consistent with the predictions of Bonhoeffer [24] for antibiotic management , is that the durability of resistance is unaffected by the cropping ratio . van den Bosch and Gilligan [23] proposed the following explanation: increasing the proportion of resistant crop initially decreases the total pathogen population , but increases the selection pressure on the pathogen and consequently the resistance is more rapidly broken down [20] . The two effects tend to compensate and the total gain is unaltered , i . e . giving the same areas for deployment of resistant cultivars under different cropping ratios as shown in Figure 1 . It is unlikely , however that in the field the solution is truly as simple as analysis of the deterministic model suggests , not least because the model [23] ignores important sources of variability , such as environmental and demographic stochasticity , rendering prediction of limited value [27] . Despite a growing body of research focused on stochastic disease dynamics , the role of demographic and environmental stochasticity on the dynamics of an epidemic is still not fully understood . For example , can we quantify the effect of random noise on the evolutionary forces classified by McDonald and Linde [20] ? Does stochasticity delay/accelerate the evolution of pathogens ? If so , how ? To our knowledge , there is no theoretical framework available that investigates the effects of demographic and environmental stochasticity on the evolution of the pathogen and thus the durability of crop resistance . Here , building on previous work of van den Bosch and Gilligan [23] we now take account of variability . More precisely we formulate a stochastic , mathematical model to test the following hypotheses , that: We show that , for the pathosystems considered here , large stochastic fluctuations , particularly at the beginning of epidemics , enhance extinction of the pathogen , and especially the virulent strain , so promoting the durability of resistant cultivars . This has important theoretical consequences as it shows that the evolution of pathogens is directly affected by demographic and environmental stochasticity . The findings also suggest possible disease control practises by exploiting natural sources of stochasticity .
Our models are motivated for a broad range of crops and plant pathogens . The target hosts and pathogens are typified by cereal rusts and mildews but are by no means restricted to these . We used a SIR epidemic model to study a system comprising two cultivars ( susceptible and qualitatively resistant ) and two pathogen strains ( virulent and avirulent ) . The unit of interest may be a plant but more usually it will be a unit of susceptible tissue such as a leaf or part thereof . In this non-spatial model , the populations of individuals are homogeneously mixed . The pathogen is transmitted from individual to individual when ‘encounters’ occur ( e . g . a spore depositing on healthy tissue ) with transition probability proportional to the number of possible encounters . The transmission of infection is described by a process like: , where T is the transmission probability from one category to another . This system can be seen as a birth-death system with many variables [28] . By writing down all possible transitions from one category to another with the adequate transition probabilities we obtain a Master Equation ( ME ) [28] , [29] . High levels of accuracy and realism are possible by using models based on the solution of the Master Equation . However , for large populations the mathematics become intractable . A compromise is represented by approximating the ME by invoking the Kramer-Moyal expansion [28] . The approximation leads to a Fokker-Planck equation ( FPE ) providing the coefficients for the Langevin equation which can be used to simulate individual stochastic realisations ( see Supporting Information ) : ( 1 ) where is the transmission rate; and are the densities of susceptible ( healthy ) and resistant hosts; and are the densities of infected hosts by the virulent and avirulent pathogen; the rate of mutations; the infectious period; is a Wiener process with mean zero and variance [28] , [29]; the terms are the entries of the diffusion matrix in the corresponding FPE , they depend solely on the state of the system and on parameters used as model input . We also superimpose that the susceptible and resistant cultivar increase continuously with rates and respectively , where is the fraction of resistant crop and is a constant planting rate . Both hosts are harvested with rate . The pathosystem is illustrated in Figure 2 . The model therefore applies to a system of continuous harvesting and sowing , typified by the management of continuous cropping systems , common in tropical regions . We have also chosen this system to enable comparison with other epidemiological models , where individuals are born with a constant birth rate and a proportion of the entire population dies with rate , analogous to the systems of Bonhoeffer and Mclean [24] , [25] for antibiotics and vaccination management . The continuous ( non-seasonal ) formulation of the pathosystem allows the analysis of periodic disturbance on the typical frequencies of the system ( see below ) that are unconfounded by seasonality . It also simplifies the mathematical analysis . In the current paper planting and harvesting are assumed to be fully farmer-controlled and therefore not subjected to stochastic fluctuations . Immigration of avirulent and virulent pathogens from an external source are also included and modelled as a Poisson process with mean and for the avirulent and virulent strains , respectively . Unless otherwise stated the values of the parameters are shown in Table 1 . As we can see the deterministic term is formally the same as the deterministic model of Mclean [25] and compatible with the model of van den Bosch and Gilligan [23] with mutations . From the system of Langevin equations we can calculate the additional number of uninfected host growth days: ( 2 ) where and are the steady states for the susceptible and resistant host ( i . e . the solutions of equation 1 when the derivatives on the lhs of equations and the stochastic terms are set to zero ) . When both pathogen strains are present , the system reaches an equilibrium in which the virulent genotype coexists with the resistant and susceptible cultivars and the avirulent genotype goes extinct [23] . As shown by [23] is interpreted as a measure of the durability of crop resistance . These methodologies result in many stochastic time-series for the population of infected and healthy hosts . For each single stochastic realisation , we calculated the durability and how this depends on parameters , such as the cropping ratio . By using the wavelet analysis [30] , a suitable tool for transient regimes like the current one , we have identified the dominant frequencies of the system . Then we replaced the fixed life-cycle parameters in the model with periodically variable parameters . Underlying this approach is the assumption that external factors such as seasonality have an immediate effect on the population . Here we tested hypotheses i ) and ii ) by:
Figure 3 shows that epidemics with the same basic reproductive number , , and the same , but with different pathogen life-cycle parameters , exhibit markedly different dynamics . The differences are expressed in the amplitudes of fluctuations , correlations and excursion times i . e . the time between an up-crossing ( down-crossing ) and subsequent down-crossing ( up-crossing ) relative to the deterministic profile . ( is defined as the expected number of secondary cases produced by a typical primary case in an entirely susceptible population , and depends solely on the life-cycle parameters ) . The importance of the intensity of such fluctuations is shown in Figure 4 . Fluctuations might lead to a critically small infected population and subsequent stochastic extinction ( and thus higher durability ) while external immigration leads to re-invasion . In contrast with the predictions of [23] for a deterministic model , average durability of crop resistance in the stochastic model increases with cropping ratio ( Figure 5 ) . The existence of dominant frequencies from wavelet analysis are illustrated for the time-series of the population infected by the virulent strain ( ) in the online Supporting Information . The dominant periods for stochastic realizations occur at ⪆ time units with a peak in the average wavelet spectrum in the range time units . The interaction of such dominant periods with a periodic perturbation , such as the intermittent application of fungicide control that affects the transmission rates ( ) , leads to large fluctuations in the infected population ( cf Figure 6 ) . This occurs in both the deterministic ( black line ) and stochastic ( red dots ) scenarios illustrated in Figure 6 . However in the stochastic case the amplitudes of the fluctuations are , in general , larger than the corresponding deterministic case . The increased amplitudes are attributable to resonance with the natural frequencies of the system . We analyse the effect further by considering how two key epidemiological variables ( maximum amplitude and number of extinctions ) respond to changes in the periodic perturbation in applying chemical control ( Figures 7 . A and 7 . B ) . In each case , the response effected by resonance with the periodic control dramatically increases in the region of dominant natural frequencies i . e . in the range time units . Our analyses also show that the effects of resonance with periodic control can reduce the amount and the proportion of the virulent form in the population . Figure 7 . C shows how the proportion of ( averaged over realisations and time ) changes with the period of control . The major effect corresponds once again with the dominant natural frequencies . The principal effects are summarised in Figure 8 . Here we show that periodic ( rather than constant ) application of control increases the durability ( ) for both the deterministic and stochastic models . The effect is substantially enhanced however for the stochastic model with a marked response corresponding to the natural frequencies .
The introduction of a resistant cultivar promotes more frequent stochastic extinctions of the pathogen population resulting in a higher for larger cropping ratios ( Figure 5 ) . The reason is attributable to the occurrence of different equilibria depending upon whether the pathogen is present or absent . For simplicity we consider the case with no external immigration and no mutations , but the result still holds in the more general case . Ignoring random effects , in the absence of the pathogen , the densities of the resistant and susceptible cultivars approach the steady state given by , reflecting the planting and harvesting rates . When the pathogen is present , the system reaches an equilibrium in which the virulent genotype coexists with the resistant and susceptible cultivars . The equilibrium is given by subject to ( see [23] ) . Thus depending upon whether the pathogen is present or not the behaviour of the pathosystem switches between these two states characterized by different equilibria . The larger the proportion of resistant crop , the smaller the density of infected hosts , especially at the beginning of an epidemic . When the density becomes critically low , the probability of extinction of the pathogen due to random fluctuations increases leading the system towards the steady state with larger yield , i . e . . Therefore the stochastic profile for the durability of resistance ( ) increases with the cropping ratio ( Figure 5 ) . The effect becomes more important when the gap between the two different equilibria is larger ( ) . Conversely , the effect is negligible when the average density of infected hosts is larger than the typical size of the fluctuations . This situation typically occurs for large initial proportions of the virulent strain or when the resistance is broken . The local minimum in the stochastic profile for at low cropping ratio ( ) is an effect of the initial conditions . The latter were chosen as the equilibrium state in the absence of the resistant cultivar and no external immigration of the pathogen . At the beginning of the simulated epidemics , the external immigration of the avirulent pathogen causes an abrupt increase in the basic reproductive number . This , in turn , leads to a sharp decrease in the durability of resistance . The contribution becomes less important for large cropping ratio , since the resistant cultivar is immune to the avirulent strain . Allowance for immigration of the virulent strain has a negligible effect since it is several orders of magnitude smaller than the immigration of the avirulent strain . This response , suggesting the existence of an optimal cropping ratio ( resistant ) , is in contrast to the predictions of van den Bosch and Gilligan [23] . However , the presence of disease resistance genes might lead to yield penalties [33] . By including a correction for yield penalty , we expect that the yield/durability would exhibit a maximum at an intermediate value of the cropping ratio . Agricultural systems are often subjected to periodic perturbation . Examples of such perturbations may arise from environmental forcing , for example temperature-driven changes in life-cycle parameters , which is particularly important as climate change has also been associated with variation in human and plant diseases [34] , [35] . Another important source of periodic perturbations are temporal variations in disease control practices e . g . due to periodic changes in the mean dosage of applied fungicides , alternation of the application of protectant and curative fungicides . To this end Gubbins and Gilligan [36] developed deterministic and stochastic models to study fungicide resistance under the application of constant and periodically varying fungicides . They found the existence of a threshold for the invasion of the resistant strain that depended upon the relative fitness of the resistant strain and the effectiveness of control , which is turn is influenced by the periodicity of fungicide application [36] . Although it recognizes the importance of periodicity , the paper by [36] does not investigate how the periodicity of application interacts with the typical frequencies of the pathosystem i . e . resonance . Resonance is well documented in many biological and ecological systems ( [31] , [37]–[40] and references therein ) . Epidemics are characterized by particular frequencies rather than others: for example Grenfell et al . [41] ( see also [42] ) investigated synchrony patterns of measles in the UK and found that the epidemic time-series are dominated by a year periodic mode . Temporal patterns of epidemics , e . g . measles , whooping cough and cholera , are also linked with environmental and human changes ( see [42] and references therein ) . In particular Rodo et al . [34] showed evidence of a relationship between El Niño/Southern Oscillation ( ENSO ) and cholera prevalence in Bangladesh . This suggests that such special frequencies might be present in plant disease epidemics too , although , to the authors' knowledge , long-term , high resolution time-series suitable for testing this hypothesis are not yet available in the literature . The existence of dominant frequencies for any epidemic described by an SIR-type mechanism is predicted by the theoretical analyses of closed systems by Alonso et al . [38] and Rozhnova and Nunes [43] . An important result for those models is that the frequency at which the power spectrum shows a peak , depends solely on the parameters underlying the disease dynamics . From a biological point of view , this suggests that the important time-scales arising from the fluctuations are expected to be related to the typical time-scales of the pathosystem ( e . g . infectious period , lifetime of the infected individual ) . It also appears that the dominant periods increase with the population of the host , as both the amplitude and the periods of fluctuations become smaller at low population densities . An external , periodic , even small perturbation with the same frequency as the dominant frequency will resonate with the system resulting in large oscillations ( Figure 7 ) . In general this leads to extinction of the pathogen and hence to longer durability of crop resistance . We make a number of simplifying assumptions in our analyses in order to test the key hypotheses discussed above . Our conclusions are derived for a system with continuous availability of healthy hosts . We do this to avoid introducing additional periodicity into the system in which we are seeking to examine resonance between a particular external perturbation ( periodic application of chemical control ) and the intrinsic periodicity of the pathosystem . It is reasonable to suppose that seasonal availability in the supply of the host would affect the distribution of the dominant frequencies . It is also possible that the combination of large fluctuations arising from resonance and an upper limit of available host imposed by a carrying capacity for the crop could lead to large outbreaks of disease rather than to extinction . Detailed analyses of these effects are beyond the scope of the current paper but will be addressed in future work . We have shown that the effect of stochasticity is important when the infected population is still low , which occurs at the beginning of the epidemics . This condition is always satisfied in a seasonal system at the beginning of each season , thus we can infer that including stochasticity in epidemic models is particularly important in seasonally variable crop management . In addition , the periodic forcing due to seasonality might interact with the dominant frequency in the absence of seasonality leading to intriguing dynamics ( see e . g . [41] , [44] , [45] ) which still need to be explored . We have also neglected explicitly-spatial effects , except for external immigration . Spatial structure and synchrony are likely to affect patterns of population fluctuation . In particular , the spatial arrangements of resistant and susceptible fields in the landscape are likely to affect extinction and re-invasion , and hence durability . For example , Park et al . [46] have previously shown that extinction times for a single pathogen strain show a non-monotonic response as the size of the sub-population increases . The effect was shown to depend upon the transit time from arrival to leaving a patch . It is conceivable that the natural frequencies of the system are affected by the spatial arrangements and sizes of resistant and susceptible fields in the landscape and by the type of dispersal of the pathogen ( long and short range rather than uniformly mixed as assumed here ) . Future research will seek to understand how the frequencies of the system depend upon the metapopulation parameters in order to establish whether or not there is an optimal patch size for the deployment of resistant cultivars . Previous theoretical [19] and experimental [47] , [48] work has investigated how individual components of the pathogen life-cycle affect the expression of crop resistance . This is particularly important for quantitative resistance , in which differing combinations of the components such as the infection efficiency , latent period , sporulation rate and infectious period , affect the expression of partial resistance [13] , [18] , [49] . We have shown in Figure 3 that different combinations of the parameters , representing different life-cycle components , can result in pathosystems characterized by the same but different amplitudes of fluctuations . Despite the pioneering work of Alonso et al . [38] and Rozhnova and Nunes , [43] , a complete understanding of the relationship between the dominant frequencies , life-cycle parameters , and other features of a pathosystem , such as spatial and temporal heterogeneities in parameter values , is still lacking . Further work is needed to tease out the effects of life cycle components on amplitudes of fluctuations and the importance of the results for plant breeders and the agrochemical industry to assist decisions over which specific pathogen life history traits to target in order to maximize the durability of crop or fungicide resistance . Experimentation will of course be necessary to confirm or reject the hypotheses posed in this paper . An empirical test would require first i ) to collect a long and high-resolution time-series of epidemiological data , perhaps starting with one cultivar only ii ) to detect at which range the dominant frequency occurs [30] , [42] , [45] iii ) then to apply a series of periodic perturbations with different periods . Even if direct measurements of durability is difficult , these kind of experiments ought to be able to detect variations in the amplitude of fluctuations ( as in Figure 7 . A ) , frequency of extinctions ( as in Figure 7 . B ) for the different periods of perturbation . It is possible that such experiments could first be undertaken for experimental microcosms ( cf [50] ) to demonstrate a proof of concept .
|
We want to understand if , and how , the evolution of a pathogen can be delayed/accelerated by random fluctuations always occurring in epidemics . We studied a simple biological system relevant to agriculture: a resistant crop immune to the disease , and a plant pathogen that defeats the resistance after a single mutation . Eventually the population of these more harmful pathogens will take over and the resistance can no longer protect the crop . As the availability of such resistant genes is limited in nature , this is an important problem to ensure food security for future generations as well as reduction in pesticide usage . We used a mathematical model to show that in general large stochastic fluctuations in epidemics enhance extinction of the pathogen , especially of the emerging mutant strains . We know that periodically forced epidemics oscillate at larger amplitude at some frequencies than at others ( resonance ) , then by adequately perturbing the system ( e . g . by alternating different types of fungicides ) we can cause massive fluctuations in the small pathogen population increasing the chances of extinction . If such hypotheses will be experimentally confirmed , we could alleviate the disease , reduce chemical control , and in general , mitigate the risk of developing highly harmful pathogens ( e . g . superbugs insensitive to antibiotics ) .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"biotechnology",
"sustainable",
"agriculture",
"high-input",
"farming",
"environmental",
"impacts",
"plant",
"biology",
"applied",
"mathematics",
"crop",
"management",
"population",
"genetics",
"crop",
"genetics",
"mathematics",
"crops",
"agricultural",
"production",
"agrochemicals",
"evolutionary",
"modeling",
"plant",
"genetics",
"crop",
"diseases",
"biology",
"agriculture",
"ecology",
"genetics",
"computational",
"biology",
"evolutionary",
"biology",
"genetics",
"and",
"genomics",
"evolutionary",
"developmental",
"biology"
] |
2013
|
Durable Resistance to Crop Pathogens: An Epidemiological Framework to Predict Risk under Uncertainty
|
Antimicrobial peptides are small , cationic proteins that can induce lysis of bacterial cells through interaction with their membranes . Different mechanisms for cell lysis have been proposed , but these models tend to neglect the role of the chemical composition of the membrane , which differs between bacterial species and can be heterogeneous even within a single cell . Moreover , the cell envelope of Gram-negative bacteria such as E . coli contains two membranes with differing compositions . To this end , we report the first molecular dynamics simulation study of the interaction of the antimicrobial peptide , polymyxin B1 with complex models of both the inner and outer membranes of E . coli . The results of >16 microseconds of simulation predict that polymyxin B1 is likely to interact with the membranes via distinct mechanisms . The lipopeptides aggregate in the lipopolysaccharide headgroup region of the outer membrane with limited tendency for insertion within the lipid A tails . In contrast , the lipopeptides readily insert into the inner membrane core , and the concomitant increased hydration may be responsible for bilayer destabilization and antimicrobial function . Given the urgent need to develop novel , potent antibiotics , the results presented here reveal key mechanistic details that may be exploited for future rational drug development .
Antimicrobial peptides ( AMPs ) are small cationic membrane-active peptides; they can be found in most living organisms and play an essential part in innate immunity [1–3] . These peptides exhibit broad-spectrum antimicrobial activity against bacteria , fungi and viruses , making them of great biomedical interest , particularly in the field of novel antibiotic design . Polymyxin B1 ( PMB1 ) is a small antimicrobial lipopeptide first derived from the bacterial species Bacilus Polymyxa in 1947[2 , 4 , 5] . It is composed of a cyclic polypeptide ring and a branched fatty acid tail , and among the amino acids forming the peptide segment are the irregular amino acids D-Phenylalanine ( DPhe ) and α , γ-Diamino Butyric acid ( DAB ) . Its full sequence is thus: DABC-Thr-Leu-DPhe-DAB-DABC-DAB-Thr-DAB-CO ( CH2 ) 4CH ( CH3 ) CH2CH3 , where DABC represents the cyclic linkage . The five non-cyclized DAB amino acids each carry a charge of +1 , and thus the cationic peptide has a total charge of +5 [6] . PMB1 is a highly potent antimicrobial peptide and is selective predominantly towards all Gram-negative bacterial species , with the exception of the Proteus groups [7] . Unfortunately , treatment of patients with PMB1 has been shown to have adverse side effects on the renal and nervous system [8–10] , and therefore clinical use of PMB1 has been limited to topical treatment as well as “last resort” therapy of patients infected with multidrug-resistant bacteria or with chronic conditions who suffer from recurring respiratory infections [11] . However , given the alarming rise in the number of bacterial strains exhibiting multidrug-resistance , there has recently been renewed interest in PMB1 [2] . PMB1 lipopolypeptides are known to permeate across the bacterial outer membrane ( OM ) by self-promoted uptake , while it is disruption of the inner membrane ( IM ) that subsequently leads to cell death . The peptides are thought to fulfil the initial stages of their bactericidal activity by anchoring themselves to the bacterial membrane via the DAB amino acids[12 , 13] . While the precise mode of action subsequent to peptide anchoring to the membrane is still unclear , it has been established that the polypeptide ring is responsible for causing an increased permeability of the bacterial membrane[4] . It has been proposed that the observed permeabilization is caused by membrane insertion of the polypeptide ring and fatty acid tail , resulting in bilayer disruption and an outflow of intracellular components , followed by cell death [4] . Because of experimental difficulties associated with characterizing dynamic , heterogeneous systems such as membrane-bound AMPs , molecular dynamics ( MD ) simulations provide a complementary approach to studying their modes of action , in unprecedented detail [14 , 15] . Here , we have used a series of MD simulations ( Table 1 ) over microsecond timescales to study , the interaction of an AMP with accurate models of both membrane components of a Gram-negative bacterial cell , there has previously been only one report of a computational study of an AMP interacting with a model OM[16] . In particular , the former membrane is represented by a realistic mixture of phospholipids representative of the bacterial IM , whilst the latter is modelled as an asymmetric bilayer containing a phospholipid mixture in the inner leaflet and rough lipopolysaccharide ( LPS ) in the outer leaflet . For comparison , we also study the interactions of the AMP with a symmetric lipid A membrane . Computational work on AMPs is well documented [17–19] and simulations of complex models of the OM are also available [20–23] , though we report one of the first combinations of the two aspects in atomistic detail . We thus investigate the molecular-level mechanisms of PMB1 binding , insertion , and bilayer disruption for both IM and OM models of the envelope of the archetypal Gram-negative bacterial species , E . coli .
To study the initial stages of AMP interaction with the envelope of Gram-negative bacteria , we simulated PMB1 in the presence of a realistic model of the asymmetric E . coli OM , composed of Re LPS in the outer leaflet and a mixture of phospholipids ( including phosphatidylethanolamine , phosphatidylglycerol , and cardiolipin ) in the inner leaflet . The molecular compositions of the simulated systems are given in Table 1 . We performed simulations of simplified OM model systems , containing lipid A in both leaflets ( Sim_LipA ) . The reasons for doing this are two-fold; firstly this setup more easily enables the peptides to permeate into the lipid part of the membrane , allowing interrogation of these membrane-peptide interactions over a tractable timescale , and secondly it abrogates the problem of peptides moving across periodic boundaries to interact with the phospholipid portion of the asymmetric bilayer . While the lack of sugars in lipid A provides a simplified model , it does enable us to study the behaviour of the peptide at the lipid headgroup/tail interface in realistic detail . Two independent simulation systems , each comprising eight PMB1 placed ~0 . 5 nm above one leaflet of the membrane , were run for 3 microseconds . Here , we report only the behaviour that differs from the asymmetric bilayer studies described above . We next turned our attention to the E . coli IM . We performed simulations of a symmetrical phospholipid membrane model ( composed of phosphatidylethanolamine , phosphatidylglycerol , and cardiolipin phospolipids ) , and exposed it to PMB1 lipopeptides ( Table 1 ) .
Our results reveal contrasting behaviour of PMB1 in the presence of different bacterial membrane models . In the case of the LPS simulations , our observations support data from previous fluorescence studies suggesting that DAB residues are key to PMB1 binding and antimicrobial function [12 , 13] . Strikingly , we observed aggregation of PMB1 on the LPS membrane surface ( Fig 1 and S2 Fig ) , in which monomers arranged themselves in a micelle-like conformation to bury their hydrophobic tails from the polar sugar rings . This aggregation , coupled with the tendency for PMB1 to cross-link the sugar hydroxyl groups resulted in formation of an immobile , “protein membrane cluster” . The adoption of this fatty acid tail orientation and aggregation may represent a necessary prelude to pore formation , in a manner that reduces the energetic cost for translocation of the PMB1 “micelle” across the polar environment of the LPS sugar groups . Intriguingly , two attempts to speed-up the process of PMB1 insertion into the outer membrane firstly by loosening the membrane by replacing the LPS cross-linking divalent cations with monovalent ions , and secondly by performing steered MD simulations to 'pull' a peptide , also provided strong support for the hypothesis that insertion of PMB1 into the outer membrane is not a facile molecular process . Because of the slow dynamics of the LPS system [23] , we also studied a simplified OM model consisting only of lipid A . Unlike the LPS system , PMB1 appeared to have no effect upon intra-lipid A hydrogen bonding , but instead the DAB residues interacted with phosphate groups , pushing them apart , leading to local membrane deformation and creating a region of reduced membrane-surface charge density that may no longer inhibit fatty acid tail insertion . During this process , Mg2+ ions were intermittently displaced and rebound to phosphate groups , but no long-term ion displacement was observed . As a result , we only witnessed complete insertion of a single PMB1 molecule on the timescales feasible with atomistic simulations . Nevertheless , it has been widely documented that divalent cations are essential for maintenance of OM stability , and PMB1 has been shown to cause destabilisation via displacement of these divalent cations [6 , 31 , 32] . For the IM model , our results are again in agreement with previous experimental work [12 , 13 , 26 , 33] highlighting the importance of the DAB residues in protein binding , confirming that electrostatic interactions between the DAB and the lipid headgroups are the initial driving force for PMB1 adsorption . However , in contrast with the OM , the distinguishing behaviour of the PMB1 molecules in the IM system was the lack of PMB1 aggregation as well as the striking result that the fatty acid tails of every PMB1 molecule spontaneously penetrated into the hydrophobic core of the membrane , some as far as the lower leaflet ( Fig 3 ) . Unlike the OM models , the IM exhibited a large increase in the disorder of the acyl tails upon fatty acid tail insertion . Indeed , PMB1 insertion seemed to be driven largely by hydrophobic interactions , not only due to fatty acid tails but also the D-Phe sidechain . The converged , inserted PMB1 conformation ( S4 Fig ) structurally resembles that observed in NMR studies performed by Mares et al [26] which focused on the interaction between LPS and PMB1 . In both cases , the majority of the ring structure remained bound to the phospholipid headgroups on the membrane surface , while the hydrophobic portion of the ring ( residues D-Phe and Leu ) adopted an embedded orientation . In the case of the IM , we also observed a further possible role of the DAB residues in antimicrobial action , namely attraction of water towards the membrane core . Unlike the OM models , upon loss of the surface-bound state and PMB1-membrane insertion , the lipid lateral diffusion rate increased beyond the PMB1-free rate , correlating with peptide/water membrane penetration . Concomitant with this , we observed a remarkable thinning effect of >0 . 7 nm and increase in area per lipid in the IM as a result of PMB1 insertion , whilst the resultant cross-leaflet interdigitation of acyl tails is consistent with data from spin-labelling and X-ray diffraction reported by Boggs et al [29] and Theretz et al [30] . Since the PMB1 does not aggregate within the IM , but instead inserts as monomers , we may hypothesise on the basis of our collective observations that PMB1 disrupts the IM not through traditional mechanisms of pore formation but through membrane insertion , bilayer thinning , and water penetration . Such effects are certainly indicative of AMP permeation and the early stages of membrane disruption [34] . The limitations of the current study arise from the slow lateral diffusion of LPS , such that the long simulation times required to witness complete PMB1 translocation within the asymmetric LPS membrane is not presently feasible with atomistic simulations . Indeed it has recently been shown that even for simple , phospholipid bilayers , the simulation times required for convergence have historically been seriously underestimated [35] ) . The slow reorganization of ionic interactions involving zwitterionic phospholipid headgroups when solutes penetrate the lipid-water interface are a particular problem , which is accentuated in the complex LPS headgroups we are studying . Other studies have suggested that atomistic molecular dynamics simulations of AMPs require multi-microsecond timescales [36] . Our simulations reported here show that even non-equilibrium methods such as steered MD are not able to access insertion behaviour into membranes and thus another approach is needed . The large molecular systems and extended timescales accessible to coarse-grain molecular dynamics ( CG-MD ) simulations provide an alternative and complementary route to studying antimicrobial peptides . Indeed , CG-MD studies have been shown to provide insights into the action of antimicrobial peptides in flat lipid bilayers and spherical vesicles [15 , 37 , 38] . To study the OM of Gram-negative bacteria using this approach , a coarse-grain model of LPS is urgently needed . Nevertheless , the simulations we have performed have helped to identify the initial stages of PMB1 action on the OM of Gram-negative bacteria , and in particular highlight the regions of the peptide that form strongest interactions with the LPS molecules of the outer membrane . In future , a multiscale simulation approach based on these studies may provide further insights regarding mechanism of action . In conclusion , in this study we have shed light on the potential mechanisms for bacterial envelope disruption by PMB1 . The aggregation witnessed in the OM models is suggestive of the possible early stages of self-regulated translocation / pore formation , whilst the fatty acid tail insertion in the lipid A environment also appears to be dependent upon aggregation in order to create a charge-free area to allow PMB1 penetration , providing further evidence for a pore model of self-regulated uptake . We may speculate that a ‘ladder’ type mechanism occurs in the context of the full LPS membrane , with the PMB1 molecules initially aggregating on the surface , prior to further penetration through the sugar region by the DAB residues , subsequently disrupting the high surface charge density of the counterion-cross-linked lipid A moieties and resulting in fatty acid tail penetration within the hydrophobic membrane core . A stepwise process for PMB1 disruption of the IM has also been established , beginning with DAB-based adsorption and followed by rapid fatty acid tail insertion within the bilayer , supported by the hydrophobic D-Phe . In this case , deep penetration of monomeric PMB1 molecules enables the DAB residues to drag water into the membrane , suggesting an alternative antimicrobial mechanism for IM destabilisation . Nevertheless , it is possible that other mechanisms ( e . g . carpet model ) may apply at higher concentrations of PMB1 .
All simulations systems are summarised in Table 1 . All simulations performed in this work used the GROMACS molecular dynamics software [53 , 54] , version 4 . 5 . 1 [55] . Standard parameters taken from the GROMOS 53A6 force field [56] were used to model the polymyxin B1 molecule in its fully ionized state . The parameters for the LPS molecules were as described and used previously [27 , 57] and the GROMOS-CKP ( Chandrasekhar[58]-Kukol[59]-Piggot[27 , 60] ) parameters were used for the phospholipids . The SPC water model [61] was used in all simulations . During the simulations , the LPS , phospholipids and solvent ( water plus counterions ) were maintained at a constant temperature of 313 K using the Nosé-Hoover thermostat [62 , 63] with a time constant of 0 . 5 ps . The only exception being the lipid A bilayer simulations which were performed at a temperature of 323 K . These temperatures were chosen as they are above the gel to liquid crystal phase transition temperatures of all the lipids used in the simulations [64–68] . A pressure of 1 bar was maintained using anisotropic pressure coupling with the Parrinello-Rhaman barostat [69 , 70] and a time constant of 5 ps . Electrostatic interactions were treated using the smooth particle mesh Ewald ( PME ) algorithm [71] with a short-range cutoff of 0 . 9 nm . The van der Waals interactions were truncated at 1 . 4 nm with a long-range dispersion correction applied to the energy and pressure . The neighbor list was updated every five steps during the simulations . All bonds were constrained using the P-LINCS algorithm [72] allowing a 2 fs time step to be applied . All LPS-containing membrane systems were neutralized with Mg2+ ions , whereas the inner membrane model was neutralized with Na+ ions .
|
Antimicrobial peptides have the ability to kill harmful bacteria through interaction with bacterial membranes . This manuscript describes the first reported computational study of antimicrobial peptide interaction with both membranes of a Gram-negative bacterium . While antimicrobial peptides have been the topic of many simulation studies , these studies have not incorporated the biochemical heterogeneity of natural membranes . Our simulations add the missing biochemical details and in doing so , reveal that the mechanisms of interaction of polymyxin B1 with the inner and outer membranes of E . coli , are really rather different . The peptides insert readily into the inner membrane , whereas the interaction with the LPS-containing outer membrane is more complex . In summary our results represent a key finding for future drug development that targets these bacteria .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
Interaction of the Antimicrobial Peptide Polymyxin B1 with Both Membranes of E. coli: A Molecular Dynamics Study
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We present a coarse-grained simulation model that is capable of simulating the minute-timescale dynamics of protein translocation and membrane integration via the Sec translocon , while retaining sufficient chemical and structural detail to capture many of the sequence-specific interactions that drive these processes . The model includes accurate geometric representations of the ribosome and Sec translocon , obtained directly from experimental structures , and interactions parameterized from nearly 200 μs of residue-based coarse-grained molecular dynamics simulations . A protocol for mapping amino-acid sequences to coarse-grained beads enables the direct simulation of trajectories for the co-translational insertion of arbitrary polypeptide sequences into the Sec translocon . The model reproduces experimentally observed features of membrane protein integration , including the efficiency with which polypeptide domains integrate into the membrane , the variation in integration efficiency upon single amino-acid mutations , and the orientation of transmembrane domains . The central advantage of the model is that it connects sequence-level protein features to biological observables and timescales , enabling direct simulation for the mechanistic analysis of co-translational integration and for the engineering of membrane proteins with enhanced membrane integration efficiency .
Most integral membrane proteins ( IMPs ) are co-translationally inserted into the membrane during biosynthesis via the Sec translocon , a multiprotein complex [1–4] . In this process , a ribosome docks to the cytosolic opening of the Sec translocon and feeds a nascent polypeptide chain ( NC ) into the translocon channel . Secretory proteins , or the soluble domains of IMPs , translocate across the lipid membrane by passing through the translocon channel [1 , 2] . Alternatively , the transmembrane domains ( TMDs ) of IMPs integrate directly into the lipid membrane via the translocon lateral gate ( LG ) . Integration is facilitated by a conformational change in the channel that separates the two LG helices to create an opening between the channel interior and the hydrophobic core of the membrane [5–7] . The likelihood of integration or translocation of polypeptide segments depends on residue-specific chemical features of the nascent polypeptide chain , such as its hydrophobicity and charge [8–12] , but is also governed by the dynamics of protein synthesis on the minute timescale [13 , 14] . To reach a stable folded structure , IMPs must integrate into the membrane with the correct topology ( i . e . , orientation of each TMD with respect to the membrane ) , which depends sensitively on the properties of both the NC and the translocon itself [3 , 15] . Even single mutations to an IMP amino-acid sequence can disrupt integration and induce disease phenotypes [16] or decrease protein expression [17–19]; similarly , mutations to the translocon channel can inhibit IMP folding [8 , 20–23] . The important role for IMPs in cellular functions , such as signal transduction , the transport of nutrients , and cell adhesion , motivates the understanding of the effect of NC and translocon properties on the efficiency of co-translational integration . However , a detailed understanding of this process presents challenges for both theory and experiment due to the long range of timescales ( from nanoseconds to minutes ) that are involved . Experimental studies have elucidated many aspects of the structure and function of the Sec translocon , although their ability to directly probe the non-equilibrium co-translational integration process is limited . Structural characterization has revealed many of the components of the translocon complex in both eukaryotes [24–28] and prokaryotes [6 , 7 , 29–32] , while biophysical assays have investigated the functional effects of NC hydrophobicity [8 , 9] , charges flanking TMDs [10–12] , soluble loop length [13 , 14] , and the forces exerted on a NC during translation [4 , 33 , 34] . Despite these findings , mechanistic details of the co-translational integration process remain in question [4] because most experiments are limited to probing final protein distributions—such as the fraction of protein in a specific topology [14] or the fraction of protein integrated in the membrane [35]—and do not typically resolve NC dynamics . Atomistic-scale molecular dynamics simulations can be used to probe detailed aspects of co-translational integration , with recent simulations providing insight into the energetics of TMD integration [36 , 37] , the dynamics of water inside the translocon [38] , the effect of NC properties on LG opening [39] , the dynamics of a NC during the initial stages of translation [40–42] , and the dynamics of IMP integration in simplified system representations [43 , 44] . However , the separation of timescales relevant to co-translational integration poses a significant challenge to conventional simulation methods: notably , ribosomal translation requires seconds to minutes to complete the biosynthesis of typical polypeptides [45–48] , while conformational fluctuations of the NC occur on the nanosecond timescale . Currently available simulation approaches either fail to reach the biological timescales of ribosomal translation [38 , 40 , 41] or lack sufficient detail to describe detailed features of the NC-translocon interactions and NC conformational dynamics [43 , 44 , 49] . The model presented here overcomes these limitations , allowing direct comparison with a broad range of available experiments . In previous work , a highly coarse-grained ( CG ) model of Sec-facilitated IMP integration was developed in which all system coordinates are projected onto a two-dimensional plane passing through the translocon LG [43] . This 2D-CG model includes an explicit representation of NC translation , translocon LG conformational gating , and a sufficiently simple system description to enable minute-timescale unbiased trajectories . Previous work has demonstrated that the 2D-CG model correctly predicts the distribution of topologies obtained by TMDs as a function of C-terminal soluble loop length [43] , the probability of membrane integration as a function of TMD hydrophobicity [43] , the effect of charge mutations on the topology of the dual-topology protein EmrE [50] , and the effect of sequence modifications on the integration efficiency of the multispanning protein TatC [19] . The 2D-CG model was also used to demonstrate a link between IMP integration efficiency and expression levels for TatC [19] , enabling the computational prediction of amino-acid sequence modifications that improve IMP expression . These successes illustrate the potential for using CG methods to capture the essential physics of the co-translational protein translocation and membrane integration processes . However , several shortcomings of the 2D-CG model have been identified . In particular , the ribosome and translocon are modeled without detailed structural features , sequence-specific ribosome and translocon chemical features are not mapped directly to the CG representation , and interactions between the NC and the translocon are independent of NC sequence . These shortcomings limit the ability of the 2D-CG model to investigate phenomena arising from sequence-specific structural and chemical features , such as variations among homologs of the Sec translocon [6 , 7] or interactions between the NC and translocon [51 , 52] . In the current work , we describe a refined CG model that enables simulation of the long time- and length-scales that are relevant to co-translational protein integration , while preserving sequence-specific properties of the NC and translocon and capturing the structure of the ribosome-translocon complex . The new 3D-CG model extends the 2D-CG model by providing a realistic three-dimensional representation of the ribosome-translocon complex mapped directly from high-resolution structural data [6 , 25] . Additionally , the model is parameterized via a bottom-up approach to reproduce sequence-specific NC-translocon interactions , and it includes a protocol for directly mapping any input amino-acid sequence to a simulation representation , enabling simulation of any polypeptide using only the amino-acid sequence as input . The improved 3D-CG model is validated by reproducing experimental measurements of TMD integration efficiency [51] and signal peptide topogenesis [14] . The model further reproduces the “biological hydrophobicity” scale derived by von Heijne and co-workers [51] , capturing the effects of single-residue mutations on stop-transfer efficiency . The strong agreement between simulation and experiment indicates that the 3D-CG model produces simulation predictions that can be confirmed by direct experimental analogues . The new model provides a framework for performing mutagenesis studies of the NC and ribosome-translocon complex to obtain a detailed mechanistic understanding of interactions that impact TMD integration and topogenesis , potentially enabling the prediction of IMP sequence modifications with enhanced membrane integration efficiency and stability .
Fig 1A presents the components of the 3D-CG model compared to an image of the ribosome-translocon complex obtained from a cryo-EM structure [25] . The SecYEG translocon ( grey/green ) , ribosome ( brown ) , and the NC ( cyan/red ) are represented with explicit CG beads , while the implicit membrane is drawn as a shaded region . As in the 2D-CG model [43] , each CG bead has a diameter of σ = 0 . 8 nm , the Kuhn length of a polypeptide chain [43 , 44] , and represents three amino-acid residues; σ sets the length scale for the 3D-CG model . The coordinate system is defined such that the origin is placed at the geometric center of the translocon channel Cα atoms , the implicit membrane spans the x-y plane with its midplane located at z = 0σ , and the axis of the translocon is aligned with the z-axis ( Fig 1C ) . The geometry of the Sec translocon is obtained by mapping all amino-acid residues of the translocon onto CG beads in a ratio of three amino acids to one CG bead , where the CG bead is positioned at the center of mass of the Cα atoms for each consecutive triplet of amino-acid residues in the translocon primary sequence . Triplets of amino acids with a net positive charge are assigned a +1 charge , and triplets of amino acids with a net negative charge are assigned a -1 charge . To determine the net charge of a triplet of amino acids the charges of the amino acids are summed , with arginine and lysine counted as +1 , and aspartate and glutamate counted as -1 ( see S2 Appendix for further discussion ) . The translocon is modeled in two distinct conformations , with the LG either closed or open ( Fig 1B ) . CG bead coordinates for both conformations are obtained from residue-based coarse-grained simulations of the Methanocaldococcus jannaschii SecYEG translocon ( PDB ID: 1RHZ ) [6] ( see S2 Appendix ) . The 3D-CG model of the translocon is oriented such that the y-axis of the simulation coordinate system passes between the helices of the LG when the translocon is in the open conformation ( Fig 1C ) . The geometry of the ribosome is obtained by mapping the ribosome-translocon complex from a recent high-resolution cryo-EM structure ( PDB ID: 3J7Q ) onto CG beads [25] . Amino-acid residues are mapped onto CG beads in a 3:1 ratio following the same procedure used for the translocon . Each RNA nucleotide in the ribosome is mapped onto two CG beads; one bead represents the sugar-phosphate backbone , while the other bead represents the nucleobase . This mapping is used to capture the excluded volume and the rigidity of the RNA scaffold and is consistent with previous work on coarse-grained DNA/RNA simulations [53–55] . Each CG bead representing a RNA sugar-phosphate backbone in the ribosome is assigned a -1 charge and each CG bead representing a nucleobase is neutral . Only the portion of the ribosome near the translocon channel is explicitly represented as CG beads in the final simulation system ( Fig 1A; additional details are in S2 Appendix ) . Ribosome CG bead positions are identical for both translocon conformations . To characterize whether the ith NC bead , with position xi = ( xi , yi , zi ) , is located in the implicit membrane region , we define the characteristic function S mem ( x i ) = [ 1 - S ( x i , y i ) ] S ( z i ) , ( 1 ) which assumes a value of 1 in the membrane and 0 elsewhere . S ( x , y ) and S ( z ) are smooth switching functions , S ( x , y ) = 1 4 1 + tanh x 2 + y 2 + 1 . 5 σ 0 . 25 σ 1 - tanh x 2 + y 2 - 1 . 5 σ 0 . 25 σ , ( 2 ) and S ( z ) = 1 4 1 + tanh z + 2 σ 0 . 25 σ 1 - tanh z - 2 σ 0 . 25 σ , ( 3 ) where x 2 + y 2 is the radial distance from the coordinate system origin in the x-y plane . S ( x , y ) is approximately 1 for the range - 1 . 5σ < x 2 + y 2 < 1 . 5σ and 0 elsewhere , while S ( z ) is approximately 1 for the range -2σ < z < 2σ and 0 elsewhere ( Fig 1C ) . Eqs 1–3 are used in Eq 8 to define the solvation of a NC bead . The potential energy function for the 3D-CG model is expressed U ( xn , xc;q , g ) =Ubond ( xn ) +Uexcl ( xn ) +Uelec ( xn , xc;q ) +Usolv ( xn;g ) +Uchan ( xn , xc;g ) +Uribo ( xn ) , ( 4 ) where xn indicates the set of NC bead positions , xc indicates the set of channel and ribosome bead positions , q is the set of all bead charges , and g is the set of all NC bead transfer free energies . All interactions in the 3D-CG model are defined using an energy scale given by ϵ = kBT , where the temperature , T , is fixed at 310 K to represent physiological conditions . Bonded interactions between consecutive NC beads are described using the finite extension nonlinear elastic ( FENE ) potential , U bond ( x n ) = - 1 2 K 0 R 0 2 ∑ b ∈ Bonds ln 1 - r b 2 R 0 2 , ( 5 ) where the sum runs over all bonds in the NC , rb is the distance between the NC beads that share bond b , K0 = 5 . 833 ϵ/σ2 , and R0 = 2σ . Short-ranged excluded volume interactions between pairs of NC beads are modeled using a purely repulsive Lennard-Jones ( LJ ) potential [56] , U excl ( x n ) = ∑ i , j ∈ NC 4 ϵ i j σ i j r i j 12 - σ i j r i j 6 + ϵ i j , r i j < 2 1 / 6 σ i j 0 , r i j ≥ 2 1 / 6 σ i j , ( 6 ) where the sum runs over all pairs of NC beads , rij is the distance between NC beads i and j , and ϵij = ϵ , and σij = σ . Electrostatic interactions are described using the Debye-Hückel potential , U elec ( x n , x c ; q ) = ∑ i , j ∈ All l B q i q j ϵ r i j exp - r i j κ , ( 7 ) where the sum runs over all pairs of charged beads , lB is the Bjerrum length , qi is the charge of CG bead i in the NC , translocon , or ribosome , and κ is the Debye length . Assuming that electrostatic interactions are screened by physiological salt concentrations [57 , 58] , the electrostatic length scales are approximated by κ = lB = σ . NC bead interactions with the implicit solvent are described using a position-dependent potential , U solv ( x n ; g ) = ∑ i ∈ NC g i S mem ( x i ) , ( 8 ) where xi is the position of NC bead i , and gi is the transfer free energy for partitioning NC bead i from water to the membrane . Residue-specific interactions between NC beads and translocon beads are given by U chan ( x n , x c ; g ) = ∑ i ∈ NC [ 1 - S mem ( x i ) ] U chan aq ( x i , x c ; g i ) + [ S mem ( x i ) ] U chan mem ( x i , x c ; g i ) . ( 9 ) Eq 9 smoothly interpolates between NC bead-translocon interactions for which NC bead i is positioned in aqueous solution inside the channel ( U chan aq ( x i , x c ; g i ) ) or positioned in the membrane near the channel exterior ( U chan mem ( x i , x c ; g i ) ) . The exact functional forms of U chan aq ( x i , x c ; g i ) and U chan mem ( x i , x c ; g i ) are described in the section Parameterization of NC-translocon interactions . Interactions between NC beads and ribosome beads are included in the Uchan ( xn , xc; g ) potential energy term ( Eq 9 ) . Contrary to interactions between NC beads and translocon beads , interactions between NC beads and ribosome beads are not bead-type specific; they are described by a repulsive soft-core LJ potential ( Eq 17 ) , with ϵij = ϵ and σj = 1 . 2σ . To prevent the NC from moving into the part of the ribosome that is not explicitly included in the simulations ( see 3D-CG Model Geometry ) , a repulsive sphere is centered at ( -10σ , -0 . 5σ , 1 . 0σ ) ( Fig 1C ) . Repulsive interactions with this sphere are described using U ribo ( x n ) = ∑ i ∈ NC 4 ϵ σ r i r - 2 σ 12 - σ r i r - 2 σ 6 + ϵ , r i r - 2 σ < 2 1 / 6 σ 0 , r i r - 2 σ ≥ 2 1 / 6 σ , ( 10 ) where rir is the distance of the NC bead i from the center of the sphere . The time evolution of the NC beads is modeled using overdamped Langevin dynamics with a first-order Euler integrator [59] , x n ( t + Δ t ) = x n ( t ) - β D ∇ x n U ( x n ( t ) , x c ( t ) ; q , g ) Δ t + 2 D Δ t R ( t ) , ( 11 ) where xn ( t ) are the positions of the NC beads at time t , U ( xn ( t ) , xc ( t ) ; q , g ) is the 3D-CG model potential energy function ( Eq 4 ) , β = 1/kBT , D is an isotropic diffusion coefficient , and R ( t ) is a random number vector drawn from a Gaussian distribution with zero mean and unit variance . The timestep , Δt = 300 ns , permits stable integration of the equations of motion with a diffusion coefficient of D = 253 . 0 nm2/s ( see S3 Appendix for discussion and Table S2 in S3 Appendix for robustness with respect to timestep ) . Ribosome CG bead coordinates are fixed throughout the simulations . Translocon CG beads undergo stochastic transitions between fixed configurations associated with the open versus closed lateral gate . NC-dependent conformational gating of the translocon is attempted at every simulation timestep . The probability that the translocon transitions from a closed ( x c closed ) to open ( x c open ) conformation , popen ( xn; q , g ) , is p open ( x n ; q , g ) = 1 τ LG exp - β Δ G open ( x n ; q , g ) 1 + exp - β Δ G open ( x n ; q , g ) Δ t , ( 12 ) and the probability that the translocon transitions from an open to closed conformation , pclose ( xn; q , g ) , is p close ( x n ; q , g ) = 1 τ LG 1 1 + exp - β Δ G open ( x n ; q , g ) Δ t . ( 13 ) The timescale for attempting translocon conformational changes , τLG = 500 ns , is obtained from prior molecular dynamics simulations [39 , 43] . The total free energy change for switching the translocon from the closed to open conformation , ΔGopen ( xn; q , g ) , is given by Δ G open ( x n ; q , g ) = Δ G empty + U ( x n , x c open ; q , g ) - U ( x n , x c closed ; q , g ) , ( 14 ) where ΔGempty = 3ϵ is the free energy penalty for opening a closed channel in the absence of a substrate [60] , U ( x n , x c open ; q , g ) is the 3D-CG model potential energy function ( Eq 4 ) with the channel in the open configuration , and U ( x n , x c closed ; q , g ) is the 3D-CG model potential energy function ( Eq 4 ) with the channel in the closed configuration . Previous simulations have found the translocon to exhibit both closed and open lateral-gate conformations [39] , and the timescale needed to perform this conformational switch is relatively small ( 500 ns ) in comparison to the other timescales modeled in the 3D-CG model [40] . Therefore , as in the 2D-CG model [43] , the lateral-gate conformational changes in the 3D-CG model are described in terms of instantaneous switches between the closed and open conformations . If an attempted conformational change is accepted , all bead positions in the translocon are immediately switched to the positions corresponding to the new channel conformation . The equations of motion described by Eqs 11–14 rigorously obey detailed balance . Translation of the NC is modeled by adding CG beads to the C-terminus of the NC during a simulation trajectory . At the initiation of the trajectory , the C-terminal NC bead is fixed at the translation insertion point ( Fig 1C ) . For each simulation timestep in which translation is performed , the C-terminal bead is moved in the +z direction by a distance equal to σΔt/ttrans , where ttrans is the timescale for translating a single CG bead . ttrans is set to 0 . 6 seconds to reproduce a translation rate of 5 residues/second [45–48] unless otherwise specified . The C-terminal NC bead is otherwise held fixed , although all interactions between the C-terminal NC bead and other NC beads are included in Eq 4 . The translation of the C-terminal bead is completed after a period of ttrans and its dynamics are described using Eq 11 for the remainder of the simulation trajectory . The next CG bead in the NC sequence is then positioned at the translation insertion point and the process is repeated until all NC beads have been translated . For the combined dynamics of the ribosome-translocon-NC system , a series of five steps is iterated at each trajectory timestep: ( i ) forces acting on each NC bead are calculated , ( ii ) NC bead positions are time-evolved using Eq 11 , ( iii ) conformational gating of the translocon is attempted ( Eqs 12 and 13 ) , ( iv ) ribosomal translation is performed if all NC beads have not yet been translated , and ( v ) the simulation is terminated if user-defined conditions are met . Specific protocols for initializing and terminating simulation trajectories are provided for each workflow described in the Results . While the system geometry , 3D-CG model dynamics , and most terms in the 3D-CG model potential energy function ( Eq 4 ) are fully described in the Methods , the functional forms of the NC-translocon interaction potentials , U chan aq ( x i , x c ; g i ) and U chan mem ( x i , x c ; g i ) in Eq 9 , have yet to be specified . Here , we describe the protocol for obtaining these potentials , which determine sequence-specific NC bead-translocon interactions . First , we define a protocol for assigning an effective water-membrane transfer free energy , gi , and charge , qi , to a NC bead , based on available experimental data . Second , potentials of mean force ( PMFs ) for translocating model tripeptide substrates across the translocon channel are calculated using the MARTINI residue-based coarse-grained force field . Finally , sequence-specific NC bead-translocon interactions in the 3D-CG model are parameterized by reproducing the MARTINI PMFs using the 3D-CG potential energy function . The interactions between a general NC bead and the rest of the system is defined by four parameters: gi , qi , λo ( gi ) , and λ i c ( g i ) . These parameters are determined as described in detail in section 3D-CG Model Parameterization . Specifically , the NC bead transfer free energy , gi , is equal to the sum of the transfer free energies of the three amino-acid residues associated with the bead according to the Wimley-White hydrophobicity scale ( Table S1 in S3 Appendix ) . For each residue that does not form secondary structure , gi is increased by 1 . 78ϵ , the cost for partitioning a peptide bond that lacks hydrogen bonds . The CG bead charge , qi , is equal to the sum of the charges of the three associated amino-acid residues . The N- and C-terminal CG beads are assigned an additional +1 and -1 charge , respectively , and have 6ϵ added to their transfer free energies to account for the additional charge [66] . The scaling parameters for NC-channel interactions , λo ( gi ) and λc ( gi ) , are determined from gi using the piecewise-linear interpolation scheme shown in Fig 2C . Fig 3 demonstrates the mapping procedure for an example amino-acid sequence . To start a 3D-CG simulation , both an input amino acid-sequence and a secondary structure assignment for this sequence must be provided . For the membrane integration simulations , the secondary structure of the experimental sequence is reported in the UniProt database and is assigned in the model directly from the available information [72] . For simulations of TMD topology , the secondary structure is not available through the UniProt database and is instead assigned using the PSIPRED secondary structure prediction server [73] .
TMDs typically contain a large number of hydrophobic residues to improve stability within the lipid membrane [74] . von Heijne and co-workers measured the probability with which a designed segment ( H-segment ) of the leader peptidase ( Lep ) protein integrates into the membrane , demonstrating that the translocon is more likely to integrate hydrophobic NC segments [51] . It was found that increasing the hydrophobicity of a poly-alanine H-segment , through mutation of alanine residues to leucine residues , monotonically increased the probability of H-segment membrane integration . Previous simulations using model sequences and the 2D-CG simulation model revealed that this trend is caused by local equilibration of the H-segment across the translocon lateral gate [43] . Reproducing the same assay using the 3D-CG model , with full structural detail and an direct mapping of the NC amino acid sequence , provides a first means to quantitatively validate model predictions . To simulate the H-segment membrane integration assay with the 3D-CG model , the Lep protein sequence is mapped to CG beads following the procedure described in Mapping amino-acid sequence properties to CG beads . Three helical secondary structure elements , including the H-segment are identified via the UniProt database ( ID:P00803 ) . Eight 19-residue H-segments are studied . Each H-segment contains between 0 to 7 leucine residues and the remaining H-segment residues are alanine [51] . All trajectories are initialized from configurations in which the two N-terminal TMDs are already translated . To reduce computational cost , simulations are initiated with the second TMD pre-inserted in the lipid membrane ( Fig 4A ) . The simulated sequences are limited to 90 CG beads in length , corresponding to a continuous stretch of amino acids starting from the second TMD ( see S2 Appendix for all simulated sequences ) . Simulations are terminated when all CG beads of the H-segment either diffuse at least 2σ away from the translocon and span the membrane ( integration , Fig 4A , S1 Movie ) or when all CG beads have translocated to the lumenal side of the membrane ( translocation , Fig 4A , S2 Movie ) . The probability of membrane integration is defined as the fraction of simulation trajectories that terminate by H-segment integration . Fig 4B shows the comparison of the experimental versus the simulated probability of H-segment membrane integration as a function of the number of leucine residues in the H-segment . The results of the experimental assay [51] are plotted in black squares and the shaded region indicates outcomes within 1 kcal/mol of the experimental measurement as determined by a best fit of the apparent free energy of integration via a sigmoidal curve [51] . The calculated results from the 3D-CG model simulations are plotted in red circles . In agreement with the experiments , the 3D-CG model shows that H-segment integration increases with the number of leucines . Although slightly shifted to the right of the experimental curve , the simulation results recover the same sigmoidal dependence of integration on leucine content and are within 1 kcal/mol accuracy of the experiment [51] . These results indicate that the 3D-CG model correctly predicts trends in NC membrane integration using only information about the protein sequence as input . Fig 4C and 4D investigate the issue of mapping from trios of amino-acid residues to a single CG bead . There are three possible CG representations ( frameshifts ) of the NC sequence that arise from the 3:1 mapping of amino-acid residues to CG beads as shown in Fig 4C . Since there is no basis for choosing any one frameshift over the other two , each of the possible frameshifts is simulated , and the calculated membrane integration probabilities shown in Fig 4B is the averaged value over all three frameshifts . For each frameshift and for each of the eight H-segment sequences , 100 trajectories are calculated ( ranging from 20–3000 s in time ) leading to 2 , 400 total simulations which required a total of 15 , 520 CPU hours on 2 . 6–2 . 7 GHz Intel Xeon processors . All CG bead sequences used in the simulations are provided in S2 Dataset . Fig 4D shows the membrane integration probability for the H-segment sequences for each individual frameshift . Results based on individual frameshifts are comparable , with a notable discrepancy for the 7 leucine H-segment in Frame 1 where the particular grouping of amino acids into triplets resulted in an H-segment for which the integration probability is relatively low . This sensitivity to the choice of triplets is addressed by simply averaging the results over all three frameshifts , which is done for the results in Fig 4B . As shown in Fig 4B , experiments and the 3D-CG model simulations both show that increasing the hydrophobicity of a H-segment by mutating alanine residues to leucine residues increases the probability of H-segment membrane integration . von Heijne and co-workers have extended this analysis by determining the effect of all twenty amino acids on the probability of H-segment membrane integration in the context of the Lep construct [51] . Assuming that there is an effective two-state equilibrium between the integration and translocation outcomes , the probability of integration can be converted into an apparent free energy of integration , ΔGapp , defined by [51] Δ G app = - k T ln p ( integration ) / p ( secretion ) . ( 18 ) By mutating the central residue of the H-segment in the same Lep construct used in the section Probability of membrane integration for NC segments of varying hydrophobicity , von Heijne and coworkers measured Δ G app aa , or the single-residue apparent free energy of integration , for all twenty naturally occurring amino-acid residues , thus deriving a “biological hydrophobicity scale” in analogy to other hydrophobicity scales [63] . Calculating the probability of membrane integration of the same set of H-segments with the 3D-CG model provides a means to validate the predicted effect of single amino-acid residue mutations . The simulation procedure for calculating the biological hydrophobicity scale is the same as illustrated in Fig 4A ) . To determine Δ G app aa for all 20 amino acids , 22 experimentally studied constructs of the mutated Lep sequence are mapped to a CG representation . Results are averaged over all three frameshifts for each of the 22 constructs , requiring a total of 66 CG bead sequences . All CG bead sequences modeled are provided in S2 Dataset . The probability of H-segment membrane integration is calculated from an ensemble of 200 trajectories ( ranging from 20–2000 s in time ) per sequence , leading to a total of 13 , 200 simulations which required a total of 77 , 003 CPU hours on 2 . 6–2 . 7 GHz Intel Xeon processors . The probability of H-segment membrane integration is converted to a Δ G app aa following the procedure of von Heijne and coworkers described below [51] . The Δ G app aa for alanine and leucine are determined first from a linear fit of ΔGapp for H-segments with 3 to 7 Leucine residues from the simulated membrane integration probability curves ( Fig 4B ) using Δ G app = n Leu Δ G app Leu - Δ G app Ala + 19 Δ G app Ala . ( 19 ) Δ G app aa for alanine and leucine are found to be 0 . 13 kcal/mol and -0 . 43 kcal/mol respectively . Experimentally determined values for alanine and leucine are 0 . 1 kcal/mol and -0 . 6 kcal/mol respectively . The difference in Δ G app aa between simulation and experiment for leucine gives rise to the slight rightward shift of the simulated membrane integration probability curve compared to the experiment in Fig 4B . To obtain Δ G app aa for the remaining amino acids , we employ [51] Δ G app aa = Δ G app x [ aa ] x - Δ G app x [ ref ] x + Δ G app ref . ( 20 ) ΔGx[aa]x is the apparent free energy of integration for an H-segment construct with the probed amino acid ( aa ) at the midpoint of the H-segment ΔGx[ref]x is the apparent free energy of integration for the same H-segment where the probed amino acid is replaced by a reference amino acid with a known apparent free energy of integration , Δ G app ref . The reference amino acids employed match those used in Ref . [51] and are specified in S2 Dataset . The H-segment constructs were chosen to have a leucine content such that the probability of membrane insertion for the sequence is nearly 50% to yield maximum sensitivity in the experimental assay [51] . For cysteine and methionine , we added two additional leucines to the simulated H-segment constructs compared to the experimental constructs to yield additional sensitivity in the computation . Fig 5 compares the values of Δ G app aa determined experimentally to the values of Δ G app aa calculated using the 3D-CG model . Each point represents a single amino acid . Points are colored by grouping amino-acid residues as charged ( black ) , polar ( red ) , aromatic ( blue ) , or non-polar ( green ) . The solid line is a linear fit to the data , while the dashed line illustrates a perfect correlation as a guide to the eye . Each Δ G app aa value is calculated from the average of three frameshifts ( defined as in Fig 4 ) . The average standard deviation between the frameshift results is 0 . 2 kcal/mol , the error bars indicate the standard error of the mean . Individual frameshift values are reported in Table S3 in S3 Appendix . The experimental and 3D-CG simulation scales are highly correlated ( r = 0 . 88 ) , confirming that the 3D-CG model reproduces trends in Δ G app aa with high fidelity . The data points largely lie above the dashed line , indicating that the 3D-CG simulations slightly overestimate the experimentally observed degree of integration . These results thus indicate that the 3D-CG is capable of reproducing the effect of single-residue mutations in good agreement with available biophysical measurements , although the quantitative agreement with experiments may still be improved via further model refinements . In addition to determining whether NC segments integrate into the membrane as TM domains , the translocon regulates the orientation with which TM segments integrate ( Fig 6A ) [14 , 33 , 75] . In particular , Spiess and co-workers found that an engineered TM signal anchor ( H1Δ22 ) integrates in either the NER/Ccyt ( i . e . Type 1 ) or the Ncyt/CER ( i . e . Type 2 ) topology; it was also found that decreasing the rate of ribosomal translation by adding cycloheximide increases the preference for the Type 2 topology [14] . Furthermore , increasing the length of the soluble loop flanking the C-terminus of the TM segment also increases the probability that the TM segment obtains the Type 2 topology until the probability eventually plateaus for a sufficiently long loop length . Previous work using the 2D-CG model qualitatively captured both these trends and revealed that the mechanistic basis for the kinetic effect is flipping of the NC from the Type 1 topology to the Type 2 topology as a function of time [43] . However , due to the lack of residue-specific interactions in the 2D-CG model , this work employed model sequences . Additionally , due to the simplified geometric representation of the 2D-CG model , it predicted that p ( Type 2 ) plateaus at shorter C-terminal lengths than is observed in the experiments . While the 2D-CG model can provide mechanistic insights [43] , quantitative agreement with the experiments is poor compared to the 3D-CG model when directly mapping the amino-acid sequence ( Fig S2 of S3 Appendix and corresponding discussion ) . Here , we test the 3D-CG model for predicting TMD topogenesis and the effect of translation kinetics on topology . The simulation approach for modeling TMD topogenesis is summarized in Fig 6A ( see S3 and S4 Movies for example trajectories ) . The H1Δ22 sequence is mapped to CG beads , and the results are averaged over all three frameshifts . Nine different lengths of the C-terminal soluble loop are mapped directly from the experimental constructs used in [14] . The first 99 residues of the sequence are assumed to be part of helical domains based on secondary structure predictions from the PSIPRED server [73 , 76] . Simulations are initialized from configurations in which four CG beads are translated and have not yet entered the translocon . Simulations are terminated when CG beads of the TMD have all integrated into the lipid bilayer in either an Type 1 or Type 2 topology and diffuse 10σ away from the translocon . The final TMD topology is determined from the position of the C-terminal CG bead relative to the membrane upon simulation termination ( Fig 6B ) . All simulations are performed with either the default translation rate of 5 residues/second ( fast translation ) or with a reduced translation rate of 1 . 25 residues/second ( slow translation ) to model the effect of adding cyclohexamide in the experimental assay . 200 trajectories ( ranging from 25–1200 s in time ) are simulated for each of the three frameshifts and for each of the nine loop lengths and at both translation rates , leading to a total of 10 , 800 trajectories which required a total of 149 , 009 CPU hours on 2 . 6–2 . 7GHz Intel Xeon processors . Fig 6B compares the simulated and experimental results for the probability with which the TMD obtains the Type 2 topology as a function of the length of the C-terminal soluble loop . The results of the experimental assay are plotted on the right for reference . Results for the normal translation rate are in solid black lines , while results for the reduced translation rate are in dashed red lines . The simulation results correctly reproduce the trends observed in the experiments , including the increased probability of the Type 2 topology for longer C-terminal loop lengths and the eventual plateau in the probability of the Type 2 topology at long C-terminal loop lengths . Furthermore , like the experimental results , the CG model predicts a significant shift to greater Type 2 integration upon reducing the rate of ribosomal translation . We present a refined CG model for co-translational membrane protein integration via the Sec translocon that captures the detailed three-dimensional geometry of the ribosome-translocon complex from high-resolution structural data [6 , 25] and that describes residue-specific interactions between the NC and translocon based on detailed MD simulations . The bottom-up parameterization approach utilized here employs extensive residue-based coarse-grained simulations to inform the model parameters without the need for additional experimental inputs . In this work , the 3D-CG model is applied to calculate the membrane integration efficiency and topology of TMDs , where the only required input is the amino-acid sequence and NC secondary structure . The 3D-CG model captures the experimentally observed [51] sigmoidal dependence of the probability of TMD integration on substrate hydrophobicity . We extend this analysis to study the effect of all twenty amino-acids on the membrane integration probability yielding values of residue-specific TMD membrane integration probabilities in good agreement with the experimentally observed “biological hydrophobicity” scale [51] . These results demonstrate that the 3D-CG model successfully combines factors that are known from previous work to affect TMD integration at the translocon , such as interactions of the nascent chain and the translocon channel interior [37 , 38 , 40] , the non-equilibrium nature of peptide elongation [37 , 43] , and the sequence context of the TMD [77] . This suggests that the 3D-CG model is well suited for future applications to investigate phenomena such as the experimentally observed position dependence of the biological hydrophobicity scale [35] and the dependence of the observed hydrophobicity values on the amino-acid residues flanking the TMD [77] . The specific interactions between the NC and the translocon , determined as part of this study , already suggest a mechanism by which flanking residues can affect TMD integration; the high barrier for the translocation of charged residues limits translocation , resulting in more integration . Finally , the 3D-CG model accurately describes the experimentally observed effect of translation rate and C-terminal loop length on TMD topogenesis [14] . The 3D representation of the model ensures the correct ribosome-translocon geometry and volume scaling behavior necessary to capture the C-terminal length dependence of TMD topology , an effect not captured in a previous 2D model [40] . The main advantage of the 3D-CG model presented here , compared to previous work , is that it requires few assumptions . NC properties are directly mapped from the underlying amino acid sequence , the ribosome/translocon geometry is mapped from available structural data , and there is no projection onto a two-dimensional subspace . Provided only with an amino acid sequence and a secondary structure assignment , the 3D-CG model obtains striking agreement with experiment , validating the ability of the 3D-CG model to predict key aspects of Sec-fascilitated protein translocation and membrane integration . We additionally emphasize that the 3D-CG model provides a refinable framework for simulating IMP co-translational membrane integration via the Sec translocon . Currently , the bottom-up parameterization strategy uses MARTINI PMFs for four distinct tripeptide substrates as input information . The 3D-CG model parameterization could be refined , either by calculating the PMF of other substrates using the MARTINI force field , by considering the role of changes in substrate protonation state in the channel interior , or by calculating PMFs using an atomistic force field . Furthermore , improved methods for parameterization and uncertainty quantification can be employed to determine parameter sets consistent with the available data [78] . All of these refinements can be made within the current 3D-CG model framework , and they will enable incorporation of additional information and improved quantitative prediction . This framework can also be naturally extended to include additional complexity , such as NC secondary and tertiary structure , other proteins that are part of the Sec translocon complex , and a heterogeneous translation rate . Future studies aimed at the prediction of multispanning IMP topology will guide model development . The 3D-CG model presented here broadens the capability of computer simulation approaches for future studies of the TMD membrane insertion process . In particular , by providing residue-specific NC-translocon interactions the current model enables direct comparison to biophysical measurements of forces between the NC and the translocon due to hydrophobic and electrostatic forces [34 , 52] . Furthermore , the realistic representation of the structure and interactions enables future mutational studies and comparison of species-specific features of the ribosome-translocon complex to obtain a detailed understanding of key residues that impact TMD integration and topogenesis . The encouraging agreement between 3D-CG model simulation outcome and experiments for single-spanning TMDs displays the capabilities of the 3D-CG framework . It enables the calculation of minute-timescale trajectories in three dimensions , facilitating computational studies that are not possible using existing models with less detail , or existing models that are unable to reach the biologically relevant timescales . The 3D-CG model , with initial model parameters obtained here using a bottom-up strategy , provides a systematically improvable framework for the simulation of co-translational membrane protein integration via the Sec translocon .
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Ubiquitous across all kingdoms of life , the Sec translocon is an essential piece of molecular machinery for protein biosynthesis . The translocon is a transmembrane channel that enables the secretion of newly synthesized proteins across the lipid membrane , as well as the integration of protein domains into the membrane interior . Understanding the function and regulation of the translocon is necessary for developing a refined view of early stage protein folding and targeting in the cell . Although computational methods are well suited to elucidating the interactions of the translocon with newly synthesized proteins , conventional simulation techniques are unable to reach the exceedingly long timescales ( i . e . , minutes ) that are relevant for protein biosynthesis . In this work , we present a novel coarse-grained approach that realistically models the ribosome/translocon/nascent-protein complex , while also allowing for the efficient simulation of biological timescales . The coarse-grained model is parameterized on the basis of extensive molecular dynamics simulations and enables the simulation of any nascent protein with only amino-acid sequence information as input . To validate the model , we perform over 26 , 000 simulations of protein biosynthesis , enabling direct comparison and demonstrating good agreement with a range of experimental studies describing this minute-timescale process .
|
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"Abstract",
"Introduction",
"Methods",
"Results",
"and",
"discussion"
] |
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2017
|
Structurally detailed coarse-grained model for Sec-facilitated co-translational protein translocation and membrane integration
|
The silent-information regulator 2 proteins , otherwise called sirtuins , are currently considered as emerging anti-parasitic targets . Nicotinamide , a pan-sirtuin inhibitor , is known to cause kinetoplast alterations and the arrested growth of T . cruzi , the protozoan responsible for Chagas disease . These observations suggested that sirtuins from this parasite ( TcSir2rp1 and TcSir2rp3 ) could play an important role in the regulation of the parasitic cell cycle . Thus , their inhibition could be exploited for the development of novel anti-trypanosomal compounds . Homology modeling was used to determine the three-dimensional features of the sirtuin TcSir2rp1 from T . cruzi . The apo-form of human SIRT2 and the same structure solved in complex with its co-substrate NAD+ allowed the modeling of TcSir2rp1 in the open and closed conformational states . Molecular docking studies were then carried out . A library composed of fifty natural and diverse compounds that are known to be active against this parasite , was established based on the literature and virtually screened against TcSir2rp1 and TcSir2rp3 , which was previously modeled by our group . In this study , two conformational states of TcSir2rp1 were described for the first time . The molecular docking results of compounds capable of binding sirtuins proved to be meaningful when the closed conformation of the protein was taken into account for calculations . This specific conformation was then used for the virtual screening of antritrypanosomal phytochemicals against TcSir2rp1 and TcSir2rp3 . The calculations identified a limited number of scaffolds extracted from Vismia orientalis , Cussonia zimmermannii , Amomum aculeatum and Anacardium occidentale that potentially interact with both proteins . The study provided reliable models for future structure-based drug design projects concerning sirtuins from T . cruzi . Molecular docking studies highlighted not only the advantages of performing in silico interaction studies on their closed conformations but they also suggested the potential mechanism of action of four phytochemicals known for their anti-trypanosomal activity in vitro .
Chagas disease is caused by a flagellated protozoan belonging to the Trypanosomatidae family of the kinetoplastida order . This disease which is mainly localized in Central and South America , affects approximately 8 million people , causing near 14 , 000 deaths worldwide [1] and leading to serious medical complications such as fatal damage to the heart muscles ( cardiomyopathy ) , central nervous system , and digestive tract ( megacolon , megaesophagus ) consequently resulting in death [2] , [3] . Unfortunately , the two drugs used for the treatment of Chagas disease ( Benznidazole and Nifurtimoz ) , have limited efficacy and have been associated with numerous adverse side effects . Moreover , the pathogen seems to be able to develop resistance against these treatments [4] , [5] . Thus , there is an urgent need to find new biotargets for the development of compounds with low toxicity and good efficacy against this parasite [6] . The silent-information regulator 2 ( SIR2 ) proteins are currently considered as emerging anti-parasitic targets because of their nicotinamide adenine dinucleotide NAD+-dependent deacetylase activity on histones and other cellular substrates . It has been demonstrated , for example , that sirtuins from Plasmodium species are involved in the regulation of the telomere-associated var gene family members that encode for proteins responsible of host immune evasion [7] , [8] . Leishmania sirtuins were found to be implicated in delaying apoptosis and providing protection from host immune responses [9] , [10] , [11] . Moreover , their species specific inhibition by bisnaphthalimidopropyl ( BNIP ) derivatives has exhibited robust anti-leishmanial activity [12] . Whereas TbSir2rp2 and TbSir2rp3 from T . brucei do not impact on parasite growth and differentiation in vitro , TbSir2rp1 has been shown to participate in DNA repair through the modification of H2A and H2B histones [13] , [14] . For an extensive review on this topic , please refer to [15] , [16] . In T . cruzi , two isoforms from the SIR2 family have been identified: TcSIR2rp1 and TcSIR2rp3 ( Figure 1 ) [17] . It was proven in cellulo that their inhibition by the well-known sirtuin inhibitor nicotinamide can cause morphologic alterations and an inhibitory growth of this parasite , suggesting a potential use of TcSIR2 proteins for the development of new drugs against Chagas disease [18] . From a structural point of view , sirtuins are formed by a large Rossmann fold domain and by a small domain composed of a helical and a zinc-binding module . Their catalytic pocket is conserved , from bacteria to mammals [19] , [20] and it is formed by three sub-pockets that accommodate the co-substrate NAD+: the A pocket , where the adenine-ribose moiety binds; the B pocket , where the nicotinamide-ribose moiety binds; and the C pocket , where nicotinamide is supposed to bind [21] . Recently , crystallography identified two possible conformational states for sirtuins: an open or non-productive conformation ( apo-form ) and a closed or productive conformation ( bound-form ) . The two conformers , experimentally observed in the human sirtuin SIRT2 , differ at the catalytic pocket in terms of shape and ligand accessibility [21] , [22] . By considering this very recent structural information , two conformers of TcSIR2rp1 have been built by homology modeling . A molecular docking approach was then used not only for evaluating the quality of the modeled proteins but also for understanding the impact of the two conformational states on the final docking results . It is important to note that the majority of molecular docking calculations involving sirtuins reported in the literature have been carried out using only the non-productive conformation of sirtuins [23] , [24] , [25] . Finally , because nature is a rich source of anti-trypanosomal agents [26] , [27] , a database of phytochemicals with a known inhibitory activity in vitro against T . cruzi has been collected from the literature . Computational interaction studies between these compounds and sirtuins in the more meaningful modeled conformation was conducted to identify a potential sirtuin-related trypanocidal activity .
Homology modeling was performed by using the Molecular Operating Environment MOE software package ( MOE 2012 . 10; Chemical Computing Group , Montreal , Canada ) . The primary sequence of SIR2rp1 from T . cruzi ( TcSIR2rp1 , sequence ID: Q4DP02 ) was retrieved from the Universal Protein Resource database and used as a target for the homology modeling . To identify a template structure , the target sequence was submitted to a PSI-BLAST search ( http://blast . ncbi . nlm . nih . gov/ ) against the Protein Data Bank PDB ( www . rcsb . org/ ) using the BLOSUM62 matrix with an E-value cutoff of 10 . The human cytosolic SIRT2 protein ( hSIRT2 ) , having ∼35% sequence identity with the target , was selected as a template for the current project . Two conformations of hSIRT2 were found in the PDB: a non-productive conformation ( PDB ID: 1J8F ) , that corresponds to the apo-hSIRT2 [21] , and a productive one ( PDB ID: 3ZGV ) [22] , in which hSIRT2 binds adenosine-5-diphophoribose A5dPR . Both proteins were used in the study . Thus , TcSIR2rp1 was modeled in the two possible conformational states by following the same strategy . The target-template sequence alignment was performed using MOE's multiple sequence alignment algorithm [28] , [29] and then refined manually . Three-dimensional model building was then carried out using the MOE homology program [30] based on a segment matching procedure [31] . Ten intermediate models of TcSIR2rp1 in both non-productive and productive forms were generated and successively minimized by using the Tripos force field [32] in Sybyl-×2 . 0 ( TRIPOS Inc . , St . Louis , MO ) . The sequence from Trp93 to His97 of the parasitic productive form was modeled based on the productive human SIRT1 structure ( PDB ID: 4I5I ) due to a lack of structural information of this specific loop in hSIRT2 [33] . Gasteiger-Huckel charges were assigned to the proteins before starting the minimization step that was conducted using the Tripos force field [32] with 700 cycles of the Powell algorithm . Finally , the stereochemical quality of the models was assessed using Ramachandran plot analysis included in the PROCHECK validation package ( http://nihserver . mbi . ucla . edu/SAVES/ ) . The models with the best stereochemical quality were then selected for the interaction studies described in the next section . The crystal structure of hSIRT2 co-crystallized with the co-substrate A5dPR ( PDB ID: 3ZGV ) was taken into account not only as a template for the homology modeling but also for validating the docking methodology . This structure was prepared for docking in Sybyl-×2 . 0 ( TRIPOS Inc . , St . Louis , MO ) by removing the crystallized water molecules , by adding the missing hydrogen atoms and by extracting the ligand . The re-docking was then conducted using GOLD version 5 . 1 ( CCDC , Cambridge , UK ) . The binding site was defined by a 12 . 5 Å radius sphere around the Ala85 residue and 100 docking solutions were generated by using 100 , 000 GOLD Genetic Algorithm iterations ( Preset option ) . Docking poses were evaluated and ranked according to the PLP score . Root-mean-square-deviation RMSD values between the docking solutions and the crystallized ligand of reference were calculated to evaluate docking performance . The homology models were then superimposed on the respective productive ( PDB ID: 3ZGV ) and non-productive ( PDB ID: 1J8F ) hSIRT2 conformations using the nicotinamide recognition sequence ( TQNXD motif ) as a reference for superimposition [16] . Further docking studies of NAD+ ( co-substrate ) , nicotinamide ( pan-sirtuin inhibitor ) and AGK2 ( selective hSIRT2 inhibitor ) were carried out on the non-productive and productive protein forms of T . cruzi sirtuins by applying the methodology described above [18] , [34] , [35] . The binding site of the parasitic targets was defined by taking Ala38 into account as a reference for NAD+ and AGK2 docking whereas , for nicotinamide , the binding site was defined by taking into account an 8 Å radius sphere around Asp125 . This correction was made to avoid false positive results due to the small size of this compound with respect to the sirtuin pocket . Clustering analysis was finally performed using the RMSD analysis tool implemented in GOLD version 5 . 1 . A library of fifty compounds with in vivo anti T . cruzi activity was created by collecting data from the literature ( Table S1 ) . Iridoids & Terpenes ( 11 compounds ) , Phenolics ( 31 compounds ) and Alkaloids ( 9 compounds ) characterize the library . 3D structures were generated by using Sybyl-×2 . 0 ( TRIPOS Inc . , St . Louis , MO ) , with particular regard to the stereochemistry and protonation states ( pH = 7 . 4 ) . To display the chemical variation of data , a heat map based on the Tanimoto similarity matrix of Morgan fingerprints [36] , was generated using RDKit [37] , SciPy [38] and Matplotlib [39] tools . The database was screened against the productive form of T . cruzi sirtuins ( TcSIR2rp1 , TcSIR2rp3 [40] ) using the previously described protocol . The best-ranked forms according to their PLP score were taken into account for docking evaluation . The compounds with a positive score with respect to AGK2 ( potent and selective class I sirtuin inhibitor [35] ) and thiobarbiturate 6 ( potent and selective class III sirtuin inhibitor [41] ) were selected for further structural analyses .
A rational template search was the first step for the construction of a reliable homology model of TcSIR2rp1 . Through a BLAST-P search , the human histone deacetylase hSIRT2 , with ∼35% of sequence identity , was determined to be the best template choice for TcSIR2rp1 modeling [21] . The primary sequence of the selected template was aligned with the respective target primary sequence ( Figure 2 ) . The alignment showed a high number of conserved residues ( ∼70% ) in the three catalytic sub-pockets ( Table S2 ) . As reported for hSIRT2 , two possible conformational states in sirtuins could exist: a so-called non-productive conformation , characterized by the absence of ligands in the catalytic site [21] , and a productive one , in which the protein is found in a bound state [22] . The main difference between the two conformations consists of a 25° rotation of the zinc-binding domain towards the Rossmann-fold domain . Moreover , a high flexibility of the zinc-binding domain was observed in the productive form ( Figure S1 ) . In this study , two conformations of TcSIR2rp1 were modeled , based on hSIRT2 structural information . After several energy minimization cycles , 98 . 3% and 98 . 6% of φ and ψ backbone dihedral angles from the non-productive and productive protein forms occupied the allowed regions of the Ramachandran plots whereas less than 1% , comprised of residues that are not part of the active site regions , occupied the disallowed ones ( Figure S2 ) . These percentages , together with the absence of clashes and anomalies in bond lengths and angles , emphasized the accuracy of the modeled proteins . In this project , a molecular docking approach was used to verify the quality of the modeled proteins . Moreover , the impact of the two modeled conformational states on the final docking results was also evaluated . To validate the docking methodology , a re-docking strategy was carried out using the structure of A5dPR co-crystallized with hSIRT2 ( PDB ID: 3ZGV , productive conformation ) . The best-ranked docking solution according to the PLP score presented an RMSD value of 0 . 7 Å with respect to the crystallographic pose . Contacts with the protein were mainly polar , involving Arg97 , Asn286 , His187 and Glu323 residues [22] . Hydrophobic interactions with Phe235 , Val266 and Phe96 were also present . When the same ligand was docked into the modeled productive TcSIR2rp1 binding site , a similar binding mode and interaction network were determined . Figure 3A shows the docking results , highlighting the successful reproduction of the crystallographic information in silico . NAD+ was successively docked into the productive form of TcSir2rp1 ( Table S3 ) . The best-ranked solution , according to the PLP score , was evaluated using the crystallographic NAD+ pose in the hSIRT1 pocket as a reference [33] . The hydrogen bond network described by Zhao et al . was retrieved and it involves Ala38 , Phe49 , Arg50 , Thr214 , Ser215 , Asn238 , and Cys308 residues . Hydrophobic contacts stabilizing the nicotinamide ring were also observed ( Figure 3B ) . Nicotinamide was then docked into the productive pocket of TcSIR2rp1 [42] . In agreement with the crystallographic information from the Thermotoga maritima-nicotinamide Sir2 homolog complex ( PDB ID: 1YC5 ) [43] , the inhibitor occupied the C-site of the protein , forming hydrogen bonds with the Ile124 backbone and Asp125 lateral chain . ∏-stacking interactions with Phe50 were also observed along with hydrophobic contacts involving Ile124 , Ala38 and Ile46 ( Figure 3C ) . AGK2 was finally docked into the productive binding site of TcSIR2rp1 ( Figure 3D , Table S3 ) . This compound was demonstrated to have a certain degree of selectivity towards class I sirtuins [44] . To date , no information about the potency of AGK2 trypanocidal activity is available . However , due to the high identity between the catalytic pocket of hSIRT2 and TcSIR2rp1 [24] , a similar binding mode was assumed in the current study ( Figure 3A , Table S2 ) . It has been described that AGK2 in hSIRT2 can preferentially bind the C-pocket of this protein [35] . However , in the absence of crystallographic information , it can be supposed , by a structure-activity relationship , that AGK2 can assume an orientation similar to A5dPR , occupying the A and B pockets . Our docking results are seemingly in agreement with this hypothesis ( Figure 3D , Figure S3 ) . By following the same methodology , in silico interaction studies were also carried out on the non-productive form of TcSIR2rp1 . The docking analysis revealed that the NAD+ best-ranked pose occupied a different position in the active site , compared to the hSIRT1 co-crystal [25] , with the adenosine-ribose moiety pointing toward the solvent . A cluster analysis ( cut-off of 2 Å ) performed on the docking solutions ( Table S4 ) , indicated that 24% of the docking positions converged to this binding mode . Moreover , numerous and diverse docking clusters , related to the high number of conformations the ligand can assume in the wide pocket , were obtained ( Figure 4A ) . Conversely , cluster analysis performed on the docking solutions from the productive form highlighted a more important convergence ( 46% ) to the correct orientation of the co-substrate in the pocket ( Figure 4B ) . In agreement with the previous findings , the docking of nicotinamide in the TcSIR2rp1 non-productive form showed that all docking poses occupied a non-crystallographic position in the pocket whereas all the docking solutions retrieved in the productive form matched the crystallographic orientation ( Table S4 , Figure S4 ) . Finally , the best-ranked pose of AGK2 seemed , even in the TcSIR2rp1 non-productive form , able to adopt a position similar to the one retrieved in the productive active site . However , this specific solution belonged to a cluster populated by a small portion of docking solutions ( 16% ) whereas , in the productive form , 27% of the docking poses converged to the expected one ( Table S4 , Figure S4 ) . Nevertheless , the PLP scores obtained for all the ligands docked in the productive TcSIR2rp1 were higher if compared to the scores obtained in the non-productive form ( Table S3 ) . This observation can be explained by the lack of key interactions between ligands and the non-productive form of the enzyme that is unfavorable for ligand accommodation . By considering all these observations , the productive form of TcSIR2rp1 , herein modeled for the first time , will be taken into account for the interaction studies described in the following section . In silico target-fishing approaches have been reported in the past to identify the possible mechanism of actions of anti-parasitic compounds [45] , [46] . By following a similar strategy , with the aim of identifying a possible action related to sirtuin inhibition , fifty diverse anti-trypanosomal natural compounds were docked into the productive forms of sirtuins from T . cruzi: TcSIR2rp1 and TcSIR2rp3 [40] ( Table S1 ) . The latter has been recently modeled in a productive conformational state based on the E . coli CobB protein and A . fulgidus sirtuin in complex with NAD+ ( PDB IDs: 1S5P , 1ICI ) . As qualitatively demonstrated by the color of the heat map reported in Figure S5 , the compounds characterizing the small dataset used in this work were diverse in terms of chemical structure . The results of the screening are listed in Table 1 . PLP scores were compared to AGK2 and thiobarbiturate 6 , because these compounds are selective inhibitors of sirtuin class I [35] and class III [41] , respectively . Four compounds , according to their positive score with respect to the score obtained for the reference compounds , were selected for further structural inspections as potential sirtuin modulators: anacardic acid derivative ( Cmp5 ) , aculeatin D ( Cmp33 ) , 16-acetoxy-11-hydroxyoctadeca-17-ene-12 , 14-diynylethanoate ( Cmp1 ) and vismione D ( Cmp50 ) . GRID surfaces were then calculated in order to better understand the interactions with the proteins [47] . DRY , N1 and O probes were used to describe the hydrophobic , electron-donor and electron-acceptor properties of the pockets . Interestingly , the analysis of the docking poses suggested that each ligand is a competitive inhibitor for NAD+ fixation , being able to occupy the NAD+ pocket of the proteins . Considering the PLP score , the anarcadic acid derivative ( Cmp5 ) obtained from the cashew nut shell liquid exhibited the best overall docking results in both parasitic sirtuins . The best-ranked pose of this compound in the TcSIR2rp1 pocket formed hydrogen bonds between the carboxylic group of the ligand and the side chain of Arg50 . Moreover , additional hydrophobic interactions with Ala38 , Phe49 , Phe188 and Val218 were observed ( Figure 5A ) . Surprisingly , in TcSIR2rp3 , the best-ranked pose exhibited a shift of 180° in the binding site with the poly-carbonated chain pointing toward the A pocket . A hydrogen bond between the polar head of the compound and Arg60 , which is known to interact with the succinyl/malonyl group of the endogenous substrate and also to be responsible for nicotinamide resistance in sirtuin class III proteins , was highlighted [15] , [48] . Hydrophobic interactions between the lipophilic chain and Ala14 , Phe24 , Phe157 and Val186 residues of pocket B and C were also detected . The second best-ranked pose matched the one observed in TcSIR2rp1 , suggesting two possible binding modes in TcSIR2rp3 ( Figure 5A , S6A , S7 ) . All these observations correlate well with the GRID surfaces calculated in the protein pockets , especially with the DRY surfaces , indicating the main role of hydrophobic interactions in the binding . Aculeatin D ( Cmp33 ) extracted from the rhizomes of Amomum aculeatum was characterized by PLP scores of 79 . 7 and 90 . 3 in the TcSIR2rp1 and TcSIR2rp3 productive forms , respectively . As in the previous case , hydrophobic-driven interactions with the sirtuin pockets , also highlighted by the DRY probe , were observed ( Figure 5B ) . In addition , for TcSIR2rp3 , hydrogen bonds with Val97 , Arg60 and His113 were reported ( Figure S6B ) , explaining the higher score observed for this specific isoform . 16-acetoxy-11-hydroxyoctadeca-17-ene-12 , 14-diynylethanoate ( Cmp1 ) extracted from the root bark of Cussonia zimmermannii formed similar interactions with both proteins . Hydrogen bonds between the hydroxyl and carbonyl group of the polar head with , respectively , Arg50 and Cys308 of TcSIR2rp1 and Asn185 and Lys224 of TcSIR2rp3 were observed . Hydrophobic interactions with Ala38 , Phe49 , Phe188 and Val218 of TcSIR2rp1 and Ala14 , Phe25 , Phe157 and Val186 of TcSIR2rp3 were also detected ( Figure 5C , FigureS6C ) . Again , GRID surface analysis highlighted a good match between DRY surfaces and the hydrophobic region of the potential inhibitor . Vismione D ( Cmp50 ) isolated from the plant Vismia orientalis Engl . ( Guttiferae or Clusiaceae ) was the last compound that was selected from the screening , according to the PLP score . Hydrogen bonds with Ala38 ( TcSIR2rp1 ) and Ala14 ( TcSIR2rp3 ) backbones were detected . Moreover , van der Waals contacts with the hydrophobic amino acids characterizing the pocket were retrieved in both parasitic proteins ( Figure 5D , Figure S6D ) . These results suggested that the possible mechanism of action of these four natural compounds , that are known to have an inhibitory activity against T . cruzi , could be related to interactions with sirtuins . Indeed , the compounds identified by molecular docking are flexible molecules that would likely bind many active sites such as other NAD+ dependent enzymes . Additional docking studies against other enzymes ( NAD+ dependent and others ) would clarify this point . Moreover , no selectivity was found when the same computational approach was performed using the homologous human enzymes hSIRT2 and hSIRT5 ( Figure S8 ) . Unfortunately , these quite large lipophilic compounds , displaying no selectivity for parasite protein over host , do not appear to be very drug-like starting points . Please refer to the works of Kaur et al . and Sacconnay et al . for information about structural differences in the binding pockets that could be exploited for species-specific sirtuin inhibitor design [40] , [24] . Nevertheless , in this study , several goals were reached: ( i ) the building ( in silico ) and the description of the three-dimensional structure of two conformational states of TcSIR2rp1 from T . cruzi; ( ii ) the evaluation of their impact on docking calculations , showing the advantages of performing computational interaction studies on the productive form; and ( iii ) the identification of a possible sirtuin-related mechanism of action of four natural trypanocidal compounds . Indeed , such results require experimental validation . The homology models of T . cruzi sirtuins are deposited in the Protein Model Database PMDB [49] with the following IDs: PM0079211 ( TcSIR2rp1 , non-productive conformational state [40] ) , PM0079212 ( TcSIR2rp1 , productive conformational state ) , and PM0078446 ( TcSIR2rp3 , productive conformational state ) .
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T . cruzi is a protozoan pathogen responsible for Chagas disease . Current therapies rely only on a very small number of drugs , most of which are inadequate because of their severe host toxicity or because of their susceptibility to drug-resistance mechanisms . To determine efficient therapeutic alternatives , the identification of new biotargets and detailed knowledge of their structures are essential . Sirtuins from T . cruzi have been recently considered as promising targets for the development of new treatments for Chagas disease . Inhibition of their activity has been shown to significantly interfere with the life cycle of the parasite . T . cruzi possesses genes encoding two sirtuin-like proteins , TcSIR2rp1 and TcSIR2rp3 . The structures of these enzymes were theoretically elucidated in this work , which also focused on the impact of their possible conformational states on computational interaction studies . A small library of phytochemicals that are active against the parasite was built and screened against the most meaningful conformations , identifying a restricted number of scaffolds that potentially interact with the modeled proteins . For these hits , a mechanism of action related to interactions with sirtuins was proposed .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"and",
"Discussion"
] |
[
"systems",
"biology",
"biochemistry",
"phytochemistry",
"chemistry",
"biology",
"computational",
"chemistry",
"chemical",
"biology"
] |
2014
|
Computational Studies on Sirtuins from Trypanosoma cruzi: Structures, Conformations and Interactions with Phytochemicals
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Circulating homocysteine levels ( tHcy ) , a product of the folate one carbon metabolism pathway ( FOCM ) through the demethylation of methionine , are heritable and are associated with an increased risk of common diseases such as stroke , cardiovascular disease ( CVD ) , cancer and dementia . The FOCM is the sole source of de novo methyl group synthesis , impacting many biological and epigenetic pathways . However , the genetic determinants of elevated tHcy ( hyperhomocysteinemia ) , dysregulation of methionine metabolism and the underlying biological processes remain unclear . We conducted independent genome-wide association studies and a meta-analysis of methionine metabolism , characterized by post-methionine load test tHcy , in 2 , 710 participants from the Framingham Heart Study ( FHS ) and 2 , 100 participants from the Vitamin Intervention for Stroke Prevention ( VISP ) clinical trial , and then examined the association of the identified loci with incident stroke in FHS . Five genes in the FOCM pathway ( GNMT [p = 1 . 60×10−63] , CBS [p = 3 . 15×10−26] , CPS1 [p = 9 . 10×10−13] , ALDH1L1 [p = 7 . 3×10−13] and PSPH [p = 1 . 17×10−16] ) were strongly associated with the difference between pre- and post-methionine load test tHcy levels ( ΔPOST ) . Of these , one variant in the ALDH1L1 locus , rs2364368 , was associated with incident ischemic stroke . Promoter analyses reveal genetic and epigenetic differences that may explain a direct effect on GNMT transcription and a downstream affect on methionine metabolism . Additionally , a genetic-score consisting of the five significant loci explains 13% of the variance of ΔPOST in FHS and 6% of the variance in VISP . Association between variants in FOCM genes with ΔPOST suggest novel mechanisms that lead to differences in methionine metabolism , and possibly the epigenome , impacting disease risk . These data emphasize the importance of a concerted effort to understand regulators of one carbon metabolism as potential therapeutic targets .
As the fourth leading cause of death and the leading cause of disability in American adults , stroke constitutes a major public health burden . Epidemiological data consistently demonstrate an association between elevated plasma homocysteine ( tHcy ) and increased risk for stroke [1] , cardiovascular disease [2] , and dementia [3] , but clinical trials of interventions to lower homocysteine have failed to demonstrate global benefit , with B12 supplementation helping to reduce risk only in subsets of the populations studied [4]–[6] . Collectively , these data support a more complicated relationship than simple biomarker and disease risk and indicate the need for new targets for risk reducing therapies . This begs the question , “Have we already missed the target of greatest clinical benefit by the time we lower homocysteine levels ? ” The folate one-carbon metabolism pathway ( FOCM ) is not only involved in the regulation of homocysteine , methionine and B-vitamin levels but also the methylation of proteins , histones , DNA and RNA . To this end , the demethylation of S-adenosyl-methionine , which gives rise to S-adenosyl-homocysteine , is the sole source of de novo methyl groups for the cell . Dysregulation of this step in the FOCM could have broad implications on many cellular processes , including risk for stroke and cardiovascular disease . The post-methionine load test is a more sensitive tool for diagnosing hyperhomocysteinemia than circulating plasma homocysteine levels [7]–[10] . Additionally , “ΔPOST” , or the difference in tHcy levels before and after methionine loading in the clinic , gives a measurement of one's ability to convert methionine to homocysteine in real time and likely reflects methyl group availability in the cell . We utilized this test to analyze genetic determinants of methionine metabolism and how these differences between individuals may be functionally regulated . Here we present a genomic , genetic and epigenetic investigation into the regulation of methionine metabolism in the Vitamin Intervention for Stroke Prevention Trial ( VISP ) and the Framingham Heart Study ( FHS ) . We first present genome-wide association ( GWAS ) data linking five loci to differences in ability to convert methionine to homocysteine . Strikingly , all of the most significant genes identified within these loci are members of the same pathway ( FOCM ) , a feature rarely observed in GWAS studies . We observed haplotype differences at the GNMT [MIM 606628] locus , our most significant GWAS finding , and devised a scheme to test methionine loading in vitro based on GNMT genotype . Additionally , we have shown epigenetic regulation of the GNMT promoter , based on a CpG-SNP rs11752813 , which likely contributes to GNMT transcription and methionine metabolism . These data may one day contribute to identification of new targets for stroke and cardiovascular disease prevention as well as other complex diseases where epigenetics play a role .
The FHS cohort is a community based longitudinal study to determine the risk for cardiovascular disease and is comprised of randomly recruited participants and their family members in the town of Framingham , Massachusetts ( Table 1 ) . VISP was a multi-center , double-blind , randomized , controlled clinical trial designed to determine if vitamin supplementation reduced recurrent cerebral infarction , nonfatal myocardial infarction or mortality and is made up of unrelated individuals . The VISP cohort has a higher proportion of men when compared to the FHS , which is not surprising when considering the VISP participants have all had a stroke ( Table 1 ) . Likewise , VISP also has a greater percentage of diabetics and hypertensive individuals ( Table 1 ) . The VISP cohort consists of individuals in the top quartile of circulating tHCY levels , which was part of the recruitment requirements; whereas FHS is made up of a normal distribution of tHCY levels ( Table 1 ) . FHS participants have higher vitamin B6 , B12 , and folate levels on average than VISP participants . BMI and smoking status are approximately the same between VISP and FHS . The VISP study consisted of 1725 ( 82 . 1% ) individuals of European descent , 258 ( 12 . 2% ) individuals from African descent and 117 ( 5 . 6% ) individuals of other ancestral populations . All VISP participants are unrelated . FHS samples are primarily Caucasian . In FHS the 3110 individuals contributing to GWAS belong to 1055 families with extended family size ranging from 1 to 140 . In FHS , 1772 individuals have at least one blood relative in the family , 279 individuals have at least one first degree relative , 278 have at least one second degree relative , and 586 have at least one third degree relative . In a GWAS of 2 , 710 persons from the FHS study , five loci ( GNMT , CBS [MIM 613381] , CPS1 [MIM 608307] , ALDH1L1 [MIM 600249] and PSPH [MIM 172480] ) reached our pre-determined genome-wide significance threshold of 5×10−8 for the ΔPOST phenotype . These findings were confirmed in the VISP study sample of 2 , 100 persons for whom two of these loci ( GNMT and CBS ) independently reached genome wide significance ( Figure S1 , S2 and Table S1 ) . The results of a sample size-weighted meta-analysis consisting of 4 , 810 subjects from both FHS and VISP confirm and strengthen the independent GWAS findings ( Figure 1 and Table 2 ) . Strikingly , all five loci identified are involved in the FOCM pathway ( Figure S3 ) . The most significant association was with GNMT ( rs9296404 , p = 1 . 60×10−63 ) located on 6p21 . 1 , a region associated with large artery atherosclerotic stroke [11] , [12] . Using ten genotyped single nucleotide polymorphisms ( SNPs ) on chromosome 6 in the GNMT region , which were significantly-associated with ΔPOST in the VISP population , ( Figure S1A ) , we conducted a haplotype analyses ( Haploview software ) [13] . Two major haplotypes emerged , encompassing ∼81% of the individuals in the VISP population ( n = 2 , 100 ) ( Figure 2A , B ) and corresponding to a high methionine metabolizing haplotype ( ΔPOST = 19 . 4 µmol/L ) and a low methionine metabolizing haplotype ( ΔPOST = 14 . 5 µmol/L ) ( Figure 2C ) . One SNP , rs10948059 , which is a genotyped and located in the GNMT promoter , captures 100% of alleles with a mean max r2 of 0 . 722 ( range 0 . 512–0 . 850 ) . These data suggest functional differences in the GNMT gene impact an individual's ability to metabolize dietary methionine . The lack of a disruptive coding mutation identified by GWAS , or in sequencing of GNMT in 24 high and 24 low methionine metabolizers ( data not shown ) , and the expectation that a higher rate of transcription of the GNMT gene should lead to higher tHcy levels , suggest a regulatory mechanism for the differences in ΔPOST rather than protein dysfunction . This metabolic difference mediated by genetic variation is of functional significance in both the general population ( FHS ) and a population with tHcy above the 25th percentile as required by the inclusion criteria for the clinical trial ( VISP ) . The known GNMT promoter [14] from the high methionine metabolizing haplotype and the low methionine metabolizing haplotype were cloned giving rise to GNMTΔHighLuc and GNMTΔLowLuc constructs ( Sequence alignments in Figure S4 ) . GNMT is most highly expressed in the liver; therefore HepG2 cells were used to test promoter activity in the two haplotype groups . There was a ∼30% difference in gene promoter activity between GNMTΔHighLuc and GNMTΔLowLuc constructs ( Figure 3A ) , when cultured with L-methionine , which correlates with the differences seen between the average ΔPOST levels in our haplotype analysis ( Figure 2C ) . The quantitative trait , ΔPOST , is dependent on methionine dosing , therefore we starved HepG2 cells of L-methionine for 24 hours and then treated them with L-methionine for 24 hours . As seen in Figure 3C , the GNMTΔHighLuc construct responded to methionine starvation and treatment with ∼2× greater activity than the GNMTΔLowLuc construct . Above standard L-methionine culturing conditions ( 0 . 2 mM ) a feedback mechanism appears to be induced , which reduces GNMT expression ( Figure 3B ) . These data suggest a mechanism by which elevated levels of tHcy may arise . Given the differences in promoter activity seen in Figure 3 , we sought to identify functional variants that may play a role transcriptional activity . SNP rs11752813 was significantly associated in our meta-analysis ( Figure 1A , p = 7 . 99×10−32 ) , and either creates or eliminates a CpG site that can be methylated depending on genotype ( rs11752813 and flanking sequence: C ( C/G ) A ) . The high methionine metabolizing haplotype ( Figure 2 ) harbors the C/C genotype ( known as GNMTΔHighLuc in Figure 3 ) at rs11752813 , and the low methionine metabolizing haplotype ( Figure 2 ) harbors the G/G genotype ( known as GNMTΔLowLuc in Figure 3 ) at rs11752813 . The presence of a “G” at the rs11752813 locus creates a CpG site while the presence of a “C” eliminates this CpG site . Analysis of ΔPOST values in the VISP ( n = 2100 ) study shows that the individuals that harbor the “G” genotype at rs11752813 have significantly lower ΔPOST on average , indicating a less active GNMT gene ( Figure 4A ) . Bisulfite pyrosequencing of the rs11752813 locus show that the G/G genotype can be methylated whereas the C/C genotype is not methylated ( Figure 4B ) . These results are consistent with the central dogma of DNA methylation that only CpG sites can be methylated . Finally , 23 individuals harboring the G/G genotype at rs11752813 were bisulfite pyrosequenced and percent methylation status was plotted against individual ΔPOST values ( Figure 4C ) . As seen in Figure 4C , even between individuals with the G/G genotype there is a strong correlation between percent methylation and ΔPOST values . These data indicate that the G/G genotype at rs11752813 creates a closed chromatin state that inhibits GNMT transcription and methionine metabolism . We next investigated cumulative effects of ΔPOST risk variants by generating a combined genetic risk score using the most significant SNPs from Table 2 . Risk scores are normally distributed in both VISP and FHS ( Figure 5A , C ) . As the risk variant load increases , the average ΔPOST levels in the VISP and FHS samples increases ( Figure 5B , D ) . This score explains 13% of the variability of ΔPOST in FHS and 6 . 3% of the variability in VISP ( Table 3 ) . Because the SNPs used in this analysis are all imputed , for both the VISP and FHS studies , we repeated the analysis utilizing the most significant genotyped SNPs from each locus . This analysis yielded similar results indicating that using imputed SNPs for this risk score does not distort the analysis ( Figure S5 ) . Importantly , when interrogating the most significant five SNPs associated with ΔPOST , we also identified an association between the aldehyde dehydrogenase 1 family member L1 gene ( ALDH1L1 ) and incident ischemic stroke in the FHS cohort ( rs10934753 , hazard ratio = 1 . 26 , p = 0 . 015 , n = 168 cases 4008 controls , analyses adjusted for age , sex and family relationships ) . The protein encoded by ALDH1L1 converts 10-formyltetrahydrofolate to tetrahydrofolate and is an essential component of the FOCM pathway . These results provide a new and significant link between the FOCM pathway and risk of initial ischemic stroke . It is important to note that the VISP population consists of exclusively ischemic stroke patients examined for recurrent stroke over a 2 year period; rs10934753 was not associated with recurrent stroke . Additionally , we did not observe an association of genetic variation in GNMT or the other 3 loci with incident ischemic stroke in FHS but our sample has limited power to detect moderate effect size ( e . g . power ranges from 20–40% to detect a hazard ratio of 1 . 20 for a variant with minor allele frequency ranging from 0 . 1–0 . 5 ) .
Elevated tHcy has long been associated with increased risk for stroke and cardiovascular disease but to date functional evidence for the driving genetic forces behind elevated tHcy levels have only been attributable , in part , to dysfunction in the methylenetetrahydrofolate reductase gene ( MTHFR [MIM 607093] ) and CBS genes [15]–[18] . MTHFR , which also participates in the FOCM pathway , is tightly related to all the genes products identified in our study and has been implicated in susceptibility to vascular disease , neural tube defects , colon cancer and acute leukemia [19]–[24] . It is interesting to note that a prior GWAS in the individual FHS and VISP cohorts or in a meta-analysis yielded no significant results for baseline tHcy alone . This highlights the usefulness of the post-methionine load test in the diagnosis of hyperhomocysteinemia as well as the fact that we can specifically detect genetic variations that lead to differential methionine metabolism . In the current study we followed up with a functional evaluation of our GWAS findings . These independent GWAS analyses and a meta-analysis of FHS and VISP find the GNMT locus as the top result . However , there were differences in the GWAS results from FHS and VISP are likely attributable to two factors: one being power ( FHS consists of 610 more individuals ) , and two , VISP is a more homogenous population than FHS lacking a normal distribution of tHcy . Our functional studies start with the GNMT gene as it contributes to the majority of the variance in both VISP and FHS . The GNMT association makes biological sense given that GNMT catalyses the conversion of S-adenosyl-methionine ( SAM ) to S-adenosyl-homocysteine ( SAH ) , using SAM as the methyl donor [25] , and affects global cellular epigenetic status as the sole source of methyl groups for the cell including those used in DNA , histone , protein and RNA modifications . Additionally , it is known that global hypomethylation is seen in atherosclerosis [26] , and we suspect that variation in GNMT could affect risk status . Further , hyperhomocysteinemia is a risk factor for stroke and cardiovascular disease , and these data indicate GNMT may represent a new pharmacogenetic target for reducing stroke risk . It is our assertion that targeting the FOCM pathway before methionine is converted to homocysteine may allow us to modulate parallel pathways , such as DNA and histone methylation , which directly impact stroke risk . A recent review by Krishna et al . [27] describes in detail a “tHcy memory effect” that may alter the epigenetic state of the cell and promote deleterious chances after tHcy is lowered . This further strengthens the argument to identify genetic risk , through use of our risk score , and examine parallel targets for therapy . We did not observe any of the deleterious GNMT mutations associated with Glycine N-Methyltransferase Deficiency [MIM 606664] ( characterized by elevated levels of plasma S-adenosylmethionine and normal plasma sarcosine ) in sequencing of GNMT in 24 high and 24 low methionine metabolizers . Additionally , a lookup of large GWAS studies of cardiovascular and cerebrovascular disease , identified many of our most significant chromosome 6 findings in a meta-analysis for blood lipids [28] , with rs2274517 being the most significant result ( p = 1 . 37×10−4 ) , suggesting that GNMT may play a broader role in risk traits for CVD beyond tHcy measures . Our functional studies support the role of GNMT in variation of methionine metabolism . The consistent and biologically plausible results from the individual GWAS and meta-analysis emphasize that the other significant associations observed in the FOCM pathway cannot be ignored . The cystathionine-beta-synthase ( CBS ) gene has been associated with stroke [20] and methionine metabolism [29] . Additionally , CBS mutations are associated with homocystinuria , iridodonesis and agitated motion of the iris [MIM 236200] [30]–[33] . Within the carbamoyl phosphate synthetase one ( CPS1 ) gene , ΔPOST rs1047891 was found to be associated with a missense Ser ( ACC ) /Phe ( AAC ) mutation ( p = 9 . 10×10−13 ) . These findings are related to a sex-specific association of CPS1 with tHcy and women , performed in the Woman's Health Genome Study [34] and a Filipino population [35] . We meta-analyzed ΔPOST , rather than tHcy , and included both men ( 54% ) and women ( 46% ) . Phosphoserine phosphatase ( PSPH ) mutations have been associated with phosphoserine phosphatase deficiency [MIM 614023] , which results in pre- and postnatal growth retardation , moderate psychomotor retardation , and facial features suggestive of Williams syndrome [36] , [37] . Taken together , these data present a new link to the genetics of the FOCM pathway with methionine metabolism both in stroke and non-stroke populations . Because of the impact that the FOCM pathway has on the biology of the cell , including overall epigenetic state and DNA methylation , gluconeogenesis , and DNA repair , understanding how individual genetic composition impacts this pathway is essential . The FOCM has also been implicated in many aspects of human health and the work presented may be relevant to several key biological mechanisms , affecting tumorogenesis [38] , B-vitamin utilization [39] , as well as cardiovascular and cerebrovascular disease risk . Additionally , it is necessary to repeat these analyses in studies of different ethnicities as both FHS and VISP are comprised of mainly individuals of European descent . While a direct link between tHcy levels and stroke and cardiovascular disease remains debated , we have shown that understanding sequence variation in the FOCM pathway may provide a link to functional differences in the population , that in turn tie one carbon metabolism to a broad range of disease risk factors . Additionally , because tHcy-lowering therapies have had variable success in reducing stroke risk in subtype populations [6] , [40] , [41] and have in some cases been harmful [42] , we believe that understanding how these genetic variants impact the overall FOCM and related pathways is essential to understanding the pathogenesis of stroke . Methionine metabolism provides a first clue to the impact of this pathway on cell biology .
All human research was approved by the relevant institutional review boards , and conducted according to the Declaration of Helsinki . The Framingham Heart Study protocol was approved by the institutional review board ( IRB ) of the Boston University School of Medicine and all participants provided written , informed consent . The VISP study protocol was approved by the IRBs of the Wake Forest University School of Medicine ( coordinating center ) and University of North Carolina Chapel Hill School of Medicine ( statistical center ) . The local IRB for each of the individual recruiting sites approved the VISP protocol and all participants provided written , informed consent . The Genomics and Randomized Trial Network ( GARNET ) analysis of the VISP data was approved by University of Virginia School of Medicine IRB . FHS started in 1948 for evaluation of cardiovascular diseases and risk factors [43] . In 1971 , 5124 children of the original cohort , and spouses of these children , referred to as Offspring cohort , were enrolled and have been examined approximately every four years [44] . Genotyping was performed on the Affymetrix 500K mapping array and the Affymetrix 50K supplemental array . Circulating homocysteine levels were measured on 3465 Offspring participants ( N = 3464 with tHcy and N = 2999 with POST ) during examination cycle 6 ( 1995–1998 ) . The study sample for GWAS consists of a subset of 3110 individuals with at least one phenotype and GWAS data ( N = 3108 with tHCY , N = 2711 with POST , N = 2710 with Δ POST ) . The sample used to examine the association between the SNPs identified in GWAS of homocysteine phenotypes and incident stroke includes 4176 original cohort and Offspring individuals ( N stroke = 200 stroke; N ischemic stroke = 168 ) . Plasma tHcy levels were measured using high-performance liquid chromatography with fluorescence detection [45] . Clinical stroke was defined as rapidly developing signs of focal neurologic disturbance of presumed vascular etiology lasting more than 24 hours . Additional details of stroke classification and diagnosis can be found in prior publications [45]–[48] . Imputation of about 11 million 1000 Genomes SNPs ( 1000G Phase I Integrated Release Version 3 Haplotypes: 2010–11 data freeze , 2012-03-14 haplotypes ) was performed using MACH version 1 . 0 . 16 ( http://www . sph . umich . edu/csg/abecasis/MACH/ ) based on 412 , 053 good quality SNPs ( excluded SNPs were characterized by call rate <97% , pHWE<1E-6 , Mishap p<1e-9 , >100 Mendel errors , MAF<1% ) . Prior to association analysis , homocysteine phenotypes were normalized by replacing its observed value with the corresponding quantile under normal distribution . For GWAS , linear mixed effects models were fitted with the transformed phenotypes as dependent variables , individual SNP genotype as a fixed effect , and person specific random effects with correlation coefficient between two individuals being twice their kinship coefficient to account for correlation within extended families [49] . FHS GWAS has adjusted for age , sex and first 10 eigenstrat principal components in the linear mixed effects mode ( Table S2 ) . Cox proportional hazard model with a robust variance to account for familial relationship was fitted to relate SNPs identified in GWAS with stroke outcomes [50] . Imputation was performed using all SNPs and samples passing basic quality filters . In brief , SNPs were selected using the recommended composite quality filter that emerged from the genotype data cleaning process . Samples were selected to have an overall missing call rate <2% , while certain sample-chromosome combinations were also excluded where a gross chromosomal anomaly was detected or when the chromosome-specific missing call rate was >5% . These study data were imputed to a phase 1 interim release from the 1000 Genomes ( 1000G ) Project [51] . Imputation target variants were defined as those with MAF≥0 . 005 across all 629 1000G samples . Imputation was carried out using BEAGLE imputation software [52] ( v3 . 3 . 1 ) for chromosomes 1–22 and the X chromosome . The imputed dataset contained total 7 , 500 , 450 variants; 766 , 577 of which ( 10 . 2% ) were observed from the array genotyping . In addition to the primary imputation analysis , additional imputations were run on chromosome 22 and the X chromosome , masking a random 10% of observed SNPs to empirically assess imputation quality . The squared correlation between observed and imputed allelic dosages ( dosage r2 ) was used to summarize the imputation quality . The median dosage r2 was 0 . 933 for chromosome 22 masked SNPs and 0 . 930 for X chromosome masked SNPs . The imputed dataset , along with a detailed report on imputation methodology , is available through the authorized access portion of the VISP dbGaP posting . Genotyping was performed on the Illumina HumanOmni1-Quad-v1 array ( Illumina , Inc . ) at the Center for Inherited Disease Research , Johns Hopkins University . The genome-wide association analysis was conducted using PLINK v1 . 0 . 7 . Multivariate linear regression model was used to test correlation of quantitative traits and SNP markers . Using the KING software , the top 10 principle components were derived from genotype data [53] and subsequently used to adjust for population heterogeneity , in addition , age and gender were also included as covariates in the model . The VISP population consists of genetically confirmed unrelated individuals and no adjustments were made to the analysis for relatedness . To normalize phenotypic traits , inverse normal transformation was applied to values of POST and ΔPOST . The same regression model was employed to perform association tests between the phenotype and expected allele counts . Meta-analysis of the 2100 VISP and 3110 FHS cohorts was conducted using the METAL software [54] . The sample size of each study was used as weight , and the sign of the beta value of each SNP coded allele was used as the direction for association ( Table S3 ) . Figure 1 regional association plots were created using the locus zoom “plot your own data” function ( https://statgen . sph . umich . edu/locuszoom/genform . php ? type=yourdata ) . Plots were created utilizing the genome build/LD population hg19/1000 Genomes Mar 2012 EUR . Unless otherwise noted , HepG2 cell lines were cultured in complete media containing high glucose Dulbecco's Modified Eagle's Medium ( DMEM ) ( Invitrogen ) with 10% ( v/v ) FBS , 2 mM L-glutamine supplemented with 1× nonessential amino acids , 1 mM sodium pyruvate ( Invitrogen ) and 1X antibiotic-antimycotic ( Invitrogen ) . Cells were maintained at 37°C in a 5% CO2 incubator . HepG2 cells were culture in complete media as described above for 24 hrs in 25 cm2 tissue culture flasks or 6 well culture plates . For methionine starvation , after 24 hrs media was removed and replaced with either complete media or complete media lacking L-methionine . After 24 hrs , cells starved of methionine were supplemented with L-methionine ( Ameresco ) at concentrations of ( 0 . 2 mM , 0 . 4 mM , 0 . 8 mM ) . RNA isolation was conducted using the Qiagen RNeasy kit according to standard manufacture's protocols . cDNA synthesis was conducted using 1 ug of total RNA and the Verso cDNA Synthesis Kit ( Fisher Scientific ) . A 3∶1 mix ( v/v ) of random hexamers and anchored oligo-dT was used following standard theromocycling conditions . For GNMT quantitative real-time PCR , Taqman MGB probes and primers were used ( Hs002219089 ) ( Applied Biosystems ) . All samples were run in triplicate in 10 µl reaction volumes . PCR conditions were the default settings of the ABI Prism 7900 HT Sequence Detection System ( Applied Biosystems ) using the standard curve setting to achieve raw data , which was analyzed in Microsoft Excel . The cycle threshold ( Ct ) was determined during the geometric phase of the PCR amplification plots as automatically set by the 7900 software . Relative differences in transcript levels were quantified using the ΔΔCt method with GAPDH [MIM 138400] ( probe 4333764F ) mRNA as an endogenous control . GNMT promoters were PCR amplified using primers ( Forward: 5′-CGGGGTACCACAGAGCGAGACTGTGTC-3′ , Reverse: 5′-GCGAGATCTCCTGCGCCGCGCCTGGCT-3′ ) as previously described [14] . One VISP case was chosen for TA cloning from the high ΔPOST and low haplotype ΔPOST haplotypes . Promoters were cloned into the StrataClone PCR Cloning vector according to the standard protocols ( Agilent Technologies ) . Promoters were next restriction digested and ligated into the pGL3 luciferase plasmid ( Promega ) using KpnI and SacI enzymes ( New England Biolabs ) giving rise to GNMTΔHighLuc and GNMTΔLowLuc . Haploview version 4 . 2 [13] was used to analyze haplotype blocks on chromosome 6 from the VISP population . 10 SNPs with p≤5×10−8 were assessed using version 3 , release 27 , analysis panel CEU+TSI . Average ΔPOST values were taken from the VISP population , and any individuals with missing data from 1 or more SNPs were excluded from the analysis . The top two haplotypes , encompassing 80% of the total VISP population were assessed . 2×104 HepG2 cells were transfected in quadruplicate with GNMTΔHighLuc or GNMTΔLowLuc and pGL4 . 74[hRluc/TK] ( Promega ) at a 10∶1 ratio using TransIt-LT1 transfection reagent ( Mirus biosciences ) in 96 well plates . Luciferase assays were conducted following the Dual-glo kit standard protocol ( Promega ) . Luciferase readings were taken using the Beckman Coulter DTX880 luminometer at a 1 second integration time . Firefly luciferase measures from GNMTΔHighLuc or GNMTΔLowLuc were taken for each well , followed by treatment for renilla luciferase activity and renilla measurement . Relative luciferase activity of each promoter was calculated by dividing the average firefly luciferase counts from GNMTΔHighLuc or GNMTΔLowLuc by pGL4 . 74[hRluc/TK] for each independent condition . L-Methionine treatment used 0 . 2 mM reagent . Total relative luciferase activity for each plasmid encompasses the average of 3 biological replicates . Pyrosequencing was performed as previously described [55] . Primers are as follows , Forward: AGTAGAGAAGTGTTAGTTAGGTTTTAT , Reverse ( Biotin labeled ) : ACCCATACAAAAAAAACAAAAAAAATCTC , Sequencing primer: TTTGGATTAGGTGGATAG . Scores were determined by using imputation dosage measures from VISP and FHS . Alleles were assessed for the average ΔPOST values in FHS and VISP . If a dosage for a homozygous SNP was associated with high homocysteine on average ( i . e . close to the value 2 ) the number was not changed . However , if the homozygous allele was not associated with the risk variant ( i . e . close to 0 ) but was represented as a number above 1 . 5 , 2 was subtracted from that number and made positive . After correction , homozygous risk variants would have a dosage value near 2 , heterozygous variants would have a value near 1 , and non risk variants would be assigned a number near 0 . All imputation dosage values were summed . The sum of each risk value was then taken for each individual to give a score from 0 to a possible 10 , and that score was rounded to the nearest integer . For calculation of the variance explained by the risk score , linear regression was used for VISP , and a linear mixed effects model was used for FHS , as these methods were used in the initial GWAS for each study . All statistics for Figures 2–4 were performed using GraphPad Prism 4 . Tests used are indicated in Figure legends . Significance threshold was set at p = 0 . 05 .
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Elevated homocysteine ( tHcy ) is strongly associated with risk for common disorders such as stroke , cardiovascular disease and Alzheimer disease . Lowering tHcy levels has proven to have variable success in reducing clinical risk , so the question remains , “Are we correctly targeting these disorders by lowering tHcy ? ” Understanding folate one-carbon metabolism pathway ( FOCM ) genetic variation will aid us in developing new targets for therapy . The FOCM is essential in regulation of the epigenome , which controls genes in ways beyond nucleotide sequence . We present data generated from stroke-only and general populations where we identify strong association of genetic risk factors for variation in one-carbon metabolism function , characterized by the post-methionine load test . We show that GNMT harbors genetic and epigenetic differences that influence gene function , which may have downstream effects on the epigenome of the cell , affecting disease risk . We developed a genetic risk score that predicts post-methionine load homocysteine levels that may be useful in clinic . Finally , we identified a novel association between ischemic stroke and ALDH1L1 , which emphasizes the clinical importance of this work . Our results highlight the importance of a concerted effort to target the FOCM ( beyond tHcy ) and parallel pathways in future pharmacogenetic work using the genetic variation we describe here .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genome-wide",
"association",
"studies",
"dna",
"modification",
"gene",
"regulation",
"genetics",
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"epigenetics",
"molecular",
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2014
|
Genome-Wide Meta-Analysis of Homocysteine and Methionine Metabolism Identifies Five One Carbon Metabolism Loci and a Novel Association of ALDH1L1 with Ischemic Stroke
|
Lipid remodeling is crucial for hypoxic tolerance in animals , whilst little is known about the hypoxia-induced lipid dynamics in plants . Here we performed a mass spectrometry-based analysis to survey the lipid profiles of Arabidopsis rosettes under various hypoxic conditions . We observed that hypoxia caused a significant increase in total amounts of phosphatidylserine , phosphatidic acid and oxidized lipids , but a decrease in phosphatidylcholine ( PC ) and phosphatidylethanolamine ( PE ) . Particularly , significant gains in the polyunsaturated species of PC , PE and phosphatidylinositol , and losses in their saturated and mono-unsaturated species were evident during hypoxia . Moreover , hypoxia led to a remarkable elevation of ceramides and hydroxyceramides . Disruption of ceramide synthases LOH1 , LOH2 and LOH3 enhanced plant sensitivity to dark submergence , but displayed more resistance to submergence under light than wild type . Consistently , levels of unsaturated very-long-chain ( VLC ) ceramide species ( 22:1 , 24:1 and 26:1 ) predominantly declined in the loh1 , loh2 and loh3 mutants under dark submergence . In contrast , significant reduction of VLC ceramides in the loh1-1 loh3-1 knockdown double mutant and lacking of VLC unsaturated ceramides in the ads2 mutants impaired plant tolerance to both dark and light submergences . Evidence that C24:1-ceramide interacted with recombinant CTR1 protein and inhibited its kinase activity in vitro , enhanced ER-to-nucleus translocation of EIN2-GFP and stabilization of EIN3-GFP in vivo , suggests a role of ceramides in modulating CTR1-mediated ethylene signaling . The dark submergence-sensitive phenotypes of loh mutants were rescued by a ctr1-1 mutation . Thus , our findings demonstrate that unsaturation of VLC ceramides is a protective strategy for hypoxic tolerance in Arabidopsis .
Lipids are essential constituents of plant cells that provide both the structural basis for cell membranes and an energy source for cellular metabolism [1 , 2] . Recently , considerable attention has been given to the function of lipids as mediators of various biological activities including growth , development and response to biotic and abiotic stresses [1 , 2] . It is increasingly apparent that the carbon length and saturation of fatty acids are altered in plants in response to environmental cues . For example , significant increases in unsaturated fatty acids occur when plants are adversely stressed [3 , 4] . Moreover , a wide range of molecules including very-long-chain fatty acids ( VLCFAs ) and their derivatives such as sphingolipids and cuticular lipids , play indispensable roles in regulation of plant stress responses [5 , 6] . In plants , de novo synthesis of VLCFAs originates from the C16 and C18 saturated fatty acids which are synthesized in the plastids . Subsequently , distinct chain lengths of fatty acids are subjected to elongation by the fatty acid elongase complex in the endoplasmic reticulum ( ER ) membrane [7] . VLCFAs are direct precursors for biosynthesis of cuticular lipids and sphingolipids; the latter act as major components of the plasma membrane and play key roles in intracellular activities as well as diverse signaling pathways [8 , 9] . Structurally , the sphingolipids are composed of a polar head group , a sphingoid long-chain base ( LCB ) and an amide-linked fatty-acyl chain . Among these components , the fatty acid chains vary in length from 16 to 26 carbon atoms , which can be either saturated or unsaturated with a cis-ω9 double bond [10] . The LCB components of sphingolipids are derived from the amino acid serine and palmitoyl-CoA in the ER by serine palmitoyltransferase to produce 3-ketosphinganine , which in turn is reduced to form d18:0 sphinganine by an NADPH-dependent 3-ketosphinganine reductase [7 , 10] . Ceramide is subsequently assembled by acylating sphinganine to an acyl-CoA or free fatty acid with acyl-chain lengths of C16 to C26 . Alternatively , ceramides can be formed via a salvage pathway , where ceramides are released from complex sphingolipids such as glycosylceramides and glycosyl-inositol-phosphoceramides [7 , 10] . Thus , ceramides serve as both intermediates for turnover of sphingolipids and backbones for synthesis of more complex sphingolipids in planta . In Arabidopsis thaliana , three genes encode ceramide synthases essential for ceramide biogenesis , namely LOH1 , LOH2 and LOH3 [7 , 10 , 11] . The levels of C16 ceramides in loh2 mutants are almost undetectable , whilst the levels of ceramides with VLCFAs are depleted in the loh1 loh3 double mutant [12 , 13] , suggesting that these three LOHs are specific for synthesis of distinct acyl-chain lengths of ceramides . Notably , the rosettes of loh1 single mutants display spontaneous cell death in short-day conditions [13] , whereas the loh1 loh3 double mutant shows lethality in early seedling development [12] . The abundance of sphingolipid species is largely dependent on the structural variation of LCB and fatty acyl chain , as well as their modifications including hydroxylation and desaturation . Moreover , increasing evidence reveals the functional significance of LCB and fatty acid ( FA ) hydroxylation and desaturation in plant cells [11] . For example , the deletion of C-4 hydroxylases in the Arabidopsis sbh1 sbh2 double mutant results in a deficiency of trihydroxy LCBs and a dwarf phenotype [14] . In contrast , the sld1 sld2 double mutant or transgenic lines of LCB Δ8 desaturases shows altered tolerance to low temperature and aluminum toxicity , respectively [15 , 16] . Meanwhile , ceramide FA hydroxylation is catalyzed by the cytochrome b5-fusion enzymes FAH1 and FAH2 . The fah1 fah2 double mutant displays increased levels of ceramides and salicylic acid , as well as enhanced resistance to the biotrophic pathogen Golovinomyces cichoracearum [17] . However , the significance of ceramide FA desaturation remained unknown until recent identification of an acyl-CoA desaturase , ADS2 , in Arabidopsis [18] . The ads2 mutant has a significant reduction in the VLC-CoAs and a decline in unsaturated sphingolipids but the biological function of this alteration awaits further investigation . Hypoxia is one of the most important abiotic stresses that affects the growth and yield of plants . Flooding , including root waterlogging and complete submergence of plants , leads to a decline in the available oxygen , and thus affects physiological activities and plant growth [19–21] . Ethylene is considered to be the primary determinant in plant response to hypoxia [20–22] . Recently , the Group VII ethylene-responsive factors ( ERFs ) have been demonstrated to be master regulators for oxygen sensing [23–25] . Specifically , one ERF transcription factor , RAP2 . 12 , interacts with the plasma membrane-anchored acyl-CoA binding proteins ( ACBP1 and ACBP2 ) under normoxia [24] . Upon hypoxic stress , RAP2 . 12 dissociates from the plasma membrane and accumulates in the nucleus to activate transcription of hypoxia-responsive genes [24] . Given the diverse cellular functions of plant ACBPs in lipid metabolism and stress responses [2] , it is conceivable that lipids or lipid signaling may play a crucial role in plant response to hypoxic stress . Recent studies in mammals have revealed that in response to hypoxia , tumor cells promote their survival and growth by scavenging unsaturated fatty acids from lysophospholipids , a process which is independent of the de novo lipogenesis pathway [26] . Similarly , dark anoxia induces substantial degradation of FAs and accumulation of unsaturated triacylglycerols ( TAGs ) in cells of Chlamydomonas reinhardtii [27] . In Caenorhabditis elegans , loss of the ceramide synthase gene HYL-1 ( hyl-1 ) results in increased resistance to anoxia , whereas deletion of the homologous gene HYL-2 ( hyl-2 ) attenuates the anoxic tolerance of C . elegans compared with normal worms [28] . Given that synthesis of C20 to C22 ceramides relies on HYL-2 , whereas formation of C24 to C26 depends mainly on HYL-1 , it appears that the in vivo homeostasis of VLC species of ceramides is important for the differential susceptibility of C . elegans to anoxia [28 , 29] . Moreover , the endogenous level of dihydroceramides ( DHCs ) is remarkably increased in mammalian cells exposed to various hypoxic conditions , which are rapidly converted to ceramides by the DHC desaturase ( DEGS ) after re-oxygenation [30] . In mammals , stress-induced ceramides can specifically bind to the cysteine-rich CR1 domain of protein kinase Raf-1 , leading to dynamic modulation of Raf kinase activity and subsequent activation of the MAPK cascade [31 , 32] . Overall , these findings demonstrate that the adaptive remodeling of lipid metabolism is a necessary process for higher organisms to respond to hypoxic stress , and such a process is likely to be conserved across eukaryotic species . In this study , we performed a comprehensive mass spectrometry-based analysis to investigate hypoxia-induced lipid remodeling in Arabidopsis . In particular , we observed a significant accumulation of ceramides in Arabidopsis upon hypoxia under both light and dark submergence conditions . Furthermore , our results demonstrate that unsaturation of VLC-ceramides is likely to be a protective mechanism to hypoxia-stressed plants by modulating ethylene signaling .
Previous investigations using a microarray approach have been performed extensively to evaluate the transcriptome profiles in Arabidopsis response to anoxia or hypoxia [33–38] . In these reports , Arabidopsis seedlings were treated with either low oxygen ( 3% O2 and 97% N2 ) or complete submergence under constant darkness , in order to induce a severe and rapid onset of anoxia/hypoxia responses in the plants . Hence , all the current available expression data were obtained from Arabidopsis seedlings exposed to anoxic/hypoxic or submergence conditions for less than 24 h . In this study , we intended to establish a method that may reflect the natural situation , i . e . , by fully submerging the Arabidopsis seedlings under light conditions . Preliminary data showed that in contrast to the almost complete death upon submergence under constant darkness within 3 d , Arabidopsis could endure light submergence stress for up to 10 d . The transcripts of hypoxia marker genes such as ADH1 , PDC1 and HUP09 in 4-week-old seedling rosettes were upregulated at 48 and 72 h after light submergence treatment ( S1 Fig ) . Therefore , a time point of 48 h upon light submergence exposure was chosen for microarray analysis to further investigate the transcription profiles of Arabidopsis rosettes in response to hypoxic stress . The transcriptomic analysis identified in a total of 7 , 320 genes differentially expressed more than 1 . 5 fold , of which 5 , 617 genes had greater than 2-fold and 3 , 528 genes had greater than 3-fold changes in expression in the 48-h light submergence-treated seedlings . Among them , 3 , 598 genes were upregulated and 3 , 722 genes were downregulated as compared to control samples ( Fig 1A and S1 Table ) . In comparison to the published data with short-time dark submergence or dark hypoxia/anoxia treatments , the number of 48-h light submergence-responsive genes was significantly higher than those of 9-h dark hypoxia and 24-h dark submergence treatments ( Fig 1A ) . Moreover , only 192 upregulated and 128 downregulated genes were shared in all of these treatments ( Fig 1A ) . Notably , light submergence induced significant changes of mRNA abundance in the genes involved in lipid biosynthesis and metabolism , as indicated by functional annotation analysis ( Fig 1B ) . Transcript levels of 89 fatty acid pathway genes were significantly altered by 48-h light submergence ( Fig 1C ) . The hierarchical clustering showed that 48-h light submergence treatment repressed the transcripts of genes in fatty acid synthesis , but significantly induced transcripts of genes in fatty acid degradation pathway ( s ) ( Figs 1C , S2A and S2B and S2 Table ) . By contrast , this difference was not evident in the dark anoxia/hypoxia or dark submergence treatments ( Fig 1C ) . Furthermore , the transcripts of several members of the KCS gene family that encodes 3-ketoacyl-CoA synthase catalyzing the initial condensation reaction in VLCFA synthesis were repressed significantly by 48-h light submergence , but most of them remained unchanged under dark anoxia/hypoxia or dark submergence at the short-time points ( Figs 1D , S2E and S3 Table ) . Additionally , many genes involved in metabolism of sphingolipids were differentially expressed during 48-h light submergence ( Figs 1D , S2G and S3 Table ) . In particular , the transcript levels of KSR2 , LOH2 , IPCS1 and DPL1 , which encode enzymes essential for synthesis and catabolism of ceramides and LCBs , were significantly induced under light submergence conditions ( Figs 1D and S2G ) . Among the 38 light submergence-responsive genes involved in cuticular lipid metabolism , two genes ( At3g49210 and At5g12420 ) encoding putative wax ester synthases were significantly upregulated , whereas the expression of a different wax ester synthase and diacylglycerol acyltransferase , WSD1 , was repressed by 48-h light submergence ( S2E and S2F Fig and S4 Table ) . In contrast to their slight up- or down-regulation in the short-time anoxia/hypoxia or dark submergence treatments , four genes ( CYP96A1 , A3 , A4 and A12 ) encoding mid-chain alkane hydroxylases ( S2E and S2F Fig ) and three genes ( DGK6 , PIPLC1 and GPAT3 ) in glycerolipid metabolism ( S2C and S2D Fig and S5 Table ) , were significantly repressed by 48-h light submergence . Taken together , our microarray data reveal that the transcripts of genes in lipid metabolism were substantially affected by the 48-h light submergence treatment . To investigate the potential role of lipids in hypoxia response , lipid profiles of Arabidopsis rosettes following light submergence exposure were analyzed by electrospray ionization–tandem mass spectrometry ( ESI-MS/MS ) . Compared to the seedlings grown under normal growth conditions , significant changes were observed in the membrane lipid contents of Arabidopsis rosettes after light submergence treatment for 48 and 96 h ( Table 1 and Fig 2A ) . By comparison , the seedlings grown in normal growth conditions , the total amounts of phosphatidylcholine ( PC ) and phosphatidyletanolamine ( PE ) of 48-h and 96-h light submergence-treated , as well as phosphatidylglycerol ( PG ) of 48-h light submergence-treated rosettes decreased significantly ( Table 1 ) . In contrast , the total levels of phosphatidylserine ( PS ) and phosphatidic acid ( PA ) of 48-h and 96-h light submergence-treated , as well as phosphatidylinositol ( PI ) of 96-h light submergence-treated rosettes increased significantly . However , few differences were detected in other lipid species including digalactosyldiacylglycerol ( DGDG ) , monogalactosyldiacylglycerol ( MGDG ) , lysoPC , lysoPE and lysoPG in the rosettes exposed to either 48-or 96-h light submergence treatments ( Table 1 ) . With regards to the lipid compositions of different molecular species , a significant accumulation in the polyunsaturated species of PC , PE and PI , such as C34:3-PC , 36:6-PC , 34:3-PE , 34:3-PI and C36:6-PI and a decline in their saturated and mono-unsaturated species were observed at 48 h and 96 h after light submergence treatment ( Fig 2A ) . Moreover , all the species of PA accumulated significantly , which correlated with the declined of species PC , PE or PI , except those of polyunsaturated species ( 34:3 and 36:6 ) . More dramatic changes of the molecular species of PS were observed in response to light submergence . As shown in Fig 2A , the levels of unsaturated species such as 34:4- , 34:3- , 34:2- , 36:6- and 36:5-PS increased , whilst those of 36:4- , 36:3- , 36:2- , 38:4- , 38:3- and 38:2-PS declined . In particular , the VLC species of 40:3- , 40:2- , 42:3- , 42:2- , 44:3- and 44:2-PS accumulated highly upon light submergence exposure ( Fig 2A ) . In addition , the species of plastidial lipids including 36:3- , 34:5-MGDG , 34:3- and 34:2-DGDG , as well as 34:2- and 34:1-PG decreased significantly after light submergence ( Fig 2A ) , which may reflect the inhibition of photosynthesis by submergence . In contrast , those of polyunsaturated 34:6-MGDG/DGDG increased significantly after light submergence . Also , the contents of 36:6-MGDG/DGDG , 34:5-DGDG , and 36:5-MGDG/DGDG , 34:4-PG increased after 48-h and 96-h light submergence , respectively ( Fig 2A ) . Since MGDG and DGDG are unique glycolipids in the photosynthetic membranes , the increases of such polyunsaturated species may be explained by the unsaturation of these galactolipids in response to submergence , which phenomenon has previously been observed during cold acclimation in Arabidopsis rosettes [39] . Previous studies suggest that anoxia or hypoxia triggers production of reactive oxygen species ( ROS ) in plant cells [40 , 41] , which may mediate the formation of oxidized membrane lipids non-enzymatically . To determine the effects of light submergence on the generation of oxidized lipids , we further analyzed the contents of oxidized galactolipids ( MGDG and DGDG ) and phospholipids ( PC , PE and PG ) , following light submergence treatment for 48 or 96 h . The levels of arabidopsides [42] , the galactolipids that conjugate to 12-oxophytodienoic acid ( OPDA ) and dinor-OPDA ( dnOPDA ) , were also measured . As presented in Fig 2B , in light submergence-treated Arabidopsis rosettes , levels of oxidized membrane lipids as well as arabidopsides ( ArA , ArB , ArC , ArD and ArE-type ) , were significantly higher than those of untreated controls ( Fig 2B ) . As compared to the dark treatment control , significant elevations in the lipid compositions of VLC-PS , PA and arabidopsides ( ArA , ArB and ArE-type ) were also found in 4-week-old seedlings treated with dark submergence ( S3 Fig ) . The slight increases in species of PC , PE and PI were also observed , which were possibly due to the remarkable loss of the absolute fresh weight of plants after 24-h dark submergence exposure . PS is a phospholipid enriching VLCFA , which serves as the main precursor for sphingolipid biosynthesis [43 , 44] . The high accumulation of VLC species of PS after light submergence treatment ( Fig 2A ) hinted that the remodeling of sphingolipids might be essential for plant hypoxia response . To confirm this possibility , total sphingolipids were extracted from 4-week-old Arabidopsis rosettes after 48-h light submergence treatment as well as untreated control , and profiled by a triple TOF LC-MS/MS system . Sphingolipids of seedlings treated with dark submergence for 24h were also analyzed and compared to those of the light submergence treatment . Results showed that both light and dark submergence treatments significantly increased the total amounts of ceramides ( Cer ) and hydroxyceramides ( hCer ) , but did not affect levels of glucosylceramides ( gCer ) ( Figs 3A and S4 ) . The total levels of LCB and its molecular species were also significantly higher in 24-h dark submergence than for 48-h light submergence treatments or the untreated control ( Fig 3A and 3B ) . In particular , most species of Cers ( 16:0 , 18:0 , 22:0 , 24:0 , 24:1 , 26:0 and 26:1 ) were induced after 48-h light submergence by 1 . 7- to 6 . 1-fold , but were more strikingly higher in the 24-h dark submergence stress treatment , which raised them by 1 . 8- to 19 . 9-fold as compared to the control . By contrast , the level of 22:1-Cer was increased 3 . 3-fold by light submergence , but not by dark submergence ( Fig 3C , upper panel ) . Additionally , the contents of most species of hCer were elicited by both submergence treatments ( Fig 3C , bottom panel ) . Moreover , certain species of gCer were slightly affected upon either light or dark submergence exposure ( Fig 3C , middle panel ) . Previous studies have observed that the long-chain ceramide ( C16 ) -containing liposomes can be delivered across cell membranes by mammalian cells [45] . To further investigate the direct link between ceramides and hypoxia response , roots of 2-week-old Arabidopsis seedlings were immersed in MS liquid medium containing commercially available ceramide ( 24:1-Cer ) liposomes and whole seedlings were collected at 0 , 1 , 3 , 6 and 12 h after treatment . Real-time PCR analysis showed that the transcripts of hypoxia-responsive genes SUS1 and PDC1 were induced by the 24:1-Cer treatment at various time points ( 6 and 12 h for SUS1 and 3 , 6 and 12 h for PDC1 ) , whilst the transcript level of ADH1 was slightly elevated by 3- and 12-h after 24:1-Cer liposome exposure ( S5 Fig ) . However , both transcripts of ADH1 and PDC1 were downregulated at the early stage ( 1 h ) after 24:1-Cer liposome treatment . In contrast , the mock treatment ( dH2O ) did not affect the transcripts of ADH1 and PDC1 genes in the early 24 h under normal light/dark conditions ( S1 Fig ) . Since the gaseous hormone ethylene and its downstream response factors are known to be important for plant response to hypoxic stress [22] , transcript levels of some ethylene downstream genes in the signaling cascade were also examined . Results showed that messenger RNA levels of EIN3 , HRE1 , HRE2 as well as RAP2 . 6 were upregulated , while those of CTR1 and EIN2 were downregulated upon 24:1-Cer liposome treatment ( S5 Fig ) . Overall , these data indicated that ceramide is a promising functional molecule essential for the regulation of hypoxia responsive genes in Arabidopsis . To address the effect of ceramides on plant response to hypoxic stress , the T-DNA insertion mutants of three genes ( loh1 , loh2 and loh3 ) encoding ceramide synthases were characterized from SALK collections [13] . PCR analysis followed by DNA sequencing localized all of the T-DNA insertions within the exons of the respective LOH genes ( S6 Fig ) . The loh1 , loh2 and loh3 single mutants did not exhibit visible phenotypic differences from wild type under normal growth conditions . However , all three loh mutants displayed enhanced sensitivity compared to the wild type , when the 4-week-old plants were dark submergence-treated for 2 d following a 3-d recovery period ( Fig 4A ) , while a 2-d dark treatment alone did not result in obvious differences between wild type and loh mutants ( S7 Fig ) . By contrast , the loh mutants ( loh1 , loh2 and loh3 ) were more tolerant than wild type under light submergence conditions , for 8 d plus 3-d recovery period , as indicated by their survival rates following recovery ( Fig 4A and 4B ) . It is noteworthy that the loh mutants showed significant phenotypic differences from wild type during the process of treatments ( Fig 4A ) , indicating that the changes of submergence sensitivities in the loh mutants is primarily due to the interruption of plant response to hypoxia . Under anoxia/hypoxia conditions , an early plant response is to alter the cellular metabolism from aerobic to anaerobic respiration , which thereby regenerates NAD+ and produces ethanol , acetaldehyde and lactate [41] . Our previous data confirmed that the final product of this reaction , ethanol , could be used to mimic hypoxic stress under certain conditions [46] . As shown in Fig 4C , when the seeds of wild type and loh mutants were germinated on MS solid medium supplemented with or without 50 mM ethanol ( EtOH ) for 2 weeks , the loh mutants displayed more tolerance than wild type in the presence of ethanol . Statistically , this data illustrated that the percentage of loh mutant seedlings with true leaves and green cotyledons were significantly higher than that of wild type ( Fig 4D ) . To investigate the biochemical nature for the distinct phenotypes of loh mutants to hypoxic stress , molecular compositions of ceramides present in their rosettes were further analyzed . Consistent with previous findings [12] , the loh2 mutant showed a dramatic reduction in ceramides with 16:0 FA , and an accumulation of VLCFAs under normal growth conditions ( Light; Fig 4E ) . By contrast , the loh1 and loh3 mutants displayed slight increases in 16:0-Cer , but decreases in certain species of VLCFAs , such as 22:1- , 24:0 and 26:0-Cer in loh1 and 20:0- and 26:0-Cer in loh3 ( Fig 4E ) . However , when the plants were subjected to 24-h dark submergence and 48-h light submergence treatments , more significant changes were observed in the loh mutants as compared to wild-type plants . As shown in Fig 4E , the mono-unsaturated VLCFA-containing ceramides including 22:1- , 24:1- and 26:1-Cer showed simultaneous decreases in the three loh mutants in comparison to that of wild type upon dark submergence treatment ( Fig 4E ) . The levels of Cers with mono-unsaturated-VLCFA were not significantly altered between light submergence-treated wild type and loh mutants , whereas the levels of saturated species such as 16:0-Cer in loh2 and loh3 mutants , 24:0-Cer in loh1 , and 20:0- , 22:0- , 24:0- and 26:0-Cer in loh3 , were significantly reduced as compared to wild type ( Fig 4E ) . To further confirm the different responses of the loh mutants to light and dark submergence stresses , two additional loh mutants , loh1-2 and loh3-2 , as well as the knockdown double mutant loh1-1 loh3-1 [12] , were exposed to either light or dark submergence conditions . As shown in Fig 5 , the loh1-2 and loh3-2 mutants responded similarly , i . e . , enhanced sensitivity under dark submergence and greater tolerance to light submergence treatments than wild type . However , the loh1-1 loh3-1 double mutant displayed attenuated tolerance to both dark and light submergence stresses in comparison with wild type ( Fig 5A ) . In agreement with the phenotypic observations , the survival rates of loh1-2 and loh3-2 mutant plants were higher under light submergence but lower under dark submergence conditions , than those of wild type ( Fig 5B ) . In contrast , the survival rates of loh1-1 loh3-1 double mutant under both dark and light submergences were significantly lower than wild type ( Fig 5B ) . Given the remarkable reduction of VLC species of Cers in loh1-1 loh3-1 double mutant [12] , these data suggest that levels of Cers with unsaturated or saturated VLC species may contribute to the differential phenotypes of loh mutants to dark and light submergence stresses . We further tested this hypothesis using the ads2 ( ads2-1 , ads2-3 and ads2-4 ) mutants , which exhibited a specific deficiency in the VLC unsaturated Cers in Arabidopsis [18] , in comparison to the wild type plants . As expected , all the ads2 mutants were more sensitive to both dark and light submergence treatments than wild type ( Fig 5C and 5D ) , indicating the unsaturation of VLC species plays an essential role in controlling submergence tolerance . Given ceramides can specifically interact with the protein kinase Raf-1 in mammals [31 , 32] , we considered the potential interaction between ceramides and the Arabidopsis Raf kinase CTR1 ( S8 Fig ) ; the latter was suggested to bind PA through the C-terminal kinase domain [47] . To test this hypothesis , the recombinant proteins consisting of CTR1 full-length protein ( rCTR1 ) as well as the C-terminal kinase domain alone ( rCTR1-K ) were expressed and purified from Escherichia coli . Membrane dot binding assays indicated that both rCTR1 and rCTR1-K proteins bound PA , but not PE , confirming previous reports using PA beads [47] . To rule out the possibility that binding of rCTR1 and rCTR1-K proteins to lipids is due to non-specific hydrophobic association , a wider range of membrane lipid class , including PA , PC , PE , PG , PI , PS , MGDG and DGDG was used to test lipid binding ( S9A Fig ) . The result again verified the binding specificity between rCTR1/rCTR1-K proteins and PA ( S9B Fig ) . We further observed that both recombinant proteins bound 24:0- and 24:1-Cer but not 18:0-Cer by the same binding approach ( Fig 6A ) . When various concentrations of 24:0- and 24:1-Cer were applied , both rCTR1 and rCTR1-K proteins bound 24:1-Cer better than 24:0-Cer ( Fig 6B ) . By using an independent microscale thermophoresis ( MST ) analysis , our data showed that the rCTR1-24:1-Cer interaction was comparable to that of rCTR1-PA binding , with a dissociation constant ( Kd ) of 1 . 6 and 1 . 9 μM , respectively ( Fig 6C and Table 2 ) . Furthermore , we found that both rCTR1 and rCTR1-K bound 24:1-Cer with higher affinities than that of 24:0-Cer , as reflected by the Kd values ( Fig 6C ) . These results revealed that CTR1 preferably binds unsaturated VLC ceramides via the C-terminal kinase domain in vitro . To investigate the biological significance of the interactions between CTR1 and unsaturated VLC ceramides , the kinase activity of CTR1 upon treatments with 24:0-Cer , 24:1-Cer and PA liposomes was measured in both in vitro and in vivo assays using the TR-FRET technique . As presented in Fig 6D , when the purified proteins were pre-incubated with 1 nM liposomes containing 24:1-Cer and PA , both rCTR1 and rCTR1-K proteins exhibited decreased kinase activities in comparison to that of an untreated control . Consistent with its lower binding affinity to rCTR1 and rCTR1-K proteins ( Fig 6B and 6C ) , 24:0-Cer liposome inhibited CTR1 activity with lesser effect than 24:1-Cer and PA liposomes ( Fig 6D ) . Moreover , the in vivo kinase activity was measured using total proteins extracted from wild-type and the ctr1-1 seedlings treated with 50 μM 24:0-Cer , 24:1-Cer and PA liposomes . As a positive control , the seedlings were also treated with 10 μM ACC . By subtracting the background signal from the ctr1-1 mutant , Arabidopsis wild-type seedlings treated with ACC as well as 24:0-Cer , 24:1-Cer and PA liposomes showed significant lower CTR1 specific kinase activities than the control treatment ( Fig 6E ) , indicating that applications of 24:0-Cer , 24:1-Cer and PA liposomes inhibit the CTR1 kinase activity significantly . To further address the effects of the CTR1-Cer interaction on the downstream signaling of CTR1 protein , the EIN2-GFP reporter was used to determine whether ceramide application could interfere with the processing and ER-to-nucleus translocation of EIN2 protein , whose activities are directly regulated by CTR1 in ethylene signaling pathway [48–50] . To this end , 7-d-old seedlings of EIN2-GFP grown on MS medium were placed in MS liquid medium supplemented with 10 μM ACC , 50 μM 24:0-Cer or 24:1-Cer liposomes for 1 h and root tip cells were subsequently observed by confocal microscopy . As presented in Fig 6F , the nuclear accumulation of EIN2-GFP fluorescence was evident upon 24:0-Cer , 24:1-Cer and PA liposome treatments , with significantly higher numbers of fluorescent nuclei in the root cells treated with 24:1-Cer liposomes . As controls , EIN2-GFP was observed to quickly move from the ER membrane to the nucleus after 1-h ACC stimulation but no such movement was detected during the mock treatment ( Control; Fig 6F ) . Based on the above results , we hypothesize that light submergence-triggered production of 24:1-Cer might be a signal for activation of the ethylene pathway through interaction with the kinase domain of CTR1 and inhibition of its activity . As a confirmation , the EIN3-GFP fusion transgenic lines in the ein3 eil1 background ( whose stabilization is used as a marker to indicate the downstream ethylene response ) [51] were used to explore the effect of ceramides on ethylene signaling . Seven-d-old EIN3-GFP/ein3 eil1 seedlings were treated with ACC , light submergence , or dark submergence , and the stability of EIN3-GFP fluorescence was monitored by confocal microscopy . Similar to the application of ACC , which was reported to stabilize EIN3-GFP protein [51] , both light and dark submergence stresses effectively induced the accumulation of EIN3-GFP fusion protein in root tip cells ( Fig 7A ) . EIN3-GFP fusion rapidly accumulated and peaked at 1 h after light or dark submergence treatment , and then gradually decreased from 1 to 3 h . Furthermore , the accumulation of EIN3-GFP fusion was significantly greater in light submergence-treated cells than those of dark submergence-treated cells ( Fig 7A ) . To further explore the effects of ceramides on the light submergence- and dark submergence-induced accumulations of EIN3-GFP , 50 μM 24:0-Cer liposomes or 50 μM 24:1-Cer liposomes were added to the light submergence- or dark submergence-exposed EIN3-GFP/ein3 eil1 lines and subsequently incubated for 3 , 4 , or 5 h . Results showed that application of 24:1-Cer liposomes under either light or dark submergence condition culminated in enhanced accumulation of EIN3-GFP fusion in the nucleus , in contrast to the singular treatments of either 24:0-Cer liposomes , light submergence , or dark submergence ( Fig 7B ) . We have shown that under normal conditions , the loh2 and loh3 mutants had enriched endogenous 24:1-Cer than 24:0-Cer in comparison to those of the loh1 mutant and wild type ( Fig 4E ) . To verify the results from confocal microscopy analyses , immunoblotting was performed to detect the EIN3-GFP protein levels in the various treatments . The data showed that EIN3-GFP fusions were induced by light and dark submergence treatments in the ein3 eil1 double mutant background; the former displayed a much higher level upon treatment ( Fig 7C and 7D ) . Moreover , when the EIN3-GFP fusion was introduced into the ein2-5 ein3 eil1 triple mutant background , the light submergence- or dark submergence-inducible stabilization of EIN3-GFP protein was blocked ( Fig 7E ) , suggesting that EIN2 is required for hypoxia-inducible EIN3-GFP accumulation . In contrast , EBF1-GFP fusion proteins were degraded upon light submergence-treatment ( Fig 7F ) . When liposomes of 24:0-Cer and 24:1-Cer were applied to light submergence- or dark submergence-treated EIN3-GFP/ein3 eil1 lines for 0 , 3 , 6 , 12 , or 24 h , both treatments supplied with 24:1-Cer liposomes accumulated significantly higher levels of EIN3-GFP protein than the 24:0-Cer liposome application ( Fig 7G ) . To test the in vivo effects of C24:1-Cer on ethylene signalling , the EIN3-GFP fusion was expressed in loh1 , loh2 and loh3 backgrounds ( Fig 8A ) and the stability of EIN3-GFP were compared among them . In comparison with the EIN3-GFP/WT line , the EIN3-GFP protein was clearly enhanced in both loh2 and loh3 backgrounds in the root tip cells under normal growth conditions ( Fig 8B and 8C ) . Also , weak EIN3-GFP fluorescence was detected in the EIN3-GFP/loh1 line , possibly due to the increase of long-chain ceramides in the loh1 mutant ( Fig 4E ) . We further found that the EIN3-GFP stabilities were significantly decreased by dark submergence for 1 h in all of the EIN3-GFP/loh1 , EIN3-GFP/loh2 and EIN3-GFP/loh3 mutant lines , in comparison with the inducible accumulation of EIN3-GFP protein in the EIN3-GFP/WT line ( Fig 8B and 8C ) . These results are consistent with the ceramide profiling data ( Fig 4E ) and suggest that the cellular levels of unsaturated ceramides ( such as C24:1-Cer ) is primarily associated with the stability of EIN3 protein in vivo . Together , these results imply that the unsaturated ceramides appear to regulate hypoxia response by modulating the kinase activity of CTR1 in the ethylene signaling pathway . To understand the genetic link between ceramide and ethylene signaling , the loh1 , loh2 and loh3 mutants were crossed to the constitutive triple response mutant ctr1-1 [52] , and the loh1 ctr1 , loh2 ctr1 and loh3 ctr1 double mutants were characterized . As shown in Fig 9A , all the double mutants displayed dwarfish phenotypes , resembling that of the ctr1-1 single mutant . When the plants were treated with dark submergence , single mutant phenotypes of loh1 , loh2 and loh3 were rescued by the ctr1-1 mutant , as indicated by the improved performance of the loh1 ctr1 , loh2 ctr1 and loh3 ctr1 double mutants in comparison with the corresponding loh single mutants under hypoxia ( Fig 9A and 9B ) . These results confirmed that ethylene signaling is likely a downstream event regulated by ceramides .
In plants , cellular membrane integrity and fluidity are largely determined by the lipid composition and the extent of desaturation , which further influences bilayer permeability , ATPase activity , and membrane-associated transportation [3] . It is generally recognized that the unsaturation of membrane lipids is an important factor in plant response to various environmental stresses such as chilling , freezing , heating , salinity and drought [53–58] . For example , unsaturated fatty acids accumulate in Arabidopsis rosettes upon exposure to chilling or freezing temperatures , whereas unsaturated species decrease markedly as the growth temperature increases to levels of heat stress [54 , 55] . Morever , two independent studies suggest that in Arabidopsis , the levels of unsaturated fatty acids are significantly increased in response to shoot-specific hypoxia or in crown galls under hypoxia/drought conditions , which processes are controlled by the phosphate-responsive transcription factor PHR1 and desaturases SAD6/FAD3 , respectively [59 , 60] . In addition , our recent findings suggest that the acyl-CoA-binding protein ACBP3 is involved in plant response to hypoxia by interacting with very-long-chain ( VLC ) acyl-CoA esters and modulating VLC-fatty acid metabolism in Arabidopsis [46] . These results demonstrate that the dynamic maintenance of lipid metabolism and proper functionality of cellular membranes are vital for plant responses to various hypoxic stresses . In this investigation , we present further evidence to show the dynamic profiles of lipid composition in Arabidopsis in response to hypoxic stress , which was mimicked by completely submerging Arabidopsis seedlings under light or dark conditions . Our data indicate that hypoxia-induced lipid changes in Arabidopsis rosettes resemble lipid profiles of tissues upon chilling or freezing treatments [39] , including a significant decrease in the levels of galactolipids and phospholipids , but elevations of unsaturated glycerolipid species , PA , as well as oxidized membrane lipids . Specifically , hypoxia activated the accumulation of VLCFA-enriched PS as well as its derivative ceramides in Arabidopsis rosettes ( Figs 2 and 3 ) . Therefore , our findings indicate that the unsaturation of VLC-ceramides is likely to be a protective mechanism that promotes tolerance to hypoxic stress in Arabidopsis . The importance of ceramides in hypoxia responses has recently been demonstrated in C . elegans and mammalian cells [28 , 30 , 61] . Our results presented here further extend the significance of hypoxia-inducible ceramides in plants , and reveal that ceramides are conserved signal molecules among eukaryotic species essential for regulation of hypoxic/anoxic adaptation . Earlier studies primarily focused on the overall ceramide species of the hypoxia-sensitive mutants under normal conditions rather than specific correlation analysis between the phenotypes and ceramide profiles upon hypoxia , and therefore multiple mechanistic understandings have been proposed . Devlin et al . [30] showed that hypoxia induces a rapid increase of all species of dihydroceramide ( DHCs ) in mammals , which returns to basal levels in a short time once the hypoxic stress recedes . The activity of DHC desaturase ( DEGS ) responsible for de novo DHC synthesis is oxygen-dependent , and overexpression of DEGS improves cell proliferation under hypoxia [30 , 62] . These findings suggest that the DEGS-triggered desaturation of DHCs is a potential oxygen sensor for synthesis of ceramides and it balances different bioactive ceramide species during hypoxic conditions . Similar to the existence of two types of ceramide synthases in Arabidopsis , there are two genes HYL-1 and HYL-2 encoding ceramide synthase in C . elegans; they show distinct roles in the synthesis of C24-26 and C20-22 ceramides in vivo [28 , 29] . Since deletions of HYL-1 and HYL-2 genes lead to attenuated and enhanced anoxia sensitivities , respectively , the C20-22 ceramides generated by HYL-2 appeared to be protective against anoxic stress [28 , 29] . In Arabidopsis , the loh1 , loh2 and loh3 mutants exhibited indistinguishable enhanced tolerances following light hypoxia treatments ( Fig 4 ) , suggesting that the different acyl-chain lengths of VLC-ceramides are not essential for plant responses to hypoxia . However , depletion of light by treating plants under conditions of dark hypoxia , all three loh mutants were hypersensitive to hypoxic stress ( Fig 4 ) . Consistently , the desaturated species of VLC-ceramides declined in the loh mutants , indicating the involvement of a light-dependent fatty acyl desaturase in hypoxia-induced desaturation of VLC-ceramides . Moreover , the VLC-ceramides in Arabidopsis are produced by the redundant function of LOH1 and LOH3 , whose knockout double mutant is embryo lethal [12] . By utilizing a leaky double mutant loh1-1 loh3-1 , in which the LOH1 and LOH3 were downregulated and the levels of VLC-ceramides were substantially reduced [12] , we observed that the light-induced tolerant phenotypes were compromised in the loh1-1 loh3-1 line ( Fig 5 ) . Therefore , our findings identified a novel VLC fatty acyl desaturation-dependent rather than the typically acyl chain length-related mechanism during hypoxia adaptation in Arabidopsis . In fact , several components of lipid metabolism including an acetyl-CoA carboxylase ACCase , two acyl-CoA-binding proteins ACBP4 and ACBP5 , and a fatty acyl desaturase FAD7 , are transcriptionally modulated by light/dark cycling [2] . Analysis of transgenic lines expressing Arabidopsis FAD7 promoter fusion with β–glucuronidase reporter has shown that expression of FAD7 is activated by light and suppressed by constant darkness [63] , suggesting that lipid desaturation is tightly regulated by light to satisfy cellular lipid demands in plant cells . A recent investigation has uncovered a role for ADS2 , an Arabidopsis acyl-CoA desaturase-like enzyme , in predominantly catalyzing the mono-desaturation of C24- and C26-ceramides [18] . Given that ADS2 is involved in lipid remodeling during establishment of cold acclimation which is a light-dependent process [64 , 65] , and is also required for hypoxic tolerance under both dark and light submergence conditions ( Fig 5 ) , we therefore suggest that ADS2 is an essential light-activated desaturase for hypoxia-triggered desaturation of VLC-ceramides in Arabidopsis . One contradictory result arising from this study is that the unsaturated species of VLC-ceramides were not upregulated in the light submergence-tolerant loh mutants . Instead , significant decreases of saturated species of long-chain ceramides ( 16:0 ) and some VLC-ceramides ( 20:0 , 22:0 and 24:0 ) were observed in the light submergence-treated loh mutants ( Fig 4E ) . It is well-known that the structural variation of ceramides causes differential physiological functions in plant cells [44] . Previous studies have clearly demonstrated that hydroxylation and phosphorylation of LCB and ceramides are important for their capability to induce cell death [17 , 66–68] . Although the contribution of fatty acyl desaturation in modulating cell death in plant cells is still unclear , it is conceivable that the enhanced resistance of loh mutants under light submergence conditions is possibly due to the decrease of saturated ceramides , the accumulation of which may induce hypoxic injury and promote cell death in plant cells . In higher plants , the gaseous hormone ethylene is a central signaling molecule in regulation of diverse hypoxia responses such as early hypoxia sensing , hypoxia-responsive gene regulation , and development of survival strategies [22] . In this report , we present several lines of evidence to show that ceramides regulate hypoxic tolerance by modulating ethylene signaling . Firstly , exogenous application of ceramides activated the transcription of hypoxia- and ethylene-responsive factors HRE1 and HRE2 [22 , 38] ( S5 Fig ) . Secondly , the C24 species of VLC-ceramides interacted with the kinase domain of recombinant CTR1 protein with high affinity in vitro ( Fig 6 ) . Thirdly , the unsaturated ceramides stimulated the ER-to-nucleus translocation of EIN2-GFP and stabilized EIN3-GFP fusion protein in vivo ( Figs 6 , 7 and 8 ) . Finally , the enhanced sensitivities of loh mutants to dark submergence were rescued by introduction of the ctr1-1 mutation that constitutively induces ethylene responses ( Fig 9 ) . In mammals , several targets of ceramides have been identified including protein kinases Raf-1 and PKCζ [31 , 69–71] and protein phosphatases [72 , 73] . Ceramides , together with PA and PS , are well-known to serve as lipid cofactors involved in activation of Raf-1 and its subsequent signal transduction cascade [32 , 74] . CTR1 is a Raf-1-like protein kinase active downstream of ethylene receptors , and a key negative regulator in ethylene signaling [47] . In the absence of ethylene , CTR1 directly interacts with ethylene receptors and represses ethylene responses by maintaining the downstream elicitor EIN2 at the ER [49 , 75] . In contrast , the presence of ethylene inhibits CTR1 activity and subsequently stimulates the phosphorylation-dependent processing and ER-to-nucleus movement of EIN2 protein , resulting in accumulation of EIN3/EIL1 and activating ethylene responses [48–50] . Our data further showed that the binding of 24:0-Cer or 24:1-Cer to CTR1 caused a reduction of its kinase activity in both in vitro and in vivo assays ( Fig 6 ) , suggesting that although both protein kinases Raf-1 and CTR1 bind ceramides , their underling mechanisms appear to be different . Unlike the positive activation of Raf-1 kinase by ceramides in mammalian cells [31] , our findings indicate that ceramides , at least their VLC species , are likely to inhibit the CTR1 activity and CTR1-mediated signaling in Arabidopsis . By analysis of transgenic lines expressing EIN2-GFP , we observed that the processing and nuclear translocation of this protein was enhanced upon treatment with unsaturated VLC-ceramides ( Fig 6 ) , supporting the theory that CTR1 activity is suppressed by ceramides . Consistently , the ceramide-induced accumulation of EIN3-GFP ( Fig 7 ) and enhanced expression of downstream transcription factors HRE1 , HRE2 and RAP2 . 6 further confirms this hypothesis ( S5 Fig ) . In support , a recent investigation showed that the recombinant CTR1 protein binds to PA and suppresses the activity of CTR1 in vitro [47] . Nevertheless , the biological significance of CTR1-PA interaction in vivo remains to be determined . Thus , further identification of the lipid-binding domain and specific binding sites in CTR1 is needed to deepen our understanding of the importance of lipid cofactors in the regulation of CTR1 function . In conclusion , we described a novel mechanism of unsaturation of VLC ceramides in protecting Arabidopsis from hypoxia-induced damages . Based on the present data that in response to hypoxia , the unsaturated VLC-ceramides bind to CTR1 and activate the subsequent downstream ethylene responses , we propose that unsaturated VLC-ceramides may function in modulating ethylene signaling and hypoxia response . Furthermore , the balance between the saturated and unsaturated species of VLC ceramides may govern the cell death and cell survival responses upon plant exposure to a hypoxic environment .
The Arabidopsis T-DNA insertion mutants of LOH1 , LOH2 and LOH3 genes were identified from the SALK collections ( http://signal . salk . edu ) with locus names of loh1 ( SALK_069253 ) , loh2 ( SALK_018608C ) , loh3 ( SALK_150849 ) , which were previously described by Ternes et al . [13] . The characterization of loh1-2 and loh3-2 mutants as well as loh1-1 loh3-1 double mutant , and generation of transgenic lines expressing 35S:EIN3-GFP in ein3 eil1 and ein2 ein3 eil1 backgrounds , as well as 35S:EBF1-GFP transgenic lines in wild-type ( Col-0 ) background have been reported [12 , 51] . The knockout mutants of ads2-1 ( SALK_079963C ) , ads2-3 ( CS817934 ) and ads2-4 ( CS873338 ) were characterized following previous descriptions [18 , 64] . The ctr1-1 mutant [52] was obtained from The Arabidopsis Information Resource ( TAIR; http://www . arabidopsis . org ) . For germination assays , Arabidopsis seeds were surface-sterilized with 20% bleach containing 0 . 1% Tween-20 for 20 min , and then washed 5 times with sterile water . Seeds were sown on MS medium , followed by cold treatment in the dark for 3 d . After germination for 7 d , seedlings were transplanted to soil and grown in a plant growth room with a 16-h-light ( 125 μmol m-2 s-1 ) /8-h-dark cycle at 22°C . Hypoxic treatment was carried out following the method of Licausi et al . [24] with minor modifications . Briefly , 3- or 4-week-old plants were submerged at depths of 5–10 cm beneath the water surface for 8 d under light conditions ( 60 μmol m-2 s-1; light submergence; Light Sub ) or for 2 d under constant darkness ( dark submergence; Dark Sub ) . Plant samples were collected or photographed at the indicated times . Dry weights and survival rates were recorded after 3-d recovery . The survival rates were calculated based on the numbers of plants with capability to produce new leaves and continue to grow after recovery from hypoxic stress . For the ethanolic treatment , simultaneous harvested seeds were sterilized and sown on MS solid medium with or without 50 mM ethanol . Following cold treatment for 2 d , the seeds were germinated under a 16-h-light/8-h-dark cycle at 22°C . Seedlings were scored and photographed at 2 weeks after germination . Microarray analysis was performed as described previously [76] . Wild-type ( Col-0; 4-week-old ) plants were light submergence-treated and rosettes were harvested at 0 and 48 h after treatment . Each sample included three biological replicates , and each replicate collected from three independent plants . Total RNA was extracted using the RNeasy Plant Mini kit ( Qiagen ) according to the manufacturer’s instructions . Labeling , hybridization , scanning , and detection on the ATH1 Arabidopsis chips ( Affymetrix ) , as well as raw data collection using the Affymetrix Gene Chip software MAS 5 . 0 were carried out as previously described [76] . The data were deposited on the Gene Expression Omnibus ( GEO ) database under accession number GSE59719 . In addition , Affymetrix CEL files of short-term anoxia ( 6 h ) in GSE2133 [77] , hypoxia ( 2 and 9 h ) in GSE9719 [78] , dark submergence treatment in GSE24077 [36] were also applied for analysis together with light submergence ( 48 h ) data . The genes involved in lipid biosynthesis and metabolism were identified according to the pathways described in Li-Beisson et al . [7] and Nakamura et al . [79] . GeneSpring 12 . 6 was used to identify differential expression genes with the criterion of 1 . 5-fold or more change and P < 0 . 05 cutoffs for the subsequent analysis . R language was used for all calculations and plots . For membrane lipid analysis , the total plant lipids were extracted following the method of Welti et al . [39] . The profiles of membrane lipids were determined by automated electrospray ionization–tandem mass spectrometry as previously described [42 , 80] . The data of polar membrane lipids described in this work were acquired from the Kansas Lipidomics Research Center Analytical Laboratory . Instrument acquisition and lipidomics method development was supported by National Science Foundation ( EPS 0236913 , MCB 0920663 , DBI 0521587 , DBI1228622 ) , Kansas Technology Enterprise Corporation , K-IDeA Networks of Biomedical Research Excellence ( INBRE ) of National Institute of Health ( P20GM103418 ) , and Kansas State University . Extraction of sphingolipids was carried out according to method IV in Markham et al . [81] using 100 mg tissue samples . Extracts were dried under nitrogen , then dissolved in 1 mL of methanol and analyzed on a triple TOF 5600 MS/MS system ( AB SCIEX , Canada ) . Separations were accomplished on an Agilent Eclipse XDB C8 column ( 50×2 . 1 mm , 1 . 8 μm ) . The column heater temperature was maintained at 40°C . The mobile phases were composed of 100% methanol and 1 mM ammonium formate with 0 . 2% formic acid and the flow rate kept at 0 . 3 mL/min . The sample volume injected was set at 10 μL . The conditions of MS/MS detector were as follows: temperature 450°C; curtain gas 30 psi; flow rate 10 L/min; ion spray voltage 5 , 000 V . Quantification was performed by normalizing the peak areas to the internal standards and response factors as previously described [82 , 83] . Liposomes were prepared according to Hu et al . [84] . For ceramide treatment , 2-week-old wild type ( Col-0 ) seedlings were floated on MS liquid medium containing 0 . 1 mM ceramide liposomes ( 24:1-Cer ) . The samples were collected at 0 , 1 , 3 , 6 and 12 h after treatment . Total RNA was isolated and real-time PCR ( qPCR ) was analyzed as described previously [76] . Gene-specific primers used for qPCR analysis are listed in S6 Table . The constructs of rCTR1 and rCTR1-K fusion were obtained by amplifying the CTR1 cDNA coding region fragment or CTR1 kinase domain alone using primer pairs XS1483/XS1484 and XS1181/XS1182 ( S6 Table ) , respectively , by RT-PCR . The confirmed PCR fragments were inserted into GST-tagged expression vector pGEX-6P-1 and ( His ) 6-tagged expression vector pRSET A , respectively . The constructs were transformed into Escherichia coli BL21 ( DE3 ) , and the recombinant proteins were expressed and purified following the manufacturer’s instructions ( Invitrogen ) . Binding of rCTR1 and rCTR1-K to ceramides and phospholipids on filters was carried out as previously described [42 , 85 , 86] . For MST analysis , the three recombinant proteins were first labeled with the Monolith NT Protein Labeling Kit RED . Labeled proteins were used at a concentration of 10 nM in 1× phosphate buffer saline ( pH 7 . 6 ) containing 0 . 05% Tween-20 . The concentration of various liposomes of ceramides and PA ( Avanti ) ranged from 1 . 5 nM to 50 μM . An optimized buffer ( 50 mM Tris-HCl pH 7 . 4 , 150 mM NaCl , 10 mM MgCl2 , 0 . 05% Tween-20 ) was prepared for incubation of proteins and liposomes for 5 min . The combined solution of labeled proteins and liposomes were transferred into standard treated capillaries and MST was measured on a Nano Temper Monolith NT . 115 ( 20% LED power; 50% laser power ) . The kinase activities of rCTR1 and rCTR1-K proteins ( 20 ng ) or Arabidopsis total proteins ( 3 μg ) from 10-d-old seedlings were measured using the STK Substrate 2-biotin in HTRF KinEASE kit according to the manufacturer’s instructions . In brief , assays were conducted in low volume , white 384-well plates ( Corning Life Sciences , MA ) , with a 20 μl assay volume containing 100 μM ATP , 1 μM STK Substrate 2-biotin . For recombinant proteins , liposomes such as 24:0-ceramide , 24:1-ceramide and PA ( 1 nM , Avanti ) were pre-incubated with protein at 4°C for 30 min . For total proteins , Arabidopsis seedlings were non-treated or treated with 10 μM ACC , 50 μM 24:0-ceramide , 24:1-ceramide and PA liposomes and the total proteins were extracted using kinase buffer containing 250 mM ( pH 7 . 0 ) HEPES , 0 . 1% NaN3 , 0 . 05% BSA , 0 . 5 mM Orthovanadate , 2 mM DTT and 10 mM MgCl2 . The kinase reaction was carried out following incubation at room temperature for 1 h . The reaction was stopped with buffered EDTA followed by the fluorescent development for 1 h at room temperature . The resulting specific TR-FRET signal was detected by a TECAN detection system ( Infinite M1000 ) and calculated based on the fluorescence emission ratio at 665/620 nm . The EIN3-GFP/loh1 , EIN3-GFP/loh2 and EIN3-GFP/loh3 lines were generated by genetic crossing 35S:EIN3-GFP line to loh1 , loh2 and loh3 mutants , respectively . The GFP fluorescence in the primary roots of 7-d-old 35S:EIN2-GFP , 35S:EIN3-GFP , EIN3-GFP/loh1 , EIN3-GFP/loh2 and EIN3-GFP/loh3 lines was observed at the indicated time points after light submergence- or dark submergence-treated in liquid MS medium , or by application of 50 μM 24:0-ceramide and 24:1-ceramide liposomes ( Avanti ) . The treatments with 10 μM ACC and 2 μM AVG were set as controls . A TCS-SP5 laser scanning confocal microscope ( Leica ) was used for analysis the stabilization of EIN3-GFP fusion protein . GFP fluorescence was excited at 488 nm , filtered through a primary dichroic ( UV/488/543 ) , a secondary dichroic of 545 nm and subsequently through 500–600 nm emission filters to the photomultiplier tube ( PMT ) detector . Total proteins were extracted by grinding samples in liquid nitrogen followed by adding ice-cold extraction buffer ( 50 mM sodium phosphate , pH 7 . 0 , 200 mM NaCl , 10 mM MgCl2 , 0 . 2% β-mercaptoethanol and 10% glycerol ) supplemented with the protease inhibitor cocktail ( Roche ) . Extracts were placed on ice for 30 min , and then centrifuged at 11 , 000 g for 30 min to collect the supernatant for electrophoresis . For immunoblot analysis , total proteins were subjected to SDS-PAGE and electrophoretically transferred to Hybond-C membrane ( Amersham ) . The anti-GFP ( Roche; 1:3 , 000 ) antibodies were used in the protein blotting analysis . The loh1 ctr1 , loh2 ctr1 and loh3 ctr1 double mutant combinations were generated by genetic crossing parental single homozygous lines of loh1 , loh2 and loh3 [13] to ctr1-1 [52] . The T-DNA insertions of loh1 , loh2 and loh3 were identified by screening all of the F2 population using gene-specific primers and paired with a T-DNA-specific primer LBb1 . 3 ( S6 Table ) . The seedlings displaying constitutive triple-response phenotypes were deemed to be homozygous for ctr1-1 [52] . The Arabidopsis Genome Initiative numbers for genes mentioned in this article are as follows: LOH1 ( At3g25540 ) , LOH2 ( At3g19260 ) , LOH3 ( At1g13580 ) , ADS2 ( At2g31360 ) , CTR1 ( At5g03730 ) , EIN2 ( At5g03280 ) , EIN3 ( At3g20770 ) , ADH1 ( At1g77120 ) , PDC1 ( At4g33070 ) , SUS1 ( At1g01040 ) , HRE1 ( At1g72360 ) , HRE2 ( At2g47520 ) , RAP2 . 6 ( At1g43160 ) , RAP2 . 12 ( At1g53910 ) , HB1 ( At2g16060 ) , HUP09 ( At5g10040 ) and LBD41 ( At3g02550 ) .
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Hypoxia is one of the most important abiotic stresses of worldwide concern that determines the crop productivity and the natural distribution of plant species . Flooding events such as root waterlogging and submergence strongly affect diffusion of gasses into plant cells , which eventually leads to hypoxia and carbohydrate shortages in terrestrial plants . The gaseous hormone ethylene is considered as the primary determinant of hypoxia response in plants . In particular , the Group VII ethylene-responsive factors ( ERFs ) have been demonstrated to be master regulators for oxygen sensing through an N-end rule protein degradation mechanism . Recent investigations have suggested that lipid molecules such VLC ceramides may play crucial roles in hypoxia signaling in both animal and plant cells . Here , we identified the unsaturated VLC ceramide species including C22:1- , C24:1- and C26:1-Cers which were essential for hypoxia response by interacting with CTR1 protein , inhibiting its kinase activity , and modulating subsequent ethylene signaling in Arabidopsis . The dynamic unsaturation of VLC ceramides is likely to serve as a novel protective strategy for enhancing plant tolerance to the frequent environmental stresses , including flooding .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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Unsaturation of Very-Long-Chain Ceramides Protects Plant from Hypoxia-Induced Damages by Modulating Ethylene Signaling in Arabidopsis
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Leptospirosis , an emerging infectious disease caused by bacteria of the genus Leptospira , is thought to be the most widespread zoonotic disease in the world . A first step in preventing the spread of Leptospira is delineating the animal reservoirs that maintain and disperse the bacteria . Quantitative PCR ( qPCR ) methods targeting the LipL32 gene were used to analyze kidney samples from 124 House mice ( Mus musculus ) , 94 Black rats ( Rattus rattus ) , 5 Norway rats ( R . norvegicus ) , and 89 small Indian mongooses ( Herpestes auropunctatus ) from five cattle farms in Puerto Rico . Renal carriage of Leptospira was found in 38% of the sampled individuals , with 59% of the sampled mice , 34% of Black rats , 20% of Norway rats , and 13% of the mongooses . A heterogeneous distribution of prevalence was also found among sites , with the highest prevalence of Leptospira-positive samples at 52% and the lowest at 30% . Comparative sequence analysis of the LipL32 gene from positive samples revealed the presence of two species of Leptospira , L . borgpetersenii and L . interrogans in mice , detected in similar percentages in samples from four farms , while samples from the fifth farm almost exclusively harbored L . interrogans . In rats , both Leptospira species were found , while mongooses only harbored L . interrogans . Numbers tested for both animals , however , were too small ( n = 7 each ) to relate prevalence of Leptospira species to location . Significant associations of Leptospira prevalence with anthropogenic landscape features were observed at farms in Naguabo and Sabana Grande , where infected individuals were closer to human dwellings , milking barns , and ponds than were uninfected individuals . These results show that rural areas of Puerto Rico are in need of management and longitudinal surveillance of Leptospira in order to prevent continued infection of focal susceptible species ( i . e . humans and cattle ) .
Along with increasing globalization , climate change , and urban expansion , the rising number of emerging infectious diseases is a major concern . Among emerging infectious diseases , over 60% are multi-host zoonoses many of which are classified as “neglected” owing to a general lack of knowledge about their epidemiology , more so in the tropics [1] . Perhaps the most widespread neglected zoonotic disease in the world is leptospirosis , which has an estimated annual global incidence of 1 . 03 million human cases with a projected number of 60 , 000 as fatal cases [2 , 3] . Leptospirosis is caused by spirochetes of the genus Leptospira [4] , with at least 15 known pathogenic species possessing over 250 serovars [5–7] . In known natural reservoirs , such as dogs , rodents , and cattle , Leptospira persists and multiplies within the renal tubules from which they are dispersed by urination of the moving hosts throughout the local landscape [4 , 6] . Once in soil and water , these bacteria can remain viable for several months and can infect susceptible species through open-skin wounds and mucus membranes [5 , 8] . Humans are incidentally infected with Leptospira following exposure to soils or water that is contaminated with animal urine [6] . Leptospirosis is an endemic disease in South Pacific island countries [9–11] . It is also widespread in the Caribbean islands including Haiti , Jamaica , Martinique , and Trinidad and Tobago [12–14] . However , incidence and prevalence of leptospirosis are largely underestimated throughout tropical environments especially since clinical signs associated to other febrile diseases , such as with dengue , malaria , and Zika , are strikingly similar; therefore , diagnosis and treatment is problematic [15–19] . In Puerto Rico , leptospirosis was first suspected in 1918 and confirmed in 1939 [20] . Current data for this island are limited , but reported incidence has increased over the past decade [16 , 21] . Leptospirosis has been known to cause abortions , birth complications , and reduced milk production in cattle [22–24] . Due to the shared environment and level of contact with the animals , livestock workers are at risk of contracting leptospirosis areas where the pathogen is present [21 , 25 , 26] . The dairy industry in Puerto Rico comprises up to 25% of agriculture-related income and is historically the most important agricultural commodity on the island [27 , 28] . Employing leptospirosis prevention regimens for cattle does not completely eliminate the threat of contraction if wildlife reservoirs are maintaining this pathogen in the farm environment . Therefore , assessing the risk associated with potential wildlife vectors in rural farm areas of Puerto Rico will inform future plans that aim to reduce transmission rates . The first step to this approach is identifying the wildlife species that are potentially acting as reservoirs on and around farms . One of the most effective methods for managing zoonotic disease outbreaks is managing the wildlife reservoirs responsible for spreading the disease [21 , 29 , 30] . Invasive and pest ( i . e . commensal rodents ) species are of particular concern , because they readily adapt to human activity and urban settings , which places them in closer proximity to humans [29 , 31] . This can be particularly true in farm settings where rodents and other pests can have direct access to animal feed and bedding areas . The objective of this study was to provide data on the prevalence of Leptospira in four invasive and pest species on rural farms of Puerto Rico; namely , House mice ( Mus musculus ) , two rat species ( Rattus rattus and R . norvegicus ) , and small Indian mongooses ( Herpestes auropunctatus ) , which are now ubiquitous throughout the island and known reservoirs for Leptospira [1 , 12] . We sampled at five rural locations in different parts of Puerto Rico , analyzed kidney tissue for renal carriage of Leptospira , and correlated Leptospira presence and absence data to individual distances from signature features within each location .
Rodents and mongooses were trapped on cattle farms from five municipalities in Puerto Rico during the summers of 2014 and 2015 ( Fig 1 ) . Municipalities sampled for 2014 included dairy cow farms in Lajas ( 18 . 041189 , -67 . 042908 ) , Isabela ( 18 . 46116 , -67 . 05652 ) , San Sebastián ( 18 . 378265 , -67 . 022423 ) , and Naguabo ( 18 . 238525 , -65 . 719208 ) . During 2015 , the same municipalities were sampled along with the addition of a beef cattle farm in Sabana Grande ( 18 . 036125 , -66 . 931173 ) . Lajas and Sabana Grande are both located in the southwestern Caribbean Sea side island and have a tropical savannah climate . Sampling sites San Sebastián and Isabela are on the northeastern Atlantic Ocean side of the island and they are characterized by a tropical rainforest climate . Naguabo represents the coastal east side of the island and receives the most amount of rain of the five farms . These sites formed part of a larger project focused on livestock health , with an emphasis on the impacts of Cattle Fever Ticks ( CFTs , Rhipicephalus spp . ) as pathogen vectors . Sampling for small mammals to ascertain their potential role as tick hosts opened the opportunity to collect samples for the present project . Selection of the farm sites was driven by the original goal of studying CFTs . Criteria for including farm in the study required the presence of the CFT as well as the willingness of farm owners to volunteer to participate in the study . An additional consideration was the distribution of these farms along different ecological zones from the island . Thus , it should be emphasized that farm selection was not driven by any previous information associated to presence of Leptospira in humans or cattle . More precisely the present project is ancillary to a larger one and thus of an exploratory nature . Sherman live traps ( 3"x 3 . 5"x 9" ) ( H . B . Sherman Traps , Inc . , Tallahassee , FL , USA ) baited with rolled oats , and Tomahawk Live Traps ( 20"x7"x7" ) ( Tomahawk Live Traps , Hazelhurst , WI , USA ) baited with tuna fish were placed in transects of either one line of 40 or two lines of 20 traps . Tomahawk traps were placed approximately 15–20 meters apart and Sherman traps were placed approximately 2–5 meters apart depending on the habitat being sampled , which included ecotones , grasslands , cattle pastures , riparian zones , and around human dwellings . The positions of captured animals were recorded with a GPS unit ( Garmin Montana 650 , Garmin Corp . , Kansas City , KS , USA ) . Tomahawk traps were checked for captures throughout diurnal hours , three times a day ( early morning , midday , and evening ) to target mongooses . Tomahawk and Sherman traps were left overnight to target rodents . Immediately after capture , animals were euthanized by cervical dislocation after first being rendered unconscious with isoflurane . Weight and size measurements of individuals were taken along with tissue samples that included kidneys , a liver fragment , the GI tract , heart , and lungs obtained using sterilized equipment . During summer 2014 , tissue samples were stored in 70% ethanol ( EtOH ) and transferred to 95% EtOH at the end of the field season . During the summer of 2015 , kidney samples were stored in 95% EtOH and kept cool at approximately 4°C throughout the field season . Samples were stored at different EtOH concentrations between years due to a lack of resources during 2014 . DNA was extracted from kidneys using the Qiagen DNeasy Blood and Tissue Extraction Kit following the manufacturer’s instructions ( Qiagen Inc . , Valencia , CA , USA ) . As per these instructions , we extracted DNA from approximately 20mg of tissue , however this measurement was not systematically standardized . DNA extracts were stored frozen at -18°C . A qPCR TaqMan assay with primers designed to target the LipL32 gene present in pathogenic Leptospira sp . was used to test for the presence of Leptospira [32 , 33] . For the purpose of this exploratory study , qPCR was used for the detection of Leptospira but was not used for quantification . TaqMan based analyses were carried out on an Applied Biosystems StepOne Plus Real-Time PCR System ( Applied Biosystems , Foster City , CA , USA ) in duplicate in a volume of 25 μl containing 12 . 5 μl of TaqMan Fast Advanced Master Mix ( Applied Biosystem , Foster City , CA , USA ) 1 μl of each forward primer LipL32-45F ( 700 nM , 5’AAG CAT TAC CGC TTG TGG TG ) and reverse primer LipL32-286R ( 700 nM , 5’GAA CTC CCA TTT CAG CGA TT ) , 1 μl of probe LipL32-189P ( 150 nM , 5’[6-FAM]- AA AGC CAG GAC AAG CGC CG -[BHQ1] ) , 1 μl of DNA template and 8 . 5 μl of water . An initial denaturation at 95°C for 5 minutes was followed by 40 cycles of 95°C for 15 seconds and 60°C for 30 seconds [32 , 33] . LipL32 gene amplicons obtained by end-point PCR with primers LipL32-45F and LipL32-286R from DNA of Leptospira interrogans serovar Copenhageni strain Fiocruz L1-130 , kindly provided by Dr . Albert I . Ko ( Yale University Schools of Public Health and Medicine , New Haven , CT ) , served as a positive control . Samples that amplified at ≤ 40 were considered positive . All samples were run in duplicate along with two negative controls . Assays were only considered valid if the negative control did not show an amplification signal . Partial LipL32 genes from a subset of positive samples were re-amplified by end-point PCR with primers LipL32-45F and LipL32-286R . Amplicons ( 242 bp ) were sequenced on an Applied Biosystems Genetic Analyzer 3500xL ( Life Technologies , Carlsbad , CA ) , and sequences deposited at Genbank . Sequences were assembled in Geneious 8 . 1 . 7 ( Biomatters Ltd , Auckland , New Zealand ) , and checked in GenBank/EMBL databases using the BLAST algorithm [34] . This subset was selected to include representative groups of samples from all species and farm locations included in this study and represents approximately 50% of the identified positive samples . Landscape features included natural ponds , slurry ponds , milking areas within farms , and human buildings for which GPS positions were recorded . GPS positions of animals collected in the field were used to calculate the distance of each individual to landscape features . Average distances between these landscape features and positive or negative samples were then used in Welch’s t-test ( unequal variances ) to assess the relationships between prevalence of Leptospira in animals and landscape features on the farm . Leptospira presence/absence data were analyzed with SaTScan v9 . 4 . 2 software to test for significant clusters of positive cases on the landscape [35 , 36] . Given that prevalence is binomial ( infected or not ) and presented as a proportion , we used Jeffrey’s confidence intervals for our estimates of prevalence among and within species and farms . We used the “prevalence” R package v . 0 . 4 . 0 and an alpha value of 0 . 05 for significance assessments . A chi-squared test was conducted to determine if there was a heterogeneous distribution of Leptospira among the five farms . Collection and handling of wild rodents followed the Guidelines of the American Society of Mammalogists for the use of wild mammals in research [37] . Our protocol was reviewed and approved by the Texas State University Institutional Animal Care and Use Committee ( protocol #0514_0303_07 ) and scientific collecting permits for wild mammal collection were provided by Departamento de Recursos Naturales y Ambientales from Puerto Rico ( 2014-IC-063 to Ivan Castro-Arellano ) . Access to private property was granted by landowners .
Over two trapping seasons , we sampled 312 mammals comprising of 124 house mice ( Mus musculus ) , 94 black rats ( Rattus rattus ) , 5 Norway rats ( R . norvegicus ) , and 89 Small Indian mongooses ( Herpestes auropunctatus ) , sampling and prevalence data are summarized in Table 1 . Sample sizes per site ranged from 33 ( Naguabo ) to 97 ( San Sebastián ) with an overall Leptospira prevalence of 0 . 38 ( 0 . 33–0 . 44 ) across all species . Although sample sizes among species were not uniform the overall prevalence values among and within sites it is of epidemiological significance to understand the level at which Leptospira is present among all animal reservoirs for both the island and each farm . Lajas had significantly higher prevalence compared to the other sites ( x2 = 9 . 97 , df = 4 , p < 0 . 04 ) . San Sebastián , Naguabo , Sabana Grande , and Isabela , had a similar prevalence values ( 0 . 30–0 . 34 ) regardless of varying sample sizes ( n = 33–97 ) . Mice generally showed higher prevalence of Leptospira ( 0 . 42–0 . 70 ) , followed by Black rats ( 0 . 00 to 0 . 61 ) , and mongooses ( 0 . 00–0 . 21 ) . Norway rats were caught at two sites and in low numbers ( n = 5 ) , only one individual was positive for Leptospira . Overall , detections of Leptospira in mice were significantly higher than in rats and mongooses ( x2 = 42 . 347 , df = 2 , p < 0 . 0001 ) . Endpoint PCRs of Leptospira positive samples for LipL32 gene fragments ( 242 bp ) from 45 mice , 7 rats , and 7 mongooses generated a sufficient number of amplicons to allow automated Sanger sequencing ( MK328816–MK328874 ) . Sequences showed either 100% identity to GenBank reference sequences for L . borgpetersenii ( KF928037 ) or L . interrogans ( U89708 , DQ149595 ) . Overall L . interrogans ( 68% ) was detected more among small mammals compared to L . borgpetersenii ( 32% ) ( t = -2 . 58 , df = 7 . 9 , p = 0 . 03 ) . Both Leptospira species were detected in similar proportions in Lajas , Naguabo , Sabana Grande , and Isabela , while samples from the fifth farm San Sebastián harbored mainly L . borgpetersenii ( Fig 1 , Table 2 ) . In rats , both Leptospira species were found , while mongooses only harbored L . interrogans ( Table 2 ) . Association of infection with landscape features was significant in Naguabo , where infected individuals tended to be closer to chosen landscape features ( including a human dwelling , dairy cow milking area , and a pond ) than were uninfected individuals ( Table 3 ) . At Sabana Grande , infected individuals were closer to the human dwelling than were uninfected individuals ( Table 3 ) . At the other locations , i . e . Lajas , San Sebastián and Isabela , infected and uninfected individuals did not differ in distance to the anthropogenic landscape features . Spatial analyses with SaTScan using a Bernoulli model identified four clusters within the studied farms but none were statistically significant . However , a cluster identified at the Lajas farm , although not significant at the level ( p < 0 . 05 ) we had chosen , had a substantially different p-value than the other clusters ( Fig 2 and Table 4 ) . This cluster was in a field in close proximity to the milking area ( 0 . 14 km SE ) and a building ( 0 . 15 km SW ) .
Although all four species of mammals analyzed in our study tested positive for Leptospira , prevalence of Leptospira was much higher in mice than in mongooses or both Black or Norway rats ( Tables 1 and 2 ) . Results from this study suggest that mice potentially play a more important role as reservoir for Leptospira in rural parts of Puerto Rico than rats . However , since mice excrete less urine than rats they are likely also shedding fewer Leptospira into the environment . A major limitation to this study was not quantifying Leptospira load among individuals . Since this was an exploratory study that aimed to identify a potential health risk to cattle and farmworkers on Puerto Rican dairy farms , the main goal was to verify the presence of the pathogenic Leptospira spp . among potential reservoirs present at each farm . Now that the presence has been verified , future studies on farms should concentrate on accurately quantifying the load of reservoir animals . Quantifying pathogen load will provide a better idea of the extent to which each species contributes to Leptospira maintenance in the environment . In Puerto Rico , all four animal species we sampled had been identified as reservoirs for Leptospira before , with high prevalence , i . e . 48% in House mice , 37% in Black rats , 40% in Norway rats , and 20% in small Indian mongooses [20] . In a previous study , both rat species were found to carry Leptospira , at a prevalence of 39% [38] . Although both of these studies were limited to the urban area of San Juan , our data confirm that House mice , black rats , and Norway rats are also important reservoirs in rural areas of Puerto Rico with high Leptospira prevalence in all animal species . It should be noted that few Norway rats were caught in sampled farms . Previous studies have found large numbers and with high prevalence of Leptospira in urban areas [39 , 40] . In both tropical and temperate urban areas , Norway rats are frequently encountered and infected with Leptospira , with prevalence values often around 40% [41–43] , but up to 89% as well [44 , 45] . In our study , Norway rats were rarely encountered and caught ( n = 5 ) , which was likely a consequence of this species preferring urban areas over rural areas [46] . Thus , our prevalence values of 20% that reflected detection of Leptospira in one individual only , are without statistical significance though still similar to other published data [47 , 48] . High prevalence of Leptospira has been shown for different species of mongooses in previous studies [49–51] . Our study resulted in few detections of Leptospira in Indian mongooses at any site on Puerto Rico corresponding to an overall prevalence rate of 13% . Results on other Caribbean islands like Barbados , however , show much higher prevalence , with prevalence values close to 41% in mongooses , while mice were infected at much lower prevalence ( 28% ) [51] . Even though similar numbers of mongooses and mice were tested in this study , the results do not match our data with 13% mongooses harboring Leptospira , and mice being the most infected with 59% prevalence . The result trends , however , are similar to numbers ascertained from a previous survey conducted in San Juan , Puerto Rico [20] . Two species of Leptospira , i . e . L . interrogans and L . borgpetersenii were detected in mice and rats , while mongooses only harbored L . interrogans . The lack of detection of L . borgpetersenii in mongooses , however , is potentially a function of the small sampling size used for species analyses of Leptospira , i . e . seven individuals from 4 locations . Since a higher prevalence of L . borgpetersenii is seen in both rats and mice , it could also be that mongooses are not regularly coming into direct contact with this pathogen due to mongooses living in lower densities than mice or rats and the short environmental persistence of L . borgpetersenii outside of the host . L . borgpetersenii is thought to survive poorly in the environment due to point mutations in environmental sensing and metabolite transport and utilization genes , and thus is transmitted most frequently through direct contact [52] . In contrast , L . interrogans survives for extended times in the environments [53] and is transmitted readily through contact with surface waters [54 , 55] . L . borgpetersenii ( i . e . serovar Hardjo ) has been reported as most prominent Leptospira sp . in cattle in Chile [56] , and cattle were proposed as maintenance host [24] . L . borgpetersenii has been detected in cattle from other countries , however , equally often in other animals , including many different rodent species [57] . Thus , while the tropical conditions on Puerto Rico generally favor environmental survival and transmission of Leptospira [53] , animal host preferences of different Leptospira species could not be established in our study . Both Leptospira species identified in this study are known to persist in urban rat populations , as demonstrated for samples from Malaysia [58] . Both species were detected in mice , in similar percentages in four of the five sampling locations in Puerto Rico , while samples from the fifth location , San Sebastián , almost exclusively harbored L . interrogans . Since there were no apparent landscape features ecologically isolating this location from the other study locations , it is interesting that there was an absence of L . borgpetersenii in mice . Low abundance and small sampling size affecting detection of L . borgpetersenii is not a likely explanation , since sampling size was similar to those of the other locations and L . borgpetersenii was identified in the only rat sample analyzed from this location . In some farms , such as those is Barbados , agricultural workers were identified as having a high risk of contracting leptospirosis due to their proximity to contaminated water and soil [59] , and in an urban slum in Brazil lower elevations were related to higher Leptospira concentrations [55] . Furthermore , previous studies have associated the persistence of Leptospira with moist environments [55 , 60] . Naguabo was the only location in this study for which several landscape feature-Leptospira prevalence relationships were found . These relationships are , in part , possibly due to the location of the farm , i . e . a valley in a mountainous area in close proximity to the El Yunque National Rainforest which experiences an average rainfall of approx . 2 , 134 mm which is much higher than in all of the other sampling sites . Heavy rainfall results in runoff that might carry bacteria to and concentrate them at structures located in areas with the lowest elevation of this farm and in a nearby stream . Here , the moist environments would provide suitable conditions for some species of Leptospira to persist for longer lengths of time; therefore , rodents would have a greater opportunity to come into contact with the pathogens . Ponds , milking areas , and human dwellings are also desirable rodent habitats due to providing easier access to resources , so rodents are more likely to persist in higher abundances in close proximity to these areas . The relationship between landscape features and the distance of positive and negative samples were inconsistent between the five farms included in this study . While it is generally thought that Leptospira is associated with the presence of environmental features such water bodies , data presented in this study do not support this hypothesis . This inconsistency could be due to interspecific interactions among animal reservoirs having a greater effect on Leptospira prevalence than the environmental features themselves . Ansersen-Ranberg et al . ( 2016 ) found that although some groups of animal reservoirs had similar prevalence values , these were inconsistently correlated to environmental factors [61] . Additionally , there is some indication that sociality in reservoir species or human effects on landscape has the potential to create hot spots for Leptospira presence . While not statistically significant , the spatial cluster of positive samples detected in the Lajas farm ( Fig 2 ) held some ecological and epidemiological significance because it coincided spatially with an area where discarded farm materials ( cut tree branches , tires , metal pieces , etc . ) had been deposited as a pile in the middle of an open field . This created a habitat suitable for mice where individuals , both positive and negative , congregated ( Fig 2 ) likely raising pathogen transmission among those individuals . As a precaution , farmers were informed that waste management around farm buildings to prevent rodent infestations should be a high priority to avoid concentration of Leptospira positive individuals . Since it is likely that the small sample size and uneven coverage of the farm influenced the significance of the cluster analysis , future research should aim to increase samples size and sample coverage at the individual farms to better identify potential disease hotspots . Another issue that needs to be addressed is the dynamics of interspecific transmission and pathogen maintenance at each farm and among farms in a landscape . The contrast between usual spatial movements and Leptospira prevalence between mongooses and mice shows a clear opposite pattern ( Fig 3 ) in which mice usually travel short distances but show greater pathogen prevalence whereas the opposite is true for mongooses . Our study found a higher prevalence of Leptospira in mice thus pointing out a potential important role for this species to maintain the pathogen at a given site . However , this rodent species is usually found in close association to human dwellings and likely will not be a relevant factor to spread the pathogen to other sites . In contrast , mongooses readily travel longer distances and would be capable of spreading Leptospira among adjoining farms and further into areas of high human use , a behavior reported for other Herpestidae species that are also reservoirs for this pathogen [50] . Since small Indian mongooses readily prey on commensal rodents a high opportunity of contact between these species exist so the need to evaluate this as a potential route for interspecies transmission and its role in maintaining the pathogen in Puerto Rico deserves further evaluation . Future studies that investigate this possible relationship should also consider other factors , such as host carriage rate , host urine extraction rates , and pathogen life history traits to gain a clear picture of how much host behaviors potentially affect prevalence between farms . According to news sources , after hurricane Maria landed in September of 2017 this event increased the number of leptospirosis infections in humans across Puerto Rico . This was likely because people were obligated to drink contaminated water as a result of failed infrastructure . As many as 76 individuals were likely infected and a small handful of these were fatal cases [62] . As climatic events such as this increase in intensity and frequency as a result of climate change , it is becoming increasingly important to monitor the epidemiological consequences on pathogenic agents of neglected infectious diseases . This is especially true in tropical areas such as Puerto Rico because these are the most severely affected by intense climatic events such as hurricanes and monsoons , as was illustrated with Hurricane Maria . This study demonstrates the need for leptospirosis monitoring programs to be implemented in rural areas of Puerto Rico along with urban areas . In conclusion , this study established baseline data on the prevalence of Leptospira species in four animal species in five rural areas , at both the east and west coasts of Puerto Rico . The capture of large numbers of rodents and mongooses with high prevalence of Leptospira in animals from all locations supports suggestions for the implementation of management plans for rodent and mongoose control to reduce the risk of susceptible focal species ( i . e . humans and cattle ) to contract Leptospira . These management plans could focus on all or selected animal species depending on their abundance at the respective location , and should include monitoring prevalence of Leptospira in cattle and adjacent soils and waters to assess environmental risks of infection in rural areas of Puerto Rico [54 , 55 , 63–67] .
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Leptospirosis is a global zoonotic disease that recently has shown an increase of human cases in many regions , Puerto Rico being one of them . To decrease human Leptospira infections it is necessary to ascertain the role of animal reservoirs to maintain the pathogen in the environment . However , no studies of wild mammal reservoirs in Puerto Rico have been done since 1963 . We addressed the prevalence of Leptospira in four species of introduced wild mammals , which included three commensal rodent species ( Mus and Rattus spp . ) and the small Indian mongoose ( Herpestes auropunctatus ) on dairy farms in Puerto Rico . Pathogen prevalence ranged from high to moderate in mice and mongooses , respectively . We also found that the pathogen is not distributed homogeneously among the sites we sampled and that landscape features , both natural and manmade , can play a role in the distribution of Leptospira . Our study provides an initial exploration of wild mammal reservoirs for Leptospira in Puerto Rico , and our data highlight the need for management of these species to potentially decrease pathogen transmission rates in both humans and cattle .
|
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2019
|
The prevalence of Leptospira among invasive small mammals on Puerto Rican cattle farms
|
Numerous psychophysical experiments found that humans preferably rely on a narrow band of spatial frequencies for recognition of face identity . A recently conducted theoretical study by the author suggests that this frequency preference reflects an adaptation of the brain's face processing machinery to this specific stimulus class ( i . e . , faces ) . The purpose of the present study is to examine this property in greater detail and to specifically elucidate the implication of internal face features ( i . e . , eyes , mouth , and nose ) . To this end , I parameterized Gabor filters to match the spatial receptive field of contrast sensitive neurons in the primary visual cortex ( simple and complex cells ) . Filter responses to a large number of face images were computed , aligned for internal face features , and response-equalized ( “whitened” ) . The results demonstrate that the frequency preference is caused by internal face features . Thus , the psychophysically observed human frequency bias for face processing seems to be specifically caused by the intrinsic spatial frequency content of internal face features .
In the brain , the structure of neuronal circuits for processing sensory information matches the statistical properties of the sensory signals [1] . Taking advantage of these statistical regularities contributes to an “optimal” encoding of sensory signals in neuronal responses , in the sense that the code conveys the highest information with respect to specific constraints [2]–[6] . Among the various constraints which were formulated we find , for example , keeping metabolic energy consumption as low as possible [7]–[9] , or keeping total wiring length between processing units at a minimum [10] , or maximizing the suppression of spatio-temporal redundancy in the input signal [2] , [11]–[14] . As for visual stimuli , natural images reveal ( on the average ) a conspicuous statistical regularity that comes as an approximately linear decrease of their ( logarithmically scaled ) amplitude spectra as a function of ( log ) spatial frequency [15]–[17] . This means that pairs of luminance values are strongly correlated [18] , and this property could be exploited for gain controlling of visual neurons . Then , visual neurons would have equal sensitivities or response amplitudes independent of their spatial frequency preference [16] . According to this response equalization hypothesis , gain should thus be incremented with increasing spatial frequency , such that the distribution of response amplitudes of frequency-tuned neurons to a typical natural image is flat . An argument in favor of employing response equalization ( “whitening” ) is that it would lead to an improvement of information transmission from one neuronal stage to another , because the output of one stage would match the limited dynamic range of a second one [19] . The present article builds upon previously reported results for whitened amplitude spectra of face images [20]: the whitened spectra reveal a spatial frequency maximum at 10–15 cycles per face , but only if external face features ( such has hair ) are suppressed . The predicted frequency maximum nevertheless agrees well with numerous psychophysical experiments , which found that face identity is preferably processed in a narrow band ( bandwidth ≈2 octaves ) of spatial frequencies from 8 to 16 cycles per face [21]–[29] . Despite of it all , the results presented in [20] indicate that the maxima in the amplitude spectra are caused by the compound effect of horizontally oriented internal face features ( eyes , mouth & nose ) . Quantitatively , the maxima thus occur in units of “cycles per face height” , whereas most psychophysical studies instead measure their results in terms of “cycles per face width” . Furthermore , although a clear enhancement of horizontal amplitudes could be observed in the spectra , horizontal amplitudes showed a somewhat “noisy” dependence on spatial frequency . Both effects are a consequence of that face features were not considered individually , what causes a mixing of the spatial frequency content of individual face features in the spectra . The mixing leads to averaging-out effects such that any possible enhancement of spectral amplitudes at other than the horizontal orientation goes unnoticed , but also may cause interference effects which lead to the mentioned noisy dependence of amplitudes on spatial frequency . The present study addresses the two issues by means of an extensive analysis of face images by means of Gabor filters . The filters were thereby parameterized ( according to [30] ) to match the spatial receptive field of band-limited , oriented and contrast sensitive neurons in the primary visual cortex [31]–[34] . ( These cortical neurons are referred to as simple and complex cells , cf . [35]–[37] ) . Great care has been taken to guarantee the correct alignment of filter responses with respect to the position of internal face features ( left eye , right eye , nose and mouth ) prior to their averaging . Doing so permits to precisely elucidate how the frequency dependence of Gabor responses ( and specifically the predicted frequency maxima ) is related to each of the four internal face features . The resulting graphs of whitened Gabor amplitudes versus spatial frequency are smooth and reveal distinct maxima at nearly all orientations . The most stable maxima , however , are observed at horizontal feature orientations in the first place , but also at vertical orientations . This observation holds true for all of the internal face features ( even for the nose ) . The present study therefore shows how the individual internal face features contribute to the psychophysically observed frequency preference , and proposes concrete mechanisms of how higher amplitudes of whitened cell responses at an early level could possibly lead to the psychophysically measured effects .
For the present study , 868 female face images and 868 male face images were used ( Face Recognition Grand Challenge database FRGC , http://www . frvt . org/FRGC or www . bee-biometrics . org ) [38] . Original images ( 1704×2272 pixels , 24-bit true color ) were adjusted for horizontal alignment of eyes , before they were down-sampled to 256×256 pixels and converted into 8-bit grey-scale . Positions of left eye , right eye , and mouth [ , respectively] were manually marked by two persons ( M . S . K . and E . C . ) with an ad hoc programmed graphical interface . The face center position ( ≈nose ) was approximated as and , where denotes rounding to the nearest integer value . Due to copyright issues it was not possible to include original sample images from the FRGC database in this paper . The persons that are shown in Figures 1 , 2 , and 3 are surrogate images that were taken in the style of the database images . The depicted individuals gave their expressive permission to publish their photographs . Sample images from the FRGC database are shown in Figure 3 of [39] , and in the supplementary material of [20] . For conversion of spatial frequency units , face dimensions were manually marked with an ad hoc programmed graphical interface . The factors for multiplying “cycles per image” to obtain “cycles per face width” were 0 . 41±0 . 013 ( females , ) and 0 . 43±0 . 012 ( males , ) . Corresponding factors for obtaining “cycles per face height” were 0 . 46±0 . 021 ( females ) and 0 . 47±0 . 018 ( males ) . Conversion factors oblique orientations were calculated under the assumption that horizontal and vertical conversion factors define two main axis of an ellipse . Pooling of results over gender implied also a corresponding averaging of conversion factors . The amplitude spectra of face images fall approximately linear as a function of frequency when both variables are scaled logarithmically [20] . Each amplitude spectrum was subdivided into 12 pie slices ( ) for computation of oriented spectral slopes ( Figure 1 ) . A straight line with slope was fitted within the spatial frequency range from to cycles per image to each pie with orientation . We used the function “robustfit” ( linear regression with less sensitivity to outliers ) provided with Matlab's statistical toolbox ( Matlab version 7 . 1 . 0 . 183 R14 SP3 , Statistical Toolbox version 5 . 1 , see www . mathworks . com ) . In total , four amplitude spectra were considered ( see Figure 1 & [20] for further details ) . A 2-D-Gabor wavelet transform was used as a simplified model of V1 visual processing [16] , [32] , [34] , [40]–[42] . Let denote the spatial frequency bandwidth in octaves and . Let , where denotes spatial frequency in units of cycles per image . Let be the phase shift of each of the components of the pair of Gabor filters ( the phase shift is not a relative phase shift: choosing makes both the even and the odd Gabor wavelet shift by , and does not affect their relative phase , i . e . they maintain their quadrature relationship ) . Let be an rotation angle in units of degrees . Then , in Fourier space , a constrained Gabor wavelet with spatial frequency and orientation is defined as ( 1 ) ( 2 ) ( Convention: means that the wave vector points to the east , cf . Figure 1; are frequency coordinates ) . Real and complex Gabor wavelets were parameterized to fit the receptive field data of even and odd simple cells , respectively [30] ( spatial frequency bandwidth octaves [41] , [43] , orientation bandwidth 30 degrees [44] , [45] , aspect ratio 1 . 5 [41] , [43] , [46] , and without loss of generality ) . Notice that here is constant ( such that wavelets are self-similar with scale ) whereas neuronal bandwidths generally decrease with the logarithm of . Gabor wavelets integrated to zero ( admissibility constraint ) . Simple cell responses were taken as the rectified amplitudes of Gabor wavelets ( positive even , negative even , pos . odd , neg . odd ) . Complex cell responses were computed with the contrast energy [16] or local energy [47] , [48] model . Convolutions were performed in the Fourier space . We considered wavelet responses at spatial frequencies from to cycles per image , with increments cycles per image . With this value of , the maximum amplitude of the impulse response function was about two orders of magnitude higher than the spurious high frequency ripples that resulted as a consequence of filter truncation . In order to make the evaluation of results tractable , each ( average ) feature map was represented by a single scalar value ( “compacted” ) , called feature map amplitude . This value is usually the spatial average . Spatial averaging could either take place over all feature map positions , or over feature-map-specific regions of interest as depicted in Figure 4 ( “ROIs” ) . The overall predictions with respect to whitened feature map amplitudes remain similar if feature maps were compacted differently , for example by taking the maximum value , or by computing the average of only those values which exceed a given threshold value .
Because the analysis is intricate at first sight , this section summarizes the main concepts and terms . The analysis takes the following steps . First , slopes of amplitude spectra are computed ( = spectral slopes ) . To this end four different types of amplitude spectra were considered , giving rise to four respective sets of slope values ( summarized in Figure 1 , see methods section ) . ( A set of slope values contains the spectral slopes computed at different orientations ) . Second , each face image is projected on Gabor filters at different scales and orientations ( Figure 2 ) . Each projection results in a new “image” that is composed of a filter's response at the corresponding position of the face image . This filtered image defines a response map at a certain spatial frequency and orientation . Five different types of response maps are distinguished: two with even symmetry , two with odd symmetry , and one combination involving both symmetries ( more details are given below ) . Third , response maps are aligned according to the position of internal face features ( left eye , right eye , mouth or nose – see Figure 3 ) and subsequently averaged . The averaged response maps are called feature maps ( Figure 5 ) . Each feature map is parameterized by . Fourth , the feature maps are response equalized ( “whitened” ) by using the spectral slopes at corresponding orientations . Fifth , to facilitate the analysis ( 18720 feature maps with 127×127 values each ) , each whitened feature map is compacted such that it is represented by a single scalar value ( = feature map amplitude ) . Compacting is carried out by computing the spatial average across the entire map ( full compacting ) , or just over a small region around a feature of interest ( ROI-compacting ) . The regions of interest ( “ROIs” ) are shown in Figure 4 . Oriented spectral slopes from the amplitude spectra were used to adjust the response gain ( = whitening ) of Gabor filters [49] . The symbols in Figure 6 indicate the four sets of . Gabor filters were parameterized such that they matched the spatial receptive fields of simple and complex cells in the primary visual cortex ( see methods section ) . Cell responses ( “response maps” ) to a face image ( size 256×256 pixels ) were simulated by projecting the image onto a wavelet with spatial frequency and orientation , that is ( convolutions were carried out in the Fourier domain , see equation 1 in the methods section ) . Response maps are complex-valued images with the same size as the face images . Cell types were distinguished by five corresponding response map types . Specifically , simple cell responses were taken as the rectified amplitudes of Gabor wavelets ( are omitted ) : positive even ( with Re[ . ] denoting the real part ) and negative even . Positive and negative odd responses , respectively , are defined analogously as the imaginary part . Complex cell responses were computed with the local energy model [16] , [48]: . To compute average cell responses over face images , each response map was centered in turn at the positions of the left eye , right eye , mouth and nose ( internal face features , Figure 3 ) , symmetrically cropped , and then summed separately for each of the four features . In this way , four types of so-called feature maps ( size 127×127 pixels ) were obtained for each of the five response maps , with 39 spatial frequencies cycles per image , and at 12 orientations degrees ( Figures 2 and 5 ) . Now , to test whether the response equalization hypothesis could account for face perception data , feature maps were whitened by multiplying each position with , that is ( with ) [50] . All in all we are left with four feature maps for each gender ( = 2 ) , response type ( = 5 ) , orientation ( = 12 ) , and spatial frequency ( = 39 ) . Each feature map in turn is composed of the responses of 127×127 model cells . This amounts to a data load of 18720×16129 ( feature maps×values ) . To reduce this data load , each whitened feature map was represented by a single scalar value ( = feature map amplitude ) . This representative value was computed by either computing the average of response magnitudes over all 127×127 feature map positions ( “full compacting” ) , or only over a region of interest that contained a single internal face feature ( “ROI compacting” , Figure 4 ) . A response distribution ( or response curve ) is then defined by considering feature map amplitudes as a function of at some orientation . If , as a result of whitening , response distributions were completely flat , we would not have gained any new insight . Therefore , we expect that the response distributions reveal residual structures as a function of ( ideally unimodal ) , which could be linked to face perception data . Figure 7A ( and corresponding Figures S1 , S2 , S3 , S4 ) shows response distributions at different orientations for full compacting . Response distributions ( “curves” ) for different response types and gender were pooled together for compiling these figures . The curves are not flat , but all have maxima ( valid maxima are indicated by encircled black crosshairs ) . The average spatial frequency ( ±s . d . ) of the valid maxima in Figure 7A is 6 . 54±3 cycles per face ( orientations ) . Observe that the maxima of response distributions at horizontal feature orientations ( 90° and 270° , turquoise curves ) are always situated at ≈10 cycles per face , irrespective of feature type . Specifically , the “horizontal” curves vary by far less than the others as a function of ROI . Furthermore , curves at horizontal and nearby oblique orientations ( ±30° ) also reveal the most pronounced deviation from a flat response distribution . Notice that horizontally oriented Gabor filters match the orientations of eyes , mouth and the nose bottom . Upon introducing a ROI , the response curves at horizontal feature orientations are shifted upwards relative to the curves at remaining orientations . This effect is particularly striking when comparing response distributions at horizontal and vertical orientations , where “horizontal” curves are getting enhanced relative to “vertical” curves with “ROI = on” . Often , curves that coincide with feature orientations revealed also clearer maxima in the sense that the maxima were lifted with respect to smaller values ( Figure 7B & corresponding panels in Figures S1 , S2 , S3 , S4 ) . In contrast , curves at oblique orientations ( e . g . , 150° ) sometimes get flatter and/or reveal multi-modal distributions . Especially interesting in this context is to consider the response distributions for the nose ( Figure S4 ) : Here , the up-shifting of the “horizontal” curve relative to the “vertical” one is the smallest ( compared to the rest of features ) , and the “vertical” curve is showing a more pronounced maximum then . A consistent interpretation of this behavior is that the nose has of course an important vertical orientation component ( the bridge of the nose ) , whereas with eyes and mouth vertical orientations are less important . Nevertheless , as with the other features , also the nose has its most “important” orientation component situated horizontally ( the bottom termination ) . Furthermore , the spatial frequency maximum of the bridge of the nose is smaller than the maximum of the “horizontal” curve . The standard deviations of the pooled data were computed from three components: ( i ) averaging the aligned response maps to compute feature maps , ( ii ) compacting the feature maps to obtain feature map amplitudes , and finally ( iii ) pooling feature map amplitudes . High standard deviations are produced ( i ) because of the variation between individual face images , and ( ii ) because Gabor wavelets produce responses to face images with only a few wavelets generating relatively high responses ( sparse responses: [16] ) . Standard deviations always decreased upon using a ROI for two reasons . First , secondary features that appear beside of the feature of interest in the center are cropped ( cf . insets in Figure 7 ) , and the variation around the aligned features is smallest between face images . Second , high Gabor wavelet responses occur mainly to the feature of interest . As a consequence , peak feature map amplitudes with ROI are bigger than without , because the relative amount of small-valued Gabor responses is smaller within a ROI . Here , the behavior of the spatial frequency maxima of the response distributions ( = valid maxima ) is summarized . Upon introducing a ROI , the great majority of the maxima shifted to higher spatial frequencies ( e . g . , Figure 7A: from 6 . 54±3 cycles per face without ROI to 8 . 97±3 with ROI ) . As already mentioned , most of the maxima which did not shift at all were those at horizontal orientations . Valid maxima of response distributions are summarized in Figure 8 and Figures S5 , S6 , S7 , S8 , respectively , with juxtapose data for “ROI = off” and “ROI = on” . The up-shifting-effect of spatial frequency maxima can be clearly seen in these figures , with valid maxima associated with ROI-compacting being situated at around 10 cycles per face . The results discussed so far were obtained with the mean spectral slopes . In order to probe the robustness of the predicted spatial frequency maxima , a further set of slope values were considered for whitening , that is the median of individual slope values ( remember: one slope value per face image ) . Whitening with led to similar predictions for the spatial frequencies of the maxima at virtually all orientations ( see corresponding colors in Figure 8 and Figures S5 , S6 , S7 ) . For a subset of all response distributions it was possible to estimate spatial frequency bandwidths ( Figure 9 ) : “ROI = off” had a greater variation of bandwidths than “ROI = on” . With “ROI = off” , most of the bandwidth estimates lie between 1 and 2 octaves . With “ROI = on” , bandwidth showed a tendency to increase on the average , with the majority of the bandwidth estimates lying in the range from 1 . 6 to 2 . 4 octaves . These estimated bandwidths are in good agreement with the psychophysically predicted bandwidths for face processing . As the function for whitening was parameterized with slopes computed from the the different types of amplitude spectra , I asked whether these slopes indeed produced the “most whitened” response distributions . Accordingly , another set of slope values was computed as follows . Feature maps were compacted without previous whitening . Whitening rather was iteratively performed through gain adjustment of feature map amplitudes – by multiplication with . For each from −2 . 5 to −1 , the degree of whitening was quantified in steps of 0 . 01 by computing the Shannon entropy [51] of the whitened response distributions . Maximally white response distributions are associated with a maximum in entropy at ( maximum entropy slope ) . Figure 6 juxtaposes averaged maximum entropy slopes ( samples for each ) with averaged amplitude spectrum slopes ( ) . Averaging took place over all parameters but orientation . Maximum entropy slopes achieve the best agreement with the corr . B . H . -slopes , both when averaging over orientations ( ) , and when evaluating statistical significance at each orientation separately ( filled symbols indicate ) . In comparison with the B . H . data , slopes from the raw spectra have the worst agreement with the maximum entropy slopes . This discrepancy is ascribable to external face features: Slopes were computed individually for each face image , and external face features like the hairline could influence individual slopes directly in the raw and corr . raw spectra . By contrast , in the feature maps the external features are averaged out and partially cropped ( fully cropped with ROI ) . The mismatch between using slopes ( raw & corr . raw ) with external face features being present in order to whiten feature maps that are nearly devoid of the external features causes corresponding response distributions to be not “optimally” white .
Here , I studied whitened and averaged responses of Gabor filters to large number of face images ( whitening refers to response equalization ) . Gabor filters were parameterized as to match spatial receptive field properties of simple and complex cells in the cortex ( see methods ) , and averaging was feature-specific ( Figure 3 ) . The results obtained here extend the predictions of a previously conducted analysis ( ref . [20] ) of averaged and whitened amplitude spectra in three important ways . ( i ) The use of Gabor wavelets permitted the examination of the orientation dependence of spatial frequency predictions , whereas in the previous study only an amplitude enhancement at horizontal orientations was revealed . ( ii ) Averaging of Gabor response maps was done according to features ( yielding corresponding feature maps ) , whereas the spatial frequency content of internal face features was mixed in the previous study ( “mixing” occurs because Fourier spectra do not retain absolute spatial information explicitly ) . Mixing caused interference effects and averaging-out of any amplitude enhancement at others than the horizontal orientation . ( iii ) The previous study showed a somewhat noisy dependence of the spatial frequency versus amplitude curve , due to mixing effects . The response amplitude curves shown here are in contrast very smooth . For the whitening procedure , the slopes of four different types of amplitude spectra were considered ( Figure 1 ) , in order to probe robustness of predictions . The slopes obtained from the corrected-Blackman-Harris-window spectrum ( corr . B . H . ) were thereby the closest to a flat response distribution in the sense that they best maximized Shannon entropy ( cf . maximum entropy slopes , Figure 6 ) . As a consequence of whitening , most response distributions ( = compacted feature maps ) were not flat or “white” ( Figure 7 ) , but revealed unimodal distributions irrespective of their orientation , with maxima centered at around 8–12 cycles per face when compacted with a feature-specific region of interest ( ROI ) , and somewhat lower without it ( ≈4–10 cycles per face , Figure 8 ) . Responses at horizontal feature orientations were scarcely affected by employing a ROI: their maxima did not shift significantly , and curve shape did not alter either ( Figure 7 , turquoise curve ) . This behavior stands in contrast to response distributions at oblique feature orientations , which showed the strongest changes . Estimated bandwidths of the response distributions were about 1 . 6 to 2 octaves with ROI . Somewhat smaller bandwidth estimates were obtained without ROI ( Figure 9 ) . Feature maps ( Figure 2 ) were obtained by properly centering Gabor response maps at feature positions prior to averaging the latter ( Figure 3 ) . In this way external face features ( e . g . , hair ) and uncentered features were averaged out ( since they varied strongly between face images ) , while centered features were kept well focused ( Figure 4 ) . The unfocused features correspond to low spatial frequencies , what generates maxima at lower spatial frequencies than with ROI . The ROI versus no-ROI data therefore demonstrate that higher responses are obtained by filters matching the orientation and spatial frequency of internal face features . The results furthermore suggest an orientation dependence of preferred spatial frequencies , similar to the oblique effect ( e . g . , [52]–[54] , but see [55] ) : Horizontal and vertical oriented features have more ecological “importance” than features at oblique orientations . Several psychophysical studies suggest that recognition of face identity works best in a narrow band ( bandwidth about 2 octaves ) of spatial frequencies from ≈8 to ≈16 cycles per face [21]–[25] , [27] , [56] , [57] . Notice that this does not mean that face recognition exclusively depends on this frequency band , as faces can still be recognized when corresponding frequency information is suppressed [27] , [29] . In addition , it seems that observers can specifically attend to the spatial frequencies that support recognition ( “diagnostic spatial frequencies” ) , and that the allocation of attended frequencies can be altered in a task-specific fashion [58] , [59] . Hence , observers could intentionally attend to other than the preferred spatial frequencies if the latter frequencies are not available , but the non-preferred frequencies may be associated with a reduced signal-to-noise ratio ( e . g . , in terms of class separation [39] ) and/or may imply a corresponding increase in time for completing a successful face recognition [29] . The preferred spatial frequencies for face recognition are not significantly affected by the structure of the background on which a face does appear [60] , so the results presented here are unlikely to be specific for the considered set of face images . How can higher response amplitudes be linked to an enhanced perceptual sensitivity for face identification ? The proposed whitening mechanism implies that neural populations which encode a natural scene at an instant in time adapt in order to match the statistics of the input such as to similar sensitivities are established for neurons with different spatial frequency selectivity ( response equalization ) . A flat or white distribution of responses is also compatible with the notion of sparse coding . For face images , we saw that a completely flat distribution could not be obtained ( at least with the proposed mechanism ) , and that the flattest possible distributions rather were unimodal ( in most cases ) . As we could readily interpret the distribution as being proportional to the underlying probability distribution , the brain could increase processing speed for face recognition if it “looked” first at those spatial frequencies which occur more often . If these frequencies are removed ( as it happened in some of the mentioned psychophysical experiments ) , then the brain has to actively examine other spatial frequencies to complete a successful recognition , what would yield to an increase in recognition time . A corresponding increase in recognition time has indeed been observed experimentally [29] . Also from an biophysical point of view , the whitened response distributions could translate into a decreased processing time . In the response-equalized population of neurons , higher response amplitudes ( which occur at around 10 cycles per face ) are associated with shorter response latencies . Or , more specifically , if we assume that whitening changes synaptic efficiency , then neurons tuned to 10 cycles per face will reach spiking threshold faster because they are driven by higher post-synaptic currents , and thus corresponding information could in principle arrive earlier at successive face recognition stages . The critical retinal illumination is the transition luminance between deVries-Rose [61] , [62] and Weber's law , describing the increasing and the saturating part , respectively , of the human contrast sensitivity function . ( The transition luminance is described by the van Nes-Bouman law [63] ) . Interestingly , this critical retinal illumination was found to vary with for foveally viewed cosine gratings [64] . This result permits to derive an explicit expression for the neural modulation transfer function ( MTF ) of the visual pathway [65] , with a linear dependence of the MTF on . So , could whitening of face images be conveyed by the neural modulation transfer function ? Amplitude spectra of natural images vary approximately with , where [15]–[17] , but for our face images ( Figure 6; [20] ) . Thus , the MTF could in principle carry out a pre-whitening of spatial frequency channels , leaving some residual whitening to the specific neural systems for face processing ( according to ) . Notice , however , that whitening with produces a smaller number of valid spatial frequency maxima in the response distribution curves ( without ROI: 8% , with ROI: 71% ) , and these maxima underestimate the psychophysically found frequencies ( without ROI≈3 , with ROI≈4 cycles per face ) . What about other stimulus classes ? A comparison can be readily drawn between the perception of letters and faces . Letter identification has been found to be sensitive to spatial frequencies of about 3 cycles per letter height , e . g . , [66]–[69] . Similar to the present study and ref . [20] , Põder performed an analysis of letter power spectra ( i . e . , the squared amplitude spectra; [70] ) . He subdivided power spectra into annuli that were one octave wide , and then integrated power across each annulus . This procedure yielded an energy maximum at 2–3 cycles per letter , consistent with psychophysical results and an interpretation of the maximum in terms of letter stroke frequency [71] . Faces and letters are examples of relatively “constrained” objects: Characters printed on a paper are two-dimensional objects which do not reveal additional information when the paper is rotated in three dimensional space . Similarly , we usually see upright faces in our visual field , and face recognition performance decreases significantly with inverted faces [72] , [73] . It seems that this drop in recognition performance is associated with corresponding changes in face processing strategies . In brief , upright faces seem to undergo an increasingly holistic or configural processing in the brain ( i . e . , in terms of relationships between internal face features or face parts , respectively ) , as opposed to inverted faces , e . g . , [74]–[79] . It has been proposed that inverted faces are processed in a similar way as arbitrary objects ( but see , e . g . , [80] or [81] for a discussion ) . Indeed , there is evidence for part-processing at early stages for face processing ( e . g . , [80] , [82] with references ) , and it appears that the familiarity with a face modulates the degree to which configural processing is evoked over part-based processing ( [83] , [84] including references ) . The findings of the present study relate best to early face processing , and specifically to part-based processing ( ROI versus no-ROI ) . In this context , it is interesting that the N170 or M170 response ( an early face-selective response which is observed in electro- or magnetoencephalography data , respectively ) can be evoked by the presence of isolated internal face features , especially the eyes [85] , [86] . This result is consistent with the present data , where all internal face features induced distinct spatial frequency maxima . Further evidence supports the notion that the eye region is especially important for face identification [87] , and that subjects use the same spatial frequencies for identifying upright and inverted faces [57] . The latter result can be interpreted such that the frequency preference for face recognition indeed reflects properties of early and part-based face processing . Different spatial frequency bands were nonetheless found to support part-based and configural face processing , respectively ( [88] - but see [81] ) . For instance , matching performance with configural changes was found to be superior for low-pass filtered faces [89] ( cut off ≈8 cycles per face width ) , whereas for detecting differences between internal face features , high-pass filtered faces ( >32 cycles per face width ) seem to give a better performance . The results here bear some loose similarity with this notion in two ways . First , the ROI versus no-ROI data revealed that feature-specific results with “ROI = on” yielded slightly higher spatial frequency predictions than the whole-face condition “ROI = off” . However , as discussed above , this frequency shift is a consequence of averaging feature-map amplitudes within a region around a feature of interest ( “ROI = on” ) , versus averaging of feature map amplitudes unspecifically ( “ROI = off” ) . The unspecific averaging includes both the feature of interest ( well focused ) , and secondary features and external face features , which appear unfocused or blurry ( Figure 4 ) , thus introducing low spatial frequency content which , upon averaging feature map amplitudes ( “compacting” ) , causes the observed frequency shift . Second , predicted spatial frequencies were higher at horizontal ( 90° , 270° ) than at vertical orientations ( 0° , 180° ) , and predicted spatial frequencies increased relatively more upon applying a ROI at vertical orientations . ( In contrast , horizontal Gabor filters match the orientations of internal face features , and consequently a ROI has only a smaller effect; oblique orientations reveal compound effects ) . The response distribution curves for vertical orientations ( Figure 7 ) show similar magnitudes for “ROI = on” and “ROI = off” . Therefore , vertically oriented Gabor filters do not only pick up spatial frequency content of internal face features , but also an important part from the rest of the face . This suggests that vertical spatial frequency content may be better suitable for processing configural parts of the face , for example for measuring inter-ocular distance . Because the predicted frequencies at vertical orientations are lower than at horizontal orientations ( both for “ROI = on” and “ROI = off” ) , this orientational effect resembles the aforementioned psychophysical findings which reported that part-based processing is supported by higher spatial frequencies than holistic processing . How general are the results of the present study ? Here it has been shown that the preferred spatial frequency band for human face recognition originates from internal face features , and that each of the internal features in isolation induces the same frequency preference . My result of course is rather invariant to inversion: the predicted spatial frequencies would not change if the study would have been conducted with a database of inverted faces . As aforesaid , a corresponding invariance has also been found by a recent psychophysical experiment: humans use the same spatial frequencies for recognition of upright and inverted faces [57] . What about horizontal head turning ? Assume a moderate head turning such that internal face features remain visible . Then , a differential effect would occur for horizontally ( 90° ) and vertically ( 0° ) oriented spatial frequencies . Horizontal spatial frequency predictions can be expected to remain approximately constant , although response distribution curves may appear noisier . Vertical and oblique spatial frequency predictions , however , can be expected to reveal a stronger variation ( this variation is suggested by comparing the ROI versus non-ROI data of the fronto-parallel case ) . Also , the magnitude and type of variation ( for all orientations ) may depend on the specific feature ( eye , mouth , or nose ) , and the degree of head turning . Recently , we were able to show that an enhanced class discrimination for face images is obtained at similar spatial frequencies which humans preferably use for face recognition [39] . This suggests that also artificial face recognition systems could exploit the spatial frequency dependency of face recognition in order to increase efficiency , either in terms of speed , accuracy , or memory economy . And it also suggests that humans may use this special range of spatial frequencies because it is best suited for distinguishing between different individuals .
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Imagine a photograph showing your friend's face . Although you might think that every single detail in his face matters for recognizing him , numerous experiments have shown that the brain prefers a rather coarse resolution instead . This means that a small rectangular photograph of about 30 to 40 pixels in width ( showing only the face from left ear to right ear ) is optimal . But why ? To answer this question , I analyzed a large number of male and female face images . ( The analysis was designed to mimic the way that the brain presumably processes them . ) The analysis was carried out separately for each of the internal face features ( left eye , right eye , mouth , and nose ) , which permits us to identify the responsible feature ( s ) for setting the resolution level , and it turns out that the eyes and the mouth are responsible for setting it . Thus , looking at eyes and mouth at the mentioned coarse resolution gives the most reliable signals for face recognition , and the brain has built-in knowledge about that . Although a preferred resolution level for face recognition has been observed for a long time in numerous experiments , this study offers , for the first time , a plausible explanation .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"computer",
"science/natural",
"and",
"synthetic",
"vision",
"computational",
"biology/computational",
"neuroscience",
"computer",
"science/numerical",
"analysis",
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"theoretical",
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"neuroscience/natural",
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2009
|
“I Look in Your Eyes, Honey”: Internal Face
Features Induce Spatial Frequency Preference for Human Face Processing
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Most filarial parasite species contain Wolbachia , obligatory bacterial endosymbionts that are crucial for filarial development and reproduction . They are targets for alternative chemotherapy , but their role in the biology of filarial nematodes is not well understood . Light microscopy provides important information on morphology , localization and potential function of these bacteria . Surprisingly , immunohistology and in situ hybridization techniques have not been widely used to monitor Wolbachia distribution during the filarial life cycle . A monoclonal antibody directed against Wolbachia surface protein and in situ hybridization targeting Wolbachia 16S rRNA were used to monitor Wolbachia during the life cycle of B . malayi . In microfilariae and vector stage larvae only a few cells contain Wolbachia . In contrast , large numbers of Wolbachia were detected in the lateral chords of L4 larvae , but no endobacteria were detected in the genital primordium . In young adult worms ( 5 weeks p . i . ) , a massive expansion of Wolbachia was observed in the lateral chords adjacent to ovaries or testis , but no endobacteria were detected in the growth zone of the ovaries , uterus , the growth zone of the testis or the vas deferens . Confocal laser scanning and transmission electron microscopy showed that numerous Wolbachia are aligned towards the developing ovaries and single endobacteria were detected in the germline . In inseminated females ( 8 weeks p . i . ) Wolbachia were observed in the ovaries , embryos and in decreasing numbers in the lateral chords . In young males Wolbachia were found in distinct zones of the testis and in large numbers in the lateral chords in the vicinity of testicular tissue but never in mature spermatids or spermatozoa . Immunohistology and in situ hybridization show distinct tissue and stage specific distribution patterns for Wolbachia in B . malayi . Extensive multiplication of Wolbachia occurs in the lateral chords of L4 and young adults adjacent to germline cells .
Filarial parasites infect more than 150 million people in tropical and subtropical countries and are responsible for important tropical diseases such as lymphatic filariasis ( elephantiasis ) and onchocerciasis ( river blindness ) . Other filarial species are important veterinary pathogens ( e . g . Dirofilaria immitis , the dog heartworm ) . Treatment of filarial infections in humans and animals is suboptimal , because available drugs do not efficiently kill adult worms . Most filarial species live in obligatory symbiosis with intracellular Wolbachia α-proteobacteria . Wolbachia are also present in many insect species , and they are among the most widely distributed bacteria that infect invertebrates . Wolbachia endosymbionts are necessary for development and reproduction of filarial nematodes , and they have been validated as a target for chemotherapy [1] . Tetracycline class antibiotics are active against Wolbachia , and depletion of endobacteria blocks reproduction and eventually kills adult worms in some filarial species [2] , [3] . While Wolbachia DNA can be detected and quantified by PCR , microscopy provides important information on morphology and localization of bacteria in parasite tissues . Immunohistochemistry has been used for years to visualize Wolbachia in filarial worms , particularly in Onchocerca volvulus [2] . Brugia malayi is the only human filarial parasite that can be maintained in laboratory animals and for which all life cycle stages are relatively easily accessible . The population dynamics of Wolbachia during the development of B . malayi has been studied by quantitative PCR; for example , the number of Wolbachia exponentially increases soon after infection of the vertebrate host [4] . Recent studies have shown that Wolbachia are unevenly distributed in intrauterine embryos and that the bacteria are not always detected in germline precursor cells [5] . However , data on the histological distribution of Wolbachia during later development of B . malayi are scarce . While it is known that Wolbachia are present in developing embryos , the mechanism of this vertical transmission is poorly understood . In situ hybridization has been used to study gene expression in filarial parasites such as B . malayi [6] and to detect Wolbachia in insects [7] , [8] , but it has not been used before to detect Wolbachia in filarial worms . In this paper , we have used optimized immunohistology , in situ hybridization , and transmission electron microscopy to systematically describe the distribution , the relative number and morphology of Wolbachia in different life stages and tissues of B . malayi . This work led to an interesting new hypothesis on the localization and migration of Wolbachia during development of filarial worms .
B . malayi worms were recovered from intraperitonial ( i . p . ) infected jirds , 2 , 5 , 8 and 12 wks post infection ( p . i . ) as previously described [9] . Aedes aegypti mosquitoes containing different larval stages of B . malayi were available from a previous study . Parasite material was fixed either in 80% ethanol for immunohistology or in 4% buffered formalin for immunohistology or in situ hybridization . At least five blocks with four or more B . malayi worms each were examined for each time point . An extensive overview about the studied material and the methods performed is provided in a supplementary table ( Table S1 ) . Up to twenty serial sections of the same block were used for comparative studies of different staining procedures . For some blocks ( especially those containing young adult worms ) more than 60 sections ( 5 µm ) were cut , but only a selection of sections was examined . For the ultrastructural analysis , 18 worms ( 39 and 56 days p . i . , Table S1 ) were fixed in 2% paraformaldehyde/2 . 5% glutaraldehyde ( Polysciences Inc . , Warrington , PA , USA ) in 100 mM phosphate buffer , pH 7 . 2 for 1 hr at room temperature . A monoclonal antibody directed against the B . malayi Wolbachia surface protein ( mab Bm WSP ) was purified from culture supernatants kindly provided by Dr . Patrick J . Lammie , Atlanta [10] . Briefly , hybridoma supernatant was incubated overnight at 4°C with ammonium sulfate , pelleted , resuspended in water and dialyzed extensively against phosphate buffered saline . The antibody solution was concentrated to 5% of the original volume using Centricon Plus-20 columns ( Millipore , Billerica , MA , USA ) and the protein content was determined . A stock mab solution of 10 mg protein per ml was used to test dilution series of 1∶10 up to 1∶500 . The best signal to background relationship was observed at a dilution of 1∶100 , and this dilution was used for all further experiments . The alkaline phosphatase anti-alkaline phosphatase ( APAAP ) technique was applied for immunostaining according to the recommendations of the manufacturer ( Dako , Carpinteria , CA , USA ) and as described earlier [11] . TBS with 1% albumin was used as negative control . Rabbit-anti mouse IgG ( 1∶25; Dako ) was applied as secondary antibody and was bound to the APAAP complex . As substrate for alkaline phosphatase the chromogen Fast red TR salt ( Sigma ) was used and hematoxylin ( Merck , Darmstadt , Germany ) served as the counter-stain . Sections were examined using an Olympus-BX40 microscope ( Olympus , Tokyo , Japan ) and photographed with an Olympus DP70 microscope digital camera . For some fluorescent analysis wheat germ agglutinin ( WGA 633 , Invitrogen , Carlsbad , CA , USA ) was used as membrane stain at 200 µg/ml for 10 minutes prior to mounting . FITC conjugated anti-mouse IgG ( 1∶300; Sigma ) was used as a secondary antibody for confocal laser scanning microscopy ( LSM ) . Sections were examined with a Zeiss LSM 510 META ( Zeiss , Jena , Germany ) confocal laser scanning microcope equipped with a plan-apochromat 63× oil objective with an argon or helium/neon laser for excitation at 488 nm or 633 nm , respectively . Confocal Z slices of 0 . 8 µm were obtained using Zeiss LSM software . The Velocity program version 5 . 4 . 2 ( Improvision , Lexington , MA , USA ) was used for high resolution interactive 3D rendering . Sections were also examined using a wide field fluorescence microscope ( WFFM , Zeiss Axioskop 2 MOT Plus ) with plan-apochromat 100× oil , 63× or 40× objectives . Wide field fluorescence microscopy and LSM were performed at the Washington University Molecular Microbiology Imaging Facility ( http://micro . imaging . wustl . edu/ ) . A 424 bp fragment of the 16S rRNA gene of Wolbachia of B . malayi was amplified ( forward primer 5′CAGCTCGTGTCGTGAGATGT , reverse primer 5′ CCCAGTCATGATCCCACTT ) and cloned into a dual promoter PCRII plasmid ( Invitrogen ) . After linearization of the plasmid , probes ( anti-sense ) and negative controls ( sense ) were prepared with Megascript T7 and Sp6 high yield transcription kits according to the manufacturer's suggested protocol ( Ambion , Invitrogen ) . For labeling of the probe a biotin-16 dUTP mix ( Roche , Indianapolis , IN , USA ) was used during in vitro transcription . The plasmid template was then removed by DNase digestion ( Roche ) . The probes were concentrated by ethanol precipitation , re-suspended in DEPC-treated water , and stored at −20°C until use . For staining , 5 µm thin paraffin sections were deparaffinized and partially digested with pepsin HCl for approximately 7 minutes . Sections were hybridized at 60°C overnight in a humid chamber with 1 µg of rRNA probe in hybridization buffer ( 50% formamide , 5XSSC , 0 . 3 mg/ml yeast tRNA , 100 µg/ml heparin , 1× Denhart's Solution , 0 . 1% CHAPS and 5 mM EDTA ) . A stringency wash was performed at 60°C for 30 min , and detection was performed using the ‘In situ Hybridization Detection System’ ( K0601 , Dako ) which uses alkaline phosphatase conjugated streptavidin to localize biotinylated rRNA probes . Sections were incubated for 20 min with streptavidin-AP conjugate at room temperature . BCIP/NBT substrate solution was added for 10 to 30 min to localize binding of the probes . Sections were deparaffinized and partially digested as described above and hybridized at 37°C overnight in a dark humid chamber using 200 ng of a custom made , labeled 30-mer antisense probe targeting the 16S rRNA of Wolbachia ( wBm16S as , 5′Alexa 488-CAGTTTATCACTAGCAGT TTCCTTAAAGTC , Invitrogen ) . The complementary sense sequence was used as a negative control probe . One stringency wash was performed at 37°C for 30 minutes . Hybridization and stringency buffers were the same as described above . Finally sections were rinsed briefly in PBS and covered with a cover slip with ProLong Gold antifade reagent that contains DAPI ( Invitrogen ) . This embedding reagent enables simultaneous fluorescence-based detection of condensed DNA in eukaryotic and prokaryotic organisms . Sections were examined using an Olympus-BX40 microscope equipped with the Olympus fluorescence filter 41001 ( excitation 460–500 nm , emission 510–550 nm ) for Alexa fluor or UN31000V2 ( excitation 325–375 nm , emission 435–485 nm ) for DAPI . For ultrastructural analysis fixed samples were washed in phosphate buffer , embedded in agarose , and postfixed in 1% osmium tetroxide ( Polysciences Inc . ) for 1 hr as described previously [12] . Samples were then rinsed extensively in dH20 prior to en bloc staining with 1% aqueous uranyl acetate ( Ted Pella Inc . , Redding , CA , USA ) for 1 hr . Following several rinses in dH20 , samples were dehydrated in a graded series of ethanol and embedded in Eponate 12 resin ( Ted Pella Inc . ) . Sections of 95 nm were cut with a Leica Ultracut UCT ultramicrotome ( Leica Microsystems Inc . , Bannockburn , IL , USA ) , stained with uranyl acetate and lead citrate , and viewed on a JEOL 1200 EX transmission electron microscope ( JEOL USA Inc . , Peabody , MA , USA ) .
Different developmental stages were stained with mab Bm WSP ( Fig . 1A , C–K ) . Results were confirmed by in situ hybridization or DAPI chromatin staining . Clusters of Wolbachia were detected in relatively few cells in microfilariae ( Fig . 1A ) . The same staining pattern was observed by rRNA in situ hybridization with a probe for Wolbachia 16S rRNA ( Fig . 1B ) . Wolbachia were sometimes detected in single cells of microfilariae within the midgut of mosquito vectors ( Fig . 1C ) or in sausage stage larvae and 2nd stage larvae in the mosquito thorax ( Fig . 1D , E ) , but most of the cells in these larval stages were free of the endobacteria . Even in infective 3rd stage larvae the vast majority of cells were devoid of Wolbachia ( Fig . 1F ) ; Wolbachia in L3 were mainly present in the cells of the lateral chord , but not in internal organs ( Fig . 1G ) . The Wolbachia density at the anterior end of 4th stage larvae was low at 2 weeks p . i . in the vertebrate host ( 3–5 days after the molt ) , and no endobacteria were detected in the tissue around the pharynx ( Fig . 1H ) . In contrast , large numbers of Wolbachia were detected in the developing lateral chords of the L4 midbody region ( Fig . 1I–K ) . In order to understand the distribution of Wolbachia in adult worms it is crucial to recall the anatomy and development of reproductive organs of filarial worms [13] , [14] . The genital opening ( vulva ) lies close to the anterior end of the female worm , approximately at the level of the esophagus ( Fig . 2A , B ) . The vagina leads into the bifurcated uterus which ends in the seminal receptacles . Theses organs are linked by oviducts with two ovaries that have an anterior growth zone , a maturation zone in the middle , and a posterior germinative zone . At 5 weeks p . i . in the vertebrate host , young adult female B . malayi worms are approximately 1 . 8 cm long and still growing . At that time point a massive accumulation of Wolbachia was observed , mainly in the lateral chords . Increased numbers of Wolbachia were observed in the lateral chords in the posterior end of the female which was still free of ovaries ( Fig . 3A ) . Sections of the posterior part of the ovaries showed large numbers of Wolbachia in the adjacent lateral chords , but the ovaries themselves were free of Wolbachia ( Fig . 3B , C , 4A ) . In the oocyte maturation and growth zones of the ovaries , Wolbachia were oriented within the lateral chords towards the pseudocoelomic cavity ( Fig . 3D–G ) , and some sections showed Wolbachia in the periphery of the ovary ( Fig . 3H , 4B , D , F ) . Two distribution patterns of Wolbachia were found in the lateral chords . Scattered Wolbachia were present in the apical part of the chords , and numerous clusters of Wolbachia were present basal border of the hypodermal chords adjacent to the ovaries ( Fig . 3I ) . A similar staining pattern for Wolbachia was observed in the lateral chords in the midbody region of 5 week old females , but their empty uterus branches were always free of Wolbachia ( Fig . 3J ) . In 8 week old female worms , less Wolbachia were detected in the lateral chords , but the mature ovaries in the posterior part of the worms were heavily infected with Wolbachia ( Fig . 5A ) . These worms contained developing microfilariae , and the ovaries showed strong staining of nuclear chromatin ( as determined by DAPI ) . Morula stage embryos were observed in the uterus with many Wolbachia , while in this region the number of endobacteria in the lateral chords was lower than in the distal parts of the lateral chords . In 12 week old females numerous Wolbachia were observed in the lateral chords and the posterior parts of the ovaries , but bacteria densities in these areas were lower than in the lateral chords adjacent to the anterior ovary and oviduct ( Fig . 5B , C , E , H ) . Numerous Wolbachia were detected in morula stage embryos in the uterus , but only a few were detected in the lateral chords of females at that level ( Fig . 5D , F , G ) . Intrauterine spermatozoa surrounding degenerated oocytes in the seminal receptacle were free of Wolbachia , but serial sections showed some Wolbachia in the oocytes in this area ( Fig . 5F , G ) . Stretched microfilariae in the vagina uterina contained Wolbachia in some cells , but the numbers were low compared to those in morula stage embryos . This suggests that Wolbachia may be necessary for rapid cell division which occurs in developing embryos but not in stretched microfilariae . The distribution of Wolbachia in the lateral chords was often asymmetrical , and this depended on the proximity to the reproductive system , body region and on the age of the worm . The genital opening of the male worm lies at the posterior end and forms with the anus a cloaca ( Fig . 2C ) . This is in stark contrast to the anatomy of females . A single vas deferens leads into a seminal vesicle that is connected to the testis; this can be subdivided into a growth zone , a maturation zone , and a germinative zone . In parallel to the distribution of Wolbachia in females , large numbers of endobacteria were observed in the lateral chords of 5 week old males , while the growing sections of the testes in the midbody region were free of Wolbachia ( Fig . 6A ) . However , Wolbachia were present in 5 week males near the testes ( Fig . 6B–E ) and in the middle part of the testis itself ( Fig . 6 F–J ) . No Wolbachia were detected within the vas deferens by immunohistology ( Fig . 7A , D ) . In contrast , Wolbachia 16S rRNA was detected by in situ hybridization in the testis tissue surrounding the spermatocytes and in the periphery of the vas deferens that contained spermatids ( Fig . 7B , C , E ) . Wolbachia were never observed in the spermatids or the spermatozoa . Comparison of four different methods on consecutive sections ( Figs . 3 D–G; 6 C–E; 6 F–J; 7A , B; 7D–G ) revealed almost identical staining patterns for Wolbachia by immunohistology with mab Bm WSP , by in situ hybridization ( using FISH and RNA in situ to detect Bm Wolbachia 16S rRNA ) , and by DAPI staining . Differences between immunohistology and 16S rRNA in situ detection were occasionally observed ( Figs . 5B , C; 7A , B; 7D , E ) . In these cases in situ hybridization detected a strong Wolbachia 16S rRNA signal , while no or very little Wolbachia surface protein was detectable by immunohistology . This may indicate a small difference of gene expression pattern or of gene product stability of both markers , but was not noticed as confounding factor . In addition , the intestine of B . malayi was sometimes nonspecifically labeled by immunohistology because of endogenous alkaline phosphatase ( e . g . Fig . 5A , F–H ) . This did not occur with in situ staining . The mab Bm WSP immunohistology assay detects a protein on the surface of Wolbachia , and it is possible that this protein is not present on all Wolbachia cells . In contrast , the in situ hybridization assay detects expression of 16S rRNA in the cytoplasm of Wolbachia . Small subunit rRNA is known to be highly expressed during the exponential growth phase of bacteria , and that has been used as marker for viability [15] . Therefore the in situ assay is an excellent marker for Wolbachia growth , and it may be suitable for assessing both the presence and viability of Wolbachia . DAPI staining , which detects A-T rich regions in DNA , is an easy and quick method to detect Wolbachia in the lateral chords , since this syncytial tissue usually does not contain condensed filarial chromosomes ( Figs . 3G; 7G ) . However , it is difficult to identify Wolbachia by DAPI staining in areas with condensed filarial chromosomes such as ovaries or in spermatids within the vas deferens ( Figs . 3G; 7G ) . This problem can be solved by combining the DAPI stain for condensed DNA with immunohistology ( Figs . 2J , 4D–G; 6H ) . This permits visualization of Wolbachia in the vicinity of filarial nuclei . Confocal laser scanning microscopy was used to study the three dimensional distribution of Wolbachia in larvae and in developing reproductive tissue of young adult worms . Although Wolbachia numbers were increasing in the lateral chords in 4th stage larvae , no Wolbachia were observed in developing reproductive organs in L4 . The higher resolution of LSM confirmed heavy Wolbachia loads in the lateral chords of young female worms ( 5 weeks ) and relatively few endobacteria in the hypodermis ( Fig . 4A ) . Entire oocytes could be examined for Wolbachia , because the size of oocytes is less than 5 µm and the scanned slices were 0 . 8 µm thick which is about the size of an endobacteria . The confocal examination of the distal end of the ovaries in 5 week old females confirmed the absence of Wolbachia from primary oocytes ( Fig . 4A , D ) . A full LSM scan and rotation of the section show that Wolbachia were present also in the hypodermal pouches that form longitudinal lines in 5 week old female worms ( video S1 ) . A membrane stain helped to demonstrate that some Wolbachia were attached to the external membrane around the proximal ovary while other bacteria were actually in the ovary ( Fig . 4B , C , videos S2 , S3 ) . The latter Wolbachia were always in the vicinity of large clusters of Wolbachia in the lateral chords adjacent to the ovaries in developing adult female worms ( Fig . 4B , C ) . Wide field fluorescence microscopy using FITC labeled mab Bm WSP with a membrane stain and an overlay of the DAPI nuclear stain showed that Wolbachia are attached to the ovary membranes ( Fig . 4D , E , F , G ) . It is possible that these endobacteria invade the ovaries of young females from the lateral chords . Wolbachia distribution in the developing ovaries was not uniform; in some cases , one branch was infected while the other branch was Wolbachia free ( Fig . 4E ) . Studies of the midbody region of 5 week old worms by transmission electron microscopy confirmed the presence of Wolbachia in the vicinity of developing reproductive tissues . Numerous rod-shaped and spherical Wolbachia were detected in the lateral chords in females , especially in adult worm tissues that are adjacent to developing ovaries . In some areas the hypodermal chord tissue was loose and vacuolized ( Fig . 8A ) . The epithelial cells surrounding the basal lamina of the ovaries were occasionally also strongly vacuolized indicating tissue degeneration , and small , electron dense Wolbachia were detected in these vacuoles ( Fig . 8B ) . Occasionally extracellular Wolbachia were seen in the pseudocoelomic cavity docking to the edge of the ovaries ( Fig . 8C , D ) or attached to the outer ovarian tissue ( Fig . 8E , F ) . While most of the Wolbachia in the lateral chords were rod-shaped or spherical and up to 1 µm in length and 0 . 5 µm in diameter , the endobacteria in the pseudocoelomic cavity were condensed , bacillary in shape and only 0 . 15 to 0 . 5 µm in length ( Fig . 8G–I ) . Within the ovaries , these small Wolbachia forms were observed in large vacuoles or in loose ovarian tissue ( Fig . 8G , I ) either as single bacteria or in groups ( Fig . 8H ) . In 5 week old male worms large clusters of large , rod-shaped or spherical Wolbachia were observed in the lateral chords in the vicinity of the testis ( Fig . 9A ) . Small , bacillary Wolbachia forms were sometimes observed in the testis tissue . At the caudal end of the testis , close to the transition to the vas deferens , Wolbachia were observed in the inner tissue , sometimes in the vicinity of peripheral spermatids ( Fig . 9B , C , D ) . These spermatids can be easily identified and differentiated from mature spermatozoa by their compact membranous organelles and the absence of major sperm protein complexes . Large amounts of membranous material were observed in the lumen between the spermatids and the inner testis epithelium . This material resembles degenerating Wolbachia ( Fig . 9B , E–G ) as they have been described previously [16] . Wolbachia were unambiguously identified in the reproductive tissue of young male worms , but not in the spermatids or spermatozoa .
Immunohistology has been extensively used to study Wolbachia and their clearance following chemotherapy in O . volvulus . Compared to O . volvulus , mature B . malayi have a thinner hypodermis and less pronounced lateral chords , and this can make the detection of Wolbachia more difficult . Our results demonstrate that the distribution and density of Wolbachia vary in different tissues and developmental stages . Our results are consistent with those from a PCR study that reported low amounts of Wolbachia DNA in vector stages and larger amounts in mammalian stages [4] . McGarry and co-workers reported an exponential increase in Wolbachia DNA in transmitted B . malayi L3 larvae as early as 7 days p . i . We detected large amounts of endobacteria by histology in the lateral chords of the midbody region in L4 larvae ( 14 d . p . i . ) . More Wolbachia were present in young adult worms at 35 d . p . i . in most parts of the lateral chords and also in an uneven distribution in the hypodermis . Observations on Wolbachia density and tissue localization may lead to hypotheses regarding their potential function in filarial worms . Antibiotic treatment experiments have suggested that Wolbachia may play a crucial role in the molting process of filarial parasites [17] , [18] , [19] , [20] . It appears clear that if Wolbachia have a direct function during molting , this function does not require localization in the vicinity of the filarial cuticle , since our localization results show that Wolbachia are not located near the cuticle during or immediately after molting . The distinct age and tissue specific distribution patterns of Wolbachia suggest also that the bacteria are not likely to be needed for housekeeping functions in all cell types of filarial nematodes . The absence of Wolbachia in the filarial nervous system , muscles , or the digestive systems suggests that Wolbachia are not needed for these functions . In adult worms the majority of mitochondria can be found in the periphery of the lateral chords , while the majority of Wolbachia are localized in or near the reproductive system . The differential distribution of Wolbachia and mitochondria within the lateral chord of filarial parasites has been reported previously [21] . Especially to the female worms the localization of Wolbachia in the lateral chords in vicinity of the reproductive system implies an important role of endobacteria for embryogenesis and intrauterine development . In agreement with this hypothesis tetracycline treatment to deplete Wolbachia in developing filarial worms has been shown to affect mainly females and causes a male-biased sex-ratio [20] , [22] . The Wolbachia genome in B . malayi encodes complete pathways for the biosynthesis of nucleotides , riboflavin , flavin adenine dinucleotide and heme , which are missing or incomplete in the filarial genome [23] . A high demand for gene products ( which may not be taken up from the mammalian host ) from these pathways might be especially necessary during the development of the reproductive system in young adult worms . Furthermore , the phylogenetically old and tight association of filarial nematodes with Wolbachia during reproduction may have led to additional interdependencies that account for their mutualistic relationship . As hypothesized for Wolbachia in insects , it is possible that Wolbachia in filarial nematodes are especially important for pre-meiotic mitosis , meiosis , and meiosis associated processes [24] , [25] , [26] , [27] . A recent study examined the dynamics of Wolbachia during intrauterine embryogenesis of B . malayi using Caenorhabditis elegans embryogenesis as a framework for the analysis [5] . Asymmetric Wolbachia segregation was observed that could explain the concentration of Wolbachia in the hypodermal chords . The early differential distribution of Wolbachia within embryonic cells corresponds well with the strong tissue specific distribution in later development described in our study . However , the authors also hypothesized that the asymmetric segregation pattern may be responsible for the presence of Wolbachia in the female germline [5] . This is in contrast to our results which clearly demonstrate the absence of Wolbachia in male and female reproductive tissue from the third stage larvae to the young adult worms . Since it is difficult or impossible to identify the germline cells or gender of microfilariae , vector stage first stage larvae , and second stage larvae of B . malayi , we cannot be sure when during development Wolbachia are lost in these cells . The terminal ends of Brugia ovaries form the germinative zones which contain the mitotic growing oogonia [28] . Our study showed that these areas were free of Wolbachia in growing , young adult worms . Our results suggest that Wolbachia from adjacent lateral chords may cross tissue zones to infect cells in maturation zone 1 ( which mainly contains primary oocytes in the pachytene stage of meiotic prophase I ) and in maturation zone 2 ( which contains oocytes in the remaining phases of meiosis I ) . The germinative zones of the ovaries seem to be populated by Wolbachia over a period of approximately three weeks following the L4–L5 molt . Large numbers of Wolbachia were present in the maturation zones of eight week or older female worms , while the attached growth zones which contain the secondary oocytes and the oviducts contained lower numbers of Wolbachia ( see Fig . 5E ) . Fertilization precedes meiosis II in filarial nematodes [28] . Wolbachia were detected in secondary oocytes surrounded by spermatozoa and unfertilized oocytes within the seminal receptacle in mature females ( see Fig . 5F , G ) . The picture was similar in male worms . Wolbachia were not observed in the germinative zone of the testis . It is possible that Wolbachia from the lateral chords infect the primary spermatocytes in maturation zone 1 , which are mostly in the pachytene stage of prophase of meiosis I [29] . This report is the first detection of Wolbachia in primary spermatocytes of developing male filarial nematodes . Although mature male worms have been previously examined for Wolbachia , prior studies did not report infection of the testis [30] . This is not contradictory to our findings , since Wolbachia appear to only infect the testis of immature adult stage B . malayi males and such worms were not studied previously . The spermatocytes of the adjacent growth zone and maturation zone 2 are difficult to differentiate morphologically , but larger secondary spermatocytes that have completed meiosis and the spherical spermatids which enter the vas deferens can be distinguished . Wolbachia were never seen in the spermatids or the mature spermatozoa . However , our in situ hybridization results clearly indicated the presence of Wolbachia 16S rRNA in the periphery of the seminal vesicle . This was confirmed by electron microscopy that showed Wolbachia in the inner epithelium of the testis or vas deferens , but not in the spermatids . These data may suggest that high Wolbachia densities are correlated with condensed chromatin and Wolbachia may be involved in chromosome segregation of filarial nematodes . Our ultrastructural studies of young adult B . malayi confirm that Wolbachia are highly pleomorphic . This pleomorphism was recognized shortly after the discovery of endobacteria in filarial nematodes , and it has been suggested that Wolbachia may have a Chlamydia-like life cycle with small dense bodies as potential infectious forms [30] , [31] . Chlamydia and filarial Wolbachia both have an obligatory intracellular life style and a small genome size due to the loss of a number of essential biosynthetic pathways . Both bacterial groups lack cell walls but retained a functional lipid II biosynthesis pathway [32] . It is also possible that Wolbachia share the requirement of Chlamydia for host cell sphingolipids supplied by the host cell Golgi apparatus and multivesicular bodies for activation [33] . Clearly , further studies are needed to assign functions to different morphological forms of Wolbachia during the filarial life cycle . Based on our results we hypothesize that the genital primordium in larval B . malayi is devoid of Wolbachia and that reproductive tissues in young adult worms become infected with Wolbachia from adjacent lateral chords which have many Wolbachia . Prior studies have shown that newly introduced Wolbachia can cross several tissue planes and infect the germline in Drosophila [34] . This could be also the case in filarial Wolbachia , and it is possible that similar host signals trigger the germline tropism of Wolbachia in filarial worms and Drosophila . Previous studies have shown that a Wolbachia htrA serine protease can be found outside bacterial cells in filarial parasites . This protease and other secreted bacterial proteins may be involved in tissue invasion [35] . In addition to tissue lysis , motility of Wolbachia may be necessary for the bacteria to cross tissue boundaries . Actin-based motility occurs in Rickettsia and many other intracellular bacteria [36] . Orthologs of genes essential for actin-based motility have been found in the Wolbachia genome . Additional work will be needed to study the localization and timing of expression for these genes [23] , [37] . Our ultrastructural results confirmed the presence of large clusters of Wolbachia in the lateral chords in the vicinity of the ovaries and in the outer ovary epithelium as previously described [30] , [38] . The new finding reported here , is the detection of extracellular Wolbachia in the pseudocoelomic cavity in young females and the presence of Wolbachia in testis of developing male worms . In summary , this study shows the value of histological techniques such as immunohistology and in situ hybridization to study the tissue distribution of Wolbachia during the life cycle of filarial nematodes . Wolbachia infection was found to be highly cell and tissue specific . No Wolbachia were found in the developing reproductive organs in fourth stage larvae and freshly molted adult worms , which had heavy Wolbachia loads in the lateral chords . Wolbachia were detected in reproductive tissues with the onset of oocyte and sperm development , and infection of oocytes results in transovarial transmission of Wolbachia to the next generation .
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Most filarial nematodes contain Wolbachia endobacteria that are essential for development and reproduction . An antibody against a Wolbachia surface protein was used to monitor the distribution of endobacteria during the B . malayi life cycle . In situ hybridization with probes binding to Wolbachia 16S rRNA were used to confirm results . Only a few cells contain Wolbachia in microfilariae and vector stage larvae; this suggests that the bacteria need to be maintained , but may have limited importance for these stages . Large numbers of Wolbachia were detected in the lateral chords of L4 larvae and of young adult worms , but not in the developing reproductive tissue . Confocal laser scanning and transmission electron microscopy showed that Wolbachia are aligned towards the developing germline . It can be hypothesized that Wolbachia invade developing ovaries from the lateral chords . In inseminated females , Wolbachia were detected in the ovaries and embryos . In young males , Wolbachia were found in parts of the testis and in the lateral chords in the vicinity of testicular tissue but never in mature spermatids or spermatozoa . The process of overcoming tissue boundaries to ensure transovarial transmission of Wolbachia could be an Achilles heel in the life cycle of B . malayi .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"developmental",
"biology",
"histology",
"infectious",
"diseases",
"veterinary",
"microbiology",
"biology",
"microbiology",
"veterinary",
"science"
] |
2011
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Tissue and Stage-Specific Distribution of Wolbachia in Brugia malayi
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During DNA replication , DNA polymerases follow an induced fit mechanism in order to rapidly distinguish between correct and incorrect dNTP substrates . The dynamics of this process are crucial to the overall effectiveness of catalysis . Although X-ray crystal structures of DNA polymerase I with substrate dNTPs have revealed key structural states along the catalytic pathway , solution fluorescence studies indicate that those key states are populated in the absence of substrate . Herein , we report the first atomistic simulations showing the conformational changes between the closed , open , and ajar conformations of DNA polymerase I in the binary ( enzyme∶DNA ) state to better understand its dynamics . We have applied long time-scale , unbiased molecular dynamics to investigate the opening process of the fingers domain in the absence of substrate for B . stearothermophilis DNA polymerase in silico . These simulations are biologically and/or physiologically relevant as they shed light on the transitions between states in this important enzyme . All closed and ajar simulations successfully transitioned into the fully open conformation , which is known to be the dominant binary enzyme-DNA conformation from solution and crystallographic studies . Furthermore , we have detailed the key stages in the opening process starting from the open and ajar crystal structures , including the observation of a previously unknown key intermediate structure . Four backbone dihedrals were identified as important during the opening process , and their movements provide insight into the recognition of dNTP substrate molecules by the polymerase binary state . In addition to revealing the opening mechanism , this study also demonstrates our ability to study biological events of DNA polymerase using current computational methods without biasing the dynamics .
DNA polymerase , which is responsible for copying DNA , is a vital enzyme involved in the transfer of genetic information for living organisms . It is also utilized by scientists to replicate DNA sequences during polymerase chain reactions ( PCR ) . DNA polymerase has the ability to quickly and accurately select the proper 2′-deoxynucleoside triphosphate ( dNTP ) to form a Watson-Crick base pair despite being outnumbered by other dNTPs and similar ribonucleoside triphosphates ( rNTP ) . In fact , replicative DNA polymerases can generate double-stranded DNA at rates of tens or hundreds of nucleotide additions per second while only incorrectly matching a nucleotide once every tens to hundreds of thousands of nucleotides added [1] . This level of specificity suggests the dynamics and conformations of DNA polymerase are important for proper substrate binding and catalysis . DNA polymerase I consists of 5′→3′ exonuclease , 3′→5′ exonuclease , and polymerase domains ( Figure 1 ) . The Klenow fragment of DNA polymerase I is an N-terminal deletion of the dispensible 5′→3′ exonuclease domain [2] . Within the Klenow fragment , the polymerase domain resembles the shape of a human hand with a thumb subdomain that grasps the DNA , a palm subdomain that contains the active site , and a mobile fingers subdomain involved in dNTP binding [3] , [4] . The thumb ( residues 496–595 ) , palm ( residue 617–655 and 830–869 ) , and fingers ( residues 656–818 ) subdomains of DNA polymerase were named based on their positioning around the bound DNA as observed in crystal structures . The fingers domain consists of multiple α-helices highlighted by the O-helix that directly interacts with the dNTP substrate upon binding . X-ray crystallography and solution kinetics studies have observed the fingers subdomain in three distinct conformations ( Figure 1 ) , which are dependent on the presence or absence of a dNTP in the active site [5] , [6] , [7] , [8] . The fingers subdomain primarily resides in an “open” conformation with no dNTP bound ( binary state ) to the polymerase . Upon binding of a dNTP ( ternary state ) that forms a proper Watson-Crick base pair with the template strand , the fingers domain enters a “closed” conformation that helps position the substrate in the active site during catalysis [5] , [9] . And recently a third “ajar” conformation was discovered that places the fingers domain in a semi-open state when a dNTP binds that forms a mismatch with the template strand [10] . The mechanism of binding reactants and subsequently releasing products post-elongation has been studied extensively , but mechanistic details for the opening and closing of the fingers domain have never been elucidated . The O-helix undergoes a ∼40° rotation when the fingers close around the bound dNTP , while the side chains of several amino acids on the helix are involved in key protein-ligand interactions . From close examination of the closed crystal structure , an arginine and lysine in the O-helix form salt bridges to help neutralize the negative charge of the triphosphate on the dNTP , while a tyrosine near the active site plays a key role in substrate specificity and closing of the fingers domain [11] . Single-molecule Förster resonance energy transfer ( FRET ) experiments of the DNA polymerase-DNA ( binary ) complex indicate that the enzyme fluctuates between the three conformations ( open , ajar , and closed ) , but incorporation of an incorrect nucleoside causes the O-helix to undergo a ∼15° rotation relative to the open structure , causing the ajar conformation to dominate [12] . More recent DNA polymerase FRET studies suggest the open conformation is present 81% of the time with DNA bound in the absence of dNTP [13] . Although the structures of the ternary ( closed and ajar ) and binary ( open ) conformations have been characterized using X-ray crystallography , the dynamics and atomistic details of the conversions between the various states occur too quickly to observe with standard experimental techniques . In this , we report the first structural images for the conversion between closed , ajar and open binary conformations . Computational methods can help describe the dynamics of biomolecules on an atomistic level not easily reached by experimental structural biology [14] . In particular , molecular dynamics ( MD ) simulations are able to simulate the movements of these molecules using Newton's classical laws of motion . Simulations of apo molecules have led to the discovery and confirmation of important biological conformational states and conformational interconversions not available to traditional experimental techniques [15] , [16] . MD has been used in many studies to understand the dynamics of protein-DNA complexes , even DNA polymerase [17] , [18] , [19] . Specifically , Golosov et al . used targeted ( i . e . biased ) MD to observe the translocation of DNA after dNTP insertion by artificially steering the simulation towards the desired endpoint [20] . Unbiased MD simulations have previously been performed , but limited to understanding localized motions of the amino acids and nucleotides in the DNA polymerase complex , and have been unable to observe any large-scale biomolecular motions during short time-scales [17] , [21] , [22] , [23] , [24] except for smaller DNA polymerase complexes [25] , [26] . In its infancy , MD could only be utilized for these short ( picosecond to nanosecond ) time-scales , but recent advances in computational hardware and MD software have made it possible to reach significantly longer time-scales into the microsecond and even millisecond range [27] . Consequently , it is now possible to computationally model domain movements that require long time-scale dynamics to observe [14] . The opening of the fingers domain in DNA polymerase I has never been studied using unbiased all-atom computational methods on the µs time-scale because of the high computational cost of modeling such a large conformational change in a large biomolecule . Biased MD simulations apply additional external forces that might unnaturally influence the dynamics , but are useful for studying conformational inter-conversions when sufficiently long unbiased trajectories cannot be simulated . When adequate computational resources are available , unbiased methods are preferred for observing conversions between structures and thus for examining biomolecular mechanistic details without biasing the dynamics . In this study , we have utilized recent computational advances to simulate the opening of the fingers domain starting from the closed ( PDB 1LV5 [28] ) and ajar ( PDB 3HP6 [10] ) conformations of Bacillus stearothermophilus DNA polymerase Klenow fragment using dynamics on the microsecond time scale , and also simulated the open state ( PDB 1L3S [28] ) for comparison . A detailed understanding of the opening process of DNA polymerase is vital as we attempt to understand the complete dynamics involved in DNA replication , and how we can apply our knowledge in biotechnology to design better polymerases for PCR . Additionally , these simulations are relevant to the polymerase community because they provide a foundation for future experimental and computational work and analysis with the ternary DNA polymerase complex . We have fully characterized the opening process that occurs prior to catalysis , and determined the key events and movements that are critical to O-helix opening . The transition from ajar to open is quick ( <20 ns ) , while the full transition from closed to open was observed taking nearly 300 ns . We observed a key intermediate step in the pathway from closed to open involving a salt bridge between an arginine side chain on the O-helix and an aspartate in the thumb domain . We have also identified critical changes in a handful of polymerase backbone dihedrals and determined the order of events involved in the transition from closed-to-open of the fingers domain . Altogether , these simulations aid in the elucidation of the O-helix opening mechanism for DNA polymerase on an atomistic level not currently available with experimental measures .
Structures of B . stearothermophilus DNA Polymerase I were acquired from the Protein Databank with the O-helix in the open ( PDB 1L3S [28] ) , ajar ( PDB 3HP6 [10] ) , and closed ( PDB 1LV5 [28] ) conformations . Prior to simulations , the substrate ligand was removed from the active site for both the 1LV5 and 3HP6 structures to create the binary complex ( enzyme+DNA ) for all simulations . The 1L3S structure was crystallized with the wild-type sequence , but the 3HP6 and 1LV5 structures were crystallized with mutations . Specifically , 3HP6 was a D598A/F710Y double-mutant , while 1LV5 had a D329A mutant that required in silico mutations to regenerate the wild-type sequence in those structures . The ff99SB force field [29] was applied to the protein along with the parmbsc0 modifications [30] for nucleic acids . Explicit hydrogen atoms were added to all initial X-ray crystal structures using the tleap module of AmberTools [31] . tleap was also used to neutralize each system with Na+ counter ions and solvate a truncated octahedron unit cell with TIP3P water molecules [32] using a 12 . 0 Å solvent buffer between the solute and the closest edge of the unit cell for a total atom count of ∼80 , 000 . The GPU-accelerated pmemd code [33] of Amber 12 [31] was used to perform all steps of MD for each system . All initial structures underwent a seven-step minimization procedure involving 1000 steps of steepest descent minimization followed by 4000 steps of conjugate gradient minimization at each step . Positional restraints on all solute heavy atoms were initially set to 10 . 0 kcal/mol/Å2 and systematically lowered during each stage down to zero for the final stage . After minimization , each system was heated linearly from 10 K to 335 K over 2 . 0 ns , while positional restraints were held constant at 10 . 0 kcal/mol/Å2 on the protein and DNA strands . The final stage of the preliminary equilibration process involved running MD at constant temperature ( 335 K ) for 3 . 5 ns , beginning with 10 . 0 kcal/mol/Å2 positional restraints on all heavy atoms of the protein and DNA for the first 0 . 5 ns , and systematically lowering the restraints every 0 . 5 ns until reaching a final restraint weight of zero ( unrestrained ) over the final 0 . 5 ns . After this equilibration protocol , unrestrained MD was performed on all solvated systems at constant pressure ( 1 atm ) using a Berendsen thermostat with isotropic position scaling and constant temperature ( 335 K ) maintained with a Langevin thermostat [34] using periodic boundary conditions , saving the coordinates , velocities , and energies every 100 ps . Long-range interactions were treated with the Particle Mesh Ewald method for periodic boundaries using a nonbonded cutoff of 9 . 0 Å and the nonbonded list was updated every 25 steps ( default ) . New random number seeds were chosen every 25 ns for each simulation to prevent simulation synchronization of the trajectories [35] . The SHAKE algorithm [36] was used to fix all covalent bond distances involving hydrogen , allowing a 2-fs time-step for dynamics . Given the time-scale of the expected conformational change , a mass repartitioning method [37] was used on the 1LV5 system to create hydrogen atoms three times heavier than normal hydrogen atom mass , which was compensated by lowering the mass of each heavy atom attached to any hydrogen atom in the system to maintain the same overall mass . The mass repartitioning method allowed us to increase the MD step size from 2 fs to 4 fs for the 1LV5 system . The 1L3S , 3HP6 , and 1LV5 starting structures were simulated without restraints for a total of 500 ns , 1 . 0 µs , and 3 . 0 µs , respectively , which were used for all analyses . The 1L3S , 3HP6 , and 1LV5 PDBs were prepared similarly for MD using the Desmond 3 . 1 MD package [38] , [39] , [40] . Each complex was checked for structural correctness using the Protein Preparation Wizard in Schrödinger's Maestro v9 . 4 . Sodium and chloride ions were added to reach a final concentration of 150 mM Na+ ( while still maintaining a neutrally charged unit cell ) and the system was solvated with TIP3P water molecules after reorientation to minimize the volume in an orthorhombic box . The Amber force fields were applied to these periodic systems . Additionally , each PDB system was simulated using the Charmm27 force field [11] , [41] , although the Charmm36 force field has been available since 2012 . The default Desmond minimization and equilibration procedure was followed , except the maximum number of steps for steepest descent and total minimization were increased to 1000 and 5000 steps , respectively . Simulations were kept at constant pressure ( 1 atom ) and temperature ( 335 K ) maintained with a Berendsen barostat and thermostat , respectively [42] . SHAKE was applied to all systems allowing a 2-fs time-step . Long-range interactions were treated with the Particle Mesh Ewald method for periodic boundaries using a nonbonded cutoff of 9 . 0 Å and the nonbonded list was updated frequently using the default settings . Coordinates and energies for the Amber ff99SB force field simulations were saved every 100 ps for a total of 500 ns , 1 . 0 µs , and 1 . 0 µs for the 1L3S , 3HP6 , and 1LV5 systems , respectively . Coordinates and energies for the Charmm27 force field were saved at the same interval for a total of 500 ns for the 1L3S simulation , while the 3HP6 and 1LV5 systems were each simulated for 1 . 0 µs . All MD analysis ( e . g . distance/angle measurements , RMSDs , etc . ) was performed using the cpptraj module [43] of AmberTools 13 . All Desmond trajectories were centered , imaged , and converted to DCD binary trajectory file format using VMD v . 1 . 9 . 1 [44] to ensure their readability by cpptraj .
In this study we performed nine MD simulations of B . stearothermophilus DNA polymerase I in the binary state ( enzyme+DNA ) starting from the open ( PDB 1L3S ) , ajar ( PDB 3HP6 ) , and closed ( PDB 1LV5 ) conformations of the fingers domain for a combined total of 9 . 5 µs . We observed DNA polymerase transition fully from the closed to open conformations starting from the 1LV5 PDB ( closed ) structure ( see movie in the Supporting Information ) . We constructed the binary conformation by removing the dNTP in silico from the active site and performed MD using two different software packages and two unique force fields to describe the dynamics ( Table 1 ) . This transition from closed to open has never previously been observed experimentally or computationally without applying a biasing potential . As expected , the simulations were not identical ( in particular , with regards to the timing of the opening process ) ; however , they all appear to have traversed similar pathways . To describe the conformation of the fingers domain at any given time , we measured the distance between the α-carbons of Pro699 at the end of the O-helix and Arg629 residue in the thumb domain of DNA polymerase ( See Figure 2A ) . This single distance is able to successfully capture the movements of the fingers domain as well as an angle used in a publication by Golosov et al . [20] and a plot of the RMSD of the fingers domain as a function of time in reference to the original crystal structure used to start each simulation ( Figure 3 ) . The plot of the Pro699-Arg629 distance ( Figure 2B ) illustrates the dynamics of the fingers domain for each simulation using the Desmond MD package with the Charmm27 force field ( See Figure 2C and 2D for corresponding plots with the Amber ff99SB force field ) . The 1L3S ( open ) simulation remains in the open conformation for the entire 500 ns trajectory . 3HP6 ( ajar ) begins in the ajar conformation , but very quickly ( <5 ns ) opens to a distance corresponding to the open conformation . Meanwhile , the fingers domain for the 1LV5 ( closed ) simulation is initially closed for more than 100 ns , but partially opens into a conformation similar to but distinct from the ajar shortly at ∼125 ns . The polymerase remains in this intermediate state for ∼50 ns before it returns the closed state for ∼100 ns duration and finally fully opens at ∼290 ns where it persists for the remainder of the simulation . The ability of the DNA polymerase fingers domain to clearly sample all three conformations ( Figure 4 and Table 2 ) coincides with experimental evidence that suggests each state is thermodynamically accessible in the binary state [13] . We have utilized two of the most recognized and accurate MD force fields ( Charmm27 and Amber ff99SB ) available for studying the motions of biological macromolecules [45] . By applying multiple force fields and MD software packages ( Desmond and Amber ) with unique sampling algorithms we tested the dependence of observed structural changes on the methodology used . We focus here mostly on the results of the dynamics simulated with Desmond using the Charmm27 force field because the observed conformational changes occurred over a shorter time-scale making the analysis simple and well-defined . The dynamics utilizing Amber ff99SB simulated with both Desmond and Amber MD software showed similar overall patterns to the Charmm27 force field , and are also represented in the Figure 2 . The results of the six simulations using the Amber ff99SB force field ( Figure 2C and 2D ) are summarized here as they relate to the Charmm27 force field simulations ( Figure 2A ) . Using the Desmond software and the Amber ff99SB force field , 1L3S remained in the open conformation for the entire simulation whereas the 3HP6 ( ajar ) simulation transitioned very quickly ( <5 ns ) to the open conformation and remained there . The 1LV5 ( closed ) Amber ff99SB simulation also transitioned into the open conformation , but this process took longer ( ∼775 ns ) than it did with the Charmm27 force field ( ∼290 ns ) . Using the same starting structure and simulating with Desmond and the Amber ff99SB force field , the polymerase was primarily in the closed conformation until ∼600 ns , and underwent a relatively slow and steady transition ( ∼175 ns ) into the open conformation and never re-visited the closed conformation after leaving it ( Figure 2C ) . With the Amber MD software using the ff99SB force field , 1LV5 did not move to the open conformation until nearly 2 . 0 µs of simulation time ( Figure 2D ) . The apparent time-dependence of these simulations is likely created by the inherent differences between the two force fields; the Amber ff99SB force field does not allow the polymerase to be as dynamic as the Charmm27 force field . Additionally , there is likely some fluctuation in the timing of the opening transition created by the nature of MD simulations that must traverse complicated potential energy surfaces ( PES ) utilizing different initial seeds/velocities , causing the simulated timing of conformational changes to vary from one trajectory to another . Thus , the timing of these transitions is likely not well defined and should not be considered the true amount of time required for each conformational change . The detailed motions of DNA polymerase during the transition from closed to open observed with Charmm27 are shown in Figure 5 . The 1LV5 ( closed ) crystal structure showed the existence of a hydrogen bond between the side chains of Tyr714 and Glu658 in the ternary ( enzyme-DNA-dNTP ) state ( Figure 5A ) , but after removing the dNTP and simulating the closed structure this hydrogen bond is quickly broken ( Figure 5B ) . This allows Tyr714 to move toward the template DNA base and causes the O-helix to open slightly ( ∼1 . 5 Å ) . The O-helix is held in this intermediate position by a salt bridge between Arg703 and Glu562 of the thumb subdomain , while Tyr714 and the guanine in the DNA template continue to compete for the insertion site ( Figure 5B ) . Shortly after the Arg703-Glu562 salt bridge interaction is broken ( Figure 5C ) the O-helix opens further , pulling Tyr714 into the insertion site , inducing a rotation of the N-β-glycosyl bond of the template nucleotide , and moving the template base out of the active site ( Figure 5D ) . The precise ordering of the last two steps is not fully established because different force fields yielded different results . The Charmm27 force field predicted the salt bridge to break prior to the N-β-glycosyl bond rotation , while the two Amber ff99SB force field simulations suggested the opposite ordering . However , in all three simulations the steps succeeding the intermediate conformation ( Figure 5B ) appear closely correlated implying that they may occur nearly simultaneously . Examination of the fingers domain dynamics in the 1LV5 ( closed ) simulation revealed an intermediate state corresponding to a Pro699-Arg629 distance of ∼13–15 Å that is stable along the pathway from closed to open from ∼100–170 ns and again from ∼280–290 ns ( Figure 2B ) . This state is not identical to the ajar state of the 3HP6 crystal structure although the observed Pro699-Arg629 distances are similar ( see below for more details ) . The intermediate conformation ( Figure 5B ) provides key insight into the opening process of DNA polymerase I . This pathway has never been observed crystallographically , likely due to the complexity of trapping such a short-lived intermediate in the binary state . However , the existence of this conformation utilizing two different MD packages ( Desmond and Amber ) and two different force fields ( Charmm27 and Amber ff99SB ) known to describe well the protein and DNA systems indicates that its presence in the opening mechanism is not dependent on the computational method and strongly supports the existence of this intermediate during opening of the fingers domain . For the 1LV5 ( closed ) Desmond simulation using the Charmm27 force field , the intermediate persisted for a total of ∼80 ns before the fingers domain opened fully . The intermediate was longer-lived using the Amber ff99SB force field ( ∼400 ns and ∼1 . 0 µs lifetimes using the Desmond and Amber MD software , respectively ) , although this could be expected since the dynamics appear to move quicker using the Charmm27 force field in general for DNA polymerase I , as previously mentioned in the Results section . The intermediate state observed in the 1LV5 ( closed ) simulation is stabilized by a key salt bridge between an arginine residue in the O-helix and a glutamate residue in the thumb domain of DNA polymerase . To test whether the salt bridge constitutes a substantial obstacle for opening , we in silico mutated Arg703 to an alanine residue in 1LV5 and re-started the simulation under the same conditions and simulated for 500 ns . The fingers domain of the R703A mutant opened in <50 ns , while the wild-type required ∼290 ns to reach the same conformation ( Figure 6 ) . Given that the only difference between these two starting structures is the mutation from arginine to alanine at position 703 , this result provides further evidence of the importance of the Arg703-Glu562 salt bridge intermediate along the opening pathway for the fingers domain of DNA polymerase . The arginine residue is highly conserved in bacterial DNA polymerase I enzymes . 28 out of 33 DNA polymerase I enzyme sequences from bacteria in UniProt contained an arginine at this location in the O-helix , including ones from Escherichia coli and Thermus aquaticus ( Taq ) , which have been structurally characterized . Arg703 is also known to be important for polymerase activity in bacteria [46] . Mutation studies of the corresponding arginine in Taq DNA polymerase I showed a clear loss of polymerase function when mutated , although the role of the arginine residue was not described [46] . Our simulations support those mutagenesis studies , indicating the importance of this arginine to the polymerase and additionally illustrate its role in forming a key intermediate during the opening of the fingers domain . As a final note on the intermediate state , although the fingers domain is clearly between the closed and open conformations , this newly observed state is not identical to the ajar state observed in the 3HP6 PDB structure . The simulated intermediate has a heavy atom root-mean-square deviation ( RMSD ) of 4 . 3 Å from the 3HP6 crystal structure . The largest structural differences between the intermediate and the 3HP6 crystal structure arise in the fingers subdomain with Arg703 and the thumb region of the polymerase where Glu562 resides ( Figure 7 ) . The Arg703-Glu562 salt bridge is not present in the 3HP6 crystal structure nor does it ever exist in any of the simulations starting from the 3HP6 ajar conformation . The 3HP6 crystal structure was generated by trapping DNA polymerase with a non-Watson Crick dNTP paired to the template strand in the active site , while our simulations are performed in the absence of a dNTP molecule to mimic the dynamics of the protein after elongation of the DNA primer strand has occurred . This means that , experimentally , the two “ajar” conformations reside on two different potential energy surfaces where the 3HP6 ajar state is only observed in the presence of a dNTP in the active site , while the proposed intermediate state is present only in the absence of dNTP ( Figure 8 ) . This is contrary to the literature reported prior to this study that assumed the polymerase conformation observed in the 3HP6 crystal structure was identical to the conformation of polymerase observed in the absence of dNTP . The single-molecule FRET experiments [13] that previously reported the presence of open , ajar , and closed conformations in the binary state probably observed the intermediate proposed in this study instead of the ajar state documented from X-ray crystallography that likely only occurs with a bound mismatch dNTP . Thus , the hypothesis for this new intermediate structure from MD is consistent with solution studies that show an intermediate state between the open and closed conformations in the absence of dNTP . The exact purpose of the intermediate is not fully understood yet , but it is clear that the presence of the intermediate slows the transition from the closed to the open conformation in the binary complex . Based on the similarity between binary and ternary pathways to the closed conformation ( Figure 8 ) , we speculate that the intermediate may also play a role in the closing of the fingers domain during dNTP binding , possibly providing an energetic barrier to opening that aids the enzyme during substrate recognition . Each backbone dihedral in the fingers subdomain of the simulation started from the closed conformation ( 1LV5 ) was compared to the corresponding open ( 1L3S ) , ajar ( 3HP6 ) , and closed ( 1LV5 ) crystal structure values . This investigation revealed four specific backbone torsions important for opening of the fingers domain—Asp680φ , Gly711φ , Val713ψ , and Ile716φ ( Figure 9 ) . These dihedrals were identified because each dihedral rotation corresponds to a significant change in the structure of the fingers domain involved in converting between the open , ajar , and closed conformations . The original rotation of each dihedral in the closed ( 1LV5 ) crystal structure is shown in Figure 10 . In the open crystal structure ( 1L3S ) the Asp680φ , Gly711φ , Val713ψ , and Ile716φ dihedrals have values of −74 . 5° , −61 . 3° , −33 . 8° , and −141 . 7° , respectively . According to the ajar crystal structure , the Gly711φ and Val713ψ dihedrals rotate by ∼11° and ∼7° , respectively , during an ajar-to-open transition . Meanwhile , the Asp680φ and Ile716φ values undergo significant ( ∼25° and ∼60° , respectively ) transitions themselves between the closed ( 1LV5 ) and open ( 1L3S ) crystal structures . These changes were all observed during our simulation that began in the closed and transitioned to the fully open conformation . Close examination of the dihedral values as the simulation progresses ( Figure 11 ) shows the ordering and impact of each dihedral . The transition from the closed state ( Figure 9A ) to the intermediate ( Figure 9B ) is initiated by the ∼30° rotation of the Asp680φ dihedral at ∼100 ns ( Figure 11A ) , which results in a large-scale movement of the N-helix in the fingers domain . Subsequently , the Gly711φ and Val713ψ dihedrals rotate by ∼20° and ∼35° ( Figure 11B–C ) , respectively , creating a bend in the O-helix ( Figure 9D ) . In the Desmond/Charmm27 simulation the fingers domain transitions back into the closed conformation after ∼170 ns . Between 280–290 ns , the fingers domain undergoes two major dihedral rotations to complete the transition to the open conformation ( Figure 9C ) . Once again , the process is initiated by the rotation about the Asp680φ dihedral ( lowering the N-helix ) , followed shortly by a ∼60° rotation of the Ile716φ dihedral ( Figure 11D ) . In this case , the rotation of the Asp680φ dihedral is enough to overcome the barrier necessary to rotate the Ile716φ dihedral and reach the fully open state . The dihedrals from the simulations appear to correlate well with the values from the existing crystal structures for each state ( Figure 9 ) . Of interest , though , is the observation that although the fingers domain appears fully open after 290 ns , the Gly711φ , Val713ψ , and Ile716φ all make substantial ( ≥20° ) transitions between 600 and 725 ns producing structures in excellent agreement with the experimental values . The ∼60° rotation about the Ile716φ dihedral actually coincides with the movement of the template DNA base flipping out of the pre-insertion site and back into the active site ( where it resided in the 1LV5 closed crystal structure ) . The rotations by Gly711φ and Val713ψ correspond to a rotation of the Tyr710χ1 dihedral so the tyrosine side chain is positioned for better π-stacking with the nucleotide of the template DNA base . The dynamical nature of this region of the O-helix is consistent with structural heterogeneity in crystal structures of open , binary complexes of Bacillus DNA polymerase before and after catalyzing DNA synthesis [7] . While the overall structure of the enzyme remains the same , the structure of the loop between the O and O1 helices ( residues 714–717 ) flips back and forth between two states after each step of processive DNA synthesis in the crystal ( Figure 12 ) , suggesting this region near Val713 and Ile716 is flexible . Although the final orientation of Tyr710 and the template DNA base is not consistent with the original 1L3S open crystal structure , this movement hints at the fundamental dynamics of the DNA polymerase active site while in the open state . Based on these simulations , we can conclude that the template base entering the active site is energetically accessible while polymerase is in the open state . Currently , it is unknown where the template base recognizes an incoming dNTP , although it has been hypothesized the preliminary interactions occur outside the active site [47] . These simulations suggest that the template DNA base could enter the active site prior to dNTP binding and recognize the incoming base while already in the active site instead of outside the active site . The Tyr714/Glu658 motif of DNA polymerase plays a vital role in dNTP binding and stability of the ternary complex due to its position in the active site . The 1LV5 and 3HP6 crystal structure suggests a stabilizing hydrogen bond between the side chains of Tyr714 and Glu658 for the ternary complex in the closed and ajar conformations respectively , while no hydrogen bond is expected for the open , binary state based on the 1L3S crystal structure . Upon removing the dNTP in the 1LV5 and 3HP6 structures , the Tyr714-Glu658 hydrogen bond is broken quickly ( <5 ns , see Figure 13 ) , suggesting this is the initial step toward opening of the fingers domain . Furthermore , recent studies have suggested this hydrogen bond plays only a minor role in stability of the ternary complex [13] , so it is not surprising the hydrogen bond is not present for long . According to crystal structures , the position of Tyr714 in the active site changes substantially based on the state of DNA polymerase . In the ternary complex with the fingers domain closed ( 1LV5 ) or ajar ( 3HP6 ) , Tyr714 is positioned next to the template base and hydrogen bonded to Glu658 . In the binary state ( 1L3S ) , Tyr714 moves into the active site , taking the place of the template nucleotide . In all of the 1LV5 and 3HP6 simulations , after early disruption of the Tyr714-Glu568 hydrogen bond , Tyr714 becomes more mobile creating van der Waals contacts with the template base . Eventually , these clashes result in a ∼90° rotation of the N-β-glycosyl bond ( Figure 14 ) of the nucleotide moving the nucleotide out of the active site entirely , while Tyr714 replaces the nucleotide in the active site and begins π-stacking with the n-1 base on the template strand , as the 1L3S ( open ) PDB structure suggested . For the 1LV5 Desmond simulation performed with the Charmm27 force field , this transition occurred at ∼300 ns and coincides with the opening of the fingers domain . By contrast , the same transition occurs after only 22 ns in the 3HP6 simulation . Although the transitions occurred later using the Amber force field , the relative timing between the N-β-glycosyl bond rotations for the 1LV5 and 3HP6 simulations remained consistent with the Charmm27 force field . The full polymerase fingers domain opening mechanism was characterized using the 1LV5 simulations , but the 3HP6 simulations were performed in the binary state to examine the opening process from ajar to open . As previously mentioned , in every trajectory beginning from the 3HP6 conformation the fingers domain opened quickly ( always <50 ns ) . The general mechanism was similar to the simulations starting from the closed simulation , except faster and without the formation of the previously proposed intermediate state . The Tyr714-Glu658 hydrogen bond breaks initially , which eventually allows Tyr714 to replace the template base in the active site upon rotation of the N-β-glycosyl bond of the nucleotide that positions the polymerase in the open conformation . Once in the open conformation , the simulations persisted in that state for the remainder of each trajectory ( up to 1 . 0 µs ) . The relative rate of opening of the polymerase starting from the 3HP6 simulations suggests the potential energy barrier between the open and 3HP6 ajar conformations is relatively low compared to other barriers in the opening process . The 3HP6 ajar conformation was trapped experimentally using a double mutant and with a non-Watson-Crick base pair in the active site ( dTTP-dG ) . Natively , the presence of a mismatch dNTP in the active site of DNA polymerase typically invokes re-opening of the fingers domain , permitting the incorrect dNTP to dissociate and allowing another dNTP to enter the active site . Considering the necessary efficiency of DNA polymerase at differentiating between correct and incorrect base pairs , it is logical to conclude the barrier between the mismatched ajar conformation ( 3HP6 ) and the original open conformation must be low so the enzyme can quickly release incorrect base pairs . This hypothesis would be consistent with our observation of a quick opening process from the 3HP6 ajar state to a conformation with the fingers domain fully open .
Although DNA polymerase I has been simulated previously using MD , no simulations have been performed using unbiased potentials over long time-scales before this study . We herein report the stability of DNA polymerase I using multiple MD packages ( Desmond and Amber ) utilizing two force fields ( Amber ff99SB and Charmm27 ) on time-scales of up to 3 . 0 µs at the high operational temperature of a thermophilic enzyme . The simulations independently and accurately reproduced large conformational changes of DNA polymerease I known from X-ray crystallography in addition to predicting a new intermediate that would be difficult to observe experimentally . Future simulations on polymerase can be performed with confidence knowing that the current force fields have the ability to reproduce experimentally derived structures , which implies we can ask more detailed and specific questions about the polymerase dynamics not already addressed in this study . From a broader perspective , this study also provides information about the state of our protein and nucleic acid force fields . Most MD studies perform simulations that do not reach the µs range , and only with recent technological advancements have we been able to reach these time-scales . As computational chemists simulate proteins and nucleic acids for longer time periods , we must ensure the force fields are able to maintain biologically stable structures . Most validation studies are performed on simulations of nucleic acids [30] , [48] or proteins [49] separately that are typically relatively small in size , but this study allows us to evaluate the performance of these parameters simultaneously on a system ( DNA polymerase I ) that is over 10 , 000 atoms unsolvated ( up to 80 , 000 atoms solvated ) . The results from the µs simulations in this study suggest the current force fields are sufficient for representing and describing the dynamics of large protein-DNA complexes on the µs time-scale . While it is unknown if these conclusions hold for even longer time-scales or larger systems , the force fields seem adequate to observe large-scale conformational changes in the current study . Future studies will focus on the mechanism of the fingers domain closing in the presence of a dNTP substrate in the active site .
|
All organisms are dependent on the proper replication of their DNA for survival . DNA polymerase is the enzyme responsible for copying our DNA during cell division . We have performed computational simulations on DNA polymerase to understand the fundamental dynamics of the enzyme . Our simulations provide new information about the way polymerase moves in solution that is not obtainable through traditional experimental techniques . In particular , we investigated the dynamics of DNA polymerase “opening” in the binary state ( enzyme+DNA with no nucleotide substrate ) starting from three different conformations . The results are consistent with available experimental data on the relative conformations of DNA polymerase in the binary state . Furthermore , we identified a novel intermediate species that we hypothesize plays a role in the dynamics of nucleotide substrate binding . Additionally , we determined the previously unknown ordering of events in the opening mechanism , and suggest new details about how the polymerase may interact with an incoming nucleotide substrate . Lastly , this research serves as a proof of principle that we can use our methodology to perform long-time scale computational simulations on DNA polymerase to explain currently unknown phenomena surrounding DNA replication .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"biochemistry",
"biochemical",
"simulations",
"computer",
"and",
"information",
"sciences",
"computational",
"chemistry",
"computer",
"modeling",
"molecular",
"dynamics",
"dna",
"replication",
"biology",
"and",
"life",
"sciences",
"dna",
"physical",
"sciences",
"computational",
"biology",
"chemical",
"biology",
"chemistry"
] |
2014
|
Molecular Dynamics Study of the Opening Mechanism for DNA Polymerase I
|
Many proteins and signaling pathways are present in most cell types and tissues and yet perform specialized functions . To elucidate mechanisms by which these ubiquitous pathways are modulated , we overlaid information about cross-cell line protein abundance and variability , and evolutionary conservation onto functional pathway components and topological layers in the pathway hierarchy . We found that the input ( receptors ) and the output ( transcription factors ) layers evolve more rapidly than proteins in the intermediary transmission layer . In contrast , protein expression variability decreases from the input to the output layer . We observed that the differences in protein variability between the input and transmission layer can be attributed to both the network position and the tendency of variable proteins to physically interact with constitutively expressed proteins . Differences in protein expression variability and conservation are also accompanied by the tendency of conserved and constitutively expressed proteins to acquire somatic mutations , while germline mutations tend to occur in cell type-specific proteins . Thus , conserved core proteins in the transmission layer could perform a fundamental role in most cell types and are therefore less tolerant to germline mutations . In summary , we propose that the core signal transmission machinery is largely modulated by a variable input layer through physical protein interactions . We hypothesize that the bow-tie organization of cellular signaling on the level of protein abundance variability contributes to the specificity of the signal response in different cell types .
Proteins do not act in isolation but interact with other proteins to fulfill important cellular functions . Often proteins are organized into pathways , which are tightly controlled cascades of protein binding events ( and those of other biomolecules ) . One important cellular function controlled by pathways is the transmission of extra-cellular signals from the cell membrane to the nucleus to provoke a response to changes in the environment of the cell . Signaling pathways are often active in many different cell types and are conserved at a large evolutionary scale [1] . Therefore , the characterization of mechanisms by which these ubiquitous pathways achieve specificity and fulfill largely different functions in different cell types or organisms is of crucial importance . One characteristic of signaling pathways is the bow-tie ( or hourglass ) architecture in which signals sensed by receptors converge onto a core consisting of a smaller number of proteins followed by a diverse response of transcription factors . The bow-tie property has been observed in different human signaling pathways such as those downstream of epidermal growth factor receptor [2] and of toll-like receptor [3] . It is generally believed to confer pathways with robustness and evolvability by buffering input signals and modularizing the response [4] . However , robustness by hierarchy comes to a price: mutations in the central core proteins might easily hijack the behavior of the entire system [5] . The robustness of pathways needs to be in balance with flexibility allowing pathways to vary their response to similar stimuli at different time points or in different cell types of the same organism . One intuitive though largely unexplored link to the bow tie model comes from investigations of protein-protein interaction ( PPI ) networks associated with gene expression information: it was observed that tissue-specific proteins tend to bind to core cellular proteins , possibly to modulate housekeeping cellular processes in a cell type-specific manner [6] . The mechanistic understanding about how activation of the same signaling pathways can lead to cell type-specific responses is rather anecdotal and involves diverse mechanisms such as cell type-specific feedback loops [7] , different abundances of transcriptional cofactors [8] , [9] or cell type-specific chromatin states [10] . However , it is largely unknown if there are functional or ( network ) topological signal protein classes that preferentially act as tissue-specific modulators of signaling . Therefore , we will explore here if we can adapt the bow-tie model to identify protein classes that show distinct evolutionary and abundance variability patterns . Recently a mass spectroscopy analysis accomplished by Mann and co-workers [11] has determined absolute protein copy numbers for 11 common human cancer cell lines with high coverage . The analyzed cell lines covered distinct tissue origins , such as lung carcinoma , hepatoma , osteosarcoma , colon carcinoma , and leukemia [11] . Multiple technical replicates allow to make robust estimates of protein expression variability by contrasting the inter-cell line with the intra-cell line variability and , therefore , make this dataset a perfect choice for quantifying differences in protein expression among different cell types . Using this dataset and defining a measure of protein conservation covering a broad set of species , we systematically investigate patterns of protein expression variability and phylogenetic conservation in human pathways . We observe large differences in protein expression and in phylogenetic conservation between and within different human pathways . Focusing on human signaling , we identify components of signaling pathways with distinct properties in respect to these features . By incorporating germline and somatic disease mutations , we show how the thereby identified pathway components underlie different selective constraints .
To estimate mean abundance and abundance variability of human proteins , we processed a recent proteomics study quantifying protein expression levels in 11 human cell lines [11] . Due to technical limitations of the mass spectrometry approach , lowly abundant proteins are associated with higher standard deviations ( Figure S1A ) . To correct for this , we computed F values to estimate the biological variability among cell lines . F values were computed by dividing the between-cell variation by the within-cell variation . Thereby we successfully eliminated any dependencies between the protein abundance and variability caused by technical biases ( see Figure S1B ) . In this study , we used the F values computed on cancer cell lines to distinguish proteins that are stably expressed across different cell types from those showing more diverse abundance profiles . We validated the underlying assumption that we can generalize our observations made on cancer cell line data to healthy tissues by contrasting the computed F values with RNA ( 16 human tissues ) [12] and protein ( 28 mouse tissues ) measurements [13] ( see Methods ) . In both cases there is generally a good agreement between protein variability across the cell lines and healthy tissues ( Figure S2A–B ) . This supports the idea that we can generalize from cancer cell lines to healthy human tissues with respect to protein abundance variability . To analyze how protein conservation relates to protein expression abundance and diversity of proteins involved in human pathways , we analyzed the conservation of all proteins in the expert-curated Reactome database [14] in selected species from Plants , Yeasts , Worms , Insects , Fishes , Birds , and Mammals . We transformed the information in which species a human protein is conserved , as indicated by HomoloGene [15] , into a phylogenetic tree-based conservation score ( see Methods and Figure S3 ) , which increases linearly with the amount of species in which homologous proteins are found and estimates of evolutionary distance separating these species from each other . We observed significant positive correlation between the evolutionary conservation of human proteins and their mean abundance ( see Figure 1A ) or negative correlation in their variability in the different cell lines analyzed ( see Figure 1B and an example on the EGFR/MAPK pathway containing both variable/lowly conserved and stably expressed/conserved proteins in Figure 1C ) . To identify cellular processes that differ in their phylogentic conservation and cross-cell variability , we selected all Reactome pathways that are expected to be general and not restricted to only some cell types ( ten pathways ) : Cell cycle , DNA replication , and chromosome maintenance [DR] , Extracellular matrix organization [MO] , Gene expression and RNA processing [GE] , Membrane trafficking [MT] , Metabolism [MB] , Signal transduction [ST] , Apoptosis [AP] , Developmental Biology [DB] , Transmembrane transport of small molecules [TM] , and Cell-Cell communication [CO] . Several other Reactome pathways are restricted to very specific body cell types ( e . g . , Neuronal system and Muscle contraction ) , or are of low coverage ( e . g , Circadian clock proteins ) , and were neglected for further analysis ( for details on the selection see Materials and Methods and Table S1 ) . We associated all members of the ten pathways with mean protein abundance , abundance variability and phylogenetic conservation values . A fraction of the proteins ( 18 . 7% from the 4069 Reactome proteins that we could associate with at least one of the investigated features ) participates in more than one pathway . We compared the distributions of mean abundance , abundance variability and phylogenetic conservation among exclusive proteins associated only with one pathway to the distributions associated with proteins involved in several pathways . We observed that exclusive proteins are significantly less conserved ( P<e−16; Wilcoxon-Mann-Whitney ) , more variable ( P<e−12; Wilcoxon-Mann-Whitney ) and less abundant ( P<e−8; Wilcoxon-Mann-Whitney ) ( see Figure S4A–C ) . Next , to elucidate the common and variable elements in 11 cell types with respect to the ten Reactome pathways , we considered only proteins found exclusively in one pathway . We observed large differences with respect to the three investigated features among human functional pathway classes ( Figure 2A ) . In general , we found two opposing groups of behavior . Housekeeping pathways ( GE , MB , MT and DR ) are enriched in conserved proteins ( Figure 2B ) , have low to average variability ( with GE being the only pathway class with a significant depletion in variability; Figure 2C ) and ( except for DR ) higher abundance ( GE and MB are significantly enriched in highly abundant proteins; Figure 2D ) . Specific pathways ( ST , MO , CO , DB ) tend to have less conserved proteins ( ST and MO show a significant depletion; Figure 2B ) , have higher variability ( Figure 2C ) and less abundance ( ST and DB are significantly associated with lower abundance; Figure 2D ) . The remaining two pathways ( AP and TM ) showed a rather average behavior , with exception of the significantly lower abundance of TM proteins . Signal transduction ( ST ) shows in all three categories ( mean abundance , abundance variability and conservation ) a significant deviation from random expectation and has a larger than average spread of the distribution of variability values ( fourth highest inter-quartile range among the ten variability distributions ) . This indicates that while average variability of signaling proteins is high , we will also find a large proportion of proteins with low variability in signaling pathways . Hence , we studied the variable and constant parts of signaling pathways . To elucidate in more depth the protein abundance variability in signal transduction pathways , we investigated whether we can relate the molecular function of proteins in signaling pathways to their abundance , variability and conservation . For this purpose , we chose two complementary protein classification strategies and signaling resources . ( a ) We assigned , where non-ambiguously possible , signaling proteins in Reactome to one of the following Gene Ontology and UniprotKB categories: membrane-bound receptor , phosphatase , kinase , transcription factor , adaptors , and GTPase binding ( see Methods for details ) . ( b ) We retrieved the full set of proteins and their classification into signaling-related sections ( ligand , receptor , mediator , cofactor , transcription factor ) from the signaling pathway database SignaLink [16] , which is another manually curated resource classifying proteins into pathway sections based on their role in signal transmission and topological properties . For example , SignaLink distinguishes between mediators and cofactors of signal transduction , where mediators are core pathway members and the cofactors merely modulate the function of signaling proteins . We only considered signaling proteins in SignaLink that are uniquely assigned to one class . Due to different curation strategies ( discussed in [16] ) , the sets of proteins associated with pathways largely differ between Reactome and SignaLink: we could automatically assign pathway functions to 802 proteins from Reactome while the SignaLink database contains 667 proteins with unique roles in signaling . The overlap between the two sets consists of only 80 proteins . The differences in protein composition and in the way proteins are associated with pathway functions allow us to study the evolution and expression of signaling proteins on two largely independent datasets . We observed large differences in conservation , mean abundance and abundance variation for different classes within both sets of annotated signaling proteins ( see Figure S5 ) . Next , we pooled all functional and topological classes into three layers: input ( receptors ) , transmission ( SignaLink: mediators and cofactors; Reactome: kinases , phosphatases , adaptors and GTPase binding proteins ) and output ( transcription factors ) . We compared the resulting feature distributions . With respect to protein abundance variability we observed for both the Reactome and the SignaLink proteins significantly higher values associated with the input layer than with the transmission layer ( P<0 . 01 , Wilcoxon-Mann-Whitney ) . The difference between the transmission and the output layer was for both data sources not ( Reactome ) or only marginally ( P = 0 . 03; Wilcoxon-Mann-Whitney; SignaLink ) significant . The conservation of proteins of the transmission layer was significantly larger than of proteins of both the input and the output layer ( for all comparisons: P<0 . 00001; Wilcoxon-Mann-Whitney ) . In summary , taking a mechanistic view on signaling pathways where an input layer receives signals from the environment , a transmission layer integrates and proceeds the signal and an output layer orchestrates the transcriptional response , two patterns emerge: ( i ) In terms of conservation , we see a bell shaped curve with a high conservation of the transmission layer and lower conservation of the input and output layer ( Figure 3A and S5 ) . ( ii ) With respect to protein abundance variability , signaling pathways show a gradient with decreasing variability from the input to the output layer ( Figure 3B ) . There is a sharp drop in variability between the input and the transmission layer while the transmission and the output layer are rather similar in terms of variability ( Figure 3B ) . These results are schematized in Figure 3C . The difference in terms of protein abundance between different functional classes was less pronounced . Interestingly , the variability and conservation of mediators and cofactors of signaling is almost the same ( Wilcoxon-Mann-Whitney test does not yield P<0 . 05 ) . This suggests that modulators in the transmission layer contribute less to cell type specific differences . We also compared the investigated features associated with proteins exclusively members of one signaling pathway ( 1253 proteins ) to those associated with proteins re-used in several signaling pathways ( 235 proteins ) ( see Figure S4D–F ) . We observed a significantly higher conservation of proteins that are members of several signaling pathways ( P<e−16; Wilcoxon-Mann-Whitney ) , while mean abundance and abundance variability did not show significant differences between the protein sets . This is in agreement with the higher number of proteins of the transmission layer among the proteins associated with multiple pathways ( e . g . , 14 adaptors and 49 kinases , which exceeds random expectation 3- and 1 . 5-fold , respectively ) . To investigate how our observation of a lowly variable and strongly conserved transmission layer might help to understand general principles by which signaling pathways are modulated in a tissue-specific manner , we overlaid our sets of signaling proteins ( merged from Reactome and SignaLink ) with PPI network data from the database HIPPIE [17] , [18] . As we observed the highest protein abundance variability in the input layer ( Figure 3B ) , we hypothesized that this variability affects the dynamics of physical interactions between the input and the transmission layer ( by removing signaling links in certain tissues or modulating competition for binding in others ) . We tested the hypothesis that PPIs between input and transmission layer tend to happen between proteins with a larger difference in variability than for PPIs between the transmission and the output layer , within one layer , or randomly chosen PPIs ( Figure 4A ) . To test this , we randomly sampled interacting protein pairs between and within the specified layer ( each distribution of differences in protein abundance variability consisted of 1000 randomly sampled interacting protein pairs ) . We found the largest difference between the variability of the interacting proteins for PPIs between the input and the transmission layer ( Figure 4B ) . The distribution of differences in variability was significantly larger than all other distributions ( P<10−16; Wilcoxon-Mann-Whitney ) . As the difference in variability is highest between the input and the transmission layer , the results met our expectation . To test if the observed difference in variability between interacting proteins between the input and the transmission layer can be solely attributed to the membership of the participating proteins in different layers , we compared the distribution of variability differences for interacting proteins to those of randomly sampled , non-interacting protein pairs where one protein is from the input and one is from the transmission layer ( Figure 4C ) . Strikingly , the differences in variability of interacting protein pairs are significantly higher than for those of non-interacting protein pairs ( P<10−16; Wilcoxon-Mann-Whitney ) . We can reproduce the same results when permuting the links between randomly sampled interacting protein pairs between the input and the transmission layer . This demonstrates that the observed differences can be attributed to both the different network positions of proteins in signaling pathways and a tendency of variable input layer proteins to physically interact with stably expressed transmission layer proteins . These observations suggest that PPIs between the input and the transmission layer might have an impact on the tissue-specificity of signaling . The higher conservation of the signal transmission layer is in agreement with the bow-tie ( or hourglass ) model proposing the presence of a conserved core with variable input and output layers modulating the signal response ( e . g . , as observed in the signaling pathway downstream of EGFR [2] ) . The trade-off between fragility and robustness of such architecture has been discussed [5] and , hence , we studied the distribution of disease mutations with respect to protein abundance , variability and conservation . Both germline and somatic mutations can lead to disease by perturbation of signaling pathways , e . g . in cancer [19] . Therefore , we investigated the dependency between different mutation types and protein abundance , variability and conservation . We found signaling proteins with somatic mutations to be significantly higher conserved than proteins with germline mutations ( P = 0 . 001; Wilcoxon-Mann-Whitney; see Figure 5A ) . Additionally , we investigated how the average number of mutations changes for proteins in different conservation intervals ( Figure 5B ) . We found that both the average number of somatic and the average number of germline mutations peak for intermediate conservation values with the distribution of somatic mutations being shifted towards higher conservation values ( resulting in the observed higher conservation values associated with somatic mutations ) . To compare the distributions of disease mutations to background mutation rates , we also computed the average number of all reported single nucleotide polymorphisms ( SNPs ) in UniProt associated with the different conservation intervals . This distribution does not peak as sharply as the two disease mutation distributions and is higher for low conservation values . To investigate functional causes for the unexpected depletion of mutations for extreme values of conservation , we computed enrichment of functional categories in the sets of very lowly and very highly conserved proteins ( see Methods ) . Among the most highly conserved proteins functions with the strongest enrichment were related to protein ubiquitination and the proteasome complex ( P = e−30 ) . The low occurrence rates for all mutation categories ( somatic , germline and all SNPs ) indicate that no mutations are tolerated in these proteins to maintain cellular integrity . Among the lowly conserved proteins the most strongly enriched functions were all related to sensory and olfactory perception ( P = e−195 ) . The lower rate of disease mutations as compared to all SNPs within this group likely reflects the tolerable effect of mutations within the sensory system on cell viability . With respect to protein expression , we found that signaling-related proteins with somatic cancer mutations have a significantly lower protein abundance variability ( P = 0 . 0005; Wilcoxon-Mann-Whitney; see Figure 5C ) as those with germline mutations . The distributions of mean number of somatic and germline mutations associated with different intervals of variability values show opposing behavior to each other ( Figure 5D ) : While low variability values are associated with high numbers of somatic mutations , the mean number of germline mutations peak for larger variability values ( before the number of germline mutations drops for the highest variability interval ) . We also found a weak though significant tendency for disease proteins with somatic mutations to be more highly expressed than proteins with germline mutations in signaling pathways ( P<0 . 05; Wilcoxon-Mann-Whitney ) . Taken together these observations support our previous hypothesis that the stably expressed and conserved core signaling pathway may perform a fundamental general role in development and generally in many cell types , and therefore germline mutations seem to be not tolerated . In contrast , non-core proteins , which tend to be expressed more cell type-specific , may tolerate germline mutations to a larger extend , presumably as the causing diseases will affect only some tissues .
We present here a systematic study of signaling proteins with respect to protein level abundances and evolutionary conservation . By doing so , we can confirm previous observations ( mainly based on mRNA levels ) but also provide novel hypotheses on the organization of human signaling pathways ( as discussed in the following ) . Some of our central findings are drawn from the analysis of protein abundance variability across cancer cell lines . As we are here interested in studying normal cellular properties , we demonstrate that there is a good agreement between protein variability across cancer cell lines and across normal cells . We report significant correlations between phylogenetic conservation and both protein abundance ( positive correlation ) and abundance variability ( negative correlation ) . These observations suggest on one hand that evolutionary conserved proteins could have an essential general function for every cell type ( see Figure 1C ) . This is in agreement with previous proteomics studies [20] , [21] identifying a central proteome of ubiquitously and abundantly expressed proteins , which are correlated in their abundances across different species . This central proteome was found to have a higher than average conservation . On the other hand , recent proteins exhibit less abundance and more cell to cell type variability , suggesting they should be more involved in cell type-specific differences . This agrees with previous studies reporting that genes with RNA expression profiles restricted to a small number of mouse tissues tend to be metazoan-specific [22] , [23] . It has been observed before [6] that tissue-specific proteins tend to interact with universally expressed proteins . To elucidate mechanisms by which the interactions between tissue-specific and general housekeeping proteins lead to tissue-specific modulation of signaling pathways , we investigated patterns of protein expression and conservation among signaling pathways . An important implication of our analyses is that the interactions between receptors and cytoplasmic proteins might have the strongest impact on the modulation of tissue-specificity of signaling . We observe a larger difference in protein abundance variability between signaling proteins associated with the input and the transmission layer than , for example , between cofactors and mediators within the transmission layer . This difference is even stronger for proteins that physically interact . The decreasing protein abundance variability from the input to the output layer might be surprising ( especially since in many of the known cases the cell type-specific response to signaling pathway activation depends on the abundance of transcriptional cofactors; see Introduction ) . However , the low variability of the output layer is additionally supported by our observation that cellular processes related to gene expression have the lowest variability among all cellular processes . Also , it is in agreement with previous studies reporting a lower mRNA variation for intracellular signaling components [24] and demonstrating how different cell types recruit a common effector network to determine the cellular response [25] . Several computational and experimental studies suggested the presence of a core signaling backbone ( e . g . , [25] , [26] ) , sometimes referred to as the hourglass or bow-tie model of signaling [4] , [5] to emphasize how signals converge from a larger input onto a conserved core . However , the mechanisms by which the core signaling machinery is modulated to respond in a cell type-specific way remain largely unknown . Here , we propose that an evolutionary conserved and stably expressed core of signaling pathways , which is modulated by less conserved and non-uniformly expressed receptors , extends the previous model and provides means to understand cell type-specific signaling as the consequence of a dynamic wiring logic between the input and the transmission layer . In addition , the conserved core is re-used in different pathways as our analysis of the conservation among proteins unique to a single pathway as compared to proteins being part of multiple pathways revealed . This holds both for top-level cellular processes as well as signaling pathways . We show how this general pathway organization principle shapes the distribution of disease mutations . As it has been discussed before [5] , the bow-tie architecture confers biological systems with robustness but at the same time creates fragilities . It allows ( due to the modularization and central control units for entire biological processes such as apoptosis or cell growth ) its hijacking by manipulating a single or a few nodes . In PPI networks , most disease proteins are located in the network periphery and are only expressed in a limited number of tissues [27]–[29] , likely due to developmental constraints selecting against mutations in central and housekeeping proteins . However , somatic mutations ( not undergoing in utero selection ) show contrary patterns and are associated to a higher degree with central and housekeeping genes [28] . In agreement , we report here that signaling proteins harboring germline mutations differ from proteins with somatic mutations with respect to protein abundance variability ( and to a weaker degree in conservation and abundance ) . It is interesting to note that a recent study [30] found mutations in the TGF-β and Wnt/β-catenin signaling pathways to be often associated with only a single cancer type ( as opposed , for example , to mutations in proteins related to genome integrity , which tend to be associated with different cancer types ) . This again highlights the importance of understanding the cell type-specific dynamics of signaling for the elucidation of tissue-specific disease mechanisms . In summary , to understand cell type-specific signaling mechanisms and , more general , to understand “what makes a cell type” , we need to distinguish between core proteins conserved through evolution , and those recently acquired and incorporate information on protein concentration to interaction networks . Ideally this should be complemented by structural information to distinguish between competing and compatible interactions [31] as well as protein localization in the cell . The effect of receptor abundance on their physical interactions with members of the transmission layer ( such as kinases , GTPase binding proteins and adaptors ) should be a major research focus to improve the understanding of the combinatorial logic of cooperativity and competition for binding .
We retrieved a recently published proteomics dataset [11] quantifying the abundances of almost 12 , 000 proteins in eleven human cell lines ( A549 , GAMG , HEK293 , Hela , HepG2 , Jurkat , K562 , LnCap , MCF7 , RKO , and U2OS ) . We standardized the given mass spectrometry intensities ( by subtracting from each measurement the sample mean and dividing by the sample standard deviation ) and extracted mean abundance and variance values . The mean abundance was computed averaging the standardized iBAQ values . To estimate the variability , we computed F values dividing the between-cell variability by the within-cell variability on the standardized label-free quantification intensities , thereby eliminating the dependence between variation and abundance ( see Figure S1 ) . Only proteins where considered that were detected in at least 50% of the MS replicates and that could be uniquely and unambiguously mapped to one protein entry in UniProt/SwissProt . For visualization purposes , distributions of F values are shown in logarithmic scale throughout the manuscript . We contrasted the computed F values with gene expression measurements from healthy tissues . First , we retrieved a set of housekeeping genes defined based on uniformly distributed RNA abundance measurements in 16 healthy human tissues [12] . Second , we retrieved protein quantifications from 28 healthy mouse tissues [13] . As in the case of the mouse proteomics study no replicates were available , we could not normalize the inter- with the intra-sample variability . Therefore we only considered highly abundant proteins ( larger than average ) to minimize the confounding impact of protein abundance on variability . We also required that the proteins had been detected in all samples . We extracted the proteins falling in the lowest and the highest standard deviation quartile and mapped these proteins to their human orthologs . Homology information for proteins was extracted from the NCBI database ( http://www . ncbi . nlm . nih . gov/sites/entrez ) using the HomoloGene search tool . We considered conservation in Pan troglodytes , Mus musculus , Rattus norvegicus , Gallus gallus , Danio rerio , Drosophila melanogaster , Anopheles gambiae , Caenorhabditis elegans , Schizosaccharomyces pombe , Saccharomyces cerevisiae , Eremothecium gossypii , Arabidopsis thaliana , and Oryza sativa . A phylogenetic tree was constructed using inferred phylogenetic relationships between these species [32] . For the purpose of associating each protein with a conservation score reflecting the evolutionary distances across the species in which the protein is conserved , we associated each human protein with a pruned phylogenetic subtree containing only those species in which the protein is conserved . The conservation score was computed as the sum of all branch lengths present in the pruned subtree divided by the sum all branch lengths present in the full phylogenetic tree . In formal notation , for each protein i a pruned tree Ti is constructed as a subtree of the full phylogenetic tree T . Branch lengths are mapped as weights to the set of edges E . The conservation score is then computed as:where Ei is the set of edges associated with subtree Ti and w ( e ) the weight corresponding to edge e . For an example of the conservation score computation see Figure S3 . Proteins involved in the 22 top-level pathway classes in the Reactome pathways database [14] were downloaded ( May 2013 ) . Several pathways were merged or removed . The complete 22 pathways defined in Reactome are listed in Table S1 together with reasons for deletion or merging . To assign proteins from Reactome to functional classes , we retrieved functional data from GO and the UniProt Knowledgebase ( UniProtKB ) . We considered the intersection of proteins being associated with the UniProtKB term ‘Membrane’ and those associated with the UniProtKB term ‘Receptor’ as membrane-bound receptors . Proteins indicated as being ‘DNA-binding’ in UniProtKB were considered as transcription factors . Kinase classification was also retrieved from UniProtKB . The definitions of phosphatases ( GO:0016791 ) , adaptors ( GO:0035591 ) , and GTPase binding proteins ( GO:0051020 ) were retrieved from GO . To construct the set of germline mutations , we retrieved all disease mutations from OMIM [33] and excluded entries labeled as somatic mutations . The set of somatic mutations was assembled by retrieving cancer mutations from COSMIC [34] including only somatic missense mutations . We computed enrichment of functional categories among the proteins falling into the first and the last quantile of the conservation distribution . We used the web tool DAVID [35] to identify gene ontology terms and domains enriched among these protein groups . We used all signaling proteins as a background . Indicated enrichment P-values correspond to the Bonferroni-corrected values given by DAVID .
|
Cell function is determined by highly organized networks of biological molecules . An important class of protein pathways maintains the transmission of signals from the cell membrane to the nucleus . These signaling pathways are reused for different purposes at an evolutionary scale and in different cell types of the same organism . However , it is largely unknown how this flexibility is achieved and how this flexibility is balanced with the high degree of evolutionary conservation of some signaling proteins and the need for robustness against intra- and extra-cellular perturbations . We show how functional roles of signaling proteins determine patterns of evolutionary conservation , protein abundance ( the average over different human cell lines and its variability ) and disease mutations . Projecting pathway annotations on protein-protein interaction ( PPI ) networks , a picture emerges in which PPIs between variable and less conserved receptors and stable and conserved proteins of the core signal transmission machinery largely modulate signaling activity in a tissue-specific manner . This has important implications for the distribution of disease mutations in signaling pathways , which need to be considered for the understanding of their effect .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"organismal",
"evolution",
"protein",
"interactions",
"signaling",
"networks",
"protein",
"abundance",
"molecular",
"cell",
"biology",
"network",
"analysis",
"eukaryotic",
"evolution",
"computer",
"and",
"information",
"sciences",
"proteins",
"proteomics",
"systems",
"biology",
"biochemistry",
"signal",
"transduction",
"cell",
"biology",
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"and",
"life",
"sciences",
"computational",
"biology",
"evolutionary",
"biology",
"cell",
"signaling",
"signaling",
"cascades"
] |
2014
|
Protein Conservation and Variation Suggest Mechanisms of Cell Type-Specific Modulation of Signaling Pathways
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The bacterium Streptococcus pneumoniae ( pneumococcus ) is one of the most important human bacterial pathogens , and a leading cause of morbidity and mortality worldwide . The pneumococcus is also known for undergoing extensive homologous recombination via transformation with exogenous DNA . It has been shown that recombination has a major impact on the evolution of the pathogen , including acquisition of antibiotic resistance and serotype-switching . Nevertheless , the mechanism and the rates of recombination in an epidemiological context remain poorly understood . Here , we proposed several mathematical models to describe the rate and size of recombination in the evolutionary history of two very distinct pneumococcal lineages , PMEN1 and CC180 . We found that , in both lineages , the process of homologous recombination was best described by a heterogeneous model of recombination with single , short , frequent replacements , which we call micro-recombinations , and rarer , multi-fragment , saltational replacements , which we call macro-recombinations . Macro-recombination was associated with major phenotypic changes , including serotype-switching events , and thus was a major driver of the diversification of the pathogen . We critically evaluate biological and epidemiological processes that could give rise to the micro-recombination and macro-recombination processes .
The evolution of many bacterial species is largely driven by horizontal exchange of sequence . Often , this can be attributed to the movement of autonomously mobile genetic elements ( MGEs ) . Many of those are able to insert into the host chromosome through site-specific recombination mediated by an integrase . However , in ‘naturally’ transformable species that possess a competence system , exogenous DNA can be imported from the environment and integrated into the chromosome through homologous recombination ( HR ) . This process was first discovered in Streptococcus pneumoniae ( the pneumococcus ) , representing some of the earliest work on molecular genetics [1] . Initially , recombination was considered by many microbiologists to be interesting but rare . However , later population-based studies demonstrated that it can have a quantifiable impact on population genetic structure of many bacteria , including S . pneumoniae [2]–[4] . Additionally , as this mechanism only requires that the acquired DNA is homologous at the ends , recombination allows for the cassette-like transfer of highly variable genes , such as those that encode for the pneumococcal capsule [5] , [6] , in a process originally defined as ‘homology-directed illegitimate recombination’ [7] . This has important clinical consequences , as this exchange of sequence has played a crucial role in the development of pneumococcal antibiotic resistance [8] , as well as the ‘switching’ of capsule types that can result in vaccine escape [9] , [10] . The rate at which the recombination process occurs is of importance when considering the adaptation of the bacterium to clinical interventions . The simplest null expectation is that HR is a homogeneous process across the species . However , recent findings suggest that homogeneity of recombination is unlikely to capture the dynamics of horizontal sequence exchange in pneumococci . In particular , heterogeneity has been observed in the rates at which different genotypes accumulate sequence diversity through HR . Analysis of multilocus sequence typing data identified a subset of ‘hyper-recombinant’ pneumococci that were more likely to be resistant to a number of antibiotics [11]; similarly , comparison of lineages within a single population found significant variation in the observed rate of HR [12] . Second , in vitro work has found that the frequency of recombination events occurring across the genome in isogenic recipient bacteria varies with the concentration of donor DNA , suggesting the environment is likely to influence the process of sequence transfer [13] . Similarly , extensive exchanges between pneumococci over short time periods have also been observed in clinical isolates , sometimes with important phenotypic consequences [14]–[16] . Third , variation has been observed in the rate at which pneumococci undergo transformation in experimental systems [17] , [18] . Therefore more detailed quantification of the observed contribution of HR will be invaluable in defining and understanding the behaviour of distinct lineages under different conditions . This in turn should help us understand how recombination contributes to the overall rate of diversification , and how it drives adaptive changes in pneumococcal populations . The opportunity for such an analysis is presented by the recent whole genome sequencing of two international collections representing contrasting pneumococcal genotypes . The first is a set of 241 pneumococcal genomes of the recently emerged pandemic multidrug resistant lineage , PMEN1 [19] . This lineage appears to have originated in Europe in 1970s , and in the following decades spread rapidly across the world . The ancestral serotype of this lineage , serotype 23F , has switched to new capsules by HR which have resulted in its evasion of the 7-valent vaccine introduced in the early 2000s . The second lineage is a set of serotype 3 isolates belonging to clonal complex 180 ( CC180 ) [20] . Serotype 3 , which causes disease associated with high levels of mortality , has been recently included in the expanded 13-valent conjugate vaccine formulation . The CC180 lineage appears to be older than PMEN1 , yet there is little evidence of it having undergone homologous recombination in recent decades , with the consequence that it is generally susceptible to antibiotics and has not altered its serotype . Hence these two genotypes , PMEN1 and CC180 , are highly distinct both in terms of their phenotypes and evolutionary dynamics . This work describes the fitting of different mathematical models of sequence exchange to the HR identified in the PMEN1 and CC180 datasets in order to identify and characterise and heterogeneity evident in the process . This resulted in the identification of two different classes of HR in both lineages: micro-recombination and macro-recombination . Potential underlying mechanistic explanations for this observation , and the implications for bacterial evolution , are discussed .
The analysis presented here is based on the inference of individual HR events , as previously described by Croucher et al . [19] . Briefly , this approach identifies independent HR events as clusters of SNPs in a genealogy reconstructed from whole genome alignments . Removal of those events allows to establish a clonal tree based on vertical transmission of SNPs . The inference for the PMEN1 lineage was based on an alignment of sequences , resulting in a genealogy with branches and homologous recombinations , whereas the inference for the CC180 lineage was based on an alignment of sequences , resulting in a genealogy with branches and homologous recombinations . Let label the branches , and let be the number of HR events assigned to branch , such that . For a given branch , let label the recombination events , and let be the length of genetic tract , in DNA base pairs , replaced by the HR event . We define the recombination rates in our models as rates per unit of branch length . Thus , their interpretation depends on the chosen measure of branch length . Since our model structure is generic with respect to this choice , by default the branch length is measured by years estimated using a dated genealogy based on a relaxed molecular clocked estimated using Bayesian methods . ( The results for alternative branch lengths are given in Tables 4–5 , Figures 8–9 and Text S2 . ) We thus use a statistical modelling approach to explain the number and size of HR events on a branch of length given the genealogy of a lineage . We use a modelling approach to test whether recombination in S . pneumoniae is heterogeneous with regard to its rate or length distribution . Four models were devised to account for patterns observed in the data: ( i ) recombination is homogeneous in frequency and in size ( Model 1 ) ; ( ii ) recombination is heterogeneous in frequency or in size , with heterogeneity modelled as deviation from the null model 1 ( Model 2 ) ; ( iii ) recombination is heterogeneous in frequency and size , and is modelled by two independent and homogeneous processes of recombination with different frequency and size: micro-recombination and macro-recombination ( Model 3 ) ; and ( iv ) recombination is heterogeneous in frequency and size , as in model 3 , but the heterogeneity in frequency is independent from the heterogeneity in size ( Model 4 ) . The models were fitted by the maximum likelihood method , namely maximising the log-likelihood function given in Text S1 . This was done using optimization functions NMaximize or FindMaxiumum in Mathematica 8 . 0 . The comparison between four different models was performed using the Akaike's Information Criterion , adjusted for finite degrees of freedom ( AICc ) . We considered one model to be a better fit than another when the difference in AICc was less than 10 ( ) . The best model was chosen as the one with the lowest value of . If multiple models were the best fit to the data , the model with the smallest number of parameters was chosen as the best by the rule of maximum parsimony . Goodness of fit was determined by verifying the ability of the model to replicate the data under re-simulation . To that end , marginal distributions of frequency and size of the simulations were compared to the equivalent marginal distributions of the data ( see Results ) . The details of the simulations are described in Text S3 . In brief , an ancestral sequence of S . pneumoniae was chosen as the earliest isolate of PMEN1 known [19] , [22] . A forward , discrete-time simulation was designed to simulate the evolution of the lineage , including diversification through recombination simulated through incorporating homologous sequence from other publically available pneumococcal genomes . We assumed that at every time step the sequence acquired a single base substitution , and could diversify into two independently diversifying lineages with a constant probability . Each sequence also had a probability of being sampled at each timestep , after which it stopped evolving . The simulation was stopped when the population reached a maximal number of sequences , . At each timestep , recombination occurred as prespecified by one of the four models: A , B , C or D . In Model A , recombination occurred homogeneously across the genome , with lengths of recombinations following a geometric distribution . In Model B , heterogeneity ( micro/macro-recombination ) was introduced in frequency but not the size . In Model C , heterogeneities in both frequency and size were correlated , as described in Model 3 above . In Model D , heterogeneity was also introduced in both frequency and size but the two were treated as independent variables for each recombination . Each model was run three times , giving 12 simulations overall .
To study the process of HR in the evolutionary history of the two lineages , PMEN1 and CC180 , we fitted mathematical models which describe how recombination events are distributed along the branches of the evolutionary tree of each lineage of S . pneumoniae . The procedure of model fitting is described in detail in Text S1 . The phylogenies of both lineages have been constructed as described previously in [19] , [20] based on vertically inherited point mutations , and were shown to be highly consistent with a molecular clock . Recombination events were reconstructed such that they were associated with particular branches of the phylogeny [19] . To remove events that may have been introduced through the movement of MGEs in PMEN1 , rather than being mediated by HR , any events affecting the prophage remnant , prophage MM1-2008 or ICE Sp23FST81 were not considered in this analysis [22] . Likewise , for CC180 , these MGEs included the OXC141 prophage locus and a single putative integrative and conjugative element ( ICE ) [20] . The distribution of recombination events on the phylogenetic trees of both lineages is summarised in Fig . 2 . The simplest model considered is that recombination events occur as a homogeneous point Poisson process through time with rate , so that the number of events occurring on a genealogical branch of length is Poisson distributed with mean , and that event sizes are geometrically distributed , with the mean length of genetic tract replaced by recombination for each event being base pairs of DNA ( see Fig . 1 and Methods ) . This model failed to capture clear heterogeneities in both the rate and size of events in PMEN1 ( Fig . 3A–C & Table 1 ) , and the same was true for the CC180 lineage ( Fig . 4A–C & Table 2 ) . A standard way to empirically describe heterogeneity is to quantify over-dispersion of the distribution of interest . To quantify heterogeneity in frequency and size in both lineages , we extended the approach in model 1 . The extension of Poisson and geometric distribution is in both cases a negative binomial distribution with parameter , which reduces to a geometric distribution for and to Poisson for very large values of ( see Fig . 1 ) . A model based on a negative binomial distribution of events per branch with mean and dispersion coefficient , and a negative binomial distribution of event sizes with mean bp and dispersion coefficient fit the data much better than the homogeneous , Poisson-based model for the PMEN1 dataset ( ; Fig . 3D–F & Table 1 ) and also for the CC180 dataset ( ; Fig . 4D–F & Table 2 ) . This demonstrates that both the recombination rate and recombination event size are heterogeneous processes , but gives little insight into the potential mechanisms generating heterogeneity . Heterogeneity in the recombination rate suggests that recombination sometimes occurs in discrete saltations rather than at a homogeneous rate . We further observed a correlation between the frequency of recombination events and their size ( Fig . 2C and 2F ) . We thus modelled the recombination process by a mixture of two , homogeneous recombination processes . The first process , which we refer to as micro-recombination , leads to single small replacements . The second process , which we refer to as macro-recombination , leads to multiple synchronous or near-synchronous larger replacements . We assumed that the micro-recombination process is described by the same parameters and as in the null model; the macro-recombination process occurs at rate , in which multiple tracts of DNA are incorporated into the genome by HR simultaneously ( or at least in a short period of time compared to the genealogical branching process , so that these end up assigned to a single phylogenetic branch ) . We model the number of gene segments incorporated per macro-recombination event by a Poisson distribution with mean , and the event sizes are geometrically distributed with mean length of genetic tract replaced by recombination for each event being bp ( see Fig . 1 ) . In this model , the heterogeneity in rates is generated dynamically through the process of near-simultaneous recombination events , but this model alone does not generate excess heterogeneity in the size distribution of recombination event . The mixture model 3 provided a much better fit than the homogeneous model 1 for both PMEN1 lineage and CC180 lineage ( and , respectively ) . It also provided a better fit than the heterogeneous model 2 ( and ) , although results of comparing non-mechanistic descriptions of heterogeneity ( Model 2 ) to mechanistic models ( Model 3 ) should be interpreted with caution , since mechanistic models are likely to be more useful even for equivalent goodness of fit . ( See also Figures 3G–I and 4G–I , Tables 1 , 2 and 3 . ) A key property of the mixture model ( Model 3 ) is that it generates correlation between the rate of recombination and the size of recombination events , since macro-recombination events , when they occur , are simultaneously larger and more numerous . To test whether this correlation was supported by the data , we compared the mixture model to a model identical in every respect , except for this correlation between rate and size ( the uncorrelated mixture model 4 ) . The resulting model fitted the data less well than the mixture model , with for PMEN1 data ( Fig . 3J–L & Table 1 ) and for CC180 data ( Fig . 4J–L & Table 2 ) . In summary , the mechanistic mixture model 3 fit to the data well and generated novel mechanistic insight . These results were not dependent on the units used to measure branch length ( see Methods and Text S2 ) . Maximum likelihood estimates of the parameters and univariate 95% confidence intervals are given in Table 3 . We then used this best fit model to determine the probability that each of the recombination events was generated either by micro-recombination or by macro-recombination . We found that of 615 events detected in PMEN1 lineage , 136 were likely to have been generated by micro-recombination , and 389 were likely to have been generated by macro-recombination , with the remainder indeterminate . In CC180 lineage , of 79 events , 14 were likely to have been generated by micro-recombination , and 64 were likely to have been generated by macro-recombination , with only one event indeterminate . The location of each event along the pneumococcal genome as well as in the inferred phylogeny of PMEN1 and CC180 lineage is shown in Figure 5 . This figure shows the heterogeneity of recombination in the phylogenies of both lineages , where certain branches exhibit multiple , long macro-recombinations , whereas short , micro-recombinations tend to be more randomly distributed . This can also be seen in supplementary Figures 10 and 11 in Text S2 , where an alternative distribution of recombination events in both lineages ( i . e . , all independent recombination events along the genome sorted by branch length ) is shown . Finally , the distribution of micro- and macro-recombination events as a function of their length and the inferred number of SNPs is given in Figure 6 . The figure shows that the inferred SNP density of micro- and macro-recombinations varies by approximately one order of magnitude , suggesting that the actual rate of micro-recombination may be considerably higher than that detectable through these data ( but see Discussion ) . In PMEN1 , 10 serotype-switching events were observed [19] ( i . e . , those which induced a change from the serotype 23F to a different one ) , and all those events were found to be with 100% posterior probability likely to have been the result of macro-recombination . More generally , to examine whether recombinations at major antigen loci are likely macro-recombinations , we counted the number of recombinations spanning or overlapping five major antigen loci in PMEN1 ( pspA , capsule biosynthesis locus , or cps , pclA , psrP and pspC ) and three major antigen loci in CC180 ( pspA , cps , and pspC ) . Of 171 such detected recombinations in PMEN1 , 93 were likely to have been generated by macro-recombination . By contrast , in CC180 only 4 recombinations at major antigens were found , however all 4 of them were likely to have been generated by macro-recombination . To assess our method of detecting heterogeneity of recombination in the genetic data we designed a simulation framework where we evolved a pneumococcal lineage over time with four prespecified mechanisms of recombination , and examined how well we can distinguish between those mechanisms ( see Methods and Text S3 ) . Specifically , we designed analyses in which the PMEN1 reference genome diversified into a sample of related sequences through discrete time-steps as specified by one of four different simulation frameworks ( Models A–D ) . We then reconstructed the evolutionary history of the lineage , with recombination events mapped onto the phylogeny , as described above and in [19] . We next fitted our four models of recombination ( Fig . 1 ) to assess which of them best explains the underlying mechanism of diversification ( see Tables 6–7 in Text S3 ) . In the first simulation ( A ) , recombination was simulated as a homogeneous process , and the homogeneous model 1 was the best fit . In the second simulation ( B ) , the distinction between micro-recombination and macro-recombination was introduced but only based on frequency and not size , and in these cases model 3 was the best fit to the data . However , there was no significant difference in the size distributions between the two modes of recombination , contrasting with the fits to the genomic data . In the third simulation ( C ) , a full mixture model of micro- and macro recombination was considered , and again model 3 was the best fit , with the likelihood of each model fits being of the same order of magnitude as in PMEN1 and CC180 data . Finally , in the fourth simulation ( D ) , an uncorrelated mixture model was assumed with independent heterogeneity in frequency and size . In this case , in two runs there was no significant difference in the fit of model 3 and 4 , while in the third model 4 was a much better fit to data than model 3 . These simulations thus demonstrate that the observation of model 3 fitting the genomic data best , with a dramatic difference in lengths between the micro- and macro-recombinations , is unlikely to be an artefact of the method used to detect recombination , or the models' formulation We next investigated whether the obtained results can explain recent observations of recombination in the pneumococcus using whole genome data . The near-simultaneous import of multiple fragments through transformation has previously been observed between a donor and recipient during a chronic infection in vivo in one patient [14] , and also inferred through reconstructing the history of another lineage , sequence type 695 [15] . In the study by Hiller and colleagues [14] , 16 recombination events varying in size from 0 . 4 kb to 235 kb ( mean of 15 kb ) were unidirectionally transferred from one donor strain into a recipient strain during an infection followed over a period of seven months . The observation that , in each case , multiple long recombinations had occurred over a defined short period suggested these examples might represent clear examples of the macro-recombination process . We found the size distribution of macro-recombinations to be in accordance with the one observed by Hiller et al . for both PMEN1 ( see Fig . 7A ) and CC180 lineage ( see Fig . 7B ) . On the other hand , the study by Golubchik et al . identified 53 recombination fragments in 5 vaccine escape recombinant lineages , ranging in size from 0 . 4 kb to 90 kb ( mean of 10 kb ) . Although the distribution of recombination sizes inferred by this analysis of re-sequencing data did not resemble any of the distributions defined by the models of recombination presented here , it nevertheless suggests a strikingly heterogeneous recombination process ( see Fig . 7C and 7D ) . A more formal approach would be needed to determine whether this is due to an actual recombination heterogeneity or due to another factor like the method used to infer recombination , or vaccine-induced selection ( see also Discussion ) . Finally , it has been demonstrated that multiple fragments of DNA can be imported by a member of the PMEN1 lineage during a single period of competence for transformation under controlled conditions [13] . While the overall distribution of sizes observed was similar to that reconstructed as happening during the lineage's diversification , there was less variation in the range of detected sizes . The discrepancy between the size distributions from the transformation experiment and the one observed in the PMEN1 lineage ( see Fig . 7E ) points to some interesting questions about varying conditions under which pneumococci undergo recombination during their evolution ( see Discussion ) . Perhaps unsurprisingly , the predicted size distribution of the CC180 lineage was even less consistent with the distribution of recombinations from the in vitro experiment ( see Fig . 7F ) . One hypothesis that could explain the observed difference between micro- and macro-recombination could be the effect of mismatch repair ( MMR; see also Discussion ) . MMR inhibits the acquisition of polymorphisms through transformation , but in the pneumococcus becomes saturated upon the import of around 150 SNPs [23] , [24] . Thus micro-recombinations could be acquired under the constraint of this system , whereas macro-recombination could represent the acquisition of sequence unlimited by MMR . In accordance with this hypothesis , when we divided branches of the phylogeny on the basis of the most common mechanism of recombination occurring on them , those on which micro-recombination predominated generally imported fewer than 150 substitutions in total , while those on which macro-recombination was more common typically acquired many more than this ( see Figures 12–13 and Text S2 ) . We also examined whether there were differences in the types of substitutions introduced by micro- and macro-recombination , as MMR varies in the efficiency with which is repairs different mutations . We found that macro-recombinations were enriched for ‘low efficiency' markers , which are repaired most effectively by MMR both in PMEN1 ( ) , and in CC180 ( ) . Interestingly , no association between the type of marker and the type of recombination was observed in the simulated pneumococcal sequences with preassumed micro- and macro-recombination mechanism ( see Table 8 and Text S2 ) .
Our analysis shows that both analysed lineages of Streptococcus pneumoniae , the multi-drug resistant PMEN1 and the older but less diverse CC180 , have likely evolved under two distinct homologous recombination processes . The first process , which we call micro-recombination , occurred at a homogeneous clock-like rate and gave rise to isolated small genetic replacements . The second process , which we call macro-recombination , was more erratic , giving rise to large , multiple synchronous ( or near-synchronous ) replacements . While in PMEN1 we found both micro- and macro-recombinations to have occurred at a similar rate ( every 17 years ) , in the less rapidly diversifying CC180 lineage micro-recombination was more frequent than macro-recombination ( once in 340 years vs . once in 770 years ) . Overall , recombination was much more heterogeneous in CC180 . Furthermore , the difference in sizes between micro- and macro-recombination was found to be greater in CC180 ( 0 . 03 kb vs . 14 kb ) than in PMEN1 ( 0 . 6 kb vs . 9 kb ) . Finally , the number of simultaneous recombinations imported during macro-event was smaller in PMEN1 than in CC180 ( 2 . 3 vs . 15 ) . The best fit parameters , together with the 95% confidence intervals , are summarised in Table 3 . The principal caveat in this analysis is that it is dependent on the correct identification of both the genealogy and the recombinations in the original analysis of the PMEN1 and CC180 lineages [19] , [20] . The main evidence given for the correct identification of the recombinations is that their removal from the set of base substitutions used to construct the phylogeny results an improved ability to detect evidence of a molecular clock at a rate similar to other bacteria that do not undergo frequent homologous recombination [19] , [25] , the length distribution of putative events is similar to that detected experimentally [13] , and that recombination events that can be inferred from phenotypic data ( e . g . , serotype switches ) are predicted at the correct locus on the expected branch of the tree [12] , [19] . However , we note that there is an inherent bias in the method described by Croucher et al . , shared with other methods that use SNP density to detect recombination ( e . g . , maximum Chi-square method , ClonalFrame [21] ) , in that it is prone to missing short recombination events that happen to bring in few SNPs into the genome . Nonetheless , such events have a relatively small effect on estimates of branch length , and therefore estimates of the molecular clock rate . However , such bias means that we have likely under-estimated the rate of micro-recombination . This is best illustrated by comparing SNP density to the observed size of the recombination ( Figure 6 ) . The observed negative correlation between SNP density and recombination size ( Spearman's rank correlation: , for PMEN1 and , for CC180 ) is likely the result of the detection bias described above , and this suggests that we may lack the sensitivity to accurately quantify the rate of micro-recombination events . Simulations of the heterogeneity suggest that the actual rate of micro-recombination is likely to be roughly three times the estimated rate . Correspondingly , we found that the methods employed in this study were able to correctly identify the underlying model of evolution when simulations were performed under different models of diversification . This suggests that our observations are unlikely to be an artefact of the method used to detect recombination . The presented analysis provides a quantitative model that could potentially explain other observations of recombination in the pneumococcus using whole genome data . The near-simultaneous import of multiple fragments through transformation has been observed previously in in vivo [14] , [15] and in vitro studies [13] . We found that the micro/macro-recombination process could be consistent with size distributions of recombinations in some patient-derived sequences ( cf . Fig . 7 ) . However , there is weak evidence that this happens in the case of transformation in vitro . Therefore the observation of these two different types of recombination requires an explanation that can link the differences in properties and kinetics . It could be that genetic transformation through the competence system is only responsible for recombination through one of the modes , like micro-recombination , while other forms of bacterial “sex” , like conjugation or transduction , would lead to the acquisition of long stretches of DNA associated with macro-recombination . Conjugation has been observed to cause extensive sequence transfer in other streptococci , which would be consistent with this hypothesis conjugative transfer can result in multiple events if multiple conjugative origins are involved [26] . However , these exchanges are associated with ori sequences from conjugative elements , and therefore result in more regular recombination boundaries than are observed for the macro recombination events in this analysis [27] . Similarly , general transduction of sequence can import large DNA fragments of variable lengths , but typically only one can be packaged into a virion . As such mispackaging events are rare , this does not provide a likely explanation for the near-simultaneous import of multiple fragments [28] . Another potential explanation of the difference between micro- and macro-recombination may be how stretches of DNA are processed within the cell . For example , the recently identified competence-specific DNA-binding protein SsbB has been found capable of storing about 1 . 15 Mb of DNA imported by the competence system [27] . As the expression of this protein varies according to regulatory processes , it could play an important role in controlling the properties of recombination . However , given the comparatively homogeneous length distribution of recombinations observed in experimental transformation of the pneumococcus , it seems likely that extracellular degradation or intracellular processing are not the best candidates to explain the observed heterogeneity . Hence it seems more likely that the observed dynamics represent transformation behaving in two distinct modes . One known threshold that could explain the variation is saturation of repair systems . MMR inhibits the acquisition of polymorphisms through transformation , but in the pneumococcus becomes saturated upon the import of around 150 SNPs [23] , [24] . Here we found moderate but significant evidence for this hypothesis , which would suggest that it is the extent and type of DNA imported that triggers the switch between the two types of exchange . In the PMEN1 dataset , each homologous recombination imports a mean of 70 substitutions ( 116 substitutions for CC180 ) , and in vitro experiments have demonstrated that multiple fragments can be imported simultaneously . Therefore the availability of high concentrations of divergent DNA , as observed in pneumococcal biofilms [29] , or a state of ‘hyper-competence’ , in which cells imported DNA more readily than normal , would seem likely to saturate the MMR system and potentially trigger the conditions required for macro-recombination . The idea of the emergence of micro-recombination and macro-recombination via saturation of the MMR has the advantage that it is consistent with the observed positive correlation between frequency and size of recombinations ( cf . Fig . 2C and 2F ) . Many macro-recombinations found in this study are considerably larger than any individual segment of donated sequence acquired by S . pneumoniae in vitro . This is likely to reflect the algorithm employed in the analysis of pneumococcal genomes , which clusters together nearby transformation events that originate from the same imported strand of DNA [13] . Therefore , integrating a larger number of imported sequence segments into the chromosome can both result in a greater number of distinct recombinations , and generate more extensive ‘mosaic’ events that would be reflected by an increase in the length of the overall transformation event in this analysis . Hence if a mechanism like MMR becomes saturated , it might not only result in more acquired recombinations but also in transformation of larger mosaic segments , resulting in a simple mechanistic link between frequency and size of recombinations . Interestingly , in vitro transformation experiments of pneumococcus , despite investigating transformation at two very different concentrations of exogenous DNA , did not find strong evidence for two distinct mechanisms of recombination [13] . This indicates that the observed difference may represent other environmental factors that affect the regulation of systems such as MMR . It is also important to consider that the observed distribution of sequence is also the consequence of selection , which could be an alternative explanation for the observed heterogeneity . However , such a selection pressure would have to be highly generic to account for such a genome-wide phenomenon . One potential pressure that affects multiple loci , in particular several affected by a high density of recombinations , is immune-driven selection . Loci which are most likely to be under selective pressure of the immune system have been shown to be recombination hotspots [19] . As this selection is likely to be diversifying , it is conceivable that longer recombinations at these loci , inducing greater phenotypic changes , are under positive selection , and are thus more frequently observed . However , the mixture model 3 remains the best fit even after those events have been removed from the dataset ( see Table 9–10 and Text S2 ) . Therefore , we conclude that , even though immune selection is likely to play a role in shaping the distribution of recombination events in the pneumococcal genome , it is unlikely to explain the observed heterogeneity of homologous recombination in S . pneumoniae . Another process that may skew the pattern of observed recombinations is the non-systematic nature of the isolate collections used in the original analyses . Two analyses were performed to assess the potential for biased sampling to affect the conclusions: the first excluded all isolates from the extensively sampled South African collection , while the second excluded all isolates serotyped as 19A to rule out potential vaccine induced selective pressure . In both cases , the results were qualitatively the same ( Table 11 and Text S2 ) . In summary , we have firmly demonstrated that homologous recombination is heterogeneous , and found that the heterogeneity shows evidence of two modes of action , which we term micro- and macro-recombination . We have also found that saturation of the mismatch repair system is the most likely mechanism for inducing macro-recombination . From a whole population survey , it has been observed that total homologous recombination rates vary substantially between pneumococcal lineages [12] , and that an increased propensity for recombination is associated with increased antibiotic resistance [11] . Given this observation , it is particularly interesting that the two lineages studied here , that are at the opposite extremes in terms of their phenotype and evolutionary history , are both characterised by a highly heterogeneous recombination process . Furthermore , the aggregate recombination distribution sizes appear quite relatively consistent across different pneumococcal genotypes [12] . This all suggests that the micro- and macro-recombination are likely to play a role across the entire pneumococcal species . Based on the results presented here , it seems that micro-recombination is the more frequent process , whereas macro-recombination is likely to be the main driver of the bacterium's diversification . How generally applicable these models are to the evolution of other species , and their relevance to wider questions about the evolution of homologous recombination itself [30] , can be addressed as more genomic datasets become available .
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Streptococcus pneumoniae , a bacterium commonly carried asymptomatically by children , is a major cause of diseases such as pneumonia and meningitis . The species is genetically diverse and is known to frequently undergo the remarkable process of transformation via homologous recombination . In this process , the bacterial cell incorporates DNA from other , closely related bacteria into its own genome , which can result in the development of antibiotic resistance or allow cells to evade vaccines . Therefore it is important to quantify the impact of this process on the evolution of S . pneumoniae to understand how quickly the species can respond to the introduction of such clinical interventions . In this study we followed the recombination process by studying the evolution of two important and very different lineages of S . pneumoniae , PMEN1 and CC180 , using newly available population genomic data . We found that pneumococcus evolves via two distinct processes that we term micro- and macro-recombination . Micro-recombination led to acquisition of single , short DNA fragments , while macro-recombination tended to incorporate multiple , long DNA fragments . Interestingly , macro-recombination was associated with major phenotypic changes . We argue that greater insight into the adaptive role of recombination in pneumococcus requires a good understanding of both rates of homologous recombination and population dynamics of the bacterium in natural populations .
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2014
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Heterogeneity in the Frequency and Characteristics of Homologous Recombination in Pneumococcal Evolution
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A variety of coarse-grained ( CG ) models exists for simulation of proteins . An outstanding problem is the construction of a CG model with physically accurate conformational energetics rivaling all-atom force fields . In the present work , atomistic simulations of peptide folding and aggregation equilibria are force-matched using multiscale coarse-graining to develop and test a CG interaction potential of general utility for the simulation of proteins of arbitrary sequence . The reduced representation relies on multiple interaction sites to maintain the anisotropic packing and polarity of individual sidechains . CG energy landscapes computed from replica exchange simulations of the folding of Trpzip , Trp-cage and adenylate kinase resemble those of other reduced representations; non-native structures are observed with energies similar to those of the native state . The artifactual stabilization of misfolded states implies that non-native interactions play a deciding role in deviations from ideal funnel-like cooperative folding . The role of surface tension , backbone hydrogen bonding and the smooth pairwise CG landscape is discussed . Ab initio folding aside , the improved treatment of sidechain rotamers results in stability of the native state in constant temperature simulations of Trpzip , Trp-cage , and the open to closed conformational transition of adenylate kinase , illustrating the potential value of the CG force field for simulating protein complexes and transitions between well-defined structural states .
Despite continuing advances in computing power , atomistic simulation remains a considerable challenge at increasingly large time and length scales for processes of biological importance such as protein folding , conformational change and assembly . Coarse-grained ( CG ) approaches have therefore enjoyed popularity , in which the polypeptide can be modeled using a reduced representation of one or more , sometimes fewer , interaction sites per residue . Early CG models employed a binary code , classifying interactions between combinations of hydrophobic and polar ( HP ) residues [1] . HP models suffered from having a degenerate global energy minimum with as many as 103 conformations . An important remaining objective therefore is the construction of a sophisticated CG potential that recapitulates the thermodynamics of the conformational landscape and identifies the native state as a stable global energy minimum consistent with energy landscape theory [2] . Such a CG potential would be of value not only to the protein folding and structure prediction communities but could prove extremely useful in general simulations of protein dynamics and conformational change . Despite an ever-growing repertoire of independent coarse-graining approaches they still have not rivaled all-atom potentials in structure prediction [3] . Nevertheless , CG models have achieved surprising success in diverse areas of protein modeling . This success is made possible by the introduction of bias towards the native state . Elastic network models are the most restrictive example , in which backbone alpha carbons within some cutoff distance of each other in the native structure are assigned pairwise harmonic restraints . Elastic network calculations have reproduced the low frequency functional motions of a large variety of proteins [4] . Less restrictive are Gō models [5] , where backbone alpha carbons in proximity in the native state are attracted via a Lennard-Jones ( LJ ) potential: ( 1 ) in which the location and depth of the attractive minimum between particles i and j are given by rmin and ε , respectively . Unlike harmonic restraints , LJ interactions can spontaneously dissociate in order to visit unfolded conformations . Gō models have been successful in predicting folding mechanisms because they mimic the minimally frustrated funneled energy landscape of evolved proteins , in which non-native interactions play a minimal role [6] , [7] . Lastly , general CG models have employed simple LJ interactions between all residues in the protein chain but rely on additional native dihedral or hydrogen bond restraints to counteract energetic frustration and thereby ensure structural stability during simulation of the native state [8]–[13] . Current efforts aim to improve the physical accuracy of CG nonbonded interaction schemes in order to alleviate the need for native structure restraints . Accurate CG interactions would provide for a correct description of protein dynamics and conformational change for large deviations from the native state as well as protein-protein interactions . Ongoing parameterization endeavors include various schemes . Certain empirical approaches parameterize ad hoc LJ functional forms using thermodynamic data such as density , surface tension , solvation energy and oil-water partition coefficients [9] , [14]–[16] , which have previously been used with some success in CG models of lipid bilayers [17] . Folding-inspired approaches utilize known folding behavior to tune CG parameters that will result in a properly folded protein [18]–[22] . In a somewhat similar fashion , knowledge-based methods invoke statistical potentials derived from distributions of residue-residue interactions and secondary structure in all known protein structures [3] , [13] , [23]–[27] . The remaining class of CG model development involves parameterization against all-atom reference simulations . Some of these approaches are based on obtaining the pairwise potentials of mean force ( PMFs ) between amino acid sidechains . Scheraga and colleagues have employed atomistic umbrella sampling to obtain PMFs for different packing arrangements of sidechain analog dimers [28] . Analytical approximations to the orientation-dependent , pairwise PMFs empower the UNRES model for structure prediction [29] . Another single site sidechain model for structure prediction has recently been developed by Betancourt and Omovie with pairwise PMFs obtained from atomistic simulations of all 210 amino acid pairs [30] . A model consisting of up to two interaction sites per sidechain has been developed for protein docking based on PMFs estimated from atomistic simulations of the 20 amino acid homodimers [31] . The goal of the present work is the construction of an accurate CG interaction model for the amino acids in which the packing energetics of sidechain rotamers is properly maintained using multiple interaction centers per sidechain . The CG potential is developed from forces generated from atomistic simulation , a process sometimes referred to as force matching ( actually a force “renormalization” ) , using the multiscale coarse-graining ( MS-CG ) method [32]–[37] . MS-CG is a variational procedure for determining the many-body potential of mean force for the CG variables ( the CG “potential” ) that reproduces the equilibrium probability distribution observed in the atomistic configurational ensemble [35] . No assumptions are made about the functional forms of the pairwise interactions between CG sites [37] , and multibody correlations [36] are implicitly taken into account in the resulting effective pairwise CG potential . In these respects MS-CG has a similar objective as the other multiscale methods iterative Boltzmann inversion [38] and inverse Monte Carlo [39] , in which the effective pairwise potential is iteratively refined until satisfactory agreement with the atomistic radial distribution functions ( RDFs ) is obtained [40] . A key difference , however , is that MS-CG uses molecular scale forces as its input and not the two-body RDFs ( rather , the latter is a prediction , not input , from the MS-CG model ) . A challenge with multiscale methods is that the resulting CG model may be limited in applicability to the substates , or region of conformational space , sampled in the reference atomistic simulations used to construct the CG model . Previous MS-CG models have been used to accurately describe specific configurations of selected peptides [41]–[43] . In contrast , the approach used here is to apply MS-CG to a variety of peptide equilibria to obtain a general set of CG interaction potentials for the amino acids that can then be used in simulations of proteins of arbitrary sequence . The MS-CG potentials are compared and combined for atomistic simulations of the unfolded ensembles of polyalanine , polyleucine and the miniprotein Trpzip , as well as the self-association of amino acid dipeptides . The force field is then validated by performing CG simulations of Trpzip , Trp-cage and adenylate kinase ( AdK ) . Parallel tempering , or replica exchange molecular dynamics ( REMD ) [44] , is used to characterize the folding energy landscapes of the three CG proteins . REMD takes advantage of simultaneous simulations at high temperature to overcome local energy barriers . Extensive sampling with CG-REMD was employed to determine the global energy minimum and illustrate potential strengths and limitations of the model . Finally , conventional constant temperature molecular dynamics ( MD ) is performed to demonstrate the stability of the native state and the promise of the CG force field for modeling protein dynamics . The present work attempts to address for the first time the question of whether protein folding and dynamics can be captured with a generic reduced representation of the sidechains derived from real physical forces . In contrast to backbone centric approaches that have had some success in predicting the global minimum of helical bundles [18] , [21] , [22] , the current sidechain centric approach appears to be more useful for simulating the native state dynamics of diverse helical and β-sheet proteins . Evaluation of our model in comparison to backbone centric models yields insight into the relative influence of the backbone and sidechains on folding and dynamics and illustrates the limitations of pairwise additive residue-level interactions in reproducing folding cooperativity .
The polypeptide backbone was represented using a single CG site per residue placed at the Cα position in order to reasonably maintain the backbone conformational degrees of freedom [45] , [46] . MS-CG was also attempted with three backbone sites per residue as in previous work on hydrogen bonding [41]–[43] but not included in the model; the resultant potentials were repulsive , likely due to the approach of averaging over all orientations present in the unfolded ensemble rather than a purely attractive native basin . MS-CG was performed by matching the instantaneous total atomistic force on the alpha carbon . As many as four CG sites were chosen for each sidechain to describe the essential orientational degrees of freedom and maintain a consistent mapping of two or three heavy atoms per site ( Figure 1 ) . MS-CG was used to determine the 15 pair potentials between combinations of five assigned CG site types ( backbone , apolar , polar , positive , negative ) . The grouping of sidechain sites according to type was a necessary approximation in order to obtain converged pair potentials using the MS-CG algorithm . MS-CG was performed by matching the sum of the instantaneous forces on all atoms in a given sidechain site . The solvent degrees of freedom were integrated out making this a “solvent-free” CG force field [47] , [48] . No atom was involved in the definition of more than one CG site , thereby allowing for consistency in momentum space as well as configuration space between the all-atom and CG many-body PMF [35] . Absolute timescales in the CG dynamics are difficult to obtain from the model , however , due to the reduced number of degrees of freedom , lack of explicit solvent molecules and smooth energy surface . Bonded terms were obtained directly from the atomistic distributions rather than the force matching results with MS-CG , allowing for residues to be treated uniquely regardless of site type to give a full description of steric packing . Potentials for bond lengths , bending angles and torsions in each amino acid were obtained using Boltzmann inversion: ( 2 ) ( 3 ) ( 4 ) where kT is the thermal energy and p is the probability distribution observed in atomistic MD; the volume normalization factors r2 and sin θ were needed to properly represent all distributions . Bond and angle potentials were fit to harmonic or fourth order polynomials where appropriate; otherwise , custom bond tables were employed in GROMACS [49] . The angle from sidechain and backbone sites of residue i to backbone residue i−1 was treated separately from the angle to backbone residue i+1 . Torsions involving sidechains were unrestricted excepting tryptophan and tyrosine rings , which were fit with improper torsions to maintain planarity . Backbone angles between three successive alpha carbons were represented using a single sequence-independent fourth order polynomial allowing rapid interconversion between α-helix and β-sheet values ( see supporting Figure S1A ) . Backbone torsions between four successive alpha carbons were represented using a set of sequence-dependent potentials developed from fitting the inverted distributions of known structures in the Protein Data Bank [50] . These consisted of 202 fourth order cosine series , one fit for each possible permutation of the middle two residues involved in the torsion . All statistical potentials were scaled by a constant factor of 0 . 54 , chosen to give good agreement with the polyalanine distribution from all-atom MD ( Figure S2A ) . The inverted distribution of polyleucine in all-atom MD was well predicted by the resulting scaled statistical potential ( Figure S2B ) . The final set of backbone torsions allowed rapid interconversion between α-helix and β-sheet , with rates generally decreasing with sidechain bulk in the order Gly∶Ala∶Cβ-branched∶Pro . Developing the bonded potentials separately from the nonbonded interactions was ultimately justified by the satisfactory agreement between bonded distributions in all-atom and CG simulations ( e . g . , see Figures S1 , S2A ) . All-atom MD was performed using the OPLS [51] protein force field with SPC solvent in the GROMACS [49] simulation package . The default parameters were employed with particle mesh Ewald for long-range electrostatics and a 1 . 2 nm cutoff for grid-based short-range neighbor searching . Constant NVT simulations were performed using the Nosé-Hoover thermostat with a 0 . 5 ps relaxation time constant . Bonds to hydrogens were constrained using LINCS and a 2 fs integration timestep was used . Each peptide system was simulated in a ( 4 nm ) 3 periodic box with a peptide∶water concentration of 10% . Coordinates and forces of protein atoms were recorded at intervals of 1 ps or longer for use in MS-CG force matching . In order to derive a general set of CG potentials from the unfolded ensemble , peptide systems were simulated at 498 K to enhance conformational sampling . Given the modest temperature dependence of interaction potentials generated from atomistic simulation [52] , [53] , polyalanine is comparably shown to exhibit modest temperature sensitivity with the MS-CG scheme employed in the present work ( Figure 2 ) . Nevertheless , the higher temperature used in the simulations to define the model CG potentials will tend to “smooth out” the resulting interactions . Potentials were developed separately for different peptide systems and compared to assess the efficacy of a single potential being used independent of sequence . Ten or more independent simulations approximately 50 ns in length were performed starting from different random configurations of the following peptide systems to yield a composite simulation length of at least 0 . 5 µs for each system ( Table 1 ) . Five Ala15 peptides were simulated in a box of water molecules at 300 K as well as 498 K . Three unfolded Leu15 peptides were simulated in one water box . Three molecules of the miniprotein Trpzip2 [54] were simulated in another water box . Lastly , 25 dipeptides were randomly placed in a single water box , one for each amino acid with the exception of two for alanine , glycine and lysine and three for aspartate . To ensure ample exchange between sidechain association partners and convergence in the developed MS-CG potentials , 200 independent simulations of the dipeptide solution were performed for a composite length of 9 µs . A few remaining bonded terms were obtained from distributions in all-atom unfolding simulations of two Trp-cage5b [55] proteins in water and five AACHMFVAA peptides in water , although neither system was force-matched . A single natively structured Trpzip molecule was also simulated in water at 300 K for comparison of structural fluctuations between all-atom MD and CG-MD with the final model . Excepting the dipeptides , each terminus was uncapped and charged . Unfolded starting configurations were generated by randomly orienting the peptides in Cartesian space and then equilibrating for 2 ns at 700 K; the random number generator was employed with varying seeds . The force matching of the atomistic reference simulations in the MS-CG method was performed using the program MSCGFM [56] , a fast and flexible implementation of MS-CG . A linear spline basis set was employed for least squares optimization of nonbonded interaction pairs separated by less than 2 nm and more than three bonds . The computation was made feasible by employing the block-averaging procedure of combining separate solutions for disjoint sets of configurations ( blocks ) [37] . Blocks consisted of 2 , 000 or more frames for each peptide system depending on memory requirements . The resulting pairwise force curves were integrated and smoothed using a cubic B-spline to obtain tabulated potentials for input in GROMACS . Repulsive positive-positive and negative-negative interactions were switched linearly to zero over the range 1 nm to 1 . 2 nm , resulting in ∼1 kJ/mol error over the switching region . Convergence of the nonbonded interactions was checked by repeating the MS-CG calculation with half the configurations and ensuring the force curves were similar excepting high frequency noise . CG simulations were performed in GROMACS 4 [49] using Langevin dynamics with a 2 ps inverse friction constant to maintain thermal equilibrium and a 2 fs integration timestep . Tabulated nonbonded interactions were updated every step and calculated between all CG sites separated by at least three bonds using a 1 . 2 nm distance cutoff . A single set of 15 site-site CG potentials , chosen as most representative across peptide systems , was employed for all CG proteins . CG-MD of polyalanine was performed on five copies of Ala15 placed in a 40 Å periodic box for 50 ns starting from a random configuration obtained from the endpoint of a 58 ns atomistic MD run at 498 K described above . Polyalanine aggregation was monitored by computing the pairwise RDF of inter- and intramolecular alpha carbons separated by at least three bonds . Native state CG-MD simulations of Trpzip , Trp-cage and the open and closed forms of AdK were begun from NMR structures 1LE1 . pdb [54] and 1L2Y . pdb [55] and crystal structures 4AKE . pdb and 1AKE . pdb , respectively , and performed for 200 ns at 0 . 6 , where is the reference temperature for the CG model . The reference temperature was defined as the folding transition , or melting , temperature observed in CG-REMD folding simulations of Trpzip . CG-REMD folding simulations were performed with exponentially spaced temperature replicas spanning 100–700 K . The number of replicas was chosen to maintain an exchange frequency between 20% and 40% throughout the simulation and was first estimated using Pdes [57] . CG-REMD of Trpzip and Trp-cage required 16 replicas while AdK required 48 replicas . Conformational exchanges between temperature windows were attempted and snapshots recorded for Trpzip/Trp-cage every 200 ps and for AdK every 20 ps . For Trpzip and Trp-cage two independent simulations of 3 µs or longer were performed starting from an extended structure to bring the total simulation length to 6 µs per replica . To check convergence 3 µs was then performed starting from the native state . Trp-cage simulations converged to a common structure . Two additional simulations were performed with it as the starting point to verify the global minimum . For the more complex folding landscape of AdK , four independent simulations were performed for 80 ns , each starting from extended , closed , open or a 50/50 mixture of open and extended states . Extended starting structures were generated from equilibration in CG-MD at 700 K for at least 10 ns . Folding landscapes were characterized by computing the root mean square deviation ( RMSD ) from the native structure of replica conformations corresponding to 0 . 6 . The RMSD from the CG representation of the native state was computed for a subset of backbone or sidechain sites after superimposing the backbone alpha carbons of the region of interest . CG-REMD was also performed with position restrained backbone alpha carbons starting from the native structure to determine the distribution of sidechain rotamers ( 3 µs for Trpzip/Trp-cage and 80 ns for open and closed AdK ) . The mean and standard deviation of the RMSD of sidechain sites from the native structure were computed over the fixed backbone CG-REMD simulations as well as the unrestrained CG-MD , both at 0 . 6 . CG conformations were visualized using VMD [58] .
The most significant approximations used in construction of the present CG model are threefold . Atomistic reference simulations were performed at elevated temperature to denature the protein ensemble and allow for rapid interchange between association pairs in aggregated peptides . Secondly , site-site interaction potentials were obtained from different peptide systems in order to obtain a single force field applicable across protein sequences . Lastly , the through-space interactions constitute an average over the chemical diversity of the amino acids grouped into five CG site types . These assumptions enabled the construction of a versatile model for simulating proteins of arbitrary sequence and conformation . Overall , a reasonable correspondence was observed between the CG potentials developed separately from different peptide systems for a given interaction . Figure 2 shows the similarity in Cα-Cα and apolar-apolar interactions across peptide sequences . For each of the 15 nonbonded interaction pairs in the model the MS-CG potential was employed that was most representative across peptide systems . The one exception is the case of positive-negative salt bridges . Force matching of Trpzip and the dipeptide solution yielded potential minima of −10 kJ/mol and −37 kJ/mol , respectively , likely due to the influence of the hydrophobic environment; the former was adopted in the CG model to avoid large forces . Figure 2 also illustrates that the temperature dependence of the developed CG potential for polyalanine is comparable in magnitude to the variation between different sequences . The modest temperature dependence of CG potentials obtained from atomistic data has been noted previously [52] , [53] . The final set of CG potentials employed ( Figure S3 ) have profiles similar to atomistic PMFs obtained at room temperature [28] . When arranged according to strength and location the attractive minima follow an expected relationship to polarity ( Table 2 ) . As is well appreciated from atomistic MD studies with continuum solvent , surface tension can be an elusive property in implicit solvent models and is often approximated as a simple function of the solvent accessible surface area [59] . Typically , CG potentials for protein folding are scaled by a constant factor so that Tf matches experiment [50] . Simulations with the present CG model exhibited a higher effective surface tension than atomistic simulations at the same temperature , as evidenced by a greater tendency to aggregate . As inferred from CG-MD of polyalanine aggregation , increasing the temperature can reproduce the pairwise RDF of nonbonded alpha carbons from atomistic MD ( Figure 3 ) . In the statistical mechanical framework of MS-CG temperature corrections should not be needed [35] , and their use may reflect inadequacies in the pairwise CG potential or basis set employed in the optimization [36] , [60] . Excessive peptide aggregation with pairwise CG PMFs has been reduced elsewhere by the inclusion of explicit waters [61] , [62] . The high sampling temperature , peptide concentration and sequences of the atomistic reference simulations are also sources of error in the developed CG potential . To determine the appropriate temperature range for protein simulations using the CG model the temperature dependence of the heat capacity was computed for Trpzip , Trp-cage and AdK from CG-REMD folding simulations ( Figure 4 ) . Unfolding transitions occurred at temperatures as low as 200 K . CG simulations of the native state were therefore performed at 0 . 6 , where is the folding transition temperature defined by the maximum in the heat capacity observed in Trpzip simulations . CG Trpzip/Trp-cage exhibited transition temperatures of 218 K/198 K , equal in ratio to their experimental melting temperatures 345 K/315 K [54] , [55] . The designed miniprotein Trpzip has an experimental melting temperature typical of natural proteins and was therefore used to define the reference temperature of the CG model . Since the CG potential is less “rough” than the actual atomistic potential , it is perhaps not surprising that a lower temperature is required for the CG protein simulations in order to effectively compensate for this feature of the model . Nevertheless , this aspect of the modeling is not completely satisfactory and will therefore be a focus of future improvements in the methodology and CG model . Parallel tempering was used to characterize the CG energy landscape . Performing REMD over a wide temperature range ( 100–700 K ) starting independently from unfolded as well as native states enabled near-canonical sampling of low energy conformations , some of which were non-native as judged from structural RMSD . Ensemble simulations were used to evaluate the accuracy of the force field in identifying the native structure as the global energy minimum . The sampling convergence of CG-REMD folding simulations can be seen in Figure 5 . Trpzip simulations starting from the unfolded state ( 7 . 1 Å Cα RMSD from native ) rapidly convert to and exchange between three stable native-like conformations ( Figures 5A , D ) . The conformation with 2 . 5 Å Cα RMSD consists of a proper β-hairpin backbone , though the Tryptophan zipper occurs on the wrong side of the β-sheet ( Figure S4A ) . The 4 Å and 6 Å Cα RMSD conformations contain the Trp zipper on the correct side of the β-hairpin but allowed for distortions in the backbone of varying degrees ( Figure S4A ) . Trp-cage simulations starting from the unfolded state ( 6 . 4 Å Cα RMSD from native ) converge to a stable global minimum with 5 . 8 Å Cα RMSD ( Figures 5B , E ) . The global minimum resembles the native helix-coil motif , albeit with a distorted helix ( Figure S4B ) . In contrast to 12-residue Trpzip and 20-residue Trp-cage , the CG energy landscape of 214-residue AdK is indicative of a frustrated random heteropolymer . Conformations with Cα RMSDs spanning the range 7–19 Å were visited with equal frequency once simulations starting from different initial structures converged ( Figures 5C , F ) . The non-two-state nature of AdK's glassy folding transition is underscored by the lack of a sharp melting transition in the heat capacity curve ( Figure 4 ) . Such deviations from the funneled landscape attributed to evolved proteins [2] emphasize the role of non-native interactions ( contacts not present in the native state ) , whose repulsive nature must be underestimated in the coarse-grained representation . The high degeneracy of AdK's global energy minimum compared to Trpzip and Trp-cage is likely due to its large domain size and vast number of possible backbone conformations . Successes and failures in ab initio folding notwithstanding , the goal of the present work was the construction of a CG force field for modeling proteins in known structural states . Conventional constant temperature simulations were therefore performed at 0 . 6 to assess the stability of the native state under CG-MD . Trpzip and Trp-cage exhibited structural stability with final configurations of 2 . 6 Å and 4 . 7 Å Cα RMSD , respectively , from the starting native structure after 200 ns of CG-MD ( Figure 6 ) . A slight bimodal distribution was observed in RMSD , but this was mainly due to fraying in the residues at the N- and C-termini ( Figures 7A , B ) . Atomistic simulation was recently used in conjunction with umbrella sampling to characterize the oft-studied open to closed conformational transition of AdK [63] , in which the LID and NMP domains undergo a 14 Å relative hinge bending motion about the CORE domain . The study suggested in the absence of ligand AdK fluctuates about the open crystal structure , occasionally visiting conformations near the closed crystal structure . Binding of an adenosine polyphosphate substrate analog to the arginine-lined active site was observed to dramatically stabilize the closed conformation . To examine the suitability of the current CG force field for studying conformational transitions , CG-MD was performed for 200 ns at 0 . 6 starting from both the open and closed AdK structures . The CORE , LID and NMP domains are stable in CG-MD of both the open and closed states ( Figure 8 ) , each of which individually has a structural RMSD between the open and closed crystal structures of less than 2 Å when domains are superimposed . The open to closed conformational transition was monitored in AdK simulations using the reaction coordinate ΔDRMSD [63] , defined as the RMSD of backbone and sidechain sites from the open state minus their RMSD from the closed state . The simulation of the closed conformer undergoes limited structural rearrangement ( 4 . 6 Å final Cα RMSD to starting structure ) compared to the simulation starting from the open conformer ( 8 . 0 Å final Cα RMSD to starting structure ) . Indeed , values of the reaction coordinate approach positive ΔDRMSD ( become more closed-like ) in simulations of the open state ( Figure 9 ) . The dynamics of AdK in CG-MD can be understood in terms of surface tension . Just as polyalanine exhibited an exaggerated surface tension for a given temperature , Trpzip , Trp-cage and AdK are more compact than the native structure under low temperature folding conditions in CG simulations ( Table 3 ) . Even under unfolding conditions , the peptide chains are disordered in compacted globules up to temperatures exceeding 500 K ( Table 3 ) , as can also be seen by the long tails in the heat capacity curves up to 700 K ( Figure 4 ) . Under CG-MD , the open state of AdK rapidly adopts a more compact and stable conformation that is structurally similar to the closed crystal structure , though the LID and NMP domains are not in contact . The simulation starting from the closed state also adopts a compacted structure in which the LID and NMP domains are in closer contact ( Figure 7C ) . Besides surface tension , other possible explanations exist for the incorrect relative arrangement of the LID and NMP domains during CG simulations . The negatively charged substrate needed to counteract repulsion in the arginine-lined binding pocket is absent from the simulations . Experimentally , the LID and NMP domains exhibit reduced thermodynamic stability compared to the CORE domain [64] . The open to closed conformational transition requires many subtle backbone rearrangements in the hinge regions connecting the three domains [64]–[66] . Lastly , an alternative explanation is that the reduced bulk of the low resolution interaction sites in the CG model fails to fully account for the effect of the underlying atomistic steric clashes . Structural compaction in peptide coarse-graining has been reported previously [42] . The reduced temperature used in the simulations could also be a contributor to structural compaction . In the case of large conformational transitions in which the rearrangement can be viewed as a local refolding event [64] , [66] , the CG force field could potentially benefit from the addition of a loose elastic network to maintain the backbone topology analogous to previous work [10] , [67] . Backbone restraints could also be used in order to predict sidechain configurations for low resolution experimental structures in which only the backbone Cα positions are known . Indeed , parallel tempering of Trpzip , Trp-cage and AdK with fixed native backbone topology yielded improved distributions of native sidechain configurations ( Table 4 , Figure S5 ) . The fact that sidechain packing is reproduced to within 3 Å RMSD suggests that the CG description constitutes a reasonable representation of sidechain sterics and polarity , although higher resolution models [15] , [68] are expected to improve accuracy .
A CG force field for the amino acids was developed based on microsecond all-atom simulations of peptide folding and association . Previously , the accuracy of CG functions has been assessed based on their ability to identify the native state as lower in potential energy than decoy structures [14] , [25] , [27] , [30] . The present CG model was evaluated based on analysis of folding energy landscapes generated from REMD simulations . Non-native structures were observed with energies similar to that of the native state , which is in accord with replica exchange investigations of other CG representations for folding [18] and structure prediction [69] . Deviations from the funneled landscape indicate that the smooth landscape of CG interactions may fail to capture the effective repulsion between non-native contacts in the rugged atomistic landscape . The current sidechain centric model emphasizes sequence at the expense of detailed backbone hydrogen bonding , both of which in conjunction have been shown to determine the tertiary structure of proteins [70] . At the other end of the spectrum , backbone centric models contain three or more backbone interaction sites per residue to incorporate geometric hydrogen bond constraints at the expense of sidechain rotamers , which are represented by a singe site at the beta carbon position [18] , [21] , [22] . Backbone centric models have successfully predicted the structure of certain α-helix bundles excepting topological degeneracy . In contrast , the current sidechain centric approach consisting of a single site per backbone and multiple sites per sidechain was demonstrated to be more useful in simulating the dynamics of diverse helical and β-sheet proteins . The limited success of pairwise alpha carbon interactions in folding prediction can be attributed to the fact that pairwise additive interactions at the residue level are not adequate to describe the highly cooperative process of protein folding [71] . Cα Gō models , for instance , require the introduction of either a desolvation barrier [72] or native dihedral backbone angular restraints to ensure a cooperative folding transition [7] . Desolvation of neighboring water molecules can be considered a multibody effect , as can the angular dependence of backbone hydrogen bonding . That the pairwise potentials developed and tested in the present work lack an appreciable desolvation barrier ( Figures 2 , S3 ) offers an additional explanation for their limited success in folding prediction . Misfolded structures have also been observed with high probability in atomistic folding studies employing implicit solvent models [59] , [73]–[75] , suggesting surface tension and solvation effects are critical in reproducing the energy landscape of proteins . With current computational resources , the ability of modern all-atom force fields to capture the energy landscape can now be assessed in explicit solvent simulations using replica exchange methods [76] , [77] . Obtaining the delicate balance between α-helix and β-sheet energetics is challenging , but ongoing all-atom efforts are showing promise [78] , [79] . Whether a single CG force field is capable of reproducing the full thermodynamic landscape of structurally diverse proteins remains a difficult question . A variety of useful CG models do exist for studying protein folding mechanisms [7] and structure prediction [3] . The present work describes a general CG force field derived from molecular-scale interactions that is capable of stable native state simulations without the need for additional structural restraints , an improvement over existing CG models . Improved structural stability can be attributed to the explicit treatment of sidechain rotamers , their steric packing and energetics , resulting in the native state being a local energy minimum . Future refinements of the model to better describe backbone hydrogen bonding are expected to improve its performance . However , the current force field may also prove useful in the modeling of protein complexes and their transitions .
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Biological function originates from the dynamical motions of proteins in response to cellular stimuli . Protein dynamics arise from physical interactions that are well-predicted by detailed atomistic simulations . In order to examine large protein complexes on long timescales of biological importance , however , coarse-grained simulation approaches are needed to complement experiment . Previous coarse-grained models have proved successful for investigations involving a given protein's native structure , including protein folding and structure prediction . We construct a model capable of simulating proteins regardless of their sequence or structure . The present coarse-grained model was , however , developed rigorously from the underlying atomistic forces as opposed to knowledge-based or ad hoc parameterizations . Examination of the model predictions on various accessible timescales reveals successes and limitations of the model . While functionally relevant conformational transitions can be studied , the coarse-grained representation has some difficulty with the ab initio folding of the peptide chain into its proper structure . Our observations highlight the complex molecular nature of a protein's underlying energy landscape , offering rigorous insight into the information missing in reduced representations of the peptide chain . With these caveats in mind , the physical interaction–based , coarse-grained model will find application in simulations of a wide variety of proteins and continue to guide future coarse-graining efforts .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"computational",
"biology/molecular",
"dynamics",
"computational",
"biology"
] |
2010
|
Multiscale Coarse-Graining of the Protein Energy Landscape
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The type VI secretion system ( T6SS ) is a widespread , versatile protein secretion system in pathogenic Proteobacteria . Several T6SSs are tightly regulated by various regulatory systems at multiple levels . However , the signals and/or regulatory mechanisms of many T6SSs remain unexplored . Here , we report on an acid-induced regulatory mechanism activating T6SS in Agrobacterium tumefaciens , a plant pathogenic bacterium causing crown gall disease in a wide range of plants . We monitored the secretion of the T6SS hallmark protein hemolysin-coregulated protein ( Hcp ) from A . tumefaciens and found that acidity is a T6SS-inducible signal . Expression analysis of the T6SS gene cluster comprising the imp and hcp operons revealed that imp expression and Hcp secretion are barely detected in A . tumefaciens grown in neutral minimal medium but are highly induced with acidic medium . Loss- and gain-of-function analysis revealed that the A . tumefaciens T6SS is positively regulated by a chvG/chvI two-component system and negatively regulated by exoR . Further epistasis analysis revealed that exoR functions upstream of the chvG sensor kinase in regulating T6SS . ChvG protein levels are greatly increased in the exoR deletion mutant and the periplasmic form of overexpressed ExoR is rapidly degraded under acidic conditions . Importantly , ExoR represses ChvG by direct physical interaction , but disruption of the physical interaction allows ChvG to activate T6SS . The phospho-mimic but not wild-type ChvI response regulator can bind to the T6SS promoter region in vitro and activate T6SS with growth in neutral minimal medium . We present the first evidence of T6SS activation by an ExoR-ChvG/ChvI cascade and propose that acidity triggers ExoR degradation , thereby derepressing ChvG/ChvI to activate T6SS in A . tumefaciens .
Pathogenic bacteria have evolved several specialized secretion systems to transport protein or DNA across membranes to extracellular milieu or even to the host cells in response to specific environmental cues . Among the 6 types of secretion systems , named type I to VI ( T1SS to T6SS ) identified in Gram ( − ) bacteria [1] , T6SS is the most recently identified system and is widespread in Proteobacteria [2]–[5] . Many T6SSs of pathogenic bacteria are induced inside the host or in response to host signals , which suggests their functions during bacterium–host interactions [6] . Numerous studies in various bacteria further suggested the diversified functions of T6SS , including survival within the host , escape from host predation , killing of eukaryotic or bacterial host cells , biofilm formation , stress response , and quorum sensing [7]–[13] . Growing evidence from structural and functional studies further reveals that T6SS may assemble into a bacteriophage tail-like structure to deliver effectors into the recipient cells [5] , [14] . The diverse functions of T6SS are reflected by its regulation by multiple mechanisms . T6SS is regulated at epigenetic , transcriptional , posttranscriptional , and posttranslational levels [3] , [15]–[17] . In enteroaggregative Escherichia coli , the sci1 T6SS gene cluster is under the control of an epigenetic switch regulated by iron availability through Fur- and Dam-dependent methylation [15] . Several T6SSs are transcriptionally regulated by various two-component systems , transcription factors , quorum sensing , alternative sigma factor 54 , and histone-like proteins [18]–[22] . The regulation of T6SS gene cluster expression by a two-component system has been reported for several T6SSs , including those from Burkholderia mallei by VirA/VirG , Edwarsiella tarda by EsrA/EsrB , and Salmonella enterica by SsrA/SsrB [20] , [23] , [24] . Posttranscriptional or translational control was revealed with the RNA binding protein RsmA , which acts as a translation repressor of the mRNA level of Pseudomonas aeruginosa HSI-1 T6SS [25] . In P . aeruginosa , HSI-1 T6SS is posttranslationally regulated by serine/threonine kinase PpkA and the cognate phosphatase PppA via threonine phosphorylation on a forkhead-associated protein , Fha1 [26] . These multiple regulatory cascades suggest that the versatile control of T6SS is critical for its function in response to specific signals . Agrobaterium tumefaciens is a soil bacterium causing crown gall disease in a wide range of plants . It integrates transferred DNA ( T-DNA ) from the tumor-inducing plasmid into the host genome [27]–[29] . When A . tumefaciens encounters signals such as acidity , monosaccharides , and phenolic compounds released from plant wound sites , the VirA/VirG two-component system , in cooperation with periplasmic sugar binding protein ChvE , is activated to induce the expression of virulence ( vir ) genes to direct the transfer of T-DNA into host cells [28] , [30] . T4SS , comprising 11 VirB proteins and VirD4 forming a transmembrane multi-protein complex , is responsible for the transfer of T-DNA and effector proteins from bacteria into host plant cells [31] . In addition to VirA/VirG , which is responsible for the expression of vir genes encoded by tumor-inducing plasmid , the chromosome-encoded ChvG/ChvI two-component system is responsible for the expression of acidity-inducible genes , including aopB , encoding an outer membrane protein; pckA , encoding phosphoenolpyruvate carboxykinase; and virG [32]–[36] . ChvG is a typical transmembrane sensor kinase that contains a large periplasmic domain located between 2 transmembrane domains and a conserved histidine kinase domain in the C-terminal cytoplasmic region [32] . The ChvG/ChvI two-component system has an essential role in tumor formation and bacterial growth under acidic conditions and in membrane integrity [33] , [37] . The ChvG/ChvI two-component system is highly conserved in α-Proteobacteria [32] , [37]–[40] . In the plant symbiont Sinorhizobium meliloti , the ChvG ortholog ExoS is the sensor kinase and with the response regulator ChvI , functions as the positive regulator for synthesis of exopolysaccharide succinoglycan , promoting biofilm formation and motility [41] . In S . meliloti , ExoR regulates its own expression through ExoS/ChvI [42] and functions as a periplasmic regulator inhibiting ExoS/ChvI signaling by physical interaction with ExoS [41] , [43] . Recent study also revealed that periplasmic ExoR is targeted for proteolysis [44]; however , whether and how ExoR perceives signals and the molecular mechanisms underlying how ExoR regulates ExoS ( ChvG ) /ChvI activity remain unknown . Interestingly , ExoR can also function independently of ChvG/ChvI in repressing succinoglycan biosynthesis and promoting biofilm formation and motility in A . tumefaciens [45] . How T6SS is regulated in A . tumefaciens remains largely unknown . Previously , we found the secretion of the T6SS hallmark protein hemolysin-coregulated protein ( Hcp ) in A . tumefaciens grown under various conditions , including nutrient-rich or minimal medium at low ( 19°C ) or room temperature [46] . Interestingly , transcriptome assays revealed that the expression of several T6SS genes encoded by the imp operon is higher under acidic than neutral minimal medium conditions [36] . However , whether the acid-induced imp gene expression is responsible for activation of T6SS secretion and the regulatory mechanisms underlying T6SS expression and activity are unknown . In this study , we aimed to investigate whether the expression and secretion of A . tumefaciens T6SS is regulated by plant-derived signals and if so , the underlying regulatory mechanism . T6SS-mediated Hcp secretion was almost silent with A . tumefaciens grown in neutral minimal medium but was induced by acidity . Further molecular analysis revealed that T6SS is activated by the ChvG/ChvI two-component system , with the sensor kinase ChvG negatively regulated by the periplasmic repressor ExoR . Importantly , we provide the first evidence that acidity induces ExoR degradation , which then may derepress ChvG to activate T6SS through a ChvI response regulator in a phosphorylation-dependent manner . This activation of T6SS by an acidic signal present in plant wound sites and apoplasts ( intercellular space ) suggests its potential role during Agrobacterium infection or replication near or inside plants .
Our previous secretome analysis revealed abundant secretion of the T6SS hallmark protein Hcp from A . tumefaciens grown in acidic minimal medium ( AB-MES , pH 5 . 5 ) [46] . Thus , we have been routinely using this growth condition to monitor T6SS activity in A . tumefaciens by Hcp secretion assay [47] , [48] . However , the signals responsible for activating T6SS for Hcp secretion remain unclear . Because A . tumefaciens can sense plant-derived signals , including acidity , monosaccharides , and phenolic compounds , which are critical components in the acidic minimal medium ( AB-MES , pH 5 . 5 ) for vir gene expression , we first tested whether any of these 3 signals plays a role in regulating T6SS activity . A . tumefaciens wild-type strain C58 cells cultured overnight in AB-MES ( pH 7 . 0 ) was sub-cultured in neutral ( pH 7 . 0 ) or acidic ( pH 5 . 5 ) AB-MES minimal medium with or without monosaccharides or acetosyringone ( AS ) phenolics for 6 h . Secretion of Hcp was abundant from A . tumefaciens cells grown in acidic minimal medium ( pH 5 . 5 ) ( Figure 1A ) , with barely detected secretion from cells grown in neutral minimal medium ( pH 7 . 0 ) . Sugar or carbon source did not seem to regulate Hcp secretion because secretion did not differ in cultures supplemented with carbon sources such as glucose , sucrose , cellobiose , or glycerol ( Figure 1A and Figure S1A ) . Intriguingly , the addition of AS in acidic minimal medium , which induces vir gene expression , as evidenced by VirE2 expression , significantly attenuated Hcp secretion as compared with no AS ( Figure 1A ) . The non-secreted protein RNA polymerase subunit A ( RpoA ) was an internal control . DMSO , used to dissolve AS , did not reduce Hcp secretion grown under acid-inducing conditions ( Figure S1B ) . Therefore , T6SS-mediated Hcp secretion was almost silent in A . tumefaciens grown in neutral minimal medium and was activated when the acidic signal was sensed . The attenuation of Hcp secretion in AS-induced acidic medium , which can induce the expression of vir genes , implied a complex regulatory network during Agrobacterium–plant interactions . Here , we investigated the regulatory mechanism underlying the acid-induced expression and secretion of T6SS . Systematic mutagenesis analysis of the T6SS locus from Edwardsiella tarda and Vibrio cholera , along with other studies , revealed about a dozen conserved components essential for mediating T6SS secretion [5] , [49] , [50] . In A . tumefaciens , the T6SS gene cluster comprises 2 divergently transcribed operons: imp , encoding 14 genes ( atu4343 to atu4330 ) ; and hcp , encoding 9 genes ( atu4344 to atu4352 ) ( Figure 1D ) . To examine the regulation of acid-induced Hcp secretion , quantitative RT-PCR ( qRT-PCR ) and western blot analyses revealed greatly upregulated expression of 3 selected genes encoded by the imp operon ( icmF , fha1 , atu4343 ) and 3 by the hcp operon ( clpV , hcp , atu4349 ) with acidity ( AB-MES , pH 5 . 5 ) ( Figure 1C ) . As controls , 23S rRNA and chvH genes , known not to respond to pH change [32] , showed similar mRNA levels with both acidic and neutral medium . The proteins encoded by the imp operon ( IcmF , Fha1 , Atu4343 ) were barely detected when grown in neutral minimal medium , whereas those encoded by the hcp operon ( ClpV , Hcp , Atu4349 ) were expressed at substantial levels under this growth condition ( Figure 1B ) . The levels of proteins encoded by the imp operon were markedly induced by acidity , whereas levels of proteins encoded by the hcp operon were only modestly higher with acidic than neutral medium ( Figure 1B ) . Therefore , T6SS secretion was activated by acid-induced expression of T6SS genes , especially those encoded by the imp operon . This pH-regulated T6SS expression and secretion were also observed in other A . tumefaciens strains such as Ach5 and 1D1609 [51] , [52] ( Figure S2A and S2B ) , which suggests that this may be a common regulatory mechanism in A . tumefaciens . The finding that acidic pH is the key to trigger T6SS gene expression and thus Hcp secretion prompted us to search for pH-responsive regulatory genes in A . tumefaciens . The ChvG/ChvI two-component system functions to regulate certain acidity-inducible genes , including the system itself , and may serve as a global pH sensor in A . tumefaciens [32] , so it may be a candidate for testing the regulatory role in T6SS . We first generated ΔchvG and ΔchvI in-frame deletion mutants for the two-component system and examined the effects on T6SS expression and Hcp secretion . Because ΔchvG and ΔchvI are sensitive to a nutrient-rich or acidic environment [37] , both mutants were grown and maintained in neutral minimal medium ( AB-MES , pH 7 . 0 ) . The proteins encoded by the imp or hcp operon showed different basal levels in neutral minimal medium ( AB-MES , pH 7 . 0 ) , and their protein levels were all further reduced with chvG or chvI deletion ( Figure 2A ) . However , as compared with chvG/chvI likely being essential for the expression of the imp operon , neither chvG nor chvI were absolutely required for hcp operon expression , as determined by western blot and qRT-PCR analyses ( Figure 2A and 2C ) . Because ExoS ( ChvG ) /ChvI signaling is negatively regulated by ExoR in S . meliloti [43] , we investigated whether ExoR functions upstream of ChvG/ChvI in regulating T6SS in A . tumefaciens . Lack of exoR enhanced the expression of both imp and hcp operons ( Figure 2A and 2C ) , which indicates that exoR negatively regulates T6SS expression in A . tumefaciens . Deletion of chvG in the ΔexoR mutant background abrogated the induced expression of imp and hcp operons ( Figure 2A and 2C ) , which indicates that chvG is epistatic to exoR in regulating T6SS . Hcp secretion was increased in ΔexoR , and this enhancement was abolished in the ΔchvG ΔexoR mutant under this growth condition ( AB-MES , pH 7 . 0 ) ( Figure 2B ) . Therefore , T6SS is regulated positively by chvG/chvI and negatively by exoR , which likely functions upstream of chvG sensor kinase . Of note , imp expression is tightly regulated by exoR and chvG/chvI , which are not absolutely required for the expression of the hcp operon . These results also suggest that acid-induced T6SS secretion is mainly controlled by the expression of imp-encoding proteins constituting the T6S machinery . The basal expression of the hcp operon in the absence of chvG or chvI further suggested the existence of an additional regulatory pathway for hcp operon expression . To relay signals via a two-component system , a sensor kinase is activated by the input signal and phosphorylates the cognate response regulator , which then exerts its function by regulating its target gene [53] . Thus , we wondered whether ChvI , as a response regulator , binds directly to the promoters of the divergent imp and hcp operons . We used electrophoretic mobility shift assay ( EMSA ) and incubated the purified His-ChvI recombinant protein with a 230-bp DNA fragment derived from the intergenic region of the imp and hcp operons but detected no binding activity ( Figure 3 ) . Because the phosphorylated state of the response regulator could modulate its DNA binding activity , we then expressed and purified the ChvI variant by replacing the conserved aspartic acid phosphorylation site with glutamic acid ( D52E ) , a phospho-mimic variant previously shown to be constitutively active in other systems [54] , [55] . EMSA revealed strong binding of the phospho-mimic variant to the probe , and the shifted complex was dissociated when challenged with the unlabeled specific DNA competitor ( Figure 3 ) . As a control , ChvI ( D52E ) did not bind to an unrelated DNA fragment derived from the A . tumefaciens genome ( Figure S3 ) . Thus , ChvI may be the response regulator directly regulating T6SS , and the phosphorylated state of ChvI is crucial for its direct binding to the intergenic promoter region between both operons . To determine whether the binding of phospho-mimic ChvI ( D52E ) to the T6SS promoter region in vitro was biologically significant in vivo , we overexpressed ChvI ( D52E ) in A . tumefaciens wild-type C58 to determine whether this phospho-mimic ChvI was sufficient to trigger T6SS expression and secretion in neutral medium , the secretion-repression condition . In parallel , we overexpressed the sensor kinase ChvG and wild-type ChvI in C58 as controls . Both the mRNA and protein levels of the analyzed imp genes were significantly induced with ChvG and ChvI ( D52E ) overexpression as compared with vector expression alone ( Figure 4A and 4B ) . In contrast , overexpression of wild-type ChvI did not elevate the mRNA and protein levels of genes encoded by the imp operon . Moreover , overexpression of ChvI ( D52A ) , with inactivation of the ChvI phosphorylation site , further reduced imp gene expression at both mRNA and protein levels . Interestingly , the expression of the hcp operon was regulated differently from the imp operon with the overexpression strains . As expected , hcp operon expression was increased at both mRNA and protein levels with ChvG and ChvI ( D52E ) overexpression ( Figure 4A and 4B , Figure S4 ) . Surprisingly , ChvI and ChvI ( D52A ) overexpression increased the hcp operon expression at both mRNA and protein levels ( Figure 4A and 4B ) . Hcp secretion was activated by overexpression of ChvG and the phospho-mimic ChvI ( D52E ) but not wild-type ChvI or ChvI ( D52A ) ( Figure 4C ) , which supports Hcp secretion being controlled by the expression of the imp operon . Moreover , phospho-mimic ChvI ( D52E ) and phospho-inactive ChvI ( D52A ) were insensitive to the acidity in regulating T6SS , with evidence that ChvI ( D52E ) was constitutively active and ChvI ( D52A ) defective in T6SS expression and secretion in both neutral and acidic medium ( Figure 4D ) . These data suggest that activation of imp operon expression requires phosphorylated ChvI , but phosphorylated or unphosphorylated ChvI can upregulate the hcp operon . The evidence that chvG is epistatic to exoR in regulating T6SS ( Figure 2 ) suggested that exoR functions upstream of chvG/chvI to abrogate ChvG/ChvI-induced T6SS activity . Because overexpression of ChvG in the presence of exoR could activate T6SS in A . tumefaciens grown in neutral medium , we hypothesized that acid-induced T6SS expression and secretion are activated by increased ChvG protein level , which is negatively regulated by ExoR in neutral medium . Thus , we first examined whether the expression of chvG , chvI , and exoR were regulated by acidity . As expected , the mRNA levels of both chvG and chvI were higher in acidic than neutral medium ( Figure 5A ) , which agreed with previous findings for acid-induced chvI-chvG autoregulation [36] . Interestingly , exoR mRNA levels were slightly upregulated by acidity ( Figure 5A ) . We next determined the protein levels of ChvG and ExoR in A . tumefaciens grown under both neutral and acidic conditions . Because of the lack of ChvG-specific antibody and inability to detect endogenous ExoR protein in wild-type C58 ( Figure 6B ) , we used heterologous promoters to overexpress ExoR and ChvG in A . tumefaciens under both neutral and acidic conditions . ChvG tagged with hemagglutinin ( HA ) at the C terminus was driven by a constitutively active lac promoter from pRL622 ( Plac-ChvG-HA ) , and ExoR was expressed by an isopropyl-beta-D-thiogalactoside ( IPTG ) -inducible trc promoter from pTrc200 ( Ptrc-ExoR ) . The ectopic expression of ExoR and ChvG also allowed us to monitor their protein expression and stability at posttranscriptional levels . The AB-MES ( pH 7 . 0 ) overnight-grown bacterial culture was subcultured in neutral ( pH 7 . 0 ) or acidic ( pH 5 . 5 ) AB-MES medium for 3 , 6 , 12 , and 24 h and collected for western blot analysis . Both neutral and acidic medium maintained the pH over this time because we detected only a slight decrease of pH after 12- to 24-h growth ( Figure 5B ) . ChvG protein level was increased steadily in acidic culture , with a pronounced increase at 12 and 24 h , whereas ChvG protein level remained at lower levels in neutral medium ( Figure 5B ) . For ExoR , which contains an N-terminus signal sequence targeting ExoR to periplasm in S . meliloti [41] , we detected 2 protein bands specifically recognized by the antibody against S . meliloti ExoR ( Figure 5B ) . Biochemical fractionation results indicated that the upper band represents the cytoplasmic ExoR precursor ( ExoRp ) , which contains the unprocessed signal sequence , whereas the lower band is the mature periplasmic ExoR ( ExoRm ) , with removal of the signal peptide ( Figure 5C ) . ExoR protein levels were decreased at 3 h after shifting to acidic medium as compared with neutral medium ( Figure 5B ) . Although ExoR continued to be synthesized in neutral or acidic medium , as revealed by increased protein levels up to 12 h , levels of both ExoRp and periplasmic ExoRm were lower in acidic medium . Next , we determined whether the low levels of periplasmic ExoRm under the acidic condition is caused by the instability of ExoRm when A . tumefaciens senses acidity . Thus , we traced both ExoR and ChvG protein stability by western blot analysis in the Plac-ChvG-HA and Ptrc-ExoR overexpression strain with protein synthesis inhibited by chloramphenicol . Periplasmic ExoRm protein level was rapidly decreased in acidic medium as compared with neutral medium , with less effect on stability of unprocessed ExoRp with neutral or acidic medium ( Figure 5D ) . Interestingly , ChvG-HA was also less stable in acidic than neutral medium with overexpressed ExoR ( Figure 5D ) . However , ChvG-HA was more stable , with similar stability in both acidic and neutral medium with endogenous exoR ( Figure 5E ) . The differential stability of ChvG with or without detectable ExoR in response to acidity suggested a negative role of ExoR in ChvG protein stability in the acidic environment . However , ChvG retains similar protein stability independent of ExoR protein levels when grown in neutral medium . Next , we determined whether the higher ChvG protein levels in acidic than neutral medium ( Figure 5B ) are regulated by exoR . To test this possibility , Plac-ChvG-HA was expressed in both ΔchvG and ΔchvGΔexoR mutants to monitor whether increased ChvG protein level is regulated by exoR at the posttranscriptional level . The level of ChvG protein was slightly higher in acidic than neutral medium at 6 h with exoR; however , the level of ChvG-HA was greatly increased without exoR and comparable in neutral and acidic medium ( Figure 6A ) . These data suggest that exoR negatively regulates ChvG protein levels in both acidic and neutral medium . Importantly , in contrast to acid-induced T6SS expression in C58 ( Figure 6B , lane 1 vs . lane 5 ) , the levels of proteins encoded by imp and hcp operons were not further increased in ΔexoR in response to acidity ( Figure 6B , lane 3 vs . lane 7 ) . This result is consistent with comparable ChvG protein levels in ΔexoR grown in neutral or acidic minimal medium ( Figure 6A ) . Furthermore , the acid-induced T6SS expression and secretion were largely compromised when ExoR was overexpressed ( Figure 6B , lane 5 vs . lane 6; and 6C ) . Because exoR mRNA levels were not reduced ( Figure 5A ) but periplasmic ExoRm was rapidly degraded in response to acidity ( Figure 5D ) , the acidity-triggered ExoRm degradation may contribute to the elevated ChvG protein abundance and thereby lead to T6SS activation under acidic conditions . The inverse association of ExoR and ChvG protein level , together with the epistasis of chvG to exoR prompted us to investigate the mode of action of ExoR in repressing ChvG/ChvI signaling . In S . meliloti , periplasmic ExoR physically interacts with ExoS ( ChvG ortholog ) , and this interaction is critical for inhibiting ExoS/ChvI signaling [43] . Thus , ExoR may negatively regulate T6SS activity by physical interaction with ChvG in A . tumefaciens grown under neutral conditions . When the acidic signal is sensed , the degradation of ExoRm may thereby allow the inner-membrane–associated ChvG sensor kinase to activate the ChvI response regulator and induce T6SS expression and secretion activity . Thus , we aligned the amino acid sequences of ExoR encoded by A . tumefaciens and S . meliloti and identified the putative amino acid residues in A . tumefaciens ExoR that may be critical for interaction with ChvG ( Figure S5 ) . ExoR contains a conserved N-terminal signal peptide and tetratricopeptide repeat ( TPR ) /Sel1-like domains , which are implicated in protein–protein interactions [56] . We generated the exoR mutants encoding ExoR variants with amino acid substitution mutations at 2 specific residues in the Sel1 repeat of ExoR ( G73 and S153 ) that are responsible for interaction with ExoS ( ChvG ) and result in increased ExoS ( ChvG ) /ChvI activity in S . meliloti [43] . We determined the effect on ChvG protein level and T6SS expression and secretion . ChvG-HA was co-expressed with wild-type ExoR , ExoR ( G73C ) , ExoR ( S153Y ) , or ExoR ( G73C S153Y ) in the ΔchvG ΔexoR mutant to determine whether these amino acid residues are critical for ChvG protein levels in neutral minimal medium . ChvG-HA protein level was lower with complementation of pExoR expressing wild-type ExoR than with the vector control ( Figure 7A ) . ChvG-HA protein level was higher with overexpression of the variants ExoR ( G73C ) , ExoR ( S153Y ) , and ExoR ( G73C S153Y ) than with wild-type ExoR ( Figure 7A ) . Furthermore , levels of proteins encoded by the imp and hcp operons were higher , and thus Hcp secretion , with the ExoR ( G73C ) and ExoR ( S153Y ) variants than with wild-type ExoR ( Figure 7B and 7C ) . T6SS expression and secretion was no longer repressed with ExoR ( G73C S153Y ) expression ( Figure 7B and 7C ) . Importantly , the abrogation of T6SS expression and secretion by ExoR was associated with its interaction with ChvG . As shown in our protein–protein interaction study with yeast two-hybrid assay ( Figure 7D ) , we detected the interaction between the periplasmic domain of ChvG and wild-type ExoR but no or little interaction with the 3 ExoR variants that were compromised by its activity in negatively regulating T6SS . The evidence that the amino acid residues of ExoR crucial for ExoR–ChvG interaction are also essential for ExoR in negatively regulating ChvG protein levels and abrogating downstream T6SS activation strongly argues that ExoR functions upstream of the ChvG/ChvI two-component system to negatively regulate T6SS by its association with ChvG sensor kinase in A . tumefaciens .
T6SS is involved in diverse functions , including promoting or repressing virulence , forming biofilm , and inducing cytotoxicity in eukaryotic or prokaryotic hosts . Many T6SSs of pathogenic bacteria are induced by host signals , which suggests their functions during bacteria–host interactions [6] . Several studies revealed that T6SS is tightly coordinated with other virulence determination systems such as T3SS , quorum sensing , and flagella synthesis [17] , [57]–[59] . In P . aeruginosa and Salmonella enterica , T3SS and T6SS are inversely regulated to allow the transition from the acute to chronic virulence phase [24] , [59] , [60] . In A . tumefaciens , T6SS is regulated by multiple environmental factors , including pH and the virulence inducer we report . The abrogation of T6SS-mediated Hcp secretion by the addition of AS phenolics in an acidic minimal medium ( AB-MES , pH 5 . 5 ) revealed an inverse association of T6SS and VirB/D4 T4SS that is highly induced by AS [31] , [61] . Interestingly , Hcp secretion levels were greatly reduced in virulence-induced medium ( AB-MES , pH 5 . 5 , +AS ) ( Figure 1A ) , but the levels of all analyzed T6SS proteins except Hcp seemed to remain similar when grown with or without AS ( Figure S1B ) , which suggests that AS might negatively regulate Hcp secretion posttranslationally . The slower migration of secreted Hcp protein as compared with cellular Hcp led us to explore the possibility of posttranslational modifications of Hcp such as phosphorylation . However , we detected no phosphorylation of Hcp ( data not shown ) . The aberrant migration of secreted Hcp is likely caused by the presence of trichloroacetic acid ( TCA ) used for protein precipitated from culture medium because both cellular and secreted Hcp migrated slower in the presence of TCA ( Figure S6 ) . Our previous study did not reveal the suppression of Hcp secretion at pH 7 . 0 [46] . The discrepancy is likely due to the use of a nutrient-rich 523 medium for overnight culture and the later time points for Hcp secretion assay in the previous work . Indeed , T6SS is almost silent when grown in neutral minimal medium ( AB-MES , pH 7 . 0 ) ( Figure 1 ) , but its expression and secretion are active when grown in nutrient-rich medium such as 523 at pH 7 . 0 [46] . Thus , T6SS might be regulated by nutrient availability in A . tumefaciens . We also noted that T6SS secretion activity depends on growth phase , with Hcp secretion greatly reduced during the late logarithmic phase ( J . Lin and E . Lai , unpublished results ) . Thus , T6SS is regulated by multiple factors via a complex regulatory network in a free-living environment or during Agrobacterium–plant interactions . By loss- and gain-of-function studies , we demonstrated that T6SS is regulated positively by the ChvG/ChvI two-component system and negatively by ExoR in A . tumefaciens . This discovery adds T6SS genes to the list of ChvG/ChvI-regulatory genes in A . tumefaciens . ExoR , ChvG/ChvI , and T6SS are widely distributed in α-Proteobacteria , which includes several animal and plant pathogens , as well as plant symbionts [32] , [37] , [45] , [46] , [62]–[64] . Thus , the regulation of T6SS via an ExoR-ChvG/ChvI cascade may be a universal regulatory mechanism in these bacteria . The acid-induced T6SS gene expression and Hcp secretion is consistent with the acidity upregulation of imp genes found in previous microarray analyses [36] , although the authors did not identify hcp operon-encoded genes as acidity-regulated genes . By investigating both the mRNA and protein levels in response to pH change together with loss- and gain-of-function studies , our data strongly argue that acid-induced imp operon expression is regulated at mRNA levels via transcriptional activation by the ChvG/ChvI two-component system . However , the hcp operon is regulated by a ChvG/ChvI pathway and by an unknown mechanism responsible for its basal expression even in the secretion-repression condition ( AB-MES , pH 7 . 0 ) . Thus , acid-induced T6SS secretion is mainly controlled by the expression of imp-encoding proteins constituting the T6S machinery . In addition , the higher induction of mRNA than protein levels by acidity ( Figure 1 ) implicated additional regulation ( s ) of the hcp operon in translation efficiency and/or protein stability . More strikingly , the phosphorylated state of ChvI was required for inducing imp operon expression and Hcp secretion but seemed to be dispensable for the increased expression of hcp operon ( Figure 4 ) . For the prototypical two-component system , the activated histidine sensor kinase phosphorylates the cognate response regulator at the conserved aspartate residue . The phosphorylation of the response regulator is generally required for binding to the target promoter [53] . The phosphorylation state of ChvI was indeed required for its binding to the intergenic region of the 2 operons in vitro and activated the expression of imp operon and T6SS secretion in vivo . Surprisingly , the overexpression of wild-type , phospho-mimic ( D52E ) , or phospho-inactive ( D52A ) variants of ChvI could increase both mRNA and protein levels of hcp operon genes , which suggests that the phosphorylated state of ChvI is not required for upregulation of hcp operon expression . Because wild-type ChvI without detectable binding activity to the T6SS promoter region can enhance the level of proteins encoded by the hcp operon in neutral medium , where ChvG is repressed by ExoR , ChvI may not directly regulate the expression of the hcp operon . In view of the chvG/chvI-independent basal level expression of hcp-operon proteins , both phosphorylated and non-phosphorylated ChvI may positively influence the expression by interacting with a yet-to-be identified regulator ( s ) of hcp operon . Future investigation should aim to identify the cis-elements critical for ChvI binding and elucidate the molecular details of how ChvI coordinates the regulation of the expression of the 2 operons from the shared or overlapped regulatory region . Orthologs of ExoR and the ChvG/ChvI two-component system are present in many α-Proteobacteria , such as Brucella , Bartonella , Sinorhizobium , and Rhizobium , which suggests a conserved negative regulation of ChvG/ChvI by ExoR among these bacteria [65] . In S . meliloti , exoR is involved in sensing ammonia or calcium signals for derepressing the expression of ExoS/ChvI target genes lpsS and exo [66] , [67] . Several two-component systems orthologous to ChvG/ChvI are activated in response to diverse signals; examples are Bartonella henselae BatR/BatS at the physiological pH of blood ( pH 7 . 4 ) [40] , Brucella abortus sensor kinase BvrS at the late logarithmic phase [68] , and A . tumefaciens ChvG/ChvI in response to the acidic signal [36] . However , a clear ExoR–ChvG/ChvI regulatory cascade was demonstrated only in regulating the synthesis of exopolysaccharide succinoglycan , forming biofilm and motility in S . meliloti [41] , [43] and regulating T6SS of A . tumefaciens in this study . The epistasis of chvG to exoR in regulating T6SS expression and secretion indicates that exoR functions upstream of chvG in sensing acidity to regulate T6SS . The disruption of A . tumefaciens ExoR amino acid residues critical for interacting with ChvG causes the increased ChvG protein levels and T6SS expression/secretion , which suggests that ExoR negatively regulates ChvG/ChvI signaling by direct binding to ChvG . This conclusion is consistent with the finding that the physical interaction of ExoR–ExoS ( ChvG ) is important in repressing ExoS/ChvI activity in S . meliloti [43] . For unknown reasons , the ExoR protein levels differed between the wild type and variants ( Figure 7A , 7C ) . However , the effect of ExoR variants on ChvG protein level and activating T6SS is indeed associated with the ability to interact with ChvG rather than the expression level ( Figure 7 ) . Together with the evidence that ChvG protein level is increased in the absence of exoR , ExoR may negatively regulate ChvG protein levels by directing binding . The inverse association of ExoR protein level and ChvG protein stability in acidic conditions ( overexpressed ExoR with unstable ChvG vs . endogenous ExoR with stable ChvG , Figure 5D , E ) implied that ExoR negatively regulates ChvG protein stability . However , ChvG protein seems to retain similar stability independent of ExoR protein levels when grown in neutral medium . Moreover , ChvG protein is not more stable in acidic than neutral medium despite acidity inducing protein levels of ChvG when driven by a lac promoter ( Figure 5B ) or its own promoter on plasmid ( Figure S7 ) . Thus , the acid-induced ChvG protein accumulation cannot be simply explained by ExoR-regulated protein stability or its transcriptional activation ( Figure 5 ) [36] . Our data suggest that additional regulations at posttranscriptional levels such as mRNA stability or translational control may also be involved but require future investigation . Because endogenous ExoR is not detectable by antibody against S . meliloti ExoR or tagged with Strep epitope on chromosomes ( Figure S7 ) , ExoR proteins can be detected only by overexpression . However , the acid-triggered ExoRm degradation also likely occurs for endogenous ExoR in A . tumefaciens because of the following evidence . First , a proteomic study revealed reduced ExoR protein level after a pH shift from 7 . 0 to 5 . 5 in Agrobacterium sp . ATCC31750 [69] . Because exoR mRNA levels were not reduced in acidic medium ( Figure 5A ) [36] , the levels of ExoR in acidic medium were not lowered by transcriptional regulation but were likely downregulated at the protein level . Furthermore , in S . meliloti , with detectable endogenous ExoR , a recent study revealed that periplasmic ExoRm is targeted for proteolysis and the ExoRm level regulates its role in suppressing ExoS ( ChvG ) activity [44] . Thus , if acid-triggered ExoRm degradation observed with overexpression indeed represents physiological relevance for endogenous ExoR , ExoR seems only to distablize ChvG in acidic medium , an adverse environment , where several proteases may be induced for protein quality control [70] . In this scenario , the increased protein levels of ectopically expressed ChvG in the absence of exoR or loss of binding to ExoR variants when grown in neutral medium must be regulated by mRNA stability and/or translational efficiency of chvG , but the potential regulations await future investigation . To this end , we propose that acid-induced ExoRm degradation is responsible or one factor for the increased ChvG protein levels or activity and T6SS activation . As illustrated in our proposed model ( Figure 8 ) , in the absence of an acidic signal , periplasmic ExoRm interacts with the ChvG sensor kinase located in the inner membrane , thus leading to inhibited ChvG/ChvI signaling pathways , including T6SS . When A . tumefaciens senses the acidic signal , ExoR loses its activity in repressing ChvG , thereby allowing the ChvG sensor kinase to relay ChvI signaling for T6SS activation . Periplasmic ExoRm may be more stable in neutral pH and rapidly degraded when sensing acidity . However , whether acid-triggered ExoRm degradation is the key or responsible for activating ChvG/ChvI signaling is not yet verified . We do not exclude that acid may also induce a conformational change of ExoR or even ChvG to regulate ChvG/ChvI signaling . Thus , it remains important to visualize the endogenous expression of ExoR and ChvG in response pH changes by pulse-chase analysis [71] or quantitation by mass spectrometry [72] . Identifying the protease responsible for ExoRm degradation and/or proteolysis-resistant variants for ExoR and ChvG will be the key for a firm conclusion for the proposed mechanism . Whether acid-induced T6SS is a common regulatory mechanism in plant-associated bacteria and the biological significance of this regulation are important questions for future investigation .
Plasmids , bacterial strains and primers are in supplemental Tables S1 and S2 . A . tumefaciens strains were grown in 523 medium [73]; ΔchvG , ΔchvI , and ΔchvG ΔexoR mutant strains were grown in AB-MES minimal medium , which contains 3 g K2HPO4 , 1 g NaH2PO4 , 1 g NH4Cl , 0 . 15 g KCl , 0 . 01 g CaCl2 , 0 . 3 g MgSO4-7H2O , 2 . 5 mg FeSO4-7H2O , 9 . 76 g 2- ( N-morpholine ) ethanesulfonic acid , and 20 g glucose per liter ( pH 7 . 0 ) [74] . For T6SS expression and secretion analysis , A . tumefaciens cells grown overnight in AB-MES medium ( pH 7 . 0 ) with appropriate antibiotics were harvested by centrifugation ( 8 , 000× g , 10 min ) and resuspended in AB-MES medium ( pH 7 . 0 or 5 . 5 ) without any antibiotics at ∼0 . 1 optical density 600 nm ( OD600 ) . After growth for 6 h at 25°C , cells were harvested , and proteins secreted into the culture medium were precipitated with trichloroacetic acid ( TCA ) as described [46] , [47] . For western blot analysis of ChvG-HA and ExoR , cells were harvested after growth for 3 , 6 , 12 , and 24 h at 25°C . For ExoR protein stability analysis , A . tumefaciens cells were resuspended in AB-MES medium ( pH 7 . 0 or 5 . 5 ) containing chloramphenicol ( 100 µg/ml ) at OD600 ∼0 . 5 from A . tumefaciens cells grown overnight in AB-MES medium ( pH 7 . 0 ) with appropriate antibiotics . Cells were harvested after growth for 1 , 2 , and 3 h at 25°C . For A . tumefaciens , the concentration of the antibiotic gentamycin was 50 µg/ml; spectinomycin , 250 µg/ml; and chloramphenicol , 100 µg/ml , and for Escherichia coli , the concentration for gentamycin was 50 µg/ml; spectinomycin , 100 µg/ml; and kanamycin , 20 µg/ml . The plasmids pJQ200KS-ΔchvG , pJQ200KS-ΔchvI , and pJQ200KS-ΔexoR were created by ligating the XbaI/BamHI-digested PCR product 1 ( ∼500 bp DNA fragments upstream of each target gene ) and BamHI/XmaI-digested PCR product 2 ( ∼500 bp DNA fragments downstream of each target gene ) into XbaI/XmaI sites of pJQ200KS [75] to generate each of the deletion mutants , for which at least 2 independent colonies were selected and confirmed by PCR . The PCR products of chvG and chvI genes containing the ribosomal-binding sequence ( RBS ) and open reading frame ( ORF ) were digested by XhoI/XbaI and cloned into the same sites of pRL662 [76] , which resulted in the plasmids pChvG and pChvI . To construct the plasmid for ChvG tagged with HA , the chvG DNA fragment containing the RBS and ORF ( without a stop codon ) was PCR-amplified , digested by SacI/XbaI , and cloned into the same sites of pBluescript SK ( + ) -HA . The resulting plasmids were further digested by XhoI/HindIII and cloned into the same sites of pRL662 to create the plasmid pChvG-HA . The PCR products of exoR containing its RBS and ORF were digested by XmaI/XbaI and cloned into the same sites of pTrc200 [77] , which resulted in the plasmid pExoR . The expression vector pET22b ( + ) ( Novagen ) was used to overexpress proteins driven by the T7 promoter via isopropyl-beta-D-thiogalactoside ( IPTG ) induction in E . coli BL21 ( DE3 ) [78] . Each ORF ( without a stop codon ) of fha1 , atu4343 , atu4349 , and rpoA was PCR-amplified and cloned into the same sites of pET22b ( + ) with appropriate enzyme sites . The clpV ORF ( without a stop codon ) was PCR-amplified , digested by HindIII , and cloned into pET22b ( + ) , which was first digested by NdeI , followed by Klenow repair , and finally digested by HindIII . N-terminal His-tagged wild-type and D52E ChvI were constructed by PCR amplification of the full-length wild-type or D52E ChvI with flanking NdeI/XhoI restriction sites and cloning into pET28a ( Novagen ) . For the constructs used for yeast two-hybrid , various exoR ORFs were PCR-amplified ( by primers AD ExoR F & AD ExoR R ) , digested ( NdeI/XhoI ) , and cloned into the NdeI/XhoI sites of pGADT7 for N-terminal fusion to the activation domain ( AD ) , pGADT7-ExoR , pGADT7-ExoRG73C , pGADT7-ExoRS153Y and pGADT7-ExoRG73C/S153Y . For plasmid pGBKT7-ChvG-peri , the DNA fragment encoding the periplasmic domain of ChvG ( 71–278 a . a . ) was PCR-amplified with primers BD ChvGperi F & BD ChvGperi R , digested with NdeI/BamHI , and cloned into the same sites of pGBKT7 . Total RNA from A . tumefaciens strains grown in AB-MES minimal medium ( pH 7 . 0 or pH 5 . 5 ) was extracted by the hot-phenol method [79] and treated with DNase I ( Promega ) to eliminate DNA contamination . RNA was reverse transcribed with random oligonucleotide hexamers ( Promega ) and the SuperScript III Reverse Transcriptase method ( Invitrogen ) . qRT-PCR involved use of specific primers with Power SYBR Green PCR Master Mix reagent ( Applied Biosystems ) and the ABI 7500 Real-Time PCR System ( Applied Biosystems ) . The program for qRT-PCR was 2 min at 50°C , 10 min at 95°C , 40 cycles of 15 s at 95°C/1 min at 60°C . Expression was normalized to that of 16S rRNA as an internal control by the 2−ΔΔCt method [80] . All in-frame deletion mutants were generated in A . tumefaciens C58 via double crossover with the suicide plasmid pJQ200KS [75] as described [46] , [47] . The detailed methods of plasmid construction , overexpression , and purification of His-tagged Fha1 , Atu4343 , ClpV , Atu4349 , and RpoA proteins for antibody production will be published elsewhere ( J . Lin and E . Lai , unpublished results ) . In brief , the expression vector pET22b ( + ) was used to overexpress His-tagged proteins driven by the T7 promoter with IPTG induction in E . coli BL21 ( DE3 ) followed by purification with an Ni2+-NTA column ( Novagen ) as described [46] , [47] . Purified proteins were separated by glycine-SDS-PAGE , and the protein band was cut out to obtain polyclonal antibody in rabbits . The pET28a-ChvI and pET28a-ChvI ( D52E ) plasmids were transformed into E . coli BL21 ( DE3 ) cells for protein expression . Briefly , cultures were induced with 0 . 4 mM IPTG for 3 h at 37°C , and cells were lysed by use of the French Press ( Aminco , Silver Spring , MD , USA ) as described [46] , [47] . Proteins were resolved by 13% glycine-SDS-PAGE and western blot analysis was performed as described [81] with primary polyclonal antibodies produced in this study and against C-IcmF [47] , Hcp [46] , ActC [82] , VirE2 [83] encoded by A . tumefaciens; ExoR [44] encoded by S . meliloti; or monoclonal antibody against HA ( Sigma ) . The secondary antibody was horseradish peroxidase-conjugated goat anti-rabbit or anti-rabbit IgG ( Chemicon ) , and signals were detected by use of the Western Lightning System ( Perkin Elmer , Boston , MA ) . Chemiluminescent bands were visualized by use of X-ray film ( Kodak , Rochester , NY ) or the BioSpectrum AC Imaging System ( Ultra-Violet Products Ltd . , UK ) to detect and quantify the photon intensity of protein signals . The 230-bp intergenic region of the imp and hcp operons and the control DNA fragment were PCR-amplified with the primers T6SSF/T6SSR and Atu4353F/Atu4353R , respectively , and purified by use of the PCR DNA fragment extraction kit ( Geneaid , Taiwan ) . The T6SS regulatory region and control DNA fragments were digested with XhoI and BamHI , respectively , and filled in with [α-32P]dCTP and unlabeled dTTP , dATG , and dGTG with the klenow fragment of DNA polymerase I . The labeled 32P-labeled DNA fragments were purified by use of G50 Mini columns ( Geneaid ) . Labeled DNA fragments ( 1 . 5 ng ) were incubated with purified ChvI protein ( 11 to 150 ng ) in 10 µl binding buffer ( 10 mM Tris-Cl [pH 7 . 5] , 1 mM EDTA , 0 . 1 mM dithiothreitol , 5% glycerol , 0 . 05 mg/ml bovine serum albumin [BSA] ) for 20 min at room temperature and then analyzed on 5% Tris-borate-EDTA non-denaturing acrylamide gels at 4°C . The separated DNA–protein complex was dried by use of a gel dryer and visualized by use of X-ray film ( Kodak ) . The DNA fragment encoding ChvI ( D52E ) , ChvI ( D52A ) , ExoR ( G73C ) , or ExoR ( S153Y ) mutations was created by PCR-based site-directed mutagenesis as described [84] . The chvI ( D52E ) and chvI ( D52A ) DNA fragments were digested by NdeI/XbaI and cloned into the same sites of pRL662 to create the plasmids pChvI ( D52E ) and pChvI ( D52A ) . The exoR ( G73C ) , exoR ( S153Y ) , and exoR ( G73C S153Y ) DNA fragments were digested by XmaI/XbaI and cloned into the same sites of pRL662 to create the plasmids pExoR ( G73C ) , pExoR ( S153Y ) , and pExoR ( G73C S153Y ) . Isolation of A . tumefaciens cellular fractions was as described [47] . The Matchmaker yeast two-hybrid system was used according to the user manual instructions ( Clontech , Mountain View , CA ) . Each of the plasmid pairs were co-transformed into Saccharomyces cerevisiae strain AH109 . The transformants were selected by their growth on synthetic dextrose ( SD ) minimal medium lacking tryptophan ( Trp ) and leucine ( Leu ) ( SD-WL medium ) . The positive interaction of expressed fusion proteins was determined by their growth on SD lacking Trp , Leu , adenine ( Ade ) , and histidine ( His ) ( SD-WLHA medium ) at 30°C for at least 2 days . Genebank accession numbers for genes: hcp ( 1136219 ) , rpoA ( 1133961 ) , virE2 ( 1137513 ) , icmF ( 1136206 ) , fha1 ( 1136209 ) , atu4343 ( 1136217 ) , clpV ( 1136218 ) , atu4349 ( 1136223 ) , chvG ( 1132071 ) , chvI ( 1132072 ) , exoR ( 1133753 ) , chvH ( 1134591 ) Genebank accession numbers for proteins: Hcp ( NP_356310 ) , RpoA ( NP_354899 ) , VirE2 ( NP_396510 ) , IcmF ( NP_356323 ) , Fha1 ( NP_356320 ) , Atu4343 ( NP_356312 ) , ClpV ( NP_356311 ) , Atu4349 ( NP_356306 ) , ChvG ( NP_353072 ) , ChvI ( NP_353073 ) , ExoR ( NP_354703 ) , ChvH ( NP_355493 )
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The bacterial type VI secretion system ( T6SS ) has diverse functions that contribute to the survival or fitness of many pathogenic bacteria in response to environmental cues . Numerous studies have shown that T6SS is highly regulated via multiple mechanisms , but the regulatory mechanisms of most T6SSs remain unknown . In this study , we discovered that T6SS is activated by acidity via an ExoR-ChvG/ChvI cascade in a plant pathogenic bacterium , Agrobacterium tumefaciens . Our data suggested that ExoR represses ChvG sensor kinase by physical interaction and the acid-induced degradation of periplasmic ExoR may derepress ChvG to activate T6SS by phosphorylation of the ChvI response regulator . The activation of T6SS by an acidic signal present in the wound site and intercellular space of plants implicates a role of T6SS during Agrobacterium–plant interactions . In view of the conservation of ExoR and ChvG/ChvI and wide distribution of T6SS in α-Proteobacteria , including many animal and plant pathogens and symbionts , the regulation of T6SS by the ExoR-ChvG/ChvI cascade may be a universal regulatory mechanism in these bacteria .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
] |
[
"bacteriology",
"plant",
"microbiology",
"gene",
"expression",
"genetics",
"molecular",
"genetics",
"biology",
"microbiology",
"host-pathogen",
"interaction",
"genetics",
"and",
"genomics",
"bacterial",
"pathogens"
] |
2012
|
Acid-Induced Type VI Secretion System Is Regulated by ExoR-ChvG/ChvI Signaling Cascade in Agrobacterium tumefaciens
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For many viruses , one or two proteins allow cell attachment and entry , which occurs through the plasma membrane or following endocytosis at low pH . In contrast , vaccinia virus ( VACV ) enters cells by both neutral and low pH routes; four proteins mediate cell attachment and twelve that are associated in a membrane complex and conserved in all poxviruses are dedicated to entry . The aim of the present study was to determine the roles of cellular and viral proteins in initial stages of entry , specifically fusion of the membranes of the mature virion and cell . For analysis of the role of cellular components , we used well characterized inhibitors and measured binding of a recombinant VACV virion containing Gaussia luciferase fused to a core protein; viral and cellular membrane lipid mixing with a self-quenching fluorescent probe in the virion membrane; and core entry with a recombinant VACV expressing firefly luciferase and electron microscopy . We determined that inhibitors of tyrosine protein kinases , dynamin GTPase and actin dynamics had little effect on binding of virions to cells but impaired membrane fusion , whereas partial cholesterol depletion and inhibitors of endosomal acidification and membrane blebbing had a severe effect at the later stage of core entry . To determine the role of viral proteins , virions lacking individual membrane components were purified from cells infected with members of a panel of ten conditional-lethal inducible mutants . Each of the entry protein-deficient virions had severely reduced infectivity and except for A28 , L1 and L5 greatly impaired membrane fusion . In addition , a potent neutralizing L1 monoclonal antibody blocked entry at a post-membrane lipid-mixing step . Taken together , these results suggested a 2-step entry model and implicated an unprecedented number of viral proteins and cellular components involved in signaling and actin rearrangement for initiation of virus-cell membrane fusion during poxvirus entry .
Entry of enveloped viruses into cells can be divided into three steps: ( i ) close apposition of viral and cellular membranes , ( ii ) lipid mixing of the outer membrane leaflets leading to formation of a hemifusion intermediate , and ( iii ) formation and expansion of a fusion pore allowing entry of the viral nucleoprotein or core into the cytoplasm [1] . One or two glycoproteins that provide cell binding and membrane fusion are sufficient to mediate entry of many enveloped viruses [2] . The process is more complex for members of the herpesvirus family , which employ four to five glycoproteins for entry [3] . Poxviruses represent an extreme case , as at least sixteen unglycosylated vaccinia virus ( VACV ) proteins participate in this process ( referenced below ) . The large number of poxvirus proteins and the absence of any that resemble conventional membrane fusion proteins by sequence suggest a novel entry mechanism . For mature virions ( MVs ) , the basic and most abundant infectious VACV particle , entry can occur by fusion at the plasma membrane [4] , [5] or in a low pH-dependent manner from within an intracellular vesicle , depending to some extent on the virus strain [6] , [7] and cell type [7]–[9] . Endocytosis of MVs is believed to occur by macropinocytosis [10]–[15] or dynamin-mediated fluid phase uptake [16] , consistent with a role for actin dynamics and cell signaling . Progeny virions that depart the cell by exocytosis contain an additional membrane that helps escape antibody neutralization and is ultimately ruptured to allow fusion of the enclosed MV with the plasma membrane or endocytic vesicle [17] , [18] . Four VACV proteins are involved in attachment of MVs [19]–[22] and twelve , conserved in all members of the poxvirus family , participate in subsequent entry steps [23]–[34] . Initial binding to target cells occurs via interactions of the MV attachment proteins with cell surface glycosaminoglycans or laminin . A cellular protein , referred to as VACV penetration factor , appears to be important for entry but exactly how is not yet understood [16] . The twelve conserved VACV entry proteins are mostly small , ranging in size from 35 to 377 amino acids , and have a N- or C-terminal transmembrane domain . The proteins are all components of the MV membrane , which is formed within the cytoplasm by incompletely defined mechanisms rather than by budding as typically occurs with other viruses [35] . This feature , as well as the association of most or all the proteins in a complex [31] , makes it difficult to investigate the roles of individual entry proteins . A useful approach has been to construct conditional lethal mutants , with one putative entry gene controlled by the Escherichia coli lac operator/repressor system and positively regulated by ß-D-isopropylthiogalactopyanoside ( IPTG ) inducer , or with an analogous tetracycline-inducible system . These mutants share similar phenotypes: in the presence of inducer , replication proceeds normally and the progeny virions contain the protein product of the inducible gene and are infectious; in the absence of inducer , progeny virions appear indistinguishable from wild type by electron microscopy and protein analysis ( except for the missing entry protein ) but have very low infectivity . Although the non-infectious virions bind to cells , immunofluorescence microscopy studies show reduced numbers of cores in the cytoplasm . With the exception of I2 [30] , repressed expression of the individual proteins does not significantly reduce the trafficking of the others to the MV membrane . However , when expression of an individual component is repressed , the formation or stability of the complex is reduced , as determined by detergent extraction and immunoaffinity purification [31] . The proteins A16 , A21 , A28 , G3 , G9 , H2 , J5 , L5 and O3 , make up the central components of the so-called entry fusion complex ( EFC ) . The L1 and F9 proteins are also required for entry; although they physically interact with the EFC , they are not required for assembly or stability of the complex , and consequently have been referred to as EFC-associated proteins [26] , [32] . The overall structure of the EFC has not been elucidated , though several pair-wise protein interactions have been identified [36]–[38] . The mechanisms involved in poxvirus entry are poorly understood . Previous studies have depended on post-membrane fusion assays and a specific role of the EFC in fusion could only be inferred from the inability of cells infected with the mutant viruses made in the absence of IPTG to undergo low pH-induced syncytia formation . Thus , direct evidence for a role of EFC proteins in membrane fusion during entry of virions has been lacking . Here , we used a variety of approaches including cell binding , membrane lipid mixing , core entry and reporter gene expression ( Figure 1 ) to evaluate the roles of host components and individual MV membrane proteins .
Fusion of viral and cellular membranes involves lipid mixing , which can be studied by loading a self-quenching fluorescent probe such as octadecylrhodamine ( R18 ) into viral membranes ( Figure 1 ) . Fusion of viral and cell membranes results in dilution of the probe and increased fluorescence [39] . Dequenching does not require full fusion of the viral and cell membrane but can occur at the initial step in which only the outer leaflets of the viral and cellular membranes fuse , known as hemifusion [1] . Therefore , dequenching could signify the occurrence of hemifusion alone or full fusion with pore formation . In a 2-step membrane fusion model ( see Discussion ) , inhibitors that prevent dequenching must operate at or prior to the hemifusion step , which precedes full fusion . In the present experiments , sucrose gradient purified VACV MVs were incubated with R18 at room temperature for 20 min . Incorporation of R18 into MVs minimally affected infectivity as shown in Figure 2A . After removal of excess R18 , the MVs were incubated with HeLa cells for 1 h at 4°C to allow adsorption and then the temperature was raised to permit fusion . R18 fluorescence was more rapid at the physiological temperature of 37°C than at 20°C ( Figure 2B ) , consistent with an active transfer process . We used WRvFire , a recombinant VACV that expresses firefly luciferase ( LUC ) regulated by an early promoter , to compare the kinetics of fusion and reporter gene expression . Whereas fusion occurred within a few minutes after incubation of virus-bound cells , LUC expression was detected at 40 min ( Figure 2C ) and was routinely assayed after 1 or 2 h . The above results supported the use of the fluorescent R18 probe for analyzing VACV-cell membrane fusion . In subsequent experiments we compared the effects of inhibitors on binding of virions to cells , fusion , and core entry as measured by LUC expression and in some cases by transmission electron microscopy . An earlier study had shown that fusion of VACV strain WR was not enhanced at low pH [40] , which in retrospect seemed surprising in view of the subsequent demonstration of low pH enhancement of core entry and reporter gene expression [6] . Nevertheless , we confirmed the similar rates of VACV WR fusion following a brief incubation with a pH 7 . 4 or pH 5 . 0 buffer and return to neutral pH ( Figure 2D ) . Furthermore , we found that bafilomycin A1 , which prevents endosomal acidification and reduces firefly LUC expression , had little effect on binding of MVs containing a Gaussia LUC core protein chimera or membrane fusion ( Figure 3A ) , similar to previous findings of membrane fusion in the presence of ammonium chloride and chloroquine [40] . Thus , low pH promotes an entry step beyond membrane lipid mixing . Depletion of cellular cholesterol reversibly prevents the accumulation of VACV cores in the cytosol at a post-attachment step [41] . Treatment of HeLa cells with methyl- ß-cyclodextrin ( mßCD ) resulted in up to a 74% reduction in total cellular cholesterol levels ( Figure S1A ) without reducing cell viability over the time-course of the experiment ( Figure S1B ) , although some cell rounding occurred . Nevertheless , MVs efficiently bound to cholesterol-depleted HeLa cells and R18 fluorescence was only mildly reduced , whereas LUC expression was greatly inhibited ( Figure 3A ) . These data indicated that the lowered level of cellular cholesterol was sufficient for membrane lipid mixing but impaired a later step in entry or reporter gene expression . Inhibitors targeting membrane blebbing , dynamin function , actin dynamics , and the activities of certain protein kinases have been shown to reduce VACV entry to varying extents as measured by reporter gene expression or detection of cytoplasmic cores [11]–[13] , [16] , [42] . In the present experiments , HeLa cells were preincubated for 30 min with inhibitors at previously used concentration ranges and the drugs were maintained in the medium during and after virus adsorption . Infection with VACV induces actin-enriched protrusions or cellular blebs [42] and entry can be partially reduced by blebbistatin , a small molecule specific inhibitor of myosin-II-dependent blebbing , virus movement along filopodia and macropinocytosis [11] , [43] , [44] . Blebbistatin was without effect on virion attachment but reduced LUC reporter expression by about 50% ( Figure 3A ) , similar to the value previously reported for a GFP reporter assay [11] . However , we found little or no effect on dequenching of the R18 probe ( Figure 3A ) , indicating that membrane fusion can occur independently of cell membrane blebbing . Dynasore is a small molecule inhibitor of the GTPase activity of dynamin1 , dynamin2 and the mitochondrial dynamin and is a rapid and potent inhibitor of dynamin-dependent endocytic pathways [45] . Dynamin also directly interacts with actin and regulates the actin cytoskeleton [46]–[48] . The effect of dynasore on VACV entry is ambiguous as it was reported not to influence entry in some studies [11] but to inhibit entry in another [16] . We found that dynasore had no effect on virion binding to HeLa cells but severely decreased LUC expression ( Figure 3A ) . Moreover , dynasore potently inhibited membrane fusion ( Figure 3B ) . These results implicated cellular dynamin as a critical factor in promoting VACV entry into HeLa cells at the membrane fusion step . We also tested several specific inhibitors of actin dynamics: CK-636 and CK-548 bind to the Arp2/3 complex and prevent actin nucleation whereas latrunculins and cytochalasins bind actin and inhibit polymerization [49] , [50] . These drugs had little effect on virion attachment but severely blocked LUC expression ( Figure 3B ) . CK-548 and CK-636 were also very effective inhibitors of membrane fusion , whereas latrunculin A and cytochalasin D inhibited fusion by approximately 50% at the concentrations used ( Figure 3B ) . These studies indicated a role for actin rearrangement in membrane fusion and raised the possibility that the effect of dynasore was related to its influence on the actin cytoskeleton rather than endocytosis . Cell signaling has been reported to have a role in VACV entry at the stage of blebbing and macropinocytosis [11] . Genestein , gefitinib ( Iressa ) and 324674 ( PD153035 ) are small molecule tyrosine kinase inhibitors [51] , [52] . These drugs did not reduce virion binding but profoundly inhibited LUC expression ( Figure 3C ) . Moreover , they also greatly inhibited membrane fusion ( Figure 3C ) . The results could be related to the relative specificity of gefitinib and 324674 for epidermal growth factor receptor signaling , which causes rapid actin polymerization and rearrangement [53] . Based on a previous report [11] , we attempted to bypass the effects of inhibitors of actin remodeling and signaling on entry by brief low pH treatment of cells with attached virions . However , in our hands , such treatments only alleviated the effects of drugs such as bafilomycin A1 , concanamycin and monensin that prevented endosomal acidification [6] but did not bypass the effects of several other inhibitors on entry as measured by LUC expression or R18 dequenching ( Figure S2 ) . Core entry steps were also analyzed by transmission electron microscopy . The results cannot be precisely compared to the above assays because a high virus multiplicity and spinoculation were used to allow counting of a sufficient number of virus particles in thin sections of infected cells . Hemifusion cannot be detected by this procedure and the earliest recognizable entry step consisted of full fusion of the viral and plasma membranes with an open pore allowing core entry ( Figure 4A ) . Although MVs can be readily detected in vesicles , full fusion of viral and vesicle membranes are rarely seen ( 5 ) . Cores that accumulate in the cytoplasm ( Figure 4B ) could have entered through the plasma membrane or an endocytic vesicle . In the absence of inhibitors , the number of plasma membrane full fusion images decreased and cores in the cytoplasm increased between 30 and 90 min ( Figure 4C , D ) . At both times , the numbers of plasma membrane full fusion images ( Figure 4C ) and cytoplasmic cores ( Figure 4D ) were reduced when the cells were treated with blebbistatin , dynasore , latrunculin A or cytochalasin D . These observations confirmed the results obtained with the LUC assay for measuring core entry . In summary , our data are generally consistent with other studies showing the importance of cell signaling and remodeling of the actin cytoskeleton on VACV entry [10]–[15] , and importantly further demonstrate that these activities are necessary for the membrane fusion step . Low pH , cholesterol and membrane blebbing appear to be more important for entry steps beyond membrane lipid mixing . Most or all of the MV membrane proteins required for entry , as distinguished from cell attachment , are components of the EFC ( A16 , A21 , A28 , G3 , G9 , H2 , J5 , L5 , O3 ) or physically associated with the EFC ( L1 , F9 ) . We employed conditional lethal mutants for all EFC and EFC-associated proteins except J5 , for which a stringent mutant was unavailable . As a control , we tested a mutant with a deletion of the gene encoding the I5 MV membrane protein that is not required for entry [54] . The recombinant viruses were replicated in the presence or absence of the IPTG inducer and the MVs were purified by sucrose gradient sedimentation . For each mutant , the number of purified virions was determined from the optical density . In some cases , virions were inactivated at 56°C prior to adsorption to cells as an additional control [55] . Equivalent numbers of particles were loaded with R18 and washed by sedimentation to remove excess dye . Dye transfer to HeLa cells was determined by increased fluorescence as in the preceding sections . In addition parallel cultures were maintained for 48 h and the yield of infectious virus determined by plaque assay . As expected , R18-loaded MVs lacking the I5 protein ( I5− ) promoted R18 probe transfer as efficiently as wild type MV ( I5+ ) , whereas transfer was reduced with the heat-inactivated MVs ( Figure 5A ) . Virions deficient in individual EFC and EFC-associated proteins had very low infectivity and except for A28 , L1 and L5 mutants exhibited severely reduced R18 dequenching as well ( Figure 5B-K ) , providing the first evidence of a direct role of EFC proteins in the membrane fusion step of virus entry . Previous studies had only shown that the EFC was required for fusion of infected cells . We used transmission electron microscopy to monitor core entry steps , following attachment of H2+ , H2− , A28+ and A28− virions . We chose H2 and A28 as examples of mutants that reduced and allowed R18 dequenching , respectively ( Figure 5G , I ) . As indicated earlier , a high multiplicity and spinoculation was needed because of the thin cell sections . The lower numbers of full fusions with pore formation at the plasma membrane and cytoplasmic cores in cells infected with H2− virions compared to H2+ virions were expected in view of the inability of the former to mediate R18 dequenching ( Figure 6A , B ) . However , there was a similar reduction in full fusion images at the plasma membrane and cytoplasmic cores after infection with A28− virions compared to A28+ virions ( Figure 6C , D ) despite the greater ability of the former to allow membrane fusion as determined by lipid mixing . Inhibition of core entry was previously shown using a confocal microscopy assay for virions deficient in L1 [32] and L5 [34] confirming an entry block despite their ability to allow lipid mixing as shown here . The above results showing that L1-deficient virions allowed membrane fusion but not core entry led us to investigate the effect of a potent L1-neutralizing monoclonal antibody ( MAb ) [56] . We found that a concentration of L1 MAb that severely inhibited core entry as determined by LUC expression and formation of infectious virus had minimal effect on membrane fusion as determined by R18 dequenching ( Figure 7 ) . This result was confirmed by a flow cytometry-based 1 , 1′-dioctadecyl-3 , 3 , 3′ , 3′-tetramethylindodicarbocyanine ( DiD ) lipid mixing assay using a wide-range of MAb concentrations ( Figure S3 ) . We still needed to consider the possibility that the role of the EFC is to activate the cell for virion entry rather than to directly participate in the entry step per se . In this context , Mercer and Helenius [11] had reported that very few VACV particles are needed to induce widespread blebbing and actin rearrangement . To further investigate the role of the EFC in entry , we coinfected cells with wild type VACV and either A28+ or A28− virions that expressed firefly LUC . We used a particle/cell multiplicity of approximately 200 for the A28+ and A28− virions and varied the multiplicity of the wild type virions from 9 to 1840 particles/cell ( equivalent to 0 . 1 to 20 plaque forming units ( PFU ) /cell ) . Coinfection with wild type virions caused a two-fold increase in LUC expression by A28+ virions and raised expression about four-fold for A28− virions ( Figure 8A ) . However , the latter was still only 3% of the value for A28+ virions indicating that efficient trans-complementation had not occurred . We also determined that soluble A28 protein [57] mixed with virions had no effect on entry of either the A28+ or A28− virions ( Figure 8B ) .
Viral and cellular membranes each consists of two leaflets and in principal membrane fusion could occur by two different pathways as discussed by Chernomordik [1] . The direct fusion model posits that pores form in each of the apposing membranes and the pore rims join forming a fusion pore that allows lipid and content mixing in a single step . In contrast , the 2-step model posits fusion of the outer leaflets of the apposing membranes to form a hemifusion intermediate followed by merging of the inner leaflets to form the fusion pore . In the latter model , lipid mixing and content mixing occur sequentially . Evidence to support the second model involving a hemifusion intermediate has been obtained for several different viruses by demonstrating membrane lipid mixing without content mixing by mutation of viral fusion proteins , slowing or interrupting fusion with inhibitors and decreasing the surface density of viral fusion proteins [58]–[61] . In the present study of VACV , we showed that membrane lipid mixing could occur without core entry under three circumstances: depletion of certain EFC proteins ( A28 , L1 or L5 ) , neutralization of VACV with a MAb to the L1 EFC-associated protein , and partial cholesterol depletion of the cell membrane . These findings are consistent with a 2-step entry model with a hemifusion intermediate for VACV . In the first part of the Results , we described the effects of inhibitors of cell processes on virion attachment , membrane fusion and core entry . Most of the inhibitors had previously been shown to reduce entry as determined by reporter gene expression or detection of cytoplasmic cores [11]–[13] , [16] , [42] . We found that none of these inhibitors prevented binding of virions to cells , many reduced membrane fusion , while others only acted at the core entry step ( Figure 9 ) . The membrane fusion inhibitors were either directly involved with actin polymerization or remodeling ( CK-636 , CK-548 , latrunculin A , cytochalasin D ) or blocked tyrosine kinases that can modulate actin cytoskeletal changes ( genestein , Iressa , 324674 ) . The action of dynasore , a specific inhibitor of dynamin GTPase , could be due to its known effect on actin since there is evidence against a role for caveolae-mediated endocytosis in VACV entry [16] . Further evidence for dynamin2 in VACV core entry has been obtained with siRNA [16] . Extensive actin remodeling and mobilization has been observed during MV binding to cell surfaces [11] , [16] , [42] suggesting that actin-enriched membrane protrusions increase the intimacy of membrane contact and promote virus-cell membrane fusion . Actin remodeling has been suggested to facilitate fusion by forcing membranes together and enlarging pores in a variety of systems [62]–[64] including virus entry and viral protein-induced cell-cell fusion [65]–[70] . With human immunodeficiency virus , actin remodeling appears to have a more important role in pore expansion and content mixing than in hemifusion [71] , [72] . We found that cytochalasin D and latrunculin A had a greater inhibitory effect on core entry ( determined by LUC expression ) than membrane fusion as determined by lipid mixing , suggesting that actin dynamics may be required for multiple steps in VACV entry . In contrast to the role of actin rearrangement , inhibitors that prevented membrane blebbing involved in virus surfing and macropinocytosis or that interfered with the reduction in pH of endosomes , had a much greater effect on core entry than membrane lipid mixing ( Figure 9 ) . It will be important to determine whether lipid mixing is occurring at the plasma membrane or in endosomes at neutral pH . Similarly , a 74% reduction of cellular cholesterol with mßCD had little effect on membrane fusion but had a major effect on core entry as measured by LUC expression . A previous study had shown that MVs associate with cholesterol-rich regions of the plasma membrane and that cholesterol depletion reduced VACV entry as measured by visualizing cores in the cytoplasm [41] . In studies with influenza virus and Semliki Forest virus in insect cells , which can be more stringently depleted of cholesterol than mammalian cells , both hemifusion and pore widening were affected [73] , [74] . The cell surface receptors for certain viruses reside in cholesterol-rich lipid rafts , but receptors for VACV have not been identified . The VACV EFC proteins were previously shown to be required for virus core entry and cell-cell fusion but evidence for a role in the fusion of viral and cell membranes had been indirect . Of the ten EFC or EFC-associated mutants tested in the present study , all were blocked in core entry as determined by infectivity or transmission electron microscopy and seven of these were unable to mediate membrane fusion . The three proteins apparently not required for membrane fusion were A28 , L1 , and L5 . It is possible that these proteins have a specific role at a later step in entry such as pore formation . However , in other systems it has been shown that the density of activated fusion proteins has to be higher for the formation and expansion of a fusion pore than for hemifusion [1] . Although these three mutants each display stringent repression of EFC protein expression as shown by Western blotting , undetectable differences could affect the sensitive lipid-mixing assay . Therefore , our main conclusion is that the EFC is required for membrane fusion and that additional studies are required to conclude that A28 , L1 and L5 have a specific role at a later step of entry such as pore formation . The L1 protein is a target of potent neutralizing and protective antibodies [56] , [75] . The structure of L1 alone and in association with a conformation-specific MAb has been solved to high resolution [76] , [77] . The Fab fragment binds to a discontinuous epitope containing two loops that are held together by a disulfide bond . Here we showed that the MAb prevents VACV entry at a step beyond lipid mixing , consistent with the effect on entry of virions deficient in the L1 protein . Since our inhibitor studies had shown that actin dynamics are required for membrane fusion and core entry , we considered the possibility that the EFC indirectly promotes entry by inducing cell signaling . Indeed , such a role could contribute to the need for multiple EFC proteins . Since Mercer and Helenius [11] had shown that cell signaling requires few virus particles , we tried to rescue EFC protein-deficient virions in trans by coinfecting with wild type VACV . Although wild type virus enhanced core entry by four-fold as measured by LUC expression , this value was still only 3% of that achieved by the control virus , suggesting that the EFC proteins have a direct role in membrane fusion and entry . Nevertheless , whether EFC protein interactions also cause signaling is an interesting question for future studies . Why so many different proteins are needed for poxvirus entry remains an enigma . None of the proteins resemble type I or type II viral fusion proteins by sequence so that determination of the 3-dimensional structure of the VACV EFC may be needed to define putative fusion loops , if the mechanism of entry involves such structures . At this time , only the structure of the L1 EFC-associated protein has been solved [76] .
African green monkey kidney BS-C-1 and human HeLa cells were maintained in minimum essential medium with Earle's salts ( EMEM ) supplemented with 2 . 5% fetal bovine serum ( FBS ) , 2 mM L-glutamine , 100 U/ml penicillin , and 100 µg/ml streptomycin ( Quality Biological ) . The recombinant VACV WRvFire expressing firefly LUC under a synthetic early/late VACV promoter was described previously [6] . Recombinant VACVs in which expression of individual EFC or EFC-associated proteins are IPTG-inducible have been previously constructed and characterized: A16 [23] , A21 [24] , A28 [25] , G3 ( A . Townsley and BM , unpublished ) , G9 [28] , H2 [29] , J5 [31] , L5 [34] , O3 [33] , L1 [32] , and F9 [26] . The recombinant VACV in which the I5L gene was deleted has been described [54] . The recombinant VACV Gauss-A4 ( parental strain WRvFire ) , which expresses the Gaussia LUC enzyme fused to the A4 core protein was generated as follows . Overlap polymerase chain reaction ( PCR ) was utilized to generate a construct in which the Gaussia LUC gene ( New England Biolabs ) was appended to the N-terminal codon of the VACV A4L gene and the EGFP coding region ( and accompanying synthetic early/late VACV promoter sequence ) was placed downstream of the Gaussia-A4L region . To achieve homologous recombination , flanking genomic sequences of A4L ( approximately 500 bp in length ) were appended to the termini of the PCR product . HeLa cells were infected with 0 . 05 PFU of WRvFire per cell and at 2 h post infection were transfected with 400 ng of purified PCR product using Lipofectamine 2000 ( Invitrogen ) according to the manufacturer's protocol . At 24 h post infection , the infected cells were lysed by five freeze/thaw cycles and clonally purified five times by picking GFP positive plaques on BS-C-1 cells . The recombinant VACV in which A28L is IPTG-inducible and expresses firefly LUC under a synthetic early/late VACV promoter has been described [25] . BS-C-1 cells were infected with VACV in the presence or absence of the inducer IPTG ( Calbiochem ) and at 48 to 72 h post infection MVs were isolated as described [78] , [79] . Briefly , infected cells were subjected to Dounce homogenization and MVs were purified by sedimentation through two 36% ( wt/vol ) sucrose cushions followed by one sedimentation on a 25 to 40% ( wt/vol ) continuous sucrose gradient; the visible virus band was collected , and virus was pelleted and stored at −80°C . Upon thawing , virus was sonicated on ice for 1 min . The infectious viral titer ( PFU per ml ) for each purified MV stock of recombinant VACV was determined by plaque assay on BS-C-1 cells as described [80] . Additionally , the number of total virus particles obtained for each purified MV stock of recombinant VACV was estimated from the optical density at 260 nm [80] . Purified MVs ( approximately 9 . 0×109 particles ) were labeled with 3 ml of 1 mg/ml of R18 ( Molecular Probes ) in phosphate-buffered saline ( PBS; Quality Biological ) + 0 . 2% bovine serum albumin ( BSA; Sigma-Aldrich ) for 20 min at room temperature in the dark . Non-incorporated R18 was removed by pelleting virions ( 16 , 000 x g for 10 min at 4°C ) and washing several times in PBS + 0 . 2% BSA . R18-labeled virions were re-suspended in PBS + 0 . 2% BSA , vortexed , and sonicated for 15 sec on ice . Virions sufficient to achieve a multiplicity of 1 to 5 PFU ( or the equivalent number of non-infectious particles ) per cell were then incubated with approximately 1 . 5×106 HeLa cells in suspension for 1 h at 4°C in cold fusion medium comprised of EMEM without phenol red and with 10 mM N-2-hydroxyethylpiperazine-N'-2-ethanesulfonicacid ( HEPES ) and 10 mM 2- ( N-morpholino ) ethanesulfonic acid ( pH 7 . 4 ) in the dark . Virus-bound cells were washed twice with cold fusion medium following low-speed centrifugation ( 750 x g for 3 min at 4°C ) . Virus-bound cells were injected into a cuvette containing fusion medium pre-warmed to 37°C and kept in suspension utilizing a magnetic stir bar . R18 fluorescence ( 560 nm excitation and 590 nm emission ) was monitored by use of a Fluoro-Max3 spectrofluorometer ( Horiba Jobin Yvon ) outfitted with a Peltier sample cooler ( Horiba Jobin Yvon ) and a temperature control unit ( Wavelength Electronics model LFI-3751 ) to maintain the desired temperature within the chamber housing the sample cuvette . For graphical presentation , the raw fluorescence data were plotted versus time . For quantitative comparisons , we determined the percent fluorescence by dividing the value obtained at 40 min by the value obtained following addition of Triton X-100 ( 1% [wt/vol] final concentration ) . HeLa cells seeded in 24-well plates ( 2 . 0×105 cells per well ) were chilled to 4°C before virus adsorption . WRvFire MVs were adsorbed in cold EMEM + 2 . 5% FBS for 1 h at 4°C . Cells were washed with cold PBS to remove unbound virions and incubated with pre-warmed EMEM + 2 . 5% FBS for 2 h ( unless indicated otherwise ) at 37°C . Cells were washed with PBS and then incubated with Cell Culture Lysis Reagent ( Promega ) for 30 min at room temperature with gentle agitation . LUC activity in cellular extracts was measured according to the manufacturer's protocol ( Promega ) and quantified on a Berthold Sirius luminometer ( Berthold Detection Systems ) . HeLa cells seeded in 24-well plates ( 2 . 0×105 cells per well ) were left untreated or treated with 10 mM mßCD ( Sigma-Aldrich ) for 30 min in EMEM at 37°C . Cells were then washed with cold PBS and cold EMEM was added to cells prior to virus adsorption at 4°C for R18 hemifusion or LUC entry assays as described above . Cholesterol levels in HeLa cells were determined using the Amplex Red Cholesterol Assay Kit ( Molecular Probes ) and was performed according to the manufacturer's protocol . The viability of mßCD-treated cells was assayed using the CellTiter 96 Aqueous One Solution Cell Proliferation Assay ( Promega ) and was performed according to the manufacturer's protocol . HeLa cells were left untreated or pre-treated with the indicated concentrations of inhibitors: Sigma-Aldrich: blebbistatin ( 75 µM ) , dynasore ( 100 µM ) , bafilomycin A1 ( 50 nM ) , latrunculin A ( 10 µM ) , cytochalasin D ( 10 µM ) , CK-548 ( 100 µM ) , CK-636 ( 100 µM ) , genistein ( 100 µM ) ; LC Laboratories: Iressa ( 40 µM ) ; EMD4Biosciences: 324674 ( 40 µM ) for 30 min at 37°C . Cells were then chilled to 4°C prior to virus adsorption for virus-cell binding , R18 hemi-fusion , or LUC assays as described . The indicated drug concentrations were maintained throughout the assay . Equivalent amounts of VACV Gauss-A4 virions ( 5 PFU per cell ) were incubated with untreated or inhibitor-treated HeLa cells in 24-well plates at neutral pH for 1 h at 4°C . Cells were washed twice with cold PBS to remove unbound virus . Cells were then incubated with LUC assay lysis buffer ( Promega ) for 30 min at room temperature with gentle agitation . Gaussia LUC activity in cellular extracts was measured according to the manufacturer's protocol ( Promega ) and quantified on a Berthold Sirius luminometer ( Berthold Detection Systems ) . Low pH stimulation of virus entry was performed as described previously [6] . Following a wash to remove unbound virions , cells were incubated for 3 min in 37°C PBS with Ca2+ and Mg2+ at pH 7 . 4 or PBS with Ca2+ and Mg2+ supplemented with 1 mM 2-morpholinoethane-sulfonic acid adjusted to pH 5 . 0 with HCl . After removal of buffers , the pH was neutralized by one wash with EMEM + 2 . 5% FBS . Cells were incubated in pre-warmed EMEM + 2 . 5% FBS for 2 h at 37°C and then prepared for the LUC entry assay as described above . BS-C-1 cells in six-well tissue culture plates ( 1 . 0×105 cells per well ) were pre-chilled at 4°C for 30 min prior to virus spinoculation . Purified MVs ( 350 PFU per cell or equivalent number of particles ) in cold EMEM + 2 . 5% FBS were sedimented onto the BS-C-1 cells at 4°C for 1 h at 650 x g in a Legend RT centrifuge ( Sorvall ) . Cells were washed with cold PBS to remove unbound virions and incubated with pre-warmed EMEM + 2 . 5% FBS for varying amounts of time at 37°C . At the indicated time , the samples were fixed on ice with 4% paraformaldehyde ( Electron Microscopy Sciences ) in 0 . 1 M phosphate buffer for 10 min and processed for transmission electron microscopy as described previously [6] . For quantitation of virus entry events , ninety randomly selected cell sections were visualized and particles therein counted . Equivalent numbers of R18-loaded MV particles ( recombinant strain WRvFire ) were incubated with 100 µg/ml of anti-L1 mouse MAb 7D11 [56] or control anti-HA mouse monoclonal ( clone 16B12 , Covance ) for 30 min at room temperature . Virion and antibody mixtures were then divided and used for R18-based fusion , LUC core entry , or plaque formation assays as described above . Purified MVs ( approximately 9 . 0×109 particles ) were labeled with 3 µl of DiD ( Molecular Probes ) in phosphate-buffered saline ( PBS; Quality Biological ) + 0 . 2% bovine serum albumin ( BSA; Sigma-Aldrich ) for 20 min at room temperature in the dark . Non-incorporated DiD was removed by pelleting virions ( 16 , 000 x g for 10 min at 4°C ) and washing several times in PBS + 0 . 2% BSA . DiD-labeled virions were re-suspended in PBS + 0 . 2% BSA , vortexed , and sonicated for 15 sec on ice . Virions sufficient to achieve a multiplicity of 1 to 5 PFU per cell were then incubated with approximately 8 . 0×104 HeLa cells in a 48-well plate for 90 min at 37°C in minimum essential medium with Earle's salts ( EMEM ) supplemented with 2 . 5% FBS , 2 mM L-glutamine , 100 U/ml penicillin , and 100 µg/ml streptomycin . Cells were washed with PBS , trypsinized , spun and fixed in 4% paraformaldehyde/PBS for 2 h at 4°C . DiD-positive cells were quantified using a FACSCalibur ( BD Biosciences ) . DiD loading had minimal effect on virus infectivity as measured by plaque assay .
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Poxviruses are large DNA viruses that cause diseases in humans and other animals . To initiate infection , the core of the large , membrane-enveloped particle must penetrate into the cytoplasm where replication occurs . For most enveloped viruses only one or two proteins are needed for attachment and penetration . However , at least sixteen poxvirus proteins are dedicated to entry: four for attachment and twelve for penetration . The latter proteins form the entry fusion complex ( EFC ) and are conserved in all poxviruses indicating that the entry mechanism has been retained since the origin of the family . The purpose of the present study was to determine the cellular processes and poxviral proteins needed for fusion of the viral and cellular membranes . We found that a variety of inhibitors that interfered with cell signaling and reorganization of the actin cytoskeleton prevented membrane fusion as determined by lipid mixing , whereas others targeted the subsequent stage in entry . In addition , seven viral protein components of the EFC were required for the initial membrane fusion step , whereas three were not . A neutralizing monoclonal antibody to one of the latter also did not interfere with membrane lipid mixing but still prevented core entry supporting a 2-step poxvirus entry model .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"viral",
"entry",
"viral",
"transmission",
"and",
"infection",
"virology",
"biology",
"microbiology"
] |
2011
|
The Membrane Fusion Step of Vaccinia Virus Entry Is Cooperatively Mediated by Multiple Viral Proteins and Host Cell Components
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Diet is a crucial determinant of organismal biology; interactions between the host , its diet , and its microbiota are critical to determining the health of an organism . A variety of genetic and biochemical means were used to assay stress sensitivity in C . elegans reared on two standard laboratory diets: E . coli OP50 , the most commonly used food for C . elegans , or E . coli HT115 , which is typically used for RNAi-mediated gene knockdown . We demonstrated that the relatively subtle shift to a diet of E . coli HT115 had a dramatic impact on C . elegans’s survival after exposure to pathogenic or abiotic stresses . Interestingly , this was independent of canonical host defense pathways . Instead the change arises from improvements in mitochondrial health , likely due to alleviation of a vitamin B12 deficiency exhibited by worms reared on an E . coli OP50 diet . Increasing B12 availability , by feeding on E . coli HT115 , supplementing E . coli OP50 with exogenous vitamin B12 , or overexpression of the B12 transporter , improved mitochondrial homeostasis and increased resistance . Loss of the methylmalonyl-CoA mutase gene mmcm-1/MUT , which requires vitamin B12 as a cofactor , abolished these improvements , establishing a genetic basis for the E . coli OP50-incurred sensitivity . Our study forges a mechanistic link between a dietary deficiency ( nutrition/microbiota ) and a physiological consequence ( host sensitivity ) , using the host-microbiota-diet framework .
Like it's genome , an organism's diet and microbiota tremendously influence its life history . Attempts to understand this have led to the development of the host-microbiota-nutrition axis model [1] . Although an avalanche of literature has reported on variations in the microbiota , diet , and genomes of model organisms , mechanistic understanding has lagged considerably behind description . The dynamic and complex interactions between these three systems form something of a "biological three-body problem" . For example , host genetics and microbiotic metabolism influence the nutritional value of the diet consumed [2 , 3] . Simultaneously , the host's environment determines its initial susceptibility to microbial colonization [4] . Despite the difficulty , it is crucial to establish a mechanistic understanding of these interrelationships to properly understand host healthspan under normal and stress conditions . Caenorhabditis elegans offers a tantalizing system for simplifying these studies without sacrificing the ability to make discoveries useful for more complex organisms . Generation of gnotobiotic worms is simple , efficient , and inexpensive , only requiring treatment of gravid adults with hypochlorite to release eggs that will hatch into microbe-free larvae . C . elegans can then simply be transferred to agar plates seeded with a wide-variety of bacteria ( either a single strain or a customized mixture ) . Generally , the host readily consumes the bacteria provided . In its most reductionist form , the host-microbiota-nutrition axis of C . elegans can be collapsed to a binary system comprised of only two species , both of which are genetically tractable: C . elegans , the bacterivorous host , and E . coli , which serves as food . Although C . elegans is typically not colonized by E . coli the way that humans are ( at least not early in its life ) , Cabreiro and Gems make a compelling argument that E . coli serves at least most of the functions for C . elegans that a conventional microbiota confers to its host , including protection against pathogens , immune maturation , digestive aid , vitamin production , and xenobiotic metabolism [5] . So by at least some definitions , E . coli can comprise a microbiota for C . elegans as well . Even this simple binary system has yielded a number of significant insights into the complicated interactions between host , diet , and microbiota . Different diets profoundly affect the worm transcriptome [6] , metabolome [7] , intestinal fat storage [8] and lifespan [6–9] . The metabolic activity of the bacterial food also influences the metabolism of C . elegans [10 , 11] . For example , C . elegans feeding on Bacillus subtilis were observed to have a greater lifespan than their E . coli-fed counterparts [12] . This effect was shown to be a consequence of the host utilizing bacterially-derived nitric oxide to stimulate expression of HSF-1 and DAF-16 , which increase lifespan [13] . In contrast , C . elegans's lifespan can be shortened by bacterial folate metabolism [14 , 15] , probably via increased S-adenosylmethionine ( SAM ) synthesis [16] . The link between SAM and lifespan was demonstrated by an elegant study that showed that metformin , by limiting SAM production and stimulating AMPK activation , increases C . elegans lifespan [16] . Unfortunately , this is unlikely to be a viable method to increase healthspan; SAM is a crucial methyl group donor necessary for a wide variety of cellular activities . For example , C . elegans sams-1 ( RNAi ) mutants exhibit constitutive immune activation , but despite this , they show increased sensitivity to Pseudomonas aeruginosa [17] . Like humans , C . elegans is incapable of creating several essential vitamins , including vitamin B12 , and they must be obtained from their diet . Vitamin B12 exists as two biologically active , readily interconvertible vitamers , methylcobalamin and adenosylcobalamin . These compounds are involved in the biosynthesis of methionine and the conversion of methylmalonyl-CoA to succinyl-CoA , respectively . This latter pathway is required for the proper breakdown of propionate and branched-chain amino acids , the failure of which is associated with mitochondrial dysfunction [18 , 19] . Serious consequences have been associated with severe vitamin B12 deficiency in C . elegans , including infertility , slowed growth , and shortened lifespan [20 , 21] . In both C . elegans and mammals , B12 deficiency causes toxic intermediates to accumulate [22 , 23] . Interestingly , the most common C . elegans lab diet , E . coli OP50 , results in a mild , chronic vitamin B12 deficiency [6 , 21] . Through a variety of assays , we discovered that the vitamin B12 deficiency caused by a diet of E . coli strain OP50 disrupts mitochondrial homeostasis , sensitizing the host to infection and a variety of abiotic stresses . B12 supplementation , even in the absence of living bacteria , increased resistance without affecting lifespan . Genetic analysis mapped this phenotype to the methylmalonyl/succinyl-CoA breakdown pathway , where vitamin B12 serves as a cofactor for MMCM-1/MUT . Our findings provide a mechanistic link between diet , cellular homeostasis , and organismal health .
While characterizing a C . elegans-P . aeruginosa Liquid Killing assay [24 , 25] , we made the unexpected observation that diet plays a large role in the hosts' survival after exposure to P . aeruginosa strain PA14 . For example , median survival time of glp-4 ( bn2 ) worms reared on E . coli HT115 was 48h , while worms reared on E . coli OP50 showed median survival closer to 30h ( Fig 1A and 1B ) . E . coli HT115 is used ubiquitously for RNAi in C . elegans; this strain is the host for the two largest , publicly available whole-genome RNAi libraries [26 , 27] . Importantly , the increase in resistance to P . aeruginosa PA14 occurred regardless of whether either E . coli strain carried the L4440 RNAi plasmid vector ( S1A Fig ) . Pathogenesis in this assay depends on intoxication with the siderophore pyoverdine , which removes iron from the C . elegans host [28 , 29] . 1 , 10-phenanthroline , a small synthetic chelator , mimics many of the aspects of siderophore-mediated killing [29 , 30] , so resistance of E . coli HT115-fed worms to this compound was also assayed . glp-4 ( bn2 ) worms fed HT115 were more resistant to phenanthroline than their OP50-fed counterparts ( Fig 1C ) . We have previously shown that exogenous iron strongly limited the ability of pyoverdine or 1 , 10-phenanthroline to cause host death [24] . Therefore , one possibility is that E . coli HT115 may be indirectly increasing resistance by providing more iron to C . elegans . To test this hypothesis , total host iron , using inductively-coupled plasma mass spectrometry , or iron ( III ) , using a fluorometric method , were measured . In each assay , iron levels were indistinguishable , regardless of the food tested ( S1B and S1C Fig ) . Since these findings suggested that the deficiency was not related to iron , we suspected the sensitivity may also exist for other pathogens . To test this , glp-4 ( bn2 ) worms were reared on either E . coli OP50 or HT115 , and then exposed to Enterococcus faecalis OG1RF , another human bacterial pathogen , in a recently-developed Liquid Killing assay [31] . To the best of our knowledge , pathogenesis in this assay is independent of siderophores . Interestingly , E . coli HT115 also increased survival of C . elegans infected with this pathogen by ~30% ( Fig 1D ) . E . faecalis OG1RF is a gram positive pathogen that likely uses different pathogenic determinants than P . aeruginosa PA14 [32] , suggesting that the mechanism of resistance has a broad spectrum of activity . The ability of E . coli HT115 to confer resistance to P . aeruginosa-mediated slow killing was also tested . Unlike the liquid-based pathogenesis assays , no statistically significant difference was seen between worms reared on the two diets ( S2A Fig ) . To ensure that the worms were not colonized differently , a strain of P . aeruginosa engineered to express DsRed was used to infect worms after they were reared on each diet . Again , no statistically significant difference was observed ( S2B and S2C Fig ) . One possible explanation for this difference is that pathogenesis in this assay is thought to take place via intestinal colonization; the biological difference induced by a diet of E . coli HT115 may be irrelevant for this mechanism of pathogenesis . Substitution of E . coli OP50 with a variety of other bacterial foods ( or dead E . coli ) increases the lifespan of C . elegans [33–36] . This is often interpreted as evidence that E . coli OP50 is weakly pathogenic to C . elegans [37 , 38] , opening the possibility that E . coli HT115 is less pathogenic than E . coli OP50 ( at least under these conditions ) . In this case , it is likely that the immune response of C . elegans would differ between OP50 and HT115 . Transcription of a number of C . elegans innate immune genes , regulated by PMK-1/p38 , ZIP-2/bZIP , DAF-16/FOXO , FSHR-1/FSH , and SKN-1/Nrf [12 , 39–43] was surveyed . Synchronized glp-4 ( bn2 ) worms were reared on either E . coli OP50 or E . coli HT115 to the young adult stage and basal gene expression for these pathways was assessed . Gene expression levels were indistinguishable between food sources ( S3 Fig ) . We also assayed induction , rather than basal expression , of the innate immune pathways using RNAi . As noted above , RNAi in C . elegans is typically performed using E . coli HT115 , in part because E . coli OP50 generally expresses dsRNA quite poorly [44] . Fortunately , an RNAseIII-deficient strain of OP50 , called xu363 , that efficiently produce dsRNA at levels comparable to E . coli HT115 has recently been engineered [45] . Comparisons of cyc-1 ( RNAi ) in the two strains ( E . coli OP50 ( xu363 ) and E . coli HT115 ) suggest that they exhibit comparable levels of gene knockdown ( S4 Fig ) . RNAi constructs for pmk-1 , zip-2 , daf-16 , fshr-1 , and skn-1 were transferred from E . coli HT115 into E . coli OP50 ( xu363 ) . glp-4 ( bn2 ) mutants were then reared on either E . coli OP50 ( xu363 ) or E . coli HT115 expressing each RNAi construct . At the young adult stage , pathogenesis in the Liquid Killing assay was tested . As expected , some gene disruptions altered the timing of death compared to bacteria containing an empty RNAi vector . For example , daf-16 knockdown , which sensitizes worms to Liquid Killing [28] , hastened death in each case . daf-2 ( RNAi ) , which constitutively activates DAF-16/FOXO , prolonged survival . Importantly , worms fed E . coli HT115 survived longer than worms fed E . coli OP50 ( xu363 ) , regardless of the gene targeted by RNAi ( S5 Fig ) . Combined , these data indicate that the weak pathogenicity reported for E . coli OP50 is unlikely to be causing the increased sensitivity to P . aeruginosa , phenanthroline , and E . faecalis . Several innate immune pathways in C . elegans ( such as those regulated by DAF-16/FOXO , SKN-1/Nrf , or the ESRE network ) also promote survival during exposure to abiotic stresses , like heat or free-radical inducing chemicals . glp-4 ( bn2 ) worms were reared on E . coli OP50 or E . coli HT115 and then exposed as young adult worms to either heat shock , juglone , or hydrogen peroxide . In each case , E . coli HT115-fed worms survived better ( Fig 1E–1G ) . Because increased stress or pathogen resistance is often associated with longer lifespan ( which has led to the theory that the former is responsible for the latter ) , we compared the average lifespan of worms reared on these bacteria . However , E . coli HT115 did not increase host lifespan ( Fig 1H ) . Another possible explanation that emerged was the temperature-dependent sterility induced by the glp-4 ( bn2 ) phenotype . For technical reasons , it is necessary to induce sterility in worms for liquid-based killing assays [24] . The glp-4 ( bn2 ) mutation compromises a valyl aminoacyl tRNA synthetase ( VARS-2 ) [46] which prevents development of the germline , but causes no other overt phenotype [47–49] . When the germline of C . elegans is removed , lifespan is extended in a DAF-16/FOXO-dependent fashion [50 , 51] . Since this transcription factor promotes broad-spectrum stress resistance , it was important to determine whether wild-type C . elegans also exhibited a difference in stress resistance when reared on E . coli HT115 . Wild-type N2 worms were reared on E . coli OP50 or E . coli HT115 and then subjected to Liquid Killing , propionate , or heat shock . Lifespans were also measured . In each case , results from wild-type worms recapitulated our findings from glp-4 ( bn2 ) mutants ( S6 Fig ) . Without a clear explanation for the diet-induced difference in stress resistance , we turned toward microarray analysis to get an unbiased representation of transcriptional events . glp-4 ( bn2 ) worms were reared to young adult stage on either E . coli OP50 or E . coli HT115 . RNA was collected and microarray analysis was performed essentially as previously described [29] . To our surprise , the number of genes differentially regulated was relatively small; only 35 genes showed upregulation between 2- and 8-fold in E . coli HT115 , while 22 genes were upregulated ( between 2- and 20-fold ) in E . coli OP50 ( Fig 2A , S1 Table ) . Interestingly , 12 of the latter 22 genes encode proteins predicted to be localized to the mitochondria ( Table 1 ) . This enrichment was strongly significant ( p = 2 . 2*10−16 ) , particularly given the small fraction of nuclear genes that encode mitochondrial proteins in C . elegans ( ~6% ) and humans ( 7% ) [52 , 56] . In contrast , only two genes encoding proteins targeted to the mitochondria ( acox-2 and T22B7 . 7 ) were upregulated in E . coli HT115 compared to E . coli OP50 ( 2/35 = 5 . 7% , p = 0 . 9896 ) . Of the genes upregulated in worms fed E . coli OP50 , two caught our attention: hsp-60 , which encodes a mitochondrial chaperone upregulated on mitochondrial damage [57 , 58] and acdh-1 , which encodes a short-chain acyl-CoA dehydrogenase that is upregulated when dietary sources are rich in branched-chain amino acids and/or propionyl-CoA , which can be toxic to mitochondria [59] . When these data were compared to a previous study , which used slightly different methodology to determine differentially regulated genes , the results had striking concordance ( S2 Table ) . Overlap between the genes downregulated in each study or upregulated in each study was significant ( p-value 1 . 42*10−11 and 4 . 97*10−22 , respectively ) . Moreover , 70% of the genes downregulated in both studies are annotated as mitochondrially-localized or have a putative function in mitochondria . We crossed the acdh-1p::GFP reporter into a glp-4 ( bn2 ) background , and then reared the resulting strain on either E . coli OP50 or E . coli HT115 . We saw increased expression of acdh-1p::GFP in E . coli OP50-fed worms , whether measured by conventional imaging ( Fig 2B , see S7A Fig for quantification ) or by flow vermimetry ( Fig 2C , see S7B Fig for quantification ) . Our transcriptome profiling and acdh-1 expression data are in concordance with previous reports , where an E . coli OP50 diet increased expression of acdh-1p::GFP and transcription of several other genes encoding mitochondrially-targeted proteins [22 , 23] . These traits have been associated with a mild to moderate vitamin B12 deficiency [6 , 21] . We attempted to use mass spectrometry to quantify adenosylcobalamin in worms fed with either E . coli OP50 or E . coli HT115 . We also tried to quantify it in bacterial slurries from each strain . Unfortunately , B12 levels for all samples measured were below the detection threshold of the instrument . Although we were unable to measure precise amounts of vitamin B12 in the host or in the bacteria , qualitative methods are available to test whether E . coli HT115 is a better source of vitamin B12 , based on acdh-1p::GFP fluorescence and propionate toxicity . First , the bacterial growth media was spiked with exogenous methylcobalamin to a final concentration of 200 ng/mL , bacteria were grown as usual , and spotted onto NGM media plates . Worms were reared on these plates and then acdh-1p::GFP expression was measured at the young adult stage using flow vermimetry . Vitamin supplementation dramatically reduced acdh-1p::GFP expression ( Fig 2C , S7A Fig ) . The same approach was used , with differing concentrations , to determine the amount of exogenous B12 required to reduce acdh-1p::GFP fluorescence in worms reared on each food . Comparable fluorescence was observed when E . coli OP50 and E . coli HT115 were supplemented with 50 μM and 3 . 1 μM , respectively ( Fig 2D and 2E ) . Second , expression of acdh-1p::GFP can be compared when worms are exposed to exogenous propionate [22 , 23] . Although worms reared on E . coli OP50 consistently showed more fluorescence than worms fed E . coli HT115 , adding propionate to media increased acdh-1p::GFP expression regardless of diet ( Fig 2F and 2G , see S7B Fig for quantification ) . For example , fluorescence was similar in worms reared on E . coli OP50 and E . coli HT115 spiked with 25 mM and 50 mM propionate , respectively . Finally , in addition to triggering acdh-1 expression , excess propionate is toxic to C . elegans [22 , 23 , 60] . Vitamin B12 sufficiency is proportional to increased survival . Young adult worms reared on either E . coli OP50 or E . coli HT115 were exposed to varying concentrations of propionate and survival was measured after 24h . Worms reared on E . coli HT115 exhibited increased resistance compared to E . coli OP50 , and the addition of methylcobalamin to their diet improved each group further still ( Fig 2H ) . All three assays qualitatively support the conclusion that E . coli HT115 is a better source of vitamin B12 than E . coli OP50 , a finding that is consistent with previous reports [6 , 22 , 23] . Because excess propionate is known to cause mitochondrial toxicity [59 , 61] , we assayed mitochondrial structure in C . elegans fed either E . coli OP50 or E . coli HT115 . Under normal conditions , mitochondrial quality control involves constant fission and fusion events that serve to pool healthy , functional mitochondrial content , while damaged material is sequestered for autophagic recycling [62] . A C . elegans strain expressing a mitochondrially-targeted GFP [57] was reared on E . coli OP50 or E . coli HT115 with or without methylcobalamin supplementation , and mitochondria were imaged in the same body wall muscle cell of young adult C . elegans . Worms fed E . coli HT115 or bacteria supplemented with methylcobalamin showed increased connectivity and fewer punctae than worms with less B12 ( Fig 3A and 3B ) . The fragmentation induced by a diet of E . coli OP50 was greater than that observed when RNAi was used to knock down fzo-1/MFN , a mitofusin essential for proper mitochondrial fusion and homeostasis ( S8 Fig ) . Breakdown of the mitochondrial network is commonly associated with reduced organellar function . We assayed other metrics of mitochondrial health in worms reared on E . coli OP50 or E . coli HT115 with or without exogenous vitamin B12 . Methylcobalamin supplementation slightly , but statistically significantly , decreased the average mitochondrial genome count as measured by quantitative PCR of mitochondrial to nuclear genome count ( Fig 3C ) . Mitochondrial mass was slightly diminished when measured using nonyl-acridine orange staining ( NAO , Fig 3D ) . Two different dyes to measure mitochondrial membrane potential ( tetramethylrhodamine ethyl ester ( TMRE ) and mitotracker red ( MTR ) ) showed no significant difference on any of the diets ( Fig 3E and 3F ) , which suggested that B12 may slightly improve the average mitochondrial membrane potential . Supplementation with methylcobalamin reduced superoxide production as an indicator of ROS in worms fed OP50 , but did not have a significant effect on worms reared on HT115 ( p = 0 . 059 ) ( Fig 3G ) . ATP production ( as measured by luciferase activity ) showed no significant difference after B12 supplementation ( Fig 3H ) . Combined , these data indicate that B12 sufficiency correlates with an increase in mitochondrial connectivity and decreased mitochondrial number . This permits mitochondrial function to be carried out at a similar level with fewer mitochondria . We also tested whether methylcobalamin supplementation could alleviate the broad-spectrum sensitivity exhibited by E . coli OP50-fed worms . Worms were reared on E . coli OP50 grown with or without exogenous methylcobalamin , and their survival was tested in Liquid Killing . A dramatic difference was observed; less than half of OP50-fed worms remained alive by the time ~10% of OP50/B12-fed worms died ( Fig 4A and 4B ) . Increased vitamin B12 availability also improved resistance to phenanthroline , infection with E . faecalis , peroxide stress , the mitochondrial poison carbonyl cyanide m-chlorophenyl hydrazone ( CCCP ) , and hyperthermia ( Fig 4C–4G ) . The resistance of wild-type worms fed on E . coli OP50 supplemented with methylcobalamin was also increased ( S9 Fig ) . A growing body of evidence suggests that decreasing mitochondrial activity , either by genetically compromising oxidative phosphorylation or by caloric restriction , extends lifespan [63 , 64] . Lifespan was unaffected by methylcobalamin supplementation ( Fig 4H ) , as was seen when E . coli OP50 was replaced with E . coli HT115 ( Fig 1G ) . All of these observations suggest that E . coli OP50 provides inadequate vitamin B12 for C . elegans . Since E . coli cannot synthesize vitamin B12 , the bacterium must import it from the extracellular milieu , a process that has been reported to require TonB [65 , 66] . On this basis , we hypothesized that a bacterial strain lacking tonB would also trigger B12 deficiency in C . elegans . This prediction was tested with two different tonB deletions in E . coli BW25113 [67] . To establish a baseline , expression of acdh-1p::GFP was determined for C . elegans reared on E . coli BW25113 . This diet largely recapitulated acdh-1p::GFP levels observed for E . coli OP50 ( Fig 5A ) , indicating that this diet also likely causes a vitamin B12 deficiency . As with E . coli OP50 and E . coli HT115 , supplementation of wild-type BW25113 with methylcobalamin completely abolished acdh-1p::GFP fluorescence ( Fig 5B ) . qRT-PCR data on log phase growth cultures of each strain indicate that tonB levels for E . coli HT115 are approximately 12 times higher than E . coli OP50 or E . coli BW25113 ( Fig 5C ) . Although tonB is important for the import of several large molecules , such as cobalamin , nickel complexes , and some carbohydrates [68] , it is also required for infection by several bacteriophages [69] , suggesting that there may be an evolutionary advantage in limiting its expression . To determine whether this limitation in tonB expression could be overcome , the tonB locus was cloned into pBAD for inducible expression and the resulting construct was transformed into E . coli OP50 . tonB expression was induced with 0 . 2% L-arabinose in LB media unsupplemented with methylcobalamin and then bacteria were spotted onto NGM . Worms were reared on this food source , and acdh-1p::GFP expression was assayed in young adults . A small , but statistically significant and repeatable reduction in acdh-1p::GFP fluorescence was seen ( Fig 5D ) . It is unclear why the overexpression did not have a more pronounced effect . One possibility is that overexpression of TonB , which is present in the cytoplasmic membrane [70] , prevents it from being properly folded and/or inserted into the membrane . Additionally , TonB is a member of a protein complex; altering the stoichiometry of proteins in complexes by overexpression often results in unpredictable consequences and poor expression , which has led to the 'balance hypothesis' [71–73] . Either , or both , of these phenomena may have limited the proper expression or function of TonB . Surprisingly , neither tonB deletion allele completely blocked the attenuation of acdh-1p::GFP fluorescence seen from methylcobalamin supplementation ( S10 Fig ) . This suggests that the bacteria may have an alternative , tonB-independent route to obtain cobalamin , although the process appears to be inefficient . Alternatively , the cells may be generating B12 de novo by salvaging precursors that they can convert into B12 . There are three obvious explanations for the vitamin B12-mediated resistance we observed . First , the additional vitamin B12 may alter the metabolism of E . coli ( e . g . , a deficiency of vitamin B12 in the LB broth used may limit the quality of the nutrition provided by the E coli , supplementation decreases the production of a toxic bacterial metabolite , or the bacteria may increase production of one or more salubrious compounds ) . Second , the bacterium may modify or metabolize the vitamin , creating the product that improves health . Finally , the bacteria may merely be the means of transporting the B12 into the intestine of C . elegans , where it directly alters host metabolism . To rule out the first two possibilities , we spotted heat-killed E . coli onto NGM plates that were themselves supplemented with methylcobalamin . glp-4 ( bn2 ) worms spotted onto these plates also exhibited increased resistance to P . aeruginosa Liquid Killing ( S11 Fig ) . On this basis , it was clear that E . coli is merely a vehicle for transporting vitamin B12 into C . elegans . As noted above , vitamin B12 functions as an essential co-factor for two enzymes: METR-1/MTR and MMCM-1/MUT ( Fig 6A ) [74] . To determine which of these functions underlies the improvement conferred by vitamin B12 , survival in Liquid Killing was assessed in worms with mutations for each pathway . RNAi targeting either metr-1/MTR or mtrr-1/MTRR had no effect on vitamin B12-mediated resistance to P . aeruginosa in the Liquid Killing assay ( Fig 6B ) . To rule out the possibility of incomplete or insufficient RNAi knockdown , we also tested whether supplementing bacteria with 3 mM methionine ( instead of methylcobalamin ) improved host survival . Since this pathway uses vitamin B12 to generate methionine , supplementation with this amino acid should have the same result if this pathway is relevant . Consistent with the results from the metr-1 ( RNAi ) and mtrr-1 ( RNAi ) mutants , methionine supplementation had no impact on survival ( Fig 6C ) . In contrast , disruption of the other B12-utilizing pathway abolished resistance even in the presence of exogenous methylcobalamin . RNAi targeting mce-1/MCEE or mutation of mmcm-1/MUT completely abolished the benefit from methylcobalamin supplementation ( Fig 6D and 6E ) . C . elegans maintains an alternative , B12-independent , pathway to metabolize propionyl-CoA . This shunt is more toxic and utilizes acrylyl-CoA and hydroxypropionyl-CoA intermediates [23] . We also tested whether this pathway plays a role in mitochondrial toxicity of dietary B12 deficiency . Mutations in hphd-1/ADHFE1 or alh-8/ALDH6A1 , two genes in this shunt , had no effect on the ability of vitamin B12 supplementation to promote survival ( Fig 6F ) . This further supports our conclusion that the MMCM-1/MUT pathway is the relevant target for B12-mediated improvements in host health .
While malnutrition is well-known to predispose animals to infection , subtle nutritional defects , and their concomitant effects on host physiology , are more difficult to detect . Interestingly , our findings may explain an intriguing finding from a recently published study [75] . Gif mutant mice , which have compromised intestinal absorption of dietary B12 , showed a striking increase in sensitivity and mortality to infection with Salmonella enterica serovar Typhimurium or Citrobacter rodentium . Although the infected mice showed no overt immune deficiencies , they had severe metabolic defects that included compromised mitochondrial function and dramatic increases in methylmalonate concentration . Importantly , these phenotypes were resolved by supplementation of the animals' diet with cobalamin . The authors postulated that the increased mortality may arise from metabolic starvation due to competition for nutrients between the host and its microbiota . However , their results can also be explained by our model . I . e . , the mitochondrial damage inflicted by vitamin B12-deficiency made the host more sensitive to stress and pathogens and significantly increased mortality . Although published analyses are limited , there is also some evidence to suggest people with genetic priopionate acidemias , which similarly cause propionyl-CoA accumulation , may also be predisposed to bacterial and fungal infections [76] . Although we began our queries of the host-microbiota-diet axis with a minimal , binary system ( C . elegans and E . coli ) , unexpected complexity has arisen . Differences in the biology of C . elegans reared on OP50 or HT115 have generally been attributed to the differences in the origins of the bacterial strains ( i . e . , OP50 was derived from an E . coli B strain while HT115 came from E . coli K12 ) or to their differences in nutrient composition ( HT115 contains less triacylglycerols and pyrimidines , and more carbohydrates than OP50 [8 , 77] and increases host concentrations of some amino acids and other metabolites [7] ) . Our data suggest that acdh-1p::GFP expression , a marker for B12 deficiency , poorly correlates with strain origin . E . coli BW25113 , which also originated from K12 , showed acdh-1p::GFP fluorescence similar to OP50 ( Fig 5A ) ) . Metabolic differences of this sort likely contribute substantial uncertainty to research outcomes , particularly since some labs use E . coli OP50 as the standard food , some use E . coli HT115 , and others routinely use E . coli HB101 . For example , despite the fact that the C . elegans literature is replete with comparisons of the lifespan of C . elegans reared on OP50 vs . HT115 , no consensus conclusion has been reached . Some researchers have seen no difference in lifespan [8 , 45] , others find an HT115-mediated extension of lifespan [9 , 78] , and still others find OP50-reared worms surviving longer [6] . Explanations for these divergences abound , including variations in media composition ( or even batch-to-batch variations from a single supplier ) , methodological differences in assays , or even accumulated mutations in the "wild-type" E . coli or C . elegans strains as they are propagated within and between labs . Ultimately , the inability to resolve what should be a simple question ( i . e . , which food allows C . elegans to live longer ? ) is troubling , particularly since the phenotype being assayed ( i . e . , death ) should be unambiguous and the experimental setup is straightforward . More elusive phenotypes with perhaps smaller effects and more complex experimental design likely have greater variability . Curran and colleagues [79] have persuasively argued the need for a rigorously-defined , standardized growth medium for studies of aging in C . elegans; our data suggest this need probably extends to all studies using this model .
All C . elegans strains were maintained on nematode growth medium ( NGM ) seeded with Escherichia coli strain OP50 , E . coli strain HT115 , or E . coli BW25113 ( see below ) . In some experiments , E . coli growth media were supplemented with methylcobalamin to a final concentration of 0 . 2 mg/L . Unless otherwise noted , worms were reared and passaged at 15°C [80] , with the exception of glp-4 ( bn2 ) worms , which were sterilized prior to use by plating diapaused , synchronized L1 larvae on concentrated E . coli on NGM plates and kept at 25°C . For RNAi-mediated gene knockdown , plasmids from the Ahringer library [26] were either used in the E . coli HT115 ( DE3 ) strain supplied or were purified and transformed into E . coli OP50 ( xu363 ) , an RNAi-competent strain of OP50 [45] . All plasmids were sequence verified . C . elegans strains used in this study included N2 Bristol ( wild-type ) , SS104 [glp-4 ( bn2ts ) ] [47] , NVK44 [glp-4 ( bn2ts ) ; zcIs14|myo-3::GFP ( mt ) |] , PE327 ( ATP reporter ) : glp-4 ( bn2ts ) ; feIs5 [sur-5p::luciferase::GFP + rol-6 ( su1006 ) ] [81] , RB1434[mmcm-1 ( ok1637 ) ] , RB2572[hphd-1 ( ok3580 ) ] , VL1176[alh-8 ( ww48 ) ] , and VL749 [wwIs24|acdh-1p::GFP + unc-119 ( + ) |] [6] . Bacterial strains used in this study included E . coli OP50 , E . coli OP50 ( xu363 ) [45] , E . coli HT115 ( DE3 ) , and E . coli BW25113 . Two E . coli tonB deletion alleles ( JW5195-1 and JW5195-2 ) were taken from the Keio Knockout Collection ( GE Dharmacon ) . To generate the tonB overexpression strain , genomic DNA was purified from E . coli strain OP50 used to amplify the tonB locus . The resultant PCR product was digested with NdeI and SalI and subcloned into pBAD33 . 1 ( a gift from Christian Raetz , Addgene plasmid #36267 [82] ) . The resultant plasmid , pBAD-OPtonB was transformed into chemically competent E . coli OP50 using conventional techniques . For pathogenesis assays , P . aeruginosa strain PA14 [83] , P . aeruginosa PA14 tranformed to express DsRed [24] , or E . faecalis strain OG1RF [84] were used . To prepare heat-killed E . coli OP50 , E . coli from an overnight culture in LB were centrifuged and concentrated tenfold in S Basal . Bacteria were then killed by incubation at 75°C for 30 min with regular mixing by inversion . Absence of live E . coli in each preparation was confirmed by plating on LB agar . For use in Liquid Killing assays , 2 mL of concentrated dead bacteria were plated onto modified NGM that was supplemented with vitamin B2 to a final concentration of 0 . 3 mg/L . Normally , C . elegans grows poorly on dead E . coli . However , exogenous vitamin B2 significantly improves C . elegans health under these conditions [85] . To test vitamin B12 supplementation , the NGM also supplemented with 0 . 2 mg/L methylcobalamin . Liquid Killing assays with P . aeruginosa PA14 were performed as described [25] . The C . elegans—E . faecalis assay was performed as described [31] . Assays were timed such that ~60–70% worms reared on E . coli OP50 would be dead . At least three biological replicates were performed per condition per experiment . Each biological replicate contained at least 10 wells ( ~200 animals ) . Statistical significance was determined using Student’s t-test . For the juglone survival assay , glp-4 ( bn2 ) worms were grown to young adulthood and placed on NGM agar media supplemented with 120 μM of juglone [86] . For peroxide and carbonyl cyanide m-chlorophenyl hydrazone ( CCCP ) exposure worms were treated with 2 mM or 100 μM of drug in S Basal , respectively . Survival was scored using Sytox Orange stain after 20 h of incubation . Phenanthroline survival assays were done similarly , except plates were supplemented with 100 μM of this chelator . In each case , plates were made fresh and used the same day . Hyperthermia assays were performed at 30°C . In all cases , unsupplemented NGM agar plates incubated at 25°C were used as controls . Worms were scored daily and worms were scored as dead when they failed to respond to touch . Longevity assays were performed similarly , except worms were incubated at 25°C on the appropriate food source for the duration of the experiment and scored every other day . Worms were considered dead when they failed to respond to a gentle prod to the head . At least three biological replicates were performed for each experiment . Each biological replicate consisted of three plates with ~50 worms/plate . Statistical significance was determined using log-rank test ( http://bioinf . wehi . edu . au/software/russell/logrank/ ) . Worms on plates were censored if they left the agar plate . The propionate sensitivity assay was performed as described in [22] with modifications . 70 L1-stage worms were dropped on to NGM agar plates containing appropriate concentrations of propionate ( or no propionate control ) using a COPAS FlowSort . After 72 h incubation at 25°C , adult worms were counted and survival was calculated . At least three biological replicates were performed for each experiment . Statistical significance was determined using Student’s t-test . In brief , approximately 24 , 000 C . elegans L1 larvae ( per sample ) were raised on NGM plates seeded with E . coli OP50 or E . coli HT115 . Upon reaching young adulthood , worms were transferred to 15 mL conicals , washed four times , and then collected with identical volumes . Samples were frozen at -80°C and processed as described [29] . At least three replicates were tested for each diet . Significance was tested using Student's t-test . glp-4 ( bn2 ) L1 larvae were spotted onto NGM plates seeded with either E . coli OP50 or E . coli HT115 and allowed to develop to young adulthood at 25°C . Worms were collected , lysed , and fluorometric determination of ferric iron was performed as previously described [29] . For RNA collection , glp-4 ( bn2ts ) worms were grown on appropriate plates until reaching young adult stage . Three biological replicates were performed . RNA was purified using Trizol following by cleanup according to Qiagen protocol . cDNA and cRNA were synthesized and hybridized to Affymetrix GeneChips for C . elegans at the Partners Center for Personalized Genetic Medicine , Boston , MA , according to manufacturer’s protocols . Three biological replicates were tested for each condition . Gene expression was analyzed using GCRMA ( http://www . bioconductor . org ) . Differentially regulated genes were determined on the basis of fold change ( >2 ) and the value of a modified Wilcoxon rank test ( >1 . 5 ) . The Wilcoxon coefficient was determined for each probeset as the smallest expression value in the condition with higher average divided by the highest expression value in the condition with lower average . Microarray data were deposited in GEO database and are available using following link: https://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? token=kjibcyuydrstvuj&acc=GSE97678 For determination of the basal level of immune genes in C . elegans , ~10 , 000 of OP50- and HT115-fed glp-4 ( bn2ts ) worms were grown to young adult stage and RNA was purified as described above . cDNA synthesis was performed according to manufacturers’ protocols ( Ambion ) . qRT-PCR was performed using SYBR Green iQ mix ( Bio-Rad ) . snb-1 gene was used as a control . For each gene , threshold cycle ( Ct ) was determined and ΔCt from snb-1 was calculated . For measurement of relative abundance of mitochondrial DNA , total DNA was extracted from ~2 , 000 synchronized young adult worms grown on appropriate food source . For genome comparisons , nd1 was used as a mitochondrial gene and act-3 as nuclear , as previously described [87] . Resultant DNA was used for to perform quantitative PCR using SYBR Green ( BioRad ) , and mitochondrial gene level was normalized to nuclear gene number using the ΔΔCt method . Thermocycler parameters were as for qRT-PCR . For measurements of tonB expression in E . coli strains , RNA was purified as described above , except samples were treated with DNAase I ( NEB ) . cysG and idnT were used as housekeeping genes [88] . Fold changes were calculated using ΔΔCt method . Primer sequences are available upon request . Three biological replicates were performed per test , each biological replicate contained duplicate wells for each primer/cDNA combination . Statistical significance was calculated using Student’s t-test . For visualization of myo-3::GFP ( mt ) and acdh-1p::GFP on slides , L1 larvae were spotted onto NGM plates seeded with appropriate bacteria , and allowed to mature into young adults at 25°C . Worms were immobilized with levamisole and transferred to slides . Images were acquired using a Zeiss Axio Imager M2 upright microscope with a Zeiss AxioCam 506 Mono camera and Zeiss Zen2Pro software . All fluorescent images were collected under identical exposure conditions . Fluorescence quantification was based on at least 50 worms per condition per biological replicate . At least three biological replicates were performed . Statistical significance was determined by Student’s t-test . For determination of ATP concentrations , approximately 2 , 000 PE327 L1 larvae [64] were placed onto NGM plates seeded with either E . coli OP50 or E . coli HT115 and allowed to develop to young adult stage at 25°C . Worms were then transferred to 96-well , white clear bottom plates and washed with S-basal five times . 150 μL Luminescence buffer ( 0 . 14M K2PO4 , 0 . 03M sodium citrate , 1% DMSO , 1mM luciferin ) was added to each well in the plate . Bioluminescence and GFP fluorescence were measured after 30 min using a Cytation5 multimode plate reader/imager ( BioTek ) . Luminescence values were normalized to GFP fluorescence to control for differences in protein expression . For measurement of acdh-1p::GFP , 5 , 000 VL749 L1 larvae were spotted onto NGM plates seeded with the food sources described . In some cases , NGM was supplemented with propionate and/or methylcobalamin . Worms were collected into 50 mL conicals , washed three times , and then GFP fluorescence was measured using a COPAS FlowPilot with 488 nm excitation and a long-pass filter . Mitochondrial membrane potential and reactive oxygen species were measured in a similar fashion , except that glp-4 ( bn2 ) worms were used , and worms were stained as described for MitoTracker Red [28] . nonyl-acridine orange ( NAO ) and tetramethylrhodamine ester ( TMRE ) dyes were used to stain mitochondria at 10 μM and 5 μM , respectively . ROS measurements were performed similarly , except dihydroethydium at a concentration of 3 μM and excitation at 488 nm was used . In each case , worms were stained for 1h , then washed 5 times prior to being measured . Measurements for each stain/diet combination were taken with the same settings . At least 1 , 500 worms/condition/biological replicate were used; at least three biological replicates were performed . Statistical significance was determined by Student’s t-test . Worm fecundity was scored as described by Bito et al . , [20] with minor modifications . E . coli OP50 , HT115 , and OP50 supplemented with 100 μM methylcobalamin were grown for 3 days in M9 medium at 37°C , then concentrated and plated onto normal NGM . Single glp-4 ( bn2 ) larvae were transferred to a new plate containing the same food . After reaching late-L4 stage , worms were allowed to lay eggs for 24 h . Young adult worms were then transferred to a fresh plate with the same food and allowed to complete egg laying . After 48 h , progeny on both plates were counted ( providing both the 24h count and the total count ) . One larva , chosen arbitrarily , was transferred to a new plate , to yield progeny for the subsequent generation . At least 24 plates were used per strain per biological replicates per generation; three biological replicates were performed . Statistical significance was determined by Student’s t-test .
|
Vitamin B12 deficiency affects ~10–40% of US adults , causing a range of health issues ranging from anemia to neurological defects . Here we provide a mechanistic link between dietary B12 deficiency and mitochondrial dysfunction . Our data indicate that B12 supports clearance of propionate , an intermediate of branched chain amino acid metabolism . Excess propionate compromises mitochondrial homeostasis , increasing susceptibility to abiotic stresses and bacterial pathogenesis . Importantly , this deficiency is absent when worms are fed a diet of E . coli HT115 . C . elegans reared on E . coli OP50 are predisposed to a variety of health defects , including increased sensitivity to bacterial pathogens and stresses; these subtle phenotypes may be difficult to recapitulate under conventional RNAi feeding conditions with HT115 , introducing unexpected experimental confounds . These findings reinforce the importance of considering the host-microbiota-nutrient axis when using this model organism .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
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"organelles",
"organisms"
] |
2019
|
Interplay between mitochondria and diet mediates pathogen and stress resistance in Caenorhabditis elegans
|
During December 2013 , the first locally transmitted chikungunya virus ( CHIKV ) infections in the Americas were reported in the Caribbean . Although CHIKV infection is rarely fatal , risk for severe disease increases with age and medical comorbidities . Herein we describe characteristics of Veterans Health Administration ( VHA ) patients with CHIKV infection and , among those with infections diagnosed in Puerto Rico , investigated risk factors for hospitalization . We queried VHA’s national electronic medical records to identify patients with CHIKV testing during 2014 . Demographics , clinical history , laboratory results , and outcomes were abstracted . We investigated risk factors for hospitalization among patients with laboratory-confirmed CHIKV infection in Puerto Rico . We identified 180 laboratory-confirmed CHIKV infections; 148 ( 82 . 2% ) were diagnosed in Puerto Rico , and 32 ( 17 . 8% ) were diagnosed among returning travelers elsewhere in the United States . In Puerto Rico , where more patients were hospitalized ( 55 . 4% versus 20 . 0% ) and died ( 4 . 1% versus 0% ) , risk for hospitalization increased with age ( relative risk [RR]/each 10-year increase , 1 . 19; 95% confidence interval [CI] , 1 . 06–1 . 32 ) and , adjusted for age , increased among patients with congestive heart failure ( RR , 1 . 58; 95% CI , 1 . 25–1 . 99 ) , chronic kidney disease ( RR , 1 . 52; 95% CI , 1 . 19–1 . 94 ) , diabetes mellitus ( RR , 1 . 39; 95% CI , 1 . 06–1 . 84 ) , or chronic lung disease ( RR , 1 . 37; 95% CI , 1 . 03–1 . 82 ) . CHIKV infection is an emerging problem among Veterans residing in or visiting areas with CHIKV transmission . Although overall mortality rates are low , clinicians in affected areas should be aware that older patients and patients with comorbidities may be at increased risk for severe disease .
Chikungunya virus ( CHIKV ) , an alphavirus most commonly transmitted by Aedes species mosquitoes , causes chikungunya fever ( CHIK ) , characterized by acute-onset of fever and what is frequently described as incapacitating polyarthralgia [1 , 2] . Since CHIKV was first identified in Tanzania in 1953 [2] , epidemics have occurred in South and Southeast Asia , Africa , and Europe [1 , 3 , 4] . Three distinct genotypes have been described , including East/Central/South African , Asian , and West African [1 , 5] . In December 2013 , locally transmitted infection in the Western Hemisphere was first reported; the predominant strain is closely related to the Asian genotype [6–8] . CHIKV has rapidly disseminated among this largely immunologically naïve population; the Pan American Health Organization ( PAHO ) reported >1 . 1 million suspect cases , involving the majority of Western Hemisphere countries by the end of 2014 [9] . During 2014 , over 31 , 000 cases ( 14% laboratory-confirmed ) were reported in Puerto Rico [10] and over 1 , 500 cases ( 17% laboratory-confirmed ) were reported in the U . S . Virgin Islands [11]; these figures are thought to underestimate disease burden because they do not include patients who did not present for care nor those who presented , but were not reported or for whom diagnostic testing was not completed [12] . During 2014 , 2 , 811 laboratory-confirmed infections in the United States were reported to the Centers for Disease Control and Prevention ( CDC ) through the ArboNET surveillance system; the majority were among returning travelers , except for 12 persons in Florida with locally transmitted infection [13] . CHIK is usually self-limited , with the majority of symptoms typically resolving in 7–10 days [1]; however , patients can have prolonged rheumatologic symptoms [14 , 15] . CHIKV infection can also be associated with severe illness , involving neurologic , cardiovascular , respiratory , renal , and ocular manifestations [16] . Although overall mortality is low , estimated at 0 . 3/1 , 000 population per year on Réunion Island [17] , risk for severe disease and death increases with age and is higher among patients with certain comorbidities [17–19] . The Veterans Health Administration ( VHA ) has health care facilities throughout the United States and U . S . territories . Because 45% of all U . S . Veterans and 62% of Veterans in Puerto Rico are aged ≥65 years [20] , and VHA patients have more comorbidities than Veterans who receive care at non-VHA facilities or non-Veterans [21] , VHA patients might be at higher risk for severe CHIK than those in the general U . S . population exposed to the virus ( i . e . returning travelers and those living in areas with CHIKV transmission ) . VHA’s Public Health Surveillance and Research Group ( PHSR ) performs surveillance for emerging infections among VHA patients . After the CHIK epidemic involved U . S . territories , PHSR began performing CHIK epidemiologic surveillance in July 2014 of all patients with laboratory-confirmed CHIKV infection diagnosed at VHA facilities during 2014 . Herein , we describe characteristics of these patients , compare clinical findings with patients who tested negative , investigate risk factors for hospitalization , and report phylogenetic analysis of CHIKV strains detected .
This study was reviewed by CDC for human subjects protection and was deemed to be non-research . It was also approved by the Stanford University Institutional Review Board and fulfilled the requirements of regulation OHRP 45 CFR 46 . 116 ( d ) for waiver of informed consent . Patient data was anonymized after data abstraction .
Patients with CHIKV infection frequently reported oligoarthralgia or polyarthralgia ( 88 . 3% ) , subjective fever ( 84 . 4% ) , generalized malaise ( 76 . 7% ) , myalgia ( 69 . 4% ) , and rash ( 44 . 4% ) , and reported these symptoms more often than CHIKV-negative patients ( Table 2 ) . Among 165 patients with laboratory-confirmed CHIKV infection who had recorded temperatures during their acute illness , only 53 ( 32 . 1% ) demonstrated objective fever ( >38 . 0°C or >100 . 4°F ) [9] , and only 48 ( 29 . 1% ) had both objective fever and arthralgia . Subjective fever and arthralgia was reported for 140 ( 77 . 8% ) of 180 patients and subjective fever or any arthralgia for 174 ( 96 . 7% ) . Among CHIKV-positive patients , 37 . 0% of 173 had leukopenia ( <4 , 000 white blood cells [WBC]/μL ) and 71 . 4% of 171 had lymphopenia ( <1 , 000 lymphocytes/μL ) ; these findings occurred more frequently compared with CHIKV-negative patients ( Table 3 ) . Thrombocytopenia ( <150 , 000 platelets/μL ) occurred among 80 ( 46 . 5% ) of 172 patients with CHIKV infection , with mean nadir platelet count of 104 , 000/μL . Acute kidney injury ( AKI ) ( ≥0 . 3 mg/dL or 26 . 5 μmol/L increase in serum creatinine from last level [28] ) was experienced by 33 ( 21 . 6% ) of 153 patients , 4 ( 12 . 1% ) of whom had stage III AKI . Hepatic transaminitis ( aspartate aminotransferase >40 U/L or alanine aminotransferase >45 U/L ) was experienced by 52 ( 40 . 3% ) of 129 patients , 16 ( 30 . 8% ) of whom had transaminases >3 times the upper limit of normal . In Puerto Rico , 82 ( 55 . 4% ) of 148 patients with laboratory-confirmed CHIKV infection were hospitalized , including 10 ( 6 . 8% ) who required intensive care; elsewhere in the United States , 6 ( 20 . 0% ) of 30 returning travelers with known hospitalization status were hospitalized ( p = . 0004 ) . Whereas the hospitalization rate increased with age among patients in Puerto Rico ( relative risk [RR]/ each 10-year increase in age , 1 . 19; 95% confidence interval [CI] , 1 . 06–1 . 32 ) , it did not increase with age among returning travelers . Because only 6 returning travelers were hospitalized , analysis is only presented for patients in Puerto Rico ( Table 4 ) . After adjusting for age , a significantly higher risk for hospitalization associated with having congestive heart failure ( CHF ) ( RR , 1 . 58; 95% CI , 1 . 25–1 . 99 ) , chronic kidney disease ( CKD ) ( RR , 1 . 52; 95% CI , 1 . 19–1 . 94 ) , diabetes mellitus ( RR , 1 . 39; 95% CI , 1 . 06–1 . 84 ) , or chronic lung disease ( RR , 1 . 37; 95% CI , 1 . 03–1 . 82 ) remained . Adjusted for age , patients had a greater risk for hospitalization if they were tachycardic ( >100 beats/minute; RR , 1 . 49; 95% CI , 1 . 12–1 . 98 ) , had leukocytosis ( >11 , 000 WBC/μL; RR , 1 . 65; 95% CI , 1 . 34–2 . 03 ) , AKI ( RR , 1 . 64; 95% CI , 1 . 33–2 . 04 ) , or hepatic transaminitis ( RR , 1 . 38; 95% CI , 1 . 07–1 . 80 ) at presentation . Two patients with CHIKV infection , confirmed by RT-PCR , developed septic shock without another identified etiology . One patient , with CHIKV infection confirmed by RT-PCR of serum , developed meningitis with a CSF profile consistent with a viral etiology , demonstrating pleocytosis ( 30 WBC/mm3 ) with initial predominant monocytosis and elevated protein ( 88 mg/dL ) . CSF was not submitted for CHIKV testing and no CSF was recovered for diagnostic testing by VA PHRL . One patient presented with Guillain-Barré syndrome one month after CHIK; recent CHIKV infection was confirmed by positive IgM serology . Two patients with CHIKV infection , confirmed by RT-PCR , presented with diabetic ketoacidosis; one patient with CHIKV infection , confirmed by RT-PCR , presented with pancreatitis; one patient with CHIKV infection , confirmed by RT-PCR , presented with colitis , and one patient with CHIKV infection , confirmed by RT-PCR of serum , experienced monomicrobial nonneutrocytic ascites ( recovered peritoneal fluid collected 20 days after symptom onset was CHIKV IgM and RT-PCR negative ) . Seven patients with CHIKV infection ( 6 confirmed by RT-PCR and 1 by IgM ) experienced pneumonia during their clinical course . Four patients with CHIKV infection , confirmed by RT-PCR , experienced congestive heart failure exacerbations; three patients with CHIKV infection , confirmed by RT-PCR , presented with syncope; and one patient with CHIKV infection , confirmed by RT-PCR , experienced a non ST-elevation myocardial infarction . One patient with CHIKV infection , confirmed by RT-PCR , presented with epididymitis . Among the 148 patients with laboratory-confirmed CHIKV infection , 6 ( 4 . 1% ) died . All had viremia demonstrated by RT-PCR ( Table 5 ) . In addition to these six patients , there were 15 patients who died after presenting with an illness compatible with CHIK that may have contributed to death; specimens for these patients were submitted for CHIKV testing but were not processed . Although attempts were made to obtain these specimens submitted to non-VHA laboratories , they were not recovered for testing . These 15 patients are thus considered suspect cases ( data available upon request ) . The mean age of patients with laboratory-confirmed CHIKV infection in Puerto Rico who died was 78 years ( range , 66–99 years ) , compared with 68 years ( range , 23–94 years ) for those in Puerto Rico who survived ( p = 0 . 12 ) . All 6 patients who died were afebrile on presentation ( only one had a recorded temperature >100 . 4°F or >38 . 0°C during hospitalization ) ; four were tachycardic on presentation . Of 5 with known medical history , all had multiple comorbidities . CHIKV E1 envelope glycoprotein gene sequences ( GenBank accession numbers: KU724228-KU724266 ) for 39 patients in Puerto Rico were closely related ( <0 . 5% nucleotide difference ) and nearly identical to strains in GenBank from St . Martin and the British Virgin Islands ( Fig 3 ) [6 , 7] .
Our study is the first to characterize U . S . Veterans with laboratory-confirmed CHIKV infection . The majority received a diagnosis in Puerto Rico , although 32 were returning travelers who received a diagnosis elsewhere in the United States . Although the majority reported subjective fever , only one-third had objective fever , indicating that use of recommended CHIK case definitions , requiring objective fever , might underestimate disease burden among VHA patients [24] . Among returning travelers , approximately one-fifth were hospitalized , whereas in Puerto Rico , where patients were older and had more comorbidities , approximately half of laboratory-confirmed patients were hospitalized , 6 . 8% required intensive care , and 4 . 1% died . The fatality rate was higher among VHA patients compared with a preliminary report from Puerto Rico in December 2014 that described only 4 deaths identified by passive surveillance on the island [12] . Although we performed population sequencing of only a portion of the E1 envelope glycoprotein gene , we did not find substantial difference among sequences from Puerto Rico compared with other strains circulating in the Western Hemisphere [6 , 7] . As in previous studies [18 , 19] , we report that age was associated with increased risk for hospitalization . After adjusting for age , CHF ( but not coronary heart disease ) , CKD , diabetes , and chronic lung disease were associated with increased risk for hospitalization . While we cannot be certain of the individual physicians' criteria for hospitalization in many cases , we do know that patients were more likely to be hospitalized if they had unstable vital signs ( e . g . tachycardia ) or had abnormal laboratory results ( e . g . leukocytosis , acute kidney injury , or hepatic transaminitis ) at presentation . Although literature review demonstrates low overall mortality , among persons with CHIKV infection presenting for care , the case fatality rate is not insignificant . During the CHIKV outbreak on Réunion Island ( East/Central/South African genotype ) [29] , among 157 patients with laboratory-confirmed infection who presented to a medical center , 61 . 8% were hospitalized and 3 . 2% died [18] . Although patients in that study were >10 years younger and fewer had comorbidities , hospitalization and case fatality rates were similar to VACHS . They reported that diabetes and ischemic heart disease , unadjusted for age , was associated with increased odds of hospitalization [18]; only 11 patients in that study had CKD . Economopoulou et al . reported that among a subset of hospitalized patients with CHIK , cardiac disease , respiratory disease , as well as hypertension were associated with increased risk for severe disease [19] . Because our study included patients with laboratory-confirmed CHIKV infection , we cannot assess the overall burden of CHIKV infection among Veterans , only those who presented to VHA facilities and had appropriate diagnostic testing completed . During 2014 , investigation of households surrounding laboratory-positive cases demonstrated that 28% of participants were laboratory-positive for current or recent CHIKV infection , and only 63% of symptomatic persons had sought care [12] . Although our query was robust , any patient with results not entered into the laboratory component of the electronic medical record ( e . g . , scanned or recorded in a progress note ) would not have been captured . Sample size limited analysis of returning travelers , and in Puerto Rico , prevented inclusion of >1 comorbidity in age-adjusted models or assessment of risk factors for intensive care or death among patients with laboratory-confirmed infection . This study identified the lack of testing availability for VACHS , as well as deficiencies in CHIKV diagnostic testing across VHA . We identified 11 patients ( 4 in Puerto Rico and 7 elsewhere in the United States ) with inadequate testing to diagnose CHIKV infection , which may have contributed to underdiagnosis of CHIKV-infection . In Puerto Rico this was because of underuse of serology for patients who presented >8 days after symptom onset . Outside Puerto Rico this was because of underuse of CHIKV RT-PCR or convalescent serology for patients who presented during the first week of symptom onset . Among returning travelers , many of whom presented for care in the U . S . during the convalescent period , when diagnosis is dependent upon serology , some diagnoses could have been missed as CHIKV IgM typically declines after several weeks to months [1] . Only 4 of the returning travelers had CHIKV IgM performed without simultaneous CHIKV IgG , and no patients had a negative CHIKV IgM and positive CHIKV IgG , however , suggesting that few cases may have been missed for this reason . Outside Puerto Rico , only 44% of patients tested for CHIKV had laboratory-confirmed infection; this not only reflects the lower prevalence of CHIKV infection outside Puerto Rico , but also improper testing of patients without symptom-onset after travel to an area with CHIKV transmission . Our surveillance activities determined that CHIKV testing availability for VACHS was lacking and resulted in VHA PHRL offering CHIKV ( and dengue virus ) testing . Further education of VHA providers regarding CHIKV infection , correct diagnostic testing ( RT-PCR versus serology ) on the basis of time from symptom onset , and available testing through VHA is needed . Lessons learned from Puerto Rico are important for areas in the Western Hemisphere with ongoing CHIKV transmission as well as countries , including the United States , with similarly immunologically naïve populations and Aedes aegypti or Aedes albopictus vectors [6] . For clinical management , newly required CHIK public health reporting , and surveillance , having adequate laboratory testing capacity for timely results is helpful . Although testing all symptomatic persons might be infeasible , sufficient capacity to test those with severe ( e . g . hospitalized patients ) or atypical illness is needed . Clinicians practicing in areas with CHIKV transmission should be aware that CHIKV infection among elderly patients and patients with comorbidities , including CHF , CKD , diabetes , and chronic lung disease may be associated with more severe disease . To determine whether the risk of atypical complications is greater for CHIKV infection compared with other viral infections , a larger cohort of patients presenting with a viral syndrome would need to be studied . Further work to examine risk factors for intensive care and death among a larger sample of patients with laboratory-confirmed infection is needed to provide closer monitoring for those at greatest risk and to investigate the effect of prevention strategies [30] targeted to populations at greatest risk should they acquire CHIKV infection .
|
Infection with mosquito-borne chikungunya virus causes fever and severe diffuse joint pain—an illness known as chikungunya fever , or "that which bends up . " Epidemics of chikungunya fever have occurred in Asia , Africa , and Europe . Not until December 2013 were there reports of chikungunya virus infection occurring in the Americas . Since then , it has involved most countries in the Western Hemisphere with >1 . 1 million cases reported by the end of 2014 . Previous data from the Réunion Island outbreak demonstrated that older patients and patients with certain chronic medical conditions may have a higher risk of severe disease . The Veterans Health Administration is the largest health care system in the United States and has facilities in U . S . territories , including Puerto Rico , which has been heavily affected by this epidemic . Among Veterans in Puerto Rico , we investigated risk factors for severe disease and described all chikungunya-associated deaths . Risk for hospitalization increased with age , and for patients of the same age , was increased among those with congestive heart failure , chronic kidney disease , diabetes , or chronic lung disease . Further work is needed to determine whether prevention strategies targeted to those who may be at greatest risk for severe disease could help decrease morbidity and mortality among these populations .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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] |
2016
|
Chikungunya Fever Cases Identified in the Veterans Health Administration System, 2014
|
Resistance against different antibiotics appears on the same bacterial strains more often than expected by chance , leading to high frequencies of multidrug resistance . There are multiple explanations for this observation , but these tend to be specific to subsets of antibiotics and/or bacterial species , whereas the trend is pervasive . Here , we consider the question in terms of strain ecology: explaining why resistance to different antibiotics is often seen on the same strain requires an understanding of the competition between strains with different resistance profiles . This work builds on models originally proposed to explain another aspect of strain competition: the stable coexistence of antibiotic sensitivity and resistance observed in a number of bacterial species . We first identify a partial structural similarity in these models: either strain or host population structure stratifies the pathogen population into evolutionarily independent sub-populations and introduces variation in the fitness effect of resistance between these sub-populations , thus creating niches for sensitivity and resistance . We then generalise this unified underlying model to multidrug resistance and show that models with this structure predict high levels of association between resistance to different drugs and high multidrug resistance frequencies . We test predictions from this model in six bacterial datasets and find them to be qualitatively consistent with observed trends . The higher than expected frequencies of multidrug resistance are often interpreted as evidence that these strains are out-competing strains with lower resistance multiplicity . Our work provides an alternative explanation that is compatible with long-term stability in resistance frequencies .
Antibiotic resistance and , in particular , multidrug resistance ( MDR ) are public health threats . Multidrug resistant infections are associated with poorer clinical outcomes and higher cost of treatment than other infections [1 , 2] and there is concern that the emergence of pan-resistant strains ( pathogens resistant to all available antibiotics ) will render some infections untreatable [3] . From the point of view of finding effective treatment options , multidrug resistance is particularly problematic because resistance to different antibiotics tends to be concentrated on the same strains: positive correlations between resistance to different drugs have been found in multiple species ( including Streptococcus pneumoniae , Neisseria gonorrhoeae , Staphylococcus aureus , Escherichia coli , Klebsiella pneumoniae , Pseudomonas aeruginosa and Mycobacterium tuberculosis ) [2] . In other words , the frequency of MDR strains is higher than we would expect from the frequencies of individual resistance determinants if these were distributed randomly in the population ( ‘MDR over-representation’ ) . Understanding the causes of this MDR over-representation is important for limiting the impact of resistance . A number of possible explanations have been suggested ( Table 1 ) [2] , but the extent to which these processes contribute to the trend remains uncertain . Many of the proposed mechanisms are specific to subsets of antibiotics and/or species . The pattern of MDR over-representation , on the other hand , is pervasive: correlations have been observed between resistance to antibiotics acting through different mechanisms , and between chromosomal and mobile genetic element ( MGE ) associated resistance determinants [2] . Explanations for MDR over-representation must therefore be either sufficiently general or sufficiently diverse to account for this pervasiveness . In this paper , we approach the problem of explaining MDR over-representation in terms of strain ecology: explaining why resistance to different antibiotics is often seen on the same strain requires an understanding of the competition between strains with different resistance profiles . For models of such competition to be credible , they must capture observed trends in resistance dynamics whilst being ecologically plausible . Developing models that fulfil these criteria has not been trivial: sensitive and resistant strains compete for the same hosts and simple models of competition therefore predict that the fitter strain will out-compete the other ( ‘competitive exclusion’ ) [19] . However , this is rarely observed: resistance frequencies have remained intermediate over long time periods in a number of species . For example , sustained intermediate resistance frequencies are observed in Europe for various antibiotics and numerous species , including E . coli , S . aureus and S . pneumoniae ( European Centre for Disease Prevention and Control Surveillance Atlas , available at https://atlas . ecdc . europa . eu ) . Stable coexistence is also observed in surveillance data from multiple other locations ( Centre for Disease Dynamics , Economics and Policy , available at https://resistancemap . cddep . org/AntibioticResistance . php ) . For further review of evidence for stable coexistence , see references [19 , 20] . Recent work has explored the role of i ) host population structure [21–23] , ii ) pathogen strain structure [20 , 21] and iii ) within-host dynamics [24] in maintaining the coexistence of antibiotic sensitivity and resistance . In this paper , we identify a structural similarity in the first two categories of model . In these models , coexistence arises through a combination of two factors . First , the presence of groups within the host or pathogen population in which the evolutionary dynamics of resistance are approximately independent from the other groups . Second , the presence of variation in the benefit gained from resistance between these groups , so that antibiotic resistance is selected for in some groups while sensitivity is selected for in others . We show that if this variation is correlated for different antibiotics , models with this structure also predict high levels of association between resistance to different antibiotics: all resistance determinants will tend to be found where the fitness benefit gained from resistance is the greatest . The observed high frequency of multi-drug resistance is therefore in line with ecologically plausible models of coexistence , making these models a parsimonious explanation for both trends .
In this section , we discuss competitive exclusion and previously proposed coexistence mechanisms in the context of multidrug resistance . We identify a structural similarity in plausible models of coexistence [20–22] and show that , in a multidrug context , models with this structure predict MDR over-representation . The model we present captures the dynamics of a bacterial species which is mostly carried asymptomatically ( e . g . E . coli , S . aureus or S . pneumoniae ) , so the probability of a host being exposed to antibiotics does not depend on whether the host is infected with the pathogen [13] . Key results , however , are also applicable when this is not the case ( see Discussion ) . In this section , we explore how introducing additional complexity to the simplified model affects our predictions about association between resistance determinants and nestedness .
In this paper , we approach the question of explaining observed patterns of association between resistance to different antibiotics ( ‘MDR over-representation’ ) in terms of understanding the competition between strains with different resistance profiles . We consider recent models of the coexistence of antibiotic sensitive and antibiotic resistant strains [20–23] in which coexistence is maintained by heterogeneity in the fitness effect of resistance , arising either from heterogeneity in the rate of antibiotic consumption and/or difference in duration of carriage . We present a generalised version of these types of models , in which competition between antibiotic sensitivity and resistance is simplified to a series of independent sub-models ( strata ) . We show that this model structure also gives rise to MDR over-representation because resistance to all antibiotics will be selected for in the strata where the fitness benefit of resistance ( ‘resistance proneness’ ) is the highest . Therefore , our results suggest that two pervasive trends in resistance dynamics , the robust coexistence of antibiotic sensitive and resistant strains and the over-representation of multidrug resistance , can both be explained by heterogeneity in the fitness effect of resistance within the host or pathogen population . We first present a simplified model for conceptual insights and then explore how additional complexity affects predicted trends . Under the strong assumption of identical antibiotic prescription patterns in all strata and no recombination , this model predicts complete linkage disequilibrium ( D′ = 1 ) between resistance to all antibiotics . Relaxing these assumption decreases the magnitude of linkage disequilibrium , giving rise to values of D′ similar to those observed in multiple bacterial datasets . High D′ is maintained even at unrealistically high recombination rates . A lower correlation in antibiotic consumption profiles between strata leads to lower values of D′ . However , the effect is gradual and the magnitude of the decrease depends on whether the strata also differ in clearance rate . Thus , even in context where patterns of prescription differ considerably between host groups , we would still expect a degree of association between resistance determinants when variation in duration of carriage contributes to variation in the fitness effect of resistance . Although the model builds on work exploring the stable coexistence of antibiotic sensitivity and resistance and coexistence is robustly observed in multiple datasets , the prediction that variation in the fitness effect of resistance leads to MDR over-representation does not require coexistence to be stable . We would expect MDR over-representation in the presence of fitness variation , even when this variation is not enough to maintain stable coexistence: for all antibiotics , the increase of resistance frequencies towards fixation would occur most rapidly in the populations with the greatest selection pressure for resistance . Under these circumstances , fitness variation would give rise to transient MDR over-representation . Our results show that when variation in the fitness effect of resistance is present and when this variation is at least partially correlated for different antibiotics , it will give rise to MDR over-representation . The extent to which this mechanism accounts for observed patterns of MDR over-representation therefore depends on the extent to which this type of fitness variation is present in pathogen populations . It is not entirely straightforward to evaluate how common variation in the fitness effect of resistance is . Wide-spread coexistence of sensitivity and resistance is not direct evidence for the pervasiveness of fitness variation because coexistence may not always arise through this mechanism . Although the majority of mechanisms proposed to date [20–23] work through fitness variation , other mechanisms are also possible [19] . In particular , recent modelling suggests that co-infection with sensitive and resistant strains gives rise to frequency-dependent selection for resistance and thus promotes coexistence [24] . However , the magnitude of this effect depends on the nature of within-host competition [24] , for which there is limited data . Thus while theoretically plausible , the extent to which this mechanism contributes in practice is still unclear . It is worth noting that different coexistence mechanisms are not mutually exclusive . If coexistence arises through a combination of fitness variation and other mechanisms , we would a priori still expect the fitness variation to give rise to MDR over-representation . In the work presented here , we consider fitness variation arising from heterogeneity in antibiotic consumption between host groups ( hospitals vs communities , geographic regions , age classes ) and from heterogeneity in duration of carriage between host groups ( age classes ) and between strains ( pneumococcal serotypes ) . This is not an exhaustive list of possible sources of heterogeneity . For example , serotype does not fully account for heritable variation in pneumococcal duration of carriage [28] , suggesting other genetic traits also play a role in determining carriage duration . In light of recent results suggesting wide-spread negative frequency-dependent selection in bacterial genomes [30 , 31] , it is not implausible to suggest these duration of carriage loci may also be under frequency-dependent selection . If so , diversity at these loci would create another source of variation in the fitness effect of resistance and hence promote coexistence and MDR over-representation . More broadly , variation in the fitness effect of resistance may arise through different mechanisms for pathogens with a different ecology than modelled in this work . For example , we have modelled a pathogen that is mostly carried asymptomatically and therefore exposed primarily to antibiotics prescribed against other infections . For pathogens where antibiotics prescribed due to infection with the pathogen itself contribute to a significant proportion of antibiotic exposure , the presence of strains differing in invasiveness would give rise to between-strain variation in antibiotic exposure and heterogeneity in the fitness effect of resistance . For bacterial species able to multiply both in hosts and in the environment , the sort of structure and heterogeneity considered in this work may also arise from differences between environmental niches . This study does not fully address the role antibiotic prescription patterns in MDR over-representation: we highlight two important remaining questions . Firstly , in the modelling framework used in this study , the distribution of drug consumption within a stratum ( i . e . a well-mixed population ) does not have an impact on MDR over-representation ( S1 Text Section 5 ) . In other words , the presence of host groups consuming antibiotics at different rates only promotes MDR over-representation if there is very little transmission between these host groups: individual-level correlation in antibiotic exposure is not predicted to promote multi-drug resistance . We have not explored this result in detail—it may arise because the model predicts competitive exclusion within a stratum . Secondly , in contrast to the distribution of antibiotic consumption within a stratum , our results suggest that the distribution of antibiotic consumption between strata does matter: the prediction of MDR over-representation is sensitive to how correlated prescription profiles are and the extent of this sensitivity depends on whether variation in duration of carriage is also present . Relating these theoretical results to observed correlations in the antibiotic consumption between different host groups and to the extent of assortative mixing between these groups will provide additional insights into observed patterns of MDR ( e . g . why the association between some drugs is higher than others ) . The fitness variation model playing a role in MDR over-representation does not preclude a potential role for other mechanisms in contributing to the trend ( Table 1 ) . This study does not address the relative extent to which the different possible mechanisms contribute to MDR over-representation . This is for two reasons . Firstly , it is unclear what the patterns of MDR predicted by alternative mechanisms of MDR over-representation are . Secondly , we do not have a full understanding of which host and pathogen characteristics are relevant in defining the strata so it is difficult to directly address whether these traits are predictors of MDR . One alternative strategy for establishing the extent to which the fitness variation model contributes to MDR over-representation would be to assess patterns of association between resistance determinants in a single strain circulating in a well-mixed host population ( i . e . a single stratum ) . The fitness variation model predicts no MDR over-representation ( as defined by D′ > 0 ) under these circumstances . Therefore , if linkage disequilibrium is observed under these conditions , this would indicate that fitness variation is not the only mechanism of MDR over-representation . Furthermore , the magnitude of linkage disequilibrium could inform the relative contribution of the fitness variation mechanism: observing similar levels of linkage disequilibrium within strata and within the whole population would suggest the fitness variation is not a necessary mechanism for generating MDR over-representation . From a public health perspective , the fitness variation model makes two concerning predictions . Firstly , we predict frequencies of pan-resistance will be high: in a perfectly nested set of resistance profiles , the frequency of pan-resistance is equal to the frequency of the rarest resistance . As a consequence , we would expect resistance arising in response to adoption of new antibiotics or increased usage of existing antibiotics to appear on already multidrug resistant lineages—an observation which has been made for the emergence of ciprofloxacin resistance in N . gonorrhoeae in the United States [32] . Secondly , our analysis has implications for the effectiveness of potential interventions against MDR . The variation in the fitness effect of resistance to different antibiotics need not be perfectly correlated for it to promote MDR over-representation . If the variation in fitness effect is maintained by multiple factors ( e . g . differential antibiotic consumption between populations and variation in clearance rates ) , removing one of these factors ( e . g . changing patterns of prescription so that consumption of different antibiotics is no longer correlated between host groups ) may have limited impact on MDR over-representation . The fitness variation model provides an explanation for MDR over-representation that is consistent with long term stability in resistance frequencies . This is relevant when considering temporal trends in resistance frequencies and predicting the future burden of resistance: other explanations for MDR over-representation ( e . g . cost epistasis , correlated antibiotic exposure at the individual level—see Table 1 ) often require MDR strains to have an overall fitness advantage over strains with lower resistance multiplicity . This would imply that the higher than expected frequency of MDR is evidence for MDR strains out-competing other strains and thus suggest that MDR strains will eventually take over . Conversely , in the model we present , MDR strains are not out-competing other strains: all resistance frequencies are at equilibrium and MDR over-representation arises from the distribution of resistance determinants . It is worth noting , however , that even in the context of the fitness variation model , on a very long time-scale , we might expect the frequency of resistance to rise if bacteria are able to evolve resistance mechanisms that carry a lower fitness cost . We show that previously proposed models in which coexistence of antibiotic sensitivity and resistance is maintained by heterogeneity in the fitness effect of resistance also predict high frequencies of multidrug resistance . The pervasive trends of coexistence and MDR over-representation can therefore be considered , at least partially , facets of the same phenomenon . We do not propose that the model we present fully explains observed patterns of association between resistance determinants . However , this effect should be considered when evaluating the role of antibiotic-specific MDR promoting mechanisms . From a public health point of view , the model we present is concerning because it predicts high frequencies of pan-resistance . On the other hand , heterogeneity in the fitness effect of resistance as an explanation for MDR over-representation allows reconciling this trend with long term stability in resistance frequencies .
The Maela pneumococcal dataset [33] , collected from a refugee camp on the border of Thailand and Myanmar from 2007 to 2010 , consisted of 2244 episodes of carriage , with associated antibiograms and carriage durations . Data were obtained from , and durations of carriage calculated by , Lees et al . [28] ( S1 File ) . Data on antibiotic sensitivity was provided for ceftriaxone , chloramphenicol clindamycin , erythromycin , penicillin , co-trimoxazole ( trimethoprim/sulfamethoxazole ) and tetracycline . Ceftriaxone was excluded from the analysis because data was missing for a large proportion of isolates ( 44% ) . The Massachusetts pneumococcal dataset , collected as part of the SPARC ( Streptococcus pneumoniae Antimicrobial Resistance in Children ) project [34] , was obtained from Croucher et al . ( 2013 ) [35] ( data available from Croucher et al [35] ) . Croucher et al . reported minimum inhibitory concentrations ( MICs ) for penicillin , ceftriaxone , trimethprim , erithromycin , tetracycline and chloramphenicol . Tetracycline and chloramphenicol were excluded from the analysis because data was missing for a large proportion of isolates ( 47% and 67% respectively ) . Non-sensitivity was defined in accordance to pre-2008 Clinical and Laboratory Standards Institute breakpoints [36] . For both datasets , ‘resistance’ as used throughout the paper refers to non-sensitivity . The four hospital datasets were obtained from Chang et al . [2] ( S2 File ) . All data were analysed anonymously . If the frequency of resistance to antibiotic a is pa and the frequency of resistance to antibiotic b is pb , the coefficient of linkage disequilibrium between resistance to antibiotics a and b is Dab = pab − papb , where pab is the frequency of resistance to both a and b . The normalised coefficient D a b ′ is given by: D a b ′ = D a b m i n ( p a p b , ( 1 - p a ) ( 1 - p b ) ) if Dab < 0 and D a b ′ = D a b m i n ( p a ( 1 - p b ) , ( 1 - p a ) p b ) if Dab > 0 . In general the sign of D′ is arbitrary because it depends on which alleles are chosen for the calculation . We consistently calculate D′ using the frequency of resistance: positive D′ therefore means resistance to one antibiotic is associated with resistance to the other , while negative D′ means association between sensitivity and resistance . All described models were implemented in Wolfram Mathematica ( version 11 . 2 . 0 . 0 ) . Modelling results are numerical solutions at t = 100000 ( equilibrium is reached considerably earlier , see Fig H in S1 Text ) . For computing D′ , numerical results for strain frequencies have been rounded to the nearest 10−10 to ensure strain frequencies for absent strains are zero ( as opposed to zero within numerical error ) . The code is provided as a supporting file . To test the effect of relaxing the assumption that the pathogen dynamics can be divided into non-interacting sub-models , we include three additional models . First , we model the dynamics of resistance to three antibiotics ( i . e . eight possible resistance profiles ) spreading in a host population consisting of five host groups . The antibiotics make up different proportions of total antibiotic consumption ( 20 , 35 and 45% of total antibiotic consumption rate τ ) . The pathogen experiences a different clearance rate within each host class p ( μp ) . In addition , sub-strain with resistance profile g experiences clearance from antibiotic exposure at rate τg which depends on its resistance status: τg = τ ( ia0 . 20 + ib0 . 35 + ic0 . 45 ) , where ia = 1 if g is sensitive to antibiotic a and 0 otherwise . Resistance to each antibiotic decreases transmission rate by a factor of c . Uninfected hosts of class p ( Up ) are therefore infected at rate c n g β [ ( 1 - m ) I g , p + m 4 ∑ x ∈ P ′ I g , x ] , where ng is the number of antibiotics strain g is resistant to , m is a parameter that sets the extent of mixing between the classes and P′ is the set of population classes excluding p . The dynamics of strain g within population p are thus described by: d I g , p d t = c n g β [ ( 1 - m ) I g , p + m 4 ∑ x ∈ P ′ I g , x ] U p - ( τ g + μ p ) I g , p ( 10 ) Second , we model the dynamics of resistance to three antibiotics in a single host population in pathogen with five strains differing in clearance rate ( i . e . eight resistance profiles and five strains , giving a total of 40 possible sub-strains ) with recombination at the duration of carriage locus . Strain i is cleared at rate μi and , as above , sub-strains with resistance profile g experience clearance from antibiotic exposure at rate τg which depends on its resistance status: τg = τ ( ia0 . 20 + ib0 . 35 + ic0 . 45 ) . Resistance to each antibiotic decreases transmission rate by a factor of c . Balancing selection is modelled similarly to Lehtinen et al . [20] , by scaling transmission rate of strain i by a factor ψi which depends on the strain’s prevalence: ψ i = ( 1 - [ ∑ x I x , i 1 - U - 1 5 ] ) k , where k is a parameter setting the strength of balancing selection and U is the uninfected host class . Recombination at the duration of carriage locus is modelled by allowing hosts infected with strain i with resistance profile g to transmit strain j with resistance profile g at a rate r∑x Ix , j . Recombination therefore decreases the transmission of strain i with resistance profile g by ρg , i = rIg , i ∑x ∑y Ix , y and increases it by κg , i = r∑y ∑x Ig , y Ix , i . Note that the recombination rate parameter r captures the probability of co-infection , the probability of recombination occurring and the probability of transmitting the recombinant sub-strain . The dynamics of strain i with resistance profile g are described by: d I g , i d t = c n g ψ i β [ I g , i - ρ g , i + κ g , i ] U - ( τ g + μ i ) I g , i ( 11 ) The third model is the same as the one above , with the exception that recombination occurs at the resistance loci instead of the duration of carriage locus . It is therefore described by Eq ( 11 ) , but the expressions for ρ and κ are different . We define resistance profile g a ′ as a resistance profile otherwise identical to g , but with the other allele at locus a ( i . e . if g is sensitive to antibiotic a , g a ′ is resistant ) , Ng , a as the set of resistance profiles with the same allele at locus a as profile g and N g , a ′ as the set of resistance profiles with the different allele at locus a than profile g . Hosts infected with strain i with resistance profile g transmit a strain i with a resistance profile g a ′ at rate r ∑ j ∑ x ∈ N g , a ′ I x , j . Recombination can occur at any of the three resistance loci ( we assume recombination rates are low enough to ignore the possibility of recombination occurring at multiple loci at the same time ) . Recombination therefore decreases the transmission of strain i with resistance profile g by ρg , i = 3rIg , i ∑x ∑y Ix , y and increases it by κ g , i = r ( I g a ′ , i ∑ y ∑ x ∈ N g , a I x , y + I g b ′ , i ∑ y ∑ x ∈ N g , b I x , y + I g c ′ , i ∑ y ∑ x ∈ N g , c I x , y ) . The parameter values for the results presented in Fig 3 are: c = 0 . 95 , β = 2 , {μ1 , ‥ , μ5} = {1 . 2 , 1 . , 0 . 8 , 0 . 6 , 0 . 4} , τ = 0 . 12 and k = 5 . To test the effect of relaxing the assumption that all host groups consume different types of antibiotics in identical proportions , we model the dynamics of resistance to two antibiotics in a population consisting of ten host groups . The dynamics within each host group are represented by Eq 3 , with parameter values cβ = 0 . 95 and cμ = 1 for both antibiotics , β = 2 , and , unless otherwise stated μ = 1 . There is no transmission between these host groups . For both drugs , five of these host groups consume the antibiotic at a rate which selects for resistance when μ = 1 ( τhigh = 0 . 075 ) , and five at a rate which selects for sensitivity when μ = 1 ( τlow = 0 . 025 ) . There are therefore six different ways in which the consumption rates of the two antibiotics can be combined: all populations consuming the first drug at a high rate also consume the second drug at high rate ( Spearman’s rho: 1 ) ; four out of the five populations consuming the first drug at a high rate consume the second drug at a high rate ( Spearman’s rho: 0 . 6 ) ; etc . We run a simulation for each of these six possible configurations . To test the effect of additional variation in the resistance proneness of strata , we introduce variation in the clearance rate of these populations: the five host groups consuming the first drug at the high rate now have different clearance rates ( evenly spaced between a maximum and minimum clearance rate ) , and similarly for the five host groups consuming the first drug at the low rate . We run a simulation for each of these possible ways the consumption of the second drug can be distributed among these host groups .
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Antibiotic resistance is a serious public health concern , yet the ecology and evolution of drug resistance are not fully understood . This impacts our ability to design effective interventions to combat resistance . From a public health point of view , multidrug resistance is particularly problematic because resistance to different antibiotics is often seen on the same bacterial strains , which leads to high frequencies of multidrug resistance and limits treatment options . This work seeks to explain this trend in terms of strain ecology and the competition between strains with different resistance profiles . Building on recent work exploring why resistant bacteria are not out-competing sensitive bacteria , we show that models originally proposed to explain this observation also predict high multidrug resistance frequencies . These models are therefore a unifying explanation for two pervasive trends in resistance dynamics . In terms of public health , the implication of our results is that new resistances are likeliest to be found on already multidrug resistant strains and that changing patterns of prescription may not be enough to combat multidrug resistance .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
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"antimicrobials",
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"drugs",
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"linkage",
"disequilibrium",
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2019
|
On the evolutionary ecology of multidrug resistance in bacteria
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Severe hepatic inflammation is a common cause of acute liver injury following systemic infection with Ehrlichia , obligate Gram-negative intracellular bacteria that lack lipopolysaccharide ( LPS ) . We have previously shown that type I IFN ( IFN-I ) and inflammasome activation are key host-pathogenic mediators that promote excessive inflammation and liver damage following fatal Ehrlichia infection . However , the underlying signals and mechanisms that regulate protective immunity and immunopathology during Ehrlichia infection are not well understood . To address this issue , we compared susceptibility to lethal Ixodes ovatus Ehrlichia ( IOE ) infection between wild type ( WT ) and MyD88-deficient ( MyD88-/- ) mice . We show here that MyD88-/- mice exhibited decreased inflammasome activation , attenuated liver injury , and were more resistant to lethal infection than WT mice , despite suppressed protective immunity and increased bacterial burden in the liver . MyD88-dependent inflammasome activation was also dependent on activation of the metabolic checkpoint kinase mammalian target of rapamycin complex 1 ( mTORC1 ) , inhibition of autophagic flux , and defective mitophagy in macrophages . Blocking mTORC1 signaling in infected WT mice and primary macrophages enhanced bacterial replication and attenuated inflammasome activation , suggesting autophagy promotes bacterial replication while inhibiting inflammasome activation . Finally , our data suggest TLR9 and IFN-I are upstream signaling mechanisms triggering MyD88-mediated mTORC1 and inflammasome activation in macrophages following Ehrlichia infection . This study reveals that Ehrlichia-induced liver injury and toxic shock are mediated by MyD88-dependent inflammasome activation and autophagy inhibition .
Human monocytic ehrlichiosis ( HME ) is the most prevalent emerging tick-borne , life threatening rickettsial disease and is caused by the obligate intracellular bacterium E . chaffeensis . Patients with HME develop severe liver inflammation and dysfunction followed by multi-organ failure and toxic shock-like syndrome . Protective immunity against Ehrlichia is mediated by Th1 cells [1–4] . On the other hand , fatal ehrlichiosis in humans and mice is due to an excessive inflammatory response and induction of pathogenic innate and adaptive immune cells , including neutrophils , NK cells and CD8+ T cells causing immunopathology [5–7] . We recently showed that virulent Ehrlichia are sensed by cytosolic pattern recognition receptors ( PRRs ) such as the nucleotide binding domain leucine-rich repeat ( NLR ) inflammasome complexes , including NLRP3[8] . Other studies have shown activation of canonical inflammasome pathways by a variety of intracellular pathogen-associated molecular patterns ( PAMPs ) and damage-associated molecular patterns ( DAMPs ) lead to cleavage of pro-caspase-1 and secretion of IL-1β , IL-18 [9–13] . Recently , non-canonical inflammasome activation involving caspase-11 activation by LPS was identified [9 , 11 , 14–16] . Caspase-11 or caspase-1 activation can lead to production of IL-1β and IL-18 , as well as promotion of pyroptosis , an inflammatory programmed cell death , and lytic release of IL-1α and HMGB1 [17–19] . Inflammasome activation is a double-edged sword , contributing to both protective anti-microbial responses as well as inflammation and cell death when excessively activated [18 , 20–22] . Our previous studies indicated that inflammasome activation is pathogenic during fatal ehrlichial infection [7 , 23–26] . IL-18 signaling mediated induction and expansion of pathogenic CD8+ T cells in murine model of fatal ehrlichiosis , which caused tissue damage [3 , 23 , 25] . Thus , regulation of inflammasome is critical for controlling Ehrlichia infection without causing collateral damage . Type-I interferon ( IFN-I ) is a major negative regulatory mechanism for inflammasome activation during infections with several intracellular bacteria [27–31] . Paradoxically , we and others have showed that type I IFN and IFN-I receptor ( IFNαR ) signaling positively regulates inflammasome activation during infection with Ehrlichia , Francisella , and Streptococcus pneumoniae [24 , 30 , 32–34] . Autophagy is another process that negatively regulates inflammasome activation . Autophagy involves the formation of double-membrane compartments ( phagophores ) that capture damaged host organelles and cytoplasm , as well as intracellular bacteria . Autophagic flux involves maturation of these phagophores into autophagosomes , which then fuse with lysosomes to form single-membrane autolysosomes where degradation of the autophagic cargo and subsequent recycling of proteins and ATP synthesis occurs [27 , 35] . Induction of autophagy is mediated by several autophagy-promoting molecules including Atg5 , Atg12 , Atg16 , Atg8/LC3 ( LC3 is the mammalian homologue of yeast Atg8 ) and beclin1 . Decreased production of any of these factors or decreased lipidation of Atg8/LC3 attenuate formation of autophagosomes and impairs autophagy process . While autophagy is a protective innate mechanism against facultative intracellular bacteria [27 , 36] , recent studies suggested that autophagy promotes survival and replication of obligate intracellular bacteria such as Ehrlichia and Anaplasma , as these bacteria capture nutrients through this process that promote bacterial growth and replication [37–39] . Unlike other Gram negative bacteria , Ehrlichia lack LPS [40–42] , a major inducer of innate responses against these pathogens . The underlying mechanisms that regulate innate immune and inflammasome activation in macrophages during infection with LPS negative Ehrlichia remain elusive . Myeloid differentiation factor 88 ( MyD88 ) is a major adaptor molecule downstream of several surface and cytosolic pattern recognition receptors [43] . In this study , we examined whether Ehrlichia-induced inflammasome activation in macrophages is regulated by MyD88 and the mechanisms involved in this regulation . Our data suggest dual protective and pathogenic roles of MyD88 in host response to Ehrlichia . MyD88 inhibited autophagy induction and controlled bacterial replication by activating mTORC1 . On the other hand , MyD88-mediated mTORC1 activation induced host-pathogenic inflammasome activation and liver damage by blocking autophagosome-lysosomal fusion and mitophagy in macrophages .
To investigate the role of MyD88 in the pathogenesis of Ehrlichia-induced liver injury , we analyzed the host response to Ehrlichia in WT and MyD88-/- mice infected with an ordinarily high lethal dose of IOE ( 104 organisms/mouse ) . Consistent with our previous studies , 100% of IOE-infected WT mice succumbed to lethal infection by days 7–11 post-infection ( p . i . ) . In contrast , only about 70% of MyD88-/- mice succumbed to infection by that time point ( Fig 1A ) . Interestingly , MyD88-/- mice had a significantly higher bacterial burden in the liver on day 7 p . i . compared to WT mice ( Fig 1B ) . H&E and TUNEL staining of the liver tissues from uninfected WT and MyD88-/- mice exhibited similarly normal liver histology with no evidences of cell death or immune cell infiltration . However , liver tissue from IOE-infected WT mice exhibited evidence of severe liver damage on day 7 p . i . as indicated by multiple foci of hepatocyte necrosis and apoptosis as well as fatty changes . In contrast , IOE-infected MyD88-/- mice had significantly attenuated liver injury at the same time point , as marked by significantly decreased TUNEL positive and necrotic ( shown by H&E staining ) hepatocytes and Kupffer cells , reduced magnitude of fatty changes/steatosis ( a hallmark of ehrlichial hepatocyte infection ) [3 , 23 , 24] ( Fig 1C ) , as well as decreased serum level of aspartate transaminases ( AST ) levels when compared to infected WT mice ( Fig 1D ) . Type I response , mainly by CD4+ Th1 cells , is critical for protective immunity against Ehrlichia , while IL-10 production by T and non-T cells inhibits bacterial clearance [1 , 5 , 6] . To determine the potential mechanism responsible for impaired bacterial clearance in MyD88-/- mice , we analyzed the phenotype of antigen ( Ag ) -specific T cells in the spleen of WT and MyD88-/- mice on day 7 p . i . by flow cytometry ( S1 Fig ) . There was no difference in the absolute number of CD4+ or CD8+ T cells between naïve WT and MyD88-/- mice ( S1B , S1C and S1D Fig ) , suggesting that MyD88-/- mice do not have an altered immune phenotype at baseline that could influence their response to Ehrlichia infection . However , IOE infection of MyD88-/- mice resulted in significantly higher percentage and number of activated Ag-specific CD4+ T cells expressing CD69 when compared to infected WT mice ( Fig 1E & S1B Fig ) , although there was no significant difference in the activation of CD8+ T cells between the groups ( Fig 1E ) . IOE-infected WT and MyD88-/- mice have significantly higher frequency of IFNγ+CD4+ Th1 cells and IFNγ+CD8+ Tc1 cells than corresponding naïve mice from each group , but there was no significant difference in the frequency of Th1 or type I CD8+ T cells between IOE-infected WT and MyD88-/- mice ( Fig 1F and S1B Fig ) . We and others have shown that TNF-α and IL-10 are key mediators of protective immunity or immunosuppression during fatal IOE infection , respectively [6 , 23 , 25] . Thus , to further examine the potential mechanism that could account for impaired bacterial elimination in MyD88-/- mice , we measured IL-10 and TNF-α production by CD4+ and CD8+ T cells at the single-cell level by flow cytometry ( S1C Fig ) , and in culture supernatant by ELISA . Splenocytes from both naïve WT and MyD88-/- mice have negligible numbers of Ag-specific TNF-α or IL-10 producing CD4+ and CD8+ T cells . However , IOE- infected MyD88-/- mice have significantly increased Ag-specific IL-10+CD4+ T cells and IL-10+CD8+ T cells in their spleens compared to infected WT mice ( Fig 1G ) . Although the frequency of Th1 cells did not differ significantly between infected WT and MyD88-/- mice , the ratio of Ag-specific IL-10+: IFNγ+ producing CD4+T cells was significantly higher in infected MyD88-/- mice compared to WT mice ( S1E Fig ) . Additionally , sera from IOE-infected MyD88-/- mice have significantly higher levels of IL-10 ( Fig 1H ) , but lower levels of TNF-α ( Fig 1I ) as well as higher IL-10: TNF-α ratio when compared to WT mice ( S1F Fig ) . The bias of adaptive immune responses in infected MyD88-/- mice towards an immunosuppressive phenotype is consistent with higher bacterial burden in these mice . Together , these data suggest that while MyD88 mediates inflammatory liver injury , it also moderates the degree of Ehrlichia-induced immunosuppression and enhances bacterial elimination . Since inflammasome activation is linked to severe liver injury in IOE-infected mice , we examined the contribution of MyD88 to inflammasome activation during IOE infection . To this end , we measured the expression of active ( cleaved ) caspase-1 in liver lysates from WT and MyD88-/- mice by immunoblotting . Consistent with our previous studies [8 , 24] , IOE infection elicited inflammasome activation in the liver of WT mice as evidenced by cleaved caspase-1 in liver lysates ( Fig 2A ) , and high serum levels of IL-1β on day 7 p . i . ( Fig 2B ) . In contrast , MyD88 deficiency inhibited caspase-1 activation in the liver ( Fig 2A ) , and decreased serum levels of IL-1β ( Fig 2B ) and IL-1α ( a marker of lytic cell death induced by caspase-1/11-mediated pyroptosis ) ( Fig 2C ) when compared to WT mice . At the transcription level , expression of caspase-1 , IL-1β and caspase-11 were also lower in IOE-infected MyD88-/- mice compared to WT mice ( Fig 2D ) , similarly to inflammasome components NLRP3 and NLRC4 ( Fig 2E ) . AIM2 levels did not change significantly with IOE infection , and were similar between WT and MyD88-/- mice ( Fig 2E ) . Next we examined MyD88-mediated inflammasome activation in bone marrow derived macrophages ( BMM ) from WT and MyD88-/- mice . Previous studies by us and other investigators suggest that most infected macrophages in the liver are derived from infiltrating blood monocytes during Ehrlichia infection[7 , 44 , 45] , so using BMM rather than resident liver macrophages better recapitulates the in vivo cell types and response to Ehrlichia . WT and MyD88-/- BMM were infected with IOE at multiplicity of infection MOI of 5 and the production of inflammasome-dependent and independent cytokines by infected macrophages was assessed at 8 , 12 , and 24h p . i . We selected these time points as they coincide with bacterial invasion and replication , without influencing cell viability . Consistent with the in vivo data , IOE induced significant secretion of inflammasome-dependent cytokines ( IL-1β and IL-1α ) and TLR-dependent , inflammasome-independent pro-inflammatory cytokine ( TNF-α ) in WT-BMM at 8 , 12 , and 24h p . i . In contrast , MyD88-/- BMM have significantly lower production of IL-1β and IL-1α , and TNF-α at the same time points ( Fig 2F , 2G & 2H ) . Decreased IL-1β secretion by IOE-infected MyD88-/- BMM correlated with defective activation ( cleavage ) of caspase-1 ( Fig 2I ) and caspase-11 ( Fig 2J ) compared to WT-BMM , suggesting that IOE induces MyD88-dependent inflammasome activation . Further , the activation of inflammasome in WT macrophages was associated with lytic cell death as indicated by increased lactate dehydrogenase ( LDH ) ( Fig 2K ) release in infected BMM culture compared to uninfected BMM culture . Cell death of infected WT-BMM was significantly attenuated when cells were incubated with caspase-1 or caspase-11 inhibitors . Although these inhibitors may also inhibit other caspases , our data suggest that IOE-induces caspase-1/11-mediated pyroptosis ( Fig 2K ) . As a positive control maximum LDH release was measured in response to stimulation of BMM with LPS+ATP ( ligands for NLRP3 inflammasome ) . To define the inflammasome complexes that contribute to IL-1β secretion following infection with virulent IOE and regulated by MyD88 , we further analyzed mRNA expression of several inflammasome complexes ( NLRP3 , NLRC4 , AIM2 ) in the liver tissues of WT and MyD88-/- mice on day 7 p . i with IOE . IOE induced significant upregulation of NLRP3 in IOE-infected WT mice compared with infected MyD88-/- mice . This result is consistent with our recent study showing that NLRP3 is an important inflammasome component during fatal Ehrlichia infection as it promotes IL-1β secretion and cell death of WT macrophages [24] . This is also consistent with a known role for MyD88 signaling in activation of NF-κB , which can then upregulate NLRP3 and pro-IL-1β message expression [46–48] . Although infected MyD88-/- mice exhibited lower mRNA expression of NLRC4 than infected WT mice , NLRC4 mRNA expression in infected WT mice was only slightly elevated ( less than 2 fold ) compared to naïve WT or MyD88-/- mice . These data are consistent with studies showing that Ehrlichia do not express the typical PAMPs known to activate NLRC4 such as flagella or type III secretion system effectors [22 , 49] , and suggests that NLRC4 is less likely to be important for inflammasome activation in our model . Another main inflammasome that was of interest to our model is AIM2 , which we previously found to be upregulated in vivo at early stages of lethal IOE infection ( day 3 p . i . ) [8] . In the current study , we did not detect significant differences in the level of AIM2 mRNA expression between IOE-infected WT and MyD88-/- mice at late stages of infection ( day 7 p . i . ) . However , our previous study has shown that AIM2 is upregulated in murine model of fatal ehrlichiosis at an early stage of infection ( Day 3 p . i . ) . Thus , we examined whether AIM2 is critical for in vivo inflammasome activation and development of liver injury following lethal IOE infection . To this end , we infected WT and AIM2-/- mice with an ordinarily lethal dose of IOE ( 104 organisms/mouse ) . IOE-infected AIM2-/- mice were susceptible to infection with 100% of the AIM2-/- mice died on day 8–10 p . i . similar to WT mice ( S2A Fig ) . H&E and TUNEL staining demonstrated liver damage in IOE-infected AIM2-/- mice as marked by presence of several foci of liver necrosis and higher number of apoptotic macrophages and hepatocytes on day 7 p . i . , when compared to infected WT mice ( S2B and S2C Fig ) . Susceptibility of AIM2-/- mice to lethal IOE infection correlated with high serum level of IL-1β and expression of active caspase-1 and IL-1β in liver tissues , which was similar to that detected in infected WT counterparts ( S2D and S2E Fig ) . Together , these data suggest that AIM2 is neither critical for inflammasome activation during fatal Ehrlichia infection , nor responsible for development of Ehrlichia-induced liver injury or fatal toxic shock . Studies have shown that inflammasome activation is regulated by autophagy [48 , 50] . To examine whether MyD88 mediates inflammasome activation via regulation of autophagy , we first determined whether IOE infection could trigger formation of autophagosomes , which entails the recruitment of cytosolic Atg8/LC3 to the phagophore [51] . Recruitment of non-lipidated LC3 ( LC3I ) to autophagosomes involves its proteolytic cleavage and lipidation , yielding LC3II . Therefore , to measure autophagy induction , we assessed conversion of LC3I to LC3II by immunoblot assay . Similar to uninfected WT mice , liver lysates from IOE-infected WT mice harvested on day 7 p . i . showed decreased accumulation of LC3II and a lower LC3II:I ratio ( Fig 3A ) . In contrast , liver lysates from IOE-infected MyD88-/- mice expressed more LC3II and a higher LC3II:I ratio when compared to naïve MyD88-/- and infected WT mice ( Fig 3A ) . We next examined the autophagy process in macrophages as the major target cells for Ehrlichia . Autophagy levels were analyzed in primary BMM from WT or MyD88-/- mice infected with IOE at MOI of 5 . Cells were harvested at 24h p . i . , and the autophagy was monitored by both immunoblotting and immunofluorescence using an anti-LC3 antibody . As shown in Fig 3B , IOE induced a significant increase of both LC3I and LC3II in WT-BMM compared to uninfected WT cells . However , the ratio of normalized LC3II: I , which reflects LC3II conversion/turnover associated with autophagosomes , was not significantly different than the LC3II: I ratio in naïve WT-BMM . In contrast , IOE-infected MyD88-/-BMM had higher accumulation of LC3II and thus higher LC3II: I ratio compared to uninfected MyD88-/-BMM and infected WT-BMM ( Fig 3B ) . Further , expression of two major autophagy proteins that initiate autophagosome formation [27 , 51]; beclin-1 ( measured by immunoblot ) and Atg5 ( measured by immunoblot and RT-PCR ) was higher in IOE-infected MyD88-/- BMM when compared to WT-BMM at 24h p . i . ( Fig 3B & 3C ) , suggesting that MyD88 impairs autophagy induction in IOE-infected macrophages . We then examined whether the accumulation of LC3I and LC3II in WT and MyD88-/- BMM was an early or late event during IOE infection . LC3 levels were analyzed in macrophages from both groups by confocal microscopy at different time points after IOE infection . Interestingly , we detected similar increases in LC3II puncta/cell ( S3A Fig ) as well as percentage of cells expressing more than 5 puncta ( S3B Fig ) in both WT- and MyD88-/- BMM at early time points of post infection , being slightly induced at 4h and significantly increased by 8h , which corresponds with the entry of Ehrlichia into macrophages . However , at 12h and 24h post infection , we observed significant decrease in the number of LC3 puncta/cell , as well as the percentage of cells expressing more than 5 puncta , in IOE infected WT-BMM ( S3A and S3B Fig ) . In contrast , LC3 puncta increased significantly in infected MyD88-/- BMM at these later time points ( S3A and S3B Fig ) . This difference correlates with the logarithmic replicating phase of Ehrlichia as previously described [52–54] . Since the differences in autophagy between WT- and MyD88-/- BMM were mainly observed at 12h and 24h , we chose these time points for further analysis . The accumulation of LC3II in IOE-infected MyD88-/- but not WT-BMM , could be due to enhanced autophagosome formation/induction , or a reduced degradation of autophagic cargo . To distinguish between these events , we analyzed autophagic flux in IOE-infected cells . To this end , we examined autophagosome-lysosomal fusion and lysosome-mediated autophagosome degradation by monitoring the effect of bafilomycin A1 ( Baf: a lysosomal inhibitor of the vacuolar-type H+-ATPase ) on the level of LC3II ( immunoblot ) and number of LC3 puncta ( confocal ) , or the colocalization of autophagosomes with lysosomes ( confocal ) . As shown by immunoblot , treatment of IOE-infected WT-BMM with Baf resulted in an increase of LC3II level [2 . 3 fold , calculated as the ratio between LC3 II level in IOE-infected WT-BMM treated with Baf ( 2 . 8 ) and those in untreated IOE-infected WT- BMM ( 0 . 5 ) ] as compared with the effect promoted by the same treatment in uninfected cells , suggesting that LC3II degradation by lysosomal enzymes is enhanced during IOE infection of WT cells ( Fig 3D ) . Similarly , treatment of IOE-infected MyD88-/- BMM with Baf resulted in an increase of total LC3II [~3 fold , calculated as the ratio between LC3 levels in IOE-infected WT-BMM treated with Baf ( 4 . 4 ) and those in untreated IOE-infected WT- BMM ( 1 . 3 ) ] as compared with the effect promoted by the same treatment in uninfected cells ( Fig 3D ) , suggesting that LC3II degradation by lysosomal enzymes is further enhanced during IOE infection in the absence of MyD88 . Confocal immunofluorescence analysis showed that IOE infection of WT-BMM and MyD88-/- BMM significantly increased the percentage of cells with more than 5 LC3 puncta ( Fig 3F ) , as well as the number of LC3 puncta/cell ( Fig 3E and 3G ) when compared to uninfected counterparts . IOE infection further increased the percentage of punctae and the number of LC3 puncta/cell in MyD88-/- BMM treated with Baf ( Fig 3E , 3F and 3G ) , which further confirm immunoblot data and suggest that MyD88 partially inhibits autophagic flux in IOE-infected BMM . Partial block of autophagosome-lysosomal fusion or autophagosome degradation in WT-BMM , but not in MyD88-/- cells , was further investigated by confocal microscopy examining the co-localization of LC3 puncta with acidic organelles ( S4A and S4B Fig ) . To this end , cells were stained with an antibody against LC3 and also with LysoTracker Red , an acidotropic fluorescent dye that accumulates in acidic lysosomes . Compared to infected WT-BMM , MyD88-/- BMM exhibited a significant increase in the number of LC3 puncta per cell that colocalized with the LysoTracker Red , suggesting formation of autolysosomes ( S4A and S4B Fig ) . Decreased autolysosome formation in WT-BMM was not due to a block in lysosomal acidification by Ehrlichiae because the number of LysoTracker Red staining in IOE-infected WT and MyD88-/- BMM was similar to LysoTracker Red staining in LPS-stimulated BMM from WT and MyD88-/- mice , respectively ( S4A Fig ) . Thus both confocal microscopy and immunoblotting analyses indicated that IOE induces MyD88-dependent partial block of the autophagosome-lysosomal fusion at late stage of the autophagic flux . To further examine autophagic flux , we analyzed the level of p62/SQSTM1 , a selective autophagy adaptor/receptor that binds to ubiquitinylated proteins and damaged organelles to target them to autophagosome-lysosomal compartments for degradation . The total cellular p62 expression levels inversely correlate with the autophagic flux and activity . Our data demonstrate a significant decrease in p62/SQSTM1 in IOE-infected MyD88-/- BMM compared to uninfected MyD88-/- BMM and infected WT-BMM ( Fig 3H ) , confirming MyD88-mediated block of autophagic flux . To determine how MyD88 inhibits autophagy induction in macrophages , we examined the mTORC1 pathway as a major negative regulatory mechanism of autophagy . mTORC1 activation is measured by phosphorylation of its downstream targets ribosomal protein S6 ( pS6 ) and eukaryotic initiation factor 4E-binding proteins ( p4E-BP1 ) , as well as phosphorylation of upstream AKT kinase . Compared to uninfected WT mice , IOE-infected WT mice had elevated pS6 in the liver tissue on day 7 p . i . ( Fig 4A ) . In contrast , mTORC1 activation was significantly abrogated in the liver of IOE-infected MyD88-/- mice ( Fig 4A ) . Consistent with in vivo data , IOE also induced MyD88-dependent mTORC1 activation in macrophages as evidenced by increased expression of phosphorylated proteins p4E-BP1 , pS6 , and pAKT ( levels of pS6 and pAKT were normalized to total S6 and AKT , respectively , as well as to GAPDH ) in the in vitro-infected WT-BMM , but not in infected MyD88-/- BMM , at 24h p . i . ( Fig 4B ) . These data suggest that MyD88 promotes mTORC1 activation in macrophages following IOE infection . To determine whether intracellular Ehrlichiae or other host factors are responsible for activating MyD88-mediated mTORC1 activation , we blocked bacterial internalization in WT-BMM with the dynamin inhibitor , dynasore . Dynasore treatment attenuated expression of pS6 ( Fig 4C ) and cellular damage as indicated by restoration of normal mitochondrial structure in dynasore-treated infected cells compared to untreated IOE-infected WT-BMM ( Fig 4D ) . Thus , these data indicate that MyD88-mediated activation of mTORC1 in IOE-infected macrophages is likely due , in part , to bacterial PAMPs . To further confirm the role of mTORC1 activation in IOE-induced autophagy inhibition , we treated IOE infected WT-BMM with rapamycin ( mTORC1 inhibitor that stimulates autophagy ) , and examined LC3II accumulation . As shown in Fig 4E , rapamycin treatment of IOE-infected WT-BMM resulted in significant LC3II turnover , resulting in a higher LC3II:I ratio compared to untreated infected cells . These results were also confirmed by electron microscopy , which demonstrated increased autophagosome formation in both uninfected and IOE-infected BMM upon rapamycin treatment ( Fig 4F ) . Interestingly , rapamycin treatment of IOE-infected WT-BMM attenuated production of both IL-1α and IL-1β ( Fig 4G ) . This correlated with decreased mRNA expression of caspase-11 as measured by RT-PCR , as well as decreased caspase-1 activation measured by immunoblotting in IOE-infected WT-BMM when compared to untreated/infected WT-BMM ( Fig 4H ) . Notably , infected MyD88-/- BMM , cultured with or without rapamycin had decreased expression of caspase-11 mRNA and did not express active caspase-1 when compared to infected WT-BMM with and without rapamycin , respectively ( Fig 4H ) . The effect of rapamycin on the production of inflammasome-dependent cytokines was not due to generalized immunosuppressive effects of rapamycin since it did not influence the secretion of TNF-α ( a TLR/NF-κB-dependent cytokine ) ( Fig 4I ) . These data suggest that mTORC1 activation may enhance inflammasome activation in macrophages following IOE infection via inhibition of autophagy induction . To determine the effect of autophagy regulation by MyD88 on host defense against IOE , we measured intracellular Ehrlichia in WT and MyD88-/- BMM by qPCR . At 12h and 24h p . i . , cells were washed twice to remove extracellular Ehrlichia , and the number of intracellular bacteria was determined by qPCR . Our data show that the number of ehrlichiae within WT-BMM at 24h p . i . was comparable to the number of intracellular Ehrlichia at 12h p . i . ( Fig 5A ) . Since the intracellular bacterial number is the net result of bacterial killing and replication , these data suggest bacteria may not be effectively replicating within WT macrophages or that they are eliminated by intracellular bactericidal mechanisms of macrophages . On the other hand , the number of viable Ehrlichia as determined by qPCR and RT-PCR of dsb and 16S rRNA , was significantly higher in the MyD88-/- BMM when compared to WT-BMM at 24h p . i . ( Fig 5B ) . These data suggest that MyD88 signaling in macrophages is critical for inhibition of bacterial survival and/or replication . To examine the effect of autophagy on bacterial survival and replication in vivo , we treated IOE-infected WT mice with rapamycin during the early stage of infection ( day 1–3 p . i . ) and measured bacterial burden on day 3 p . i . Interestingly , rapamycin treatment significantly increased IOE bacterial burden in the liver compared to untreated infected mice ( Fig 5C ) . Consistent with the in vivo data , rapamycin treatment ( inducer of autophagy ) of IOE-infected WT-BMM resulted in a significant increase in the number of viable intracellular Ehrlichia as measured by 16S rRNA at 24h p . i . when compared to infected untreated WT-BMM or uninfected WT-BMM treated with or without rapamycin ( Fig 5C ) . Together , these results suggest that MyD88-mediated inhibition of autophagy induction is also a host-protective mechanism limiting survival and/or replication of IOE . Since lack of MyD88 enhances colocalization of LC3 and lysosomes , we asked the question why replicating intracellular Ehrlichia in MyD88-/- BMM are not effectively eliminated . To address this question , we analyzed the colocalization of Ehrlichia with LC3 and lysosomes in both WT and MyD88-/- BMM . Our data demonstrate that IOE did not colocalize with LC3 ( Fig 5D ) or lysosomes ( S5 Fig ) in WT or MyD88-/- macrophages . These data are consistent with recent studies showing defective co-localization of LC3 with a closely related Ehrlichia species , E . chaffeensis [37 , 55] , and this does not appear to be MyD88-dependent . Virulent IOE infection triggers upregulation of several TLRs in liver tissue of infected mice including TLR2 , TLR7 , and TLR9 [8] . To determine which TLR signal activates MyD88- mediated mTORC1 and inflammasome activation , we measured mTORC1 activation and production of IL-1β in BMM from TLR7-/- and TLR9-/- mice . We focused on endosomal TLRs as Ehrlichia are located intracellularly within endosomes . IOE infection of TLR7-/- BMM elicited IL-1β at slightly lower levels when compared to WT-BMM ( Fig 6A ) . On the other hand , lack of TLR9 signaling significantly attenuated the secretion of IL-1β following IOE infection in TLR9-/- BMM ( Fig 6A ) , and abrogated S6 phosphorylation ( Fig 6B ) . Reduced secretion of IL-1β in IOE-infected TLR9-/- BMM correlated with reduced expression of active caspase-1 and caspase-11 mRNA compared to infected WT-BMM ( Fig 6C ) . To directly investigate the role of TLR9 in Ehrlichia-induced liver injury and toxic shock , we infected WT and TLR9-/- mice with an ordinarily lethal dose of IOE . Interestingly , TLR9-/- mice were highly resistant to lethal ehrlichiosis as evidenced by 85% survival of mice ( n = 6 ) until day 60 p . i . , while 100% of WT mice succumbed to infection on days 10–12 p . i . ( Fig 6D ) . Protection of TLR9-/- mice against fatal toxic shock was associated with significant attenuation of liver injury as evidenced by decreased cell necrosis and fatty changes/steatosis , as well as enhanced cellular infiltration as measured by H&E staining ( Fig 6E ) . TUNEL staining also revealed decreased number of TUNEL positive apoptotic hepatocytes and Kupffer cells in IOE-infected TLR9-/- mice ( Fig 6E and 6F ) at day 7 p . i . , when compared to WT mice . Together , these data suggest TLR9 is a key upstream pattern recognition receptor ( PRR ) mediating MyD88-dependent activation of mTORC1 and inflammasome during Ehrlichia infection . We and others have shown that fatal IOE infection induces secretion of IFN-I cytokines , mainly by monocytes and plasmacytoid dendritic cells [8 , 24 , 32] . Binding of IFN-I cytokines to IFNAR induces multiple downstream signaling pathways that lead to diverse biological effects [56–59] . Our recent study showed IFNAR signaling induced caspase-11 activation , host cell death and detrimental inflammasome activation [24] . Since the host responses in IOE-infected MyD88-/- mice partially phenocopies the response in IOE-infected IFNAR-/- mice described in our previous study [24] , we hypothesized that MyD88 signaling may regulate IFN-I response in macrophages . To this end , we analyzed mRNA expression levels of interferon regulatory factor 7 ( IRF7 ) , which regulates transcription of IFN-I genes , as well as IFNβ in IOE-infected WT and MyD88-/- BMM . Consistent with previous in vivo data [8] , IOE infection enhanced expression of IRF7 and IFNβ in WT-BMM ( Fig 7A ) , but not in MyD88-/- BMM . This suggests that IFNβ production in macrophages is downstream of MyD88 signaling during fatal ehrlichial infection . To further determine whether inflammasome activation can be rescued in infected MyD88-/- BMM by addition of IFNβ , we treated BMM from WT and MyD88-/- mice with or without IFNβ followed by IOE infection , and measured levels of IL-1β . Stimulation of WT-BMM with LPS and ATP ( positive control for NLRP3 inflammasome ) induced a significantly higher production of IL-1β compared to unstimulated WT cells . Lack of MyD88 signaling partially decreased IL-1β production by both LPS treated and IOE-infected cells , suggesting that LPS/ATP and IOE induce inflammasome activation via MyD88-dependent and independent pathways ( Fig 7B ) . Notably , the addition of IFNβ to IOE-infected WT-BMM , but not MyD88-/- BMM , induced higher levels of IL-1β secretion when compared to uninfected and untreated/infected BMM controls ( Fig 7B ) . Failure of IFNβ to rescue IL-1β production in MyD88-/- BMM was not associated with significant difference in mRNA expression of IFNAR in these cells when compared to WT-BMM ( Fig 7C ) . Since MyD88 is critical for transcription and upregulation of pro-IL-1β and pro-caspases-1/11 as shown in Fig 2 , these data suggest that IFN I- mediated inflammasome activation during IOE infection requires priming by MyD88 . TLR9 signaling is known to be triggered by either bacterial DNA or mitochondrial ( mt ) DNA . Based on the above data showing that TLR9 mediates IOE-induced activation of mTORC1 and inflammasome in macrophages and is a key mediator of Ehrlichia-induced liver injury , we hypothesized that TLR9/MyD88 pathway and subsequent inflammasome activation in infected macrophages could be triggered by mt DNA that accumulates in infected cells as a result of inhibition of mitochondrial autophagy ( mitophagy ) . To examine this hypothesis , we first analyzed the mitochondrial membrane permeability , as an important parameter of mitochondrial function , in infected WT and MyD88-/- BMM using JC-1 dye and confocal microscopy . We stained WT and MyD88-/- BMM infected with IOE for 24h with JC-1 for 30min . Uninfected BMM from both mice groups were stained at the same time points as controls . JC-1 monomers aggregate on the mitochondrial surface , which shows as red fluorescence in healthy cells with high mitochondrial potential . On the other hand , JC-1 remains in the monomeric form in apoptotic or unhealthy cells with low mitochondrial potential , which shows as green fluorescence . Our data demonstrate a significantly higher ratio of fluorescent intensity of JC-1 monomers ( green fluorescence ) to fluorescent intensity of aggregates ( red fluorescence ) in IOE-infected WT-BMM , indicating a predominance of cells with low mitochondrial potential consistent with damaged mitochondria ( Fig 8A and 8B ) . In contrast , red-fluorescing , highly energized mitochondria were proportionally more prevalent in IOE infected MyD88-/- BMM , suggesting healthier cells with high mitochondrial potential ( Fig 8A and 8B ) . To further determine whether damaged mitochondria are eliminated , we analyzed colocalization of p62 with damaged mitochondria as evidence of enhanced mitophagy . To this end , cells were stained with an antibody against p62 and also with MitoTracker , a fluorescent dye that stains healthy and damaged mitochondria . Similar to immunoblotting data in ( Fig 3H ) , immunofluorescence staining of IOE-infected WT-BMM demonstrated higher p62 expression than MyD88-/- BMM . However , p62 failed to colocalize with MitoTracker in IOE-infected WT-BMM compared to p62-MitoTracker colocalization in LPS-treated WT-BMM ( Fig 8C ) . On the other hand , there was significantly higher double positive p62-Mitotracker staining in IOE-infected MyD88-/- BMM when compared to WT-BMM , suggesting a better colocalization of p62 with damaged mitochondria and enhanced mitophagy in MyD88-/- BMM ( Fig 8C and 8D ) . Additionally , electron microscopy analysis indicated that IOE-infected WT-BMM have few double-membrane autophagosomes and autolysosome , but many vacuoles resembling swollen lysosomes or damaged mitochondria ( Fig 8E ) . In contrast , IOE-infected MyD88-/- BMM had healthier mitochondrial morphology , and cells contained several autolysosomes containing mitochondria ( Fig 8E ) . To further examine whether damaged mitochondria that colocalize with p62 are effectively eliminated via the autophagy flux in MyD88-/- BMM , we analyzed colocalization of LC3/autophagosomes with mitochondria . Our data show that IOE-infected MyD88-/-BMM has increased LC3 colocalization with mitochondria , while IOE-infected WT-BMM has little or no colocalization of LC3 with mitochondria ( Fig 9A and 9B ) . These data suggest that MyD88-mediated inhibition of autophagy results in defective mitophagy and accumulation of mitochondrial DAMPs . Although the contribution of Ehrlichia-derived PAMPs to inflammasome activation needs to be investigated , these data suggest that mitochondrial DAMPs are potential ligands for TLR9/MyD88-mediated inflammasome activation during lethal infection with LPS-negative Ehrlichia .
Liver is the major target organ in human monocytic ehrlichiosis . HME patients develop severe liver injury marked by an initial elevation of liver transaminases followed by progressive hepato-splenomegaly , hepatic lobular lympho-histiocytic cholestasis , foamy and activated macrophages , and bile duct epithelial injury , which progress to liver failure [60] . Our study demonstrates , for the first time , a novel mechanism whereby virulent Ehrlichia ( IOE ) causes liver damage via the MyD88-mTORC1 pathway . Our data indicate that Ehrlichia activates the MyD88-mTORC1 pathway in macrophages to induce host-pathogenic inflammasome activation . Mechanistically , these events occur via MyD88-mediated inhibition of autophagic induction and flux as well as blocking mitophagy leading to accumulation of inflammasome activators including PAMPs and DAMPs . We have previously shown that severe liver injury in the fatal murine model of ehrlichiosis is caused by IL-18 and IL-1β-mediated expansion of pathogenic cytotoxic CD8+ T cells and neutrophils [26] . These cells not only induce death of infected cells , but also induce death of CD4+ Th1 cells that are important for protective immunity against Ehrlichia via mechanisms that involve TNF/TNFR and FAS signals . In this study , lack of MyD88 decreased the frequency of activated CD8+ T cells and increased the number of CD4+ T cells , which could also account for the attenuated liver immunopathology at late stages of infection . Autophagy is a critical innate immune host defense mechanism against several facultative intracellular bacterial pathogens such as Legionella and Salmonella [61–64] . However , the role of autophagy in protective innate immunity against infection with obligate intracellular virulent bacteria such as Ehrlichia is not clearly defined . Our study suggests that autophagy promotes survival and/or replication of Ehrlichia , and that MyD88 signaling impairs bacterial replication by inhibiting autophagy induction . This conclusion is supported by the finding that enhanced autophagy induction in vivo in IOE-infected MyD88-/- mice and rapamycin-treated/infected WT mice as well as its corresponding in vitro validation in macrophages promoted bacterial survival and replication ( Fig 1 and Fig 5 ) . A recent study by Lin et al . [37] , indicated that autophagy provides amino acids to Ehrlichia that enabled their survival and replication and that blocking autophagy induction using 3-MA treatment or knockdown of Atg5 or beclin-1 attenuates bacterial growth [37 , 38] . Thus , the high bacterial burden in MyD88-/- mice and macrophages , as major target cell for Ehrlichia , is likely due to ability of IOE to hijack nutrients via autophagy proteins . Although MyD88 deficiency enhanced autophagosome-lysosome fusion in macrophages , the lack of colocalization of IOE organisms with the LC3II/autophagsomes and lysosomes suggest that the replicating bacteria in MyD88-/- cells cannot effectively be eliminated . Several studies in mice and humans reported the negative regulation of inflammasome by autophagy . Genetically or pharmacologically defective autophagy in monocytes leads to accumulation of damaged mitochondria and increased concentration of mitochondrial DAMPs such as reactive oxygen species ( ROS ) and oxidized mitochondrial DNA ( mtDNA ) . Robust generation of mt ROS or mtDNA and their release into cytosol causes activation of cytosolic NLRP3 inflammasome complex , which in turn cleaves pro-caspase-1 or pro-caspase-11 and leads to cleavage of mature IL-1β and IL-18 . Our study highlights a novel positive role of MyD88 in regulation of inflammasome activation via two pathways . First , MyD88 signaling induces NF-κB activation and subsequent expression of pro-IL-1β and other inflammasome components , which provides “signal 1” for inflammasome activation . This conclusion is supported by our data showing reduced production of NF-κB-dependent TNF in MyD88-/- cells as well as decreased mRNA and protein levels of NLRP3/NLRC4 and pro-caspase-1/11 ( Fig 2 ) . Second , MyD88-mediated inhibition of autophagy induction and flux leads to defective mitophagy and accumulation of damaged mitochondria , which in turn results in release of mtDNA or other mitochondrial DAMPs ( e . g . , ROS or oxidized cardiolipin ) . These mt DAMPS act as “signal 2” triggering inflammasome activation . This conclusion is supported by TEM analysis showing MyD88-dependent accumulation of swollen damaged mitochondria ( Fig 8E ) , which we further confirmed by confocal microscopy analysis showing lack of colocalization between p62 and damaged mitochondria ( Fig 8C and 8D ) as well as lack of colocalization between LC3 and mitochondria ( Fig 9A and 9B ) in WT-BMM , but not in MyD88-/- BMM . This clearly demonstrates that IOE induces mitochondrial damage and block mitophagy in macrophages via MyD88 signaling . These data are consistent with recent studies showing that infection of macrophages with E . chaffeensis , a human pathogen closely related to IOE , inhibits mitochondrial metabolism [54 , 65] . The mechanism that account for enhanced mitophagy in MyD88-/- , but not in WT , macrophages remains elusive . One mechanism by which mitophagy is induced is through Parkin/PINK1 where PINK1 binds selectively to damaged mitochondria and oxidized cardiolipin , which leads to the recruitment of Parkin ( E3 ubiquitin ligase ) to mitochondria and ubiquitination of mitochondrial substrates . This process is followed by recruitment of the ubiquitin-binding adaptor p62 , which deliver the parkin-ubiquitylated cargo into autophagosomes for degradation by binding to LC3 . Future studies will examine whether a defect in the PINK1-Parkin-Cardiolipin-p62 pathway is responsible for defective mitophagy in WT macrophages following infection with virulent Ehrlichia . This study strongly indicates that MyD88 is a key regulator of inflammasome activation . However , the data showing partial decrease in serum levels of IL-1β and IL-1α in IOE-infected MyD88-/- mice ( Fig 2 ) , suggest a potential role of MyD88-independent pathways such as TIR-domain-containing adaptor-inducing interferon-β ( TRIF ) and type-I IFN receptor signaling pathways in IOE-induced inflammasome activation . In support of MyD88-independent pathway ( s ) , we found that infected TLR9-/- mice are completely protected against lethal ehrlichiosis compared to partial protection of infected MyD88-/- mice ( Fig 6 ) . Lack of TLR9 signaling in macrophages also completely abrogated canonical and non-canonical inflammasome activation . These data suggest that TLR9 is the major PRR causing inflammasome activation , subsequent liver damage , and fatal toxic shock following lethal Ehrlichia infection . TLR9 signaling is known to trigger two downstream pathways . The first pathway leads to transcriptional activation of NF-κB-dependent proinflammatory cytokines , and the second pathway leads to the activation of IFN-I genes through phosphorylation of IRF7[66 , 67] . Although both pathways are MyD88-dependent , the IFN-I pathway and responses also requires additional signaling[59] . We and others have previously showed that IFNAR signaling is pivotal not only for inflammasome activation and secretion of IL-1β , but also for development of IOE-induced toxic shock , via mechanisms that involve caspase-11 activation [23 , 24 , 32] . In the current study , we show that IFNβ promotes IL-1β secretion by IOE-infected WT macrophages compared to untreated/ infected cells ( Fig 7B ) . Together , our data indicate that the heightened resistance of TLR9-/- mice to fatal Ehrlichia infection compared to MyD88-/-mice could be due to abrogation of MyD88 and IFN-I pathways; both of which clearly play synergistic pathogenic roles in induction of inflammasome activation and development of Ehrlichia-induced liver injury and toxic shock . Our data demonstrate that MyD88 deficiency partially attenuates mRNA expression of IRF7 and IFNβ ( Fig 7A ) , suggesting that MyD88 could mediate , in part , inflammasome activation by promoting IFN-I response . However , stimulation of IOE-infected MyD88-/- BMM with IFNβ did not restore IL-1β secretion . We have examined IFNAR expression , as a possible mechanism that account for failure to restore IL-1β in IOE-infected MyD88-/- BMM . We did not detect significant difference in IFNAR expression , at mRNA level , between IOE-infected WT and MyD88-/- cells ( Fig 7C ) . Although we have not examined whether MyD88 signaling influenced the IFNAR expression at the protein level , we believe this is unlikely scenario based on other studies showing that MyD88 is dispensable for upregulation of IFNAR expression and the induction of interferon stimulated genes . Indeed , IFN-I response is known to be promoted via number of complementary and/or redundant pathways during viral infections in mice and humans [59 , 68 , 69] . Based on these data , we conclude that failure to induce IL-1β in IOE-infected MyD88-/- BMM is not due to attenuated expression of IFNAR , but possibly due to defective transcriptional upregulation of pro-IL-1β and pro-caspase-1/11 ( signal 1 ) as described above . Data from our previously published studies [8 , 26] suggest that multiple inflammasome complexes including NLRP3 , AIM2 , and NLRC4 are upregulated during infection with virulent Ehrlichia and may play a role in the development of Ehrlichia-induced liver injury . The current study does not support a role for AIM2 in Ehrlichia-induced liver injury . Similarly , although expression of NLRC4 was downregulated in infected MyD88-/- compared with WT mice , NLRC4 is less likely to be a critical inflammasome complex in our model since Ehrlichia does not posses flagella or type III secretion system effectors , which are known PAMPs for NLRC4 activation . Further studies are required to directly examine the contribution of NLRC4 or other inflammasome complexes in our model . Nevertheless , we believe that NLRP3 is one of the major inflammasome complex that contributes to the pathogenesis of Ehrlichia-induced liver injury and toxic shock . Our data demonstrating MyD88-dependent block of mitochondrial autophagy ( mitophagy ) suggest that oxidized mitochondrial DNA ( mtDNA ) or , ROS are potential DAMPs that induce activation of NLRP3 inflammasome . mtDNA can also provide positive feedback amplification of inflammasome pathways by triggering TLR9 , NLRs , and cytosolic DNA sensors that induce type I IFN . This does not exclude the possibility that ehrlichial ligands ( PAMPs ) such as; bacterial DNA or type I and IV secreted effectors [70 , 71] , induces activation of the TLR9/MyD88 and inflammasome pathways , respectively . In conclusion , based on our results , we propose a novel mechanism that explains the immunopathology and suppression of protective immunity during Ehrlichia-induced toxic shock ( Fig 10 ) . Sensing of vacuolar ehrlichial ligands ( e . g . bacterial DNA ) by TLR9 can trigger MyD88 signaling , which in turn causes activation of mTORC1 and suppression of autophagy . As Ehrlichia exploit autophagosomes to obtain nutrients and survive , MyD88-mediated mTORC1 activation and subsequent inhibition of autophagy induction is , therefore , a host-protective mechanism . On the other hand , MyD88-mediated partial block of autophagy flux/mitophagy contributes to excessive inflammation and immunopathology via activation of NF-κB and inflammasome ( s ) . Importantly , TLR9/MyD88 signaling contributes to IFNβ/IFNαR- mediated non-canonical , caspase-11-dependent inflammasome activation and inflammatory host cell death . Cross presentation of infected apoptotic cells by dendritic cells and other antigen presenting cells ( APCs ) , together with excessive inflammatory environment elicit activation of CD8+ T cells [72] , which in turn cause further host cell death as suggested by our previous studies[3 , 6 , 23 , 25] . In addition , IOE-induced inhibition of autophagy via MyD88 could impair MHC-class II mediated antigen presentation resulting in decreased expansion of Ag-specific CD4+ Th1 cells . Decreased magnitude of protective CD4+ Th1 response would result in uncontrolled bacterial replication that further induces excessive inflammation through a positive feedback loop . In-depth understanding of the intricate host-microbial interactions during infection with these obligate intracellular bacterial human pathogens will provide new insights for the development of effective therapeutics , diagnostics , as well as preventive strategies against fatal Ehrlichia-induced toxic shock .
This study was conducted according to the principles species in the Declaration of Helsinki and under ethical Guidelines ( University of Pittsburgh Institutional Review Board ) . All animal studies and procedures were carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institute of Health’s office of Laboratory Animal Welfare; the Assurance Number is A3187-01 . The Division of Laboratory Animal Resources at University of Pittsburgh is accredited by the American Association for the Assessment and Accreditation of Lab Animal Care ( AAALAC ) . The protocol ( IACUC protocol number: 14125020 ) was approved by the Institutional Animal Care and Use Committees , University of Pittsburgh . Bone marrow was isolated from naïve , WT , MyD88-/- , TLR7-/- and TLR9-/- mice and prepared as described previously [73 , 74] . Briefly , femurs were excised and flushed under aseptic conditions . Bone marrow cells were seeded in 100 mm petri dishes at 1x106 cells/10ml/dish in DMEM/F12-GlutaMAX ( Invitrogen ) supplemented with 10% FBS ( Invitrogen ) , 20ng/ml MCSF ( PeproTech ) , 10 mM Hepes ( Invitrogen ) , and 10 mM glutamine ( Invitrogen ) . Approximately 4 ml of fresh media was added after 3 days of culture . On day 6 , cells were collected and the BMM were characterized by flow cytometry and stained with fluorophore conjugated antibodies specific to CD11b , CD11c , and F4/80 . The number of CD11c-CD11b+F4/80+ BMM isolated by this method was ~ 95% . BMM collected on day 6 were seeded into 12 well plates for 20 h prior to infection at a density of 106 cells/well in DMEM/F12 ( Invitrogen ) supplemented with 5% FBS , 20 ng/ml MCSF , 10 mM HEPES . Cell-free IOE organisms were prepared from IOE-infected splenocytes as previously described [3] . IOE organisms were added to the BMM cultures at MOI of 5 . Control cells were cultured with mock control Ag ( antigens prepared from uninfected splenocyte ) . To confirm that host cell death did not account for differences between experimental groups , we measured cell viability at all time points ( 4 , 8 , 12 , 24 , and 48 h p . i . ) using trypan blue staining . For analysis of autophagy flux , bone marrow macrophages ( 5X 106 cells/ml ) were treated with autophagy inhibitor , bafilomycin A1 ( 100nM , InvivoGen ) 4h before the termination of IOE infection at 24h . For blocking internalization of ehrlichiae , cells were pre-treated for 1h with a cell-permeable inhibitor of dynamin , Dynasore ( 30μM , Abcam ) . For blocking mTORC1 activation , cells were treated with mTORC1 inhibitor , rapamycin ( 10μM , InvivoGen ) . For stimulation of cells with type I IFN cytokines , IFNβ ( Millipore , 500 IU/ml ) was added to the cells along with IOE infection . For LPS/ATP positive controls , cells were treated with 200 ng/ml LPS for 18-24h followed by 30 min stimulation with 5mM ATP . For inhibition of caspase-1 and caspase-11 activation , cells were treated with caspase-1 ( Ac-YVAD-cmk , in vivoGene ) and caspase 11 ( 40 μM- Wedelolactone; CAS- 524-12-9 , Santa Cruz ) inhibitors for 2h . Both caspase-1 and caspase-11 inhibitors are used according to manufacturer's recommendation , and were shown to significantly inhibit caspase- 1 and caspase-11 expression in cultured cells , respectively . However , these inhibitors may also non-specifically inhibit other caspases or apoptosis-related proteins . Infected culture without the inhibitors and uninfected cells were used as positive and negative controls for all the above experimental conditions , respectively . For detection of intracellular bacteria , cells were collected at 12h and 24h post-infection , and washed two times with PBS to remove the extracellular bacteria . The number of ehrlichiae was determined by qPCR or RT-PCR analysis of the Ehrlichia dsb gene or 16S rRNA , respectively , as described below . Supernatants were collected and stored at −80°C for cytokine analysis at a later time . Splenocytes from naïve or infected mice were stimulated in vitro with IOE antigens for 12h to determine the frequency of antigen-specific T cells . In vitro stimulated spleen cells were re-suspended in fluorescence-activated cell sorter-staining buffer at a concentration of 106 cells/well . FcRs were blocked with a mAb ( clone 2 . 4G2 ) against mouse cell surface antigens CD16 and CD32 for 15 min . The following fluorescence-conjugated Abs were used ( all antibodies were purchased from either BD Biosciences or Biolegend ) : anti-CD3 ( clone 145- 2C11 ) , anti-CD4 ( clone RM4-4 ) , anti-CD8a ( clone 53–6 . 7 ) , anti-CD69 ( clone RA3-6B2 ) , anti-IL-10 ( clone JES5-16E3 ) , anti–TNF-α ( clone MP6-XT22 ) , anti–IFN-γ ( clone XMG102 ) . Isotype control mAbs , including FITC- , PE- , Percep , and allophycocyanin-conjugated hamster IgG1 ( A19-3 ) , rat IgG1 ( R3-34 ) , rat IgG2α ( R35-95 ) , mouse IgG2α ( X39 ) , mouse IgG2b ( MPC-11 ) , mouse IgG1 ( X40 ) , and rat IgG2b ( A95-1 ) . For intracellular cytokine staining , the splenocytes were stimulated with IOE antigen , with addition of BD Golgi Plug . Following blocking and primary antibody staining , cells were permeabilized with CytoFix-CytoPerm kit ( BD Biosciences ) for detection of IFN-γ and IL-10 . Lymphocyte and granulocyte populations were gated based on forward and side-scatter parameters . Approximately 50 , 000 events were collected for the spleen cells and 100 , 000 for the LMNCs using the BD-LSR II ( BD Immunocytometry Systems , San Jose , CA ) flow cytometry , and the data were analyzed using FlowJo software ( TreeStar , Ashland , OR ) . Liver tissues and BMM were lysed in T-PER lysis buffer or RIPA buffer ( Thermo Fisher Scientific , Waltham , MA ) respectively , supplemented with protease inhibitors and 1 mM phenylmethylsulphonyl fluoride ( PMSF ) . Protein extraction was performed at 4°C for 30 min and the protein content of the lysates was measured using a Bicinchoninic Acid Assay Kit ( Pierce ) . Lysates ( 15–40 ug ) were resolved in 4% to 20% gradient SDS–PAGE under reducing conditions . After electrophoresis , proteins were transferred onto PVDF membranes ( BioRad ) , and blocked for 1 h in Tris-buffered saline ( TBS ) containing 5% non-fat milk and 0 . 1% Tween 20 . Blots were probed with the appropriate primary antibodies and peroxidase-conjugated bovine anti-rabbit secondary antibodies ( 1:10000 ) ( Santa Cruz Biotechnology ) . The membranes were processed and probed with the following antibodies , according to standard protocols: anti–caspase-1 ( 1:100 ) ( EMD Millipore , Billerica , MA ) , anti–caspase-11 ( 1:100 ) and anti-LC3B ( Sigma-Aldrich , St . Louis , MO ) and anti-IL-1β ( 1:100 , GeneTex ) . The following antibodies were from Cell Signaling Technology , Inc . , and used at 1:1000 dilution; anti-beclin 1 , anti-p62 ( SQSTMI ) , anti-phospho 4E-BP1 , anti-AkT , anti-phosphoAkT , anti-S6 , anti-phospho S6 , anti-Atg5 . Specific signals were developed using the ECL-Plus system ( GE ) . Blots were stripped with Restore Western Blot Stripping Buffer ( Pierce ) and re-probed with anti-GAPDH ( Sigma , 1:5000 ) or anti-beta actin ( Abcam; 1:2500 ) as loading controls . The density of bands in Western blots was determined using ImageJ software version 1 . 48 ( NIH , Bethesda , MD ) . The ratio of LC3II:I was determined by normalization of the LC3II and LC3I to GAPDH , and then presented the normalized LC3II:LC3I ratio . Staining of LC3 puncta , p62 aggregates , acidified lysosomes , and mitochondria ( healthy and damaged ) and quantification by confocal microscopy were performed as previously described [35 , 75] . Briefly , BMM cultured on cover slips were infected with IOE at MOI of 5 . Negative and positive controls included are uninfected cells ( stimulated with mock Ags ) or LPS stimulated cells , respectively . Cells were then washed 3X with PBS , fixed with 2% paraformaldehyde for 20 min , and permeabilized with 0 . 1% Triton X-100 in PBS for 30 min . After blocking with 5% BSA ( Sigma-Aldrich , A2153 ) for 60 min , the primary antibodies; anti-LC3 ( Sigma , 50 ug/mL ) , anti-p62 ( Cell Signaling , 1:100 ) were added for 1 h at room temperature . Cells were washed and then incubated with fluorescent labeled anti-rabbit secondary antibody DyLight ( VectaFluor , 1:500 ) for 1 h . Nuclei were stained with DAPI and cells were analyzed by confocal microscopy ( Olympus Flouview 1000 ) . For localization of IOE with LC3 , infected cells and uninfected controls were incubated with polyclonal rabbit anti-Ehrlichia chaffeensis antibody ( added at 1: 1000 dilution ) that cross-react with IOE organisms as we described before [3 , 6] for 2h followed by incubation with fluorescent labeled anti-rabbit secondary antibodies . Analysis of mitochondria ( healthy and damaged ) or acidified lysosome was performed using Mitotracker Red ( #M-7512 , Life Technologies ) or LysoTracker Red ( #L-12492 , Thermofisher ) , respectively , at 37°C for 1 h and assessed with a confocal microscope ( Olympus flouview 1000 ) . Analysis of LC3 puncta or colocalization was performed using the NIH Image J software package and analyzing 40–50 cells per group from 3 independent experiments . The LC3 puncta was identified as highly fluorescent green aggregates . Colocalization analysis was performed with a Olympus fluorescent microscope equipped with a CCD camera and magnifier software , which allows capturing images digitally . The colocalization images is analyzed using PCI software from a Olympus fluorescent microscope by counting number of yellow aggregate per cell , with 30–45 cells per experiment and data collected from 3 independent experiments . Results were presented as average LC3 puncta per cell , percentage of cells with more than 5 LC3 puncta/field , or the number of colocalized puncta/cell . At least 10 fields of view for each sample were quantified for each experimental group across at least three independent experiments Data were analyzed as average ± standard deviation . Mitochondrial membrane potential was assayed using JC-1 ( Biotium , Fremont , CA ) kit according to the manufacturer’s protocol . Briefly 100μl of the JC-1 staining solution was added per ml culture medium of coverslip cultured naïve and IOE infected WT and MyD88-/- BMM ( 24h p . i . ) , and incubated for 30 minutes at 37°C . Photographic images were taken using an Olympus confocal microscope and quantification performed using Image J as described above . Bone marrow derived macrophages from WT or MyD88-/- mice were infected with IOE at MOI of 5 , in the presence or absence of rapamycin or Dynasore for 24h . After infection and treatment , cells were fixed with 2 . 5% glutaraldehyde 0 . 1 M phosphate buffer ( pH 7 . 4 ) , and post-fix monolayer in 1% osmium tetroxide with 1% potassium ferricyanide , and dehydrated with a graded series of alcohol . Invert beam capsules of resin over relevant areas of monolayers and polymerize . Ultrathin sections were processed by transmission electron microscopy ( JEM-1011 Transmission Electron Microscope , JEOL Ltd . ) For quantitative evaluation of autophagosomes and mitochondria in BMM , 10 image fields ( 10 , 000 X ) were selected for each sample . Total DNA was isolated from liver tissues ( in vivo ) or bone marrow macrophages ( in vitro ) using the DNeasy Blood and Tissue kit ( QIAGEN ) . Bacterial burden was determined by quantitative Real-Time PCR , using an ABI 7500 FAST System ( Applied Biosystems , USA ) and using specific primer sets ( S1 Table ) amplifying the IOE dsb gene as previously described [76] . The eukaryotic housekeeping gene gapdh was amplified using the GAPDH primers ( S1 Table ) . The absolute IOE dsb copy number was determined using a standard curve and was normalized to qPCR-detected levels of the gapdh in the same sample and expressed as copy number per 104 copies of gapdh . The number of intracellular Ehrlichia within BMM was measured at early stage of infection by immunofluorescence microscopy since qPCR cannot distinguish between live and dead bacteria . In certain experiment , the Ehrlichia copy number determined by dsb was correlated with the copy number based on detection of Ehrlichia 16S rRNA in the samples , which measures viable bacteria as described before [76–78] . Briefly , mRNA was isolated from the samples and quantitative RT-PCR was performed as described below . RNA from the liver tissues or bone marrow macrophages was extracted using TRIzol Reagent ( Invitrogen Life Technologies , Carlsbad , California , USA ) . RNA ( 2 μg ) was reverse transcribed in a final volume of 20 μl using RT2 first strand kit ( Qiagen , USA ) , as specified by the manufacturer , and then subjected to PCR using specific primer sets ( S1 Table ) . qRT-PCR was conducted using an ABI 7500 FAST System ( Applied Biosystems , USA ) . cDNA ( 100 ng ) was subjected to qRT-PCR in a 25 μl reaction volume using SYBR-Premix ( Qiagen ) . The GAPDH gene was amplified for normalization of the cDNA amount used in qRT-PCR . Reactions were carried out in triplicate , and the data were analyzed using the 2−ΔΔCt method . Liver samples were fixed in a 10% solution of neutral buffered formalin , dehydrated in graded alcohols , embedded in paraffin wax , and stained with hematoxylin and eosin ( H&E ) . Histological sections were evaluated qualitatively for morphological differences and quantitatively for apoptosis ( by TUNEL immunohistochemical assays ) . TUNEL staining ( Research Histology Services , University of Pittsburgh ) was performed on the formalin-fixed , paraffin-embedded tissue sections . Apoptotic Kupffer cells and hepatocytes were counted in 10 high-power fields ( HPFs ) for each mouse . Stained slides were viewed under an Olympus ( Tokyo , Japan ) BX40 microscope and were scanned using a Mirax MIDI slide scanner ( Carl Zeiss Microscopy , Jena , Germany ) ; images were captured using Pannoramic Viewer software ( 3DHistech , Budapest , Hungary ) . Cell culture supernatants and cell pellets from infected or uninfected bone marrow-derived macrophages ( BMM ) isolated from WT mice were collected at 24h p . i . and assayed for LDH activity using the CytoToxo96 LDH-release kit ( Promega ) following the manufacturer’s recommendations . The levels of IL-1α , IL-1β , IL-10 , and TNF-α in serum of infected mice or produced by the WT and MyD88-/- BMM , IOE infected and uninfected culture supernatants were determined by commercially available enzyme-linked immunosorbent assay ( ELISA ( eBioscience , San Diego , CA; Vienna , Austria ) ) kits according to the manufacturer’s instructions . All of the data presented are representative of at least three independent experiments that yielded similar results . Two group analyses was performed using an unpaired two-tailed t-test . For comparison of multiple experimental groups , we used one-way analysis of variance ( ANOVA ) with Bonferroni’s procedure . To determine whether the difference in survival between different mice groups was significant , data were analyzed by the Breslow-Wilcoxon Test . All statistical analyses were performed using Graph Pad Prism ( GraphPad Software Inc . , La Jolla , CA , USA ) . Data are represented by means and standard deviations ( SD ) . Differences with P values of <0 . 05 , <0 . 01 , and <0 . 001 were considered slightly ( * ) , moderately ( ** ) , and highly ( *** ) significant , respectively .
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Human monocytic ehrlichiosis ( HME ) is the most prevalent emerging infectious disease in the United States . Ehrlichia chaffeensis , etiologic agent of HME , is a Gram negative obligate intracellular bacterium transmitted by infected tick bites and can infect different cell type . Although Ehrlichia lack lipopolysaccharide ( LPS ) , they induce potentially life threatening HME that mimic sepsis or toxic shock associated with multi-organ failure . The clinical diagnosis of HME is difficult , and definitive diagnosis is most often retrospective . Late antibiotic treatment is frequently ineffective in preventing disease progression to fatal multi-organ failure . Liver failure is one of the most serious complications and the most frequent cause of death in patients with HME , however we only have a limited understanding of how this liver failure is caused during fatal Ehrlichia infection . The objective of this study is to determine how LPS-negative Ehrlichia activates inflammatory responses in macrophages during Ehrlichia infection to promote liver damage . We show here that MyD88-signaling causes detrimental derangement of the immune system and subsequent liver damage by regulating two key innate immune events in macrophages: autophagy and inflammasome activation . Targeting host-pathogenic pathways in ehrlichiosis can be incorporated into future design of novel therapeutic approaches for HME .
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2017
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MyD88-dependent inflammasome activation and autophagy inhibition contributes to Ehrlichia-induced liver injury and toxic shock
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Alternans of cardiac action potential duration ( APD ) is a well-known arrhythmogenic mechanism which results from dynamical instabilities . The propensity to alternans is classically investigated by examining APD restitution and by deriving APD restitution slopes as predictive markers . However , experiments have shown that such markers are not always accurate for the prediction of alternans . Using a mathematical ventricular cell model known to exhibit unstable dynamics of both membrane potential and Ca2+ cycling , we demonstrate that an accurate marker can be obtained by pacing at cycle lengths ( CLs ) varying randomly around a basic CL ( BCL ) and by evaluating the transfer function between the time series of CLs and APDs using an autoregressive-moving-average ( ARMA ) model . The first pole of this transfer function corresponds to the eigenvalue ( λalt ) of the dominant eigenmode of the cardiac system , which predicts that alternans occurs when λalt≤−1 . For different BCLs , control values of λalt were obtained using eigenmode analysis and compared to the first pole of the transfer function estimated using ARMA model fitting in simulations of random pacing protocols . In all versions of the cell model , this pole provided an accurate estimation of λalt . Furthermore , during slow ramp decreases of BCL or simulated drug application , this approach predicted the onset of alternans by extrapolating the time course of the estimated λalt . In conclusion , stochastic pacing and ARMA model identification represents a novel approach to predict alternans without making any assumptions about its ionic mechanisms . It should therefore be applicable experimentally for any type of myocardial cell .
In cardiac physiology , alternans designates the alternation of action potential ( AP ) parameters ( e . g . , AP duration ( APD ) , calcium transient ) from beat to beat [1] , [2] . It leads to dispersion of refractoriness and represents a well established mechanism of conduction block and thus of severe reentrant arrhythmias [3] , [4] . At the cellular level , alternans results from complex dynamic interactions between membrane potential ( Vm ) , ion currents and intracellular calcium cycling , which can together lead to different types of dynamical instabilities [2] , [5]–[8] . The classical understanding of the genesis of alternans is based on the concepts of restitution functions and iterated map models [5] , [9]–[11] . In the classical theory [5] , [9] , the relation between APD and the previous DI is first characterized by the APD restitution function f as APD = f ( DI ) . During pacing at a given basic cycle length ( BCL ) , the next DI can then be inferred as BCL–APD and the next APD mapped using f . By iteration , successive APDs and DIs can be reconstructed . The steady state is determined by the intersection of the line APD = BCL – DI with f . If the slope of the restitution function α = df/dDI is <1 at this point , the system is stable , and if α>1 , the system is unstable . Because of the nonlinear nature of f , this instability results in alternans , period doubling cascades and chaos via a variety of dynamical routes [12]–[14] . However , it has been shown that the criterion α = 1 for the onset of alternans is only approximate or even inappropriate . Indeed , alternans can be present even if α<1 , or conversely , APD may not alternate although α>1 [15]–[18] . These discrepancies can be explained by the notion of “memory” [5] , [16] , [19]–[23] , reflecting the fact that APD depends not only on the previous DI , but on several previous DIs and APDs . In multicellular tissue , these discrepancies can also be explained by the fact that electrotonic interactions and a steep conduction velocity restitution relation can further affect the APD restitution slope at which alternans occurs by exerting important stabilizing or destabilizing effects [15] , [16] , [24] . In experiments , restitution is conventionally investigated by pacing at steady-state and by introducing premature or delayed stimuli ( S1S2 protocol , S1S2 restitution curves ) , or by decreasing BCL stepwise and examining steady-state APD vs . DI at the end of each step ( dynamic restitution curves ) . Results at odds with the classical theory have then motivated researchers to develop refined pacing protocols and analyses incorporating the notion of memory to investigate alternans , such as the “perturbed downsweep protocol” [17] , [20] , [21] , [25] . To untangle the effects of memory , recent studies used pacing protocols in which the DI is varied slowly in a sinusoidal manner , revealing hysteresis between APD and DI [26] , or in a random manner , minimizing the influence of previous APDs and DIs on the current pacing cycle [22] . With this latter protocol , it was also demonstrated using regression analysis that APD depends on the APDs and DIs during several previous cycles [27] . In mathematical cardiac cell models , seminal insights have been obtained using eigenmode analysis [28] , [29] , in which one considers the deviation of the time course of model parameters from their steady-state periodic time course during pacing at a given BCL . This deviation is then decomposed as a sum of eigenmodes associated to corresponding eigenvalues ( λ ) . For every eigenmode , the time course of model variables is scaled by the corresponding λ after each pacing cycle . When |λ|<1 , the eigenmode is attenuated from beat to beat and eventually dissipates . In contrast , the eigenmode is amplified when |λ|>1 , which implies instability . When λ is a real number , the sign of λ associates the corresponding eigenmode with either memory ( λ>0 , same polarity for every beat ) or alternans ( λ<0 , polarity changes every beat ) . Thus , at least in single cell models , eigenmode analysis formally defines the exact criterion for the onset of alternans as λalt = min{Re ( λ ) } = −1 . However , eigenmode analysis requires accessing the internal model variables and , therefore , it is not feasible experimentally . Thus , applications of eigenmode analysis have so far been limited to computer simulation studies [30] , [31] and no straightforward approach has been designed on this basis to predict alternans in an experimental setting . In previous work [24] , we introduced the concept of cardiac tissue as a “filter” transforming an input ( e . g . , a series of pacing intervals varying stochastically ) into an output ( e . g . , the series of APDs or DIs ) . We examined the filter characteristics in the frequency domain in terms of gain and phase shift using the transfer functions between the series of pacing intervals and the series of APDs ( Ht→a ) and between the series of pacing intervals and the series of DIs ( Ht→d ) , respectively . In the present study , we developed a generalized framework for a straightforward and accurate prediction of alternans . We devised an approach permitting to quantify the eigenvalue λalt and these transfer functions by using only experimentally measurable quantities ( APD , DI ) without the requirement to access internal model variables . The first step of this approach consists of using pacing intervals varying stochastically around a mean BCL . In the next step , the poles ( including λalt ) and zeros of the transfer functions Ht→a and Ht→d are identified by fitting an autoregressive-moving-average ( ARMA ) model to the recorded values of APD and DI [32] . The power of this approach was evaluated in the cardiac cell model of Sato et al . [8] . This model is formulated in three versions ( based on different sets of model parameters ) implementing the following mechanisms of alternans: 1 ) Vm-driven ( alternans attributable to the gating kinetics of membrane ion channels , with a steep APD restitution curve ) , 2 ) Ca2+-driven with positive Ca2+ to APD coupling ( large Ca2+ transients generating longer APDs ) , and 3 ) Ca2+-driven with negative Ca2+ to APD coupling ( large Ca2+ transients generating shorter APDs ) . In the latter two versions , alternans originates from an instability of Ca2+ cycling and occurs even in the presence of shallow APD restitution curves , thus reproducing recent experimental and theoretical findings [6] , [7] , [33] . The three versions of the Sato et al . model thus offered the advantage to test our approach for three fundamentally different ionic mechanisms of alternans . For this purpose , we compared the marker λalt obtained using ARMA model identification during stochastic pacing with the exact control value of λalt derived using eigenmode analysis . Our results provide the proof of principle that the eigenvalue λalt can be estimated from the time series of pacing cycle lengths , APDs and DIs , and thus that the criterion λalt = −1 could be utilizable experimentally .
We used the model of Sato et al . [8] , who combined the Fox et al . canine ventricular myocyte model [34] with the model of intracellular cycling proposed by Shiferaw et al . [33] . As mentioned above , this model is formulated in three versions implementing different mechanisms of alternans , including alternans originating from an instability of Ca2+ cycling . The three different versions of the model were stimulated with 1-ms current pulses of 50 µA/µF as in the original study of Sato et al . [8] , which corresponded approx . to 1 . 23 times diastolic threshold . Simulations were run with a constant time step of 0 . 005 ms . Gating variables were integrated using the method of Rush and Larsen and other model variables were integrated using the forward Euler method . Activation time was defined at depolarization to −35 mV and repolarization time at −85 mV , respectively . APD was defined as the interval between activation and repolarization times and corresponded approximately to APD at 92% of repolarization . Intervals between successive activations were equivalent to pacing intervals due to the short and quasi constant latency in response to stimulation . In all ionic cardiac cell models , including the Sato et al . model , the state of the cell at any time t is fully described by a vector v of N linearly independent variables ( N = 16 in the Sato et al . model ) , and the temporal evolution of the model is described by dv/dt , which is defined as a function of v . N defines the order of the cell model . At a given basic cycle length ( BCL ) , there exists a unique function that maps vi ( v at the onset of the ith stimulus ) to vi+1 ( at the onset of the i+1th stimulus ) . At steady state , v maps onto itself , defining the steady state vector vBCL . As shown by Li and Otani [28] , the mapping function can be linearized near steady state as ( 1 ) where δv = v−vBCL is a small perturbation of vBCL and J is the Jacobian matrix of this mapping . The element of J in column c and row r is defined as ( 2 ) where vi+1 , r is the rth element of vi+1 and vi , c is the cth element of vi . In our simulations , J was computed by introducing a small perturbation δ of the corresponding element of vBCL , applying the modified v as initial condition and by evaluating ∂v near the limit δ→0 after running the model for one BCL . As shown previously [28] , the response at BCL is stable if the eigenvalues λ of J all lie within the unit circle in the complex plane ( <1 in absolute value ) , and the onset of alternans coincides exactly with one of the eigenvalues ( λalt ) being equal to −1 . For each BCL tested ( in decremental steps of 5 ms from 1000 ms to 500 ms , and then in steps of 1 ms ) , the 3 versions of the Sato et al . model were paced at this BCL until a steady state 1∶1 response was obtained or until sustained alternans was documented . Because our goal was to be as close as possible ( within a reasonable computational limit ) to the true steady state when considering all model variables , steady state was considered to be attained when the relative beat to beat variation of all model variables was <10−7 . Steady state defined according to this criterion was obtained after 150–1000 beats . In presence of a stable 1∶1 response , the following protocols and analyses were conducted: The results of these three analyses were compared and evaluated in terms of the ability of SS1S2 , Sdyn and λalt to predict the onset of alternans in the different versions of the Sato et al . model . In some simulations , a random error was added to APD to mimic experimental measurement error .
Figure 1 A depicts steady-state APD vs . BCL in the three versions of the Sato et al . model and illustrates the bifurcations to alternans . The shortest BCLs at which alternans was absent during steady state pacing were 308 ms , 330 ms and 370 ms in the Vm-driven model , the Ca2+-driven model with positive Ca2+ to APD coupling and the Ca2+-driven model with negative Ca2+ to APD coupling , respectively . Shortening these BCLs by 1 ms resulted in sustained alternans . In Figure 1 B , the corresponding eigenvalues with an absolute value >0 . 1 are represented . As predicted by eigenmode analysis [28] , the onset of alternans occurred exactly when λalt . reached −1 in all three model versions . The principal memory eigenvalue λmem remained close but always less than +1 at all BCLs tested . All the other eigenvalues were close to 0 . Eigenvalues with an absolute value <0 . 1 ( not shown ) correspond to eigenmodes which dissipate by >99% after 2 beats , and which therefore have only very small influences on the dynamics of the model . The time course with which the model stabilizes towards steady state can be inferred from λmem . Because λmem is the eigenvalue closest to 1 ( in absolute value ) , it determines the slowest time scale in the model . During pacing at a given BCL , the corresponding eigenmode ( Emem ) decays as ( 22 ) where n is the number of beats . The time constant of this process is ( 23 ) ( expressed in number of beats ) , i . e . , ( 24 ) in absolute time units . This time constant is shown in Figure 1 C and D . Thus , in the Sato et al . model , accommodation of model variables ( and thus of APD ) to a given pacing rate occurs with a time constant in the range of 10–40 beats , which corresponds to 8–40 s , depending on BCL . Figure 2 illustrates dynamic and S1S2 restitution curves obtained using conventional pacing protocols . The dynamic restitution curves were generated from the steady state APD and DI values at each individual BCL ( i . e . , at each S1S1 pacing cycle length ) . Each S1S2 restitution curve was then obtained by introducing a modified cycle length ( S1S2 interval ) and by representing the APD of the subsequent AP vs the previous DI . With this approach , a family of S1S2 restitution curves was obtained . In all versions of the model , S1S2 and dynamic restitution functions never overlapped . As shown previously [19] , this absence of overlapping proves that APD depends on more parameters than the previous DI . In the Vm-driven model , the S1S2 restitution curves formed the closest pattern gathered around the dynamic restitution curve , and the S1S2 restitution curves were always monotonically increasing . In contrast , in the two Ca2+-driven versions of the model , the S1S2 restitution curves deviated substantially from the dynamic restitution curve , especially at larger DIs . In the Ca2+-driven model with positive Ca2+ to APD coupling , the prominent increase of APD at long DIs was the consequence of larger Ca2+ transients , resulting in AP prolongation . In the Ca2+-driven model with negative Ca2+ to APD coupling , the S1S2 curves were non monotonic and each curve exhibited a segment with a negative slope . This phase of decreasing APD with increasing DI was the consequence of larger Ca2+ transients , resulting in this case in AP shortening . Cherry and Fenton [23] introduced the concept of “memory amplitude” as a measure of short term memory . This measure is defined as the range of APD values covered by the S1S2 restitution curves at a predefined long DI . For DI = 800 ms , memory amplitude was 25 , 152 and 151 ms in the Vm-driven and the Ca2+-driven model versions with positive and negative Ca2+ to APD coupling , respectively . According to this criterion , the Ca2+-driven models exhibit a larger amount of short term memory compared to the Vm-driven model . However , memory amplitude and λmem cannot be compared directly , because the former reflects APD changes over 2 ( or a very few ) beats , whereas the latter reflects the longest time scale of the model dynamics . In the example illustrated in Figure 3 , the Ca2+-driven cell model with positive Ca2+ to APD coupling was first paced at a constant BCL of 400 ms and exhibited a stable 1∶1 response at steady state . Subsequently , the cell was paced at CLs varying randomly with a SD of 5 ms around 400 ms . Figure 3 A depicts simulated APs and Figure 3 B represents successive CLs , DIs , and APDs . As shown in Figure 3 B , the series of APDs during random pacing was well fitted by a 3rd order ARMA model , accounting for >99% of APD variance with a residual variance <1% . The pole of the ARMA model closest to −1 was −0 . 780 , very near to λalt = −0 . 790 computed using eigenmode analysis . Figure 3 C compares the transfer functions Ht→a and Ht→d of the ARMA model with those derived using eigenmode analysis and those calculated directly from the ratios of the Fourier transforms of the APD , DI and CL time series . The three computations were all in agreement . Furthermore , the transfer functions obtained with the ARMA model matched almost exactly those predicted using eigenmode analysis , except at low frequencies <0 . 05 beat−1 . These transfer functions represent the model behavior in terms of gain and phase shift in the frequency domain . The negative gain of Ht→a indicates that at a mean CL of 400 ms , variations of APD are small relative to variations of CL , and thus that the effects of APD restitution are moderate . Conversely , the gain of Ht→d around 0 shows that CL variations translate primarily to DI variations . However , the increase of Ht→d to +3 . 5 dB at f = 0 . 5 beat−1 indicates that DI variations at higher frequencies are actually amplified , revealing the propensity of the model to generate alternans . Figure 4 shows the restitution portraits of the three model versions in more detail and compares the behavior of the S1S2 and dynamic restitution slopes ( SS1S2 and Sdyn , respectively ) and the markers λalt ( alternans eigenvalue ) and ztd1 ( first zero of the transfer function Ht→d ) as a function of BCL . The restitution portraits ( Figure 4 A ) reflect the clearly distinct restitution dynamic in the three model versions . In these portraits , it is once more apparent that dynamic and S1S2 restitution curves are not equivalent , a behavior which reflects memory [17] , [20] . Figure 4 B first explores the behavior of the different markers extracted with the different methods , as BCL approaches the bifurcation to alternans . In all versions of the model , the conventional slopes SS1S2 and Sdyn derived using eigenmode analysis were indistinguishable from those obtained using conventional restitution protocols . However , both SS1S2 and Sdyn were poor predictors of alternans as they always were <1 at the critical BCL at which alternans appeared . In contrast , λalt was always exactly −1 at the onset of alternans as predicted by theory . The behavior of ztd1 further reflects the different dynamic mechanisms governing restitution . While ztd1 remains near 0 in the Vm-driven model , it follows a nearly parallel course to λalt in the Ca2+-driven models , but with ztd1 > λalt for positive Ca2+ to APD coupling and ztd1 < λalt for negative Ca2+ to APD coupling . Figure 4 B then explores the ability of stochastic pacing combined with ARMA model fitting to estimate these different markers . This approach permitted the robust estimation of both λalt in all three versions of the ventricular cell model , and this estimation was excellent at regimes when λalt was close to −1 ( a feature which is essential for the practical prediction of alternans ) . Similarly , ztd1 could be accurately estimated when it was larger than >0 . 5 ( in absolute value ) . When these markers were close to 0 ( e . g . , in the Vm-driven model ) , the estimation became less reliable , in agreement with the notion that poles and zeros near 0 exert only a small influence on the dynamics of a time series , which renders their identification difficult [32] , [37] . The combination of stochastic pacing and ARMA model fitting also permitted the reliable estimation of SS1S2 according to Eq . 20 without actually conducting an S1S2 protocol . However , the estimates of Sdyn with the ARMA model according to Eq . 21 were prone to a large variability ( not shown ) . This is explained by the fact that Ht→a and Ht→d obtained with the ARMA model do not capture the transfer functions with a sufficient reliability at very low frequencies ( see Figure 3 C ) . Because Sdyn is given by Ht→a and Ht→d at f = 0 ( Eq . 14 ) , the estimation of Sdyn with ARMA model fitting is thus prone to be less robust . Representations of frequency response spectra are intuitively easier to interpret than corresponding sets of poles and zeros . Therefore , we investigated how the aspect of the transfer functions Ht→a and Ht→d behaves at regimes closer and closer to the bifurcation to alternans . As a reference , we first computed these transfer functions for the classical first-order memoryless restitution function APDn = f ( DIn−1 ) [9] with a slope α = df/dDI at the operation point . These transfer functions are Ht→a = α/ ( z+α ) and Ht→d = z/ ( z+α ) , with z = e2πif , as can be deduced from Eqs . 12 and 15–19 and as we showed previously [24] . They are represented in Figure 5 A for α ranging from 0 . 1 to 0 . 9 . The transfer functions in the three versions of the Sato et al . model are then shown in Figure 5 B–D for BCLs approaching the bifurcation to alternans . From Figure 5 , it is apparent that the relationship between stochastic variations of CL and resulting variations of APD and DI is nonlinear , non-additive and frequency-dependent . At CLs far from the alternans regime ( darker purple curves ) , restitution was less involved and CL variations translated primarily into variations of DI , while APD variations were comparatively small . This is reflected by a gain close to 0 dB for Ht→d and a negative gain ( attenuation ) for Ht→a . However , for all models and at regimes progressively closer and closer to the bifurcation to alternans ( lighter redder curves ) , CL variations resulted in a more and more positive gain for both Ht→d and Ht→a at frequencies >0 . 4 beat−1 , with a peak at 0 . 5 beat−1 . This observation can be interpreted as an increasing propensity to alternans . This gain reached values up to 20 dB , which corresponds to amplification by a factor of 10 . Thus , in regimes close to the development of alternans , variations of APD and DI may reach a level which is comparatively one order of magnitude higher compared to variations of CL . The behavior of the transfer functions in the Vm-driven model ( Figure 5 B ) was qualitatively similar to that in the first-order model , suggesting a low level of memory in the Vm-driven Sato et al . model . In contrast , the behavior in the Ca2+-driven models was clearly different . With positive Ca2+ to APD coupling ( Figure 5 C ) , the curves appear skewed towards the right . With negative Ca2+ to APD coupling ( Figure 5 D ) , the aspect is fundamentally different . First , at f = 0 . 5 beat−1 , the phase shift of Ht→a is −2π instead of −π , a difference explained by the presence of a zero ( ztd1 ) more negative than λalt . Second , a singularity appears at f = 0 . 5 beat−1 in Ht→d ( abrupt change in polarity ) when this zero leaves the unit circle at −1 ( see Figure 4 B ) . Thus , all these transfer function “portraits” are able to picture and reveal the dynamical differences regarding both the propensity to alternans generation and memory in the different models . Similar to Figure 3 , all these transfer functions could be estimated using the stochastic pacing protocol and ARMA model identification , with small deviations in the low frequency range <0 . 05 beat−1 . This range corresponds to time scales of >20 beats and is thus determined by long-lasting effects of memory ( poles and zeros near +1 ) , which cannot be captured accurately by the ARMA model . These effects manifest themselves in Figure 5 B–D as inflections of both the gain and the phase shift curves at f<0 . 05 beat−1 . The analyses presented above were conducted in stationary regimes , for which mean BCL and cellular properties did not evolve with time . However , in electrophysiological experiments , the propensity to alternans is typically assessed by decreasing BCL ( either stepwise or progressively ) until alternans appears . Therefore , we examined whether determination of λalt using ARMA model identification would permit to anticipate the onset of alternans during a slow decrease of BCL . Results with the Ca2+-driven model with positive Ca2+ to APD coupling are shown in Figure 6 . In Figure 6 A , the model was paced using a protocol consisting of CLs decreasing progressively ( −0 . 1 ms/beat ) to which random Gaussian variations ( SD: 1 ms ) were added . The resulting series of APDs and CLs were then segmented in windows of 30 cycles with an overlap of 15 cycles , and λalt as well as SS1S2 were estimated from the data in each window using a 2nd order ARMA model . In the illustrated example , λalt progressively approached −1 and the onset of alternans coincided with the moment when λalt reached −1 ( vertical dotted line ) . Thus , observing the course of λalt as it gets closer to −1 allows anticipating alternans . Figure 6 A shows once more that SS1S2 is a poor predictive marker , as its value was only 0 . 4 at the onset of alternans . In Figure 6 B , the model was paced using the same ramp protocol as in Figure 6 A , but without adding random CL variations , i . e . , using a control ramp protocol without stochastic variations . Although manifest APD alternans appeared later than in Figure 6 A , the difference series of APD ( ΔAPD = APDi – APDi−1 ) reveals that microscopic alternans ( micro-alternans ) actually appeared at the same moment as anticipated in Figure 6 A from the behavior of λalt . Similar results were obtained with the Vm-driven model and the Ca2+-driven model with negative Ca2+ to APD coupling . In an experimental setting , APD is always subject to measurement error . To evaluate how our analyses would be influenced by measurement error , we conducted 10 simulations as in Figure 6 A and added a random Gaussian error on the APD time series before the evaluation of λalt and SS1S2 . In these simulations , the SD of the random CL deviations was increased to 5 ms and the SD of the error added to APD was 1 ms . As illustrated in Figure 6 C , adding noise to the APD time series resulted in an underestimation of λalt ( in absolute value ) and a larger variability of the estimates , which necessitated increasing the number of cycles used for ARMA model fitting to 150 . However , the estimation of λalt became progressively more accurate as it approached −1 , and extrapolating the time course of λalt towards its intercept with the line λalt = −1 permitted to anticipate the onset of alternans as in the control situation without adding noise to APD . Interestingly , the estimation of S1S2 was not influenced by measurement noise . It is also informative to analyze λalt as a function of BCL , as done in Figure 6 D for the data presented in Figure 6 A and C . In this analysis , linear regression of λalt vs . BCL was conducted for data points with λalt>−0 . 85 . By extrapolating the regression line to λalt = −1 , it was possible to anticipate the BCL at which alternans developed . We then evaluated whether estimating λalt during a slow change of cellular properties would also allow anticipating the onset of alternans . The Vm-driven model was paced at a stationary rate ( BCL = 320 ms ) and the conductance of the slow delayed rectifier K+ current ( IKs ) was progressively reduced at a rate of 0 . 2% per second , starting from its nominal value of 100% , to mimic the slow application of an IKs channel blocker . Figure 7 A depicts the behavior of APD , DI and λalt ( estimated in windows of 30 cycles as in Figure 6 A ) during stochastic pacing ( mean CL: 320 ms; SD of CL: 1 ms ) . As in Figure 6 A , λalt progressively approached −1 and alternans appeared when λalt reached −1 . This indicates that observing the course of λalt as it gets closer to −1 may also allow anticipating alternans during pharmacologic interventions . Figure 7 B represents the control situation , in which the model was paced at a constant CL of 320 ms without random variations , but with the same decrease of IKs conductance . Manifest alternans appeared later than in Figure 7 A , but micro-alternans ( visible in the ΔAPD series ) appeared at the same moment as anticipated from the evolution of λalt . The sensitivity of the estimation of λalt on noise added to the APD time series was investigated in Figure 7 C with an approach similar to that used in Figure 6 C . A random Gaussian error was added on the APD time series before the evaluation of λalt and the simulation was repeated 10 times . In these simulations , the SD of the random CL deviations was 2 ms and the SD of the error added to APD was 1 ms . The number of cycles used for ARMA model fitting was adjusted to 150 . Adding noise to the APD time series resulted in a slight underestimation of λalt ( in absolute value ) but did not affect the time of its intercept with the line λalt = −1 . Thus , predicting the onset of alternans was not precluded by the noise added to APD . To investigate whether ARMA model identification during stochastic pacing offers a significant advantage over a simpler time domain analysis consisting of quantifying the decay of APD oscillations following a perturbation , we examined the response of the Sato et al . model to a step change of BCL . An example is illustrated in Figure 8 A for the Ca2+-driven model with positive Ca2+ to APD coupling after a step decrease of BCL from 400 to 390 ms . The step decrease of BCL caused transient decaying APD alternans , followed by an exponential convergence of APD to its new steady state at BCL = 390 ms . These two patterns reflect the alternans and memory eigenmodes , respectively . To quantify the decay of the alternans eigenmode , an exponential function was fitted to the absolute value of the APD difference series ( |ΔAPD| ) . The time constant of this function provided an estimate of λalt of −0 . 800 , which was close to the control value of −0 . 820 derived using eigenmode analysis . However , as shown in Figure 8 B , this estimation was compromised when noise was added to APD ( SD = 0 . 1 ms ) . In Figure 8 C , this time domain method is compared statistically to ARMA model identification during stochastic pacing ( SD of APD: 5 ms; 3rd order ARMA model , identification over 30 cycles ) . In the presence of noise , the variability of λalt estimates was significantly smaller for ARMA model identification during stochastic pacing . Similar results were obtained at other BCLs and for the two other versions of the cell model . Thus , in the presence of noise , ARMA model identification is more robust than quantification of the exponential decay of APD alternation following a step decrease of BCL .
In the present study , we revisit restitution by examining it in a generalized framework based on eigenmode analysis , a sound mathematical approach for the characterization of dynamical systems . Previous studies based on eigenmode analysis [28] , [29] have shown that in cardiac electrophysiology , alternans and memory are actually the two faces of the same dynamics . By looking into eigenmode analysis in the frequency domain , we first showed that in the linear limit , the eigenmode description of a cardiac cell is equivalent to the description using a memory model of cardiac restitution . Both descriptions can be understood in terms of transfer functions . In the frequency domain , memory and alternans can then be regarded as the two extremes on the frequency axis , which ranges from 0 to 0 . 5 beat−1 . Based on engineering notions of signal processing , we then devised a practical method to determine these transfer functions together with their poles and zeros using only time series of CLs , APDs and DIs . Our key finding is that the propensity to alternans can be quantified and monitored and thus the onset of alternans can be anticipated using the eigenvalue ( first pole ) λalt obtained via ARMA model identification during pacing at intervals varying randomly . The results of the computer simulations , conducted with models exhibiting three fundamentally different ionic mechanisms of alternans , not only support the general validity of this approach but also suggest that it may also be applied in non stationary regimes such as during a slow acceleration of the ( average ) pacing rate or the progressive application of a drug . Our approach is general because it can be applied to any model based on a biophysical description of ion currents as well as any higher-dimensional iterated map model of cardiac dynamics , such as the models of Vinet et al . [10] , Chialvo et al . [13] or Qu et al . [38] , for which eigenmode analysis can also be conducted and thus the transfer functions determined . Our approach develops its full strength when pacing at randomly varying intervals is considered . Our simulations provide the proof of principle that stochastic pacing permits the estimation of the transfer functions Ht→a and Ht→d and thus of their first pole as a marker for the propensity to alternans . It is worth to note that in our simulations , ARMA model identification was excellent in estimating SS1S2 , even in the presence of measurement noise . Thus , the S1S2 restitution slope can actually be determined by stochastic pacing without the need to conduct an S1S2 protocol . However , the performance of ARMA model identification to estimate Sdyn was low . This observation is explained by the fact that the determination of Sdyn ( using Eq . 21 ) is exquisitely sensitive to errors in the determination of the higher order coefficients of the ARMA model and that these errors cannot be decreased by increasing the order of the ARMA model . Indeed , in our simulations , >99% of APD variance was already described with a model of order 2 or 3 , and increasing this order provided neither a better description of the dynamics , neither a higher reliability in computing λalt or restitution slopes . This indicates that the dynamics of the Sato et al . model , which has 16 variables , can be well represented by a lower dimensional model of order 2 or 3 in near stationary regimes . This also explains that the estimation of Sdyn using ARMA model identification is prone to a large variability , and thus that it would not be superior to a conventional pacing protocol in a practical experimental setting . In this study , we used APD and DI as the system's output time series . It must be noted that our approach can also be used to derive the transfer function between pacing cycle lengths and any other output parameter such as the peak Ca2+ transient or the peak of a given ion current . Therefore , in an experimental setting , ARMA model identification during stochastic pacing could also be applied on series of peak Ca2+ transients , or , a fortiori , on local conduction velocities or mechanical parameters ( e . g . , peak force or shortening ) . Our approach also offers the advantage to be versatile . For example , exploring the frequency response of the Ca2+ transient in addition to the response of APD may uncover additional insights regarding the primary cause of alternans ( voltage vs . Ca2+ driven ) , which may be pertinent in determining appropriate clinical therapeutic strategies ( e . g . conventional pharmacotherapy targeting ion channels vs . new agents that may target the cellular Ca2+ handling machinery ) . This analysis was however beyond the scope of this work . As demonstrated in the Methods section , the classical one-dimensional memoryless map [9] represents a particular case for which the criteria Sdyn = 1 , SS1S2 = 1 and λalt = −1 are equivalent . However , the equivalence of these criteria breaks down as soon as cardiac dynamics exhibit memory . Memory can thus be defined as any deviation from the first order map behavior . Thus , the notion of memory clearly explains why , in a more general setting , any prediction of alternans based on Sdyn or on SS1S2 [17] , [18] should not be expected to be reliable . In a theoretical study , Tolkacheva et al . [25] derived criteria for alternans and stability based on measuring dynamic and S1S2 restitution slopes in an iterated map model given by APDn+1 = f ( APDn , DIn ) . The authors then generalized their analysis to mapping models with an arbitrary amount of memory [21] ( corresponding to Eq . 15 ) . The mapping models were investigated in the time domain using the perturbed downsweep protocol . We note that our framework is fully consistent with their time domain analyses , as it yields , for example , an equivalent result for Sdyn . However , our approach provides additional insights in the Z and frequency domains and links restitution to eigenmode analysis . As a principle , frequency domain analysis permits to understand cardiac dynamics in response to any arbitrary sequence of pacing intervals , including stochastic pacing and pacing at cycle lengths varying in an oscillatory manner . In this latter context , the recent studies of Wu and Patwardhan deserve attention . To demonstrate memory effects , these investigators paced a ventricular cell [26] or a mathematical cell model [39] while controlling the DI and varying it as a sinusoidal function with a period of 100 beats . This sinusoidal variation resulted in hysteresis of APD vs . DI , i . e . , in a phase shift between both . Because a sinusoidal pacing protocol can be regarded as probing the transfer functions at the corresponding frequency ( f = 0 . 01 beat−1 ) , memory effects should become apparent at this frequency in graphical representations of transfer functions . Accordingly , at frequencies ≤0 . 01 beat−1 , the three versions of the Sato et al . model are characterized by manifest phase shifts ( Figure 5 B–D ) , whereas the phase shift is vanishingly small in the memoryless first-order model ( Figure 5 A ) . While a sinusoidal pacing protocol thus represents a suitable approach to probe memory , the advantage of the stochastic pacing protocol is that it examines all frequencies at the same time , thus probing both alternans and memory . Stochastic pacing and ARMA model identification would be straightforward to implement in any electrophysiological apparatus . Therefore , our approach could readily be translated to in vitro and in vivo models , opening the perspective of new diagnostic approaches during clinical investigation of heart rhythm disorders . In the future , one could for example envision stochastic pacing for clinical electrophysiological testing as a built-in extension to cardiac mapping systems or implanted defibrillators for the purpose of risk-stratification . Obviously , our theoretical framework must withstand the challenge of experimental validation . Because experimental data such as APD measurements are always subject to measurement error and because APD variability may also result from the stochastic gating of ion channels [40] , it will first be necessary to carefully optimize the SD of stochastic pacing variations , the number of cycles used for ARMA model identification and the order of the ARMA model . Our simulations suggest that our approach performs well as long as the system remains near its linear limit . If the SD of CL is set to be too large , our approach will eventually be limited by nonlinearities in the system and some stimuli may fall in the refractory period . To minimize the intrinsic variability of APD , it may then be appropriate to use small pieces of cardiac tissue in which this intrinsic variability is strongly decreased by gap junctional coupling between individual cells [40] . It will also be necessary to evaluate the effects of other sources of variability in both experiments and further computer simulations . Nevertheless , our computational results indicate that our approach should be robust in predicting the onset of alternans . From a theoretical point of view , it will also be necessary to extend the theory to multicellular systems in order to understand the influences of intercellular interactions , the effects of conduction velocity restitution [41] , [42] and the consequences of multidimensional phenomena such as wavefront curvature [43] . In particular , it has been shown in spatially extended systems that electrotonic interactions can exert a large influence on the occurrence of alternans and the APD restitution slope at which alternans occurs [16] , [23] . Furthermore , the occurrence of alternans can be significantly modulated by steep conduction velocity restitution slopes [15] . These aspects will require a careful computational evaluation . Such computational studies will permit to understand the possibilities and limitations of our framework in greater detail , and they are expected to provide additional insights into dynamical phenomena emerging at multicellular scales , such as spatially discordant alternans [41] , [42] . In conclusion , stochastic pacing combined with ARMA model identification represents a novel frequency domain approach to study cardiac dynamics . This approach should be applicable experimentally for the accurate evaluation of the propensity to alternans and the prediction of its onset . Because its mathematical foundation does not make any a priori assumptions about the ionic mechanisms of alternans , it pertains to any type of myocardial cell or tissue , irrespective of species , disease status or pharmacological interventions .
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Cardiac arrhythmias are frequent complications of heart disease and an important cause of morbidity and mortality . The rhythmic activity of the heart relies on the action potential , a bioelectrical signal characterized by complex dynamics involving ion currents and intracellular calcium cycling . When these dynamics become unstable , arrhythmogenic patterns can emerge . A typical example is the beat-to-beat alternation of action potential parameters , a phenomenon called alternans , which represents a well known mechanism precipitating severe arrhythmias . Alternans results from the interaction of action potentials during consecutive beats . Classically , this interaction is investigated by describing the dependence of action potential parameters on previous diastolic intervals and action potential durations . However , experiments have shown that quantitative markers derived in this way are only approximate or even inappropriate to predict alternans . Here , we devised a novel procedure for the reliable prediction of alternans , based on introducing small random variations of pacing intervals followed by signal processing in the frequency domain . Using a biophysical model of the cardiac cell , we demonstrate that our algorithm accurately predicts the onset of alternans during pacing at an accelerating rate or during the application of a drug . Our approach may thus open new perspectives for the clinical evaluation of arrhythmias .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"physiology",
"signal",
"processing",
"biology",
"anatomy",
"and",
"physiology",
"cardiovascular",
"system",
"biophysics",
"computational",
"biology",
"engineering"
] |
2012
|
Uncovering the Dynamics of Cardiac Systems Using Stochastic Pacing and Frequency Domain Analyses
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Flavivirus envelope protein ( E ) mediates membrane fusion and viral entry from endosomes . A low-pH induced , dimer-to-trimer rearrangement and reconfiguration of the membrane-proximal “stem" of the E ectodomain draw together the viral and cellular membranes . We found stem-derived peptides from dengue virus ( DV ) bind stem-less E trimer and mimic the stem-reconfiguration step in the fusion pathway . We adapted this experiment as a high-throughput screen for small molecules that block peptide binding and thus may inhibit viral entry . A compound identified in this screen , 1662G07 , and a number of its analogs reversibly inhibit DV infectivity . They do so by binding the prefusion , dimeric E on the virion surface , before adsorption to a cell . They also block viral fusion with liposomes . Structure-activity relationship studies have led to analogs with submicromolar IC90s against DV2 , and certain analogs are active against DV serotypes 1 , 2 , and 4 . The compounds do not inhibit the closely related Kunjin virus . We propose that they bind in a previously identified , E-protein pocket , exposed on the virion surface and although this pocket is closed in the postfusion trimer , its mouth is fully accessible . Examination of the E-trimer coordinates ( PDB 1OK8 ) shows that conformational fluctuations around the hinge could open the pocket without dissociating the trimer or otherwise generating molecular collisions . We propose that compounds such as 1662G07 trap the sE trimer in a “pocket-open" state , which has lost affinity for the stem peptide and cannot support the final “zipping up" of the stem .
Enveloped viruses penetrate into the cytosol of their target cell by fusion of viral and cellular membranes [1] , [2] . Flaviviruses , such as dengue , penetrate from endosomes , following uptake by clathrin-mediated endocytosis [3] , [4] . At endosomal pH , proton binding by their envelope protein , E , triggers a fusion-promoting conformation change [5] , [6] . The flavivirus envelope fusion protein , E , forms a well-ordered lattice of 90 dimers on the surface of a mature , infectious virus particle [2] , [7] . Crystal structures of soluble forms of E ( “sE" ) , which include the first ∼395 of ∼445 ectodomain residues but lack a conserved , membrane-proximal “stem" region , have contributed to molecular descriptions of flavivirus fusion [8]–[12] . The three domains ( DI–III ) of the E protein reorient with respect to one other during the fusion-promoting conformational transition , which includes dissociation of the prefusion dimer and reconfiguration of the subunits into trimers [2] . At an intermediate stage a hydrophobic “fusion loop" at one end of the extended E subunit inserts into the outer leaflet of the target bilayer [2] , [13] . The driving force for pinching the two membranes together appears to come from contacts made by domain III , as it folds back against domain I , and by the stem , as it “zips" up along adjacent domain II monomers [1] , [2] . Molecular understanding of the fusion pathway and the proteins involved has enabled discovery of small-molecule and peptide inhibitors that target intermediates in these fusion-inducing rearrangements . The best-known example of the latter type of entry inhibitor is T-20/enfuvirtide , a peptide used to treat HIV-1 infection [14]–[17] . The T-20 peptide interferes with a late stage in the fusion-inducing conformational transition of HIV-1 gp41 . Certain small molecules block HIV-1 fusion by a similar mechanism , binding in a conserved pocket on the gp41 inner core [18] . Inhibitors that target the fusion glycoprotein , F1 , of respiratory syncytial virus ( RSV ) also prevent infection by blocking a conformational transition [19] , [20] . Targeting the HIV-1 and RSV glycoproteins is possible , because fusion occurs at the plasma membrane , where exposure of the relevant fusion intermediates allows straightforward access to the specific inhibitors . For viruses such as flaviviruses that fuse from endosomal compartments , however , targeting an intermediate of the rearranging fusion protein requires concentrating the inhibitor within the endosome , as its potential binding sites are not available until reduced pH has induced their exposure . Kielian and co-workers have reported reconstitution of an sE trimer for both alpha- and flavivirus envelopes , suggesting that one might use reconstitution strategies to identify inhibitors that block steps in fusion [21] , [22] . We found recently that we could target a fusion intermediate of dengue virus E with peptides derived from its ectodomain stem [23] , [24] . These peptides bind the postfusion form of DV2 sE trimer , mimicking late steps in stem rearrangement . They inhibit in vitro fusion and DV2 infectivity . C-terminal modification with membrane targeting sequences increases their inhibitory strength [23] . A series of experiments support a two-step mechanism , in which a reversible , non-specific interaction with the viral membrane brings virion-associated peptides into the low-pH endosome , where full exposure of the peptide site on the E-protein conformational intermediate leads to tight , specific binding , which interfere with the final “zipping" of the stem [24] . Can small molecules also inhibit this step in the fusion pathway ? We have adapted the assay we used to study interaction of stem-derived peptides with stem-less sE trimer , to screen for small-molecule inhibitors that target this fusion intermediate . We have identified compounds that compete for stem peptide association , and we show that they reversibly inhibit DV infectivity . We further show , using model liposomes , that these molecules specifically block viral fusion . They appear to bind the virion before adsorption to cells , by interacting with the prefusion , dimeric E protein , possibly in a previously identified , hydrophobic pocket . This association presumably permits their virus-associated transfer into endosomes . Limited structure-activity relationship studies have yielded compounds that inhibit DV2 ( NGC isolate ) with IC90∼1 µM . Our competition screen has thus identified a group of potent , small-molecule inhibitors of DV entry validating experimental screens for small molecules that block viral entry from internal compartments .
We have described a fluorescence polarization ( FP ) assay to identify stem-derived peptides that bind the trimeric postfusion conformer of DV2 sE [24] . We found peptides from the C-terminal stem region that bind tightly to this proposed fusion intermediate . With one such peptide , DV2419–447 , tagged at its N-terminus with FITC , we adapted the FP assay to screen for small molecules that compete for binding to trimeric sE ( Figure 1 ) . We screened ∼30 , 000 compounds in a 384-well format and found several that were active , as measured by a reduction in the fluorescence polarization signal . We chose to pursue further work on one such “hit" , 1662G07 ( Figure 2 ) , for which several structurally similar compounds were commercially available . We tested a set of related compounds , both from the screening libraries and from commercial vendors , to obtain preliminary structure activity relationships ( SAR ) . Several modifications to the parental scaffold affected competition with peptide ( Table S1 ) . Modification or removal of the nitrile moiety ( R1 position ) impaired or abolished activity . Removal of the halogen in the m-position on the R2 ( as phenyl ) decreased competition; addition of strongly electron withdrawing groups ( either trifluoro- or trifluoromethoxy ) in the o- or m-positions increased it . A thiophene at R3 was equivalent to the furan in 1662G07 , but a cyclopropane in that position eliminated competition with peptide . From preliminary SAR analysis with commercial analogs described above , we chose to synthesize two series of compounds based on the parental scaffold . Series 3-148 , 3-149 and 3-151 varied the R2 position while the 3-110 varied at the R3 positions . ( Figures 2 and 4 , Tables S2 and S3 ) . Among the 51 compounds from these series , we found that sixteen competed with stem-derived peptide for binding to the DV2 sE trimer . Strongly electron withdrawing groups in the o- , m- , or p- positions on R2 ( as phenyl ) enhanced activity; substitution with methyl or methoxy-methyl groups at these positions yielded inactive compounds . Some larger , heterocyclic rings impaired activity ( Figures 2 and 4 . Tables S2 and S3 ) . Do these small molecules , identified in a screen for binding to a late-stage fusion intermediate , inhibit DV2 viral infectivity ? We used a standard plaque forming assay to test the effect of the analogs from the 3-148 , 3-149 , 3-151 and 3-110 series at a single concentration on growth of DV2 NGC . The virus inoculum was preincubated with compound for 15 minutes and then adsorbed to BHK-21 cells , at a multiplicity of infection ( MOI ) of 1 , for 1 hour at 37°C . Supernatants were harvested after 24 hours and titred by standard plaque assay [23] . A subset of the compounds from both series reduced DV2 infectivity ( Figures 2 and 4 , Tables S2 and S3 ) . Compounds were inactive against vesicular stomatitis virus , an unrelated enveloped virus and were noncytotoxic at the concentrations tested ( Figure S7 , Figures 2 and 4 , Tables S1 , S2 and S3 ) . Comparison of the activity profiles from the 3-148 , 3-149 and 3-151 series in the peptide-competition and viral infectivity assays revealed a clear concordance between active compounds in one assay and actives compounds in the other ( Figure 3 ) . The likelihood that this degree of concordance could be random is less than 10−4 . Compounds in the 3-110 series were particularly active against DV2 . We therefore tested this series at a single concentration ( 5 µM ) against isolates from the other three dengue serotypes: DV1 WP74 , DV3 THD 3 and DV4 TVP360 . The compounds of series 3-110 inhibit DV1 , 3 and 4 infectivity to varying degrees ( Figures 4 and S1 and Table S3 ) . DV3 was particularly insensitive to inhibition , as we had also found when testing its response to stem-derived peptides . From this initial screen , we looked more closely at three analogs that appeared to have the strongest antiviral effect , 3-110-5 , 3-110-14 and 3-110-22 . We examined their inhibition of DV2 and DV4 viruses to determine IC90s . As seen in Figure 4 these compounds had strong antiviral activity against DV2 and DV4 , with IC90s in submicromolar and micromolar ranges , respectively . At the same concentration used with the DV serotypes , none of the compounds had detectable activity against Kunjin , a subtype of West Nile virus ( Figure S2 ) ; the analogs most potent for inhibiting dengue , 3-110-5 , 3-110-14 and 3-110-22 , had no effect on Kunjin , even at 20 µM . The small-molecule inhibitors were selected in a screen that detects formation of an E-protein conformation adopted only after a virion has arrived in the low-pH environment of an endosome – an intracellular compartment presumably inaccessible to the free compounds . A series of order-of-addition experiments using the 3-148 , 3-149 and 3-110 series show that to have a significant inhibitory effect , the compounds must be preincubated at 37°C with the viral inoculum before adsorption to cells ( Figure 5 ) . We observed the same level of inhibition using a direct plaque assay as a readout ( Figure S8 ) . When compound and virus inoculum were added to cells at the same time , we detected an approximately tenfold drop in viral titre compared with the DMSO control for compounds in the 3-110 series and little or no effect for compounds in the 3-148 and 3-149 series . Postinfection treatment of cells with compound one hour after initial adsorption of virus did not reduce viral titre , nor did pretreatment of cells for one hour before virus adsorption ( Figure 5 and data not shown ) . These results imply a direct association of compound and virion before endocytosis of the virus . To detect DV2 fusion with liposomes , we used the content-mixing assay we previously applied to characterize peptide inhibitors of DV [24] . Selected compounds from the 3-148 , 3-149 and 3-110 series were incubated for 15 minutes at 37°C with virus , which was then added to trypsin-loaded liposomes . We adjusted the pH of the medium to ∼5 . 5 for 10 minutes , back-neutralized the samples , and incubated for an additional 45 minutes at 37°C to allow trypsin to act . Digestion of the viral core protein , which would have been exposed to protease only after fusion of virions with liposomes , was assessed by SDS-PAGE and immunoblotting . Protection of the core protein from proteolysis with retention of the envelope protein indicated an effective fusion inhibitor . A subset of the compounds we tested specifically blocked content mixing of virus with trypsin-loaded liposomes ( Figure 6 ) . We used stem peptide DV2419–447 , previously shown to inhibit fusion , as a positive control . The assay we used to find inhibitory compounds detects an interaction with trimeric E , which forms on virions only after exposure to low pH . Yet the inhibitory small molecules appear to bind virions at pH 7 . Thus , the inhibitors can associate with both the pre- and postfusion E-protein conformers . Unless their binding site is at an interface between adjacent subunits in one of the two conformational states , we expect the compounds also to bind a monomeric form of E . We expressed and purified DI/DII , a soluble , monomeric fragment of E comprising only the first two domains ( Figure S3 ) and showed by surface plasmon resonance ( SPR ) that the inhibitory small molecules from both series indeed bind directly and reversibly to DI/DII ( Figure 7 ) . Control compounds , which do not inhibit viral infectivity and do not compete for peptide binding to trimeric sE , do not bind DI/DII . To rule out the possibility that the small molecules inactivate virions nonspecifically , we tested whether addition of DV2 DI/DII to an inoculum preincubated with a small-molecule inhibitor could restore infectivity . We incubated a virus inoculum for 10 minutes with selected compounds from the 3-148 , 3-139 and 3-110 series at inhibitory concentrations and then added DI/DII in molar excess . Exogenous DI/DII indeed reversed the small-molecule inhibition ( Figure 8 ) . DV2 DI/DII alone did not affect viral titre . WNV DI/DII did not restore infectivity in the presence of these compounds ( Figure S9 ) , consistent with their failure to inhibit Kunjin virus .
We have shown that 1662G07 and it analogs inhibit growth of DV2 and that they block low-pH triggered fusion of virus with liposomes . We identified the parent compound , 1662G07 , in a high-throughput screen that detected competition by active molecules with binding to trimeric sE of a fluorescein-tagged , stem-derived peptide . We designed this assay to represent the final step ( s ) in the low-pH triggered , E-protein conformational change – the process that induces penetration from endosomes through fusion of viral and endosomal membranes . Nonetheless , the inhibitory activity of these small molecules depends on binding to a virion before the virus encounters a cell , indicating that the compounds can also associate with E in its dimeric , pre-fusion conformation . Indeed , they bind in solution to a monomeric , DI/DII fragment of E , and addition of this fragment to an inoculum preincubated with one of the compounds restores infectivity , presumably by sequestering the inhibitor . How can small molecules that bind the prefusion E dimer and the DI/DII fragment also block association of a stem-derived peptide with the sE trimer ? From the known structures of sE dimers ( prefusion ) [9] and trimers ( postfusion ) [25] and from accurate docking of the former into subnanometer-resolution cryoEM reconstructions of virions [26] , we can propose both an answer to this question and a model for the mechanism of action of the small-molecule inhibitors we have studied . The activity of these molecules in an assay for infectivity correlates well with their capacity to compete with a stem-derived peptide for binding to sE trimer . The most straightforward explanation for this correlation is that the compounds bind at a site accessible on both prefusion and postfusion E conformers . The most obvious site on E for small-molecule binding is a pocket , adjacent to the hinge between domains I and II , which accepts a β-octyl-glucoside ( β-OG ) molecule when sE dimers are crystallized in the presence of the detergent . This pocket closes down in the trimer conformation seen in the crystal structure , and the closed pocket is incompatible with occupancy by a bulky ligand ( e . g . , 1662G07 ) . A dimer-to-trimer conformational transition will then require expulsion of the ligand , imposing a barrier to completion of the fusion process . For this reason , several groups have used in silico screens to find potential pocket-binding compounds , and in at least two cases , the results of those screens have yielded active inhibitors [27]–[31] . It has not yet been shown , however , whether the compounds found in this way indeed bind in the pocket as predicted . One of those computational screens used the Maybridge library for its search , and one of two active inhibitors identified is related to 1662G07 , including most of the scaffold in Figure 2 [27] . That compound was not represented , however , in the version of the Maybridge library we used in our experimental screen . We have docked several of our compounds , using the GLIDE program [32] . We obtain fits consistent with the crystallographically observed interactions of β-OG ( Figure S6 ) . Although the β-OG pocket is closed in the trimer , its mouth is fully accessible . Examination of the E-trimer coordinates ( PDB 1OK8 ) shows that conformational fluctuations around the hinge could open the pocket without dissociating the trimer or otherwise generating molecular collisions ( Figure 9 ) . We suggest that compounds such as 1662G07 inhibit peptide binding by trapping the sE trimer in a “pocket-open" state , which has lost affinity for the stem peptide and cannot support the final “zipping up" of the stem ( Figure 9 ) . Binding of a compound in the β-OG pocket can explain how it accompanies virions into endosomes . Then , even if structural rearrangements of the E protein as it transitions at low pH from dimer to trimer expel the compound from the pocket , it would still remain at relatively high concentration in the endosomal space and be able to rebind rapidly . Effective inhibition would simply require that the rate of rebinding be higher than the rate at which the stem zips up along domain II . One potent inhibitor of infectivity for all four dengue serotypes , compound 3-110-22 , failed to inhibit stem-peptide binding in the competition assay ( Figure 4 ) . A likely explanation is that modification of the parental scaffold to produce 3-110-22 gave a compound with high affinity for the β-OG pocket in the E dimer , but lower affinity for the pocket-open state of the E trimer . The region that surrounds the mouth of the pocket differs in the two conformations , because of the fold-back of domain III ( Figure 9 ) , and 3-110-22 has a bulky substituent group . It appears that compound 3-110-22 indeed binds tightly to the E dimer , because unlike a number of others , it blocks the dimer-to trimer-transition of sE in vitro ( Figure S4 ) . There are less likely alternative explanations for the inhibitory action of the compounds we have studied . One postulates a binding site , other than the β-OG pocket , that is present on monomeric DI/DII , on dimeric sE and on trimeric sE and that overlaps the peptide site on the trimer; another is a multi-site and multi-step mechanism . There is no evident candidate site for the former mechanism . The latter requires correlated affinities and properties of multiple sites . We therefore suggest that our peptide-competition , high-throughput screen has identified a large set of molecules that bind the β-OG pocket and that we have devised a useful assay for pocket-binding inhibitors , potentially applicable to any flavivirus for which one can prepare a stable , trimeric sE .
Stem peptide 419–447 , with the DV2 NGC sequence and an RGKGR solubility tag appended at its C-terminus , was synthesized using standard Fmoc chemistry on an ABI 431 Peptide Synthesizers at the Tufts University Core Facility ( Boston , MA ) , purified using reverse phase HPLC , and analyzed by mass spectrometry . Fluorescein-isothiocyanate ( FITC ) was conjugated to the N-terminus of the peptide through a β-alanine linker . Liposomes [made with 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine ( POPC ) , 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine ( POPE ) ( Avanti Polar Lipids ) and cholesterol ( Sigma-Aldrich ) in a 1∶1∶2 molar ratio in TAN buffer ( 20 mM triethanolamine , 100 mM NaCl , pH 8 . 0 ) ] were prepared by freeze-thaw extrusion through a 0 . 2 µ filter as described previously [24] . The postfusion sE trimer was produced as described [24] . Purified sE ( Hawai'i Biotech ) was incubated at 37°C in the presence of liposomes of the composition described above and acidified with MES buffer . Liposomes were solubilized with n-octyl-β-D-glucoside ( β-OG ) and n-undecyl-maltopyranoside ( UDM ) . The solution was applied to a monoS column ( GE Healthcare ) ; sE trimer was eluted with a 2 M NaCl step gradient and further purified by size-exclusion chromatography on Superdex 200 ( GE Healthcare ) . Protein was dialyzed extensively using a 50-KDa molecular-weight cutoff membrane ( Spectrapor ) . Synthetic analogs of 1662G07 were prepared as described in Text S1 [33] . All screens were preformed at the NSRB at Harvard Medical School . Binding experiments were carried out in Corning , low-volume 384 well microplates and analyzed in a PerkinElmer EnVisions instrument ( excitation wavelength , 485 nm; emission wavelength , 535 nm ) . sE trimer was added to each well at 0 . 150 µM in 30 µL of TAN buffer . 0 . 1 µL of compound was transferred to each well and incubated at room temperature for 1 hr before the addition of DV2419–447 at a final concentration of 20 nM . After a 3-hour incubation , plates were read and fluorescence polarization measurements recorded . The original hit described in this paper came from the Maybridge 5 screening library at the NSRB at Harvard Medical School . This assay was essentially as described previously [24] , [34] . Liposomes were made with added trypsin at 10 mg/ml . Unencapsulated trypsin was removed by passage of the suspension through a Superdex 200 gel filtration column ( GE Healthcare ) . Small molecules and peptide prepared as DMSO stocks were diluted into 50 µL of TAN buffer in the presence of purified virions . Reactions were incubated at 37°C for 15 mins , before the addition of trypsin-loaded liposomes , acidified with MES pretitrated to reach a final pH of 5 . 5 , and incubated at 37°C for 15 mins . Reactions were neutralized to pH 8 . 0 with 1 M TEA . Trypsin digestion proceeded for 1 hr at 37°C . Aliquots of the reaction were resuspended in SDS-loading buffer with 2 mM PMSF , incubated for 20 mins at 100°C , and analyzed by SDS-PAGE followed by immunoblotting with an anti-dengue core and anti-E antibody . Experiments were performed in duplicate on a Biacore 3000 instrument . DI/DII protein was immobilized to a CM5 biosensor chip per manufacturer's instructions . All experiments were carried out at 25 C in HBS-EP buffer ( 10 mM HEPES , 150 mM NaCl 3 mM EDTA and 0 . 005% ( vol/vol ) P20 surfactant ) . Sensorgrams were obtained by passing over small molecules diluted in HBS-EP buffer at specified concentrations at a flow rate of 50 µL/min with a 2 minute association phase and 10 minute dissociation phase . The sensor surface was not regenerated between experiments . Identical injections over blank lanes without protein were used and subtracted from the data to account for background and nonspecific interactions with the biosensor chip . C6/36 cells were maintained in L-15 medium supplemented with 10% fetal bovine serum penicillin and streptomycin ( Invitrogen ) . For viral plaque assays , BHK-21 cells were seeded ( 5×104cells/well ) in 24-well , treated tissue-culture plates in α-MEM supplemented pen/strep antibiotics , and 5% Fetal Bovine Serum ( FBS ) . Cells were plated <12 hrs before use and stored at 37°C with 5% CO2 . Dengue virus serotype 2 New Guinea Clone ( NGC ) was adsorbed to confluent layers of C6/36 cells for 1 hr at 25°C with rocking every 15 mins . L-15 medium ( Mediatech ) was added , and cells were incubated at 25°C until syncytium formation was observed . The supernatant was clarified by centrifugation at 1600 RPM at 4°C and stored at −80°C . BHK-21 cells were seeded as described above . Aliquots from infections were diluted in 10 fold dilutions in Earle's balanced salt solution ( EBSS ) , and 100 µl of each dilution were added to cells . Plates were incubated for 1 hr at 37°C and rocked every 15 mins . Unadsorbed virus was removed by washing with 1 ml PBS , after which 1 ml of α-MEM supplemented with 2% carboxymethylcellulose ( CMC ) , pen/strep antibiotics , HEPES and 2% FBS , was added to each well and incubated at 37°C for 4 days . The CMC overlay was aspirated , and cells were washed 2× with 1 mL PBS and stained with crystal violet . Virus supernatant was diluted in EBSS to a stock concentration that would allow for infection at MOI of 1 , based on 50 , 000 seeded cells . Small molecules ( or carrier ) were added to the inoculum as indicated for each experiment . Cells were infected for 1 hr at 37°C with gentle rocking every 15 mins . Virus ( or virus∶small-molecule mixtures ) were washed from cells with 1 mL of PBS and overlay medium ( α-MEM supplemented with HEPES , pen/strep antibiotics and 2% FBS ) added . Plates were incubated at 37°C for 24 hrs . Aliquots of the supernatant were withdrawn and stored at −80°C . BHK-21 cells were seeded at a density of 15 , 000 cells in a 96 well format . Compounds or vehicle were serially diluted in EBSS and 100 µl were transferred to each well . Plates were incubated at 37°C for 1 hr , media was aspirated and cells were washed 2× with 200 µl of PBS . 200 µl of α-MEM supplemented with pen/strep antibiotics , and 2% FBS was added and incubated for 24 hrs at 37°C . 20 uL of alamarBlue ( Invitrogen ) was added directly to each well and incubate for 2 hrs and read for absorbance at 570 nm .
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Fusion of viral and cellular membranes is necessary to establish infection by an enveloped virus . This process is facilitated by rearrangement of protein ( s ) present on the virion surface in response to molecular cues from the compartment from which fusion occurs , such as low pH of an endosome . Dengue virus is an enveloped virus in the flavivirus family; its “E" ( for envelope ) protein is the fusion mediator . We previously showed that peptides derived from the membrane proximal “stem" of the E protein bind a form of E that represents a late-stage fusion intermediate . We used this assay to screen for small-molecule inhibitors that compete for stem-peptide association with E . We describe one such inhibitor and its analogs that block viral fusion . These inhibitors also block infectivity if added to dengue virus before infection . Withdrawing the inhibitor before fusion reverses the blockage . We propose that these small molecules bind a hydrophobic pocket on the virion surface and that the virus carries them into the endosome , where they prevent viral fusion by stabilizing an intermediate conformation of the E protein that cannot complete the fusion-promoting conformational change . Identification of these fusion inhibitors shows that viral entry is a possible target for anti-flavivirus drugs .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"medicinal",
"chemistry",
"virology",
"chemistry",
"biology",
"microbiology",
"viral",
"structure"
] |
2012
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Small-Molecule Inhibitors of Dengue-Virus Entry
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Alternative splicing ( AS ) and gene duplication ( GD ) both are processes that diversify the protein repertoire . Recent examples have shown that sequence changes introduced by AS may be comparable to those introduced by GD . In addition , the two processes are inversely correlated at the genomic scale: large gene families are depleted in splice variants and vice versa . All together , these data strongly suggest that both phenomena result in interchangeability between their effects . Here , we tested the extent to which this applies with respect to various protein characteristics . The amounts of AS and GD per gene are anticorrelated even when accounting for different gene functions or degrees of sequence divergence . In contrast , the two processes appear to be independent in their influence on variation in mRNA expression . Further , we conducted a detailed comparison of the effect of sequence changes in both alternative splice variants and gene duplicates on protein structure , in particular the size , location , and types of sequence substitutions and insertions/deletions . We find that , in general , alternative splicing affects protein sequence and structure in a more drastic way than gene duplication and subsequent divergence . Our results reveal an interesting paradox between the anticorrelation of AS and GD at the genomic level , and their impact at the protein level , which shows little or no equivalence in terms of effects on protein sequence , structure , and function . We discuss possible explanations that relate to the order of appearance of AS and GD in a gene family , and to the selection pressure imposed by the environment .
Alternative splicing ( AS ) and gene duplication ( GD ) are two main contributors to the diversity of the protein repertoire with enormous impact on protein sequence , structure , and function [1–5] . Interestingly , several recent studies point to a direct equivalence between AS and GD . There are some cases where alternative splice variants in one organism are similar to gene duplicates in another organism [6–9] . For example , the eukaryotic splicing factor U2AF35 has at least two functional splice variants in human , U2AF35a and U2AF35b , which differ by seven amino acids in the RNA recognition motif ( Figure 1A ) . The fugu orthologue U2AF35-a has no splice variant; instead there is a duplicate gene U2AF35-b with changes identical to those found in the human splice variant U2AF35b [9] . Further , the changes introduced to a sequence are constrained by the need to preserve a stable and functional three-dimensional ( 3-D ) fold [10] . Indeed , structural studies have shown that insertions and deletions between gene duplicates tend to happen at sequence locations where they are less damaging [11] , such as loops at solvent-accessible locations . These restrictions will apply irrespective of the source of the changes and thus may introduce a certain degree of similarity between the sequence changes associated with GD and AS . Finally , recent studies have shown that AS and GD are inversely correlated on a genome-wide scale [12 , 13] , i . e . , small gene families tend to have more genes with alternative splice variants than do large families . These findings together—i . e . , anecdotal examples , structural constraints , and anticorrelation at the genomic level—suggest that AS and GD are interchangeable sources of functional diversification [12] . Genes with AS would not need to produce additional variants in the form of duplicates , and vice versa . Here , we first tested the anticorrelation between AS and GD with respect to sequence divergence , function , and gene expression . Second , we studied the interchangeability hypothesis at the protein structure level and asked to what extent AS and GD introduce changes to the sequence that are equivalent in their nature and effect on structure and function . To this end , we conducted a large-scale comparison of the effects of AS and GD on human and mouse proteins ( Figure 1B and 1C; the results for the analysis of the mouse data can be found in Figure S4 ) . For the vast majority of cases , the two processes result in different protein modifications with different functional implications . This finding , while consistent with the different molecular mechanisms underlying both phenomena , contradicts the anticorrelation observed at the genomic level . We discuss some possible explanations for this paradox .
In accordance with recent findings [12 , 13] , AS and GD are anticorrelated at the genomic level ( Figure 2A ) . There are fewer alternatively spliced genes in large families of gene duplicates than expected in an unbiased distribution . This depletion is strongest in gene families of high sequence identity ( seq . id ) ( >80% seq . id . , GD80 , Figure 2A ) , and weaker but still present for more diverged families ( >40% seq . id , GD40 ) . The same trend holds true when examining orthologues: the bias is stronger amongst human–mouse or human–fly orthologues ( GD80 , χ2-value = 150 and χ2-value = 105 , respectively; p-value ≪ 0 . 001 in both cases ) than amongst human–yeast orthologues ( GD80 , χ2-value = 84; p-value ≪ 0 . 001 ) . To test whether AS and GD are interchangeable at the structural and thus functional level , we compare sequence changes between duplicate proteins to those between alternative splice isoforms ( Figure 1B–1C ) . There are two basic types of sequence changes in both AS and GD: substitutions , and insertions or deletions ( indels ) ( Figure 1B ) . Both can be described in terms of their length , the nature of the affected amino acid residues , and their location in the structure or relative to each other in the sequence . We use these properties as changes in their values can be directly related to changes in protein structure and function [19] . We analyzed the properties strictly from a protein structure and function point of view; we do not examine the evolutionary processes ( such as selection ) that led to them . We focus on gene families defined using two seq . id . thresholds: 80% and 40% . The former was chosen because the anticorrelation at the genomic level is stronger ( Figure 2A ) , and global seq . id . between alternative splice variants is >80% ( see below ) . However , GD data at the 80% seq . id . level may display only a few sequence changes , resulting in larger than expected differences between AS and GD . To avoid this bias we have also considered more diverged families , defined by a 40% seq . id . threshold . Figure S5 contains additional analyses on GD , using families defined by common protein domains . In our analyses described below , we focus on gene families that have both alternative splice variants ( AS+ ) and gene duplicates ( GD+ ) , i . e . , AS+/GD+ , except in the case of local sequence identities , for which we also extend our study to families AS–/GD+ and AS+/GD– . First we examine substitutions , i . e . , the extent and nature of amino acid changes and the length of the substituted region . In general , substitutions amongst GD range from a small number of amino acid replacements in recent homologues to a large number of replacements in proteins as divergent as haemoglobin , for instance [10] . In AS , substitutions in one isoform as compared with another one arise by the use of mutually exclusive exons [20] , although they can also be due to intron retentions accompanied by stop codons [21] . AS and GD are anticorrelated at the genomic level ( Figure 2A ) [12 , 13] . This relationship holds true for genes of different degrees of sequence divergence and of different functions , and suggests functional interchangeability between both phenomena [12] . This hypothesis is supported by known examples of equivalence [6–9] and by general , structural constraints on sequence changes [10] . In contrast , our sequence analysis shows that AS and GD followed by divergence are not directly interchangeable in their effect on protein structure and function . Indeed , they introduce very different changes with respect to indels and substitutions . The differences are summarised in Table 1 . Indels caused by AS are more drastic than those observed in GD , both in type and location of affected residues . In the case of substitutions , we find that for GD they are more conservative and more broadly distributed along the whole sequence than those of AS . However , we also observe a small number of cases in which the interchangeability hypothesis may be true , and the sequence changes introduced by AS and GD are equivalent ( Figure 3C , Figure 7 ) . To explain the apparent paradox between the relationship of AS and GD at the genome and at the protein level , we speculate on alternative explanations for the depletion of AS observed in large GD families ( Figure 2A ) [12 , 13] , discussing several effects that may contribute to it . Duplicated genes tend to have fewer exons than genes that have no duplicates but alternative splice variants ( see Figure S8 ) . This trend may in part be due to retrotransposition events which create duplicates which are single-exon genes that cannot have splice variants . However , when accounting for single-exon genes , the anticorrelation between AS and GD is still observed ( see Kopelman et al . [12] and Figure S11 ) . In addition , the anticorrelation may arise from a combination of the negative selection pressures introduced by dosage balance effects . In singleton genes that have evolved a splice variant , duplication may be disfavoured due to a multiple simultaneous dosage balance effect [36]: a single duplication event would produce a multitude of additional gene variants whose functions interfere with the existing , tightly regulated system of biological processes . This effect would be weaker for genes without AS , thus introducing a bias towards their duplication . This bias may be reinforced , under conditions of environmental stress [37 , 38] , by the need to increase expression levels of a desirable function [39 , 40] . Further , the retention of duplicates as a potential backup system may be fostered if a gene is essential for the cell's survival upon gene knockout [41] . In contrast , the introduction of AS in essential genes does not directly support robustness against null-mutations , and may even be slightly deleterious . Thus , essentiality of genes may contribute to the inverse relationship between AS and GD . Finally , if a gene with AS has duplicated , subsequent loss of an isoform in one of the copies may be tolerated due to the existence of an identical version of this isoform in the other copy of the gene . This explanation is supported by recent findings on the evolution of AS upon GD [13] , and the fact that the depletion in AS is stronger for closely related duplicates [12 , 13] ( Figure 2A , 80% seq . id . ) . Such a scenario would also be an extension of the backup role shown for paralogues [42] . A combination of these effects would , in general , result in a smaller proportion of genes with AS in gene families with more than one duplicate , in particular for recent duplicates , suggesting that the chronological order of events plays a role . Subsequent divergence of the gene duplicates may alleviate the negative impact of the dosage balance effect , allowing the evolution of AS and reducing the anticorrelation between AS and GD .
The accession numbers used in this paper are from Swiss-Prot ( http://www . ebi . ac . uk/swissprot ) : rat Piccolo C2A Q9JKS6 ) , human MAPK9 ( P45984 ) , MAPK10 ( P53779 ) , and MAPK13 ( O15264 ) ; and from the Protein Databank ( http://www . rcsb . org/pdb ) : MAPK10 ( 1jnk ) .
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Alternative splicing ( AS ) and gene duplication ( GD ) followed by sequence divergence constitute two fundamental biological processes contributing to proteome variability . The former reflects the ability of many genes to express different products , while the latter results in several copies of the same gene that are similar but not identical . In spite of these obvious differences , recent computational studies as well as anecdotal experimental evidence suggested that AS and GD produce functionally interchangeable protein variants . We provide a detailed study of the differences between alternative splicing and gene duplication and discuss potential interchangeability with respect to variation in expression , protein structure , and function . In general , the contribution of these two processes to the proteome variability is substantially different , and we advance some explanations that may explain this apparent contradiction and contribute to our understanding of the evolution of complex , eukaryotic proteomes .
|
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"Introduction",
"Results/Discussion",
"Supporting",
"Information"
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"computational",
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2007
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The (In)dependence of Alternative Splicing and Gene Duplication
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Despite partial success , communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing , a state called complete locked-in state ( CLIS ) . Based on a motor learning theoretical context and on the failure of neuroelectric brain–computer interface ( BCI ) communication attempts in CLIS , we here report BCI communication using functional near-infrared spectroscopy ( fNIRS ) and an implicit attentional processing procedure . Four patients suffering from advanced amyotrophic lateral sclerosis ( ALS ) —two of them in permanent CLIS and two entering the CLIS without reliable means of communication—learned to answer personal questions with known answers and open questions all requiring a “yes” or “no” thought using frontocentral oxygenation changes measured with fNIRS . Three patients completed more than 46 sessions spread over several weeks , and one patient ( patient W ) completed 20 sessions . Online fNIRS classification of personal questions with known answers and open questions using linear support vector machine ( SVM ) resulted in an above-chance-level correct response rate over 70% . Electroencephalographic oscillations and electrooculographic signals did not exceed the chance-level threshold for correct communication despite occasional differences between the physiological signals representing a “yes” or “no” response . However , electroencephalogram ( EEG ) changes in the theta-frequency band correlated with inferior communication performance , probably because of decreased vigilance and attention . If replicated with ALS patients in CLIS , these positive results could indicate the first step towards abolition of complete locked-in states , at least for ALS .
Communication is the process of expressing and sharing feelings , thoughts , and intentions with one another by verbal and various nonverbal means . Communication skills appear automatic but can pose severe challenges to individuals suffering from motor neuron disorders . The most devastating of motor neuron diseases is amyotrophic lateral sclerosis ( ALS ) [1] , which is progressive and renders an individual motionless , severely affecting his or her communication ability [2] . As the disorder progresses , it destroys the respiratory and bulbar functions , forcing the individual to make vital decisions . If they opt for life and accept artificial respiration , they can no longer communicate verbally , and assistive communication devices that rely on nonverbal signals such as finger movement and gaze fixation are then used for communication [3] . In ALS , the disorder progresses in most patients until the patient loses control of the last muscular response , usually the eye muscles , a condition known as completely locked-in state ( CLIS ) [4] . Brain–computer interface ( BCI ) represents a promising strategy to establish communication with paralyzed ALS patients [5–7] , as it does not need motor control . BCI research includes invasive ( implantable electrodes on or in the neocortex ) [8–11] and noninvasive means , including electroencephalography [12 , 13] , functional magnetic resonance imaging ( fMRI ) [14] , and functional near-infrared spectroscopy ( fNIRS ) [15] , to record brain activity for conveying the user’s intent to devices such as simple word-processing programs [12] . The first BCI for communication in ALS patients with intact eye muscles was demonstrated by Birbaumer et al . ( 1999 ) [12] . With at least intact eye muscles and the rest of the body paralyzed , the condition is known as locked-in state ( LIS ) [4] . Since then , several invasive and noninvasive BCIs have been developed for communication in ALS patients . Noninvasive methods , namely slow cortical potential ( SCP ) -BCI [12 , 16 , 17] , sensory motor rhythm SMR-BCI [18–20] , and P300-BCI [21–24] , have been utilized more frequently than invasive methods [25–27] for communication in people with ALS [6 , 12 , 27–30] . Irrespective of the types of BCI , during the BCI session patients selected letters or words after learning self-regulation of the particular brain signal or by focusing their attention to the desired letter or a letter matrix [21 , 23] , and the attention-related brain potential selects the desired letter . In a meta-analysis of the scientific literature of all ALS patients in CLIS [13] , it was found that none of the existing techniques such as the P300 event-related brain potential ( ERP ) , SCP , frequency analyses of various frequency bands of the electroencephalogram ( EEG ) , and invasive electrocorticogram ( ECoG ) recordings [31] allowed reliable and meaningful communication with BCI . All BCI procedures mentioned above were based on effortful and explicit ( conscious ) voluntary control of a neuroelectric brain response such as learning with feedback and reward , during which patients learned to increase or decrease amplitudes of the SCP [12] to produce event-related desynchronization ( ERD ) of the central alpha-rhythm [32] to focus attention on a visually or auditorily presented sequence of letters in order to select a desired letter with the brain response . P300 [21 , 22 , 24] is also used in a similar manner to visually select a desired letter . The required activation of explicit-voluntary ( controlled ) attention in these BCI tasks , none of them resulting in stable learning of brain-based communication , prompted us to propose the theoretical psychophysiological notion of “extinction of goal directed cognition and thought” [13] in complete paralysis with otherwise intact cognitive processing . This theoretical account—certainly highly speculative in light of the complete lack of data about cognition and inner speech and motivational processing in CLIS—we substantiated with the failure to replicate initial positive reports about instrumental ( volitional ) [33 , 34] learning of autonomic responses in the curarized ( paralysed ) rat . The persistent incapability to replicate these experiments suggests that intact or partially intact motor functions and somatic-motor system mediation of autonomic functions ( i . e . , subtle postural or muscle tension changes to affect the desired physiological changes ) is a mandatory requirement for instrumental learning and control of physiological functions . Theoretical views of this problem , like the one proposed , are not new but were expressed already in Greek philosophy by Aristotle [35] and by the philosophers of volition , particularly Arthur Schopenhauer in his monumental account of “Die Welt als Wille und Vorstellung” [36] ( The World as Volition and Imagery ) and during twentieth century learning theory [37 , 38] . Conscious of the fact that it is problematic to justify a theory on negative facts ( lack of instrumental learning in the curarized rat and missing BCI control of BCIs requiring controlled attention in CLIS ) , we argue that [27] classical reflexive conditioning and learning might circumvent volitional effort in instrumental control . Thus , an experimental procedure involving processing of overlearned ( “automatic” ) questions ( i . e . , “Berlin is the capital of France , ” “You are in pain” ) asking for automatic cognitive processing only may fulfill this criterion . Thinking but not voluntary imagining affirmative “yes” and negative “no” to overlearned questions occurs effortlessly , such as automatic nodding of the head in a conversation: the extensive literature ( mostly Russian ) on semantic classical conditioning [39] and implicit attention and memory [40] provides ample support of this notion . However , for the case of patients in CLIS , one is faced with the dilemma that we cannot expect a learning curve characteristic of skill learning ( usually exponential ) or classical conditioning in a BCI task asking for overlearned “yes” and “no” responses as used on the present BCI system [29 , 30] . Patients were confronted in their lifetime with these questions ( “Berlin is the capital of France” ) before entering ( or on the verge of ) CLIS , and we can assume with certain confidence that no further learning at the time of assessment with the BCI is necessary and thus no learning curve can be expected . The same holds true for personal questions ( “Your husband’s name is Joachim” ) . Thus , at an experimental level , it remains difficult to prove the speculation of intact classical conditioning but lack of instrumental voluntary learning in CLIS patients involved in a BCI task after entering CLIS . Only one patient in the literature [31] using an electrocorticographic-based BCI before and after transitioning from LIS to CLIS was published . This patient was unable to communicate with the BCI after entering CLIS . However , observation of a single case cannot serve as strong evidence comparable to the animal experiments [33 , 34] using curarization for the creation of reversible paralysis . We are also aware of the fact that a single case of a cognitively intact CLIS patient or curarized organism learning to instrumentally drive a BCI disproves our hypothesis . Experimental descriptions of patients with ALS or subcortical stroke in locked-in state ( LIS ) or who are severely paralysed using spiking frequency changes of motor neurons to move a robotic arm [10 , 11 , 41] cannot disprove our account . Patients in those studies [10 , 11 , 41] still had intact motor control of eye movements and some remaining muscles and thus could use the remaining muscular forces ( somatomotor mediation ) for instrumental learning and BCI control . Because none of the BCI techniques outlined above are able to provide viable means of communication [5] , the patients in CLIS due to ALS , without any muscular control , are rendered communicationless . We are then faced with the dilemma of defining communication in CLIS . Does it only mean to express one’s feelings , thoughts , and intentions in a fluent , automatized manner ? Or , alternatively , does it mean to convey one’s intent or one’s feelings and thoughts to questions ? As mentioned , all the existing BCIs rely on two elements: first , the neuroelectric signal ( EEG or ECoG ) control and second at least an intact eye muscle; the neuroelectric signal–based BCI did not work so far in patients in CLIS , in which eye movement control is lost . A single case report by Gallegos-Ayala et al . ( 2014 ) [42] used fNIRS to measure and classify cortical oxygenation and deoxygenation following the “yes” or “no” thinking of the CLIS patient in response to true or false questions , respectively . The report described a CLIS patient with ALS achieving BCI control and “yes” and “no” communication to simple questions with known positive answers or negative answers and some open questions over an extensive time period . Although it was not spontaneous and voluntary , controlled communication , it at least enabled the individual without any means of communication to transmit “yes” and “no” to questions framed by family members and/or caregivers . The result opened a venue to provide at least some means of communication to individuals in CLIS who are otherwise left communicationless . Hence , an extensive study was performed on four ALS patients in CLIS to train them to communicate “yes” and “no . ” In the present study , which is the first of its kind , fNIRS-based BCI was used for binary communication in four ALS patients in CLIS . The fNIRS-based BCI was employed successfully to train patients to regulate their frontocentral brain regions in response to auditorily presented questions . After training a classifier separating “yes” from “no” answers for several days , the patients were given feedback of their affirmative or negative response to questions with known answers and open questions over weeks .
The support vector machine ( SVM ) classifier’s classification of true sentences as true , false sentences as false , true sentences as false , and false sentences as true was used to calculate the false positive rate ( FPR ) and true positive rate ( TPR ) for each training and feedback session and also for all the training sessions and feedback sessions separately for each patient . FPR and TPR was plotted to obtain the receiver operating characteristic ( ROC ) curve of the binary SVM classifier during training and feedback sessions for each patient , as shown in S2 Fig , S3 Fig , S4 Fig and S5 Fig for patients F , G , B , and W , respectively . The change in oxygenated hemoglobin ( O2Hb ) , EOG , and EEG power spectrum in response to true and false questions , obtained from the frontocentral region of the brain , across all the sessions from each patient was used to determine the SVM classification accuracy of "true/yes" and "false/no" answers . Successively , the daywise CA ( i . e . , averaging CA of all sessions in a single day ) of each patient was compared to the adjusted chance-level threshold ( described in BCI effectiveness metric section ) , as shown in Figs 2 , 3 , 4 and 5 for patients F , G , B , and W , respectively , and Table 2 . The offline CA is reported using O2Hb , EEG , and EOG signals for training sessions , while online CA is reported using only O2Hb for feedback and open question sessions because feedback was provided online only using the O2Hb signal . The answering concordance between semantically paired questions ( "Paris is the capital of Germany , " "Paris is the capital of France" ) , expressed as the percentage of concordant answers over pairs' repetition , was as follows: F , 68%; G , 67%; B , 67%; W , 70% . Thus , the semantic concordance rate ( SCR ) ranges from 67 to 78% ( see S12 Table ) . Median values of SCR are significantly different from 50% ( all p < 0 . 0001 ) , in which 50% is the SCR expectation of a random classifier . The results of ANOVA and post hoc t-test ( see Table 1 , row G , H , I , and J ) further emphasize a significant difference between the classification accuracy of O2Hb versus EEG and O2Hb versus EOG , with no significant difference between EEG and EOG . There is one exception in the case of patient W , as no significant difference was found between the classification accuracy of O2Hb versus EOG . Patient W ( 23 y of age ) suffering from juvenile ALS with an extremely rapid disease progression ( 2 y from diagnosis to CLIS ) was not asked open questions because of the supposedly difficult emotional state at that early stage of BCI communication but continues to train the BCI at present . The patients showed the following stable dominant frequencies: F , 6 . 75 Hz; G , 6 . 25 Hz; B , 7 Hz; and W , 8 Hz . Power spectrum density of electroencephalographic signals corresponding to "true/yes" and "false/no" sentences’ ISI acquired from channel FC6 is shown in S1 Fig . The middle-frequency bands’ ( high-theta , low-alpha , and high-alpha ) mean power comparison between "true/yes" and "false/no" sentences’ ISI revealed no main effects of conditions and channels in any patient ( all p > 0 . 05 ) . The middle-frequency bands’ ( high-theta , low-alpha , and high-alpha ) spectral features comparison between sentence presentation interval and sentences’ ISI revealed some main effects of the intervals factor , as reported in S1 Table , section A . In two ( G and B ) out of four patients , a smaller low-alpha band “power variability” in the sentences’ ISI compared to the sentence presentation interval was found ( p < 0 . 05 ) . In patient W , a higher high-theta , low-alpha , and high-alpha bands’ mean power in the sentences’ ISI compared to the sentence presentation interval was found ( ISI was synchronized compared to sentence presentation ) . Patient F did not show any significant difference in the middle-frequency bands’ mean power and “power variability” ( all p > 0 . 05 ) . See Results section of S1 Text for details . The correlation analysis between fNIRS classification accuracy and low-frequency bands’ ( those more related to vigilance ) mean power revealed some interesting results ( see S1 Table , section B ) . In three ( G , B , and W ) out of four patients , the median of the negative averaged correlation between low-theta band mean power and fNIRS classification accuracy was significantly different from zero ( patient G: r = –0 . 365; patient B: r = –0 . 264; patient W: r = –0 . 386; all p < 0 . 05 ) . However , in patient F , who had the longest time period in CLIS , the median of the positive averaged correlation between delta and high-theta band mean power and fNIRS classification accuracy was significantly different from zero ( delta: r = 0 . 233; high-theta: r = 0 . 213; all p < 0 . 05 ) . The low-frequency bands’ mean power distribution medians of successful and unsuccessful days ( i . e . , days with classification accuracy above chance-level threshold were considered successful ) was further investigated for each patient to ascertain the difference , if any . In patient G , the low-theta band mean power of successful days was significantly smaller than that of unsuccessful days ( p < 0 . 05 ) . In patient B , the high-theta band mean power of successful days was significantly smaller than that of unsuccessful days ( p < 0 . 05 ) . This strengthens the above results of lower-frequency bands’ dominance for more unsuccessful performance . Additional details are provided in the Results section of S1 Text and in S1 Table .
Three patients ( G , B , and W ) showed a negative averaged correlation between low-theta band mean power and fNIRS classification accuracy [53] , meaning the smaller the low-theta band mean power , the higher the performance , except in patient F , who has been in CLIS for more than 4 y . The correlations analysis between daywise classified BCI performance and low-frequency bands across all intervals and all electrodes for each patient separately gave consistent and highly significant results ( see Results section of S1 Text for details ) . The binary communication performance worsened with lower frequencies in two patients ( G and B ) as predicted . The number of days for patient W was limited ( i . e . , 6 d ) , thus it is quite unlikely to expect a significant difference in low-frequency bands’ mean power for successful and unsuccessful days ( see S1 Table , section B ) . Decrease in vigilance reflected in slow frequencies impedes BCI performance and communication . Patient F , who had an extremely long history of CLIS without any communication over the years , showed a positive correlation in the delta and high-theta band with performance . She was the patient with very slow dominant frequency during rest , and it may be speculated that in such a deprived brain , superposition of delta and high-theta frequency represent a sign of increased attention and focus . For instance , low-theta band mean power can be used in future BCIs to stop a BCI session or to avoid the presentation of the sentences and/or questions during decreased vigilance . For a robust validation of the BCI binary communication system in CLIS , two main unsolved questions remain: ( i ) the physiological identification of the cognitive processes underlying the listening to “yes–no” questions and the answerer’s mental state and ( ii ) the online identification of decreased vigilance states that are detrimental ( lowering performance ) for BCI binary communication purposes , such as decreased alertness , drowsiness , and sleeping . Multielectrode EEG recordings used simultaneously with the fNIRS system and quantitative source analysis of the different frequency bands at different sites are necessary to clarify these questions . For the study reported here , only portable devices and a few EEG channels could be used in the interest of the bedside , home-based strategy selected . Thus , our interpretation of the EEG frequency bands’ variations remains speculative . In patients completely motionless over a period of years with restricted vision because of eye muscle paralysis and compromised vision because of drying and reduced or absent afferent input from the sensorimotor system , reduced vigilance measured with EEG and an irregular sleep–wake cycle was documented by Ramos et al . ( 2011 ) [31] and Soekadar et al . ( 2013 ) [54] . De Massari et al . ( 2013 ) [45] have shown that reduction of P300 amplitude across the BCI paradigm presentation predicted negative performance , again suggesting excessive loss and excessive variation of wakefulness and attention as a major limiting factor for BCI applications in such severely compromised patients . Thus , we modified the existing fNIRS–BCI–system in a hybrid EEG–fNIRS–BCI , with the EEG allowing online corrections of excessive reduction of vigilance indicated by appearance of delta and low-theta periods . This new hybrid system should allow further improvement of communication in CLIS . The results on four CLIS patients reported here allow the following conclusions:
A continuous wave ( CW ) -based fNIRS system , NIRSPORT ( NIRX ) , which performs dual-wavelength ( 760 nm and 850 nm ) CW near-infrared spectroscopic measurement at a sampling rate of 6 . 25 Hz , as shown in in Fig 6A , was used . The NIRS optodes were placed on the frontocentral regions as shown in Fig 6B . During the BCI sessions , the EEG was also recorded with a multichannel EEG amplifier ( Brain Amp DC , Brain Products , Germany ) from ten Ag/AgCl passive electrodes mounted on the head cap . Six electrodes ( FC5 , FC1 , FC6 , CP5 , CP1 , and CP6 ) were used to acquire EEG signals and four electrodes were used to acquire the vertical and horizontal EOGs . The signals were bandpass filtered using a finite impulse response filter with a bandpass of 0 . 5–30 Hz . The EOG was filtered with different bandpass filters ( 0 . 5–3 . 5 Hz , 0 . 5–10 Hz , and 0 . 5–30 Hz ) , but none of these filters led to significant differences of neurophysiological patterns related to the ocular activity . Question- or response-related eye movements were not detected in any of the patients over the whole time period of many weeks . Each EEG channel was referenced to an electrode on the right mastoid and grounded to the electrode placed at Fz location of the scalp . Electrode impedances were kept below 10 kΩ and the EEG signal was sampled at 500 Hz . During all BCI sessions , the spontaneous EEG was visually controlled by one of the authors ( NB or BX ) to avoid longer periods of slow-wave sleep during the BCI evaluation . A BCI session was initiated only if the EEG was free of high-amplitude slow activity below 4 Hz . Patient F ( female , 68 y old , completely locked-in state ) was diagnosed with bulbar sporadic ALS in May 2007 , was diagnosed as locked-in in 2009 , and was diagnosed as completely locked-in May 2010 , based on the diagnoses of experienced neurologists . She has been artificially ventilated since September 2007 , fed through a percutaneous endoscopic gastrostomy tube since October 2007 , and is in home care . No communication with eye movements , other muscles , or assistive communication devices was possible since 2010 . Further details of this patient are described in Gallegos-Ayala et al . ( 2014 ) [30] . Patient G ( female , 76 y old , CLIS ) was diagnosed with bulbar ALS in 2010 . She lost speech and capability to walk by 2011 . She has been fed through a percutaneous endoscopic gastrostomy tube since September 2011 , artificially ventilated since March 2012 , and is in home care . She started using assistive communication devices employing one finger for communication in February 2013 . Later , she was diagnosed with degeneration of vision because of cornea defects in September 2013 . After the failure of the finger-communication device , an attempt was made to communicate using eye tracking in early 2014 . She stopped communicating with the eye in August 2014 , before the BCI was introduced , and an attempt was made to communicate with the subtle twitch of an eye lid , which was not reliable . The husband and caretakers declared no communication with her since August 2014 . Patient B ( male , 61 y old , CLIS ) was diagnosed with nonbulbar ALS in May 2011 . He has been artificially ventilated since August 2011 , fed through a percutaneous endoscopic gastrostomy tube since October 2011 , and is in home care . He started communicating with a speech device in his throat from December 2011 , which ultimately failed , and he started using the MyTobii eye-tracking device in April 2012 . He was able to communicate with MyTobii until December 2013 , after which the family members attempted to communicate by training him to move his eyes to the right to answer “yes” and to the left to answer “no , ” but the response was variable . No communication was possible since August 2014 . Patient W ( female , 24 y old , locked-in state on the verge of CLIS ) was diagnosed with juvenile ALS in December 2012 . She was completely paralyzed within half a year after diagnosis and has been artificially ventilated since March 2013 , fed through a percutaneous endoscopic gastrostomy tube since April 2013 , and is in home care . She was able to communicate with eye tracking from early 2013 to August 2014 but was unable to use the eye-tracking device after the loss of eye control in August 2014 . After August 2014 , family members were able to communicate with her by training her to move her eyes to the right to answer “yes” and to the left to answer “no” questions until December 2014 . In January 2015 , eye control was completely lost , she tried to answer yes by twitching the right corner of her mouth , that too varied considerably , and parents lost reliable communication contact . All four patients reported in this manuscript were enrolled consecutively . Patients’ family approached us to get enrolled in the study because of the past work and public appearance of the corresponding author . Patients were never screened and excluded for this study . The only criterion for the inclusion in this study was that the patient should be in completely locked-in state ( CLIS ) or on the verge of CLIS , and family members could not communicate with eye movements or any other response with the patient . The CLIS state was then verified with confirmation of the attending neurologist , EOG recordings , and video recordings of the families’ failures to achieve contact with the patient . The schematic depicting the experimental procedure , acquisition , and analysis of fNIRS and EEG data during BCI sessions is shown in Fig 6 . An auditory paradigm was employed to ( a ) train patients on questions with known answers , termed as training sessions; ( b ) give feedback on questions with known answers , termed as feedback sessions ( i . e . , “Your husband’s name is Joachim , ” and after classification during ISI: “your answer was recognized as ‘yes’/‘no’ ) ; and ( c ) answer open questions , termed as open question sessions ( “You have back pain” ) . Known questions are personal questions based on patient’s biography . For every known question with a clear “yes” answer , a semantically related question with a clear “no” answer was constructed and vice versa; for example , “You were born in Berlin” and “You were born in Paris . ” Patients were asked to think “yes” or “no” answers and , if possible , also to use their previously successful eye movements . They were explicitly instructed not to imagine the answer or visually or auditorily imagine the word ( i . e . , as a visual or sound form ) “yes” or “no” . Open questions are general questions related to quality of life and questions of caretakers whose answers can only be known by the patient . A total of at least 200 known questions and 40 open questions were constructed for each patient with family members before the initiation of the BCI study . Each patient was visited for 4 to 5 d in a month , except patient W . Three to four sessions were performed each day depending upon the health condition reported by the caretakers of the patient . Every session lasted for 9 min , and a session in progress was terminated extremely rarely ( i . e . , if removal of saliva became urgent ) . In such a rare event , the session was started again . Since each session lasted for 9 min , the caretaker or the family member was always instructed to take care of the needs of the patient before the start of the session , and the session was always started with the permission of the caretaker or the family member . A session , once in progress , was never terminated for patients F , G , and W . For patient B , a session was terminated while in progress three times because of removal of saliva , and the data were not included in any kind of analysis . Acoustically presented instructions about the procedure were given repetitively before each training , feedback , and open questions sessions , allowing patients to recall and consolidate the required task to listen and answer mentally . Each BCI session started with training sessions , during which the patients were instructed to listen to 20 personal questions ( with known answers ) consisting of 10 true and 10 semantically equivalent false sentences . The sentences were presented randomly in such a way that two semantically related questions never played one after another . Family members were always present throughout the BCI session , and they never prompted the patient to answer the question . Complete pin-drop silence was maintained during the session , and only the recorded sentences were presented via audio presentation software connected to sound box with the voice of a family member or caregiver . Patients were asked to think “ja , ja…” ( German for “yes” ) and “nein , nein…” ( German for “no” ) for 15 s during the ISI until they heard the next sentence after an interval of 5 s of rest , as shown in Fig 6 . After the end of each session , the fNIRS feature necessary to differentiate between “yes” and “no” answers during ISI was extracted and classified . Only training sessions were performed during the first few days , and upon several successful training sessions ( as described below in BCI effectiveness metric section ) , the online feedback session was performed . During training sessions , both the patient and the algorithm were trained . Patients learned to mentally answer the question , and the algorithm learned to classify the “yes” and “no” fNIRS pattern of a particular patient . This kind of “mutual learning” seems important to optimize the “yes” versus “no” classification outcome and to customize and/or adapt the BCI system to each individual patient . At the end of each training session with 20 sentences ( questions ) , patients were told the average classification accuracy of the session ( calculated using the SVM classifier ) to motivate and help patients in learning . In the course of an online feedback session , patients were presented the known questions as described above , but now at the end of the 15 s ISI they were given auditory feedback of accuracy , during which the computer said , “Your answer was recognized as yes” or “Your answer was recognized as no” depending upon the question ( all sessions were videotaped and are available on request ) . Feedback to strengthen the conditioned response was provided only if the classification accuracy was greater than the chance-level upper limit to guide the conditioned learning toward meaningful answers and to avoid frustration by negative feedback already at the beginning of a daily session . Feedback was driven by the fNIRS classifier , calculated using the data acquired during the training sessions . After successful training and feedback sessions , the patients were presented with open questions , during which they were always given the auditory feedback of their answer . The validity of answers to open questions can only be estimated by ( a ) face validity ( i . e . , questions of pain in the presence of an open wound ) ; ( b ) stability over time; ( c ) external validity , estimated by family members and caretakers; and ( d ) internal validity between questions ( i . e . , the concordance between the answer to “I love to live” with the answer to “I rarely feel sad” [presented to all patients—except W—regularly] ) . Table 1 , rows A , B , and C enumerate the total number of training , feedback , and open questions sessions performed by each patient , respectively . Patient W received no open questions because of low classification accuracy , which we and the parents attributed to her emotional state distracting her from concentrating on the responses because of the short time period of adaptation to the CLIS . The binary BCI system effectiveness and robustness depends on its capability of correctly classifying the neurophysiological correlates of “yes” and “no” answers to true and false questions . The proposed true and false questions have two possible outcomes only , which are equally distributed with a probability of 0 . 5 . To ensure that the classification of “yes” and “no” answers is not at chance-level , a reliable metric has to be used . Based on binomial distribution theoretical background , Müller-Putz et al . [59] defined a metric for experimental procedures with a binary outcome and multiple repetitions to determine the chance-level threshold above which the classification accuracy results can be considered as not resulting from chance . Because type and number of questions ( personal questions with known answers and open questions ) are partly different over days ( i . e . , the experimental conditions were different ) the chance-level threshold was calculated on a daily basis . The daily-based chance level was computed using the formulas described in Müller-Putz et al . [59] and by taking into account the number of true and false sentences presented in a single day to each patient . The fNIRS data was acquired online throughout all the sessions , namely training , online feedback , and open question sessions . The fNIRS data acquired online was normalized , filtered using different bandpass filters ( 0 . 0016–0 . 3 ) , ( 0 . 01–0 . 3 ) and ( 0 . 02–0 . 3 ) and processed using modified Beer–Lambert law [60 , 61] to calculate the relative change in concentration of oxyhemoglobin ( O2Hb ) and deoxyhemoglobin ( RHb ) . The choice of bandpass filter had no effect on the waveforms of signal . The relative change in O2Hb computed online during each session was used to train a SVM classifier model . The mean of relative change in O2Hb across each channel was used as a feature to train the SVM model through a 5-fold cross-validation procedure . In this study , only the relative change in O2Hb was used , as after the end of sessions with known answers it was observed that O2Hb provided stable and higher cross-validation classification accuracy than RHb . In an invasive animal study with nonhuman primates , we have also measured a superior covariation of oxygenation changes compared to deoxygenation , with intracortically recorded neural activity [49] supporting this clinical observation . Since the classification accuracy achieved was higher for O2Hb , the SVM model generated using O2Hb was used to provide online feedback for known as well as open questions sessions . If the classification accuracies for at least three consecutive “training” sessions with questions with known answers were greater than the chance-level threshold , a new model was generated using the relative change in O2Hb across three training sessions to give online feedback . During an online feedback session , fNIRS data acquired online corresponding to each ISI was processed to obtain the relative change in O2Hb , as described above , across all the channels . The mean of the relative change in O2Hb across all the channels was used as test feature to map onto model space . Upon mapping of this test feature onto the model space , the SVM predicted ( called predict label ) the side of the hyperplane the test feature fell on . Depending on the value of the predict label , appropriate feedback was provided to the patient: if the predict label was 0 , the patient was given feedback that his or her answer was recognized as “no , ” and if the predict label was 1 , the patient was given feedback that his or her answer was recognized as “yes . ” fNIRS provides three different signals: oxyhemoglobin ( O2Hb ) , deoxyhemoglobin ( RHb ) and total hemoglobin ( THb ) [60 , 61] . As mentioned in the section Online data analysis , since the classification accuracy achieved was higher for O2Hb , only the results from the offline processing of O2Hb data will be shown along with the EEG and EOG data . The relative change in O2Hb , EEG , and EOG data were processed offline to determine: To ascertain the difference between the averaged ISI of true and false sentences , t-tests were performed . t-test was performed separately for O2Hb , EEG , and EOG signals acquired from all the sessions and across all the channels in a session , averaged over many sessions varying slightly between patients . Furthermore , t-tests were also performed for each session between the ISI of all the ten true sentences and all the ten false sentences ( “Berlin is the capital of France , ” “Berlin is the capital of Germany” ) across different channels in a session . For the EEG , frequencies between 0 and 30 Hz , estimated by Welch’s method [62] , were used for classification and statistical testing . ANOVA and post hoc t-test were used . Frequency bands’ mean power and their “variability” were estimated using Welch’s method [44 , 58] . For each patient , middle-frequency bands’ ( i . e . , high-theta , low-alpha , and high-alpha ) features of “true/yes” and “false/no” sentences’ ISI were compared , as well as middle-frequency bands’ features of sentence presentation and interstimulus intervals . Successively , the averaged correlation between each low-frequency band ( i . e . , delta , low-theta , and high-theta ) mean power and fNIRS classification accuracy was computed to find relevant relationships of low EEG rhythms with the BCI experimental procedure outcome . Details are provided in the Methods section of S1 Text . The performance of the binary SVM classifier was ascertained by plotting the ROC curve . The ROC curve was created by plotting the TPR against the FPR ( obtained from the contingency table created for each session ) and the average of all the sessions , separately for each patient , using the four possible outcomes of a binary SVM classifier . The formation of contingency table for training and feedback sessions for each participant is described in the Receiver operating characteristic curve section of S2 Text . Further chi-square test was performed to determine the statistical significance of the observed outcomes in the contingency table , also described in the Receiver operating characteristic curve section of S2 Text . Semantic concordance rate ( SCR ) was calculated to ascertain the consistency and/or concordance of the answers between semantically equivalent but contrasting true and false sentences requiring “yes” and “no” answers , respectively . SCR ( i . e . , the percentage of concordant answers over pairs’ repetition ) was calculated for all semantically related sentences presented to each patient . The method employed to calculate the semantic concordance rate is described in the Semantic concordance rate ( SCR ) section of S2 Text . This measure also provides indirect information about the intact cognitive processing of the presented sentences in a CLIS patient .
|
Despite scientific and technological advances , communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing , a condition that is called completely locked-in state . Brain–computer interfaces based on neuroelectrical technology ( like an electroencephalogram ) have failed at providing patients in a completely locked-in state with means to communicate . Therefore , here we explored if a brain–computer interface based on functional near infrared spectroscopy ( fNIRS ) —which measures brain hemodynamic responses associated with neuronal activity—could overcome this barrier . Four patients suffering from advanced amyotrophic lateral sclerosis ( ALS ) , two of them in permanent completely locked-in state and two entering the completely locked-in state without reliable means of communication , learned to answer personal questions with known answers and open questions requiring a “yes” or “no” by using frontocentral oxygenation changes measured with fNIRS . These results are , potentially , the first step towards abolition of completely locked-in states , at least for patients with ALS .
|
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"Introduction",
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2017
|
Brain–Computer Interface–Based Communication in the Completely Locked-In State
|
The epidemiologically most important mechanism of antibiotic resistance in Staphylococcus aureus is associated with mecA–an acquired gene encoding an extra penicillin-binding protein ( PBP2a ) with low affinity to virtually all β-lactams . The introduction of mecA into the S . aureus chromosome has led to the emergence of methicillin-resistant S . aureus ( MRSA ) pandemics , responsible for high rates of mortality worldwide . Nonetheless , little is known regarding the origin and evolution of mecA . Different mecA homologues have been identified in species belonging to the Staphylococcus sciuri group representing the most primitive staphylococci . In this study we aimed to identify evolutionary steps linking these mecA precursors to the β-lactam resistance gene mecA and the resistance phenotype . We sequenced genomes of 106 S . sciuri , S . vitulinus and S . fleurettii strains and determined their oxacillin susceptibility profiles . Single-nucleotide polymorphism ( SNP ) analysis of the core genome was performed to assess the genetic relatedness of the isolates . Phylogenetic analysis of the mecA gene homologues and promoters was achieved through nucleotide/amino acid sequence alignments and mutation rates were estimated using a Bayesian analysis . Furthermore , the predicted structure of mecA homologue-encoded PBPs of oxacillin-susceptible and -resistant strains were compared . We showed for the first time that oxacillin resistance in the S . sciuri group has emerged multiple times and by a variety of different mechanisms . Development of resistance occurred through several steps including structural diversification of the non-binding domain of native PBPs; changes in the promoters of mecA homologues; acquisition of SCCmec and adaptation of the bacterial genetic background . Moreover , our results suggest that it was exposure to β-lactams in human-created environments that has driven evolution of native PBPs towards a resistance determinant . The evolution of β-lactam resistance in staphylococci highlights the numerous resources available to bacteria to adapt to the selective pressure of antibiotics .
The most important antibiotic resistance mechanism in staphylococci is associated with the mecA gene , which confers resistance to the large class of β-lactam antibiotics . mecA is carried on a mobile genetic element called staphylococcal cassette chromosome mec ( SCCmec ) [1] , which always inserts at the same locus in the chromosome , in the 3’ end of orfX ( which encodes a RNA methyltransferase ) [1 , 2] . Several studies have demonstrated that acquisition of mecA confers to staphylococci a competitive advantage in the hospital , community and veterinary environments [3 , 4] . Introduction of the mecA determinant into the S . aureus genome on multiple occasions , has led to the emergence and worldwide dissemination of several methicillin-resistant S . aureus ( MRSA ) clones [5] . The mecA determinant encodes an extra penicillin-binding protein ( PBP2a ) . The expression of resistance is achieved by a slow rate of acylation of PBP2a as well as a low affinity of the enzyme for β-lactams [6] . Structural studies have revealed that the poor acylation rate , that PBP2a presents when in contact with β-lactams , is due to a distorted active site , provided by the flexibility of the non-binding ( NB ) domain and regions surrounding the active site groove in the transpeptidase ( TP ) domain [7] . Furthermore , the position of Ser403 is crucial for the nucleophilic attack of the β-lactam ring , which leads to acylation of the protein [7] . The first clinical MRSA isolates were identified in the UK in 1961 , shortly after the introduction of methicillin into clinical practice [8 , 9] . Early MRSA were found to present a heterogeneous profile of resistance to β-lactams [10] . Further studies have revealed that mutations in genes associated with cell division as well as central metabolism ( the so-called auxiliary genes ) influence the expression of β-lactam resistance and the resulting phenotype [11] . Moreover , the expression of homogeneous high level resistance has been associated with the activation of the bacterial stringent response , provoked by mutations in the relA system [12 , 13] and related regulons and genes [14] . These findings underline the importance of the S . aureus genetic background in the expression of β-lactam resistance . The rapid emergence of MRSA raised the hypothesis that mecA was already present in the staphylococcal gene pool prior to the introduction of methicillin . In fact , a ubiquitous homologue named mecA1 , with 80% nucleotide identity to mecA has been identified in the primitive coagulase-negative Staphylococcus sciuri [15] . Several lines of evidence suggest that mecA1 is the precursor of mecA . While mecA1 does not confer resistance to β-lactams in S . sciuri , there are reports of β-lactam-resistant strains that have alterations in the promoter region of this gene [16] . When introduced experimentally into a S . aureus strain , mecA1 was able to confer β-lactam resistance and produce a protein with properties resembling that of MRSA PBP2a [17 , 18] . Additional mecA homologues have been identified in related species , including a mecA homologue ( mecA2 ) with 90% nucleotide identity with mecA in Staphylococcus vitulinus [19] . Furthermore , mecA along with its regulators , mecI and mecRI , has been identified in a small number of Staphylococcus fleurettii isolates [20] . Despite the importance of mecA in the epidemiology of antibiotic resistant staphylococci , the evolutionary history of this gene has remained unclear . The purpose of this study was to shed light on the evolutionary steps linking the native mecA homologues identified in primitive coagulase negative staphylococci to the β-lactam resistance gene mecA and the resistance phenotype .
The putative precursor of mecA is mecA1 , previously shown to be ubiquitous in S . sciuri ( 15 ) , but the frequency of the other mecA homologues ( mecA2 and mecA ) in the remaining species of the S . sciuri group remained unclear . Additionally , the location of mecA homologues in the chromosome was unknown . A search for mecA homologues by BLAST analysis in the genomes of 106 S . sciuri , S . vitulinus and S . fleurettii isolates collected from humans and animals showed that all strains carried at least one copy of mecA homologue . These were found either in the orfX ( SCCmec insertion site ) or 200 kb from orfX , a site that from now on , we will call native location . We confirmed that , in our collection , mecA1 was present in all S . sciuri isolates [15] and mecA was present in all S . fleurettii strains [20] at the native location . S . vitulinus was different from the other species , since half of the strains ( n = 9 ) carried mecA2 [19] , and the remaining strains either carried mecA ( n = 6 ) or did not carry any mecA homologue in this region ( n = 3 ) . Alignment of all mecA1 , mecA2 and mecA sequences ( S1A Fig ) showed that mecA1 was extremely diverse , including a total of 44 different alleles ( SID = 97 . 2% , CI = 95 . 7%-98 . 7% ) that varied between 93–100% in nucleotide identity ( S1B Fig ) . In contrast , mecA2 and mecA were highly conserved ( mecA2: SID = 70 . 4% , CI = 60 . 5%-80 . 2%; mecA: SID = 21 . 6% , CI = 9 . 7%-33 . 6% ) varying from 99 . 75 to 100% in nucleotide identity . Furthermore , amino acid sequence predictions showed that the SID of mecA1-encoded PBP4 was still very high , 96 . 2% ( CI = 94 . 5%-97 . 9% ) . Interestingly , although both the nonbinding ( NB ) and transpeptidase ( TP ) domains were under purifying selection ( dN/dS<1 ) , the NB domain accumulated many more amino acid substitutions ( 36% ) and showed a higher dN/dS per site ( 0 . 19 ) than the TP domain ( 8%; dN/dS = 0 . 05 ) . The genetic diversity observed for mecA1 appears to have resulted both from recombination and mutation events , wherein the average recombination/mutation rate/site was estimated to be 0 . 15:1 . According to RDP4 analysis , the recombination observed in mecA1 has been driven by recombination between different S . sciuri mecA1 alleles ( Supplementary S1B Fig ) . Although according to our data recombination in mecA1 was not such a frequent event , the recombining mecA1 alleles represented 60 . 5% of the S . sciuri population . In addition , all but two S . sciuri isolates showing oxacillin resistance carried recombining alleles , suggesting that recombination in mecA1 was important for the development of resistance in this species . Overall , neither genetic diversity nor recombination were features affecting the entire S . sciuri genomes . This was obvious by the lower fraction of conserved positions of mecA1 ( 84 . 7% ) when compared to the remaining core genes ( 91 . 57%; stdev 3 . 43 ) ( Fig 1A ) , and by the fact that mecA1 was among the 2 . 5% most variable genes in the core genome ( see supplementary S2 Table and Fig 1A ) . Additionally , for the great majority of strains ( n = 57 , 75% ) the order of the 1759 core genes was conserved ( 0 discontinuities in gene order ) , when compared with the S . sciuri sciuri reference genome NCTC12103 ( see Table 1 and Fig 1B ) . The only subspecies that , according to our data , has probably a higher recombination rate is S . sciuri rodentius , since the genomes of all the strains belonging to this subspecies showed at least one discontinuity in their genome . Furthermore , this subspecies comprised the highest number of discontinuities in gene order ( 1–5 discontinuites ) . The other subspecies showing discontinuities were the subspecies S . sciuri sciuri and a putative new subspecies ( see item The genetic background was associated with the emergence of β-lactam resistance in S . sciuri ) ) , but this corresponded to a single discontinuity and was a rare occurrence among these subspecies . Altogether , these results suggest that mecA1 is a hotspot for diversification in S . sciuri . To assess the level of resistance to oxacillin , we determined the epidemiological cut-off ( ECOFF ) value for oxacillin in the S . sciuri group of species ( S2 Fig ) , since the currently available MIC breakpoints are defined only for clinically significant Staphylococcus species . According to this analysis , the oxacillin breakpoint for resistance was set at 3 μg/ml oxacillin . Considering this breakpoint , the great majority of S . sciuri strains carrying only mecA1 ( 54/60 ) was susceptible to oxacillin , but six strains were resistant ( K4 , K5 , K7 , Jug17 , SS37 and SS41 ) as determined by Etests . From the 54 susceptible strains , 24 produced heterogeneous profiles when analysed by oxacillin population analysis profiles ( PAPs ) , and almost half of these isolates ( 11 out of 24 ) were able to grow at concentrations up to 6–100 μg/ml ( Fig 2A ) . Moreover , the 16 S . sciuri strains that carried mecA in addition to mecA1 were all resistant ( MIC 16 to >256 μg/ml ) , and representative strains showed an heterogeneous profile and were able to survive at concentrations up to 800 μg/ml of oxacillin ( Fig 2A and Table 2 ) . Like in S . sciuri , in S . vitulinus , the great majority of strains carrying either mecA2 or mecA were oxacillin-susceptible , but some of these strains showed a heterogeneous profile in which sub-populations could grow above the MIC ( 100–400 μg/ml ) ( Fig 2B ) . Moreover , a few strains displayed a resistant phenotype ( CH15 , CH2 and CH5 ) ( see Table 2 and Fig 2B ) . In contrast , the great majority of S . fleurettii isolates were resistant to oxacillin ( MIC 4->256 μg/ml ) with subpopulations that were able to grow at concentrations up to 25–400 μg/ml ( Fig 2C ) , but two strains showed a susceptible phenotype ( CH22 and CH28 ) ( see Table 2 and Fig 2C ) . In order to understand the mechanisms associated with the oxacillin resistance phenotypes exhibited by S . sciuri , S . vitulinus and S . fleurettii we looked for: differences in the structure of proteins encoded by mecA homologues; changes in the expression of mecA homologues; presence of SCCmec; and differences in the genetic background . S . sciuri is widely disseminated in nature , being found in different animal species and , occasionally , isolated from human infections , whereas S . vitulinus and S . fleurettii host range is mainly restricted to animals ( 36–39 ) . Nonetheless , the three species live in environments created by humans in which antibiotic usage is frequent , namely in hospitals and farms . Data resulting from this study provided evidence that antibiotic deployment in these environments were probably the drivers of β-lactam resistance development . This is illustrated by the finding that all β-lactam resistant S . sciuri , were collected from human sources or from animals in close contact with humans ( horses and dogs ) , and not from wild animals ( p<0 . 05 ) . Moreover , all S . vitulinus and S . fleurettii included in this study originated from animal species in close contact with humans and were either intrinsically resistant to β-lactams or had the capacity to develop resistance . Antibiotic resistance is believed to be the result of antibiotic pressure imposed on bacteria . Bayesian phylogenetic reconstruction was used to explore the association between the time of emergence of resistant phenotypes and antibiotic use . The MCC tree resulting from BEAST analysis of mecA homologs using a random molecular clock and a constant size population model showed that the time to the most recent common ancestor ( tMRCA ) of all mecA homologues alleles was estimated to be in 1891 ( 1845–1976 95%HPD ) ( see Fig 5 ) , before the introduction of antibiotics in the clinical practice , and that two clusters split in 1937 ( 1892–1968 95%HPD ) , one originating mecA1 and the other mecA2 and mecA ( Fig 5 and S1B Fig ) . The mecA1 began diversifying in 1968 ( 1951–1986 95%HPD ) , which coincided with the emergence of recombining alleles and the emergence of β-lactam resistance . This was contemporaneous with the use of penicillin and methicillin as a treatment in humans ( 1940 and 1960 , respectively ) and of penicillin as a feed additive in production animals ( 1950–1960 ) . According to our results , the first mecA2 allele emerged in a S . vitulinus strain , approximately in 1967 ( 1930–1976 95%HPD ) . The development of mecA occurred later , in S . fleurettii , in 1977 ( 1961–1997 95%HPD ) , suggesting mecA could have been already present in the population when methicillin was first introduced into clinical practice , in 1961 . The SCCmec was estimated to have emerged afterwards ( 1982–1994 95%HPD ) in S . sciuri rodentius [22] ( Fig 5 ) . Once created , SCCmec appears to have been rapidly disseminated to other staphylococcal species like S . aureus; coincidently , it was during the 1980s and 1990s that MRSA pandemic clones began to expand worldwide . The 95% HPD values obtained for the dates presented are wide , particularly at deeper nodes , thus comprising considerable uncertainty . This may result from the type of strain collection analyzed , which was non-random and enriched for more recent isolates . Alternatively , the findings may reflect occurrence of purifying selection and recombination in mecA1 . In particular , leaps of diversity due to recombination in mecA1 or to a weak/mild purifying selection may have led to the estimation of a date that is posterior to the true date of emergence . However , the MCC tree based on mecA genes in the absence of recombination sites ( and using the same molecular clock and population models ) , showed no relevant differences when compared to the tree constructed in the presence of recombination , neither in the population structure nor in the dating of evolutionary events .
The mechanism of β-lactam resistance mediated by mecA in Staphylococcus is one of the most efficient mechanisms of resistance to antibiotics , providing resistance to virtually all members of the large class of β-lactams . Several studies have shown that the mecA precursor was a native gene ( mecA1 ) not providing resistance in Staphylococcus sciuri , the most primitive staphylococcal species [15 , 18] . However , the evolutionary steps leading to phenotypic resistance remained unclear . In this study , we showed that species of the S . sciuri group developed multiple strategies during their evolutionary history to develop β-lactam resistance including ( i ) structural diversification of a native PBP , ( ii ) changes in the promoter of the mecA homologues , ( iii ) SCCmec acquisition and ( iv ) adaptation of the genetic background . Although the TP domain has been described as the crucial domain for PBP activity [7] , our results are the first to identify a fundamental role of the NB domain for its full performance . In particular , we found that alterations in the NB domain of proteins encoded by the mecA homologues can have impact on the level of distortion of the active site groove and on the consequent access of the substrate ( or the antibiotic ) to the Ser401/Ser403 , the key aminoacid residues at the catalytic site . The existence of subtle changes in the NB domain of PPBs can give rise to proteins with different levels of activity . Since the different mecA homologues have a very conserved TP domain , the evolution from a susceptible to a resistance determinant probably involved alterations mainly in the NB domain of the protein . An additional mechanism driving β-lactam resistance involved alterations in the promoter of mecA homologues: either deletions around the RBS site or alterations in -10 and -35 regions . The association of changes in the promoter with an increased mecA1 expression and a resultant resistance phenotype was a phenomenon previously observed in a few strains of S . sciuri [16] . In this study we confirmed that these type of events probably occurred with a relatively high frequency in the overall S . sciuri population and also in S . vitulinus , during their evolutionary history . This event occurred in the promoters of mecA1 and mecA2 only , and may represent a molecular strategy used by the bacteria to circumvent antibiotic pressure , in the absence of a low affinity PBP . We found a good correlation between the alterations in the promoter of these genes and both the expression of their encoded proteins as well as the corresponding resistance phenotypes . A previous study analyzing the mechanism of β-lactam resistance in S . sciuri strains showed a total correlation of the resistance phenotype with an increase in both mecA1 transcription and mecA1-encoded PBP translation ( 16 ) , suggesting that post-transcriptional and post-translational regulation , if occurring , appear not to have disturbed the link between the alterations in the promoter and the observed expression of resistance . Another event associated with the emergence of resistance was the acquisition of SCCmec by S . sciuri and S . vitulinus . However , in this case the correlation between the phenotype and the genotype was only observed for S . sciuri . The absence of a resistant phenotype in S . vitulinus strains carrying mecA either in the native location or within SCCmec is puzzling . We show that the susceptibility is not associated with the absence of gene expression , but post-translational modifications may be involved . Another alternative is that access of the antibiotic to its target may be blocked , by an unknown mechanism . In addition , our results demonstrate that–similarly to the case of MRSA [11 , 14 , 27]–the genetic background also plays an important role in the expression of β-lactam resistant phenotype of this primitive group of staphylococci . The most obvious examples are the absence of resistant phenotype in the presence of mecA in S . vitulinus and the development of resistance in particular phylogenetic clusters of S . sciuri . Genes involved in general metabolism were already shown to play important roles in the expression of β-lactam resistance in S . aureus suggesting an interplay between the overall metabolism and β-lactam resistance [27] [28] . The observation that unknown factors in genetic background are important for the expression of resistance does not allow to establishing definitely a direct correlation between nucleotide substitutions observed in the promoter sequence and mec homologue genes and the resistance phenotype . To substantiate this , an ideal approach would be to test the different promoters and express the different mec homologue variants in an appropriate S . sciuri genetic background . However , these studies are very difficult to perform due to the lack of genetic tools available in this species . The fact that such diversified mechanisms leading to β-lactam resistance were found in different species of the S . sciuri group together with accumulation of more than one of these mechanisms at different time points , generating redundancy , are evidence for the persistent antibiotic pressure that these species experienced during their evolutionary history . Moreover , the diversification , recombination and purifying selection , observed in mecA1 gene , in opposition to the remaining chromosome , in the majority of S . sciuri strains further highlights the specific response of a bacterial species to the environmental pressure by antibiotics . Antibiotic pressure giving rise to β-lactam resistance appears to be directly linked to exposure to human created environments , since resistance was exclusively observed in clinical isolates of human origin or from production animals , where high doses of antibiotics are generally used , and absent from wild animals where antibiotic pressure is limited to the level of antibiotics present in nature . This is in accordance with the Bayesian analysis performed , in which the estimated dates of occurrence of key events in mecA homologues evolution coincided with the time of introduction of antibiotics in veterinary and human clinical settings [29] . A limitation of this study is the fact that the reconstructed phylogeny of the mecA homologues was based on the Bayesian analysis of genes , which we showed to be under recombination and purifying selection . Additionally , it was based on a sampling framework that was non-random and that constituted an underrepresentation of S . sciuri , S . vitulinus and S . fleurettIi population diversity , namely in host range , dates of isolation and geographic region [30] . Consequently , inexact estimations of the evolutionary path of mecA homologues , mainly of mecA1 , may have been generated , adding uncertainty to the dating of the evolutionary events of mecA homologs , namely to the existence of overlap between the emergence of resistance phenotypes and the use of antibiotics . Overall our data suggest that the first evolutionary steps leading to mecA-mediated β-lactam resistance in Staphylococcus occurred in the most primitive staphylococcal species by several molecular mechanisms , in response to β-lactam pressure , both in humans and livestock . These results highlight the complexity of the evolution of mecA-mediated β-lactam resistance .
Human isolates were obtained as part of routine surveillance and laboratory testing and were analyzed anonymously . All data was collected in accordance with the European Parliament and Council decision for the epidemiological surveillance and control of communicable disease in the European community [31 , 32] . Ethical approval and informed consent were for that reason not required . The animal isolates originated from nasal and skin swabs and bovine milk and some were collected as part of previous studies in Denmark [33 , 34] and Switzerland [19 , 35] . According to the national legislations , formal ethical approval was not required since samples were collected by non-invasive sampling procedures and no animal tissues were collected . A collection of 106 staphylococcal isolates , comprising 76 S . sciuri , 18 S . vitulinus and 12 S . fleurettii was assembled . This is a convenience sample , however , we believe it reasonably reproduces the species distribution and diversity of hosts that exist in nature , as previously described [36–39] . Regarding S . sciuri , 28 isolates were obtained from humans , while the remaining 45 isolates were recovered from both wild and domesticated mammals ( Supplementary S1 Table ) . Isolates were collected in different countries ( Czech Republic , Denmark , Portugal , Switzerland , Sweden , former Yugoslavia , Mozambique , Panama and USA ) between 1972 and 2012 . S . vitulinus and S . fleurettii isolates were collected from horses and bovine mastitis milk samples , in Denmark , Switzerland and the Netherlands , in 2004 , 2005 and 2010 . The S . sciuri isolates were identified at the species level by 16S rRNA ribotyping and API-Staph ( Biomerieux , France ) . S . fleurettii and S . vitulinus were identified at the species level by sequencing of 16S rRNA or sodA and Maldi-tof analysis ( Microflex LT , Bruker Daltonics GmbH , Bremen ) [19 , 35 , 40] . Species identification was confirmed by phylogenetic analysis of tuf gene nucleotide sequence [41] . Was assessed by oxacillin Etest ( bioMérieux , France ) . The breakpoint for defining susceptibility was evaluated as suggested by EUCAST ( www . eucast . org ) . An epidemiological cut-off value ( ECOFF ) was determined by considering the MIC to oxacillin of S . sciuri and S . vitulinus isolates not carrying mecA ( wild type ) and isolates carrying mecA ( non-wild type; resistant ) . All S . fleurettii strains carried mecA and were therefore considered resistant . The distribution of MIC values was plotted ( S2 Fig ) and isolates were considered susceptible when MIC < 3 μg/ml . Moreover , population analysis profiles ( PAPs ) for oxacillin were determined for representative isolates ( 28/60 S . sciuri exclusively carrying mecA1 , 23/37 isolates carrying mecA , 9/9 isolates carrying mecA2 ) as previously described [42] . The PAP results of S . sciuri isolates have already been published [15 , 21] . DNA was extracted using the phenol/chloroform extraction method ( S . sciuri ) and the DNEasy Blood & Tissue Kit ( S . vitulinus and S . fleurettii ) ( Qiagen , Limburg , The Netherlands ) . Sequencing was performed using a HiSeq ( Illumina , San Diego , USA ) with an estimated coverage of 40x and a read length of 100 bp . The reads were assembled de novo using VELVET [43] and VelvetOptimiser ( https://github . com/Victorian-Bioinformatics-Consortium/VelvetOptimiser . git ) . DNA of S . fleurettii 402567 was prepared by phenol/chloroform extraction and was sequenced using PacBio RS apparatus ( Pacific Biosciences , Menlo Park , USA ) . De novo assembly was performed using HGAP 3 ( https://github . com/PacificBiosciences/Bioinformatics-Training/wiki/HGAP-in-SMRT-Analysis ) . A reference genome was produced by combining Illumina and PacBio sequencing data for a single strain , S . fleurettii isolate 402567 . PacBio reads were combined with Illumina reads obtained for each isolate in CLC Genomics Workbench ( Qiagen , Hilden , Germany ) , using the Genome Finishing module . The resulting contigs were ordered using the closed genome of Staphylococcus xylosus , the species most closely related to S . fleurettii with a closed genome ( NCBI accession number CP007208 . 1; average nucleotide identity with S . sciuri , 78%; S . vitulinus , 77 . 1%; and S . fleurettii , 78 . 5% ) . Gaps ( eight ) were closed by mapping Illumina data of remaining S . fleurettii strains to the contigs . The resulting closed genome was annotated with RAST ( http://rast . nmpdr . org/ ) . The reference genome S . fleurettii 402567 was used to perform a SNP analysis of the predicted core genome of S . sciuri , S . vitulinus and S . fleurettii isolates . SNP analysis was performed using Stampy ( version 1 . 0 . 11 ) where reads were mapped to the reference genome . SNP calling was performed using SAMtools ( version 0 . 1 . 12 ) , and Neighbor Joining ( NJ ) analysis was used to assess the phylogeny . Trees were drawn using FigTree ( http://tree . bio . ed . ac . uk/software/figtree/ ) . Nucleotide sequences of mecA homologues were identified by BLAST analysis and were extracted from the sequence of the contigs . Alignments with the entire gene or regions corresponding to specific domains were performed with ClustalW [44] . Phylogenetic trees were constructed with a neighbor-joining algorithm . We used BEAST software ( v1 . 8 . 3 ) [45] to investigate the temporal evolution of mecA homologues . Estimation of substitution rates and divergence times of the tree internal nodes was performed using the HKY nucleotide substitution model . The Markov chain Monte Carlo ( MCMC ) analysis was run up to 107 generations and checked for convergence by examining that the effective sample size ( ESS ) values were greater than 200 for all parameters . Strict , random , uncorrelated and fixed clock models under a constant population size model were compared for their fit to the data using marginal likelihood ( stepping stone and path sampling ) ( see S3 Table ) and Bayes factor ( see S4 Table ) . The best-fit clock model ( random clock ) was then tested with the constant size population and the exponential growth population models . No significant differences in timescales or tree topology were obtained when the two different population size models were used . A burn-in of 10% was removed of each BEAST run and the maximum clade credibility ( MCC ) tree was selected from the posterior tree distribution using the program TreeAnnotator ( available as part of the BEAST package ) . Final trees were annotated with FigTree ( http://tree . bio . ed . ac . uk/software/figtree/ ) . The BEAST analysis was performed for the entire set of mec homologue sequences and for the sequences belonging to each mec gene separately . To assess the impact of recombination on the phylogeny , the same analysis was repeated with the mec gene homologue sequences from which nucleotides under recombination were deleted ( RDP4 software ) . To verify if the non-binding and the transpeptidase domain of mecA1 were under positive selection , estimates of overall dN/dS ratios ( number of non-synonymous substitutios per site/number of synonymous substitutions per site ) were produced for the nucleotide sequence of each domain , using the program MEGA6 [46] . RDP4 [47] was used to predict which parts of the mecA homologue sequences were under recombination and to estimate the mecA1 recombination/mutation rate . The structure of representative proteins encoded by the mecA homologues was predicted using ModWeb ( https://modbase . compbio . ucsf . edu/modweb/ ) [48] . Structures of one mecA allele , one mecA2 allele and six mecA1 alleles ( representing each major clade of the phylogenetic tree , 0 . 015 distance cut off ) were obtained . Alignments of the structures modeled with PBP2a ( protein database , PDB code 1MWU ) were produced in PyMol ( The PyMOL Molecular Graphics System , Version 1 . 5 . 0 . 3 Schrödinger , LLC ) and visually inspected for relevant alterations of the protein structure . The genetic diversity of the different mecA homologues was assessed by the Simpson’s index of diversity ( SID ) [49] , using a confidence interval of 95% . The online tool available at http://darwin . phyloviz . net/ComparingPartitions/ was used . To find the core genes of the 76 S . sciuri genomes , we used Prokka [50] and Roary [51] . The sequences of the aligned core genes were compared using Weblogo version 2 . 8 . 2 [52] and the number of conserved positions were determined for each core gene . To determine the order of the 1759 core genes in each contig of the 76 S . sciuri assembled genomes a consensus sequence of each core gene was blasted against the 76 S . sciuri genomes and the reference genome NCTC12103 and their position assigned . The order of the core genes in each contig of the 76 genome sequences was compared to the order of the core genes in the NCTC12103 genome , and the total number of discontinuities was determined using an in-house script . Association between variables within the data was done using the Qui2 test with 95% confidence level . S . aureus strain COL , S . epidermidis strain ATCC12228 , S . sciuri strains K11 , K7 , K5 and K4 , S . fleurettii strains 402567 , CH22 , CH28 and S . vitulinus strains H91 , CH10 and CH15 , were grown in 250 ml of TSB at 37°C with aeration to an OD600nm of 0 . 7 . Cells were harvested , washed and ressuspended in buffer A ( 50 mM Tris pH 7 . 5 , 150 mM NaCl , 5 mM MgCl2 ) with phenylmethylsulfonyl fluoride ( 0 . 5 mM ) and submitted to freeze-thaw cycles . All subsequent steps were performed at 4°C . Lysostaphin ( 100 μg/ml ) , Lysozyme ( 50 μg/ml ) , DNase ( 10 μg/ml ) , RNase ( 10 μg/ml ) , PMSF ( 0 . 5 mM ) β-mercaptoethanol ( 10 mM ) were added and the cell suspensions were incubated on ice for 30 min , followed by 5 cycles of sonication of 30 sec and 2 min intervals . Unbroken cells and cellular debris were removed by centrifugation of 5 min at 5000 g and the resulting supernatants were centrifuged at 50 , 000 g for 1h and washed in 50 mM phosphate buffer , pH 7 . 0 . The obtained membrane fraction was resuspended in 25mM phosphate buffer pH 7 . 0 , 1% Triton X-100 , 10 mM MgCl2 , 20% glycerol . Total protein concentration was determined using the BCA assay ( Pierce , Thermo Scientific , USA ) . Membrane preparations ( 50 μg ) were separated by SDS polyacrylamide gel electrophoresis ( 8% acrylamide-0 . 06% bisacrylamide ) at constant current of 20 mA . The proteins were transferred onto nitrocellulose Hybond-ECL membranes ( GE Healthcare Life Sciences , USA ) using the wet blotting system ( Bio-Rad , USA ) for 90 min . Membranes were kept on PBS-Tween with 5% low-fat milk O/N and incubated with 5 mM diethyl pyrocarbonate ( DEPC ) , to inhibit binding of S . aureus protein A to IgG [53] and rabbit polyclonal anti PBP2a antibody ( raised against the synthetic peptide NH2-CGSKKFEKGMKKLGVGEDIPSDYPF; RayBiotech ) at 1:1000 dilution for 1 hour . After two washes the membranes were incubated with the anti-rabbit secondary antibody conjugated to horseradish peroxidase ( PerkinElmer , USA ) at 1:5000 dilution for 1 hour . The chemiluminiscent signal was detected using Western Lightning Plus-ECL ( PerkinElmer ) and CL-XPosure film ( Thermo Scientific ) . The membrane was incubated in stripping buffer ( 62 . 5 mM Tris-HCL pH 6 . 7 , 100 mM β -mercaptoethanol , 2% SDS ) at 50°C for 30 min and re-hybridized with 5mM DEPC and polyclonal antibody raised against the amidase domain of S . aureus Atl protein , at 1:1000 dilution for 5h . The raw reads of the 106 isolates analyzed in this study and the closed genome of S . fleuretti were deposited in ENA with the following accession number PRJEB18761 .
|
The emergence and rise of mecA-mediated β-lactam resistance in staphylococci has been one of the greatest concerns of the scientific and medical communities worldwide . However , little is known regarding the origin of the mecA gene determinant . In this study we demonstrate that antibiotic pressure in the human environment and in food additives used in livestock was the major driving force of the evolution and spread of resistance to β-lactams . Furthermore , we confirm the previous findings suggesting that the development of resistance occurs in primitive species of staphylococci through diversification of a native penicillin binding protein involved in cell wall synthesis . We also demonstrate that resistance was achieved through four distinct mechanisms: accumulation of substitutions in a specific domain of the protein; diversification of the promoter of the gene; acquisition of SCCmec , and adaptation of the genetic background . Our results highlight the resources that primitive bacteria used to thrive in a changing environment that has led to the methicillin-resistant Staphylococcus aureus ( MRSA ) pandemics .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"antimicrobials",
"taxonomy",
"medicine",
"and",
"health",
"sciences",
"pathology",
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"laboratory",
"medicine",
"pathogens",
"drugs",
"microbiology",
"vertebrates",
"staphylococcus",
"aureus",
"animals",
"mammals",
"methicillin-resistant",
"staphylococcus",
"aureus",
"antibiotic",
"resistance",
"phylogenetics",
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"antibiotics",
"phylogenetic",
"analysis",
"pharmacology",
"bacteria",
"bacterial",
"pathogens",
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"sequence",
"analysis",
"computer",
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"antimicrobial",
"resistance",
"bioinformatics",
"staphylococcus",
"medical",
"microbiology",
"microbial",
"pathogens",
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] |
2017
|
Evidence for the evolutionary steps leading to mecA-mediated β-lactam resistance in staphylococci
|
The C . elegans eat-3 gene encodes a mitochondrial dynamin family member homologous to Opa1 in humans and Mgm1 in yeast . We find that mutations in the C . elegans eat-3 locus cause mitochondria to fragment in agreement with the mutant phenotypes observed in yeast and mammalian cells . Electron microscopy shows that the matrices of fragmented mitochondria in eat-3 mutants are divided by inner membrane septae , suggestive of a specific defect in fusion of the mitochondrial inner membrane . In addition , we find that C . elegans eat-3 mutant animals are smaller , grow slower , and have smaller broodsizes than C . elegans mutants with defects in other mitochondrial fission and fusion proteins . Although mammalian Opa1 is antiapoptotic , mutations in the canonical C . elegans cell death genes ced-3 and ced-4 do not suppress the slow growth and small broodsize phenotypes of eat-3 mutants . Instead , the phenotypes of eat-3 mutants are consistent with defects in oxidative phosphorylation . Moreover , eat-3 mutants are hypersensitive to paraquat , which promotes damage by free radicals , and they are sensitive to loss of the mitochondrial superoxide dismutase sod-2 . We conclude that free radicals contribute to the pathology of C . elegans eat-3 mutants .
Dominant optic atrophy ( DOA ) is one of the leading causes of inherited blindness . DOA is a progressive eye disease caused by degeneration of the retinal ganglion cell layer with ascending atrophy of the optic nerve [1] . The most prevalent form of DOA is caused by heterozygous mutations in the nuclear encoded , but mitochondrially targeted , Opa1 protein [2] , [3] . Opa1 is a member of the dynamin family of proteins . This family consists of several large GTP binding proteins with diverse cellular functions . The archetypal dynamin is required for endocytosis [4] , [5] , but two other dynamin-related proteins , Drp1 and Mitofusins in mammals , act along with Opa1 to control mitochondrial fission and fusion . Mitochondrial fission and fusion are dynamic processes required for the replenishment of mitochondria , for example in long neuronal projections and during cell growth and division . Mitochondrial fission facilitates the redistribution of mitochondria in response to local changes in the demand for ATP , while mitochondrial fusion is needed to exchange mtDNA and other components that may become damaged over time [6] , [7] . The rates of fission and fusion vary depending on cell type and environmental cues , but these rates are usually balanced . This balance is controlled by the opposing actions of the different dynamin family members on or in mitochondria . The three dynamin-related proteins that affect mitochondria have different topologies and play different roles in fission and fusion . Mammalian Drp1 and the homologous proteins in C . elegans and yeast are cytosolic proteins that are required for mitochondrial division [8]–[10] . These proteins wrap around constricted parts of mitochondria where they control a late stage of mitochondrial outer membrane division [8] , [11] . Mutations in Drp1 homologues give rise to a highly interconnected mesh of mitochondria [8]–[10] . Fusion between mitochondrial outer membranes is mediated by a different set of dynamin family members [12] . These proteins are called Mitofusins in mammals and Fzo1 in yeast and Drosophila . They have two transmembrane segments that anchor the proteins in the mitochondrial outer membrane . There are two Mitofusins in mammals ( Mfn1 and Mfn2 ) , which are often coexpressed but are not redundant [13] . Mutations in Mfn2 cause peripheral neuropathy in Charcot Marie Tooth ( CMT ) disease [14] . Mutations in Fzo1 and Mitofusins give rise to fragmented mitochondria [12] , [15] , [16] , but this fragmentation can be suppressed by mutations in Drp1 homologues in yeast and mammalian cells . Evidence for the role of Opa1 in fusion between mitochondrial inner membranes initially came from studies of the yeast homologue of Opa1 , which is called Mgm1 . The mitochondria of yeast Mgm1 mutants are fragmented , they form aggregates and they lose their mtDNA [17]–[19] . Conditional mutations show that the loss of mtDNA is preceded by the changes in mitochondrial morphology , indicating that loss of mtDNA is a secondary defect [18] . The mitochondrial fragments in Mgm1 mutants are converted into a closed network of mitochondria by additional mutations in mitochondrial fission proteins , suggesting that Mgm1 is a mitochondrial fusion protein [20]–[23] . This role was substantiated by experiments in which two yeast cells with differently labeled mitochondria are allowed to fuse . The mitochondria of Mgm1 mutant cells do not mix the two labels showing that they are unable to fuse [24] . A direct role in mitochondrial fusion was then shown with in vitro reconstitution experiments using mitochondria isolated from yeast Mgm1 mutants [25] . Biochemical analysis shows that yeast Mgm1 and mammalian Opa1 are localized to the mitochondrial intermembrane space [19] , [24] , [26] , [27] . The mitochondrial leader sequences of Mgm1 and Opa1 are cleaved upon import into mitochondria . In yeast , roughly half of the protein is further processed by a rhomboid protease [28]–[32] . A homologue of this protease , called PARL , exists in mammals , but cleavage in higher eukaryotes may require other proteases [33] . Immuno-electron microscopy of mammalian cells shows the bulk of Opa1 protein distributed throughout cristae with only a small portion localized to the boundary space between mitochondrial inner and outer membranes [27] . The importance of Opa1 for housekeeping functions , such as mitochondrial fusion and redistribution of mtDNA , is apparent from these cell biological studies . It has , nevertheless , been difficult to establish the exact sequence of events leading to retinal ganglion cell death in DOA , even with the mouse models that have recently become available [34] , [35] . The effects on retinal ganglion cells are restricted both in time and place and they occur with the mild loss of Opa1 function that results from haploinsufficiency of the Opa1 gene [36] . In contrast , cultured mammalian cells transfected with Opa1 siRNA typically show the stronger effects that are associated with complete loss of Opa1 function . Late time points after transfection with Opa1 siRNA show mitochondria that are reduced to small dispersed fragments [27] , [37] , [38] , while early time points show that this fragmentation is preceded by internal rearrangements of the mitochondrial inner membrane [27] . At these times the mitochondria swell and stretch forming localized constrictions , similar to the changes in mitochondrial morphology that are observed during early stages of apoptosis [39] . Transfection with Opa1 siRNA also increases susceptibility to apoptosis by promoting cytochrome c release [40] . Increased susceptibility to apoptosis , exacerbated by photo-damage , was therefore proposed as a possible cause of retinal ganglion cell death in patients with DOA [41] . However , alternatives , such as the effects of reduced levels of ATP , are also considered as possible causes of DOA [42] . Here we show that the previously described C . elegans eat-3 ( ad426 ) strain [43] has a mutation in the D2013 . 5 gene , which encodes the ortholog of yeast Mgm1 and mammalian Opa1 . The ad426 mutation leads to fragmented mitochondria similar to those cause by mutations in Opa1 and Mgm1 . Electron microscopy shows that eat-3 ( ad426 ) mitochondria have disorganized inner membranes and a large number of inner membrane septae . We also find that eat-3 ( ad426 ) growth defects are attributable to impaired oxidative phosphorylation and increased damage from free radicals within mitochondria .
BLAST homology searches show that C . elegans has a single homologue of yeast Mgm1 and mammalian Opa1 . This protein is encoded by the D2013 . 5 gene . It has a predicted molecular weight of 106 . 8 kDa and 46% amino acid identity to human Opa1 . Similar to yeast Mgm1 and mammalian Opa1 , this C . elegans protein has a putative mitochondrial targeting sequence followed by domains that are typical of dynamin family members: a conserved GTPase domain , a middle domain and a GED or assembly domain [44] ( Figure 1A ) . Pilot experiments with D2013 . 5 RNAi yielded worms that grew slowly , remained small and had small numbers of progeny . These phenotypes led us to investigate the eat-3 mutant , which was previously identified in a screen for mutations that cause abnormal or defective eating in C . elegans [43] . The D2013 . 5 gene is very close to the eat-3 locus ( within 0 . 2 map units ) and the overall appearance of D2013 . 5 RNAi animals is similar to that of eat-3 animals . Upon sequencing the D2013 . 5 gene from eat-3 ( ad426 ) animals , we found a single point mutation , changing a valine at position 328 to an isoleucine ( Figure 1B ) . Although this is a surprisingly conservative change , there are other examples where such a change has a dramatic effect on protein function [45] . The affected residue is just downstream of the G2 threonine in the effector binding loop of the dynamin-like GTPase , where it may disrupt the GTPase cycle . A C . elegans dynamin mutant , dyn-1 ( ky51 ) , has a mutation that is also very close to the G2 threonine [46] . Surprisingly , this dynamin mutation can be suppressed by a second mutation at the same position as that mutated in eat-3 ( ad426 ) , which further demonstrates the importance of this particular residue ( Figure 1B ) . To verify that eat-3 ( ad426 ) is indeed a D2013 . 5 mutant , we injected this strain with a wildtype D2013 . 5 cDNA under control of the D2013 . 5 gene promoter . The number of progeny reaching the L4 larval stage increased from 10 per uninjected eat-3 animal ( SD = 8 , n = 28 ) to 30 per transgenic animal ( SD = 22 , n = 26 ) , showing that a wildtype D2013 . 5 construct partially rescues the eat-3 mutant . Partial rescue is common for C . elegans genes with a maternal effect , since transgenes are often poorly expressed in the germline . We obtained further evidence that D2013 . 5 encodes the eat-3 locus with a second allele , named tm1107 . The tm1107 allele is most likely a null , since it has a 419 bp deletion that causes a frameshift at position 329 and thus eliminates two thirds of the D2013 . 5 protein . The absence of EAT-3 protein in tm1107 animals , but not in ad426 animals , was confirmed by Western blot analysis using an antibody raised against the C . elegans EAT-3 protein ( Figure S1 ) . Homozygous eat-3 ( tm1107 ) animals survive but they have fragmented mitochondria , a decrease in broodsize , sluggishness and slow growth phenotypes , similar to the phenotypes of eat-3 ( ad426 ) animals . More importantly , tm1107 fails to complement eat-3 ( ad426 ) , indicating that ad426 and tm1107 are both alleles of eat-3 and that the phenotypes are due to mutations in the D2013 . 5 gene ( data not shown ) . The C . elegans D2013 . 5 locus is henceforth called eat-3 . Additional alleles of eat-3 were isolated in an F2 screen for suppressors of eat-3 ( ad426 ) mutant phenotypes . The progeny of 36 , 000 F1 animals were screened for restored growth rate , size and fecundity . This screen yielded seven new mutants with restored growth rates . Five of these mutants have second site mutations in the eat-3 gene ( cq6-cq10 ) , while two mutants have mutations that lead to premature stops in the drp-1 gene ( cq5 and cq11 ) . The new mutations in the eat-3 locus all cause substitutions in the GTPase domain ( Figure 1B ) . A similar screen with the dyn-1 ( ky51 ) also yielded a series of substitutions in the GTPase domain ( Figure 1B ) . When the new mutations are mapped onto the crystal structure of the dynamin GTPase domain [47] , they reveal a striking pattern of convergence on the G2 motif of the GTPase domain ( Figure 1C ) . It seems likely that they restore the ability of the G2 threonine to interact properly with GTP or make the conformational changes that occur during GTP hydrolysis . To investigate how eat-3 affects mitochondria , we focused on mitochondrial morphology in C . elegans body wall muscles . Mitochondria were detected with mitochondrial matrix markers , consisting of an N-terminal mitochondrial leader sequence fused to GFP , cyan fluorescent protein ( CFP ) or yellow fluorescent protein ( YFP ) , and mitochondrial outer membrane markers , consisting of a resident outer membrane protein ( TOM70 ) fused to GFP , CFP or YFP [8] . The functions of the EAT-3 protein were disrupted by expressing dominant negative mutant proteins or antisense cDNA , which effectively causes localized RNAi , under control of the muscle specific myo-3 promoter . The dominant negative mutations that we used here are T322A , which disrupts the G2 motif of the GTPase domain , and K300A , which is analogous to the K44A mutation in the G1 motif of dynamin [5] . Both dominant negative mutations and the loss of function induced by antisense cDNA cause mitochondria to fragment into a large number of small pieces ( Figure 2B and 2C; 83% of cells were affected , n = 200 ) . Labeling with a mitochondrial outer membrane marker shows that the mitochondrial fragments truly are detached ( not shown ) . The exact size and distribution of mitochondrial fragments varied between the different treatments , but it was not evident that these phenotypes represent different levels of severity ( Figure 2B–2C ) . Mitochondrial fragmentation is also observed in eat-3 ( ad426 ) and in eat-3 ( tm1107 ) animals and this phenotype is reversed in transgenic animals expressing wildtype eat-3 cDNA under control of the myo-3 promoter ( Figure 2D–2F ) . Similar mitochondrial fragmentation is observed in muscle cells of fzo-1 ( tm1133 ) mutants ( Figure 2G ) , which have a mutation in the C . elegans homologue of Drosophila and yeast Fzo1 and mammalian Mitofusins , proteins required for fusion of the mitochondrial outer membrane [12] , [13] , [15] , [16] , [48] , [49] . The mitochondria of eat-3 and fzo-1 mutants are similarly fragmented consistent with their roles in mitochondrial fusion . However , the gross anatomical defects ( size , growth rate and broodsize ) are less severe in fzo-1 ( tm1133 ) mutants and fzo-1 RNAi animals than in eat-3 mutants ( data not shown ) , even though fzo-1 ( tm1133 ) is also a null allele ( it has a chromosomal deletion that truncates the protein after 65 amino acids ) . These results suggest that there might be functional differences in the ways that EAT-3 and FZO-1 proteins affect the gross anatomy of C . elegans . In contrast , intragenic revertants of eat-3 ( ad426 ) show a range of mitochondrial morphology defects ( Figure 2H–2J ) ; the mitochondria are still fragmented in eat-3 ( ad426cq10 ) , they are partially restored to their filamentous morphology in eat-3 ( ad426cq8 ) and completely restored in eat-3 ( ad426cq6 ) commensurate with the suppression of gross anatomical defects . We conclude that mutations in fzo-1 and eat-3 both cause mitochondrial fragmentation , but their effects on size , growth rate and broodsize are different . To further investigate how mitochondria are affected , we conducted electron microscopic analysis of wildtype and eat-3 ( ad426 ) worms . Figure 3A and 3C show longitudinal sections of wildtype worms . The mitochondria in muscle cells are long , while mitochondria in other cell types , such as intestinal cells , appear to be short or round , because they are randomly oriented with respect to the plane of sectioning . The mitochondria contain many short pairs of membrane segments that criss-cross the mitochondrial matrix ( Figure 3A , insert ) . These segments are likely to be oblique sections of randomly oriented cristae . Their morphology suggests that C . elegans mitochondria contain tightly packed tubular cristae . In contrast , eat-3 mitochondria are almost all round ( Figure 3B , 3D , and 3E ) and often further divided by inner membrane septae ( “1” in Figure 3E ) . The number of mitochondria transected by inner membrane septae , as detected in thin sections , is less than 0 . 5% in wildtype animals ( n = 220 ) and 63% in eat-3 ( ad426 ) animals ( n = 221 ) . The frequency of internal septae in eat-3 animals is likely to be even higher , because the thin sections will have missed septae outside of the plane of sectioning . We conclude that the majority of eat-3 ( ad426 ) mitochondria are divided by internal membrane septae . In contrast , wildtype mitochondria are rarely if ever further divided by septae . The mitochondria of eat-3 ( ad426 ) animals often have shorter and reduced numbers of cristae . These cristae typically project no more than 100 nm into the matrix ( “2” in Figure 3E ) , while cristae in wildtype mitochondria are more densely packed and appear to traverse the width of mitochondria . To quantify the difference between eat-3 and wildtype cristae , we traced the lengths of mitochondrial membranes detected in thin sections . The eat-3 mitochondria had on average 1 . 21 µm total cristae length ( n = 20 , SD = 0 . 78 ) , compared with 7 . 34 µm in wildtype mitochondria ( n = 16 , SD = 3 . 80 ) . The length of inner boundary membranes is also decreased: 2 . 62 µm per eat-3 mitochondrion ( n = 20 , SD = 0 . 91 ) , compared with 5 . 38 µm per wildtype mitochondrion ( n = 16 , SD = 2 . 64 ) . There is , however , still a 66 . 2% decrease of total cristae length when normalized with the lengths of inner boundary membranes or a 70 . 3% decrease when normalized with the surface area of the mitochondrial section . We conclude that most eat-3 mitochondria have fewer cristae than wildtype mitochondria . Some mutant mitochondria have long inner membrane invaginations , which could in principle be enlarged cristae , but are more likely membrane folds resulting from a surplus of inner membrane ( “3” in Figure 3E ) . It is , however , not clear from the EM sections whether these membrane folds are attached to the rim . In addition , many eat-3 mitochondria have internal curved or ring-shaped structures formed by two concentric membranes enclosing a matrix-like material ( “4” in Figure 3E ) . These membrane inclusions are eat-3-specific , since they were not observed in wildtype animals . The matrices of eat-3 mitochondria also contain electron-dense inclusions ( “5” in Figure 3E ) , but these are not specific for the eat-3 mutant , since wildtype mitochondria contain similar ( albeit smaller ) inclusions . Given their internal location , all of these membrane inclusions are likely to be derived from the inner membrane . To determine whether the various membrane inclusions observed in thin sections are connected outside of the plane of view , we made three-dimensional reconstructions of mitochondria using electron tomography . In this technique , a thick section is viewed at different angles and the imaging data is used to reconstruct a three dimensional model . Images of an eat-3 mitochondrion are shown in Figure 4 . This mitochondrion has several matrix “bubbles” , divided from the rest of the matrix by septae of inner membrane . These bubbles are sealed off , indicating that the septae observed in the thin sections reflect completed inner membrane divisions . The three-dimensional reconstructions of eat-3 mitochondria also show that some of the membrane inclusions that appear free floating in the matrix are indeed physically separated from the inner membrane ( Figure 4 ) . This separation suggests severing of membranes within the mitochondrial matrix by an as yet unknown mechanism . Similar free-floating structures were previously observed in a mitochondrial myopathy of unknown etiology in humans [50] and in mitochondria of apoptotic cells [51] . To investigate the role of eat-3 in whole worms we first determined the expression pattern of the eat-3 gene with transgenic animals that carry an extrachromosomal array with the eat-3 gene promoter fused to green fluorescent protein ( GFP ) and β-galactosidase coding sequences . This pattern was similar to that of C . elegans drp-1 [8] with high levels in intestinal cells , in muscle cells and in neurons and low levels in other cell types ( data not shown ) . Cell types with high levels of expression may be metabolically more active than other cell types , but basal levels of this protein are most likely required in all cells . We then conducted experiments to assess the effects of eat-3 loss of function on the growth and brood size of worms . Worms injected with eat-3 dsRNA give viable progeny but their brood size is reduced ( 90 viable eggs per worm , SD = 14 , n = 20 , compared with 270 for wildtype , SD = 10 , n = 20 ) . The F1 worms remain small , are sluggish and develop slowly . Similar effects were observed with chromosomal mutations in the eat-3 gene . In an experiment with ad426 and tm1107 alleles , the averages were 302 for wildtype ( SE = 8 . 2 , n = 7 ) , 51 for eat-3 ( ad426 ) ( SE = 8 . 4 , n = 7 ) and 50 for eat-3 ( tm1107 ) ( SE = 10 , n = 7 ) . In an experiment with intragenic revertants of eat-3 ( ad426 ) , the averages were 75 for eat-3 ( ad426cq6 ) ( SE = 48 , n = 6 ) , 190 for eat-3 ( ad426cq7 ) ( SE = 14 , n = 6 ) and 65 for eat-3 ( ad426cq8 ) ( SE = 27 , n = 6 ) . These numbers are variable , as one might expect from different allele strengths , but they are all reduced when compared with wild type animals . Growth was quantified by measuring the lengths of progeny from RNAi injected worms ( Figure 5A ) . Progeny from worms injected with eat-3 dsRNA were on average only 0 . 15 mm in length at four days after hatching ( SD = 0 . 02 , n = 20 ) , whereas wild type animals were 1 mm in length ( SD = 0 . 04 , n = 5 ) . Even after three weeks , the eat-3 RNAi worms rarely reach 0 . 5 mm , consistent with a previous study showing that the eat-3 ( ad426 ) mutant also remains small [52] . Similar effects on length were observed with chromosomal mutations in the eat-3 gene ( Figure 5B ) . Worms with eat-3 deficiencies live longer than wildtype animals ( 33 days for RNAi worms , versus 20 days for untreated worms , and as shown previously with eat-3 ( ad426 ) animals [53] ) , but it also takes them longer to reach adulthood ( 10 days for eat-3 RNAi progeny whereas wildtype animals take 2 days ) . It would thus appear that developmental decisions are normal , but the rate of development is greatly reduced as one might expect from a general decrease in metabolic activity . To see how mitochondria in the gonads of eat-3 RNAi animals are affected , we stained the gonads of injected worms with Rhodamine 123 , as was previously done with C . elegans drp-1 RNAi animals [8] . We find that the mitochondria are more dispersed , but do not appear to be less abundant than in untreated gonads ( Figure 5C–5D ) . The effect of eat-3 RNAi on mitochondria is , however , much less dramatic than that of drp-1 RNAi , which causes mitochondria to form large aggregates [8] . However , Hoechst staining shows that there is a paucity of nuclei when compared with wildtype ( Figure 5C–5D ) . This paucity suggests reduced numbers of mitotic divisions at the tips of the gonads , which would lead to the production of fewer oocytes in agreement with the low brood sizes of eat-3 mutant and RNAi treated animals . Two of the mutants that were isolated in our screen for suppressors of eat-3 ( ad426 ) have premature stop codons in the drp-1 gene , showing that defects in mitochondrial fission suppress the defect in mitochondrial fusion caused by a mutation in eat-3 . Similar genetic interactions were previously observed with mutations in the orthologous yeast genes [19] , [21] . Since mitochondrial fission and fusion proteins not only act antagonistically on mitochondrial morphology , but also affect the viability of worms , we conducted additional experiments to further determine the extent of eat-3 suppression by drp-1 loss of function . First , we tested whether the fragmentation of mitochondria is reversed by the dominant negative mutant DRP-1 ( K40A ) , which blocks division of the mitochondrial outer membrane [8] . Constructs encoding Pmyo-3::DRP-1 ( K40A ) and a mitochondrial outer membrane marker were injected into eat-3 ( ad426 ) worms or into wildtype worms along with the Pmyo-3::antisense-eat-3 construct . DRP-1 ( K40A ) gives rise to interconnected mitochondria , regardless of whether it is expressed in a wildtype background , with antisense eat-3 , or in an eat-3 mutant ( 100% of cells , n = 50 , data not shown ) . The drp-1 ( cq5 ) allele , which was isolated as a suppressor of eat-3 ( ad426 ) , also causes hyperconnectivity of mitochondria in eat-3 ( ad426 ) animals , similar to the connectivity observed in the drp-1 ( cq5 ) single mutant ( Figure 6A–6D ) . We conclude that a functioning mitochondrial division apparatus is required for the mitochondrial fragmentation induced by mutant eat-3 . To find out whether other abnormalities of the eat-3 ( ad426 ) mutant are reversed by a defect in mitochondrial division , we determined the brood-size of eat-3 ( ad426 ) mutants grown with or without drp-1 RNAi . Our results show that drp-1 RNAi significantly restores the brood-size of eat-3 ( ad426 ) mutants ( Figure 6E ) . A chromosomal mutation in drp-1 also restores the brood size as shown with eat-3 ( ad426 ) ; drp1 ( cq5 ) animals ( Figure 6E ) . We conclude that defects in C . elegans DRP-1 and EAT-3 proteins compensate each other's physiological defects . Similar effects were observed in yeast , where the effects of mutations in the EAT-3 homologue Mgm1 are suppressed by mutations in the DRP-1 homologue Dnm1 [19] , [21] . To our surprise , however , the brood size defect of the eat-3 ( ad426 ) allele was also partially suppressed by fzo-1 RNAi , while the eat-3 ( tm1107 ) allele , which is most likely a null allele , was not suppressed by drp-1 or fzo-1 RNAi ( Figure 6E ) , even though mitochondrial fragmentation in eat-3 ( tm1107 ) animals is reversed by drp-1 RNAi ( Figure S2 ) . These results suggest that the eat-3 ( ad426 ) allele has some residual protein function that is masked by wildtype DRP-1 and FZO-1 proteins . In support of this residual activity , we find that eat-3 RNAi reverses the restoration of brood size by the drp-1 mutation in eat-3 ( ad426 ) ; drp1 ( cq5 ) animals ( Figure 6E ) . The suppressive effects of drp-1 and fzo-1 loss of function can be explained by the fact that they both act upstream of inner membrane fusion . Loss of drp-1 prevents the formation of inner membrane fusion intermediates by introducing a fission defect that is epistatic to fusion defects , while loss of fzo-1 does this by blocking outer membrane fusion , which also precedes inner membrane fusion . It seems likely that the inner membrane fusion intermediates , formed with wildtype drp-1 and fzo-1 , sequester mutant EAT-3 protein , while loss of drp-1 or fzo-1 function frees this protein for other essential functions within the mitochondrial intermembrane space . It is well-established that Opa1 has an anti-apoptotic function in mammalian cells [37] , [40] , [54] , [55] . We therefore tested whether apoptosis contributes to the various eat-3 phenotypes in C . elegans by making double mutants with eat-3 ( ad426 ) and ced-3 ( n717 ) or ced-4 ( n1894 ) mutations . The ced-3 gene encodes a caspase and the ced-4 gene encodes APAF-1 . Mutations in either gene block programmed cell death in C . elegans . The effects on broodsize were determined by counting the numbers of progeny that survive to the L4 larval stage . The brood sizes were reduced to varying degrees in each of the single mutants , but the brood size defects of the eat-3 ( ad426 ) animals were not significantly affected by the additional mutations in ced-3 and ced-4 loci ( Figure 7A ) . Although ced-3 encodes the caspase that is utilized for all programmed cell death in C . elegans and inducible cell death in C . elegans gonads [56] , there are three other caspases ( csp-1 , csp-2 and csp-3 ) that might contribute to cell death under other circumstances . We tested these csp genes with feeding RNAi , but saw no effect on the brood size of eat-3 ( ad426 ) mutants . Some redundancy between the caspases remains possible , but redundancy does not apply to ced-4 , since it encodes the single C . elegans homologue of APAF-1 . The ced-4 gene is central to all caspase dependent cell death in C . elegans . The absence of an effect of ced-4 mutations on the eat-3 broodsize defect , as shown here ( Figure 7A ) , is therefore a reliable indication that caspase dependent cell death does not contribute to the reduced broodsize of eat-3 mutants . To verify that eat-3 mutants show no increase in cell death , we counted the numbers of dying cells by looking for light-refractory cells with DIC microscopy in eat-3 ( ad426 ) and eat-3 ( tm1107 ) embryos at the comma stage . Those numbers were not significantly different from the numbers for wildtype embryos ( Figure 7B ) . To verify that the ced mutants used here were effective , the numbers of dying cells were also counted in ced-3 ( n717 ) and ced-4 ( n1894 ) mutant embryos . As expected , these mutants show strongly reduced numbers of dying cells . We conclude that ced-3 and ced-4 dependent cell death does not contribute to the reduced brood size of eat-3 animals . The eat-3; ced-3 and eat-3; ced-4 double mutants also grow slowly and remain small similar to the eat-3 single mutants ( data not shown ) , suggesting that cell death does not contribute to these other maladies . The growth and brood size defects of eat-3 mutants resemble those of gas-1 and mev-1 mutants , which have defects in Oxidative Phosphorylation complexes . Those mutants are also more susceptible to damage from free radicals , as shown by their sensitivity to paraquat , which produces superoxide radicals through a radical ion intermediate [57] . To test whether eat-3 mutants are also sensitive to free radicals , we grew eat-3 ( ad426 ) animals with increasing concentrations of paraquat . We find that eat-3 ( ad426 ) animals are significantly more sensitive to paraquat than wildtype animals ( Figure 8A ) . Values for IC50 were on average 0 . 25 mM for eat-3 ( ad426 ) animals and 0 . 44 mM for wildtype ( N2 ) animals ( averages of four independent experiments ) . Increased sensitivity to paraquat is also observed with eat-3 ( tm1107 ) animals ( Figure 8B ) , confirming that this effect is caused by loss of eat-3 function . The sensitivity of eat-3 ( ad426 ) animals to paraquat is suppressed by the drp-1 mutations in eat-3 ( ad426 ) ; drp-1 ( cq5 ) and in eat-3 ( ad426 ) ; drp-1 ( cq11 ) animals ( Figure 8A–8B ) . These two drp-1 mutations were isolated independently , confirming that they are the cause of this reversal . Since these results suggests that mitochondrial outer membrane fission and fusion processes affect paraquat sensitivity , we tested whether the fzo-1 ( tm1133 ) mutant , which has a defect in mitochondrial outer membrane fusion , are also sensitive to paraquat . Our results show that this mutant is not more sensitive to paraquat than wildtype animals ( Figure 8A ) , from which we conclude that mitochondrial fusion defects are not enough to promote free radical damage . The increased paraquat sensitivity of eat-3 mutants , but not of fzo-1 mutants , therefore indicates that the EAT-3 protein affects free radical formation or sequestration in ways that are unrelated to its role in mitochondrial fusion . To test whether the induction of superoxide dismutase genes aids survival of eat-3 mutants , we tested possible genetic interactions between eat-3 and superoxide dismutase genes in C . elegans . C . elegans has five superoxide dismutase genes . The sod-1 , sod-4 , and sod-5 genes encode Cu2+/Zn2+ superoxide dismutases . One splice variant of sod-1 and all variants of sod-4 have a signal peptide , suggesting that these proteins are sent through the secretory pathway to the extracellular matrix . Other sod-1 isoforms and all proteins encoded by sod-5 lack recognizable targeting sequences , suggesting that those are cytosolic . A fraction of Cu2+/Zn2+ superoxide dismutases might also be localized to the mitochondrial intermembrane even without recognizable targeting sequences , similar to Cu2+/Zn2+ superoxide dismutases in yeast and mammals [58] . The two remaining sod genes ( sod-2 and sod-3 ) encode Fe/Mn superoxide dismutases . These proteins have mitochondrial leader sequences , which most likely target them to the mitochondrial matrix . We first grew eat-3 ( ad426 ) animals on feeding RNAi bacteria with RNAs for the sod genes that are not secreted ( sod-1 , sod-2 , sod-3 and sod–5 ) , since those might affect the survival of eat-3 mutants . There were little or at best modest effects with sod-1 , sod-3 and sod-5 RNAi treatments , but the effects of sod-2 RNAi on eat-3 ( ad426 ) animals were consistent and strong ( Figure 9A ) . To verify these differences , we grew mutants of each of the sod genes on eat-3 RNAi bacteria . As with the converse experiment , sod-2 ( gk257 ) mutant animals grow much more poorly with eat-3 RNAi ( Figure 9B ) . The effectiveness of eat-3 ( ad426 ) in one experiment and eat-3 RNAi in the second experiment confirms that the enhancement of sod-2 defects are indeed caused by eat-3 loss of function . We conclude sod-1 , sod-3 and sod-5 are not necessary for survival of the eat-3 mutant , but a mutation in the sod-2 gene and sod-2 RNAi both strongly affect survival of animals with eat-3 deficiencies . The weak or negligible enhancement of eat-3 by sod-3 RNAi and the sod-3 ( gk235 ) mutant is noteworthy since SOD-2 and SOD-3 have 88% amino acid identity and both proteins have mitochondrial leader sequences , indicating that they are both targeted to the mitochondrial matrix . The genetic interactions between sod-2 and eat-3 might , however , be different from those between sod-3 and eat-3 , because sod-2 and sod-3 genes are differentially expressed [59] and their expression is regulated by different pathways [60] . We used Western blots probed with a cross reacting Fe/Mn-SOD antibody to determine whether differential expression of sod genes correlates with the different effects that we observe with sod-2 and sod-3 genes . Our blots show that Fe/Mn-SOD expression is induced more than two-fold in eat-3 ( ad426 ) animals ( Figure 9C ) . This induction is almost entirely attributable to SOD-2 since sod-2 RNAi , but not sod-3 RNAi largely abolishes this expression ( Figure 9D ) . The induction is reversed by a secondary mutation in drp-1 ( cq5 ) and in the intragenic revertant of eat-3 ( ad426cq8 ) ( Figure 9C ) . Similar reductions were seen with other revertants ( data not shown ) . Consistent with their lack of paraquat sensitivity , fzo-1 ( tm1133 ) animals show little or no induction of SOD expression . We conclude that SOD-2 protein levels are dramatically increased in eat-3 ( ad426 ) animals , but not in fzo-1 ( tm1133 ) animals . This increase is partially reversed in intragenic revertants and fully reversed by the drp-1 mutation in the eat-3 ( ad426 ) ; drp-1 ( cq5 ) double mutant . It seems likely that increased expression of SOD-2 helps prevent damage from free radicals , but this increase is still not enough to prevent the hypersensitivity of eat-3 mutants to paraquat .
C . elegans eat-3 mutants have many of the same features that were previously observed with yeast Mgm1 mutants and mammalian cells transfected with Opa1 siRNA . The mitochondria in eat-3 mutants are fragmented , these fragmented mitochondria are further divided by inner membrane septae and fragmentation is reversed by loss of Drp1 . C . elegans eat-3 mutants are also affected at the organismal level . The mutant animals grow slowly , are sluggish and have greatly reduced broodsize , consistent with severely compromised mitochondrial function . However , heterozygous eat-3 mutations have no overt defects in worms , unlike heterozygous Opa1 mutations in humans , which cause optic neuropathies through haploinsufficiency . The eat-3 mutants are nevertheless still useful for unraveling pathogenic mechanisms , since the phenotypes in C . elegans and in mammal are both due to loss of protein function and therefore their effects on other cellular pathways are also most likely similar . It was conceivable that the broodsize defects of eat-3 mutants are due to increased apoptosis in the gonad . In wildtype worms , approximately 50% of germ cells die prior to oogenesis , but more death can be induced by DNA damage , by pathogens and by other forms of stress . These death-inducing conditions all converge on the classic apoptosis machinery that requires the caspase CED-3 and the APAF1 homologue CED-4 [61] . We investigated the possibility that apoptosis contributes to the pathogenesis of eat-3 mutants by analyzing eat-3; ced-3 and eat-3; ced-4 double mutants and by counting the numbers of dying cells in eat-3 mutants . There was no increase in the numbers of dying cells in eat-3 embryos , nor was there suppression of the eat-3 broodsize defects in the double mutants . C . elegans does have several other caspases ( csp-1 , csp-2 and csp-3 ) , but RNAi of these genes had no effect on eat-3 animals ( data not shown ) nor are they known to contribute to apoptotic cell death in C . elegans [56] . Redundancy is not an issue with ced-4 , which encodes the only APAF1 homologue in C . elegans . In summary , none of the RNAi treatments or chromosomal mutations in cell death genes showed a suppressive effect on eat-3 mutants , from which we conclude that caspase-dependent cell death does not contribute to the pathology of eat-3 in worms . Mammalian cells transfected with Opa1 siRNA are more sensitive to apoptosis inducing agents [62] , but there is also evidence that patients with dominant optic atrophy have reduced levels of ATP , which could trigger retinal ganglion cell degeneration [42] . The gross anatomical phenotypes of C . elegans eat-3 mutants , such as small size , slow growth and reduced broodsize are consistent with caloric restriction as observed in feeding mutants with pharyngeal defects [43] , [52] . The small brood sizes of eat-3 mutants are not due to retention of eggs , nor are there increased numbers of dead eggs or larvae on plates ( data not shown ) . There is , however , a paucity of nuclei in the gonads of eat-3 RNAi animals ( Figure 5C ) , consistent with the production of fewer oocytes . Fewer oocytes could reflect reduced rates of mitotic division at the distal tip of the gonad , since it was previously shown that mutations in mitochondrial proteins can inhibit cell division through the actions of AMP kinase and cyclin E [63] . Oocyte production might also be compromised at later stages , since the availability of yolk protein and other major constituents of oocytes is affected by the metabolic state of the animal . Many of the eat-3 mutant phenotypes are therefore attributable to a general breakdown in mitochondrial function . Earlier studies of yeast Mgm1 mutants show progressive loss of mtDNA [18] , [19] . Loss of mtDNA is also observed in patients with dominant optic atrophy where it will affect assembly of oxidative phosphorylation complexes [64] . Defects in oxidative phosphorylation proteins can result in fewer and shorter cristae [65] , which would be confined to those matrix compartments that have lost their mtDNA . Selective loss of cristae due to stochastic loss of mtDNA agrees with our electron microscopy data , since that data shows a heterogeneous mixture of mitochondrial matrix compartments , some with severely disrupted cristae and others with seemingly wildtype cristae ( pairs of arrows in Figure 3D ) . The observation of different types of matrices enclosed by a single mitochondrial outer membrane suggests that the outer membranes of eat-3 mutant mitochondria fuse irrespective of their mtDNA content , but the mitochondrial inner membranes fail to fuse , similar to the results obtained with Mgm1 in yeast [25] . The ability to suppress eat-3 ( ad426 ) defects with drp-1 RNAi and mutations in drp-1 is consistent with stochastic loss of mtDNA in eat-3 mutants . Mutations in the yeast DRP-1 homologue Dnm1 similarly suppress Mgm1 growth defects and they restore cristae morphology [20] . There are , however , several observations suggesting that the mitochondrial fusion defect and the resulting loss of mtDNA might not be the only causes of sickness in eat-3 mutants: First , drp-1 RNAi does not rescue the C . elegans eat-3 ( tm1107 ) deletion allele , while it does rescue the eat-3 ( ad426 ) allele . Second , C . elegans fzo-1 ( tm1133 ) mutant animals are not as severely affected as eat-3 mutants , nor are they rescued by drp-1 RNAi ( data not shown ) , even though one might expect them to be equally susceptible to loss of mtDNA , since yeast Fzo1 mutants do lose their mtDNA [12] , [48] and they are rescued by mutations in Dnm1 [19] , [21] , [22] . Extensive loss of mtDNA also occurs in mouse Mitofusin mutants ( the mammalian homologues of Fzo1 ) [66] . We conclude that lack of ATP due to loss of mtDNA is not enough to explain why optic nerves are singled out for destruction in patients with dominant optic atrophy . Our results suggest an alternative explanation for the sickness of C . elegans eat-3 mutants , which may also be relevant for the selective degeneration of retinal ganglion cells in patients with dominant optic atrophy . C . elegans eat-3 mutants are hypersensitive to paraquat and sod-2 RNAi , suggesting increased production of free radicals or an impaired disposal mechanism . A drp-1; eat-3 double mutant and an fzo-1 mutant are not more sensitive to paraquat , suggesting that there might be something specific about the effects of eat-3 on mitochondria , for example contributing to the maintenance of cristae , as was suggested for Opa1 in mammalian cells [27] , [37] , [40] , [55] , [67] . The enhancement of eat-3 phenotypes by sod-2 RNAi and a mutation in the sod-2 gene , but not by RNAi or mutations in other superoxide dismutase genes , suggests that damage from free radicals is confined to the mitochondrial matrix or the mitochondrial inner membrane . The effects are most likely not direct , since SOD-2 is a mitochondrial matrix protein while EAT-3 is primarily localized to the mitochondrial intermembrane space and other mutations that affect oxidative phosphorylation in C . elegans , such as the mev-1 and gas-1 mutants with mutations in complex I and II proteins , also show increased sensitivity to paraquat [57] . Disruption of the electron transport chain , for example through altered cristae morphology , can increase production of free radicals , while conversely free radicals in the mitochondrial matrix can further disrupt the electron transport chain . These two problems are therefore likely to reinforce each other , possibly leading to catastrophic breakdown of mitochondrial function . If free radicals also contribute to dominant optic atrophy in humans , then the underlying cause of this disease might be more similar to that of other optic neuropathies than previously understood . Patients with Leber's hereditary neuropathy ( LHON ) have mutations in subunits of Oxidative Phosphorylation complex I , which increases free radical production by disrupting the flow of electrons through complex I along with their more obvious effects on ATP production [68]-[70] . Optic neuropathies triggered by macular degeneration and optic neuropathies triggered by dietary deficiencies are also linked to damage from free radicals in mitochondria . Increased levels of free radicals in these diseases are compounded by the effects of light entering the eyes , since light triggers additional free radical production through absorption by cytochrome c oxidase and flavin containing oxidases in mitochondria [71] . Damage from free radicals will exacerbate the effects of ATP deficiency and increased susceptibility to apoptosis in patients with dominant optic atrophy . It is even possible that some of the increased susceptibility to apoptosis in Opa1 deficient cells is caused by damage from free radicals . In conclusion , mutations in C . elegans eat-3 have many of the same effects on mitochondrial morphology that were previously observed with mutations in yeast Mgm1 and mammalian Opa1 . Mutations in key components of the major cell death pathway show that this pathway does not affect the eat-3 phenotype . Instead , eat-3 mutants are sensitive to damage from free radicals and they show hallmarks of ATP deficiency . The effects of sod-2 loss of function and partial compensation by induced expression of SOD-2 suggest that damage from free radicals is localized to the mitochondrial matrix . These observations might help design more effective treatments for patients with DOA .
The D2013 . 5 gene of eat-3 ( ad426 ) was sequenced using amplified genomic DNA from two independent PCR reactions . The C . elegans eat-3 cDNAs yk10h8 and yk21c2 were obtained from Y . Kohara ( National Institute of Genetics , Mishima , Japan ) . The pPD expression vectors were kindly provided by A . Fire , J . Ahnn , G . Seydoux , and S . Xu ( Carnegie Institution of Washington , Baltimore , Maryland ) . The Peat-3::NLS::GFP::β-galactosidase construct was made with an eat-3 gene promoter fragment ( positions 23335 to 25288 of cosmid D2013 ) , fused to the reporter sequences of pPD95 . 67 . The rescue construct contained this same promoter fragment fused to the yk21c2 cDNA . This cDNA lacks the N-terminal 70 amino acids . The missing sequence was generated by PCR of genomic DNA . Mutations were introduced by PCR and verified by sequencing . EAT-3 was expressed in muscle cells using the myo-3 promoter of pPD96 . 52 . The antisense construct has the insert of yk21c2 cloned in the antisense orientation in pPD96 . 52 . Production of dsRNA , mitochondrial markers , microinjection , light microscopy and feeding RNAi procedures were described previously [8] , [72] . Feeding RNAi bacteria were kindly provided by Dr . J . Ahringer ( University of Cambridge , UK ) . C . elegans strains were obtained from the C . elegans stock-center ( CGC , University of Minnesota ) and from Dr . S . Mitani ( National Bioresource Project of Japan . Tokyo Women's Medical University School of Medicine , Tokyo ) . Strains provided by Dr . Mitani were backcrossed with wildtype ( N2 ) animals to remove adventitious mutations . Revertants of dyn-1 ( ky51 ) and eat-3 ( ad426 ) were generated with EMS mutagenesis . The dyn-1 ( ky51 ) is temperature sensitive for growth and motility [46] . L3 larvae of either strain were treated with 50 mM EMS as described [73] . F2 progeny of mutagenized animals were screened for revertants by looking for restored growth and motility . The dyn-1 ( ky51 ) animals were screened at the restrictive temperature ( 25°C ) while eat-3 ( ad426 ) animals were screened at 20°C . Newly identified mutants were backcrossed with wildtype ( N2 ) worms to determine whether the new mutations are intra- or extragenic and to rid them of adventitious mutations . The three revertants of dyn-1 ( ky51 ) were genetically inseparable from the original dyn-1 mutation and five of the seven eat-3 ( ad426 ) revertants were inseparable from eat-3 , suggesting that these are intragenic revertants . New mutations in the intragenic revertants were identified by sequencing their respective dyn-1 and eat-3 genes . New mutations in the two extragenic revertants of eat-3 ( ad426 ) were identified by sequencing their drp-1 genes . To determine paraquat sensitivity , increasing concentrations of paraquat ( N , N′-Dimethyl-4 , 4′-bipyridinium dichloride from MP Biomedicals LLC , Solon Ohio ) were added to 30 mm NGM agar plates . These plates were seeded with OP50 bacteria [73] and fifty L1 larvae were transferred to each plate . The plates with worms were incubated at 20°C and tracked for several days by counting the numbers of worms that reached adulthood . Young gravid worms were mixed with E . coli or dry baker's yeast and 10% methanol [74] . This mixture was cryofixed in a Bal-Tec HPM 010 high pressure freezer ( Technotrade , Manchester , New Hampshire ) , followed by freeze-substitution with 2% osmium tetroxide and 0 . 1% uranyl acetate in acetone . The temperature was slowly increased to −20°C and then to room temperature . The samples were rinsed with acetone and infiltrated with Epon-Araldite ( 1 hr in 1 part resin and 3 parts acetone; 2 hr in a 1∶1 mixture; 4 hr in a 3∶1 mixture; 1 hr and 16 hr in resin alone ) . The samples were then incubated in resin with accelerator for 4 hr , flat-embedded between Teflon-coated slides and cured in a 60°C oven for 48 hr . Longitudinal sections ( 60 nm thick ) were post-stained with uranyl acetate and lead citrate . All specimens were examined using a Tecnai 12 transmission electron microscope at 100 kV . Membrane lengths and surface areas were measured with NIH Image software . For tomography , 500 nm thick sections were cut and stained with uranyl-acetate and lead citrate . Colloidal gold particles ( 10 nm ) were applied as alignment markers . A tilt series of 122 images was made on the Albany AEI EM7 MkII HVEM at 1000 kV . The images were recorded around two orthogonal tilt axes , each over an angular range of 120° with a 2° tilt interval . The double-tilt images were aligned , further processed to make a tomographic reconstruction , followed by surface rendering as previously described [75] . Samples for Western blot analysis were prepared by freeze/thawing worms , followed by solubulization in SDS-PAGE sample buffer , boiling for 10 min and clearing of debris by centrifugation for 2 min at 3 , 000 rpm in an Eppendorf microfuge . Western blots were probed with superoxide dismutase antibody from Abcam ( Cambridge , Massachusetts ) . Western blots were quantified with densitometry using a Personal Densitometer SI and ImageQuant software ( Molecular Dynamics , Sunnyvale , California ) .
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Dominant Optic Atrophy is a progressive eye disease caused by degeneration of retinal ganglion cells . The most prevalent form of DOA is caused by mutations in the Opa1 protein . This protein is required for fusion between mitochondria , it has an anti-apoptotic function , and it is required for mitochondrial DNA segregation . It has , nevertheless , been difficult to understand why mutations in Opa1 specifically affect retinal ganglion cells . We used rhe nematode C . elegans as a model to study the underlying causes of Opa1 pathologies . C . elegans Opa1 is encoded by the eat-3 gene . Mutants are sluggish , grow slowly , remain small , and have small broodsizes . These phenotypes are not suppressed by mutations in cell death genes , suggesting that apoptosis does not contribute to eat-3 pathogenesis . Instead , eat-3 mutants are hypersensitive to paraquat , which promotes damage by free radicals , and they are sensitive to loss of the mitochondrial superoxide dismutase sod-2 , which is needed to eliminate free radicals from the mitochondrial matrix . Moreover , eat-3 mutants overexpress SOD-2 , most likely compensating for increased free radical production . These results show that C . elegans EAT-3 is important for resistance to free radicals and they raise the possibility that free radicals contribute to DOA in humans .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"biology/membranes",
"and",
"sorting",
"genetics",
"and",
"genomics/disease",
"models"
] |
2008
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The C. elegans Opa1 Homologue EAT-3 Is Essential for Resistance to Free Radicals
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The promoter regions of approximately 40% of genes in the human genome are embedded in CpG islands , CpG-rich regions that frequently extend on the order of one kb 3′ of the transcription start site ( TSS ) region . CpGs 3′ of the TSS of actively transcribed CpG island promoters typically remain methylation-free , indicating that maintaining promoter-proximal CpGs in an unmethylated state may be important for efficient transcription . Here we utilize recombinase-mediated cassette exchange to introduce a Moloney Murine Leukemia Virus ( MoMuLV ) -based reporter , in vitro methylated 1 kb downstream of the TSS , into a defined genomic site . In a subset of clones , methylation spreads to within ∼320 bp of the TSS , yielding a dramatic decrease in transcript level , even though the promoter/TSS region remains unmethylated . Chromatin immunoprecipitation analyses reveal that such promoter-proximal methylation results in loss of RNA polymerase II and TATA-box-binding protein ( TBP ) binding in the promoter region , suggesting that repression occurs at the level of transcription initiation . While DNA methylation-dependent trimethylation of H3 lysine ( K ) 9 is confined to the intragenic methylated region , the promoter and downstream regions are hypo-acetylated on H3K9/K14 . Furthermore , DNase I hypersensitivity and methylase-based single promoter analysis ( M-SPA ) experiments reveal that a nucleosome is positioned over the unmethylated TATA-box in these clones , indicating that dense DNA methylation downstream of the promoter region is sufficient to alter the chromatin structure of an unmethylated promoter . Based on these observations , we propose that a DNA methylation-free region extending several hundred bases downstream of the TSS may be a prerequisite for efficient transcription initiation . This model provides a biochemical explanation for the typical positioning of TSSs well upstream of the 3′ end of the CpG islands in which they are embedded .
DNA methylation is essential for mammalian development [1 , 2] , playing an important role in maintaining transcriptional silencing of genes on the inactive X chromosome , imprinted genes , and parasitic elements [3 , 4] . In mammals , DNA methylation occurs predominantly on cytosines in the context of the 5′-CpG-3′ dinucleotide ( mCpG ) , and this epigenetic mark is propagated on both parent and nascent strands after DNA replication . The CpG dinucleotide is generally found at a lower than expected frequency in the mammalian genome , with the exception of G + C-rich regions known as CpG islands , which have the statistically expected frequency of CpGs [5] . Analysis of the distribution of DNA methylation reveals that while the majority of cytosines in the context of the CpG dinucleotide are methylated in normal adult somatic tissues , promoter regions containing a high concentration of CpGs , which encompass approximately 70% of mammalian genes [6] , typically remain methylation-free [7] . Surprisingly , the relatively high CpG density associated with CpG island promoters frequently extends ∼400–1 , 000 bp downstream of the transcription start sites ( TSS ) of such genes [6 , 8] , indicating that an unmethylated region extending 3′ of the TSS may be required for efficient transcription . While it is clear that methylation of promoter regions , including that of the Moloney Murine Leukemia Virus ( MoMuLV ) [9] , leads to silencing at the level of transcription initiation [4 , 10 , 11] , several lines of evidence suggest that DNA methylation in the promoter proximal region 3′ of the TSS can also have an adverse affect on transcription . Methylation exclusively in the coding region of an episomal reporter for example , yields an ∼10-fold reduction in expression , relative to an unmethylated control [12] . Similarly , transient transfection of reporter constructs methylated in vitro in regions exclusive of the promoter yields a dramatic decrease in expression level relative to unmethylated controls [13 , 14] . Furthermore , microinjection experiments of mammalian cells [15] or Xenopus oocytes [16] with in vitro methylated reporter constructs reveals that dense methylation 3′ of an unmethylated promoter can dramatically decrease expression level , particularly when located in close proximity to the promoter . Using the Cre/loxP-based recombination system , recombinase-mediated cassette exchange ( RMCE ) [11 , 17 , 18] , we recently showed that a region of dense methylation located ∼1 kb downstream of the TSS of a p16/CDKN2A CpG island promoter attenuates expression level by decreasing elongation efficiency [19] . Taken together , these results reveal that methylation 3′ of the TSS may adversely affect the efficiency of transcription . However , a detailed analysis of the interplay between proximity of DNA methylation to the TSS , transcription efficiency , and chromatin structure has not yet been reported . Here we used RMCE to target a MoMuLV long terminal repeat ( LTR ) -based transgene encoding “humanized” green fluorescent protein ( GFP ) , either unmethylated or methylated in vitro exclusively in a region 3′ of the TSS , to a specific intergenic genomic site in murine erythroleukemia ( MEL ) cells . We show that the methylation pattern introduced in vitro is unstable in vivo , with spreading of methylation towards the TSS occurring in a subset of the clones isolated . When methylation spreads to within ∼320 bp 3′ of the TSS , expression is dramatically reduced , relative to clones bearing an unmethylated cassette integrated at the same site . Surprisingly , such promoter-proximal methylation inhibits RNA polymerase II ( RNAPII ) recruitment and TATA-box-binding protein ( TBP ) binding at the unmethylated promoter . Analysis of the modification state of the amino-terminal tail of H3 reveals that while H3 trimethylated on lysine 9 ( H3K9me3 ) is confined to the patch-methylated region , the unmethylated promoter is hypo-acetylated at this residue , and nucleosome positioning is dramatically altered around the TSS . Based on these observations , we propose that while methylation ∼1 , 000 bp 3′ of the TSS has a relatively modest effect on transcription elongation , methylation ∼300 bp 3′ of the TSS generates a chromatin structure that precludes efficient transcription initiation from a methylation-free promoter .
To determine if DNA methylation exclusive of the promoter/TSS region of a CpG island promoter influences transcription initiation , we introduced a transgene containing the MoMuLV LTR driving expression of GFP , either unmethylated or “patch” methylated in vitro exclusively in a region ∼1 kb 3′ of the TSS ( Figure 1A ) , into the RL5 integration site in MEL cells by RMCE [11 , 17] . This integration site was recently cloned and mapped to the intergenic region between the Tal1 and Map17 genes on Chromosome 4 [20] . Flow cytometric analysis of the pool of ganciclovir resistant cells electroporated with the control unmethylated ( − ) cassette revealed a high and homogeneous level of GFP expression ( Figure 1B ) . In contrast , analysis of the pool of ganciclovir resistant cells harboring the patch-methylated cassette revealed heterogeneous GFP expression , with one population expressing at a level approaching that of the unmethylated cassette and another expressing at a relatively low level . To study the transcriptionally active subpopulations in greater detail , GFP+ cells were sorted , and subclones generated . As expected , the majority of clones generated with the unmethylated cassette harbor the transgene at the RL5 site in one of two possible orientations , as determined by Southern blotting ( Figure 1C ) . Thus , consistent with our previous work , these data reveal that an unmethylated cassette is expressed at relatively high levels , irrespective of genomic orientation . In all subsequent experiments , control clones harboring an “orientation-matched” unmethylated cassette were analyzed in parallel with clones harboring the patch-methylated cassette . To determine whether the heterogeneity in expression detected in the pool of cells harboring the patch-methylated cassette reflects the presence of cells with distinct , stable expression states , clones were analyzed by flow cytometry at day 38 post-electroporation ( Figure 2A and 2B ) . Patch-methylated clones ( identified with an affixed “P” throughout the remainder of this article ) of two distinct classes were detected; one expressing GFP at levels close to the unmethylated cassette and another expressing at significantly reduced levels . Comparison of the median GFP fluorescence values of ten clones harboring the patch-methylated cassette reveals that the “dull” clones express at a level ∼2%–10% of the unmethylated control ( with the exception of clone 3P , which shows a heterogeneous pattern of expression; unpublished data ) , while the “bright” clones express at a level approaching that of the unmethylated control ( Figure 2B ) . To independently determine the relative expression level of the “low-expressing” class of clones , the steady state level of mRNA of an unmethylated control ( 6− ) , and a representative low-expressing patch-methylated clone ( 9P ) was determined by RT-PCR ( Figure 2C ) . Consistent with the flow cytometry results , the methylated clone was found to express mRNA at ∼2% of the level of the unmethylated clone . To confirm that the clonal populations showing a low level of expression do not include transcriptionally silent cells , subpopulations of clone 9P cells showing relatively high ( above the 98th percentile of the GFP negative parent line ) or low ( indistinguishable from the parent line ) expression were sorted , cultured for 5 d , and reanalyzed by flow cytometry ( Figure 2D ) . The expression profiles of these populations were very similar , indicating that the cells in this clone are homogeneous with respect to expression level . Taken together , these results reveal that a subset of patch-methylated clones harbor a cassette from which expression is dramatically reduced relative to an unmethylated control cassette integrated at the same site in the same orientation . Given the heterogeneity in expression profiles of clones harboring the initially patch-methylated cassette , we next determined the methylation status of the cassettes in each of the clones described in Figure 2 . Genomic DNA isolated 35 days post-electroporation was digested with BamHI alone , or in combination with the methylation-sensitive restriction enzyme HpaII , and Southern analysis was conducted using a GFP probe ( Figure 3A and 3B ) . Two classes of clones are readily apparent , one with a relatively high molecular weight band of ∼1 . 65 kb ( clones 1P , 4P , 8P , 9P , and 10P ) , and another with relatively low molecular weight bands between ∼0 . 5 and 0 . 9 kb ( clones 2P , 5P , 6P , and 7P ) . The former is indicative of the absence of methylation at the first HpaII site 5′ of the patch-methylated region , but maintenance of methylation at all HpaII sites 3′ of this site , while the latter is indicative of loss of methylation at some or all of the premethylated sites . Comparison of the methylation state with the expression data , as measured by flow cytometry ( see Figure 2B ) , reveals that clones harboring cassettes with varying degrees of demethylation of the patch-methylated region are all in the high-expressing class , while clones showing maintenance of methylation at the premethylated sites , are all in the low-expressing class of clones . To characterize the methylation status of representative low- and high-expressing patch-methylated clones in greater detail , we analyzed clones 2P , 5P , 8P , 9P , and the unmethylated control clone 6− by bisulphite sequencing , using primers specific for the LTR ( I ) , GAG ( II ) , “methylation-junction” ( III ) , and GFP/in vitro methylated ( IV ) regions ( Figure 3C; Figure S1; and unpublished data ) . Sequencing of cloned amplification products revealed that “alleles” of the high-expressing unmethylated control clone 6− and the initially patch-methylated clone 2P are virtually methylation-free , explaining why the latter clone shows an expression profile similar to that of the initially unmethylated cassette . Interestingly , clone 5P , which shows an “intermediate” level of expression , retains the general methylation pattern introduced in vivo , with loss of methylation at some sites in the premethylated region but no spreading upstream of this region . In contrast , “alleles” of the low-expressing clones 8P and 9P not only retained the initial methylation state at the majority of CpG sites , but also showed significant spreading of methylation upstream of this region ( Figure 3C , Figure S1 ) . Thus , the apparently unmethylated HpaII site ( as determined by Southern blotting ) is embedded within the “de novo” methylated domain in these clones . Unfortunately , as this site is within the 3′ primer sequence of the GAG amplicon ( see Table S1 ) , the bisulphite data is uninformative for this site . However , several other CpGs in the initially unmethylated promoter proximal region remain unmethylated , despite being flanked by newly methylated sites , indicating that specific CpGs flanking the patch-methylated region are de novo methylated with very different efficiencies . Surprisingly , in the two low-expressing clones that were analyzed , methylation did not spread beyond ∼320 bps 3′ of the TSS . With the exception of a few dispersed CpGs that are methylated in a subset of sequenced alleles , the promoter and TSS region in these clones remain methylation-free . These data indicate that spreading of methylation to within ∼320 bp of the TSS is sufficient to dramatically reduce transcriptional efficiency from an unmethylated promoter , while methylation ∼1 , 000 bp 3′ of the TSS has a relatively modest effect on transcription . Previously , we found that the histone deacetylase ( HDAC ) inhibitor Trichostatin A ( TSA ) dramatically increased transcription from MoMuLV proviral clones constitutively expressing at a low level , but is incapable of activating expression in the same clones at a later time point when DNA methylation has accumulated in the TSS region [9] . These results indicate that deacetylation of histones enhances transcription from active promoters , but is ineffective at promoting transcription from promoters that are methylated . Treatment of representative clones ( 3− and 6− ) harboring the unmethylated L1-LTRGFP-1L cassette with 50nmol TSA for 48 h yielded a dramatic increase in expression level ( Figure S2 ) . In contrast , the same treatment of the low-expressing clones 8P and 9P yielded no increase in GFP expression , indicating that in the presence of promoter-proximal methylation , inhibition of HDAC activity is not sufficient for transcriptional induction . To determine whether methylation in close proximity to the promoter region influences recruitment of RNAPII to the transgene , formaldehyde cross-linked chromatin was generated from a representative unmethylated control clone ( 6− ) and a low-expressing patch-methylated clone ( 9P ) . Chromatin immunoprecipitation ( ChIP ) was performed using antibodies specific for the N-terminal domain ( sc−899 ) or the unphosphorylated C-terminal domain ( CTD ) ( 8WG16 ) of RNAPII . Quantitative real-time PCR was conducted using primers specific for the transgene ( Figure 4A ) , as well as for the endogenous β-major ( β-maj ) globin gene , which is not expressed in uninduced MEL cells [21] . Analysis of the data generated from two independent chromatin preparations reveals that , as expected , no enrichment of RNAPII is detected in the promoter region of the β-maj gene , relative to control immunoglobulin G ( IgG ) ( Figure 4B and 4C ) . While clone 9P also shows no enrichment of RNAPII in the promoter ( TATA ) and downstream ( GFP ) regions of the transgene , the unmethylated clone shows significant enrichment in both regions using both α-RNAPII reagents ( Figure 4B and 4C ) . Not surprisingly , a higher level of enrichment of RNAPII was detected in the promoter than in the downstream region . Similar results were obtained using an antibody specific for RNAPII phosphorylated on Serine 5 of the CTD , the elongation-competent form of the holoenzyme ( Figure S3 ) . Taken together , these results reveal that methylation beginning ∼320 bp downstream of the TSS is sufficient to inhibit recruitment of RNAPII to the unmethylated transgene promoter . To determine whether the failure to recruit RNAPII is the result of the inhibition of formation of the preinitiation complex ( PIC ) , we next carried out ChIP analysis of TBP , a subunit of the transcription initiation factor ( TFIID ) complex that binds to the TATA box and initiates formation of the PIC . Analysis of the data generated from two independent chromatin preparations reveals that relative to control IgG , no TBP is detected in the promoter region of the endogenous β-maj gene or in the GFP region of the transgene in either clone ( Figure 4D ) . However , while the unmethylated clone shows significant enrichment of TBP in the TATA box region , clone 9P shows no enrichment . These data indicate that the reduced expression observed in the patch-methylated clone is the result of transcriptional inhibition at a step prior to recruitment of TFIID to the promoter region . Given that recruitment of RNAPII and TFIID to the promoter region of the patch-methylated transgene is clearly inhibited , we next investigated whether the histone marks normally associated with “active” genes are similarly affected . The 5′ ends of actively transcribing genes are marked by histone H3 trimethylated ( H3K4me3 ) and dimethylated ( H3K4me2 ) on K4 [22] , and several of the HMTases with specificity for H3K4 are associated with the elongating RNAPII holoenzyme . Interestingly , methylation of H3K4 precedes acetylation of H3 in the promoter regions of inducible genes [23] , and a low but clearly detectable level of H3K4me3 is found associated with the 5′ end of some genes “poised” for transcription [22 , 24] . To determine whether promoter-proximal DNA methylation influences H3K4 methylation state , two independent ChIP experiments were conducted using antibodies specific for H3K4me3 or H3K4me2 ( Figure S3 ) and primers specific for the transgene or the endogenous pancreatic Amylase 2 ( Amy2 ) gene , which is transcriptionally silent in MEL cells . In the intragenic regions analyzed , H3K4me3 enrichment is significantly higher in the transcriptionally active unmethylated control , likely the result of transcription-coupled deposition of H3K4me3 . Surprisingly , enrichment of this mark in the promoter region is 2-fold higher in the patch-methylated than the unmethylated clone , perhaps as a consequence of decreased turnover of nucleosomes in the promoter region of the patch-methylated cassette . Analysis of H3K4me2 revealed low but significant levels of enrichment on both clones , with clone 9P showing a ∼2–4-fold lower level of enrichment throughout the transgene . These results reveal that while DNA methylation starting ∼320 bp 3′ of the TSS yields a lower level of H3K4 methylation in the transcribed region of the cassette , this mark is still present in the promoter region . We next tested whether acetylation of the H3 tail , another mark associated with “active” chromatin , is affected by promoter-proximal methylation . Acetylation of histone tails neutralizes the positive charge on lysines and is believed to promote an allosteric change in nucleosome conformation that renders nucleosomal DNA more accessible to the transcription machinery [25 , 26] , perhaps by altering higher order chromatin structure [27] . While binding of TBP to the TATA box is severely inhibited by incorporation of template DNA into a nucleosome [28] , histone acetylation can facilitate TBP binding to a naturally positioned nucleosome [29] . Furthermore , in vitro and in vivo experiments are consistent with the model that histone acetylation is an early step in the cascade of events leading to transcription initiation [30–33] . We hypothesized that a condensed chromatin structure extending 5′ of the methylated region and/or a methylation-mediated alteration in nucleosome positioning may be responsible for the observed effect on TBP binding and transcription initiation . We isolated chromatin from clones 6− and 9P and determined the acetylation state of H3 on K9 and K14 ( H3K9/K14ac ) , the latter of which is the major substrate of the mammalian p300 , PCAF and GCN5 histone acetyltransferases [34 , 35] . Similar levels of enrichment of H3K9/K14ac were detected at the endogenous β-maj gene in both the unmethylated and patch-methylated clones ( Figure 5A ) , indicating that the efficiency of the immunoprecipitation was similar for both clones . Surprisingly , analysis of the TATA and GFP regions of the transgene revealed significant enrichment of H3 acetylation exclusively in the unmethylated clone , despite the fact that the promoter/TATA box region of the patch-methylated cassette is also unmethylated . Given the absence of H3K9/K14ac throughout the patch-methylated cassette , and the reported association of DNA methylation and H3K9 methylation [36–38] , we next tested whether the patch-methylated cassette is marked by H3K9me3 , using the endogenous Gnas gene , previously shown to be marked by H3K9me3 in MEL cells [39] , as a positive control ( Figure 5B ) . Analysis of the Gnas gene revealed similar levels of enrichment in the unmethylated and patch-methylated clones ( ∼26- and 23-fold , respectively , relative to the IgG control ) , indicating that the H3K9me3 ChIP worked with similar efficiency in both chromatin preparations . A low level of enrichment , was also detected in the promoter region of the endogenous β-maj gene in both clones , consistent with the previous report showing H3K9me3 1 kb 3′ of the promoter region in uninduced MEL cells [40] . Analysis of the TATA box region of the transgene reveals that both clones 6− and 9P show a modest level of enrichment for H3K9me3 . In contrast , analysis of the GFP region reveals a high level of H3K9me3 enrichment ( ∼22-fold relative to IgG ) exclusively in clone 9P . The unmethylated clone shows a relatively low level of enrichment in the same region , ∼4 . 7-fold relative to IgG . Given the size of chromatin fragments generated via sonication , the slightly higher level of enrichment detected in the TATA region of clone 9P may be the result of amplification of template fragments bearing H3K9me3 in the downstream DNA methylated region . These data reveal that targeting of H3K9me3 to “euchromatic” regions in mammalian cells may be facilitated by the presence of DNA methylation per se , and that methylation-associated deposition of this mark may not spread beyond the DNA methylated regions . To directly determine whether the observed inhibition of transcription in the low-expressing patch-methylated clones is associated with an altered chromatin structure in the upstream unmethylated CpG island promoter/TSS region , DNase I hypersensitivity ( HS ) analysis was conducted . Previous experiments have shown that a transcriptionally active MoMuLV LTR harbors two HS sites , one in the upstream enhancer region , and another around the TSS [41 , 42] . Nuclei from clones 6− and 9P were isolated and treated with DNase I . Subsequently , genomic DNA was extracted , digested with BamHI , and analyzed by Southern blot using the indirect end-labeling technique ( Figure 6A ) . The unmethylated clone showed a predominant band at 2 . 3 kb , and an additional band at 2 . 5 kb , corresponding to the TSS and the enhancer regions , respectively . In contrast , the clone bearing promoter-proximal DNA methylation showed a band of comparable intensity in the enhancer region , but only a faint HS site around the TSS , indicating that the chromatin structure around the TSS is indeed altered by DNA methylation ∼320 bp downstream of this site ( Figure 6B ) . To establish that the difference in DNase I digestion patterns was not the result of a difference in the efficiency of digestion , the blot shown in 6B was subsequently stripped and reprobed with a probe specific for the endogenous “HS3” site in the Locus Control Region of the β-globin locus ( Figure 6C ) . A single DNase I-dependent band is present in clone 6− and 9P samples , demonstrating that both clones were digested with comparable efficiency . To determine whether the loss of the TSS-specific HS site reflects an alteration in nucleosome positioning at the TSS of the patch-methylated cassette , “methylase-based single promoter analysis” ( M-SPA ) , a method recently developed by Fatemi et al . [43] , was conducted using nuclei isolated from clones 6− and 9P . M-SPA involves treatment of isolated nuclei with the cytosine-C5 CpG-specific DNA methyltransferase SssI ( M . SssI ) , followed by genomic bisulphite sequencing of individual progeny DNA molecules . In the context of chromatin , the accessibility of individual CpGs to M . SssI activity is dependent upon nucleosome positioning or the presence of bound transcription factors [44] . Patches of ∼150 bp of relatively under-methylated regions correlate with the approximate positions of nucleosomes on individual sequenced molecules [43] . As the promoter region of the transgene is virtually methylation-free in both the patch-methylated and unmethylated clones ( see Figure 3 ) , the methylation detected predominantly reflects the accessibility of individual CpGs to M . SssI activity . Bisulphite analysis of isolated genomic DNA treated with M . SssI for 5 min revealed complete methylation of all 24 CpGs in the promoter region ( unpublished data ) . In contrast , analysis of isolated nuclei treated with M . SssI for 15 min revealed a clone-dependent methylation pattern ( Figure 7A ) . While clone 6− shows a relatively low level of methylation downstream of the TSS , clone 9P showed a relatively low level of methylation across a ∼150-bp region centered on the TSS ( Figure 7B ) . These distinct patterns of methylation sensitivity likely reflect the positioning of a nucleosome over the promoter/TSS region in clone 9P , and the absence of a nucleosome in this region in the unmethylated , highly transcribed control . Taken together , these data reveal that the chromatin structure around the unmethylated TSS is indeed altered in cells harboring DNA methylation ∼320 bp 3′ of this region , demonstrating that this epigenetic mark can act at a distance to disrupt nucleosome positioning and in turn , transcription initiation in mammalian cells .
Here we utilized RMCE to target a patch-methylated construct into a defined chromosomal region . Spreading of methylation towards the TSS in a subset of clones allowed us to study the influence of methylation on transcription when present at different distances downstream of a methylation-free promoter . When methylation spreads to within ∼320 bp of the TSS , expression is dramatically reduced , relative to an unmethylated cassette integrated at the same site . Clones bearing such promoter-proximal methylation show loss of RNAPII and TBP binding and a reduction in the level of H3K4me3 in the transcribed region . Surprisingly , while H3K9me3 is enriched in the patch-methylated region , this mark does not spread upstream into the unmethylated promoter , despite the fact that this region is hypo-acetylated on H3K9/14 . Nevertheless , the observed loss of a promoter-specific DNase I HS site and alteration of nucleosome positioning around the promoter/TSS region reveals that DNA methylation beginning 320 bp—three nucleosomes—downstream of the TSS generates a chromatin structure in the methylation-free promoter region that precludes efficient transcription initiation . Previously , we conducted a similar set of experiments using a construct with the p16 promoter driving expression of the GFP gene [19] . This construct , which differs from the construct used in this study at the promoter region only , was patch-methylated in vitro , in the same region as the L1-LTRGFP-1L construct and integrated at the same genomic site via RMCE . In contrast to the LTR-based construct , only one GFP+ population was detected by flow cytometry . Analysis of the methylation status of GFP+ clones harboring the p16 promoter-based construct , which showed a decrease in expression of ∼40% relative to unmethylated control clones , revealed no spreading of methylation upstream of the premethylated region ∼1 kb downstream of the promoter , and no apparent effect on transcription initiation rate , as determined by polII ChIP and run-on analyses of the transcribed region upstream of the patch-methylated domain . Given that the level of expression from the unmethylated LTR is approximately five times greater than that of the unmethylated p16 promoter ( at the RL5 integration site ) , it is quite possible that methylation spreading inhibited transcription from the p16 promoter to such an extent that the level of GFP expression was below the threshold for detection by flow cytometry . If so , this class of clones was inadvertently excluded from our original study , as only cells showing detectable GFP expression were chosen for further analysis . In a subpopulation of cells harboring the L1-LTRGFP-1L cassette described here , a region upstream of the in vitro methylated domain was de novo methylated , yielding a dramatic decrease in expression that was nevertheless above the threshold for detection by flow cytometry . Why methylation spreading did not extend into the promoter region remains to be determined . Given the persistence of H3K4 methylation in the promoter region of the patch-methylated cassette , it is tempting to speculate that this “active” mark may serve in part to protect this region from de novo DNA methylation . Regardless , these data reveal that while transcription is only modestly reduced in the presence of a dense patch of methylation ∼1 kb downstream of the TSS , methylation within ∼320 bp of the TSS is sufficient to dramatically reduce transcription initiation efficiency , results consistent with those of Graessmann and colleagues , who found using microinjection of premethylated HSV-based reporter constructs that methylation of specific CpGs >570 bp downstream of the TSS had no effect on expression , while methylation of CpGs within 570 bp of the TSS had a dramatic effect on transcription [15] . Similarly , using a transiently transfected luciferase reporter , Hisano et al . showed that methylation of a region ∼100 bp downstream of the TSS of the minimal promoter ( which lacks CpGs altogether ) of the murine Tact1 gene yielded a dramatic reduction in expression , relative to an unmethylated control [45] . Taken together , these observations indicate that methylation of the promoter region per se is not a prerequisite for DNA methylation-mediated transcriptional inhibition . As outlined in the model described in Figure 8 , we propose that the distance of DNA methylation downstream of the TSS dictates the nature and level of transcriptional inhibition , with a modest effect on elongation efficiency occurring in the presence of dense DNA methylation on the order of 1 kb downstream of the TSS [19] , and a dramatic decrease in initiation efficiency occurring in the presence of DNA methylation on the order of 300 bp downstream of the TSS . Recruitment of HDAC and/or H3K9 MTase complexes to the methylated region may play a role in inhibiting transcriptional initiation at a distance . However , while we did detect a high level of enrichment of H3K9me3 in the DNA methylated/GFP region in the patch-methylated clone analyzed here , enrichment of this mark was only 2-fold higher in the promoter region of the patch-methylated cassette than the control unmethylated cassette , indicating that H3K9me3 acts at a distance to prevent recruitment of TBP , or is not responsible for the observed initiation block . As Vakoc et al . recently showed that H3K9me3 is frequently found in the transcribed regions of actively transcribing genes [40] , we favor the latter possibility . Regardless , given that the unmethylated control cassette shows a relatively low level of enrichment of the H3K9me3 mark in the otherwise identical but unmethylated GFP region , our experiments clearly reveal that the presence of a short patch of DNA methylation is sufficient to recruit an HMTase with H3K9me3 activity to a “euchromatic” region in mammalian cells . Consistent with the hypothesis that DNA methylation can act “upstream” of H3K9 trimethylation in mammalian cells , Feng and colleagues recently showed that the presence of CpG dinucleotides is required for maintenance of H3K9 trimethylation [39] . Genome-wide analysis of the distribution of H3K9/K14 acetylation reveals that this mark typically extends ∼1 kb downstream of TSSs , and along the entire length of CpG islands [46] . Acetylation of histone tails in promoter regions facilitates efficient remodeling of nucleosomes via recruitment of SWI/SNF chromatin remodeling complexes and/or TFIID [29 , 33 , 47 , 48] , and a recent analysis of PIC formation in 29 ENCODE regions in human cells reveals a very strong correlation between H3 acetylation and the presence of a PIC [49] . As the unmethylated promoter region of the patch-methylated cassette described here is hypo-acetylated relative to the identical but unmethylated control cassette integrated at the same site , we propose that the observed decrease in histone H3 acetylation reduces nucleosome “fluidity” in the promoter proximal region , which inhibits repositioning of the nucleosome around the TATA-box and in turn , recruitment of TBP . Support for this hypothesis comes from the DNase I and M-SPA analyses described here . For a number of genes , formation of promoter DNase I HS sites precedes active transcription [50–52] , perhaps reflecting the activity of chromatin remodeling complexes . Indeed , maintenance of expression from the MoMuLV LTR requires Bramha , the catalytic subunit of one of the mammalian SWI/SNF complexes [53] . Clones bearing methylated CpGs ∼300bp downstream of the TSS showed loss of the HS site normally found at the TSS and the presence of a nucleosome centered on the TSS , clearly demonstrating that promoter-proximal methylation can alter the chromatin structure of an unmethylated upstream promoter . Recently , Frigola and colleagues demonstrated that silencing of all of the genes within a 4-Mb band of Chromosome 2q . 14 . 2 occurs frequently in colorectal cancer [54] . Surprisingly , while several “CpG island suburbs” within this region harbor genes with hypermethylated CpG island promoters , other genes within this band are silenced despite the absence of promoter-specific DNA methylation , indicating that DNA methylation within promoter regions per se is neither a prerequisite nor a necessary consequence of transcriptional silencing in cancer . In light of the results reported here , it would be of interest to analyze the methylation state of the regions flanking the unmethylated but silenced CpG island promoters in this region . Bird and colleagues noted some time ago that a high density of CpGs extend 3′ of the TSSs associated with CpG islands [55] . Indeed , recent bioinformatic analyses reveals that a relatively high density of CpGs is frequently found to extend on the order of 1 kb downstream of the TSSs of mammalian promoters [6 , 8] , indicative of the absence of methylation within these regions , at least in the germ line [56] . Our results reveal that a methylation-free region extending downstream of the TSS may be necessary for efficient promoter activity , perhaps explaining why CpG islands promoters are structured in this way .
A HindIII to BamHI fragment including the MoMuLV LTR and GFP from the previously described L1-MFGGFP-1L plasmid [42] was cloned into the vector L1polylinker1L . Subsequently , a fragment containing the SV40 polyA signal was inserted into the BamHI site at the 3′ end of the GFP gene of this intermediate construct , yielding the L1-LTRGFP-1L construct used in this study . To generate the “patch”-methylated cassette , this vector was digested with BglII and BamHI , yielding two fragments , one containing the LTR and transcribed GAG sequence , and the other the GFP gene and plasmid backbone . The latter fragment was methylated in vitro with M . SssI and ligated to the unmethylated promoter fragment , generating a plasmid in which the TSS of the LTR is ∼1 . 0 kb from the 5′ end of the methylated region . To confirm that the methylation reaction was carried to completion , methylated DNA was , digested with the methylation-sensitive enzyme HpaII following organic extraction and visualized by electrophoresis on an agarose gel ( unpublished data ) . Unmethylated and patch-methylated plasmid was introduced into RL5 MEL cells [17] by electroporation , and selected in the presence of ganciclovir as previously described [11] . GFP+ cells were isolated by FACS , cloned by limiting dilution , and screened for successful Cre-mediated exchange by Southern blotting . For treatment with TSA ( Wako Pure Chemicals Industries , http://www . wako-chem . co . jp/english ) , aliquots dissolved at 5 mg/ml in methanol and stored at −20 °C were thawed , diluted in fresh complete medium , and added to MEL cells at a final concentration of 50 nmol . Using this protocol , more than 90% of cells are viable , as measured by propidium iodide ( PI ) staining . For FACS analyses , MEL cells were processed as previously described [42] . Data on at least 10 , 000 viable cells ( as determined by PI staining ) were collected for each sample and analyzed using FlowJo software ( Treestar , http://www . treestar . com ) . For RT-PCR , total RNA was isolated from 5 × 106 cells using the RNeasy kit ( Qiagen , http://www . qiagen . com ) following the manufacturer's protocol . Quantitative RT-PCR was carried out using random hexamer as described [42] . For second strand synthesis , each 25 μl reaction was supplemented with 1 μCi of [α-32P]dCTP ( NEN ) . Primers spanning the transgene intron ( see Table S1 for primer sequences ) were used in a duplex PCR reaction with primers specific for the endogenous β-actin gene as an internal control . Amplification products were quantified via storage phosphor imaging using a Typhoon 8600 and ImageQuant software ( Molecular Dynamics , http://www . ump . com/mdynamic . html ) . Preparation of genomic DNA , the DNA probe , restriction digests , and membrane transfers were performed as described previously [42] . Clones harboring a single copy integrant at the RL5 integration site were identified by digestion of genomic DNA ( isolated at day 21 post-RMCE ) with BamHI , which cuts once in the MFGGFP provirus , followed by Southern blot analysis using the indirect end-labeling technique with the GFP probe . Methylation status of the cassette was determined by digestion of genomic DNA with BamHI alone or in combination with the methylation sensitive enzyme HpaII . For bisulphite analyses , genomic DNA was digested with BamHI , purified by phenol/chloroform extraction , denatured , treated with bisulphite as described previously [9] , and subject to nested or semi-nested PCR using primers designed to amplify bisulfite-converted template . Primers used for the LTR ( I ) , GAG ( II ) , and GFP regions ( IV ) are listed in Table S1 . PCR products were cloned via T/A cloning using the pGEM-T easy kit ( Promega , http://www . promega . com ) , and individual inserts were sequenced using the Big Dye version 3 . 1 . Sequencing data was analyzed using Sequencher software ( Gene Codes , http://www . genecodes . com ) . DNase I digestion of nuclei was performed as described previously [57] , using from 0 . 67–7 . 67 μg DNase I/ml . DNase I digested genomic DNA was purified and digested with BamHI , prior to electrophoresis on a 0 . 8% agarose gel . The GFP probe used for hybridization was generated by digestion of the plasmidL1-MFG-GFP-1L with NcoI and BamHI , yielding a restriction fragment including the complete 720-bp GFP gene . The blot was stripped by pouring a boiling solution of 5% SDS and 5 mmol NaOH over the blot and allowing it to cool to room temperature . After repeating this step with 0 . 5% SDS only , the blot was rinsed with 2× SSC . The blot was subsequently exposed to film to establish the efficiency of stripping . The probe used for rehybridization was generated by digestion of the plasmid BSKSIImHS3TIM with EcoRI and BamHI , generating a 360-bp fragment that is specific for the “HS3” region of the murine ß-globin Locus Control Region . M-SPA was conducted as described previously [43] , with minor modifications . For in vitro methylation of purified DNA , 6 μg of EcoRI-digested genomic DNA was treated with 60 Units M . SssI for 5 min and the reaction stopped with 2× lysis buffer . For M . SssI treatment of nuclei , 1 × 107 cells were harvested , and nuclei were isolated via dounce homogenization . Intact nuclei were treated with 60 Units M . SssI for 15 min , and processed as described [43] . Subsequently , 500 ng of genomic DNA was subject to bisulphite conversion using the EZ-DNA methylation Gold kit ( Zymo Research , http://www . zymoresearch . com ) and processed as described above . To generate cross-linked chromatin , 2 . 4–4 × 107 exponentially growing cells were incubated in the presence of 1% ( v/v ) formaldehyde for 10 min at 37 °C . Chromatin was generated and ChIP conducted as described previously [58] . Polyclonal antibodies specific for TBP ( kindly provided by N . Hernandez ) H3K9/14ac ( 5 μg; Upstate , 06–599 ) , which is reported to preferentially recognize H3 acetylated on K9 [59] , H3K4me2 ( 5 μl ) ( Upstate , 07–030 , http://www . upstate . com ) , H3K4me3 ( 4 . 5 μg ) ( Abcam , ab8580 , http://www . abcam . com ) , and H3K9me3 ( 4 . 5 μg ) ( Upstate , 07–442 ) were used in combination with control , purified rabbit IgG ( 20 μg ) ( Sigma , http://www . sigmaaldrich . com ) . To generate cross-linked chromatin for ChIP with antibodies recognizing RNAPII , 1 . 6 × 107 exponentially growing cells were incubated in the presence of 1% ( v/v ) formaldehyde for 10 min at 25 °C , and ChIP was conducted , as described previously [60] . Sonicated fragments were generated using a Bioruptor water bath sonicator ( Diagenode , http://www . diagenode . com ) , with chromatin fragments ranging in size from 250–750 bp , as determined by electrophoresis through a 0 . 8% agarose-Tris-borate-EDTA gel and ethidium bromide staining . α-RNAPII antibodies used include: the polyclonals SC-899 ( 2 μg ) ( Santa Cruz , http://www . scbt . com ) and 8WG16 ( ∼25 μg ) ( Covance , MMS-126R , http://www . covance . com ) , specific for the amino- and carboxy-termini , respectively , of the large subunit of RNAPII and the monoclonal H14 ( 20 μg; Covance , MMS-134R ) , specific for RNAPII phosphorylated on S5 of the CTD . For experiments using the α-RNAPII antibodies SC-899 and 8WG16 as well as , the TBP , H3K9/14ac and H3K9me3 specific antibodies , enrichment via ChIP was determined by real-time quantitative PCR using an Opticon 2 thermal cycler ( Bio-Rad , http://www . bio-rad . com ) , with EvaGreen ( Biotium , http://www . biotium . com ) , and hot-start Taq polymerase ( Fermentas , http://www . fermentas . com ) . Primers specific for the TATA box and GFP regions of the transgene were used , as well as primers specific for the endogenous Gnas and β-maj genes ( sequences shown in Table S1 ) . Conditions are available upon request . The percentage of material bound , relative to total input , was determined using the standard curve method . For ChIP using the H14 , H3K4me2 , and H3K4me3 antibodies , quantitative duplex PCR was performed using a PerkinElmer 9700 thermocycler ( http://www . perkinelmer . com ) , as described [42] . Conditions of linear amplification were determined empirically for all primer combinations . Each 25 μl reaction was supplemented with 1 μCi of [α-32P]dCTP ( PerkinElmer ) . Primers specific for the LTR , GAG , and GFP regions of the transgene , as well as the endogenous Amy2 gene are shown in Table S1 . The reaction product was subject to electrophoresis on a 5% ( v/v ) nondenaturing polyacrylamide gel , and the amount of amplified product was quantified as described for RT-PCR . To determine “fold-enrichment” values for a given region in the cassette , the ratio of the two PCR products ( transgene/control ) was calculated for the antibody bound fraction and normalized to the ratio obtained from the input material . Relative enrichment values were subsequently calculated by taking the ratio of the fold-enrichment values of the unmethylated/methylated samples in each of the regions analyzed .
The GenBank ( http://www . ncbi . nlm . nih . gov/gquery/gquery . fcgi ) accession numbers for the genes and genomes discussed in this paper are MoMuLV ( AF033811 ) ; β-major ( β-maj ) globin ( NM_008220 ) ; Gnas ( NM_010309 ) ; and Amy2 ( NM_009669 ) .
|
Genes , the functional units of heredity , are made up of DNA , which is packaged inside the nuclei of eukaryotic cells in association with a number of proteins in a structure called chromatin . In order for transcription , the process of transferring genetic information from DNA to RNA , to take place , chromatin must be decondensed to allow the transcription machinery to bind the genes that are to be transcribed . In mammals , promoters , the starting position of genes , are frequently embedded in “CpG islands , ” regions with a relatively high density of the CpG dinucleotide . Paradoxically , while cytosines in the context of the CpG dinucleotide are generally methylated , CpGs flanking the start sites of genes typically remain methylation-free . As CpG methylation is associated with condensed chromatin , it is generally believed that promoter regions must remain free of methylation to allow for binding of the transcription machinery . Here , using a novel method for introducing methylated DNA into a defined genomic site , we demonstrate that DNA methylation in the promoter-proximal region of a gene is sufficient to block transcription via the generation of a chromatin structure that inhibits binding of the transcription machinery . Thus , methylation may inhibit transcription even when present outside the promoter region .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"mus",
"(mouse)",
"in",
"vitro",
"genetics",
"and",
"genomics"
] |
2007
|
An Unmethylated 3′ Promoter-Proximal Region Is Required for Efficient Transcription Initiation
|
When two mutations , one dominant pathogenic and the other “confining” nonsense , coexist in the same allele , theoretically , reversion of the latter may elicit a disease , like the opening of Pandora's box . However , cases of this hypothetical pathogenic mechanism have never been reported . We describe a lethal form of keratitis-ichthyosis-deafness ( KID ) syndrome caused by the reversion of the GJB2 nonsense mutation p . Tyr136X that would otherwise have confined the effect of another dominant lethal mutation , p . Gly45Glu , in the same allele . The patient's mother had the identical misssense mutation which was confined by the nonsense mutation . The biological relationship between the parents and the child was confirmed by genotyping of 15 short tandem repeat loci . Haplotype analysis using 40 SNPs spanning the >39 kbp region surrounding the GJB2 gene and an extended SNP microarray analysis spanning 83 , 483 SNPs throughout chromosome 13 in the family showed that an allelic recombination event involving the maternal allele carrying the mutations generated the pathogenic allele unique to the patient , although the possibility of coincidental accumulation of spontaneous point mutations cannot be completely excluded . Previous reports and our mutation screening support that p . Gly45Glu is in complete linkage disequilibrium with p . Tyr136X in the Japanese population . Estimated from statisitics in the literature , there may be approximately 11 , 000 p . Gly45Glu carriers in the Japanese population who have this second-site confining mutation , which acts as natural genetic protection from the lethal disease . The reversion-triggered onset of the disesase shown in this study is a previously unreported genetic pathogenesis based on Mendelian inheritance .
A nonsense mutation may , in theory , disrupt and thus “confine” the effects of another dominant pathogenic mutation when the two mutations coexist in the same allele of a single gene . Furthermore , in such cases , reversion of the confining nonsense mutation may paradoxically elicit a congenital disease , although proven cases of this hypothetical pathogenesis have not been reported . Keratitis-ichthyosis-deafness ( KID ) syndrome ( OMIM 148210 ) is a rare congenital ectodermal disorder characterized by vascularizing keratitis , ichthyosiform erythroderma and sensorineural hearing loss [1] . KID syndrome is mainly caused by a heterozygous germ line missense mutation in GJB2 ( Entrez Gene ID: 2706 ) encoding connexin 26 ( Cx26 ) ( RefSeq: NM_004004 . 5 ) [2]–[4] . Here we report a case of KID syndrome where the reversion of a missense mutation induced a lethal disease . We encountered a girl with KID syndrome from obviously healthy parents , and sequence analysis of GJB2 revealed a heterozygous missense mutation , p . Gly45Glu , in the patient . Unexpectedly , her healthy mother also had the heterozygous missense mutation p . Gly45Glu , as well as another heterozygous nonsense mutation: p . Tyr136X . From these findings , we hypothesized that the p . Tyr136X mutation confines the pathogenic effect of p . Gly45Glu in the mother and that the reversion of p . Tyr136X triggered the onset of KID syndrome in the patient . In the present study , TA cloning and haplotype analysis of the family confirmed that an allelic recombination event involving the maternal allele carrying the two mutations generated the pathogenic allele unique to the patient . Furthermore , cotransfection experiments and a neurobiotin uptake assay clearly demonstrated that the p . Tyr136X mutation confines the pathogenic effects of the p . Gly45Glu mutation . Thus , to our knowledge , the present findings provide the first evidence of reversion-triggered onset of a congenital disease .
The KID syndrome patient is a girl born from apparently healthy Japanese parents . She showed ichthyosiform erythroderma at birth , and later she developed typical manifestations that lead to the diagnosis of KID syndrome ( Figure 1A ) . Despite intensive care , she died of the disease . Sequence analysis of GJB2 was performed to confirm the diagnosis . Direct sequencing of PCR fragments spanning all the exons of GJB2 revealed a heterozygous missense mutation , c . 134G>A ( p . Gly45Glu ) , in exon 2 of GJB2 in the patient and her mother , but not in her father ( Figure 1B ) . Her mother had an additional heterozygous nonsense mutation , c . 408C>A ( p . Tyr136X ) , in the same exon ( Figure 1B ) . TA cloning analysis showed that the c . 408C>A and c . 134G>A mutations were in cis configuration . All family members uniformly harbored the two known non-pathological SNPs [5] c . 79G>A ( p . Val27Ile ) ( rs2274084 ) and c . 341A>G ( p . Glu114Gly ) ( rs2274083 ) heterozygously and in trans configuration with the c . 134G>A or c . 408C>A mutation ( Figure 1C and 2A ) . The existence of the GJB2 mRNA harboring the c . 134G>A missense mutation in the patient's skin was verified by a RT-PCR assay ( Figure S1 ) . To confirm the biological relationship between the patient and her parents , we genotyped for 15 short tandem repeat ( STR ) loci with tetranucleotide repeat units using a multiplex kit . Since all of the genotypes for 15 STR loci were consistent with the relationship between the parents and child and each combined probability of exclusion and paternity was calculated as 0 . 999999997 and 0 . 9999999986 , respectively , the authenticity of biological relationship between the parents and the child was confirmed accurately ( Tables S1 and S2 ) . To elucidate the origin of the c . 134G>A mutation in the patient , haplotype analysis was performed . Forty SNPs annotated by the International HapMap Project [6] spanning the >39 kbp region surrounding the GJB2 gene were sequenced . Fourteen SNPs were found to be heterozygous in one or more of the family members ( Figure 1C and S2 ) . TA cloning analysis mapped the heterozygous SNPs into three separate genetic regions ( Figure 1C ) . All family members had at least one common haplotype in each genetic region , suggesting that they share a haplotype in the >39 kb genetic region we studied . Unexpectedly , the patient harbored a unique haplotype that was not seen in either of her parents ( Figure 1C ) . No evidence of spontaneous mutations was found besides these SNP sites through the direct sequencing of the entire coding region of GJB2 . We performed an extended SNP microarray analysis spanning 83 , 483 SNPs throughout chromosome 13 . No apparent chromosomal aberration was detected besides a 1 , 430 kbp copy-number neutral loss-of-heterozygosity region on 13q31 . 1 which was unique to the patient's genome . From these findings , we reasoned that an allelic recombination event involving the shared allele ( Figure 1C , shown in blue ) and the maternally unique allele ( Figure 1C , shown in orange ) generated the haplotype unique to the patient ( see also the Discussion section below ) , since it differs by three or more base pairs from the counterparts carried by either parent , giving only a remote possibility of coincidental accumulation of spontaneous point mutations at these specific SNP sites . The latter possibility , however , cannot be completely excluded . The blood cells of the patient did not show mosaicism , and the patient's skin symptoms were fairly evenly distributed over the entire body surface . These findings suggest that the patient was not mosaic for the GJB2 mutation . Thus , we consider the reversion leading to the pathogenic allele in the patient to be a pre-zygotic event . As described above , the patient who harbored the p . Gly45Glu mutation manifested the disease , while the mother who harbored the mutations p . Gly45Glu and p . Tyr136X was apparently unaffected ( Figure 2A ) . A heterozygous de novo p . Gly45Glu mutation is known to cause the lethal form of KID syndrome [3] , and its molecular pathogenic mechanism has been well described [7]–[9] . Cx26 , the product of GJB2 , is a gap junction protein with four transmembrane domains and two extracellular domains ( Figure 2B ) . The Cx26 molecule is a protomer of a hexameric connexon , and two connexons expressed on the membranes of neighboring cells connect to form a gap junction channel [10] . Gly45 locates at a domain that lines the channel pore and probably mediates voltage sensing [10] . Connexons containing p . Gly45Glu mutants function as hemichannels with aberrantly increased activity [7] , [8] that leads to the disease manifestations 3 , [9] . It is also known that , besides KID syndrome , biallelic loss of function of GJB2 causes autosomal recessive non-syndromic hearing loss ( NSHL ) [11] . The fact that the p . Gly45Glu/p . Tyr136X mutation homozygously or compound heterozygously causes NSHL suggests that this mutation leads to total loss of function for the GJB2 product [5] . These considerations lead us to hypothesize that the p . Tyr136X mutation confines and rescues the dominant pathogenic effect of the p . Gly45Glu mutation . Since inter-protomer interactions of Cx26 require the fourth transmembrane domain [10] that is terminated by the p . Tyr136X mutation ( Figure 2 A and B ) , a Cx26 carrying p . Gly45Glu/p . Tyr136X alteration would be excluded from the hexameric connexons . This phenomenon , in which a second-site mutation cancels an exsisting pathogenic mutation , was previously reported; it is called “partial reversion” , because the wild-type allele itself is not attained , although the seoncd-site mutation rescues the disease [12] . To test this hypothesis , we observed the colocalization of fluorescent-tagged Cx26 variants in HeLa cells . The father had wild-type and p . Val27Ile/p . Glu114Gly variant alleles ( Figure 2A ) . When these Cx26s were cotransfected , they together formed gap junctions , suggesting that both proteins retain their native functions ( Figure 3A ) . The Cx26 p . Gly45Glu/p . Tyr136X mutant failed to enter the gap junction generated by Cx26 p . Val27Ile/p . Glu114Gly , demonstrating that only the latter form comprises the functional gap junctions in the mother ( Figure 3A ) . Cx26 p . Gly45Glu colocalized with the p . Val27Ile/p . Glu114Gly variant but failed to form gap junctions ( Figure 3A ) . In a neurobiotin uptake assay , which monitors channel activity as cellular uptake of a neurobiotin tracer [9] , only the p . Gly45Glu mutant and not the p . Gly45Glu/p . Tyr136X mutant induced the aberrant uptake ( Figure 3B ) .
Many cases of revertant mosaicism have been reported as “natural gene therapy” where the mitotic recombination results in revertant mutations that mitigate the disease symptoms [13]–[16] . However , the present study is the first report to demonstrate a mutant reversion triggering a genetic disease . The present data of genomic DNA sequencing and haplotype analysis demonstrate that the patient and her father share an identical haplotype ( Figure 1C , shown in blue ) . We hypothesized that the entire blue allele in the patient's genome was derived from the father , while the other allele ( Figure 1C , shown in yellow ) was basically derived from the mother . It seemed , however , that this allele underwent pre-zygotic reversion during meiosis of the maternal gamete . The fact that the patient's unique allele ( Figure 1C , shown in yellow ) differs by three non-continuous SNPs from the unique maternal allele ( Figure 1C , shown in orange ) while the neighboring SNPs are conserved might be explained by multiple events of gene conversion involving both of the maternal alleles ( Figure 1C , shown in blue and orange ) that may have occurred in this genetic region . Double cross-over also might account for the recombination , but it is less likely , considering that the non-conserved and conserved SNPs in the patient's allele reside within close proximity ( Figure 1C ) ; the average length of the gene conversion tract is estimated to be in the range of 55–290 bp , whereas the cross-over tracts are typically longer [17] . Mitotic gene conversion has been found in some cases of revertant mosaicism in cutaneous disease , including generalized atrophic benign epidermolysis bullosa [12] , [13] . We are unaware of any previous report of multiple gene conversions within a relatively short genetic segment as in the present case . However , the present data compel us to consider that it occurred . Since the patient's unique allele differs by three or more base pairs from the counterparts carried by either parent , we judge the possibility of coincidental accumulation of spontaneous point mutations at these specific SNP sites to be highly unlikely . This possibility , however , cannot be completely excluded . As evidence supporting our hypothesis , consistent with a previous report [9] , we clearly demonstrated that Cx26 p . Gly45Glu colocalized with the p . Val27Ile/p . Glu114Gly variant but failed to form gap junctions ( Figure 3A ) . Previous studies have shown that Cx26 p . Gly45Glu forms hemichannels that are aberrantly activated at low extracellular Ca2+ levels [9] . The present study used a neurobiotin uptake assay [9] to show that only the p . Gly45Glu mutant and not the p . Gly45Glu/p . Tyr136X mutant induces the aberrant uptake ( Figure 3B ) . These results taken together support the model in which the p . Tyr136X mutation confines the dominant gain-of-function effect of the p . Gly45Glu mutation to prevent the onset of the disease ( Figure 4 ) . Such secondary effects of revertants may pose a challenge in genetic analyses of extended genes or more than one gene with functional interactions . Interestingly , in the group of Japanese patients with bilateral sensorineural hearing loss , it is not uncommon to find GJB2 p . Gly45Glu carriers , but none of them are affected by KID syndrome [5] . They uniformly have a tandem p . Tyr136X mutation , as in the mother of the present case [5] . Thus , we hypothesized that , in the Japanese population , carriers of p . Gly45Glu are protected from the lethal form of KID syndrome by the tandem , confining mutation p . Tyr136X . To clarify the frequency of the p . Gly45Glu mutation in the entire Japanese population , we performed screening analysis for the two mutations p . Gly45Glu and p . Tyr136X in a normal control group consisting of 920 overall healthy Japanese individuals ( 1 , 840 alleles ) . Neither p . Gly45Glu nor p . Tyr136X was found in any of the 1 , 840 alleles ( data not shown ) . Tsukada et al . [5] also reported that neither p . Gly45Glu nor p . Tyr136X was found in 252 Japanese healthy control individuals ( 504 control Japanese alleles ) . These results indicate that the alleles with tandem p . Gly45Glu and p . Tyr136X mutations are infrequent in the general Japanese population . However , in the epidemiological statistics of Tsukada et al . [5] , we found screening data for GJB2 mutations in Japanese patients with sensorineural hearing loss . The report revealed that , among 1 , 343 Japanese patients with hearing loss , 33 patients had one or two p . Gly45Glu alleles ( 34 p . Gly45Glu alleles in 2686 alleles for an allele frequency of 1 . 27%; 33 carriers in 1 , 343 patients for a carrier rate of 2 . 46% ) . This means 2 . 46% of Japanese patients with bilateral sensorineural hearing loss have one or two p . Gly45Glu alleles . As for the prevalence of sensorineural hearing loss , it was reported that 3 . 5 per 1 , 000 individuals in the entire population have bilateral sensorineural hearing loss [18] . Thus , calculating from these epidemiological statistics , we estimate that 8 . 6 per 100 , 000 individuals , or approximately 11 , 000 individuals in the entire Japanese population , have one or two p . Gly45Glu alleles . However , no patient with the lethal form of KID syndrome due to p . Gly45Glu has been reported in the Japanese population as far as we know , although the mutation p . Gly45Glu has been reported as a cause of the lethal form of KID syndrome in several European patients [3] , [19]–[22] . Tsukada et al . [5] reported that all 34 alleles with p . Gly45Glu found in the Japanese patients with sensorineural hearing loss also had p . Tyr136X , suggesting that p . Gly45Glu is in complete linkage disequillibrium with p . Tyr136X in the Japanese population . In our mutation screening , no allele carrying either or both mutations , p . Gly45Glu and p . Tyr136X , was found in 920 Japanese individuals ( 1 , 840 alleles ) and these results support the idea that p . Gly45Glu is in complete LD with p . Tyr136X in the Japanese population . In light of this , we conclude that , even though individuals may have the dominant lethal mutation p . Gly45Glu , the confining mutation p . Tyr136X in cis configuration protects against the disease , KID syndrome , in the approximately 11 , 000 Japanese individuals in the entire Japanese population who harbor p . Gly45Glu . The allele with the tandem mutations p . Gly45Glu and p . Tyr136X causes hearing loss in an autosomal recessive manner . Most carriers of the tandem mutations in the Japanese population are heterozygous for the allele , such as the patient's mother in the present study , and are not affected with hearling loss . In summary , our findings demonstrate that the second-site confining mutation is an imporatant genetic protection mechanism , and its loss , like the opening of Pandora's box , is a novel genetic pathogenesis that releases the hidden genetic disease .
This study was approved by the Bioethics Committee of the Nagoya University Graduate School of Medicine and was conducted according to The Declaration of Helsinki Principles . Written informed consent was obtained from the parents . The patient was referred and seen at the Outpatient Clinic of Dermatology , Nagoya University Hospital . Genomic DNA extracted from peripheral blood was used as a template for PCR amplification , followed by direct automated sequencing . The entire coding regions of GJB2 including the exon/intron boundaries were sequenced as reported elsewhere [4] . TOPO-TA cloning kit ( Life Technologies ) was used for TA cloning analyses . PCR primers were designed to amplify the genetic regions containing the selected SNPs , and the acquired PCR products were analyzed by direct sequencing . For amplification of PCR fragments longer than 1 , 000 base pairs , KOD-Plus-Neo polymerase ( Toyobo ) or PrimeScript GXL polymerase ( Takara Bio ) was used and the PCR products were cloned with the TOPO XL-TA Cloning Kit ( Life Technologies ) after the addition of a 3′-adenine overhang . The parent-child relationship was validated using AmpFlSTR Identifier plus PCR amplification kit ( Applied Biosystems ) according to the manufacturer's instructions . The combined probability of exclusion and the combined probability of paternity ( in the case of the odds ratio for prior probability = 1 ) for 15 STR loci were calculated to confirm the authenticity of the biological relationship between the parents and the child . Genomic DNA was extracted from whole blood using the QIAamp DNA Blood Maxi Kit ( Qiagen ) . Real-time PCR-based genotyping of the GJB2 mutations was performed with TaqMan MGB probe genotyping assay according to the manufacturer's instructions provided by Roche Diagnostics . To detect an allele of each mutation , a set of two TaqMan MGB probes labeled with a fluorescent dye ( FAM or VIC ) and a quencher dye ( non-fluorescent dye; NFD ) followed by minor groove binder ( MGB ) , and sequence-specific forward and reverse primers were synthesized by Life Technologies Corporation . The sequences of assay probes/primers are shown in Table S3 in this article's supplementary material . Real-time PCR was performed with LightCycler 480 system II 384 plate ( Roche Diagnostics ) in a final volume of 5 µl containing 2× LightCycler 480 Probes Master ( Roche Diagnostics ) , 200 nM probes for wild type and mutant each and 900 nM forward and reverse primers each , with 5 ng genomic DNA as the template . The thermal conditions were the following: 95°C for 10 min , followed by 45 cycles of 95°C for 10 s , 60°C for 60 s and 72°C for 1 s , with a final cooling at 40°C for 30 s . Endpoint fluorescence was measured for each sample well . Afterward , genotyping was performed using endpoint genotyping analysis in LightCycler 480 software . Eight hundred and twenty controls were analyzed with the real-time PCR-based genotyping of GJB2 mutations , and another 100 controls were analyzed with the direct automated sequencing for the entire coding region of GJB2 . Total RNA from the formaldehyde-fixed paraffin-embedded skin sample of the patient was extracted using the RNeasy FFPE kit ( Qiagen ) and Deparaffinization Solution ( Qiagen ) according to the manufacturer's instructions . The total RNA was reverse-transcribed with a GJB2 specific primer , 5′-GGATGTGGGAGATGGGGAAGTAGTG-3′ , using PrimeScriptII 1st strand cDNA synthesis kit ( Takara , Japan ) . The PCR fragment harboring c . 134G>A mutation was amplified with primer sets , 5′-GGAAAGATCTGGCTCACCGTCCTC-3′ and 5′-CGTAGCACACGTTCTTGCAGCCTG-3′ , and directly sequenced with the same primers . Chromosome-wide genotyping was performed using HumanOmni2 . 5–8 BeadChip ( Illumina ) , which covers a total of 2 , 379 , 855 SNPs throughout the genome , including 83 , 482 SNPs on chromosome 13 . Genomic DNA was hybridized according to the manufacturer's instructions and data analysis was carried out using GenomeStudio software ( Illumina ) . HeLa cells were cultured in Dulbecco's modified Eagle's medium ( DMEM ) containing 10% fetal calf serum . For transfection of plasmids , cells were seeded onto 8-well LabTek chamber slides ( Thermo Scientific ) and transfected with FuGene HD Transfection Reagent ( Roche Applied Science ) according to the manufacturer's instructions . The coding sequences of Cx26 variants were amplified from the genome of the patient or the parents , fused to cDNAs coding enhanced green fluorescent protein ( EGFP ) ( Clontech ) or monomeric red fluorescent protein ( Clontech ) , and subcloned into pcDNA3 . 1 ( - ) plasmid using the InFusion HD Cloning Kit ( Takara Bio ) . The coding sequences of the generated vectors were checked for PCR errors by direct sequencing . HeLa cells were cotransfected with the EGFP-tagged and mRFP-tagged vectors . Forty-eight hours after transfection , the cells were fixed with 4% formaldehyde . Fluorescent images were obtained using FSX-100 microscope system ( Olympus ) . HeLa cells were cotransfected with EGFP-tagged and mRFP-tagged Cx26 variant expressing vectors , and neurobiotin uptake assay was performed as described elsewhere [9] . Briefly , cells were washed with calcium free Hank's buffered salt solution for 20 minutes and incubated with phosphate-buffered saline ( PBS ) containing 0 . 1 mg/ml neurobiotin ( Vector Laboratories ) for another 20 minutes . Cells were washed three times with PBS supplemented with 2 mM CaCl2 for 10 minutes at 37°C . The cells were fixed with 4% formaldehyde and permeabilized and blocked with 3% BSA/0 . 1% Triton X-100/PBS for 1 hour . Then the cells are stained with 3% BSA/0 . 1% Triton X-100/PBS containing 10 µg/ml Alexa Fluor 350-streptoavidin ( Life Technologies ) for 1 hour , followed by three washes with 0 . 1% Triton X-100/PBS . Stained cells were fixed with ProLong Gold antifade reagent ( Life Technologies ) and fluorescent images were obtained .
|
Loss of gene functions due to nonsense mutations is a typical pathogenic mechanism of hereditary diseases . They may , however , in certain genetic contexts , confine the effects of other dominant pathogenic mutations and suppress disease manifestations . We report the first instance in the literature where the reversion of a “confining” nonsense mutation in GJB2 gene released the dominant pathogenic effect of a coexsisting gain-of-function mutation , eliciting the lethal form of keratitis-ichthyosis-deafness syndrome ( KID ) . We describe this form of KID syndrome caused by the reversion of the GJB2 nonsense mutation p . Tyr136X that would otherwise have confined the effect of another dominant lethal mutation , p . Gly45Glu , in the same allele . The patient's mother had the identical misssense mutation which was confined by the nonsense mutation . An epidemiologic estimation demonstrates that approximately 11 , 000 individuals in the Japanese population may have the same lethal GJB2 mutation , nonetheless protected from the manifestation of the syndrome because they also inherit the common “confining” nonsense mutation . The reversion-triggered onset of the disease shown in this study is a previously unreported genetic pathogenesis based on Mendelian inheritance .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"eye",
"infections",
"dermatology",
"otology",
"medicine",
"and",
"health",
"sciences",
"genetic",
"dominance",
"autosomal",
"dominant",
"traits",
"epidemiology",
"ophthalmology",
"genetics",
"keratitis",
"biology",
"and",
"life",
"sciences",
"hearing",
"disorders",
"dermatologic",
"pathology",
"otorhinolaryngology",
"pediatric",
"dermatology",
"genetic",
"epidemiology",
"clinical",
"genetics"
] |
2014
|
Revertant Mutation Releases Confined Lethal Mutation, Opening Pandora's Box: A Novel Genetic Pathogenesis
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Unraveling the interplay between connectivity and spatio-temporal dynamics in neuronal networks is a key step to advance our understanding of neuronal information processing . Here we investigate how particular features of network connectivity underpin the propensity of neural networks to generate slow-switching assembly ( SSA ) dynamics , i . e . , sustained epochs of increased firing within assemblies of neurons which transition slowly between different assemblies throughout the network . We show that the emergence of SSA activity is linked to spectral properties of the asymmetric synaptic weight matrix . In particular , the leading eigenvalues that dictate the slow dynamics exhibit a gap with respect to the bulk of the spectrum , and the associated Schur vectors exhibit a measure of block-localization on groups of neurons , thus resulting in coherent dynamical activity on those groups . Through simple rate models , we gain analytical understanding of the origin and importance of the spectral gap , and use these insights to develop new network topologies with alternative connectivity paradigms which also display SSA activity . Specifically , SSA dynamics involving excitatory and inhibitory neurons can be achieved by modifying the connectivity patterns between both types of neurons . We also show that SSA activity can occur at multiple timescales reflecting a hierarchy in the connectivity , and demonstrate the emergence of SSA in small-world like networks . Our work provides a step towards understanding how network structure ( uncovered through advancements in neuroanatomy and connectomics ) can impact on spatio-temporal neural activity and constrain the resulting dynamics .
Neuronal ensembles exhibit a broad repertoire of activity patterns . Such dynamics are governed by a time-evolving network of synaptic connections with an intricate , yet structured , organization . Due to the advancement of connectomics , our knowledge about such networks is rapidly growing , and increasingly detailed maps of neuronal wiring are becoming available . In parallel , modern recording techniques , such as calcium imaging and multi-electrode arrays , allow neuroscientists to monitor the activity from thousands of neurons simultaneously , with recordings from entire brains at single neuron resolution becoming technologically feasible [1–3] . The observed dynamics of neural networks exhibit an interplay of structured spatio-temporal scales , which underpin a wide range of cognitive functions [4] . The idea that neuronal group activity induced by network structure is at the core of neural computation dates back at least to the work of Hebb [5] , who hypothesized that the transient activity of groups of neurons ( so called cell assemblies ) is the currency of information processing [4 , 6] . This notion is supported by recent experiments showing that reciprocal connections between neurons occur above chance level [7 , 8] , especially if neurons receive common inputs [9 , 10] . In the case of the visual system , for instance , excitatory neurons with similar response features tend to be more connected to each other [11 , 12] . Moreover , studies have demonstrated that neurons exhibit layer-specific connectivities within rodent sensory cortex [13] and neocortex [14] . In addition , organized architectures have been observed to occur at multiple hierarchical scales [15–18] and in non-mammalian organisms [19] . These findings suggest that cortical regions contain well-defined subnetworks . However , the underlying question is whether given a network topology , we can predict the potential of the network to sustain structured spatio-temporal activity . Such questions are not only of interest for network dynamics , but have also implications for memory formation and learning , since neural networks undergo topological changes over time due to plasticity [20 , 21] . Recently , it has been shown computationally ( see e . g . Ref . [22] ) that leaky-integrate-and-fire ( LIF ) networks with equal excitatory and inhibitory connection net strengths ( i . e . balanced [23] ) yet with clustered excitatory connections , can exhibit prolonged heightened group activity , with the activity transitioning between groups in the network ( Fig 1A ) . Here we characterize the emergence of such slow-switching segregated dynamics in balanced LIF networks as a result of the network connectivity . Specifically , we find that the spectral properties of the synaptic weight matrix ( i . e . , the existence of an eigenvalue gap and a block-localized dominant subspace ) provide a criterion to predict the appearance of such activity in the network . We then use simple linear rate models to gain insight into the mechanisms underpinning the origin of such dynamics in structurally clustered LIF networks . Using these insights , we construct novel LIF topologies that display slow-switching group activity with distinct properties: involving both inhibitory and excitatory neurons; exhibiting multiple slow time-scales; as well as demonstrating the possibility of such dynamics in networks with no obvious clustered connectivity , such as small-worlds . Finally , we discuss briefly possible implications of the different wiring schemes for neural computation .
Clustered excitatory topologies in a balanced LIF network can lead to dynamics in which localized high activity states transition between assemblies of neurons within the network [22] . This is illustrated in Fig 1A , where the dynamics of an unstructured and a balanced clustered network with 20 groups are shown side by side ( see Materials and Methods for a description of the networks ) . Hereafter , we will refer to such activity as slow-switching assembly ( SSA ) dynamics . Visually , SSA dynamics manifests itself as bands of increased activity in the raster plots , and can be statistically quantified from the resulting spike-train dynamics a posteriori ( see Eq ( 18 ) in Materials and Methods ) . Ideally , however , we would like to establish a priori , solely from the given connectivity , the possibility of such dynamical patterns emerging . The full dynamics of LIF networks are notoriously difficult to analyze due to their inherent non-linearity; hence an exact analytical treatment of the dynamical evolution for an arbitrary clustered topology is essentially intractable . However , two concepts from spectral graph theory and linear systems provide valuable insights: ( i ) for symmetric , non-negative connectivity matrices , it can be shown that a modular network structure implies a gap in the spectrum of the graph ( i . e . , in the set of eigenvalues of the weight matrix ) [24 , 25] , as well as the block-localization of the associated eigenvectors on the modules of the network ( noting that isolated eigenvalues may also be the result of other features [26] ) ; ( ii ) for linear systems , a gap in the spectrum of the graph results in a separation of time scales in the dynamical process [27–29] . This relation between the modular structure , the eigenvalues and associated eigenvectors , and linear network dynamics can be used to discover modular structures in graphs from a dynamical perspective [30–33] . In fact , the weighted connectivity matrices of unclustered and clustered LIF networks [22] display different spectral characteristics . Fig 1B shows the spectra of two networks with 1600 excitatory and 400 inhibitory neurons with unclustered ( left ) and clustered ( right ) topologies . In the unclustered case , we find the expected circular distribution of eigenvalues , which follows from the properties of random graphs [34 , 35] , although the presence of groups of neurons with different cardinalities and variances means that the eigenvalue distribution is not completely uniform on the circle [34] . Note also that the balanced construction of the LIF network , with a marginally larger inhibitory input for each neuron in order to keep the network stable , leads to the existence of one pair of complex conjugate eigenvalues which lies separate from the main bulk of the spectrum ( black arrows in Fig 1B ) . This eigenvalue pair is associated with the global activation mode of the network ( as explained in the following sections and in Ref . [29] ) . As shown in Fig 1A , this unclustered network exhibits the expected asynchronous , unstructured neuronal spiking dynamics . In contrast , the LIF network with clustered excitatory neurons displays banded SSA dynamics . Spectrally , its weight matrix exhibits a clear gap Δλ along the real axis of its spectrum ( Fig 1B , right ) and , as shown in a later section , the associated Schur vectors also exhibit a measure of structural block-localization . We remark that , in general , this should not be expected a priori , since the weight matrices are asymmetric and include both positive ( excitatory ) and negative ( inhibitory ) couplings . To ascertain the dynamical relevance of the spectral gap , we examined the relation between the clustering strength in the network , defined as the ratio of probabilities of connections inside and outside the neural assemblies ( R E E = p in E E / p out E E ) , the spectral gap ( Δλ ) , and the magnitude of the numerically observed SSA dynamics . To quantify the assembly spike-rate variability , we have defined two complementary metrics . First , the metric S ̂ measures the heterogeneity in the firing rates of the putative cell-assemblies averaged over the simulation ( see Eq ( 18 ) in Materials and Methods ) . A large value of S ̂ indicates that the average firing rates of the assemblies are diverse , whereas a low S ̂ indicates that all groups have very similar firing rates at all times and no group shows elevated firing . Under SSA dynamics , the variability of firing rates across groups increases in time as the assemblies transition between high and low firing rates . As discussed below , it is possible that the heightened firing activity is localized in a particular assembly and does not switch between assemblies . This non-switching dynamics where only a group of neurons exhibits elevated firing over the entire simulation time can be detected using the second variability measure S ̂ T , defined in Eq ( 20 ) . For SSA dynamics , both S ̂ and S ̂ T give similar results ( see S1 Fig ) . In the rest of the paper , we focus on SSA dynamics and mainly use S ̂ , except when discussing the transition to non-switching behavior . Definitions of these quantities are given in Materials and Methods . Fig 2 shows that SSA becomes observable above a threshold of the clustering strength REE , although the dependence of this threshold on the size of the network and number of clusters does not seem to follow an obvious pattern . On the other hand , our numerics show that the spectral gap provides a more direct indicator of the presence of SSA dynamics in the network . The relationship between REE and Δλ ( Fig 2C ) shows that knowing REE is not sufficient to determine Δλ as the spectral gap depends on other factors such as the network size and the number of groups in the network . Together with the examination of the structure of the associated orthogonal subspace ( see Section “The block-localization of the dominant linear subspace” ) , such spectral characterization may be used to establish the potential of networks to sustain SSA activity , even if there is no obviously clustered network model known a priori . The observed spectral properties of clustered LIF networks lead us to consider a stylized linear rate model for neuronal activity in the next section , as a means to gain insights into the defining factors of the wiring diagram leading to SSA . So far we showed that a linear rate model can provide valuable insights into the mechanisms underpinning SSA dynamics in networks with clustered excitatory neurons . The crucial point for the emergence of SSA is the separation of time scales , dictated by the splitting of the leading eigenvalues of the weight matrix , together with the block-localization of the associated dominant subspace . These spectral properties can be introduced into LIF networks by entirely different synaptic couplings , and we now consider a mechanism in which the inhibitory neurons are involved more centrally in generating SSA dynamics . Consider the wiring diagrams presented in Fig 7A and 7B , representing the simple rate model and its corresponding LIF schematic . In this case , the wiring does not involve clustered coupling between groups of excitatory neurons , but rather relies on preferential connectivity patterns between inhibitory and excitatory neuron groups only . Each group of excitatory neurons activates preferentially an associated group of inhibitory neurons and , in turn , this group of inhibitory neurons feeds back more weakly to its associated group of excitatory neurons ( see Materials and Methods for a full description of the network ) . Therefore the effective functional circuitry consists of both excitatory and inhibitory neurons embedded in a feedback loop and , as a consequence , the inhibitory neurons play an integral role in generating the spatio-temporal dynamics and display SSA dynamics too , as we show below . This wiring mechanism was suggested by the stylized firing rate model with two coupled pairs of inhibitory/excitatory feedback loops in Fig 7A . This system is described by Eq ( 1 ) with a vector of firing rates for the four groups r = ( re1 , re2 , ri1 , ri2 ) T and a synaptic weight matrix defined as: W = ( w w - k ϵ - k s w w - k s - k ϵ s ϵ - k w - k w ϵ s - k w - k w ) , ( 8 ) s > ϵ , k ≥ 1 , w = ( s + ϵ ) / 2 . ( 9 ) Similarly to Eq ( 2 ) , the system is balanced and s−ϵ captures the clustering strength within the excitatory-inhibitory pairs . The Schur decomposition of W leads to the following Schur form Q = ( - w + w ff 0 k ( s - ϵ ) w ff , 2 - k ( s - ϵ ) ) , w + = ( k - 1 ) ( s + ϵ ) , w ff = ( k + 1 ) ( s + ϵ ) , w ff , 2 = - ( k - 1 ) ( s - ϵ ) , ( 10 ) where the eigenvalues of W are on the diagonal . When the leak term is considered , the largest eigenvalue of Eq ( 1 ) is ( − 1 + k ( s − ϵ ) ) . Hence , to keep the system stable we need to constrain 0 < k ( s - ϵ ) < 1 , ( 11 ) and the spectral gap is again controlled by s−ϵ . In the associated orthonormal Schur basis , the first two modes of the firing rate dynamics u 1 = 1 2 ( 1 1 1 1 ) , u 2 = 1 2 ( 1 1 - 1 - 1 ) ( 12 ) are again global ‘sum’ and ‘difference’ modes , which interact via a balanced amplification mechanism [29] . However , there are also two localized modes u3 and u4 that can lead to slow structured activity: u 3 = 1 2 k + 2 ( k - k 1 - 1 ) , u 4 = 1 2 k + 2 ( 1 - 1 - k k ) . ( 13 ) Of these , u3 is associated with the largest eigenvalue and describes the slow ( est ) dynamics . This mode corresponds to a firing pattern of correlated activity within the pairs ( re1 , ri1 ) and ( re2 , ri2 ) , and anti-correlated activity across the pairs . As before , this analysis extends to networks with more groups and/or stochastic coupling between groups ( for related arguments see supplementary material of Ref . [29] ) . In Fig 7C and 7D , we show that the implementation of this wiring mechanism into a full LIF network displays SSA dynamics with a spectral gap , as predicted by our simple rate model , and the paired excitatory/inhibitory neurons act as a functional circuit displaying synchronous firing behavior . Importantly , note that , in contrast to the excitatory clustered scenario , there are no groups with dense reciprocal couplings in this network topology . Our numerics also show that varying the strength of the functional grouping ( WEI = WIE ) leads to the emergence of SSA dynamics , linked to the presence of the spectral gap Δλ ( Fig 8A ) , and to the alignment of the leading Schur vectors with the ‘correct’ functional circuits , i . e . , pairs of excitatory and inhibitory groups together ( Fig 8B ) . It is interesting to note that in this topology there is also a group of eigenvalues bounded away in the negative direction ( see Fig 7D ) . These modes are the quickest decaying and correspond to ‘anti-correlated’ firing states , in which excitatory neurons act in synchrony with the inhibitory groups to which they are not functionally associated . Such fast decay reinforces the survival of synchrony within the functional groups in the network . Interestingly , these modes were already present in our linear rate model: u4 in Eq ( 13 ) shows the same firing pattern with the fastest eigenvalue λ 4 = − k ( s − ε ) . Although perhaps the most intuitive way of generating SSA dynamics follows from clustering the connectivity of the LIF network , our analysis above suggests that alternative types of structural organization support SSA dynamics in LIF networks , as long as they are characterized by a gap in their eigenvalue spectrum and a measure of block-localization of the Schur vectors . More generally , one could conjecture that any low rank perturbation of the weight matrix of a balanced network which leads to an eigenvalue gap might be a valid candidate for a mechanism to generate SSA in neuronal dynamics . A few such network architectures are worth highlighting , as they have been considered of particular relevance in a neuroscience context .
Answering the question of how the wiring of neural circuits governs neural dynamics is a key step in understanding neuronal computations . Here , we showed how knowledge of certain spectral features of the matrix of neuronal connectivity can help understand what dynamics a network can support . In this paper we focused on LIF networks exhibiting slow-switching assembly ( SSA ) dynamics , where groups of neurons show a sustained and coherent increase in their firing rates , with slow switching between epochs of localized firing in different groups across the network . We found that the presence of the slow switching time scale is reflected in the spectral properties of the synaptic weight matrix: a gap separating the leading eigenvalues together with a block-localization of the associated Schur vectors on groups of neurons is a key indicator of the presence of SSA dynamics in the network . In line with this observation , multiple gaps in the eigenvalue spectrum are indicative of further time scales in the network dynamics ( see Fig 10 ) . Moreover , when the leading eigenvalue becomes larger than one , the SSA dynamics becomes localized on one of the assemblies ( see Fig 6 ) . First , we revisited the case of balanced LIF networks with clustered excitatory neurons [22] and observed that only when there is an eigenvalue gap and Schur block-localization does the network display SSA dynamics . Further analytical understanding from a stylized firing-rate model allowed us to determine that the clustering strength drives the development of the eigenvalue gap responsible for the slow switching between localized firing modes . We remark that clustered excitatory connectivity leads to a Hebbian amplification regime [29]: the more stable the pattern formation , the slower the dynamics of any banded activity . Fast switching between well-defined , stable patterns is thus only achievable for strong input changes . As suggested from our spectral characterization , and confirmed by simulations , we showed that SSA dynamics can be achieved through the structured tuning of synaptic strengths , rather than through the clustered rewiring of the connections . While the equivalence between topological and weight organizations is clear for a linear system , such a direct correspondence is not guaranteed a priori for a non-linear system . In fact , the clustering of weights appears to have a slightly larger influence compared to topological organization in our simulations of non-linear LIF systems . More importantly , this dynamical equivalence between clustered topology and clustered synaptic weights has potential ramifications for learning and synaptic plasticity; it indicates that the alteration of the weight structure can lead to the emergence of grouped activity without the need for structural rewiring of physical synaptic connections , thus suggesting a cost-effective adaptation to stochastically encode different firing patterns [47] . Within LIF networks with clustered excitatory connectivity [22] , inhibitory neurons only play a balancing background role , whereas experimental studies have revealed a vast diversity of interneuron subtypes that play key roles in computation [48 , 49] . We thus investigated mechanisms in which inhibitory neurons have an active , functional role in generating SSA dynamics . We demonstrated both analytically and via numerical simulations how such functional circuits can be constructed with the help of a feedback loop between excitatory and inhibitory neurons , so that inhibitory neurons themselves exhibit SSA dynamics and become an integral functional part of the dynamics . As fewer inhibitory neurons are present , adapting the weights according to this functional co-clustering scheme may also provide a cost effective alternative to generate SSA activity in neuronal networks . The emergence of SSA dynamics can be achieved not only by clustering excitatory neurons but also by shaping the network structure in several ways . For instance , a small-world network topology of the excitatory neurons is also able to support SSA dynamics due to the spectral properties of small-worlds , which induce a separation of the slow eigenvalues and the localization and switching of firing activity associated with slowly spatially-varying dominant Schur vectors . In contrast , scale-free networks lack the block-localization of the Schur vectors on multiple groups which is necessary to consistently induce SSA dynamics in LIF networks . Further topologies will be explored in the future . In particular , inhibitory neurons that inhibit other inhibitory neurons ( so-called disinhibition patterns [48 , 50–54] ) provide an interesting example . A different avenue might be provided by balanced amplification mechanisms [29] , which could be potentially used for creating grouped activity , yet without the introduction of a slow time scale . Other interesting questions in this respect are: which wiring mechanisms provide the most economical , or evolutionarily fit , variant [47 , 55] to induce a given dynamics; and how does the observed diversity of interneurons ( and the multiple roles they can play ) relate to this dynamical picture . Our work emphasizes the importance of the spectral characterization of the weight matrix for the dynamics taking place on these topologies . Although spectral properties are also key in characterizing the dynamical response of networks operating at criticality [56] , our observations here correspond to a different phenomenon . The emergence of a dominant assembly when λmax ≳ 1 leads to a saturation of the network and a decrease of its dynamical heterogeneity , in contrast to systems of coupled non-modular excitatory units at criticality , which maximize their dynamic range for λmax = 1 [56 , 57] . Our work links up with experimental findings that cortical states can arise spontaneously and exhibit switching behavior . Recordings from anesthetized cat visual cortex have revealed activity states that dynamically switch , and these states match closely to recorded orientation maps [58] . Such cortical states likely arise due to similarly tuned neurons having higher intra-cortical connectivity [11 , 12] , as is also predicted through models of orientation maps [59–61] . These observations are similar to our theoretical outcomes which indicate that dynamic transitioning between different network states can be driven and sustained based only on the underlying network topology . Other experiments have identified states in CA3 network activity of the hippocampus [62] where active cell assemblies exist for tens of seconds before sharply transitioning to a new state . However , these were metastable states ( hence unlikely to be reactivated ) and included a core population of cells consistently active in all states . While such complex dynamics are beyond the simple models presented here , it may be feasible to model such metastable dynamics through more elaborate network topologies , which may include synaptic plasticity [63] . Finally , let us remark that our reduced model descriptions effectively focused on networks implementing a rate-based coding mechanism and do not include spike-timing of cell assemblies , which has been identified to be an important component in neuronal computation , e . g . in rodents [64 , 65] , monkeys [66] , songbirds [67] , and grasshoppers [68] . Whether our results can be translated to a time-coding regime would be an interesting question for future work . As connectomics continues to advance the mapping of connections and synaptic strengths in wide areas of the brain , experimentally obtained weight matrices can be analyzed spectrally as described above to determine if SSA dynamics can be supported by the networks under study . While we focused here on the simplest mechanisms underlying SSA activity , future work could also consider the construction of biophysically realistic models that take into account the growing literature on the distribution of synaptic strengths , synaptic contacts , firing rates , and other relevant cortical parameters that tend to show a lognormal distribution [7 , 69–71] . Although knowledge of the network structure ( connectomics ) is not sufficient to predict the dynamics a network circuit will display ( for instance , the network dynamics can be dominated completely by a strong input ) , it can still give valuable insights on the firing patterns the network can support . Conversely , the observation of neuronal dynamics alone may not be sufficient to understand neural computation in detail , since different network topologies can yield similar dynamics . Hence , our work hints at how connectomics and neuronal dynamics data can provide complementary and intertwined routes for systems neuroscientists to study the computational principles implemented by the brain . Finally , it is important to remark that in order to get a fuller picture of the relation between structure and dynamical network properties many other factors not considered here will be of interest , including the distribution of inputs , the precise location of synapses on the post-synaptic neuron , and the plasticity rules that govern the evolution of the network over time [72] . Linking the insight gained from our work to experimental observations or to highly detailed computational models [3 , 73] would thus be a fruitful next step in order to bridge the gap between neuronal structure , dynamics , and ultimately function .
We simulated leaky-integrate-and-fire ( LIF ) networks where the non-dimensionalized membrane potential of each neuron ( Vi ( t ) , i = 1 , … , N ) was modeled by: d V i ( t ) d t = 1 τ m ( μ i - V i ( t ) ) + ∑ j W i j g j E / I ( t ) , ( 14 ) with a firing threshold of 1 and a reset potential of 0 . The constant input terms μi were chosen uniformly in the interval [1 . 1 , 1 . 2] for excitatory neurons , and in the interval [1 , 1 . 05] for inhibitory neurons . The membrane time constants for excitatory and inhibitory neurons were set to τm = 15 ms and τm = 10 ms , respectively . The refractory period was fixed at 5 ms for both excitatory and inhibitory neurons . Note that although the constant input term is supra-threshold , balanced inputs guaranteed that the average membrane potential is sub-threshold [22] . The network dynamics is captured by the sum in Eq ( 14 ) , which describes the input to neuron i from all other neurons in the network . The topology of the network is encoded by the weight matrix W , i . e . , Wij denotes the weight of the connection from neuron j to neuron i , where Wij is zero if there is no connection . Synaptic inputs are modeled by g j E / I ( t ) , which is increased step-wise instantaneously after a presynaptic spike of neuron j ( g j E / I → g j E / I + 1 ) and then decays exponentially according to: τ E / I d g j E / I d t = - g j E / I ( t ) , ( 15 ) with time constants τE = 3 ms for an excitatory interaction , and τI = 2 ms if the presynaptic neuron is inhibitory . Eq ( 14 ) can be rewritten in matrix notation as: d V ( t ) d t = T - 1 [ μ - V ] + W g ( t ) , ( 16 ) where T = diag ( τi ) and V = [V1 , … , VN]T and the vectors V , μ , and g are N × 1 vectors . The spectral analyses in the main text ( eigenvalues and Schur vectors ) refer to the weight matrix W . Different network topologies correspond to different weight matrices W , as explained below . In all simulations , the ratio of excitatory to inhibitory neurons was fixed to be 4:1 . Unless otherwise stated , the networks comprise N = 2000 units ( 1600 excitatory , 400 inhibitory ) and were simulated over 20 seconds to calculate the statistics reported . The time step for all simulations was 0 . 1 ms . All simulations were run in MATLAB ( version 2011b or later ) . Different network topologies were simulated using different W matrices but maintaining a general balanced network . If not indicated differently , all parameters below correspond to the case N = 2000 . To evaluate the extent to which a network displays slow-switching assembly dynamics , we used the following two spike-rate variability measures . Given a partition of the neurons into c groups , we compute the average spiking frequency fi ( t ) of the neurons in each cluster i ∈ {1 , … , c} over non-overlapping windows of 100ms . For each time window , we obtain the firing rate vector f ( t ) = [f1 ( t ) , … , fc ( t ) ]T of which we compute the standard deviation σ ( t ) . The standard deviations are then averaged over the duration of the simulation: S = 1 T ∑ t = 1 T σ ( t ) , ( 17 ) where T is the total number of time windows in the simulation . We then obtain a boot-strapped expectation ⟨Sshuff⟩ computed by reshuffling neurons at random into groups of the same sizes as those in the partition . The spike rate variability score is then defined as: S ^ = S - ⟨ S shuff ⟩ , ( 18 ) where the average ⟨Sshuff⟩ is obtained over 10 random reshufflings of the neurons . If the network fires uniformly ( with no localized patterns ) , S ̂ is low; whereas S ̂ increases if the network displays heterogeneous activity aligned with the partition under investigation . As explained in the text , we introduced a second spike-train variability measure to quantify the variations of the group firing-rates across time . This complementary measure allows us to discern scenarios in which there is a group of neurons dominating the firing ( thus leading to a large variation across groups ) , but no switching between groups . To quantify these effects we use an analogous measure to S ̂ above . Given a partition of the neurons into c groups , we compute the average spiking frequency fi ( t ) of the neurons in each cluster i ∈ {1 , … , c} over non-overlapping windows of 100ms . For each group i , we compute the standard deviation σT ( i ) of the vector of coarse-grained firing rates across time fi = [fi ( t1 ) , … , fi ( tN ) ] , where tk stands for the kth time bin and i = 1 , … , c . We then average over groups to obtain: S T = 1 c ∑ i = 1 c σ T ( i ) . ( 19 ) As above , we obtain a boot-strapped expectation ⟨ S T shuff ⟩ by reshuffling neurons at random into groups of the same sizes as those in the partition . The spike rate variability score over time is then defined as: S ^ T = S T - ⟨ S T shuff ⟩ , ( 20 ) where the average ⟨ S T shuff ⟩ is obtained over 10 random reshufflings of the neurons . Again , if the network fires homogeneously ( with no localized patterns in time ) , S ̂ T is low; whereas S ̂ T increases if the network displays heterogeneous firing rates over time . In S1 Fig , we show the behavior of S ̂ T for the examples discussed in the main manuscript . As expected , for all cases where SSA dynamics is present , both measures S ̂ and S ̂ T behave consistently . On the other hand , S ̂ T detects the end of the SSA region when , through increased clustering , the dynamics gets localized on one cell assembly , as shown in Fig 6 . First , we find the dominant firing patterns in the network dynamics . We perform simulations of the LIF network and obtain the firing rates of every neuron in 250 ms bins to generate an N × T matrix , where N is the number of neurons and T is the number of bins . On this matrix , we perform a principal component analysis ( PCA ) and select the first c−1 principal components {pi}i=1c−1 . This set of N-dimensional vectors captures most of the variability observed in the simulated dynamics . We then assess how aligned the c−1 principal components are with the dominant Schur vectors of the weight matrix of the network . More precisely , we compute the Schur decomposition of the weight matrix W; keep the c−1 dominant Schur vectors {ui}i=1c−1 associated with the eigenvalues with largest real part; and then compute the ( first ) principal or canonical angle θ between the subspaces spanned by the two sets of vectors 𝓟 = span{pi} and 𝓤 = span{ui} [38 , 39]: cos ( θ ) = max { u T p ∥ u ∥ ∥ p ∥ | u ∈ 𝓤 p ∈ 𝓟 } , ( 21 ) The first principal angle measures how ‘close’ the observed firing patterns are to the dominant modes computed solely from the weight matrix . If cos ( θ ) ≈ 1 , there is a large alignment between the span of both subspaces .
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Neural networks display a wide range of spatio-temporal behaviors . Understanding how this complex orchestration of neuronal firing activity is determined by the structure of the network ( i . e . , its wiring ) is an important step towards comprehending how neural computation is manifested , especially given the growing experimental access to temporal recordings and connectomics . Here we investigate the link between network structure and the dynamics of neuronal assemblies in the context of leaky-integrate-and-fire ( LIF ) networks . We show how structural features in the wiring of the network can introduce additional time-scales to the dynamics , and how such structured wiring can lead to spatio-temporally segregated , coherent activity of groups of neurons . Using rate models we gain insight into how such spatio-temporal dynamics emerge as a direct consequence of the spectral properties of the network , and use this understanding to examine further circuit topologies that enable phenomenologically similar behavior , yet rely on fundamentally different connectivity and functional patterns .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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Emergence of Slow-Switching Assemblies in Structured Neuronal Networks
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In order to decrease the prevalence of trachoma within the country , the Republic of South Sudan has implemented components of the SAFE strategy in various counties since 2001 . Five counties in Eastern Equatoria state were surveyed in order to monitor progress of programmatic interventions and determine if additional rounds of Mass Drug Administration with azithromycin were needed . Five counties ( Budi , Lafon , Kapoeta East , Kapoeta South and Kapoeta North ) were surveyed from April to October 2015 . A cross-sectional , multi-stage , cluster-random sampling was used . All present , consenting residents of selected households were examined for all clinical signs of trachoma using the World Health Organization ( WHO ) simplified grading system . 14 , 462 individuals from 3 , 446 households were surveyed . The prevalence of trachomatous inflammation-follicular ( TF ) in children ages one to nine years ranged from 17 . 4% ( 95% Confidence Interval ( CI ) : 11 . 4% , 25 . 6% ) in Budi county to 47 . 6% , ( 95% CI: 42 . 3% , 53 . 0% ) in Kapoeta East county . Trachomatous trichiasis ( TT ) was also highly prevalent in those 15 years and older , ranging between 2 . 6% ( 95% CI: 1 . 6% , 4 . 0% ) in Kapoeta South to 3 . 9% ( 95% CI: 2 . 4% , 6 . 1% ) in Lafon . The presence of water and sanitation were low in all five counties , including two counties which had a complete absence of latrines in all surveyed clusters . To our knowledge , these were the first trachoma surveys conducted in the Republic of South Sudan since their independence in 2011 . The results show that despite years of interventions , four of the five surveyed counties require a minimum of five additional years of SAFE strategy implementation , with the fifth requiring at minimum three more years .
Trachoma , the leading cause of preventable blindness , is a disease caused by ocular infection with the bacterium Chlamydia trachomatis [1] . Over time , repeated infections can lead to conjunctival scarring of the upper eyelid , which may result in the eyelid turning inwards against the globe and cornea ( entropion ) and causing irritation and abrasion of the eye by the lashes ( trachomatous trichiasis ) . If left untreated , scarring and opacification of the cornea can occur leading to irreversible blindness in the eye . In order to address the public health burden of trachoma globally , the World Health Organization ( WHO ) has endorsed the SAFE strategy: Surgery for those with trichiasis , mass distribution of Antibiotics to reduce infection , and promotion of Facial cleanliness and Environmental improvement through a focus on latrine use as a way to decrease modes of transmission . According to WHO recommendations , the full SAFE strategy is warranted at the district level if the prevalence of trachomatous inflammation-follicular ( TF ) is greater than 10% in children ages one to nine years [2] . Previous prevalence surveys conducted across many counties in South Sudan between 1999 and 2010 demonstrated the need for the SAFE strategy throughout assessed regions [3–7] . Furthermore , some suspected endemic areas of the country still require baseline mapping to understand the magnitude of the prevalence of trachoma and the interventions needed . Despite years of conflict and weak infrastructure , South Sudan has implemented components of the SAFE strategy in various counties since 2001 [8] . Since 2007 , SAFE activities have been implemented in five Eastern Equatoria counties , with all five receiving at least four rounds of MDA , multiple surgical campaigns , and health education activities . The Ministry of Health , Republic of South Sudan ( MoH-RSS ) , with the support of The Carter Center , implemented population-based trachoma surveys in five counties to determine if full SAFE strategy interventions were still warranted .
Ethical clearance was received from the Ethical Committee of the Ministry of Health of South Sudan and the Emory University Internal Review Board ( IRB 079–2006 ) . Due to high illiteracy among the population and logistical constraints of written consent forms , IRB approval was obtained for verbal informed consent to be collected from all participants and recorded electronically . For those under 16 years of age , verbal consent from a parent or guardian was required . Participants were free to withdraw consent at any time without consequence . Between April and October 2015 , we conducted population-based surveys in the following five counties: Budi , Lafon , Kapoeta East , Kapoeta North , and Kapoeta South ( Fig 1 ) . Administratively , South Sudan is divided into states , counties , payams , bomas , and villages . Counties are the equivalent of a district and are the level at which trachoma activities are implemented . In 2015 , the government of South Sudan re-defined and renamed state borders; however , since the surveys were conducted before this time , this report refers to the original political boundaries of Eastern Equatoria state . Of the baseline surveys conducted in Eastern Equatoria state between 2001 and 2004 [3] , the populations in Kimotong and Narus payams were examined . Kimotong payam , located in Budi county , had a TF prevalence of 40% ( CI: 34 . 6% , 45 . 7% ) in children ages one to nine years and TT prevalence in persons 15 years and above of 17% ( CI: 14 . 6% , 19 . 6% ) , while Narus payam , located in Kapoeta East county , had a TF prevalence of 35% ( CI: 31 . 6% , 39 . 3% ) and TT prevalence of 6 . 3% ( CI: 4 . 7% , 8 . 2% ) . For programmatic purposes , Kimotong’s prevalence data was applied to all of Budi county , while Narus’ data was applied to Kapoeta East , Kapoeta North , and Kapoeta South counties . Due to Lafon’s proximity to these counties , it was assumed to be equally endemic . To estimate TF prevalence among children ages one to nine years with 95% confidence , a sample size was calculated using an assumed TF prevalence in this age group of 10% ±5 . 0% precision , and a design effect of five [9] . We assumed a 15% non-response rate , five people per household and that 29% of the population was one to nine years old [4] . Based on these assumptions , we estimated that 794 children ages one to nine years would need to be surveyed in each county to obtain accurate prevalence estimates . To achieve this sample size , we surveyed 20 clusters , with a cluster consisting of one village of 30 households . Budi and Lafon surveys were conducted in April and May 2015 , while Kapoeta East , Kapoeta North , and Kapoeta South were surveyed August to October 2015 . Based on a higher than expected TF prevalence and a lower mean household size in Budi and Lafon counties ( 4 . 6% ) , survey assumptions were modified in order to estimate a TF prevalence of 20% ± 5% , which led us to survey 25 clusters of 35 households for the three remaining counties of Kapoeta East , Kapoeta North , and Kapoeta South . For all five surveys , the same multi-stage cluster-random sampling method was employed to assure a known , non-zero probability of selection for every individual in each county . In the first stage , clusters were systematically selected from a geographically ordered list using probability proportional to population size for each county based on village population estimates provided by the Ministry of Health South Sudan Trachoma Control Program and the South Sudan Guinea Worm Eradication Program . Villages with a population <100 persons or towns >5 , 000 persons were excluded from the sampling frame [10] . In the second stage , households were selected randomly using a sketch map and segmentation method [11] . Households within a cluster were selected with equal probability , and all present consenting household members were examined for all signs of trachoma [12] . Data recorders were selected from the counties in which data was being collected to ensure they were able to communicate with the survey participants in their local dialect . All recorders underwent a six-day training concerning how to use electronic tablets to collect data , conduct interviews , and randomly select households and individuals to be interviewed following the standard protocol . Following training , all recorders were required to pass an examination on their data collection and interview skills in order to participate on the survey team . Trachoma graders were selected from a pool of currently practicing ophthalmic clinical officers working in tertiary eye care facilities throughout South Sudan . Grader training consisted of in-class and field practice using the WHO simplified grading system [12] . Each grader was required to pass both an in-class slide test and a field reliability exam with a score of 84% agreement and ≥0 . 70 kappa score against the consensus grade of the three grader trainers . Seven of the 10 trainees achieved the required agreement , and the top six were selected to participate in data collection . Each data collection team consisted of a trachoma grader , a data recorder , and driver , with one supervisor responsible for two teams . All residents of selected households were enumerated regardless of their presence and/or willingness to be examined . All verbally consenting persons living within the household were interviewed in their local language and examined for all five signs of trachoma , as defined by the WHO simplified grading scheme [12] using a 2 . 5X loupe and a flashlight . Graders also observed the face of each child , ages one to nine , to determine if their face was unclean , defined as the presence of ocular or nasal discharge [13] . Any participant that was found to have TF or trachomatous inflammation-intense ( TI ) was provided with tetracycline eye ointment . Those who were found to have TT were registered and encouraged to undergo TT surgery when the next surgical campaign was held in their county . Survey teams made one attempt to follow-up at the end of the day to examine children ages one to nine years who were absent during the examination process by returning to the child’s house . Households that were empty were not replaced by another household . Structured questionnaires were used to interview a representative from each household , with special preference to interviewing female caregivers , who in this cultural context are the primary caregivers to children and are responsible for household chores such as fetching water . The household interview included questions regarding: demographics , latrine ownership and use , primary type of water source used during the dry season , distance to water source , face washing practices with children , cattle keeping , and ownership of radios and mobile phones . In order to clarify the type of water source , pictures of various water sources were shown to the respondent . An improved water source was defined as piped water into dwelling , a public tap , a protected dug well , or a protected spring [14] . When a latrine was present , recorders and/or graders verified use through asking the respondent if they used the latrine and directly observing signs of use , including a worn path to the latrine , presence of fresh feces in the latrine [15] , and presence of materials for anal cleansing and/or hand washing . All data were collected electronically on Samsung Galaxy tablet computers loaded with custom built survey software , Swift Insights [16] . Data were downloaded from tablet computers periodically by field supervisors . While in the field , supervisors reviewed the data collected by recorders and documented errors encountered during data collection to assist in data cleaning . Sampling weights were calculated as the inverse of the probability of selection at both stages of sampling . Confidence intervals were calculated using Taylor linearization through svy survey procedures in Stata 13 . 1 ( StataCorp LP . [http://www . stata . com] ) taking into account the multi-level structure of the sampling . All reported percentages with confidence intervals are weighted . Post-stratification weighting using five-year age-sex bands from the survey census population was used when estimating the prevalence of trachomatous scarring ( TS ) , TT , and corneal opacity ( CO ) among the whole population and among those 15 years and older to account for systematic missingness among older males in this population . The missingness of males was perceived to be an issue because women carry an increased burden of TT compared to men [17] . All statistical analysis was conducted using Stata 13 . 1 .
In five counties of Eastern Equatoria State , a total of 14 , 462 individuals in 3 , 446 households were enumerated , and of these 11 , 367 ( 78 . 6% ) were present at the time of the survey ( Fig 2 ) . Among present household members , 10 , 614 ( 93 . 4% ) consented to the examination and were included in the analyses . Among children ages one to nine years enumerated , 4 , 744/5 , 126 ( 92 . 6% ) were present and examined . In the examined population with complete sex data , 6 , 580/10 , 596 ( 62 . 1% ) were female and 4 , 016/10 , 596 ( 37 . 5% ) were male , while among children one to nine years 2 , 344/4 , 735 ( 50 . 5% ) were female and 2 , 391/4 , 735 ( 49 . 5% ) were male . Eighteen individuals were missing data on sex and three individuals were missing data on age . The mean age of study for participants was 18 . 8 years , which was statistically significantly lower than the mean age of individuals not present at the time of the survey ( 22 . 2 years; P≤0 . 0001 ) . The respondent provided demographic information about absent household members , and anecdotal evidence at the time of the survey was provided that men were away at temporary cattle camps . Indicators of water , sanitation , and hygiene were low within all five counties ( Table 1 ) . The prevalence of an improved primary source [14] for water ranged from 1 . 2% ( 95% CI: 0 . 4% , 4 . 0% ) in Lafon to 11 . 7% ( 95% CI: 3 . 0% , 36 . 4% ) in Kapoeta East . In all three Kapoeta counties , nearly one third of households reported that it took them greater than 60 minutes to fetch water and return home . The county with the highest household latrine coverage was Kapoeta South ( 10%; 95% CI: 4 . 0% , 22 . 7% ) , which is the location of the major town for all three Kapoeta counties , while in Kapoeta East and Kapoeta North , no household latrines were found by survey teams . Overall , 54 . 1% ( 95% CI: 49 . 7% , 58 . 5% ) of children ages one to nine years showed signs of a clean face . The prevalence of TF in children ages one to nine years was 17 . 4% ( 95% CI: 11 . 4% , 25 . 6% ) in Budi , 35 . 3% ( 95% CI: 28 . 7% , 42 . 5% ) in Lafon , 47 . 6% ( 95% CI: 42 . 3% , 53 . 0% ) in Kapoeta East , 39 . 7% ( 95% CI: 32 . 3% , 47 . 6% ) in Kapoeta North , and 30 . 1% ( 95% CI: 23 . 4% , 37 . 9% ) in Kapoeta South counties ( Table 2 ) . All five of these counties were therefore over the 10% TF threshold and require the full SAFE strategy , including the continuation of MDA with azithromycin ( Fig 3 ) . Budi had the lowest prevalence of TI , 5 . 8% ( 95% CI: 2 . 5% , 12 . 7% ) , and Kapoeta East had the highest prevalence of TI , 15 . 4% ( 95% CI: 11 . 7% , 20 . 0% ) . There were no statistically significant differences between males and females ages one to nine years for the prevalence of TF ( 35 . 6% vs . 36 . 5%; P = 0 . 58 ) or TI ( 10 . 2% vs . 10 . 8%; P = 0 . 62 ) . Within this age group , both TF and TI were higher among younger children ( < 7 years ) and lower in those children between seven and nine years old ( Fig 4 ) . The prevalence of TF in participants age 15 years and older was low in all counties , ranging from 0 . 6% ( 95% CI: 0 . 2% , 2 . 1% ) in Budi to 1 . 9% ( 95% CI: 0 . 9% , 4 . 0% ) in Lafon . The prevalence of TS among participants 15 years and older ranged from 2 . 9% ( 95% CI:0 . 9% , 8 . 6% ) in Budi to 6 . 6% ( 95% CI: 3 . 5% , 12 . 1% ) in Lafon . Among this age group , the prevalence of TS was higher in females than in males ( males = 1 . 9% vs . females = 4 . 9%; P = 0 . 0001 ) and increased with age ( P≤0 . 0001 ) ( Fig 5 ) . TT was observed across the age spectrum; however , only nine cases were detected among children younger than 15 years . The prevalence of TT in adults 15 years and above ranged from 2 . 6% ( 95% CI: 1 . 6% , 4 . 0% ) in Kapoeta South to a high of 3 . 9% ( 95% CI: 2 . 4% , 6 . 1% ) in Lafon county . Post-stratification weighting , used to account for systematic missingness among adult males 15 years and older , caused a reduction of TT estimates for all five counties . Similar to TS , TT was more prevalent in females than males in this age group ( males 1 . 3% vs . females = 4 . 6%; P≤0 . 0001 ) and increased with age ( P≤0 . 0001 ) . All five of the counties exceed the <0 . 2% elimination target set by the WHO [2] ( Fig 6 ) . Among participants 15 years and above found to have TT ( n = 182 ) , 28 ( 21 . 6% , 95% CI:14 . 6% , 30 . 9% ) reported having had surgery and 50 ( 29 . 8% , 95% CI: 20 . 6% , 41 . 0% ) were observed by the grader as having signs of epilation . Among those with TT who reported not having had surgery , five participants ( 3 . 6% 95% CI: 1 . 4% , 9 . 1% ) reported refusing surgery and 110 participants ( 96 . 4% , 95% CI: 90 . 9% , 98 . 6% ) reported not knowing about or not being offered surgery .
The results of these population-based impact surveys demonstrate that trachoma remains a major public health problem in these areas of South Sudan . The prevalence of TF among children ages one to nine years was greater than 10% in all five counties , with four of five above 30%; therefore , all counties require the continuation of MDA for trachoma control . The prevalence of TT in those 15 years and older was above WHO thresholds in all counties , demonstrating the need for continued surgical interventions to avoid preventable blindness . Water and sanitation factors , such as presence of latrines , access to water , and child facial cleanliness , were low in all counties , suggesting that the environmental and behavioral conditions in this part of the country enable the transmission of the causative agent of trachoma . Despite years of civil unrest prior to South Sudan’s independence from Sudan in 2011 and the recent ethnic conflict beginning in December 2013 , South Sudan has carried out components of the SAFE strategy in varying degrees since 2001 . Within the five counties surveyed , SAFE activities began in 2007 in coordination with the South Sudan Guinea Worm Eradication Program . Budi and Lafon received seven rounds of MDA , with the last MDA conducted in 2013 . Kapoeta North received five rounds of MDA , with the last MDA in 2011 , and both Kapoeta South and Kapoeta East received four rounds of MDA , with the last MDA completed in 2010 . Despite these reported rounds , according to programmatic records , drug coverage varied between counties and years , with poor coverage often coinciding with local insecurity that prevented distribution teams from accessing all villages . Surgical campaigns were conducted at various points throughout the 2007 to 2013 time period . Some water provision was provided through the partners of the South Sudan Guinea Worm Eradication Program’s infrastructure , and health education was conducted during MDA and surgical campaigns . Previous baseline and impact surveys showed that the prevalence of trachoma varied throughout the country , with low levels of trachoma detected in Western Equatoria [5] , Northern Bahr-el-Ghazal [6] , and Central [18] states . Hyper endemic levels of trachoma were detected in the Greater Upper Nile region of Jonglei , Upper Nile , and Unity states [3 , 7] , and Eastern Equatoria state [3] . The baseline survey in Eastern Equatoria state was conducted in smaller evaluation units ( payams ) and is therefore difficult to directly compare to the current results , which were conducted at the county level . At baseline , Kimotong payam in Budi county was hyperendemic for TF ( 40% ) and TT ( 17% ) in 2004 . The current data from Budi county as a whole demonstrates that lower levels of these key indicators may reflect successful SAFE implementation in the county . Narus payam in Kapoeta East county was also hyperendemic for TF ( 35 . 4% ) and TT ( 6 . 3% ) in 2004 . The current county-level data from Kapoeta East county demonstrates the county remains hyperendemic . This is likely due to the county only receiving four rounds of MDA , with the last being in 2010 , a five-year gap between MDA and the 2015 impact survey . As the WASH related data shows , there is limited presence of latrines and access to water . The other three counties ( Lafon , Kapoeta South , and Kapoeta North ) cannot be compared , due to baseline data not being collected from those counties . Moving forward , all components of the SAFE strategy need to be implemented . Four of the five counties require five rounds of MDA; however , given that all five counties border each other and consist of nomadic and semi-nomadic communities , in addition to the burden of trachoma in surrounding countries and parts of South Sudan , the trachoma program should consider , at minimum , five annual rounds of MDA for all five counties . Each MDA should aim for greater than 80% coverage . In order to do this , MDAs should be conducted during months of the year when cattle herding members of the community are staying within the village . In regards to surgery , there is need for sustained surgical interventions in all five counties . However , this can only be achieved if there is also a corresponding investment in increasing ophthalmic services in the country at all administrative levels . According to an assessment of eye care workers in 21 countries of sub-Saharan Africa in 2011 , less than one ophthalmologist per one million population were present in South Sudan , thereby making it the country with the lowest ratio of ophthalmologists to population in the countries surveyed and revealing the need for more ophthalmic professionals in the country [19] . The current surveys found that prevalence of TT in all five counties was ≥2 . 8% among adults 15 years and older . This estimate takes into account the fact that older women are more likely to be examined in household surveys of this nature . As demonstrated in Table 2 , failing to account for this fact in Eastern Equatoria state would have led to increased TT prevalence estimates in all five counties . The surveys demonstrated that epilation was an existing practice in these areas , and that lacking immediate surgical services , it may be possible to promote proper epilation practices within these populations [20] . The 96 . 4% of TT cases identified as not being offered surgery highlights the challenge in accessing patients and reinforces the need to scale up training of TT surgeons , the need for continual implementation of surgical camps in remote areas of each county , and the need for increased efforts to inform community members about surgical campaigns so that they may attend . Lastly , 21 . 6% of participants with TT reported having had surgery previously . This suggests a fairly high level of recurrence; therefore , future plans should include accommodating these complicated cases . Facial cleanliness and environmental improvement education campaigns will need to be an integral part of the trachoma program; however , there will be immense challenges as access to water and sanitation services are limited . Based on this population-based data , these five counties have not achieved basic minimum standards for clean water and sanitation , with at least 88% of the population lacking access to an improved source of drinking water and at least 90% lacking latrine access , services which are not only important for preventing trachoma , but also in reaching the Sixth Sustainable Development Goal of access to water and sanitation for all by 2030 [21] . Within South Sudan , education campaigns will need to be developed within the context of communities that have low school attendance ( less than 1% in Kapoeta East county ) , low adult education ( ranging from 0 . 7% to 30 . 6% ) , and limited phone and mobile radio ownership . The Trachoma Control Program of South Sudan overcame many challenges while implementing these surveys , most notably a paucity of experienced trachoma graders , insecurity , and difficulty accessing some villages . In order to ensure the training of highly reliable trachoma graders , the MoH-RSS requested assistance from its neighboring endemic countries , and Ethiopia , Sudan , and Uganda responded by providing experienced grader trainers to South Sudan . This successful cross-border training activity provided South Sudan with much needed technical assistance . Insecurity in the surveyed counties was addressed through pre-survey meetings with appropriate representatives from the national , state , payam , and village levels . Because of this pre-survey communication , communities were less suspicious of the survey teams’ motives and activities when the survey teams arrived . Additionally , armed security escorts were organized for main transport corridors that had reported security incidences . In Budi and Lafon , villages are often located along hill and mountain ridges in order to provide security to the communities . These locations resulted in teams having to hike long distances to reach villages , which extended the duration of the survey . Despite these challenges , the teams reached all randomly selected clusters and households and examined the necessary minimum number of required individuals . Outside of strengthening the national Trachoma Control Program’s ability to implement SAFE activities , partners should provide South Sudan with the resources needed to conduct more prevalence surveys . Many counties still require baseline “mapping” , with most of these assumed to be highly endemic; thereby , needing many years of SAFE implementation . Knowing the true extent of trachoma in the country will better enable the Ministry of Health to advocate for resources and plan interventions to help avoid preventable blindness . The five counties surveyed border trachoma-endemic countries . According to the Global Trachoma Atlas [22] , to the East they border parts of Ethiopia with a reported TF prevalence between 10–29 . 9% and Kenya with a reported TF prevalence over 30% [23] , and to the South , these counties border areas of Uganda with a TF prevalence between 5–9 . 9% . Successful trachoma elimination programs in those three countries will likely depend on continuing progress in South Sudan , given the cross-border movement that is common with cattle herding communities and the continued displacement of people , internally and internationally , due to ongoing conflict . A strategy which brings together these Ministries of Health to target these cross-border areas is needed in order to control trachoma regionally by 2020 . The global community cannot expect to eliminate trachoma by 2020 if South Sudan is not provided with the human and financial resources it needs to not only understand the full extent of the disease in the country , but also to implement the various components of the SAFE strategy .
Despite insecurity , lack of infrastructure , and limited in-country trained eye care personal , South Sudan , with leadership in the Ministry of Health , has shown its desire to eliminate trachoma through its willingness to implement the SAFE strategy and conduct impact surveys under challenging circumstances . All five counties surveyed in Eastern Equatoria state of South Sudan face considerable trachoma burdens . TF was above 30% in four counties and 17% in one county , showing the need for multiple years of MDA . TT was above the WHO threshold of 0 . 2% in those 15 years and above in all five counties , thereby requiring surgical interventions . The presence of water and sanitation were low in all five counties , including two counties which had a complete absence of latrines in all surveyed clusters . These survey results will enable the national program to use valuable , updated data to better plan programmatic activities , thereby helping the country make significant progress towards eliminating blindness from trachoma .
|
Trachoma is the leading cause of preventable blindness in the world; however , with proper interventions the disease can be controlled . Despite a paucity of experienced trachoma graders , insecurity , and difficulty accessing some villages , population-based impact surveys were conducted in five counties of South Sudan in 2015 , with over 10 , 600 persons examined by trained and certified graders . Results showed that all five counties surveyed in Eastern Equatoria state of South Sudan face considerable trachoma burdens . The presence of water and sanitation were low in all five counties , including two counties which had a complete absence of latrines in all surveyed villages . The global community cannot expect to eliminate trachoma by 2020 if South Sudan is not provided with the human and financial resources it needs to not only understand the full extent of the disease in the country but also to implement programmatic interventions .
|
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2017
|
Burden of trachoma in five counties of Eastern Equatoria state, South Sudan: Results from population-based surveys
|
Plasmodium vivax infects a hundred million people annually and endangers 40% of the world's population . Unlike Plasmodium falciparum , P . vivax parasites can persist as a dormant stage in the liver , known as the hypnozoite , and these dormant forms can cause malaria relapses months or years after the initial mosquito bite . Here we analyze whole genome sequencing data from parasites in the blood of a patient who experienced consecutive P . vivax relapses over 33 months in a non-endemic country . By analyzing patterns of identity , read coverage , and the presence or absence of minor alleles in the initial polyclonal and subsequent monoclonal infections , we show that the parasites in the three infections are likely meiotic siblings . We infer that these siblings are descended from a single tetrad-like form that developed in the infecting mosquito midgut shortly after fertilization . In this natural cross we find the recombination rate for P . vivax to be 10 kb per centimorgan and we further observe areas of disequilibrium surrounding major drug resistance genes . Our data provide new strategies for studying multiclonal infections , which are common in all types of infectious diseases , and for distinguishing P . vivax relapses from reinfections in malaria endemic regions . This work provides a theoretical foundation for studies that aim to determine if new or existing drugs can provide a radical cure of P . vivax malaria .
Plasmodium vivax is the most widespread of the human malaria parasite species with 2 . 85 billion people living in areas at risk for P . vivax infection [1] . Worldwide there are approximately 100 million cases of P . vivax annually , and the severity of P . vivax infection is increasingly recognized as more cases of death and drug resistance are reported [2] . The predominant biological mechanism that accounts for the increased range of P . vivax is the ability of these parasites to persist as dormant liver stages known as hypnozoites . This unique parasite stage is metabolically inactive and can remain dormant for months to years before reemerging to cause clinical disease [3] , [4] . Asymptomatic hypnozoite carriers therefore represent a major impediment to malaria elimination efforts . Despite the large burden of P . vivax malaria throughout the world , little is definitively known about hypnozoite reactivation . Recent reports in the literature have shown that relapses occurring in patients with a low number of hypnozoites in their liver are usually clonal and the relapse parasites are genetically homologous to the parasites from the initial infection [5]–[7] . In contrast most relapse infections in endemic settings , where patients harbor hypnozoites from multiple infectious mosquito bites , are polyclonal infections caused by parasites genetically heterologous to the initial infection [8]–[10] . The heterologous infections do share some alleles suggesting the parasites share a common ancestor [8] but the polyclonal nature and higher allelic diversity [11] of these infections along with the limited number of genetic markers used in previous studies make it difficult to assess the specific genetic relationship . The complex dynamics of P . vivax relapse infections prevents using genotyping methods to classify recurrent malaria episodes in the field as relapse infections caused by hypnozoites , as recrudescent infections caused by a failure to clear the initial infection , or as reinfections . The inability to distinguish between the three causes of recurrent malaria infection in endemic areas prevents accurate estimates of hypnozoite prevalence and inhibits the ability to study this parasite stage directly . Furthermore , the inability to distinguish relapse infections from reinfections prevents drug efficacy trials in endemic countries , impeding the development of the next generation of anti-hypnozoite drugs and hindering a thorough understanding of resistance to primaquine , the only currently licensed drug able to clear hypnozoites and achieve a radical cure . Due to these confounding aspects of relapse infection , studies of travelers who move into an endemic region , contract malaria once , and then leave could be particularly informative . Here we report the analysis of whole genome sequencing data from sequential recurrent parasite infections obtained from a patient who had a P . vivax malaria episode shortly after arriving in Canada from Sudan ( where the infection occurred ) , and subsequently experienced two relapses , 3 months and 33 months after the first episode , despite treatment with the recommended drug regimen . Analysis of single nucleotide variants ( SNVs ) identified in the three recurrent malaria infections demonstrate that while the first recurrent infection was polyclonal , the two subsequent infections , which can be definitively categorized as relapse infections , were clonal . In addition , comparison of the SNVs identified in the three infections demonstrate that the parasites isolated from the patient are most likely meiotic siblings and are the result of a single sexual cross in the mosquito vector .
The protocol used to collect human blood samples for this work was approved by the Health Research Ethics Board of the University of Alberta and written informed consent was obtained from the patient . The consent form states in English that blood samples collected from the patient may be used to genetically characterize the parasites and that samples may be shared with other researchers for scientific purposes . P . vivax DNA used in this study was isolated from a symptomatic patient who was blood smear positive for P . vivax malaria in Alberta , Canada as previously described [12] . The patient was a 38-year-old male originally from Eritrea . In December 2008 he spent nearly one month in Sudan where , according to patient history , he contracted malaria for the first time in his life . The patient was treated with chloroquine but not primaquine . After treatment , he relocated to Canada in mid-January 2009 and experienced his first recurrent episode ( EAC01 ) within two weeks of arrival . He was treated with chloroquine ( 600 mg base immediately , 300 mg base at 6 , 24 , and 48 h ) and primaquine ( 30 mg daily P . O . for 14 d ) as standard clinical care . The patient recovered clinically and was blood smear negative for malaria on day 16 . Subsequently , the patient experienced two relapse episodes within 3 months ( EAC02 ) and 33 months ( EAC03 ) of his initial recurrent infection in Canada . During EAC02 , the patient was treated with the same regimen of chloroquine as before but was given an extended 28 d dose of primaquine ( 30 mg P . O . daily ) and was blood smear negative after two days . During the final episode of this study , the patient was treated with chloroquine for three days and received standard primaquine ( 14 d 30 mg P . O . daily ) and was again blood smear negative after two days and recovered clinically . At each episode , EDTA-preserved blood was collected from the patient and submitted to the Provincial Laboratory for Public Health ( Edmonton , Canada ) for routine confirmation of malaria by real-time PCR . Blood from the first two episodes was stored at −20°C prior to use for sequencing . Whole blood from the third episode was centrifuged to pellet red blood cells and stored at −80°C . Parasite DNA was extracted from 40 µL of whole blood using the PSS GC12 instrument ( Precision System Science Co . Ltd . ) with the DNA 200 protocol and kits ( E2003 ) . DNA was eluted into a 100 µL volume . Genotyping was based on sequence repeats in the microsatellites 1 . 501 , 3 . 502 , 3 . 27 , and MS16 , as well as the genes msp1F3 , msp3α , msp4 and msp5 using the primers and protocol as previously described [13] . All molecular markers were amplified by nested or semi-nested PCR using 3 µL of extracted DNA as a template in the first amplification step and 1 µL of the first PCR product for the second amplification . The PCR reaction was performed in a final volume of 20 µL containing 1X PCR buffer ( Qiagen ) , 2 mM of MgCl2 ( Qiagen ) , 200 µM of each dNTP ( Takara Bio ) , 0 . 25 µM of each primer , and 1 . 5 units of HotStar Taq DNA polymerase ( Qiagen ) . The cycling program was as follows: 5 min at 95°C followed by 30 cycles of denaturation for 1 min at 95°C , annealing for 1 min at 56°C–62°C ( depending on the marker analyzed ) , and elongation for 1 min at 72°C with a final elongation for 5 min at 72°C . PCR was performed in a Thermal Cycler 2720 ( Applied Biosystems ) . Amplification was confirmed in a 2% agarose gel and PCR products were stored at 4°C in the dark . The product size was resolved by capillary electrophoresis in an ABI Prism 3100 Genetic Analyzer ( Perkin Elmer Applied Biosystems ) , using GS500 LIZ as the internal size standard and the microsatellite settings . The results were analyzed using GeneMapper software ( version 3 . 5; Applied Biosystems ) . All electropherograms were inspected visually and peaks above a cut off of 300 relative fluorescent units ( RFU ) were considered true amplification products . Based on the repeat length , alleles were grouped into 3-bp bins for MS16 , msp1F3 , msp3α , msp4 and msp5 , 4-bp bins for 3 . 27 , 7-bp bins for 1 . 501 or 8-bp bins for 3 . 502 . Multiple alleles per locus were scored if minor peaks were >33% of the height of the predominant allele present for each locus . For samples EAC01 , EAC02 , and EAC03 , bulk genomic DNA was isolated from frozen whole blood samples using the DNeasy Blood and Tissue kit ( Qiagen ) as per the manufacturers instructions . A Taqman qPCR assay for P . vivax b-tubulin ( PVX_094635 ) was used to assess the P . vivax DNA quantity in the bulk gDNA isolated from the patient blood sample ( Primer 1: CGAAAGGAAGCAGAAGGATG and Primer 2: GGGGAGGGGAATACTGAAAA with a Hydrolysis Probe of CAGGTAGTGGTATGGGAACCTTGCTGA ) . The qPCR reaction was conducted using Applied Biosystems Taqman 2× Genotyping Master Mix ( Life Technologies ) , 20 ng bulk genomic DNA , 900 nM of each primer , and 250 nm of the fluorescent hydrolysis probe . Reactions were carried out on an Applied Biosystems StepOne Plus ( Life Technologies ) using the manufacturers standard protocol . A 12-point standard curve was made from Sal1 reference DNA originally obtained from the CDC by serially diluting 20 ng of Sal1 gDNA 1∶2 for a theoretical lower limit of quantitation of 0 . 02% P . vivax DNA . Total P vivax DNA was calculated by comparing the Ct value of the sample to the 12-point standard curve of Sal1 reference DNA . Whole genome capture ( WGC ) of the initial infection and the relapse samples was performed as previously described [14] . Briefly , Illumina TruSeq v . 3-style Y-adaptors ( CCACTCATGCAGGTGAGCGTC*T and /Phos/GACGCTCACCTATGTCTCCCT ) were ligated onto Sal1 reference genomic DNA that had been sheared to 200 bp using an S-series Covaris Adaptive Focused Acoustic machine ( Covaris ) . The T7 promoter sequence ( bold ) was added into the standard Illumina amplification primers ( TTC[TAATACGACTCACTATAGGG]AGACATAGGTGAGCCTC and CCACTCATGCAGGTGAGCGTCT ) used to amplify the ligated products . To create the whole genome baits , the resulting library was used in an in vitro transcription reaction following the manufacturers protocol ( Ambion MEGAshortscript T7 Kit , Life Technologies ) with the exception that biotin labeled dUTP was used in replacement of the supplied dUTP . Bulk genomic DNA was carried through the standard Illumina whole genome sequencing ( WGS ) library preparation process using Adaptive Focused Acoustics for shearing ( Covaris ) , end-repair , A-tailing and ligation ( New England Biolabs ) . Hybridization capture was carried out as previously described [14] , [15] . Briefly , 750 ng of the whole genome baits were incubated with 500 ng of the bulk genomic DNA-fragment library along with 2 . 5 µg of human Cot-1 DNA , 2 . 5 µg of salmon sperm DNA , 2 . 5 ug of Human genomic DNA , and 1 unit of blocking oligonucleotides complementary to the Illumina TruSeq v . 3 adaptor and incubated for 24 hours at 65°C . After the hybridization , the captured targets were selected by pulling down the biotinylated probe/target hybrids by using streptavidin-coated magnetic beads ( Dynabeads MyOne Streptavidin T1; Life Technologies ) as previously described [14] . Whole genome capture samples were sequenced on an Illumina Hi-Seq2000 at the TSRI Next Generation Sequencing Core Facility . Samples were paired-end sequenced for 101 bp per read and one 7 bp index read using Illumina v . 3 chemistry . Base calls were made using Illumina RTA ( v . 1 . 12 ) software . Data for each sample sequenced in this study is available in the NCBI Sequence Read Archive [SRA057904] . Fastq files obtained from sequencing were aligned to the Sal1 reference using BWA ( v . 0 . 5 . 9 ) with soft clipping of bases with quality score 2 and below [16] . PCR duplicates were next identified and marked using Picard ( v . 1 . 51 ) MarkDuplicates . Aligned reads were then realigned around indels and areas of high entropy using GATK ( v . 1 . 3+ ) IndelRealigner , and the base quality scores of realigned reads were then recalibrated using GATK TableRecalibration [17] , [18] . After realignment and recalibration the samples were considered “clean” and ready for use in downstream analysis . Genome wide coverage and loci covered to a certain percentage were calculated using GATK DepthOfCoverage [18] . For all GATK DepthOfCoverage analyses the minimum mapping quality ( mmq ) was set to 29 and the minimum base quality ( mbq ) was set to 20 . SNV discovery was conducted on the ten publicly available P . vivax genomes [North Korea I: SRP000316 , Mauritania I: SRP000493 , Brazil I: SRP007883 , India VII: SRP007923 , IQ07: SRP003406 , SA94–SA98: SRA047163] . Only those reads from each sample that aligned in proper pairs were used in the SNV discovery process ( samtools view –f 2 ) [19] . SNVs were identified in each sample individually using GATK UnifiedGenotyper and stringent filters were applied to achieve the highest confidence SNV set possible with GATK VariantFiltration . The filters used included minimum depth of coverage of 20 , minimum ReadDepthAndAllelicFractionBySample of 1 . 0 , maximum Fisher's Exact test for strand bias of 3 . 0 , and maximum HaplotypeScore of 3 . 0 . Additionally SNVs were required to be biallelic with respect to Sal I and SNVs that were within 50 bp of each other were both excluded . The resulting 10 high stringency genotype sets were combined into a single set of 55 , 399 high confidence SNVs . The 55 , 399 high confidence SNVs identified in the SNV discovery process outlined above were then genotyped in all three samples using GATK UnifiedGenotyper . Those loci with multi-allelic genotypes were used only for analysis of clonality . The resulting VCF file was annotated using SnpEff v . 3 . 3 ( snpeff . sourceforge . net ) [20] and principal components analysis and all SNV plots were completed with MATLAB v . 7 . 12 . 0 . 635 ( The Mathworks ) . For the Fws calculation the reference and alternate read depths were extracted from the VCF file and used in Equation 1 where pw and ps are the allelic frequency of the reference allele within the sample and within the population , respectively , and qw and qs are the allelic frequency of the alternate allele within the sample and within the population , respectively [21] . ( 1 ) Regions of contiguous DNA that were identical between samples were identified and a weighted average block size metric ( HapBlockMet ) was calculated for each pairwise comparison using all identified haplotype blocks according to Equation 2 where d is the length of the block . ( 2 ) Copy number variants were detected using a novel CNV detection algorithm [22] . Briefly , after reads were aligned to the reference genome , depth of coverage was normalized for GC bias across the entire genome excluding the apicoplast and mitochondria . Regions were considered amplified if the average of continuous bases normalized by a Gaussian curve with standard deviation of 50 bases showed a two fold or greater read coverage relative to the rest of the genome . Genome fold coverages were analyzed in a per region fashion , with each region that had a statistically significantly higher coverage ( p<0 . 05 , normalized for number of regions , two-proportion z-test compared to average ) being called as a copy number variant . The size of the region was varied , with the first and last base pair positions being considered the boundaries of the CNV , and the region that produced the most significant result was considered to have the true CNV boundaries .
The patient , a 38-year-old male from Northeast Africa , moved to Canada in mid-January 2009 and presented with P . vivax malaria one month after experiencing his first primary infection in Sudan . During this initial recurrent infection , the patient was treated with the recommended regimen of both chloroquine and primaquine . After subsequent recovery , the patient presented with P . vivax malaria again three months later and was treated with chloroquine and an extended 28-day course of primaquine . Thirty months later the patient presented with a third recurrent P . vivax malaria infection and was given a standard dose of chloroquine and primaquine . Throughout the three malaria infections the patient had not travelled outside of areas in North America known to be non-endemic for malaria , thereby ruling out reinfection [12] . According to the patient's medical history , the first recurrent infection ( EAC01 ) was classified as either a recrudescence caused by a failure to clear parasites from the primary infection in Sudan or a relapse infection caused by reactivation of a dormant hypnozoite . The second ( EAC02 ) and third ( EAC03 ) recurrent infections were classified as relapse infections since parasites were cleared from the patient after each preceding infection in Canada . Infected blood samples were collected during each malaria episode and frozen . Because of the unique history of this patient , the samples were thawed and examined after the third malaria episode . Although the P . vivax-infected patient blood samples had not been collected using the leukocyte depletion protocol required for efficient direct sequencing analysis of P . vivax samples [23] , [24] , we were able to sequence the three parasite strains using a whole genome capture technique utilizing RNA baits derived from the SalI reference strain of P . vivax [14] using an in-solution hybridization capture procedure [15] , [25] . Briefly , in this method the P . vivax DNA from a patient sample hybridizes to the biotinylated RNA baits and the DNA/RNA hybrids are then purified using streptavidin beads , resulting in the depletion of most of the contaminating human DNA . This method enriched the P . vivax DNA , which initially comprised less than 1 . 0% of the total genomic DNA ( gDNA ) in the patient infected whole blood samples , to 20%–40% of total DNA content allowing efficient whole genome sequencing analysis of the parasite DNA ( Table 1 ) . Enriched P . vivax gDNA was sequenced on an Illumina HiSeq 2000 using 100 base-pair paired end reads and 5 . 1–8 . 5 billion bases were obtained for each parasite strain resulting in genome wide coverage of 35X–118X ( Table 1 ) . In addition , for all three samples , >88% of the genome could be assigned a confident genotype ( Table 1 ) . Several types of reads were evident in comparison to the SalI reference sequence . These include clear homozygous single nucleotide variants such as the one causing the S117N change in the P . vivax dihydrofolate reductase gene ( PVX_089950 ) ( Figure 1A ) , biallelic reads ( Figure 1B ) , such as the “T” in the isoleucine codon and the “C” in the valine codon at amino acid 1478 in the P . vivax multidrug resistance associated protein ( PVX_097025 ) , and multiallelic reads in a noncoding region on chromosome 13 ( Figure 1C ) . Although the biallelic reads appeared real and , in many cases , involve alleles present in other P . vivax isolates , multiallelic reads , such as that shown in Figure 1C , appeared to be due to alignment errors based on the fact that numerous mismatches are found throughout the read . These alignment errors were subsequently removed by excluding cases where there were more than 1 SNV in 50 bases . These misalignments were not used in further analyses . The single and biallelic reads , their readcount , and their position in the genome are given in Supporting Dataset 1 . In order to examine the origins of the parasites , we first sought to identify the genetic differences between the three infections by examining their genome sequence . Using the whole genome sequencing data , we genotyped the three P . vivax infections at 55 , 399 positions ( Table 2 ) . These loci were selected by analyzing the genome sequences of 10 diverse P . vivax strains from the NCBI Sequence Read Archive and identifying the location of SNVs that differed from the SalI reference sequence . SNVs included in this set of markers were required in at least one of the 10 sequences to have a minimum coverage of 20 reads and all reads indicating the presence of a single allele . These markers were spaced , on average , 408 bases apart across the genome and the distribution was similar across all chromosomes ( data not shown ) . Approximately 44% of the SNVs genotyped were located in coding regions , which constitute 54 . 6% of the genome ( Table 2 ) . The samples analyzed here were different from the SalI reference genome at 23 , 379 , 20 , 734 , and 20 , 934 of the genotyped loci for EAC01 , EAC02 , and EAC03 , respectively ( Table 2 ) . Of these variant loci , 19 , 667 , 19 , 623 , and 20 , 110 had five or more reads mapping to the locus in EAC01 , EAC02 , and EAC03 , respectively , and were considered high quality genotype calls ( Table 2 ) . In order to establish that all the infections were of East African origin , as suggested by the patient's medical history , we compared the three infections isolated here with five geographically diverse P . vivax strains for which both sequencing data and geographical data are publicly available using the same 55 , 399 loci [23] , [26] . Using principal components analysis [27] , we show that our three parasite strains from East Africa are very closely related to one another ( Figure 2 ) . In addition , the three samples from our patient are most closely related to the West African Mauritania I and India VII strains and are diverged from P . vivax samples from South America and the North Korea I strain ( Figure 2 ) . Principal components analysis corroborates the patient's medical history and offers further proof that he was not reinfected by an unrelated P . vivax strain while residing in Canada . With the dense set of genetic markers obtained from WGS data we next sought to establish the clonality of the three infections . For this analysis we used both single and biallelic genotype calls in EAC01 , -02 , and -03 , and calculated the percentage of loci containing more than one allele in this haploid organism as an indicator of clonality . The parasites in the first blood sample ( EAC01 ) were determined to be polyclonal with 43 . 7% ( 8 , 603 of the 19 , 667 variant loci with 5 or more reads ) possessing more than one allele ( Table 2 ) . In addition , the Fws value , another indicator of multi-clonal infections which compares the parasite diversity within a single patient to the parasite diversity seen on the population level [21] , [28] , is 0 . 51 ( clonal = 1 . 0 ) for the initial infection and is suggestive of more than one clone ( Table 2 ) [28] . In contrast , both relapses arising from hypnozoites activated 30 months apart ( EAC02 and EAC03 ) appeared to be clonal based upon the same metrics as above ( Table 2 ) , which is consistent with previous reports of relapse infections in non-endemic settings [6] , [29] . The first relapse ( EAC02 ) had an Fws of 0 . 97 and the second relapse had an Fws of 0 . 94 ( Table 2 ) . In addition both relapse samples had very few loci with multiple alleles ( 258 ( 1 . 31% of total SNVs ) for EAC02 , 303 ( 1 . 51% of total SNVs ) for EAC03 ) at the variant genotyped loci ( Table 2 ) . For EAC03 , 69% ( 45 ) of the 303 multi-allelic loci also gave mixed reads in one of the other sequenced samples ( e . g . India VII , IQ07 , Brazil 1 , North Korea or Mauritania ) suggesting these loci are in an area of the P . vivax genome which is problematic to sequence using current short-read technology . In addition , more than 35% of these multi-allelic loci in EAC02 and EAC03 were clustered in subtelomeric regions and regions encoding internal variable gene families , which comprise only 12% of the P . vivax genome ( Table 2 ) . Also of note , the distribution of multi-allelic loci was nonrandom with many of the mixed read alleles mapping to one 70 kb fragment on the right arm of chromosome 7 , suggesting that this region might be duplicated in the three East African isolates . We therefore conclude that these few loci containing multiple alleles in the clonal samples are most likely the result of sequencing/alignment errors to these highly variable sequences , which frequently duplicate and recombine during mitotic growth [30] . To compare these findings using conventional methods , the three P . vivax infections were subjected to eight-marker RFLP genotyping , which represents the current standard in characterizing P . vivax diversity . The markers used here consisted of four microsatellites and four genes of the highly variable merozoite surface protein family , all of which have been shown to be variable in previous studies [13] , [31] , [32] . These regions were amplified using PCR ( see methods ) , and the size of the PCR products were analyzed on an ABI Prism 3100 Genetic Analyzer . As expected , the first infection ( EAC01 ) showed multiple bands for 6 of the 8 markers , while EAC02 and EAC03 appeared monoclonal , with only a single band for each of the 8 markers for EAC02 , and 7 of the 8 markers for EAC03 , where three bands were observed for the 3 . 27 microsatellite marker ( Table 3 ) . It seems unlikely that this extra band is informative given that the regions appear perfectly identical in EAC02 and EAC03 in this region by whole genome sequencing ( Figure 3 ) . It is possible that the extra bands for the 3 . 27 marker are due to either contamination or PCR artifacts resulting from mis-hybridization of the PCR primers for this marker in the East African isolates . These data suggest that microsatellite genotyping of field isolates , although standard , may lead to inaccurate conclusions about polyclonal infections . We next sought to determine the genetic relatedness between the recurrent parasite infections using the genotyped markers . Genotyping at the 55 , 399 loci showed that 4 , 434 ( 32 . 8% ) of the 13 , 536 genotyped positions ( 5 or more reads ) that were variant between the three infections produced different base calls in the two relapse samples ( Supporting Dataset 1 ) , contradicting the microsatellite analysis showing that the two relapses are virtually identical ( Table 3 ) . This result initially suggested that these two episodes might have come from different infection events . To investigate further , EAC02 and EAC03 were subsequently subjected to a pairwise comparison in which confidently genotyped markers were plotted as a function of chromosome position ( Figure 3 ) . Surprisingly , these data indicate that the parasites exhibited a highly non-random pattern in which regions of identity were organized into large blocks with a weighted average size of 715 kb and that the microsatellite markers were located by chance in regions that happened to lack variant SNVs . As a control , the two relapse samples were also compared in a pairwise manner to the Brazil I strain since this strain is also believed to be derived from a relapse infection . We observed no evidence that the relapse samples shared large contiguous sequences of DNA with this South American strain ( Figure 3 ) . We additionally calculated the haplotype block sizes from pairwise comparisons between EAC02 and Brazil I and EAC02 and Mauritania 1 ( the strain most closely related by PCA to the East African samples investigated here ) . The haplotype block sizes for these comparisons were 5 . 8 kb and 6 . 3 kb , respectively , indicating that EAC02 shared no large contiguous pieces of DNA with these other P . vivax stains . We next compared the two definitive relapse samples to the first sample ( EAC01 ) at those loci that were unambiguously genotyped in all three infections . We again found that the strains obtained from our patient over 33 months shared substantial portions of the P . vivax genome and that the regions of identity were organized into contiguous blocks of genomic sequence ( data not shown and Supporting Dataset 1 ) . These analyses further suggest that parasites from all three infections are highly related , yet genetically distinct , to one another . Since EAC01 was a polyclonal infection , we sought to determine if it was comprised exclusively of parasites directly related to EAC02 and EAC03 . If EAC01 only contained parasites that were directly related to the second and third infections , then genotyped loci in EAC01 would not contain more than two alleles . Of the 10 , 775 loci that contained more than one allele in EAC01 only six loci ( 5 . 6×10−4% ) possessed more than two alleles with high confidence ( greater than 5 reads ) . These data suggest that the first infection is comprised of two or more parasites directly related to EAC02 and EAC03 . Additionally , when EAC01 variant mixed-read alleles from chromosome one were sorted by read count ( Table 4 ) one resulting haplotype perfectly matched that of EAC02 and EAC03 ( which are identical on chromosome 1 ) and the other was completely different . These data suggest that either there are three clones , one with the chromosome 1 EAC02/03 haplotype and two with the alternative haplotype , or that there are only two different clones present , but that EAC02/03 haplotype clone is less abundant ( Table 3 ) . Overall these data suggest that EAC02 and EAC03 and the two clones in EAC01 are separated by only a single meiosis . In the related human parasite , P . falciparum , sexual crosses performed using chimpanzees showed that the average number of bases per 100 recombination events ( one centimorgan = 10 kb ) is 9 . 6 kb [33] . Based on the 27 breakpoints shown in Figure 3 over the 26 . 9 Mb P . vivax genome [34] we estimate that the average number of bases per 100 recombinations is a similar 10 kb . Although no laboratory crosses of P . vivax have been performed , our clones appear to be the progeny of a natural cross . The malaria parasite undergoes sexual reproduction during the mosquito life cycle stage including recombination between male and female gametes . Since the infections were most likely caused by “sibling” parasites , we next sought to determine if they had arisen from a single zygote and if there was evidence of reciprocal recombination events , which would indicate a direct genetic relationship between the parasite strains from the recurrent infections . To accomplish this task we analyzed the whole genome sequencing data in multiple two way comparisons that separated the two clones in EAC01 into two different virtual clones ( EAC01A and EAC01B ) based on read count ( Figure 4 ) . While it is recognized that this is not ideal because stochastic differences in read count are highly likely , without the parental haplotype a third sample is , nevertheless , necessary to visualize reciprocal events . This is because although a recombination event can be found that occurred in one sibling but not the other using WGS , reciprocal events would be invisible . Because of the potential for noise we further filtered the set to include only the highest quality base calls . Specifically , we used a dataset of 5938 positions that had at least 20 reads across EAC01 , EAC02 , and EAC03 major alleles . Regions were plotted across the chromosome and colored based on whether they were identical or different . Comparisons between EAC01A and EAC01B , EAC02 , and EAC03 and a fourth comparison between EAC02 and EAC03 are shown in Figure 4 . The two clones ( EAC02 and EAC03 ) and the two virtual clones ( EAC01A and EAC01B ) were identical across much of the genome . As suggested previously , on chromosome 1 , EAC01B , EAC02 and EAC03 were almost identical ( different at 1 of 94 loci genotyped ) , but different from EAC01A at 93 of these loci . It is not clear that the microsatellite genotyping ( Microsatellite 1 . 501 , Table 3 ) would have detected the EAC01A virtual clone , but conventional microsatellite genotyping of chromosome 1 showed only 1 product in all three isolates for marker 1 . 501 . Likewise , there was no evidence of recombination on chromosome 5 ( 183 of 183 loci identical in EAC01B , EAC02 , and EAC03 ) . Nevertheless , clear evidence of reciprocal recombination events were visible on all other chromosomes with most chromosomes having between 1 and 4 . These breakpoints were identified as areas of the genome where one clone transitions from sharing a haplotype to not sharing a haplotype ( Figure 4 ) . It should be noted that breakpoints detected using read counts of the two virtual clones ( EAC01A and EAC01B ) were confirmed with the two independently sequenced samples , EAC02 and EAC03 . An example of one of these events on chromosome 9 at base pair resolution is shown in Figure 4 . The high-resolution data show that the recombination event occurred between bases 1 , 872 , 017 and 1 , 879 , 466 . The four clones analyzed here share 21 recombination breakpoints , which is in concordance with the number of crossovers postulated to occur per meiosis in P . falciparum [33] , [35] . As noted above , the patient here was treated with primaquine after both the first and second infection , yet still suffered additional relapse infections , suggesting that the parasites could be resistant to primaquine . Although the number of samples here is too small to map a resistance gene we nevertheless looked to see if known drug resistance genes were heterozygous or homozygous across the different infections and thus could indicate evidence of positive selection . The P . vivax pfcrt homolog ( PVX_087980 ) on chromosome 1 was identical across all three infections with only two synonymous mutations ( D328 and F339 ) in comparison to the SalI reference strain within a region of homozygosity that spanned 11 , 912 bases . For the multidrug resistance loci pvmdr ( PVX_080100 ) , pvmrp ( PVX_097025 ) , and gtp cyclohydrolase ( PVX_123830 ) we found there were two haplotypes present across the three infections . But for pvdhfr ( PVX_089950 ) and pvdhps ( PVX_123230 ) , genes involved in anti-folate resistance , there was only one haplotype across all three infections , revealing an area known to be under selection in that region of the world [36] . For pvdhfr the region of homozygosity on chromosome 5 was 23 kb , but for pvdhps on chromosome 14 , the region of homozygosity spanned 132 kb . Along with SNVs in putative resistance genes , we also looked for copy number variants ( CNV ) , a key mechanism of resistance in P . falciparum [37]–[40] , in P . vivax homologs of known P . falciparum drug resistance genes ( Table 5 ) . In the three infections analyzed here , we were unable to detect any amplification CNVs ( >2 . 0 sequencing coverage relative to the genomic average ) at the genes of interest using a novel CNV detection algorithm [22] . In addition , we did not detect any significant differences in relative sequencing coverage between the three infections . However , although the algorithm used here ( see Methods ) has been shown to robustly identify CNVs in P . falciparum , in the absence of genome-captured P . vivax DNA samples with known CNVs that could serve as positive controls , false negatives could be possible .
Here we show whole genome sequencing results for three sequential recurrent P . vivax infections , two of which could be definitively categorized as relapse infections , occurring in a patient over 33 months in a non-endemic country . These results demonstrate the feasibility of using the whole genome capture technique on archived samples after long term storage along with standard samples directly from the field to dramatically improve our understanding of P . vivax infections within a single patient . These data also demonstrate the power available from the more comprehensive datasets produced with whole genome technologies to more accurately describe the genetic structure both within a geographic region and also temporally within a single patient . Comparing the whole genome sequencing data to the current standard of RFLP on 8–15 markers demonstrates that data from small genotyping sets are more susceptible to common errors . For instance EAC03 had three bands by RFLP analysis at the microsatellite 3 . 27 , but analysis of this region by whole genome sequencing using hundreds of markers showed this region to be clonal . The most likely cause for this error is a PCR error , and while whole genome sequencing is not immune to PCR errors the high level of read counts at each individual locus along with the dense set of markers across the entire genome make the technique more robust than the current standard genotyping methods . Additionally , as the cost of sequencing declines , new methods like whole genome capture will make it feasible to generate larger more complete genetic datasets of malaria infections . These new datasets will allow more robust characterization of recurrent infections; better classification of recurrent P . vivax infections will be crucial in designing next generation anti-malarials as well as public health interventions directed specifically at P . vivax . The latter two of the three recurrent malaria infections analyzed here can be definitively classified as relapse infections , which arose from the activation of hypnozoites . Prior to each of these two infections the patient had been negative for malaria infection by diagnostic PCR , ruling out a recrudescence . Furthermore , the patient history stated that the patient had not been to a malaria endemic country since the previous malaria infection , ruling out a reinfection . Both of the definitive relapse samples ( EAC02 and EAC03 ) were clonal based on analysis of the whole genome sequencing data . This suggests that a single hypnozoite was activated through an unknown trigger and emerged from the liver to cause symptomatic disease . In contrast the first recurrent infection , whose origin could not be distinguished between a relapse and recrudescence , was polyclonal . These results are in concordance with previous studies looking at relapse samples from patients with limited or no previous malaria infection [5] , [6] . Additionally , the clonality seen in the final infection might be a result of the extended course of primaquine the patient received after the second infection . This treatment regimen might have selected only one highly resistant hypnozoite leading to a clonal infection as an artifact of treatment . Therefore clonality might be suggested as a marker for relapse infection only in these specific circumstances . In contrast , reports from the literature indicate that relapse infections occurring in endemic areas can be either clonal or polyclonal due to the hypnozoite load in the liver that has accumulated from previous mosquito inoculations [8]–[10] . The high degree of relatedness between the two infections also corroborates the patient's medical history indicating that the parasites causing these two infections separated by 30 months were acquired in the same region at the same time . If the patient had travelled to another malaria endemic region , the parasites causing infection would be genetically different , and , if the patient had returned to East Africa , the recombination breakpoints would have been different . These data also support the idea that the parasites are from the same sub-geographic region within East Africa and not a combination of parasites from Sudan and Eritrea , for instance . From a public health perspective , these data highlight the need for analyzing P . vivax samples from the field with a denser genetic array than has previously been performed . While EAC02 and EAC03 are highly related as described above , there are still substantial portions of the P . vivax genome where they differ . Nevertheless , the traditional eight-marker genotyping panel indicated that these two parasites were identical at all eight markers suggesting that these two infections were clones of one another , which is not corroborated by the denser genomic data . Also , looking at the pairwise comparison between EAC02 and EAC03 demonstrates that selecting a different set of eight random markers from the genome could just as easily have indicated the parasites where completely unrelated . We demonstrate here that the current gold standard techniques investigating 8–15 genes or microsatellites will be unable to fully describe the complex genetic structure of P . vivax infections . Further genomic analysis of the two definitive relapse samples indicates that these two infections are highly related . These two infections share large amounts of DNA , and the DNA that is homologous between these two P . vivax strains is organized into large contiguous blocks of genomic DNA , or haplotypes . The average size of the haplotype blocks shared by these two parasite strains ( 715 kb ) is roughly half the size of the average chromosome ( 1 . 5 Mb ) indicating that these two samples are separated by a single round of meiosis ( Figure 5 ) . Comparing the second and third infection to the first infection also indicated that these parasite strains arose from the same parental gametes . Additionally , genomic analysis demonstrated that these three parasite samples shared reciprocal recombination breakpoints . While it is not uncommon to find distinct recombination breakpoints in malaria samples collected from a specific region , especially one where transmission is quite low [41] , [42] , the fact that reciprocal recombination breakpoints were found , and that all recombination breakpoints identified were reciprocal , further illustrates the direct genetic relatedness of these three infections . In single-celled eukaryotes such as S . cerevisiae , a single zygote can give rise to four meiotic progeny that constitute a “tetrad” and show reciprocal recombination breakpoints . We hypothesize based on the recombination data that the direct genetic relationship hypothesized here is of meiotic siblings from the same zygote . This meiotic sibling hypothesis would imply that sporozoites from a single viable oocyst were injected into the patient at the time of the infectious mosquito bite . Due to the low transmission rate in the area of infection and the low oocyst burden of mosquitoes seen in the field , this is the most likely scenario . A potential alternative to the meiotic sibling hypothesis is that the parasites seen here constitute a family trio . This parent-child relationship could be seen one of two ways . One , the patient might have been infected by three separate mosquitoes each carrying a separate member ( mother , father , and child ) of the trio . Further analysis of the epidemiology of P . vivax in East Africa and , in particular in Sudan , indicates that a parent-child relationship between multiple infections , while genetically feasible , is unlikely due to low transmission rates in the area [41] , [42] . The second mechanism by which the patient could have been infected by this nuclear family of parasites is for the patient to have been infected by one mosquito harboring multiple oocysts . In the parent-child relationship structure , sporozoites from at least three oocysts would have had to be injected into the patient: one with the cross , and two self-crosses for each of the parental strains . In addition , there would have most likely been additional oocysts present that contained additional crosses that would have also been injected . Recombination data to date from the initial polyclonal infection has not found evidence of additional parasite crosses between two putative parental strains further suggesting that the genetic relationship between these parasites are not a parent-child relationship . There is also the possibility that there was previously one cross in the region of inoculation with the four descendants of this cross circulating in the area and maintaining themselves by consecutive self-crosses in the mosquito . Under this hypothesis , the patient in this study would have been sequentially infected by three of these strains . Based on the data and malaria transmission characteristics in the Sudan , we believe this hypothesis is of low probability for two reasons . One , the patient is unlikely to have received three infectious bites during his short stay in Sudan , an area of low malaria transmission . Two , if the sexual cross had occurred more than one generation ago , it is unlikely that the parasites would share all recombination breakpoints . For this to happen , the parasites from the initial cross would have to have been segregated via multiple human infections or multiple mosquitoes immediately after the initial cross since otherwise the chance of all four progeny only self-crossing in the next generation is unlikely . In this area of low transmission it is unlikely that multiple mosquitoes would have transmitted progeny from the initial cross to three or four other individuals and it is equally unlikely that the same mosquito would have infected three of four other people , each with a different progeny from the same cross . For these two reasons , both relating to the low transmission dynamics of the region of inoculation , the possibility of the cross seen here occurring >1 generation ago is small . We of course cannot definitively distinguish between the alternative hypothesis relationships without the putative fourth member of the tetrad . Despite this fact , the meiotic sibling hypothesis is the most likely of the proposed hypotheses at this time as it best conforms to the P . vivax population structure and transmission rate present in Sudan . Additionally , the dense genetic maps obtained via whole genome sequencing highlight a new , unique aspect of P . vivax immune evasion . It is known from therapeutic malaria experiments in the first half of the 20th century that patients can become immune to successive relapse infections [3] . However , these infections were initiated by only a few strains usually propagated by blood transfusion which were likely genetically identical . Here we suggest that in a natural infection resulting from the sexual cross of two different parasites , up to four genetically unique parasites emerge from each oocyst ( Figure 5 ) . These diverse parasites have a greater chance to evade the human immune response as they are subsequently activated and emerge from the liver . These high-resolution data in which haplotypes can be distinguished simply based on read count offer the opportunity to use linkage analysis of complex patient samples in mapping drug resistance genes . Higher levels of read coverage could also allow physical mapping and de novo assembly of clones in a mixed infection . Whole genome sequencing of sequential P . vivax infections including definitive relapses also strengthens a classic model of P . vivax biology with genomic data . Studies in the first half of the 20th century of P . vivax relapse using malaria therapy for neurosyphilis as a model demonstrated that both a short latency relapse and a long latency relapse can arise from parasites that are directly related to one another [43] . Confirming these initial results , we show here on the genomic level that genetically related parasites from the same cross can cause both a short and long latency relapse infection . This further confirms that P . vivax parasites are inherently capable of remaining dormant in the liver for months to years unless activated by the appropriate ( unknown ) trigger . These data also highlight the need to better understand the trigger that activates hypnozoites , currently hypothesized to be a febrile illness [44] , and/or hypnozoite biomarkers that can be used for long-term surveillance for P . vivax infections in tropical areas as part of malaria elimination programs . Another area of interest highlighted by this particular case is primaquine resistance . The patient was treated with a standard dose of primaquine after the initial infection and a double dose after the first relapse , but the parasites were obviously able to evade this treatment . At this point , it is unclear whether the failure of primaquine was due to parasite resistance or the patient's inability to metabolize primaquine to its active form . If these genetically related parasites are resistant to primaquine then it is possible that the patient initially harbored a more diverse hypnozoite load in the liver that was cleared after primaquine treatment . However , analysis of the initial recurrent infection , obtained before primaquine treatment , does not suggest this to be the case , but it cannot be definitively ruled out . This patient's history nevertheless demonstrates that primaquine is not always effective and that safe , efficacious replacements are needed . A major obstacle to both testing new anti-hypnozoite drugs and for monitoring the effectiveness of primaquine in endemic countries is the inability to distinguish between the sources of recurrent infection . This study is limited by its inherent uniqueness . With only three samples to date , we are unable to definitively answer many of the outstanding questions , such as the exact genetic relationship between strains and primaquine resistance . In addition , the answer to the relationship structure between the three infections would be more easily answered if the P . vivax community possessed a more solid understanding of the population structure in Sudan , but due to the political instability in the region this is currently not feasible . In order to overcome these limitations , more relapse samples , and preferably multiple sequential relapse samples from a single patient , need to be obtained . These infections can be either naturally occurring or controlled P . vivax infections based on new protocols [45] . Further in depth analysis of definitive relapse infections will shed more light on this crucial parasite stage , but , as demonstrated in this study , current relapse analysis methods lack the power to fully characterize the hypnozoite stage for either research or public health purposes . The hypnozoite will therefore need to be the focus of specific intervention programs if the goal of malaria elimination is to be realized in areas endemic for P . vivax .
|
Plasmodium vivax is capable of remaining dormant in the human liver for months to years after an initial infection , creating an asymptomatic human reservoir . This unique aspect of parasite biology makes eliminating P . vivax distinctly different from P . falciparum elimination , and yet very little is known about this dormant parasite stage . Lack of knowledge about the dormant liver stage prevents the creation of new drugs and public health interventions directed at P . vivax . In order to better understand this particular parasite stage , we used whole genome sequencing to analyze three sequential P . vivax infections , two of which could be definitively categorized as having arisen from dormant liver stages . Our whole genome sequencing data demonstrates that dormant liver stage parasites are closely related yet not , as had previously been postulated , identical . These data highlight the need for a new paradigm to investigate P . vivax dormant liver stages in order to design the next generation of P . vivax drugs and effective global health interventions .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"sequencing",
"techniques",
"genome",
"sequencing",
"genetics",
"biology",
"and",
"life",
"sciences",
"molecular",
"biology",
"techniques",
"microbiology",
"genomics",
"molecular",
"biology",
"parasitology"
] |
2014
|
A High Resolution Case Study of a Patient with Recurrent Plasmodium vivax Infections Shows That Relapses Were Caused by Meiotic Siblings
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Proteolytic processing of Gag and Gag-Pol polyproteins by the viral protease ( PR ) is crucial for the production of infectious HIV-1 , and inhibitors of the viral PR are an integral part of current antiretroviral therapy . The process has several layers of complexity ( multiple cleavage sites and substrates; multiple enzyme forms; PR auto-processing ) , which calls for a systems level approach to identify key vulnerabilities and optimal treatment strategies . Here we present the first full reaction kinetics model of proteolytic processing by HIV-1 PR , taking into account all canonical cleavage sites within Gag and Gag-Pol , intermediate products and enzyme forms , enzyme dimerization , the initial auto-cleavage of full-length Gag-Pol as well as self-cleavage of PR . The model allows us to identify the rate limiting step of virion maturation and the parameters with the strongest effect on maturation kinetics . Using the modelling framework , we predict interactions and compensatory potential between individual cleavage rates and drugs , characterize the time course of the process , explain the steep dose response curves associated with PR inhibitors and gain new insights into drug action . While the results of the model are subject to limitations arising from the simplifying assumptions used and from the uncertainties in the parameter estimates , the developed framework provides an extendable open-access platform to incorporate new data and hypotheses in the future .
The morphological maturation of human immunodeficiency virus type 1 ( HIV-1 ) depends on the proteolytic processing of the Gag and Gag-Pol polyproteins by the virus encoded PR that occurs concomitant with or shortly after virus release [1] . PR inhibitors ( PIs ) that interfere with this process result in the production of immature , noninfectious virus particles , and constitute an important drug class in anti-HIV-1 therapy [2] , [3] . While currently approved drugs act by competitive binding to the PR active site , thereby affecting all cleavage events , individual steps of the maturation process are also potential targets for future drug development [4] , [5] . The development and therapeutic application of HIV-1 maturation inhibitors requires a detailed understanding of the cleavage process , which has multiple layers of complexity . First , PR itself is embedded in the Gag-Pol polyprotein , and Gag-Pol auto-processing is required to initiate the maturation process [6]–[9] . Liberated PR molecules then catalyze further cleavage events , which might result in accelerated PR release by a positive feedback loop . Second , due to its relatively broad substrate specificity [10] , HIV PR targets 11 canonical cleavage sites in the Gag and Gag-Pol polyproteins ( Figure 1 ) , generating 66 distinct molecular species ( substrates , intermediates and products ) , and a large number of competing reactions occur simultaneously within the confined space of the virion . Cleavage at the individual sites occurs with different rates; a 400-fold difference in rate between the fastest ( SP1-NC ) and slowest ( CA-SP1 ) cleavage site in Gag has been determined in vitro [11] . Third , several intermediates include an active protease domain , but also one or more uncleaved cleavage sites: these molecular species have dual roles as both substrates and enzymes in the reaction network . The complexity of the reaction system ( number of reactions/reactants ) is comparable to that of the cell cycle , which has been among the most important targets of systems modelling in biology so far [12] . Understanding and predicting the behaviour of this complex system requires systems modelling and extensive empirical data to parameterize the model . Accumulating data on the kinetic parameters of the cleavage reactions [6]–[9] , [13]–[20] and on the biology of HIV-1 virion assembly and maturation [21]–[26] now allow us to tackle this problem , and we here present the first full reaction kinetics model of proteolytic processing by HIV-1 PR . We use the model to characterize the general time course of the process , to identify the parameters with the strongest effect on the maturation process , to assess interactions between the parameters and the potential of individual parameters to compensate for drug effects or changes in other parameters , and to explain the steep dose response curves associated with PIs [27] . While subject to inevitable limitations arising from the simplifying assumptions used and from uncertainty in the parameter estimates , our model results provide a level of resolution exceeding that of currently available experimental data . The time course of proteolytic processing has been characterized quantitatively for purified Gag in vitro [11] , but was determined only qualitatively for Gag-Pol [28] , and experiments are typically limited to tracking a small number of molecular species in simplified in vitro systems . In contrast , the model presented here tracks all intermediates and products of the complex reaction system . Furthermore , the model can be used to predict the effect of quantitatively characterized mutations or drugs alone or in combination . It can also be applied to predict the effect of potential perturbations induced by compounds in drug development . Systems modelling has been used to identify and characterize synergistic drug interactions that can enhance the effect of drugs [29] , [30] . The model presented here opens the possibility to apply this approach to HIV-1 PR inhibition .
We built a full model of PR catalyzed Gag- and Gag-Pol processing based on mass action Michaelis-Menten reaction kinetics ( Materials and Methods ) . Based on published data on the architecture of the immature HIV-1 particle [22] , we set initial concentrations to correspond to 2 , 280 molecules of Gag ( corresponding to 3 . 619 mM ) and 120 molecules of Gag-Pol ( corresponding to 0 . 195 mM ) within the confined space of a spherical virus particle with a radius of 63 nm [22] as the starting point for our simulations . By parameterizing all cleavage reactions according to in vitro empirical estimates ( Table 1 ) , our model generated a detailed predicted time course for the cleavage process ( Figure 2; major intermediates are shown in Figure S1 ) . The timing of virion maturation ( virion maturation time , VMT; dashed red line in all panels of Figure 2 ) was estimated based on two criteria for maturation: i ) the presence of a sufficient number of liberated CA molecules to form a mature conical capsid ( one “capsid unit” corresponding to 1 , 500 CA monomers [31] ) , and ii ) the concentration of the late processing intermediate CA . SP1 falling below a critical level . Processing at this site is required for mature capsid formation [26] , [32]–[34] . A CA . SP1 concentration below 5% of the total initial Gag content is needed to fully alleviate the trans-dominant inhibition effect of this fragment on HIV-1 infectivity [35] , and we used this threshold as a criterion for attaining VMT . The time needed for the assembly of the mature cone shaped capsid was not considered in our definition of the VMT , which implies that our estimates for VMT can be regarded as lower bounds; however , assembly is likely to be fast compared with the preceding steps of proteolytic processing ( see Discussion ) . Using the default parameters , our model predicted morphological maturation to occur ∼30 min after the start of the process , which is thought to be initiated at the formation of the virion . While there are no reliable data on the timing of virion maturation in vivo , the fact that morphological maturation intermediates have not been detected by electron microscopy indicates that the process is comparatively fast . Our result is roughly consistent with the current assumption that maturation occurs during or shortly after budding [36] , taken together with fluorescence imaging results indicating that most HIV-1 virions are released from the cell within 30 min after formation [37] , apparently with a ∼15 min delay after the assembly of the Gag shell beneath the cell membrane [23] . Total proteolytic activity ( Figure 2E , green line ) peaks around 39 min , and declines afterwards due to the internal cleavage of PR monomers . Remarkably , the combined catalytic activity of all intermediate enzyme forms exceeds that of the fully cleaved PR homodimer until ∼35 min after the start of the process ( Figure 2E , blue line indicates the relative contribution of mature PR dimers to the proteolytic activity ) . Up to the time of VMT ( dashed red line ) , intermediate enzyme forms are predicted to have catalyzed ∼80% of all cleavage reactions . Functional p66/p51 heterodimers of reverse transcriptase ( RT ) also decline after a peak due to the cleavage of the p66 subunit into p51 and p15 fragments; however , this decay is arrested as PR activity is lost ( this might occur even faster in vivo: see Discussion ) . Finally , we have verified that the total concentration of uncleaved cleavage sites greatly exceeds the total concentration of active enzyme forms throughout the simulated cleavage process ( Figure 2E ) , which justifies the use of Michaelis-Menten kinetics ( assuming quasi steady state for the enzyme–substrate complexes ) . We also plot the time course of the overall processing of individual cleavage sites in Figure 3A: the figure shows what fraction of a given cleavage site is yet uncleaved ( the total concentration of all molecular species that contain the uncleaved site , divided by the initial concentration ) . The order of cleavage can be defined for fixed thresholds of processing: Figure 3B depicts the order obtained for 50% and 95% processing; Figure 3C presents a schematic representation of the order of cleavage events based on 50% processivity . The order of events in our simulations is roughly consistent with the order of events observed in vitro [11] , [38] , with two exceptions: the removal of the spacer peptide from CA and the cleavage at the N-terminus of PR ( p6pol/PR ) occur much faster in the simulations than in vitro . These discrepancies arise from the relatively faster rates of cleavage observed during the processing of oligopeptides , which were used to parameterize the model . However , slowing down the processing of the CA/SP1 site to reproduce the results of in vitro processing of full-length Gag ( as in [39] ) results in VMT>2 hours ( see Discussion ) ; we therefore used the parameter set derived from oligopeptide cleavage ( Table 1 ) in the subsequent analyses . We thus conclude that our model is able to capture most known characteristics of the cleavage process , and proceed to analyze further properties of the system , for which little or no empirical data exist yet . We next investigated the sensitivity of the maturation time to the parameters of the model . These analyses provide insight into the sensitivity of the results to the uncertainty of the parameters , and also predict the response of the system to possible interventions that affect individual steps of the process . We first varied one parameter at a time ( see Table 1 for the list of all 33 parameters ) in the range of 0 . 1 to 10 times its default value , while fixing all other parameters at their default values ( Figure 4A ) . Within the studied range , varying most parameters had hardly any effect on the virus maturation time , with the exception of two critical parameters , which emerged as dominant factors: the rate constant of auto-cleavage by the full length Gag-Pol dimers , and the catalytic rate constant of heteromolecular cleavage at the CA/SP1 cleavage site . The dependence of VMT on both dominant parameters was very similar: at the lower ( slower ) end of the studied range , VMT is very sensitive to small changes in these parameters , while at the higher ( faster ) end , further increase in either rate constant yields diminishing reductions in VMT . We also tested the effect of initial Gag content of the virus . Since HIV-1 particles are not homogeneous , but have been shown to vary with respect to diameter [40] and completeness of the spherical Gag shell [22] , [25] , this parameter will vary among individual virions [41] . Varying initial conditions from 1 , 600 to 3 , 500 molecules of total Gag content ( while keeping the Gag∶Pol ratio of 20∶1 constant ) had negligible effect on the time course of virion maturation ( variation in VMT was ≤1 second ) . We next performed a multivariate exploration of the parameter space . Parameters were drawn randomly from lognormal distributions parameterized such that 95% of the values fell in the range of 0 . 1 to 10 times the default value of the parameters; for the few parameters with no direct empirical estimates ( the association and dissociation rate constants of full-length Gag-Pol and partially cleaved PR enzyme forms , and the KM values for the NC/TFP and TFP/p6pol cleavage sites ) , we allowed a range with plus/minus two orders of magnitude around the default value . We performed 10 , 000 simulation runs with independently generated random parameter sets , of which 8 , 937 achieved virion maturation by 120 min . Median VMT ( when censoring uncompleted runs at VMT = 121 min ) was 38 . 5 min ( IQR: 22 . 4–66 . 6 min ) ; the distribution of VMT was non-normal ( Kolmogorov-Smirnov test , p<10−10; Figure S2 ) . Maturation was triggered by the loss of CA . SP1 inhibition in 7 , 141 ( 80% ) of the cases where maturation occurred , and we verified that the criterion for the Michaelis-Menten approximation ( Stot>Etot ) was fulfilled for nearly all ( >99% ) parameter sets . The dominance of the catalytic rate constants of initial auto-cleavage and CA/SP1 cleavage was confirmed in this analysis . Of the 33 parameters , only four had a significant effect on VMT ( Spearman rank correlation test; p<0 . 0015 after Bonferroni correction ) : this included both dominant rate constants , which also displayed considerable correlation strength ( Spearman's Rho of −0 . 58 and −0 . 45 for the CA/SP1 catalytic rate constant and for the rate constant of Gag-Pol auto-cleavage , respectively ) . The catalytic rate constant of the NC/SP2 cleavage and the association rate constant of active ( N-terminally free ) partially cleaved PR forms also affected the VMT according to this analysis , but displayed only very weak correlation ( Spearman's Rho around −0 . 03 ) . Only the two dominant rate constants had discernible impact on the distribution of plotted VMT values ( Figures 4B and C show the influence of a dominant rate constant and of a representative “neutral” rate constant , respectively ) . We thus conclude that the time-limiting steps in virion maturation ( with current maturation criteria ) are the initial auto-cleavage of full-length Gag-Pol and the processing of the CA/SP1 cleavage site . Given the comparable magnitude of the impact of both dominant parameters on VMT , any effect ( mutation or drug ) involving one of the rate constants might be compensated by a change in the other . We investigated the potential for such compensation and for interactions ( synergy [29] , [30] or antagonism ) between the rate constants . Figure 5 shows isoclines of VMT ( isoboles [30] ) with the rate constant of Gag-Pol auto-cleavage plotted against the CA/SP1 catalytic rate constant , with all other parameters fixed at their defaults . All points of an isocline yielded a fixed VMT ( analogous to isoboles of combined drug doses of equal activity [30] ) . The isoclines are hyperbola-like functions with both vertical and horizontal asymptotes . This shape of the functions implies that for any given VMT , there is a minimum value for both parameters needed to achieve maturation within that given time; the vertical asymptotes indicate the minimum rates for CA/SP1 cleavage , the horizontal asymptotes indicate the minimal rate constants for Gag-Pol auto-cleavage . Close to the asymptotes , the corresponding slow rate becomes rate limiting , and very small changes in the limiting rate constant can only be compensated by large changes in the other parameter to maintain VMT . The default ( empirical ) parameter setting happens to fall in the regime where both parameters have comparable effect . This result indicates that small decreases in either rate constant ( by drug or mutational effect ) can be compensated by increases in the other parameter; however , compensation becomes increasingly difficult and eventually impossible as the affected rate parameter approaches its critical ( asymptotic ) value . Even where compensation is possible in terms of VMT , the time course of Gag-Pol processing cannot be forced to return to the original behaviour . When a change in one of the dominant parameters is compensated by a change in the other to yield the same VMT , the time course of the process remains different from that obtained with the default parameters ( Figure S3 ) . “Complete” compensation of the time course would be possible only between parameters that have very similar local sensitivity functions [42]; however , the local sensitivity functions of the two dominant rate constants have different shapes ( Figure S4 ) . While some of the other parameters have similar sensitivity functions ( for example , the sensitivity function of the Gag-Pol dissociation rate constant has similar shape to that of the catalytic rate constant of Gag-Pol auto-cleavage ) , the small magnitude of the effect of these on VMT precludes any meaningful compensation of changes in either of the dominant rate constants . We next investigated the potential interactions between the effects of the two dominant parameters when both are changed . In particular , we tested whether combined changes are characterized by either of two simple types of interaction: additive or multiplicative effects . We used the following simple definitions for the two types of interaction: using the notations VMTdef , VMTA , VMTB and VMTAB to denote VMT obtained with the default parameters , with one , the other , or both of the parameters changed to a defined extent , we denote the absolute changes in VMT due to changes in each parameter with d1 = VMTA-VMTdef and d2 = VMTB-VMTdef , and the fold changes with f1 = VMTA/VMTdef and f2 = VMTB/VMTdef . The expected VMT when both parameters are changed is then VMTAB = VMTdef+d1+d2 under the additive model , and VMTAB = VMTdef*f1*f2 under the multiplicative model . We use the comparison with these two simple reference cases to illustrate the nature of the interaction depending on the direction of intervention and possible compensatory effects . We varied both parameters along a geometric series ranging from 0 . 16 to 6 . 25 times the default value , both separately and in all possible combinations . We used the results from the univariate series to predict the effect of combined changes assuming both additive and multiplicative effects , and tested the deviation of the simulations with combined changes from both predictions ( Figure 6 ) . We found that the additive model fits qualitatively better when both parameters are changed in the same direction ( both increased or both decreased; Figure 6A ) , while the multiplicative model fits better when one parameter is increased and the other decreased ( Figure 6B ) . Two scenarios might be most relevant biologically . First , compensatory mutations in one parameter might restore VMT in the presence of drugs or mutations that decrease the other parameter . In this case , one parameter is decreased and the other increased , which results in multiplicative interactions , consistent with the shape of the VMT isoclines ( Figure 5 ) . This implies that a given fold increase in one of the parameters can be compensated by a similar factor of decrease in the other parameter to restore the default VMT . Second , combinations of drugs might target both rates in concert , which corresponds to a decrease in both parameters . For this scenario , our results predict additive effects: the increase in VMT induced by such a combination can be approximated by the sum of the increases induced by monotherapy with the individual drugs . Synergistic drug effects are not expected . The modelling framework also allowed us to characterize the effect of PIs . As a test case , we selected darunavir , which is a potent inhibitor of HIV-1 PR [43] , [44] . Figure 7A depicts the dependence of virion maturation time on the concentration of darunavir ( red symbols ) in the model . The response is very steep: VMT rises from the default value to infinity within about an order of magnitude range ( ∼0 . 01–0 . 1 mM ) of the drug concentration , which is consistent with the steep dose response curves observed for PIs [27] . However , the PI concentration , where maturation is lost in the model is several orders of magnitude higher than the IC50 estimated for darunavir in infected cells in vitro [44] , which calls for an explanation ( see below ) . The vertical asymptote where maturation fails to occur is very close ( at ∼0 . 12 mM ) to the possible maximal concentration of PR dimers ( at ∼0 . 095 mM; half of the initial Gag-Pol content ) , which implies that the majority of the enzyme needs to be blocked by the highly efficient inhibitor , if slower maturation still produces viable virions . This situation corresponds to the “critical subset” model of drug action [45] , [46] , which applies when enzyme function is insensitive to the drug concentration as long as a critical subset of enzyme molecules is unbound , but is lost quickly in the regime where the increasing drug concentration saturates the critical subset . Approximating the critical subset with the concentration of PR dimers that remain free in the presence of varied concentrations of the drug , the size of the subset is predicted to be around 30 PR dimers , if VMT = 60 min is required for viability , or around 15 dimers , if VMT>100 min is still tolerated ( Figure S5 ) . This result also predicts that the critical drug concentration needed to block virion maturation depends approximately linearly on the initial Gag-Pol content , and mutations affecting Gag-Pol frameshift will therefore have limited potential to compensate for the effect of PR inhibitors [47] . Figure 7B confirms this prediction . While the shape of the dose response curve was consistent with the observations , there was a strong quantitative discrepancy between the model predictions and the empirical dose response observed in vitro . In the simulations , inhibition occurs where drug concentration is in the range of the maximal PR concentration ( corresponding to half of the initial Pol content ) , which is in the ∼0 . 1 mM range . In contrast , in vitro experiments estimated an IC50 ( half maximal inhibitory concentration ) for darunavir in the nanomolar range [44] , which implies a four to five orders of magnitude discrepancy between the estimates . The critical drug concentration in the model depends only on the assumption that a single drug molecule binds to and blocks a single PR dimer , and on the estimated Pol content ( ∼120 molecules ) of a single virion . For a nanomolar drug concentration to take effect , a single molecule of drug should be able to block 104–105 PR dimers; in fact , a nanomolar concentration would imply that the average drug content of individual virions would be well below a single drug molecule per virion ( which would correspond to a “concentration” of ∼1590 nM ) . That is , most virions would contain no drug molecules , unless there is drug enrichment . This result is independent of the details of the model , and the discrepancy highlights an important additional process , which has been overlooked previously . We propose that at low ( nanomolar ) drug concentrations in the medium , the critical drug concentration within the virion can be generated by diffusion and ( near ) irreversible binding to PR , which together result in the accumulation of drug from the surrounding medium to a form bound to PR in the virion . Darunavir has relatively high membrane permeability [48] and can even accumulate within cells [49] , [50] . Assuming a drug concentration of 5 nM in the medium , and free diffusion of darunavir to the nascent virion , we calculate that the critical concentration ( ∼0 . 1 mM; a 2×104 fold enrichment ) required to block maturation can accumulate and bind PR in as little as a few minutes ( Materials and methods ) . The rate limiting step is the association of the drug to PR , rather than diffusion to the virion , and the concentration of unbound PR is approximately halved per minute . Assuming that the critical subset comprises 1/2 , 1/4 , 1/8 of the total PR pool , it would thus take about 1 , 2 or 3 minutes to accumulate the critical drug concentration needed to inhibit maturation . This simplistic calculation provides a lower boundary for the length of time when Gag-Pol processing is susceptible to the drug effect [51] . Note , however , that the beginning of the susceptible period might precede the budding of the virions , if the PR embedded in Gag-Pol can already be targeted by the PI within the cell . More realistic estimates for diffusion ( that take into account possible barriers or the extensive binding of darunavir to proteins [48] ) might in the future provide additional insight on the time window of susceptibility to PIs during the viral life cycle . The model can also be used to predict the dependence of the drug effect on the binding affinity ( parameterized by the dissociation rate constant ) of the drug ( Figure 7C ) . We found that the response to changes in the dissociation rate constant is similarly critical ( steep ) as to the concentration of the drug . Furthermore , the critical binding affinity required for the inhibition of maturation is several orders of magnitude lower than the estimated binding affinity of darunavir ( and other potent drugs ) , which indicates that potent PIs operate with a broad “safety margin” . This is consistent with the observation that for darunavir a nearly 1000-fold decrease in binding affinity did not translate into a weaker antiviral activity [43] , and might contribute to the relatively high genetic barrier of the drug . Implementing a hypothetical inhibitor that binds to full-length Gag-Pol to block the initial auto-cleavage produced dose response curves of very similar shapes; however , such inhibitors require much stronger binding affinity ( close to that of darunavir ) to take effect on VMT ( Figure 7C: blue symbols ) , have weaker effect at the same fixed affinity and concentration ( Figure 7A: blue symbols ) , and imply a smaller critical subset of unbound target molecules ( Figure S5 ) . This difference probably arises because unbound Gag-Pol molecules that undergo auto-cleavage generate active PR forms that can no longer be targeted by an ( exclusive ) inhibitor of Gag-Pol; in contrast , unbound PR remains a target for PIs until the cleavage of its internal cleavage site , upon which protease activity is lost . We also tested the potential of the catalytic rate constants to compensate the effect of PIs ( for example , by compensatory mutations in the cleavage sites [52] , [53] ) . Figure 7D shows the compensation plots ( isoclines of VMT = 30 min ) of both dominant catalytic rates against the concentration of darunavir , demonstrating a limited potential for compensation . The vertical asymptote of the isocline for the CA/SP1 catalytic rate constant ( at 10−1 . 36≈0 . 0436 mM ) indicates that even an “infinite” catalytic rate could only compensate a drug dose of about 36% of the critical concentration ( 0 . 12 mM ) that inhibits maturation completely , and in vivo drug levels with current dosing are likely to exceed the critical concentration considerably . The prediction of limited compensatory potential is in apparent contradiction with some empirical data that show clear compensation by substrate mutations in tissue culture and selection of such mutations in vivo [52] , [53] ) ; see the Discussion for a possible explanation . Finally , we investigated whether a small initial inoculum of mature PR would be able to accelerate virion maturation . Such an inoculum could potentially be derived either from the infecting virion or from Gag-Pol processing within the cell before virion assembly and budding . Figure S6 illustrates that a small initial inoculum has only a modest effect on the time to virion maturation; for example the addition of PR corresponding to 10% of Gag-Pol content reduces VMT from 30 min to about 25 min . A greater initial concentration of PR is unlikely at the beginning of the maturation process , given that premature proteolysis prior to confining the components in an assembling virion abolishes particle formation [54] , which suggests that the bulk of proteolysis of virion associated proteins only occurs in the assembled virion ( at or shortly after the time of budding ) . We therefore conclude that an initial inoculum of PR is unlikely to contribute substantially to proteolytic activity during maturation . The time scale of virion maturation therefore depends on Gag-Pol auto-cleavage within the virion , as has been assumed in our model . This result is also consistent with the observation that N-terminal cleavage follows first-order kinetics in protein concentration [8] , [9] , which implies that the dominant mechanism is intramolecular , rather than heteromolecular cleavage .
Our simulations of Gag-Pol processing are consistent with most of the known features of Gag-Pol processing ( approximate time scale , order of release of final products ) , and can offer important insights into further details of the process that are not amenable to empirical study . In particular , we predicted the rate limiting steps in the maturation process , and our results suggest that the auto-cleavage of Gag-Pol dimers and the PR catalyzed cleavage at the CA/SP1 site are the most promising candidates for future drugs that would target individual steps of the proteolytic process . Importantly , bevirimat , the first clinically tested HIV-1 inhibitor that targets an individual cleavage site , as well as the chemically unrelated inhibitory compound PF-46396 , affect cleavage at the CA/SP1 boundary [4] , [5] . Unfortunately , drug combinations ( or mutations ) that inhibit or impair both dominant steps are not predicted to have a synergistic effect . Our model also provides a simple mechanistic explanation for the steep dose response curves associated with PIs [27] , and highlights the importance of diffusion mediated drug accumulation in the virions ( or in the infected cells before virion budding ) , which calls for further analyses . We demonstrated that the maturation process is robust with respect to variation in Gag content , and therefore also to stochastic biological variations in virion assembly , and showed that a small initial inoculum of mature PR is unable to “kick-start” the process . The model predicted that intermediate PR forms ( with uncleaved C termini ) may contribute substantially to proteolytic processing ( this result clearly depends on the assumption that such intermediate forms have efficient catalytic activity [8] , [55] ) . We also found that the self-cleavage of PR results in a loss of PR activity after the completion of maturation , which might be an evolutionary adaptation to avoid the loss of RT activity due to the cleavage of all p66 monomers and the possible loss of CA monomers due to cleavage of CA at non-canonical internal cleavage sites [17] . Finally , we were also able to predict the compensatory potential between drugs or mutations that affect the rate limiting steps or block PR activity . The compensatory potential of mutations affecting Gag-Pol frameshift [47] could also be investigated with the models . While our “full model” of Gag-Pol processing provides valuable insights , this simplistic modelling approach clearly has a number of limitations . The use of mass action reaction kinetics assumes a well-mixed homogeneous system with concentrations described on a continuous scale , while both immature and mature virions have organized spatial structure [21] , [40] , [56] , [57] and the number of enzyme and substrate molecules within a virion has a limited discrete scale of the order of hundreds and thousands , respectively [22] , [31] . These constraints are likely to affect HIV-1 proteolytic processing and limit the validity of our model predictions [58] . To mitigate these limitations , the low number of interacting molecules could be addressed relatively simply by discrete stochastic modelling [59] , while the introduction of explicit space would require a major re-structuring of the model , and might be a promising direction for further study . Furthermore , the criteria that we used for maturation may have been incomplete . Our criteria for VMT involved the steps of proteolytic processing required for the morphological maturation of the capsid [25] , [31] , [35] , but not the time needed for the assembly of the mature capsid . However , two observations indicate that assembly of the mature cone probably does not take very long: EM analyses have never revealed distinct maturation intermediates and in vitro assembly of CA seems to be very rapid following induction by high salt [60] ( although assembly must be induced by a different trigger in vivo ) . Given these caveats , our definition of VMT based on processing criteria can be regarded as a lower bound but is likely to be a good approximation of the time to morphological maturation . However , there are probably other criteria for the viability ( infectivity ) of virions: each of the enzymes and structural proteins of the virus probably has a critical required count . Should quantitative data on other criteria be identified in the future , the model can easily be extended to accommodate the further requirements . We note also that the current limited maturation criteria are likely to introduce a bias on the set of parameters that can potentially influence VMT: the MA/CA , CA/SP1 and SP1/NC cleavage sites have direct effect on the molecular species that appear in the maturation criteria ( of these , the processing of the MA/CA and SP1/NC cleavage sites occurs much faster than that of the CA/SP1 site , with kcat/KM ratios of 45 and 74 vs . 9 , which explains why only the latter has strong rate limiting effect ) , while Gag-Pol auto-cleavage is strictly needed to initiate processing , its rate therefore has a ubiquitous effect on the time course of the process . However , even with the current criteria , other rates ( cleavage sites ) could have strong indirect effects by competing for the available enzymes . The identity of the dominant rates is therefore not hard-wired in the structure of the model , but depends on the relative magnitude of all kinetic rates . For example , re-parameterizing the model with cleavage rates estimated with full-length Gag changes also the identity of rate limiting steps ( see below ) . To be able to use a consistent set of parameters , we had to rely on estimates obtained from the in vitro cleavage of oligopeptides containing individual cleavage sites in solution . However , flanking regions around the cleavage sites [20] and the extended context of variations in both the substrates and the enzyme [58] are likely to have an effect on the rates of cleavage by influencing the access of PR to the sites . In addition , the incorporation of Gag and Gag-Pol molecules in the strictly organized lattice architecture and the limited movement of molecules within the confined space of the virion are also likely to influence the effective cleavage rates . These effects may be particularly apparent regarding the removal of the spacer peptides by cleavage at the CA/SP1 and NC/SP2 processing site , respectively . Based on all available virological data , SP cleavage is considered to be a late event occurring after all other cleavages within Gag , which is not reflected in our simulations that were parameterized with peptide kinetic data ( Figure 3B ) . There is indeed evidence that the CA/SP1 sites and NC/SP2 sites are sensitive to the lattice and RNA context . For example , the presence of uncleaved MA upstream [26] , uncleaved NC downstream [11] or a proportion of uncleaved CA-SP1-NC molecules in the particle [35] reduces cleavage at the CA/SP1 site . Furthermore , exposure of the NC/SP2 site has been shown to be sensitive to the spatial organization of the RNA strands within the virion [61] . Such indirect effects might be responsible for the potency of the NC/SP2 cleavage site to affect the time scale of Gag processing and to compensate for inhibition by PIs [52] , [53] ) , even though such an effect was not predicted in our simulations . Cleavage at this site was found to be much slower in the context of full-length Gag compared with that of short oligopeptides [11] , which might increase the impact of this cleavage site on the time scale of virion maturation . For Gag , quantitative empirical data are also available for the cleavage of the full polyprotein in vitro [11] , and the relative rates estimated for some of the cleavage sites are different from those obtained in the context of oligopeptides , which might reflect the effect of flanking groups . We tested how an alternative set of cleavage rates in Gag , fitted to the empirical data on full-length Gag cleavage ( adopted from [39] ) affects the results of our model . The most important difference in the parameters is that CA/SP1 cleavage was much slower ( kcat decreased , KM increased ) compared with the estimates obtained with oligopeptides . As a result , virion maturation occurred much later , around ∼144 min ( Figure S7 ) , which is inconsistent with the fact that many released virions captured close to the cell by electron microscopy are mature and no morphological maturation intermediates are observed . Thus , although the true in vivo relative rates might be better approximated in the experiments that worked with full-length Gag instead of oligopeptides , the absolute rates of cleavage are likely to be much faster in vivo in the maturing virions . Furthermore , even full-length Gag in vitro is unlikely to reflect the spatial effects occurring in whole virions . However , the testing of this alternative parameter set yielded an important insight: the sensitivity of VMT to individual parameters also changes ( Figure S8 ) compared with the default settings; a greater number of parameters have discernible effect on VMT with this parameter setting . This indicates that while our predictions on the identity of rate limiting steps are very robust as long as the dominant rate constants remain relatively close to their original values , large changes in these parameters ( in this case in both CA/SP1 kinetic rate constants ) may re-define the rate limiting steps of the process . In particular , it remains to be seen how well the currently used parameter estimates that were obtained with oligopeptide substrates reflect the real relative rates of cleavage in vivo: if future empirical work calls for major changes in the parameters , the simulation analyses should be repeated to accommodate the effect of the new estimates . Functional p66/p51 heterodimers of reverse transcriptase ( RT ) decline to a relatively low level due to the cleavage of the p66 subunit into p51 and p15 fragments in the model . However , immunoblot analyses indicate that p66 and p51 persist in mature virions at approximately equal concentrations [62] , [63] , which suggests that some mechanism might protect RT from proteolysis in vivo . For example , the p51/p15 cleavage site might be cleaved preferentially in the context of p66 homodimers [38] , [64] , with greatly reduced cleavage both in monomers and in the functional heterodimer . Finally , in all analyses we could only investigate VMT ( the time to virion maturation ) as a surrogate metric for the success of virion maturation . VMT predicted with the empirical set of kinetic parameters was consistent with current models of release and maturation [23] , [36] , [37] , although some studies have estimated a longer time scale of several hours [65] , while another retrovirus ( Moloney Murine Leukemia Virus ) seems to achieve partial maturation in as little as 5 minutes after release [66] . However , we have no direct data on how VMT translates to infectivity . As a rough approximation , maturation should be completed within the mean lifespan of virions , which is around ∼1 h in the blood plasma [67] , and a few hours [68] or possibly even shorter [69] in the lymphoid tissues . Furthermore , cell-to-cell transmission ( which plays a major role in HIV infection [70] ) would also require rapid maturation after release; indeed , mature particles have been clearly observed in the virological synapse [71] , although another study claimed that maturation only occurs upon endocytic uptake of the virus across the synapse [65] . However , further studies will be needed to better characterize VMT and its influence on replicative capacity of the viruses . In terms of the predicted drug effects , our results are likely to be robust due to the steep response curves: in the regime of the critical concentration and efficacy of drugs , VMT rises very sharply towards infinity ( Figure 7 ) , which firmly nails down the critical threshold required to block virus maturation effectively . To our knowledge , our model is the first attempt to build and analyze a full model of proteolytic Gag-Pol processing in maturing HIV-1 . Previous mathematical analyses have been restricted to approximating drug effect on series of subsequent cleavage reactions [72] , without tracking individual substrates and products , while our previous analysis addressed the in vitro processing of Gag only [39] . The model developed here represents a major extension by taking into account all processing events for Gag and Gag-Pol , initial auto-cleavage and explicit enzyme dynamics . While subject to a number of limitations in its current form , this simulation framework provides a flexible platform to incorporate emerging empirical data in the future , including additional criteria for virus maturation or infectivity , measurements on the time course of proteolytic processing in whole virions ( not available currently , but possibly amenable to emerging new technology ) , or more accurate data on the virion maturation time . Even current technology would allow the estimation of all kinetic rate constants ( as in Table 1 ) for mutant PR forms , which could be used to generate detailed predictions on the influence of the mutations on the proteolytic process . The estimation of kinetic rates for combinations of PR forms , cleavage site variants and defined drug conditions could be used to characterize , and predict , complex evolutionary pathways under drug pressure . Quantitative data on the relationship between viral fitness and concentrations of functional RT or integrase could be used to predict the interactions of these drug classes with PR inhibitors [73] or PR mutations that impair the processing of these enzymes . Finally , the framework will be applicable to other viruses that rely on proteolytic processing of virion components , given that sufficient data on the respective cleavage rates are collected ( for example , some kinetic data are available for the retroviruses feline immunodeficiency virus [74] , Rous sarcoma virus [75] and murine leukaemia virus [76] ) . The publication of our full computer code under open access as a supplement to this paper will further facilitate the extension and broad application of this modelling framework in the future .
Fully cleaved HIV-1 proteins and peptides are denoted by their standard abbreviations and nomenclature ( see legend to Figure 1 ) . Partially processed intermediates are denoted by concatenating the notations for the starting and the end fragment , for example , the intermediate species spanning CA-SP1-NC is denoted by CA . NC , the species spanning NC-TFP-p6pol-PR-p51 is denoted by NC . p51 , etc . Dimers are denoted in the form M1dM2 , where M1 , 2 denote the monomers; for example , PRdPR denotes the mature homodimer of fully liberated protease , MA . INdPR . IN denotes the initial product of the first step of Gag-Pol autocleavage . Gag-Pol processing by the HIV-1 PR is a specific case of catalyzed competitive heteropolymer cleavage , for which we have developed a generic modelling framework in terms of Michaelis-Menten reaction kinetics under the quasi-steady-state ( QSS ) approximation [39] . In an abstract notation , let Si denote the ith monomer of the heteropolymer; Si , j a fragment spanning monomers i to j; and let the cleavage site h refer to the site C terminal to the hth monomer . Free protease can bind reversibly to any of the cleavage sites of a fragment such that i≤h<j , and let refer to an enzyme-substrate complex in which the enzyme is bound to cleavage site h within the fragment Si , j . Assuming Michaelis-Menten kinetics , the steady-state concentration of enzyme-substrate complexes can be written as , where Efree denotes the concentration of free enzyme and denotes the Michaelis-Menten constant of the cleavage of fragment Si , j at cleavage site h . Using Etot = Efree+Ebound and summing over all distinct enzyme-substrate complexes we obtain: , where with n denoting the length ( number of distinct monomers ) of the heteropolymer [39] . The rate of change in the concentration of Si , j can then be calculated as shown in Figure 8 ( with denoting the catalytic rate constant of the cleavage of fragment Si , j at cleavage site h ) . For example , the fragment CA . NC can be produced by the cleavage of MA . NC at the MA/CA cleavage site or by the cleavage of any CA . X fragment with X being a monomer downstream of NC at the NC/TFP cleavage site; and can be lost by internal cleavage at the CA/SP1 or the SP1/NC cleavage sites . Proteolytic processing is initiated by the auto-cleavage of the full-length Gag-Pol dimer , which liberates the N-termini of both embedded PR domains in two steps [9] , [28] , [77]–[79] . PR activity is hampered primarily by the N-terminal flanking fragments of Gag-Pol [9] , [55] , [77] , [80] , and PR intermediates extended at the C terminus have been shown to have catalytic activity comparable to that of the mature enzyme [8] , [55] . We therefore assumed that dimers of partially cleaved PR forms that have free N termini but uncleaved flanking groups at their C terminus already possess catalytic activity . For simplicity , we assumed that all active enzyme dimers have the same catalytic activity , and C terminal groups affect the efficiency of dimerization [81] , rather than the rates of catalysis . The model also tracked the formation and breakup of homo- and heterodimers of full-length Gag-Pol , intermediate and mature PR forms , and of p51/p66 heterodimers of the reverse transcriptase ( RT ) . Because the quasi steady state of competing dimerization reactions cannot be computed in a closed form , we implemented a “hybrid” time scale in which enzyme-substrate complexes were kept in QSS within one time step , even though enzyme dimers were not . At the beginning of each time step , we summed up the level of enzymatic activity ( total concentration of active enzyme dimers , Etot ) , and calculated the QSS for the enzyme-substrate complexes . The QSS concentrations of the complexes determined the rates of the net cleavage reactions within a time step . Enzyme dimers were allowed to form and dissociate within one time step , but the level of enzyme activity was only updated at the beginning of the next time step . Thus the iterative steps of our model were the following: 1 ) Sum up all active ( intermediate and mature ) enzyme dimers to calculate Etot; 2 ) calculate the QSS of enzyme-substrate complexes; 3 ) numerical integration of reactions involving the association and dissociation of enzyme dimers , the auto-cleavage reaction and the net cleavage reactions . Because cleavage rates are typically estimated in the context of oligopeptides , the effect of flanking groups on cleavage could not be taken into account , that is , the same cleavage site was always cleaved with the same rate , irrespective of the substrate it was located in . We thus set and for all ( i , j ) . All parameters are listed in Table 1 . For simplicity , dimers were assumed to be resistant to cleavage . In addition to the 11 canonical cleavage sites , we also implemented a non-canonical cleavage site within the mature PR monomer [15] , which might be important for the auto-inactivation of proteolytic activity . Finally , we implemented two types of inhibitors: “classic” PIs that bind to active ( intermediate and mature ) PR dimers , and another hypothetical type that binds to dimers of full length Gag-Pol and blocks auto-cleavage ( for simplicity , cross-inhibition of both types of dimers was not allowed ) . The model was implemented in the C programming language , using an adaptive fifth order Cash-Krap Runge-Kutta algorithm for numerical integration of ordinary differential equations [82] . The full computer code of the simulations is available in the supplement Text S2 . For the local sensitivity analyses , to be able to use the DASAC package [83] , we have re-implemented the model in the Fortran programming language . We have verified that the two implementations yield consistent results . The following 33 parameters were varied either individually or in combination: kcat and KM values for the 11 canonical cleavage sites and for the non-canonical internal cleavage site within PR; the rate of Gag-Pol auto-cleavage; the association and dissociation rates for the three classes of PR containing enzyme forms: mature PR , intermediate enzyme forms with cleaved N terminus , and full-length and intermediate forms with N terminal flanking groups; finally , the association and dissociation rates of RT heterodimers . Local sensitivity analyses demonstrate how a small change in a particular parameter ( from its default value ) affects the time course of the concentrations during the simulations . The sensitivity of each variable ( molecular species ) with respect to each parameter is obtained as a function of time ( along the time course of the simulated proteolytic process ) . We used half-normalized sensitivity functions that express the change in substrate concentrations as a function of relative change in a parameter ( how much , in absolute units , does the concentration change , if we change the parameter by 1% ) . Local sensitivity analyses were performed with the help of the DASAC package [83] using the Fortran implementation of the model . Our simple calculation was based on diffusion from bulk medium to the surface of a sphere , assuming steady-state concentration gradient around the sphere and immediate uptake at the surface . Under these assumptions , total flux to the sphere ( rate of accumulation ) can be calculated as: , where D is the diffusion coefficient , r is the radius of the sphere and C0 is the concentration in the bulk medium ( at “infinite” distance from the sphere ) [84] . The diffusion coefficient can be estimated by the Wilke-Chang method [85] as , where Φ is a dimensionless association factor of the solvent ( equal to 2 . 6 for water ) , Mb is the molecular weight of the solvent ( 18 . 02 Da for water ) , T is the temperature in kelvins , ηb is the viscosity of the solvent ( 0 . 862 cP for water at 300 K ) and Va is the molar volume of the solute ( 408 . 4 cm3 for darunavir ) , which yields a diffusion coefficient of D = 4 . 78×10−6 cm2 s−1 for darunavir at T = 300 K in water . Given the estimated radius of a virion at r = 63 nm [22] and using a nanomolar concentration of C0 = 5 nM for the bulk medium , the rate of accumulation of darunavir in the virion is estimated as Q≈2×10−21 mol/s . Considering an initial Gag-Pol content of 120 molecules per virion [22] , the maximum amount of PR dimers is 60 , or Emax = 10−22 mol per virion . The time needed to accumulate an equal amount of drug molecules can then be calculated as ta = Emax/Q = 0 . 05 s . Assuming that this idealized diffusion process continuously replenishes the drug molecules that bind PR dimers within the virion , the drug concentration within the virion can be approximated with that in the bulk medium , and the binding kinetics of PR dimers by the drug can be characterized by the equation dE/dt = kass*C0*E , where E denotes the concentration of unbound PR dimers and kass is the association rate of the drug . The time until a fraction f of all dimers remains unbound can then be calculated as −ln ( f ) /kass*C0 . Using the association rate of darunavir ( Table 1 ) and C0 = 5 nM , the concentration of unbound enzyme is approximately halved per minute ( t1/2≈63s ) . Given that the dissociation rate of darunavir is <10−6/s ( Table 1 ) , the dissociation of drug-enzyme complexes can be neglected on the time scale of minutes . Statistical tests were performed using the R statistical environment [86] . Mathematical formulae were fitted to data points using the nls ( ) function of R .
|
Human Immunodeficiency Virus ( HIV ) produces its structural proteins and key enzymes in the form of polyproteins , from which the individual proteins need to be released in a complex and tightly regulated series of cleavage reactions to give rise to a morphologically mature , infectious virus particle . This process is catalyzed by a viral protease ( PR ) , which is itself embedded in one of the polyproteins , and is one of the main targets of antiretroviral drugs . We have developed the first full reaction kinetics model that addresses the several layers of complexity ( multiple cleavage sites and substrates; multiple enzyme forms; PR auto-processing ) associated with the proteolytic processing of HIV polyproteins . The model allows us to identify the rate limiting step of virion maturation and the parameters with the strongest effect on maturation kinetics . We predict how changes in the individual cleavage rates and the effects of drugs might interact and possibly compensate each other , characterize the detailed time course of the process , and explain why the effectiveness of PR inhibitors rises very steeply at a critical threshold concentration of the drugs . These new insights promote our understanding of the viral life cycle and may guide the future development of antiviral drugs .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"systems",
"biology",
"biochemical",
"simulations",
"immunodeficiency",
"viruses",
"virology",
"microbiology",
"biology",
"computational",
"biology"
] |
2013
|
Gag-Pol Processing during HIV-1 Virion Maturation: A Systems Biology Approach
|
Similar to developmental programs in eukaryotes , the death of a subpopulation of cells is thought to benefit bacterial biofilm development . However mechanisms that mediate a tight control over cell death are not clearly understood at the population level . Here we reveal that CidR dependent pyruvate oxidase ( CidC ) and α-acetolactate synthase/decarboxylase ( AlsSD ) overflow metabolic pathways , which are active during staphylococcal biofilm development , modulate cell death to achieve optimal biofilm biomass . Whereas acetate derived from CidC activity potentiates cell death in cells by a mechanism dependent on intracellular acidification and respiratory inhibition , AlsSD activity effectively counters CidC action by diverting carbon flux towards neutral rather than acidic byproducts and consuming intracellular protons in the process . Furthermore , the physiological features that accompany metabolic activation of cell death bears remarkable similarities to hallmarks of eukaryotic programmed cell death , including the generation of reactive oxygen species and DNA damage . Finally , we demonstrate that the metabolic modulation of cell death not only affects biofilm development but also biofilm-dependent disease outcomes . Given the ubiquity of such carbon overflow pathways in diverse bacterial species , we propose that the metabolic control of cell death may be a fundamental feature of prokaryotic development .
The balanced progression of cell division and apoptotic events is a classic hallmark of eukaryotic development [1] . Intriguingly , a similar homeostatic control of cell death , lysis and proliferation is predicted to benefit the development of adherent multicellular bacterial assemblages ( called biofilms ) by providing nutrients and critical biofilm building matrix components like extracellular DNA ( eDNA ) [2] , [3] . Consistent with this assumption , recent investigations have revealed that bacteria , like eukaryotes not only harbor elaborate regulatory systems that modulate cell death , but also display biochemical and physiological hallmarks characteristic of programmed cell death ( PCD ) [4] , [5] , [6] . The molecular components that mediate cell death in S . aureus are regulated , in part , by the LysR-type transcriptional regulator , CidR [7] and include a set of membrane bound proteins , CidA and CidB , whose functions are predicted to be analogous to the Bcl-2 family of apoptotic modulators in eukaryotes [2] , [3] . However , less clear are the mechanistic contributions of other members of the CidR regulon in cell death , specifically those enzymes that are active during overflow metabolism , pyruvate oxidase ( CidC ) and α-acetolactate synthase/decarboxylase ( AlsSD ) [8] , [9] . Given that these enzymes are the only additional members of the CidR regulon and that multiple physiological signals that directly affect both central metabolism and cell senescence coordinate their expression [10] , we predicted an intricate role for these proteins in the physiology of cell death . Here , we report that both CidC and AlsSD carbon-overflow pathways contribute to staphylococcal cell death . Our results demonstrate that cell death is potentiated by acetate , a major weak acid byproduct of glucose catabolism , whose levels are antithetically modulated by CidC and AlsSD activities . We also report that the physiological features accompanying staphylococcal cell death resemble eukaryotic PCD ( apoptosis ) wherein cell death is associated with respiratory dysfunction , increased ROS production and DNA damage . Finally , we demonstrate a role for staphylococcal PCD in biofilm development and pathogenesis .
Multiple studies have linked the uptake and metabolic fate of glucose to the regulation of PCD during eukaryotic development [11] . To determine whether such correlations are broadly conserved in bacteria , the effects of glucose on staphylococcal cell death were assessed over a period of five days by monitoring the colony forming units ( cfu/ml ) of wild-type cells grown aerobically in rich media ( tryptic soy broth , TSB ) containing either 14 mM or 35 mM glucose . Although there appeared to be no significant difference in the viable cell counts after 24 h of growth in either type of media , subsequent stationary phase survival of wild-type cells was dependent on initial glucose concentrations wherein S . aureus grown in TSB-35 mM glucose displayed a steep decline in viability ( ∼7 log10 difference ) compared to the modest decline ( ∼1 . 2 log10 ) observed for cells grown in TSB-14 mM glucose over the same period of time ( 120 h ) ( Fig . 1A ) . These results indicate that growth in excess glucose reduced the survival of S . aureus in stationary phase . To ascertain whether excess glucose-mediated staphylococcal cell death bore similarities to PCD , we explored the physiological status of the dying population by flow cytometry after 72 h of growth and compared them to a 24 h reference point when cells were relatively healthy based on viable counts . Respiratory potential was estimated using the cell permeable redox dye , cyano-2 , 3-ditolyl tetrazolium chloride ( CTC ) . Reduction of CTC into a red insoluble fluorescent formazan that accumulates intracellularly is achieved by dehydrogenases of the electron transport chain ( ETC ) that are expressed within actively respiring bacterial populations [12] . In addition to CTC , cells were co-stained with 3′- ( p-hydroxyphenyl ) fluoroscein ( HPF ) , a cell permeable fluorescent reporter that has widely been used to detect levels of highly reactive oxygen species like hydroxyl radicals ( OH• ) [13] . CTC staining of S . aureus grown in TSB-14 mM glucose revealed a healthy respiring sub-population ( ∼34% ) at 24 h and a relatively smaller population of cells ( 13% ) undergoing oxidative stress ( Fig . 1B , Table S1 ) . By 72 h the respiring population under these very same conditions increased to 79% ( Fig . 1B , Table S1 ) . This is expected as dependency on the TCA cycle and oxidative phosphorylation for cellular energetic needs increases in stationary phase , upon exhaustion of glucose from the media . Interestingly , S . aureus grown in TSB-35 mM glucose revealed an even larger population ( ∼61% ) that reduced CTC at 24 h when compared to 14 mM glucose ( Fig . 1B , Table S1 ) . The increased reduction of CTC under these conditions could not have resulted from a corresponding increase in the rate of cellular respiration , as the ability of these cells to consume oxygen as a terminal electron acceptor had significantly decreased ( Fig . 1C ) . These observations suggest that aerobic growth in excess glucose not only results in the inhibition of respiration , but may also promote the promiscuous transfer of electrons to alternate acceptors like CTC , due to a bottleneck in the ETC . The transfer of electrons via a functional ETC has previously been proposed to ameliorate oxidative stress by curtailing the single electron reduction of oxygen to superoxide radicals ( O2•− ) , a precursor of the highly reactive hydroxyl radical ( OH• ) [14] . Hence , we argued that the decreased functionality of ETC observed for cells grown in excess glucose may eventually promote the production of ROS . Although we did not observe OH• at 24 h of growth , we detected a dramatic increase of HPF stained cells by 72 h of growth in TSB-35 mM glucose but not in TSB-14 mM glucose ( Fig . 1B , Table S1 ) . The temporal production of ROS was confirmed by electron paramagnetic resonance ( EPR ) spectroscopic analysis of samples incubated with the spin probe , 1-hydroxy-methoxycarnonyl-2 , 2 , 5 , 5-tetramethyl-pyrrolidine hydrochloride ( CM-H ) . Cells grown in TSB-35 mM glucose exhibited approximately 3-fold increase in EPR peak amplitude by 72 h relative to those grown in TSB-14 mM glucose ( Fig . 1D ) . To determine the chemical nature and relative levels of various ROS produced , we incubated samples with either superoxide dismutase ( SOD; O2•− scavenger ) or dimethyl thiourea ( DMTU; OH• scavenger ) prior to the addition of CM-H . This approach revealed that cells undergoing cell death produced both superoxide and hydroxyl radicals ( Fig . S1 ) . An abundance of cellular ROS mediates several types of DNA damage , including single and double stranded breaks that lead to DNA fragmentation [15] . We performed TUNEL ( terminal deoxynucleotidyl transferase dUTP nick end labeling ) assays on S . aureus undergoing oxidative stress to estimate the population of cells undergoing DNA fragmentation by flow cytometry . Consistent with the temporal pattern of ROS production observed earlier , we detected a sub-population of cells with fragmented DNA ( TUNEL positive ) by 72 h of growth under excess glucose conditions ( Fig . 1E ) . Notably , only minimal TUNEL staining ( Fig . 1E ) was detected after 72 h for cultures supplemented with 14 mM glucose . Collectively , these observations suggest that cell death resulting from growth under excess glucose exhibits multiple hallmarks of eukaryotic PCD . Interestingly , the phenotypic hallmarks of PCD were not restricted to growth of S . aureus under glucose rich conditions alone , but were also observed when grown in the presence of excess fructose , mannitol and sucrose suggesting a strong association between carbon catabolism and cell death ( Fig . S2 ) . How does excess glucose mediate cell death ? It is well known that S . aureus cultured in excess glucose undergoes CcpA-mediated catabolite repression [16] . This ensures that acetate accumulates in the media as a byproduct of glucose catabolism . However , once glucose is completely exhausted from the media , the TCA cycle is progressively relieved of CcpA repression and excreted acetate is oxidized to generate energy required for subsequent growth [16] . Growth of S . aureus in TSB-14 mM glucose displayed such a classic diauxie , where glucose was consumed within 5 h and subsequent growth was dependent on the consumption of acetate by 9 h ( Fig . 2A–C ) . The temporal dynamics of acetate levels in the media were also reflected in the pH shift of the culture supernatant from 7 . 2 to 5 . 5 during acetate accumulation and from 5 . 5 to 7 . 2 during its depletion ( Fig . 2C , 2D ) . As observed previously [17] , growth of S . aureus in TSB-35 mM glucose did not display the expected diauxic shift ( Fig . 2A ) . Although excess glucose was consumed within 9 h , acetate remained unutilized and the pH of the culture was maintained at 4 . 6 ( Fig . 2B–D ) . Based on these observations , we hypothesized that cells grown in excess glucose would eventually be inhibited by high concentrations of acetate and low pH . The growth inhibitory effects of weak organic acids like acetate are largely dependent on pH [18] . As the extracellular pH nears the pKa of acetate ( pKa = 4 . 76 ) , the protonated ( uncharged ) membrane permeant form of the acid ( CH3COOH ) replaces its corresponding ionic forms ( CH3COO−; H+ ) , thus allowing the former species to passively breach bacterial membranes and dissociate within their relatively neutral cytoplasm [18] . If left unchecked , such an event may lead to growth inhibition and lethality through cytoplasmic acidification [18] . Given that the pKa of acetate is easily met during growth in excess glucose , it seemed plausible that this acidic metabolite may represent a physiological trigger for cell death . To test whether acetate was capable of inhibiting S . aureus growth under acidic conditions , we buffered TSB at pH 4 . 8 using 30 mM HOMOPIPES ( homopiperazine-N , N′-bis-2- ( ethanesulfonic acid ) and challenged cultures with the sodium salt of acetate . As expected , acetate inhibited the growth of S . aureus under these conditions , but neither acidic pH alone nor addition of an equimolar concentration of sodium chloride ( 50 mM ) inhibited growth to the same extent as sodium acetate ( Fig . 2E ) . Further , the addition of a non-metabolizable weak acid , benzoate ( pKa = 4 . 2 ) was as toxic as acetate under low pH ( Fig . 2E ) . These observations suggest that acetate mediated growth inhibition is a direct consequence of intracellular acidification and not due to secondary metabolites resulting from intracellular catabolism of acetate . To confirm that cell death is dependent on the weak acid properties of acetate , we grew S . aureus in TSB-35 mM glucose that was buffered to a pH of 7 . 3 with 50 mM MOPS ( 3- ( N-morpholino ) propanesulfonic acid ) . We reasoned that although cells would utilize excess glucose to generate acetate , the relatively neutral pH of the medium would allow it to remain in the ionic state and prevent it from permeating and acidifying the interior of cells . Indeed as predicted , S . aureus under these conditions did not undergo cell death despite its growth in excess glucose ( Fig . 2F ) . Remarkably , we also observed a dramatic reduction in the generation of ROS ( Fig . 2G–H , Table S1 ) , decreased DNA damage ( Fig . 2I ) and comparable rates of respiration relative to wild-type ( Fig . 2J ) under these conditions , suggesting that excess glucose per se was not responsible for the phenotypes associated with cell death . Rather these observations collectively demonstrate that acetate , a metabolic byproduct of glucose catabolism triggered these phenotypes under acidic pH . Although acetate is primarily produced in S . aureus by the phosphotransacetylase ( Pta ) -acetate kinase ( AckA ) pathway , this metabolic pathway is unlikely to be directly involved in cell death as its activity is evident even during growth in 14 mM glucose , a condition where cell death is not triggered [19] . Additionally , disruption of this pathway surprisingly enhanced the rate of cell death during growth despite a decrease in acetate production [19] . This led us to reason that acetate-dependent cell death must be controlled by an alternate pathway , such as CidC . The cidC gene encodes a pyruvate oxidase that directly converts pyruvate to acetate and carbon dioxide and its expression is partly under the control of the regulator , CidR , whose activity is up-regulated in response to excess glucose [7] , [19] . As the alsSD metabolic operon that results in the conversion of pyruvate to acetoin is also co-regulated by CidR , we hypothesized that both these pathways may modulate acetate-dependent cell death by competition for their common substrate , pyruvate , under conditions of excess glucose ( Fig . 3A ) . To test this hypothesis , we initially determined the levels of acetate and acetoin in 24 h culture supernatants of both ΔcidC and ΔalsSD mutants relative to WT . Compared to the wild-type strain , the ΔcidC mutant grown in TSB-35 mM glucose accumulated less acetate and relatively higher levels of acetoin ( Fig . 3B , 3C ) . Conversely the ΔalsSD mutant excreted an excess of acetate ( Fig . 3B ) suggesting that both pathways competitively displaced pyruvate . We then tested the effects of cidC and alsSD pathways on cell death . In agreement with earlier studies [17] , [20] , mutation of either of these pathways resulted in opposing survival trends in stationary phase . Accordingly , a metabolic block in CidC activity ( ΔcidC ) enhanced stationary phase survival , while that of AlsSD ( ΔalsSD ) resulted in an increased rate of cell death compared to the wild-type strain ( Fig . 3D ) . Consistent with the increased survival of the ΔcidC mutant and in contrast to WT and the ΔalsSD mutant , HPF-CTC double staining of 72 h cultures revealed a healthy population of respiring cells that exhibited low levels of ROS ( Fig . 3E , Table S1 ) , a phenotype that was also confirmed by EPR spectroscopy ( Fig . 3F ) . In addition , flow cytometry detected fewer TUNEL-positive cells in the ΔcidC mutant , suggesting decreased DNA damage in these cells ( Fig . 3G ) . Indeed , the cell death phenotypes associated with both ΔcidC and ΔalsSD mutants could be complemented in trans ( Fig . S3 ) confirming their role in modulating cell death . Taken together , these data support the hypothesis that both CidC and AlsSD pathways modulate cell death by controlling flux through the pyruvate node . Surprisingly , the ΔalsSD mutant generated ROS and exhibited DNA damage at levels similar to the wild-type strain ( Fig . 3E–G , Table S1 ) despite an increase in loss of viability ( Fig . 3D ) . This raised the possibility that in addition to affecting excreted acetate levels , there may be additional mechanisms by which the ΔalsSD mutant regulates cell death . To test this hypothesis we constructed a double ΔcidCΔalsSD mutant . We reasoned that if regulation of extracellular acetate by substrate competition was the primary mechanism by which AlsSD modulated cell death , then the ΔcidCΔalsSD double mutant would phenocopy the ΔcidC mutant . However , our results clearly demonstrate that the ΔcidCΔalsSD double mutant exhibited increased loss of viability ( Fig . 3D ) , increased generation of ROS ( Fig . 3E–F , Table S1 ) and excessive levels of DNA damage ( Fig . 3G ) than the ΔcidC mutant despite excreting comparable levels of acetate ( Fig . 3B ) . Such an effect may occur if mutation of alsSD caused cells to be more susceptible to lower concentrations of weak acids in the culture media . Indeed , growth of ΔalsSD mutants challenged with acetate , lactate or pyruvate was more easily inhibited than wild-type ( Fig . S4 ) . We tested two plausible hypotheses to explain the observed hyper-susceptibility of ΔalsSD mutants to weak acid stress . First , we argued that the end product of AlsSD catabolism , acetoin , itself may be necessary to withstand weak acid stress as it is known to contribute to the maintenance of cellular redox status upon being converted to butanediol or serve as a carbon source during stationary phase of growth . To test this hypothesis we subjected the ΔalsSD mutant to pyruvic acid stress and asked whether supplementation of excess acetoin in culture could rescue the pyruvate-mediated growth inhibition of this mutant . Our results demonstrate that acetoin could not restore pyruvate-mediated growth inhibition of the ΔalsSD mutant ( Fig . S5 ) . Furthermore , transformation of the ΔalsSD mutant with a plasmid bearing the alsS gene alone ( in the absence of its cognate partner , alsD ) under the control of its native promoter was able to rescue this mutant from pyruvic acid-mediated stress to growth rates comparable to the parental control , thus excluding any role for acetoin in promoting weak acid resistance ( Fig . 4A ) . We next hypothesized that AlsSD might play an active and crucial role in detoxifying intracellular acidification that accrues from the deprotonation of weak organic acids in the relatively neutral bacterial cytoplasm [21] . In such a scenario , intracellular protons would be consumed during multiple stages of decarboxylation , first of pyruvate into acetolactate catalyzed by AlsS , followed by that of the acetolactate intermediate into acetoin by AlsD , both leading to a gradual alkalization of the cytoplasm during weak acid stress . Direct evidence confirming a role for AlsSD in regulating intracellular pH was obtained by using cells loaded with the fluorescent pH probe 5 ( and 6- ) -carboxyfluorescein succinimidyl ester ( cFSE ) . Resting cells that were suspended in potassium phosphate buffer ( pH 4 . 5 ) maintained a slightly acidic interior ( pHinternal of 5 . 9; Fig . 4B , top ) , resulting in a transmembrane pH gradient ( ΔpH = pHinternal- pHexternal ) of approximately 1 . 4 units . Addition of pyruvate under these conditions initiated a pH gradient ( ΔpH ) decay across the membrane that was exacerbated in the ΔalsSD and ΔcidC ΔalsSD backgrounds compared to either the parental or ΔcidC strains ( Fig . 4B , bottom ) . Although the pH gradient decay recovered and stabilized over time , the rate and magnitude of the pHi recovery in different strains appeared to be dependent on the activity of AlsSD ( Fig . 4B ) . In the presence of pyruvate , both the parental control and the ΔcidC mutant displayed comparable recovery rates of ( 23 . 83±1 . 9 ) ×10−3 min−1 and ( 27 . 45±5 . 5 ) ×10−3 min−1 , respectively , and reached a pHi comparable to those of control cells ( untreated resting cells ) within 20 minutes ( Fig . 4B , 4C ) . In contrast , the pHi of both the ΔalsSD and ΔcidCΔalsSD mutants had stabilized approximately 0 . 2–0 . 3 units below that of the pyruvate treated parental control and exhibited significantly decreased pHi recovery rates ( P<0 . 05 ) of ( 11 . 66±2 . 1 ) ×10−3 min−1 and ( 5 . 24±3 . 8 ) ×10−3 min−1 , respectively leading to incomplete recovery from acidic stress even after 60 minutes ( Fig . 4B , 4C ) . These data demonstrate a role for the enzymatic activity of AlsSD in countering weak acid mediated intracellular acidification of the bacterial cytoplasm . In eukaryotes , there is increasing evidence that ROS plays a key role in mediating PCD [22] , [23] . Given that the physiological induction of ROS in S . aureus is dependent on the accrual of extracellular acetate , we next asked whether cell death is a direct consequence of acetic acid-mediated intracellular acidification or is an indirect result of oxidative stress . To this end we devised a strategy to determine the contribution of intracellular acidification on triggering cell death , independent of the ROS generated after 72 h of growth under aerobic conditions . Wild-type S . aureus was aerobically grown for 24 h in TSB-35 mM glucose , followed by a sudden shift to anaerobic conditions . While this process ensured as much acidification as aerobically grown cultures , it conveniently eliminated ROS ( due to the absence of oxygen ) . Although it is plausible that a sudden transition of cultures to anaerobic conditions may induce cell death independent of acidic stress , we controlled for this possibility by performing a similar experiment with wild-type S . aureus grown in TSB-35 mM glucose buffered to a pH of 7 . 3 with 50 mM MOPS . Cell viabilities monitored over a 120 h period showed only minimal loss of viability for neutrally buffered cultures , suggesting that cell death following anaerobic transition was dependent on culture acidification , similar to aerobically grown cells ( Fig . 5A ) . Most importantly , unbuffered cultures that were shifted to anaerobic conditions displayed a partial restoration of viability compared to their corresponding aerobic cultures ( Fig . 5A ) . Similarly a partial rescue was also observed when well-aerated cultures grown for 24 h were left standing without further agitation to minimize aeration ( Fig . S6 ) . Together , these data suggest a contributory role for oxidative stress in cell death . Consistent with trends in cell viability , HPF-CTC staining of 72 h unbuffered cultures confirmed the absence of ROS following transition to anaerobiosis and the presence of a respiring population in contrast to that observed for the same time period under aerobic conditions ( compare Fig . 5B with Fig . 1B , Table S1 ) . Surprisingly , there was also a dramatic reduction of TUNEL positive cells following the shift to anaerobiosis ( Fig . 5C ) , suggesting that ROS rather than intracellular acidification played a crucial role in DNA damage . Cell death in staphylococcal biofilms is often spatially restricted to developing microcolonies [24] . Given that the CidR regulon is also actively expressed in microcolonies [24] , we predicted that cell death may be modulated by both CidC and AlsSD activities and further contribute to the structural and developmental integrity of the maturing biofilm . To test these hypotheses , we assayed the ability of the ΔcidC and ΔalsSD mutants to form biofilms , relative to the wild-type strain on glass surfaces exposed to a continuous flow of nutrients . Wild type biofilms appeared as a confluent biomass frequently interspersed with microcolonies that differentiated from the primary biofilm mat . As previously noted , live/dead cell staining of wild-type biofilms confirmed that dead cell populations were predominantly localized within microcolonies ( Fig . 6A ) . However compared to the wild-type strain , COMSTAT analysis of ΔcidC biofilms revealed significantly decreased total biofilm biomass and thickness , indicative of developmental defects during biofilm formation ( Fig . 6B , 6C ) . Additionally , the roughness coefficients ( a measure of the biofilm architectural heterogeneity ) of the ΔcidC mutant biofilms were significantly lower than those of wild-type ( Fig . 6D ) , a phenotype that was also consistent with the decreased ability of the ΔcidC mutant to differentiate into microcolonies . Finally , the ΔcidC biofilm revealed significantly less dead cell biomass compared to the parental strain , strongly suggestive of its involvement in promoting cell death in biofilms ( Fig . S7 ) . Similar to the ΔcidC biofilm , COMSTAT analysis of biofilms formed by the ΔalsSD mutant also exhibited decreased biofilm biomass and thickness compared to the wild-type strain ( Fig . 6B , 6C ) . However it is unlikely that the observed decrease in biomass of one-day old ΔalsSD biofilms was due to an early developmental defect as they were able to differentiate into microcolonies and attain similar roughness coefficients to that of its isogenic wild-type strain ( Fig . 6D ) . Rather , these defects are consistent with increased sensitivity of the AlsSD mutant to weak acids and low pH environments of biofilm microcolonies . Collectively , these observations suggest that the activities of both CidC and AlsSD regulate cell death at the population level to achieve optimal biomass and structural integrity during biofilm development . Since the ability of bacteria to develop as biofilms on heart valves constitutes the root cause of infective endocarditis , we speculated that staphylococcal cell death may also contribute towards pathogenesis in a rabbit model of infective endocarditis . This model not only provides an estimate of the organism's biofilm forming capability in vivo but also allows for the simultaneous assessment of embolization ( dissemination of the bacterial vegetation to secondary sites due to blood flow associated shear forces in the heart ) . To test this hypothesis we induced left-sided endocarditis in rabbits and infected them with wild-type , ΔcidC and ΔalsSD mutant strains . At 48 h post-infection , the bacterial burden in the primary vegetation ( heart valves ) , heart tissue , kidney and blood were determined . Consistent with being the primary site of biofilm infection , the heart valves exhibited the maximum bacterial burden ( ∼108 cfu/gm of tissue ) among the various tissues harvested ( Fig . 6E ) . Relative to the wild-type strain , both ΔcidC and ΔalsSD mutants had similar bacterial loads at this site ( Fig . 6E ) suggesting that these metabolic pathways did not affect the growth of these strains or their ability to colonize the primary infection sites in vivo . Interestingly however , the ΔcidC mutant displayed significantly decreased bacterial burdens in the blood and other secondary sites of infection including heart tissue ( excluding valves ) and kidneys ( Fig . 6E ) . As bacterial colonization of these secondary sites primarily results from infectious emboli originating from the heart valve , it may be argued that the cell-death associated CidC pathway may play a role in metastasis of the valvular vegetation .
In the present study , we demonstrate that both CidC and AlsSD pathways , that have traditionally been considered metabolic routes for excess carbon flow , antithetically modulate staphylococcal cell death by regulating the levels of excreted acetic acid . Exercising such metabolic control over cell death affords S . aureus a means to modulate biofilm development and possibly disperse and colonize alternate sites during the course of biofilm-associated infections . How does acetate potentiate cell death ? Our results reveal that both intracellular acidification and ROS generation may play a role in acetate dependent cell death . Although intracellular acidification can result from other fermentative metabolites like D- or L-lactate , we were unable to detect excretion of L-lactate and observed only small differences in the excretion of D-lactate in the ΔcidC and ΔalsSD mutants relative to WT ( Fig . S8 ) . Furthermore , given that the pKa of D-lactate ( pKa = 3 . 86 ) is lower than acetate and its levels in culture supernatants were minute ( ∼35 fold less than acetate ) we reasoned that it is unlikely to have a similar effect to that of acetic acid on cell death during aerobic growth . At the molecular level , it is not clear how acetate initiates ROS production . The evidence presented in this study suggests that acetate may contribute to a bottle-neck in electron transport by reducing the functionality of the respiratory chain . This could catalyze the promiscuous reduction of molecular oxygen and result in the production of ROS . Consistent with this argument , we have confirmed increased levels of superoxide and hydroxyl radicals following growth of S . aureus in 35 mM glucose , a condition that leads to acetate stress . Given that the pKa of superoxide anion is 4 . 88 , it is very likely that this species freely traverses the cytoplasmic membrane and mediates oxidative damage during acetic acid stress . In addition to ROS , cytoplasmic acidification due to acetate influx itself can be a significant cause of cell death . Evidence for this conclusion arises from the observation that both metabolizable ( acetate , lactate and pyruvate ) and nonmetabolizable ( benzoate ) weak acids inhibit S . aureus growth . Such inhibition can result from acid catalyzed intracellular protein unfolding and aggregation . In support of this conclusion , a recent transcriptomic analysis of S . aureus challenged with the weak acid , lactate , revealed various clp genes ( including clpB , clpC and clpP ) involved in protein folding and recycling to be strongly up regulated [25] . Extending these findings to biofilms , we argue that the acidic pH microenvironments of biofilm microcolonies [26] , [27] may spatially bias these biological structures as sites of respiratory inhibition and cell death . Weak acid metabolic byproducts like acetate and lactate are thought to accumulate within the biofilm interior , primarily due to diffusion limits resulting from reduced fluid flow and accumulation of biofilm matrix components [28] . We propose a model of staphylococcal PCD wherein the acidic pH within microcolonies activates expression of the CidR regulon [29] in a subpopulation of cells . This subsequently would lead to a feed-forward loop in which toxic acetate levels are reached through CidC activity . Ultimately cell death would ensue when macromolecular repair mechanisms are exhausted and cells within the biofilm are overwhelmed by the damaging effects of acetate ( Fig . S9 ) . To prevent a disproportionate number of cells from undergoing cell death , S . aureus co-expresses the AlsSD pathway along with CidC . Enzymatic activity of acetolactate synthase ( AlsS ) results in the condensation of two molecules of pyruvate to acetolactate and thereafter to acetoin by acetolactatae decarboxylase ( AlsD ) . This effectively minimizes the carbon diverted to the generation of toxic acetate through the CidC pathway . More importantly evidence presented here also shows that the AlsSD pathway consumes protons from the cytoplasm and helps maintain pH homeostasis similar to the enterococci and lactobacilli [21] , [30] . We contend that the ability of AlsSD to antagonize CidC activity effectively results in the modulation of intracellular pH and constitutes a robust mechanism to limit cell death and optimize biomass within the microenvironment of a microcolony . The pathways that generate acetate vary among organisms . Whereas most bacteria , including E . coli and S . aureus generate acetate under aerobic conditions through the Pta-AckA and the CidC pathways , most eukaryotes ( yeasts and mammals ) lack these enzymes . Instead yeasts produce acetate as a natural byproduct of ethanol fermentation from acetaldehyde using acetaldehyde dehydrogenase [31] . Mammalian cells rarely generate significant quantities of acetate . But under certain conditions , acetate is produced by the enzymatic hydrolysis of acetyl-CoA in the cytoplasm [32] . Irrespective of the diversity in production routes , multiple studies have demonstrated that acetate itself can act as a potent inducer of PCD in yeasts and mammalian cells [33] , [34] . Consistent with these studies , we demonstrate multiple hallmarks of eukaryotic PCD including respiratory dysfunction , generation of ROS and DNA fragmentation to be conserved during acetate mediated cell death in S . aureus . Further , similar to eukaryotic PCD [1] , acetate-mediated cell death functions in a developmental context and appears to be crucial for optimal staphylococcal biofilm development . It is noteworthy that two different activities of pyruvate oxidase ( CidC , also annotated as PoxB in E . coli and SpxB in S . pneumoniae ) have been described previously [35] , [36] . In E . coli and S . aureus , this enzyme catalyses the decarboxylation of pyruvate into acetic acid and carbon dioxide , whereas acetyl phosphate and hydrogen peroxide are the predominant products of a similar reaction in L . plantarum and S . pneumoniae . Remarkably , both these reactions appear to induce stationary phase cell death in bacteria with either acetate or hydrogen peroxide as principal determinants of cell death [37] . Additionally , similar to S . aureus , cell death due to pyruvate oxidase activity is associated with apoptotic hallmarks in S . pneumoniae [37] . These observations appear to clearly mark pyruvate oxidase activity as a suicidal marker in bacteria . Finally , what are the biological implications of regulating PCD ? We used a well-established rabbit model of infective endocarditis to assess the effects of altering PCD on in vivo biofilm development . S . aureus injected intravenously in rabbits is rapidly cleared from the blood within the first 30 minutes leaving only minute residual amounts to linger over longer periods of time [38] . However any injury to the heart valves marks a preferred site for bacterial colonization and eventual development into a biofilm ( vegetation ) . The pathological progression of infective endocarditis subsequently involves embolization of bacterial vegetations to alternate sites including surrounding heart tissues and other peripheral organs like the brain and kidneys [39] . This process not only poses a constant seeding source of infection but also hinders the normal functioning of peripheral organs and is often associated with a high degree of mortality [39] . Our investigations failed to reveal a colonization defect of the ΔcidC and ΔalsSD mutants on heart valves relative to the wild-type strain . However we observed a significant decrease in ΔcidC burdens in the blood , heart and kidneys after 48 h of infection . Although not conclusive , these findings strongly suggest that the ΔcidC mutant had lower rates of dissemination to secondary infection sites in vivo . Alternately , it is also possible that the ΔcidC mutant exhibits tissue specific fitness and survival defects in vivo . Either way , these findings suggest that alterations to the activity of cell-death associated metabolic pathways during biofilm development could affect staphylococcal pathogenesis . In conclusion , the activation of pathways that generate metabolic acids from glucose during carbon-overflow and oxygen replete conditions have long been considered paradoxical in bacteria that are capable of undergoing oxidative phosphorylation , as it results in low energy yields , potentially toxic acid by-products and activation of cell death pathways [40] . Based on the current study we propose that the extracellular accumulation of metabolic acids is a developmental strategy that bacteria undertake to initiate cell death , a necessary precursor to optimal biofilm development . The initiation of staphylococcal cell death by intracellular acidification bears some striking resemblance to eukaryotic PCD . For instance , the dimerization and insertion of the pro-apoptotic modulator , Bax , into the membrane is thought to be triggered by intracellular acidification of eukaryotic cells [41] just prior to the release of cytochrome c into the cytoplasm . In this regard , it is possible that membrane oligomerization of CidAB and LrgAB ( functional analogs of Bax and Bcl-2 in S . aureus , respectively ) may also be initiated following intracellular acidification . Additionally , cytoplasmic acidification in eukaryotes also activates caspases , essential components of the apoptotic pathway [42] . Collectively , these observations are suggestive of a conserved role for glycolysis-mediated intracellular pH regulation in the modulation of PCD in eukaryotes and prokaryotes .
Animal experiments were conducted in compliance with a protocol ( # 12-048-08-FC ) approved by the Institutional Animal Care and Use Committee ( IACUC ) . The University of Nebraska Medical Center is accredited by the Association of for Assessment and Accreditation of Laboratory Animal Care International ( AALAC ) . In addition , all animals at the University of Nebraska Medical Center are maintained in accordance with the Animal Welfare Act and the DHHS “Guide for the Care and Use of Laboratory Animals . ” Strains and plasmids used in this study are listed in Table S2 . The ΔcidCΔalsSD double mutant was created by moving the ΔcidC::erm allele from KB1058 into the ΔalsSD mutant ( UAMS-1489 ) using bacteriophage Φ11-mediated transduction . In addition to growth on selective antibiotic media , the ΔcidC transductants were confirmed phenotypically and by PCR using the following primer pairs: cidC UP ( 5′-CACATGCATTTGGCACAGCT-3′ ) and cidC DN ( 5′-TGCTCATGCCTGCATTACCA-3′ ) . The plasmid , pVCT2 , containing the alsS gene with its native promoter was constructed by amplifying an approximately 2-kb region from the UAMS-1 genome using the primers , alsS-comp-F ( 5′-GATCGAGCTCTCCCTTATAATCACTCCCTTCA-3′ ) and alsS-comp-R ( 5′-AGTCTCTAGATGTGCCTAATGTACCATGTTG-3′ ) , and inserting the resulting DNA fragment into the Sac1 and Xba1 sites of the shuttle vector , pLI50 [43] . Similarly , plasmid pVCT3 ( containing cidC gene with its native promoter ) was amplified from a ΔcidAB double mutant using primers , cidC comp-F ( 5′-GATCGAATTCACTCATTATTTGTGATTCCTCA-3′ ) and cidC comp-R ( 5′-AGTCGTCGACCAATTCAGTACAATCATTTGTG-3′ ) . The resulting amplification product was inserted into the EcoR1 and Sal1 sites of pLI50 . Subsequently , both pVCT2 and pVCT3 were transformed into RN4220 and transduced into the ΔalsSD and ΔcidC mutants respectively , using bacteriophage ϕ11 for phenotypic complementation . E . coli cultures were grown in Luria Bertani ( LB ) broth . S . aureus cultures were grown in trypticase soy broth ( TSB ) supplemented with 35 mM glucose ( unless specified otherwise ) . Bacterial cultures were aerobically grown at 37°C in either Erlenmeyer flasks fitted with bug stoppers to minimize evaporation during long-term growth or in 96-well flat , clear bottom micro-titer plates . For anaerobic growth , cultures were supplemented with cysteine ( 0 . 5 mg/ml ) and 10 mM nitrate ( or fumarate ) and agitated at 250 rpm in an anaerobic hut . When necessary , antibiotics were added to cultures as follows: ampicillin ( 100 µg/ml ) ; erythromycin ( 5 µg/ml ) ; tetracycline ( 10 µg/ml ) ; and chloramphenicol ( 10 µg/ml ) . All analyses were performed using 1- and 3- day old stationary phase cultures of S . aureus on a BD LSRII flow cytometer ( Beckton and Dickinson , San Jose , California ) . Cell samples were washed twice and diluted to a final concentration of 107 cells per ml in PBS . Cells were stained for 30 min with 5-cyano-2 , 3-ditolyl tetrazolium chloride ( CTC , 5 mM ) and 3- ( p-hydroxyphenyl ) fluorescein ( HPF , 15 µM ) followed by FACS analyses at a flow rate of ∼1000 cells per second . A total of 10000 events were collected for each sample . Bacteria were discriminated from background using a combination of forward scattered ligt ( FSC ) and side scattered light ( SSC ) . Samples were excited at 488 nm using an argon laser and HPF emission was detected at 530±30 nm ( with a 505 nm long-pass mirror ) whereas CTC emission was detected at 695±40 nm ( with a 685 nm long-pass mirror ) . Raw data were analyzed using the FlowJo software . Quantitative assessment of DNA fragmentation was performed using the ApoDirect kit ( BD bioscience ) . Samples were collected at the appropriate time points and fixed in 1% paraformaldehyde for 30 minutes . Cells were then washed twice in PBS , resuspended in 70% ethanol and stored at −20°C . Subsequent labeling of 3-OH ends of fragmented DNA was performed according to the manufacturer's instructions . Flow cytometry to detect TUNEL positive cells was performed as previously described [4] . Aliquots from stationary phase cultures ( 1- and 3 days ) were withdrawn and resuspended to an OD600 of 10 units in 1 ml KDD buffer ( Krebs-HEPES buffer , pH 7 . 4; 99 mM NaCl , 4 . 69 mM KCl , 2 . 5 mM CaCl2 , 1 . 2 mM MgSO4 , 25 mM NaHCO3 , 1 . 03 mM KH2PO4 , 5 . 6 mM D-glucose , 20 mM HEPES , 5 µM DETC and 25 µM deferoxamine ) . The resuspended culture aliquots were then incubated with 200 µM of cell-permeable ROS sensitive spin probe 1-hydroxy-3-methoxycarbonyl-2 , 2 , 5 , 5-tetramethylpyrrolidine ( CMH; Noxygen Science Transfer and Diagnostics , Elzach , Germany ) for 15 minutes at room temperature prior to analysis using a Bruker e-scan EPR spectrometer with the following settings: field sweep width , 60 . 0 gauss; microwave frequency , 9 . 75 kHz; microwave power , 21 . 90 mW; modulation amplitude , 2 . 37 gauss; conversion time , 10 . 24 ms; time constant , 40 . 96 ms . To identify the nature of ROS produced , cells resuspended in KDD buffer were incubated with either 400 units of superoxide dismutase ( SOD; O2•− scavenger ) or cell permeable dimethyl thiourea ( 20 mM DMTU; OH• scavenger ) prior to the addition of CMH . S . aureus was cultured at 37°C in TSB supplemented with either 14 or 35 mM glucose and aerated at 250 rpm with a flask-to-medium ratio of 10∶1 for 24 h . Cultures were subsequently diluted to an OD600 of 0 . 1 in fresh TSB ( 14 mM glucose ) . Oxygen consumption rates were determined for a period of 30 minutes at 37°C by using a MitoXpress oxygen-sensitive probe ( Luxcel Biosciences ) according to the manufacturer's instructions . The data were normalized to the corresponding OD600 units . For these analyses , bacterial growth was allowed to proceed at 37°C and 200 rpm in BugStopper-sealed flasks containing TSB ( 35 mM glucose ) in a 1∶10 , flask to volume ratio . Preliminary experiments suggested that the assayed metabolite by-products were not significantly consumed following exhaustion of glucose from the media for up to 24 h . Therefore metabolite excretion profiles were determined from culture supernatants that were harvested at 24 h of growth . Acetate and glucose from culture supernatants were measured using commercial kits ( R-Biopharm , Marshall , MI ) , according to the manufacturer's instructions . Acetoin was measured as previously described [44] . Overnight grown ( 16 to 18-hr ) S . aureus cultures were resuspended to an OD600 of 0 . 06 in TSB ( 35 mM glucose ) . Bacterial suspensions were dispensed into 96-well microtiter plates and grown for 24 h at 37°C in a Tecan infinite 200 spectrophotometer under maximum aeration . The absorbance signals ( OD600 ) were recorded every 30 minutes for the entire period of growth . For various experiments bacteria were challenged with the following compounds ( final concentrations ) : acetic acid ( 30 mM ) , lactic acid ( 40 mM ) , pyruvic acid ( 30 mM ) and acetoin ( 10 mM ) . Intracellular pH was determined as previously described [30] with the minor modifications . Briefly , bacterial cells were grown in TSB ( 35 mM glucose ) and 1 mL was harvested by centrifugation upon reaching an OD600 of 2 . Cells were washed twice with an equal volume of 10 mM potassium phosphate buffer ( pH 7 ) and resuspended in an equal volume of the same buffer . To load cells with the intracellular pH probe , 10 µl of 1 mM CFDA SE ( 5- ( and 6 ) - carboxyfluorescein diacetate succinimidyl ester ) was added to the suspension and incubated for 15 minutes at 30°C . Excess dye was removed by incubating the cells for 15 minutes at 30°C in potassium phosphate buffer ( pH 7 ) containing 10 mM glucose . Cells were subsequently washed twice in the same buffer and finally resuspended in 50 mM potassium phosphate buffer ( pH 4 . 5 ) . Labeled cells were kept on ice until use . To measure intracellular pH , 100 µl of the labeled cell suspensions were introduced into a 96-well flat bottom , black polystyrene plate ( COSTAR 3916 ) . Fluorescence was measured using a Tecan Infinite 200 spectrofluorimeter with excitation and emission wavelength set at 490 nm and 525 nm , respectively . Fluorescence emission units were converted to pH units using a standard calibration curve derived from labeled cells whose internal pH was equilibrated to the external pH ( in citric acid buffers ) ranging from pH 4 to 8 , by the addition of 1 mM valinomycin and 1 mM nigericin . Biofilms were grown in either FC280 or FC285 flow-cell systems ( Biosurfaces Technology Inc , Bozeman , MT ) and were analyzed by CLSM as described previously [45] . Briefly , biofilms stained with SYTO-9 ( 1 . 3 µM final concentration ) and TOTO-3 ( 2 µM final concentration ) fluorophores were excited with the 488 nm and 633 nm lasers respectively , and the emissions were collected using a 525±25 nm and 680±30 nm band-pass filter . For pictorial representation , the biofilms were imaged using an Achroplan 40×0 . 8 n . a . water dipping objective and for COMSTAT image analysis , images were acquired using a 20×1 . 2 n . a . dry objective to achieve a larger biofilm surface area for statistical purposes . Regions of interest within the biofilms were selected from similar areas within each flow-cell chamber and each confocal experiment was repeated a minimum of three times . Biofilm architecture was characterized using the COMSTAT software and measures of total biomass , average thickness , maximum height and roughness coefficients were determined [46] . Images were rendered using Imaris software ( Bitplane , Saint Paul , MN ) . Experimental endocarditis on the aortic valve of female New Zealand White rabbits ( 3 kg ) were carried out as previously described with minor modifications [47] . Briefly , rabbits were anesthetized by intramuscular injection of ketamine hydrochloride ( 35–50 mg/kg ) , xylazine ( 2 . 5–6 mg/kg ) and atropine ( 0 . 005–0 . 01 mg/kg ) cocktail . An incision was made dextrolateral to the trachea and a polyethylene catheter ( Becton Dickinson , MD ) was then introduced into the left ventricle via the right carotid artery to produce sterile thrombotic endocarditis , and the skin incision sutured . To induce bacterial endocarditis , animals were intravenously challenged with 1 ml inocula ( 105 cfu/ml ) via the marginal ear vein 24 h after catheterization . The animals were challenged with either the wild-type or mutant strains ( ΔcidC and ΔalsSD ) and subsequently ( 48 h post-infection ) euthanized by lethal injection of a solution containing sodium pentobarbital ( 200 mg/kg ) . Bacterial loads from various tissue homogenates were determined by serial dilutions on THB agar plates .
|
Many bacterial species including the pathogen Staphylococcus aureus are capable of adhering to surfaces and forming complex communities called biofilms . This mode of growth can be particularly challenging from an infection control standpoint , as they are often refractory to antibiotics and host immune system . Although developmental processes underlying biofilm formation are not entirely clear , recent evidence suggests that cell death of a subpopulation is crucial for its maturation . In this study we provide insight regarding the metabolic pathways that control cell death and demonstrate that acetate , a by-product of glucose catabolism , potentiates a form of cell death that exhibits physiological and biochemical hallmarks of apoptosis in eukaryotic organisms . Finally , we demonstrate that altering the ability of metabolic pathways that regulate acetate mediated cell death in S . aureus affects the outcome of biofilm-related diseases , such as infective endocarditis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biofilms",
"bacteriology",
"bacterial",
"physiology",
"bacterial",
"biofilms",
"ecology",
"biology",
"and",
"life",
"sciences",
"microbiology",
"microbial",
"ecology"
] |
2014
|
A Central Role for Carbon-Overflow Pathways in the Modulation of Bacterial Cell Death
|
Human African trypanosomiasis ( HAT ) , also known as sleeping sickness , persists as a public health problem in several sub-Saharan countries . Evidence-based , spatially explicit estimates of population at risk are needed to inform planning and implementation of field interventions , monitor disease trends , raise awareness and support advocacy . Comprehensive , geo-referenced epidemiological records from HAT-affected countries were combined with human population layers to map five categories of risk , ranging from “very high” to “very low , ” and to estimate the corresponding at-risk population . Approximately 70 million people distributed over a surface of 1 . 55 million km2 are estimated to be at different levels of risk of contracting HAT . Trypanosoma brucei gambiense accounts for 82 . 2% of the population at risk , the remaining 17 . 8% being at risk of infection from T . b . rhodesiense . Twenty-one million people live in areas classified as moderate to very high risk , where more than 1 HAT case per 10 , 000 inhabitants per annum is reported . Updated estimates of the population at risk of sleeping sickness were made , based on quantitative information on the reported cases and the geographic distribution of human population . Due to substantial methodological differences , it is not possible to make direct comparisons with previous figures for at-risk population . By contrast , it will be possible to explore trends in the future . The presented maps of different HAT risk levels will help to develop site-specific strategies for control and surveillance , and to monitor progress achieved by ongoing efforts aimed at the elimination of sleeping sickness .
Human African trypanosomiasis ( HAT ) , or sleeping sickness , is a vector-borne disease caused by two sub-species of the parasitic protozoa Trypanosoma brucei ( i . e . T . b . gambiense and T . b . rhodesiense ) . Trypanosomes are transmitted to humans by the infected bite of various species of tsetse fly ( genus Glossina ) . Transmission of the disease only takes place in sub-Saharan Africa , in discrete areas of endemicity , or ‘foci’ , within the geographic distribution of the tsetse fly . The Gambian form of sleeping sickness is normally characterized by a long asymptomatic period and it is found in western and central Africa . The Rhodesian form , which is encountered in eastern and southern Africa , displays a much more rapid onset of overt symptoms and a faster progression . In the early 1960s , the reported incidence of the disease was at a trough , with only a few thousand cases being reported annually . However , a decline in surveillance in the post-independence period allowed sleeping sickness to regain ground . By the end of the 20th century , the World Health Organization ( WHO ) estimated that 300 , 000 people contracted the infection every year [1] . Since then , a global alliance led by WHO set elimination as the goal of its strategy against HAT [2] , [3] . This renewed commitment by international and national institutions , including the private sector , succeeded in reverting the trend . As compared to the peak in 1998 , when 37 , 991 new cases of HAT had been reported at the continental level , 6 , 743 cases were reported in 2011 , corresponding to a reduction of 82 . 3% . Also , many countries considered as endemic have not reported any cases in recent years [4] . The magnitude of the recent advances in HAT control and surveillance is such that up-to-date estimates of the number and geographic distribution of people at risk are urgently needed . In the past , estimates of sleeping sickness risk at the continental , regional and national levels could only be based on educated guess and rough estimations of experts , rather than on a clearly laid out , objective analysis of the epidemiological evidence . In 1985 , a WHO Expert Committee indicated that a population of 78 . 5 million was at risk of HAT in sub-Saharan Africa [5] . This figure was based on national-level information provided by the Ministries of Health of affected countries . In 1995 , a new WHO Expert Committee indicated that 60 . 8 million people were at risk of contracting sleeping sickness [1] , thus providing what was , to date , the latest global estimate of HAT risk . To derive this latest figure , a semi-quantitative method was used , whereby rural populations involved in agricultural activities within known HAT transmission areas were considered at risk . In both estimates , subjectivity remained high and the link to the epidemiological evidence loose . Since the latest estimations were made , HAT control and surveillance were scaled up [6] , and data collection and reporting were substantially improved , with WHO coordinating the efforts of the National Sleeping Sickness Control Programmes ( NSSCPs ) , bilateral co-operation , Non-Governmental Organizations ( NGOs ) , Research Institutes and the private sector [7] . Also , over the last 10 to 15 years , the increased availability and utilization of the Global Positioning System ( GPS ) , remote sensing data and Geographical Information Systems ( GIS ) triggered the development of novel , more objective methodologies to map the risk of many diseases [8] , [9] , [10] , [11] . Till recently , geospatial analysis had never been used to estimate HAT risk at the regional or African scale . In 2008 , the Atlas of HAT was launched , aiming at assembling , harmonizing and mapping datasets on the geographic distribution of sleeping sickness in sub-Saharan Africa [12] . Comprehensive and accurate epidemiological maps were generated [4] , [13] , which laid the foundations for more objective , evidence-based estimations of sleeping sickness risk . Thereafter , a GIS-based methodology for risk estimation was developed and tested in six Central African countries [14] . In this methodology , harmonized epidemiological data and global human population layers were combined , thus enabling different levels of HAT risk to be estimated and mapped . ‘Risk’ was regarded as the likelihood of infection , and the likelihood was estimated as a function of disease intensity and geographical proximity to HAT reported cases . In the present study , the methodology tested in the six Central African countries was applied at the continental level in order to map the risk of sleeping sickness in sub-Saharan Africa and to estimate at-risk population . In an effort to generate comparable estimates for both T . b . gambiense and T . b . rhodesiense infections , the same methodology was applied to all HAT-endemic countries and to both forms of the disease .
Georeferenced layers of sleeping sickness occurrence and human population for the period 2000–2009 constituted the input for the present HAT risk mapping exercise . The number and the geographic distribution of HAT cases were provided by the latest update of the Atlas of HAT ( reference date: 31 May 2012 ) , thus including 170 , 492 cases of T . b . gambiense infection and 5 , 084 of T . b . rhodesiense , for a total of 175 , 576 HAT reported cases . Reported cases originated from twenty countries , namely Angola , Cameroon , Central African Republic , Chad , Congo , Côte d'Ivoire , Democratic Republic of the Congo , Equatorial Guinea , Gabon , Ghana , Guinea , Kenya , Malawi , Mozambique , Nigeria , Sudan , Uganda , United Republic of Tanzania , Zambia and Zimbabwe [4] . The Atlas provided village-level mapping for 81 . 0% of the cases , corresponding to 19 , 828 different locations mapped . The average spatial accuracy for reported cases mapped was estimated at ≈1 , 000 m using methods already described [4] . For the remaining 19 . 0% of the cases , village-level information was unavailable but the area of occurrence was known ( e . g . focus , parish , health zone , etc . ) . For the purpose of risk estimation , these cases were apportioned among the endemic villages of their area of occurrence by means of proportional allocation [14] . Reported cases also included those diagnosed in non-endemic countries – most notably in travellers and migrants – which in the Atlas of HAT are mapped in the probable place of infection and flagged as ‘exported’ [15] . For T . b . rhodesiense exported cases , the place of infection most frequently corresponds to a park or another type of protected area . For the sole purpose of risk estimation , T . b . rhodesiense exported cases were randomly distributed within the boundaries of their respective park/protected area of origin . The geographic distribution of human population was derived from Landscan ™ databases [16] . Landscan provides global grids where census counts are allocated to grid nodes on the basis of probability coefficients . The spatial resolution of Landscan is 30 arcseconds ( ≈1 km at the equator ) , and the population layer is updated on a yearly basis . To delineate risk areas , an average of the ten Landscan population datasets from 2000 to 2009 was used . Subsequently , Landscan 2009 was combined with the risk map to provide estimates of people at risk at the end of the study period [14] . Both input layers ( i . e . sleeping sickness cases and human population ) can be regarded as spatial point processes , and thus amenable to spatial smoothing . Spatial smoothing methods are used in epidemiology to facilitate data analysis , and they allow to transform point layers into continuous surfaces of intensity . In this context , the intensity λ ( s ) of a point process is the mean number of events per unit area at the point s [17] . The term ‘event’ is used to distinguish the location of an observation ( si ) from any other arbitrary location s within a study region R . Spatial smoothing techniques can be based on localized averages or more complex , three-dimensional mathematical functions ( e . g . kernels ) , but they all rely on a moving window , whose size and shape determines how far the effect of an event will reach [18] . For this study , intensity was estimated through a kernel function k ( · ) , so that the intensity estimate could be expressed as:Here , s was a location anywhere in the study region R , s1 , . . , sn were the locations of the n observed events , and k ( · ) represented the kernel weighting function . τ>0 is normally referred to as the bandwidth or search radius , and si were the events that lay within the area of influence as controlled by τ . There are various shapes of kernel to choose from , all usually represented by symmetric bivariate functions decreasing radially . The choice of shape has relatively little effect on the resulting intensity estimate [19] , [20] and we used a quadratic kernel [20] . A more important choice is the selection of the bandwidth τ , the rule being that the higher τ , the smoother the intensity surface . Although different techniques are available for selecting τ [21] , [22] , no optimal value exists , and characteristics of the biological process under study are often better suited to guide the choice , so that the smoothed surface provide insights into the underlying data [18] . By taking into account the epidemiological features of HAT , the behaviour of the tsetse vector and the mobility of people in the average rural African milieu where HAT occurs , a search radius of 30 km was chosen [14] . In particular , a few studies investigated the daily distance covered by people living in HAT foci [23] , [24] , [25] and revealed that this tends not to exceed 15 km . The distance of 30 km enabled to take into account , at least in part , also people's movements that do not occur on a daily basis . Figure 1 provides a three-dimensional illustration of the output of spatial smoothing . In the example , the point layer used as input comprised one single ‘event’ ( i . e . one HAT case ) localized at the centre of the grid . Prior to spatial smoothing , the number of HAT cases reported in 2000–2009 was divided by ten , thus providing the average number of cases per annum ( p . a . ) . Similarly , Landscan human population layers from 2000 to 2009 were averaged [14] . Both averaged layers were subjected to spatial smoothing using the same quadratic kernel function . Importantly , both intensity surfaces were generated using the same 30 km bandwidth [26] . Spatial smoothing resulted in the two surfaces and , which represent the average annual estimates of disease intensity and population intensity respectively . The input to and output of spatial smoothing are exemplified in Figure 2 . The ratio between the intensity of HAT cases and the population intensity can be defined as the disease risk [18] , so that a risk function was estimated as:Thresholds were applied to the risk function in order to distinguish and map different categories of risk , ranging from ‘very low’ to ‘very high’ ( Table 1 ) . Outside the areas mapped as at risk of HAT , i . e . in areas where <1 HAT case per 106 inhabitants p . a . was reported , the risk to contract the disease was considered ‘marginal’ . These marginal areas were not taken into account further in this study . The term ‘marginal’ was chosen because , in such areas , risk could not be considered as non-existent , since residents of these zones could still expose themselves to infection if visiting transmission areas . The map depicting the different categories of HAT risk was combined with Landscan 2009 dataset to estimate the number of people at risk at the end of the study period [14] .
A total of 57 million people are estimated to be at risk of contracting Gambian sleeping sickness ( Table 4 ) . This population is distributed over a surface of 1 . 38 million km2 ( Table 2 ) . Approximately 19 . 6 million ( 34 . 4% ) of the people at risk live in areas classified at moderate risk or higher , which correspond to areas reporting ≥1 HAT case per 104 inhabitants p . a . The remaining 65 . 6% ( ≈37 . 4 million ) live in areas classified at low to very low risk . Central Africa accounts for the vast majority of people at risk of T . b . gambiense infection ( Figure 3 ) . The risk patterns in Cameroon , Central African Republic , Chad , Congo , Equatorial Guinea , and Gabon have already been described in some detail elsewhere [14] . In essence , areas at very high to high risk are localized in southeastern and northwestern Central African Republic , southern Chad , along lengthy stretches of the Congo river north of Brazzaville , and by the Atlantic coast on both sides of the border between Gabon and Equatorial Guinea . The Democratic Republic of the Congo is , by far , the country with the highest number of people at risk ( ≈36 . 2 million ) and the largest at-risk area ( ≈790 thousand km2 ) . Areas at risk can be found in the provinces of Bandundu , Bas Congo , Équateur , Kasai-Occidental , Kasai-Oriental , Katanga , Kinshasa , Maniema , Orientale , and South Kivu . More details on the risk and the geographic distribution of sleeping sickness in the Democratic Republic of the Congo will be provided in a separate paper . In South Sudan , a sizable area ( ≈100 thousand km2 ) and over a million people are estimated to be at risk of sleeping sickness , including a number of high to very high risk areas in Central and Western Equatoria provinces . These findings highlight the need for continued surveillance in this country [27] . In neighbouring Uganda , the area at risk of T . b . gambiense infection ( ≈17 thousand km2 ) is located in the North-west of the country . It mostly falls in the category ‘moderate’ , and it accounts for over two million people at risk . In Angola , sleeping sickness is found in the northwestern part of the country ( ≈180 thousand km2 – 4 . 8 million people at risk ) , and most of the high-risk areas are located in the Provinces of Bengo , Kwanza Norte , Uige and Zaire . In western Africa , the most affected endemic areas are categorized at moderate risk and they are localized in costal Guinea and central Côte d'Ivoire ( Figure 4 ) . Areas at lower risk fringe the main foci , but they are also found in other zones such as southern Guinea and southern Nigeria . Rhodesian sleeping sickness is estimated to threaten a total of 12 . 3 million people in eastern and southern Africa ( Table 5 ) . This population is distributed over a surface of 171 thousand km2 ( Table 3 and Figure 5 ) . Of the total population at risk of T . b . rhodesiense , a minor proportion ( ≈1 . 4 million – 11 . 8% ) live in areas classified at moderate risk or higher , the rest ( ≈10 . 9 million – 88 . 2% ) live in areas classified at low to very low risk . In Uganda , Rhodesian HAT threatens a population of ≈7 . 9 million , and the risk area ( 29 thousand km2 ) stretches from the northern shores of Lake Victoria up to Lira District , north of Lake Kyoga . The areas in Uganda where risk is relatively higher ( i . e . ‘moderate’ ) broadly correspond to the districts of Soroti , Kaberamaido and northwestern Iganga . Because of a comparatively lower human population density , some areas in the United Republic of Tanzania are estimated to be characterized by higher levels of risk than Uganda , despite fewer reported cases of HAT . In particular , risk is estimated to be high in proximity to the Ugalla River Forest Reserve ( Tabora Province ) . Also all of the other risk areas in the United Republic of Tanzania are associated in one way or another to protected areas , most notably the Moyowosi Game Reserve and the natural reservations in the northeast of the country ( i . e . Serengeti , Ngorongoro and Tarangire ) . Overall , ≈1 . 8 million people ( 66 thousand km2 ) are estimated to be at risk in this country . In Kenya , HAT risk ranging from low to very low is localized in the western part of the country , adjacent to risk areas in neighbouring Uganda . Also , although no cases were reported from the Masai Mara National Reserve during the study period , part of its area is estimated to be at risk , as influenced by the risk observed in the neighbouring Serengeti National Park ( United Republic of Tanzania ) . Interestingly , two cases have been reported recently ( 2012 ) in travellers visiting the Masai Mara [28] . Nature reserves also shape the patterns of HAT risk at the southernmost limit of T . b . rhodesiense distribution , most notably in Malawi , Zambia and Zimbabwe . In this region , the highest number of people at risk is found in Malawi ( ≈0 . 9 million people ) , where risk is associated to the wildlife reserves of Vwaza Marsh , Nkota-Kota , and the Kasungu National Park . In Zambia ( ≈0 . 4 million people at risk ) , risk areas are scattered across the country , predominantly in the East and most notably around the North and South Luangwa National Parks . In Zimbabwe , an area of 7 . 8 thousand km2 is estimated to be at risk ( 94 thousand people ) . This risk zone in associated to the Mana Pools National Park and the Lake Kariba .
Approximately 70 million people ( 1 . 55 million km2 ) are estimated to be at various levels of HAT risk in Africa . This corresponds to 10% of the total population and 7 . 4% of the total area of the endemic countries . This figure is not far from estimates made by WHO over the last thirty years , ( 78 . 54 million in 1985 [5] and 60 million in 1995 [1] ) . However , the meaning and interpretation of these various figures substantially differ , and it is unwarranted to make comparisons between the results of the present study and previous figures , especially if the goal is to explore trends . In the early 80 s , the only way to derive country- and continental-level estimates of people at risk of HAT was to collate heterogeneous information from the Ministries of Health of the affected countries [5] . A decade later , an attempt was made to update the estimates [1] , but the degree of subjectivity in the methodology and the reliance on expert opinion remained high . By contrast , the present methodology is quantitative , reproducible , based on evidence and provides a categorization of risk . The use of global human population layers [16] and the regular update of the Atlas of HAT [4] will enable regular and comparable updates to be made . The presented maps of different HAT risk categories will help to plan the most appropriate site-specific strategies for control and surveillance , and they will contribute to ongoing efforts aimed at the sustainable elimination of the sleeping sickness . However , the reported incidence levels underpinning the different risk categories differ by orders of magnitude , so that a more accurate representation of HAT risk can be given by focusing on the different risk categories . For example , 21 million of people ( 0 . 7 million km2 ) are estimated to live at ‘moderate’ to ‘very high’ risk of infection . These are the areas where the most intensive control measures need to be deployed . Low to very low risk categories account for ≈48 million people ( 0 . 8 million km2 ) . In these areas , cost-effective and adapted measures must be applied for a sustainable control . From the methodological standpoint , assumptions affect all estimates of disease risk , including those presented in this paper . One important assumption in the proposed methodology is that it is possible to use the same approach based on human cases of trypanosomiasis to estimate risk of both forms of sleeping sickness . This assumption met the primary goal of generating continental risk estimates in a consistent fashion . However , especially for T . b . rhodesiense , different approaches could be explored , explicitly addressing the pronounced zoonotic dimension of this form of the disease . Another important choice in the proposed methodology is that of the 30 km bandwidth – the distance from affected locations beyond which disease intensity is considered zero . Sensitivity analysis conducted for six central African countries showed that there is a positive linear relationship between bandwidth on the one hand , and the extent of risk areas and the at-risk population on the other [29] , [30] . However , the categories at higher risk were shown to be the least affected by bandwidth . Therefore , as a rule , increasing the bandwidth would inflate the low-risk categories , but it would have a more limited effect on the delineation of areas at higher risk . The estimates presented here also rest on the assumption of isotropy for the risk function . In the future , anisotropy may be explored in an effort to account for the linear nature of some important landscape features such as rivers or roads . When interpreting the presented risk estimates it is important to acknowledge the uncertainty inherent in the human population datasets used as denominator [31] . Also , it has to be borne in mind that no attempt was made to model HAT under-detection and under-reporting , which , despite recent progress in surveillance [12] , are still known to occur . HAT under-detection can occur both in areas covered by active or passive surveillance and in areas that , because of remoteness or insecurity , are off the radar of health care services , and therefore sometimes referred to as ‘blind spots’ . These two types of under-detection are expected to have different effects on risk estimation and mapping . The former is likely to impinge mainly on the level of risk , with a limited effect on the delineation of risk areas and on the estimates of the total population at risk . By contrast , if under-detection occurs in zones were no surveillance is in place , a few areas at risk will fail to be captured and mapped , which is bound to result in underestimation of the total population at risk . In the proposed risk mapping methodology , the latter areas would have been included in the ‘marginal’ risk category . Efforts should be made to identify and accurately delineate these hypothetical transmission zones , finding adaptive strategies to cope with the constraints of remoteness and insecurity that affect them . Knowing the true epidemiological status of these areas has vast implications not only for risk estimation but most crucially for the prospects of HAT elimination . For the chronic T . b . gambiense infection [32] , under-detection can be addressed by continuous passive case detection and regular active screening surveys . The fact that we took into consideration ten-year data on disease occurrence and control activities should contribute to the robustness of the T . b . gambiense risk estimates . However , in the case of T . b . rhodesiense , due to the acuteness and rapid progression of infection , under-detection poses more serious challenges . Although attempt were made to model under-detection for T . b . rhodesiense [33] , both data and methodological constraints prevent these methods from being applied at the continental-level . In the future , methodologies should be developed to estimate and map the coverage of active and passive surveillance . These would provide valuable information complementing risk maps , whilst also assisting in optimizing field interventions . The temporal dimension is also crucial when interpreting risk maps . The proposed estimates were based on an average of HAT reported cases for a ten-year period . No weighting for the different reporting years was applied , despite the fact that a reduction in reported cases was observed during the last years of the study period . As a result , all cases contributed equally regardless of when exactly they were reported . Importantly , the estimates of people at risk presented in this paper , being based on reported cases , can not account for the possible future spread of HAT , and the risk thereof , into presently unaffected areas . Other approaches to risk modelling could be more interested in predicting the future risk of sleeping sickness , focusing on the environmental suitability for HAT rather than on its present occupancy . To this end , the relationships are to be explored between HAT occurrence and a range of factors , including human and livestock population movements [34] , environmental , climatic and socio-economic variables , as well as disease and vector control . The potential of this type of models has been investigated in a few local contexts , for example in southeastern Uganda for T . b . rhodesiense [35] , [36] , [37] , [38] , and coastal Guinea for T . b . gambiense [25] . Recent attempts have also tried to address risk forecasts at the regional level in relation to climate change [39] . The potential of various modelling frameworks could be explored for modelling the future risk of HAT [40] , [41] . The growing range of spatially explicit environmental datasets [42] and increased computational power enable these models to be applied even across large geographical areas . Interpretation of model outputs will probably be the most serious challenge . In fact , incompleteness and biases in the real-world epidemiological records often blur the line between concepts such as the theoretical fundamental niche of a pathogen and its realized niche . Where estimates of prevalence are available , most notably in T . b . gambiense areas , model-based geostatistics could also be applied , which utilize Bayesian methods of statistical inference and enable rigorous assessment of uncertainty [11] . Their potential for , and applicability to , a low-prevalence , focal disease such as HAT would be interesting to explore .
|
The present thrust towards the elimination of human African trypanosomiasis ( HAT , or sleeping sickness ) requires accurate information on how many people are at risk of contracting the disease , and where they live . This information is crucial to target field interventions effectively and efficiently , as well as to monitor progress towards the elimination goal . In this paper , a Geographic Information System was used to delineate areas at different levels of risk . To this end , accurate data on the spatial distribution of HAT cases ( period 2000–2009 ) were collated and combined with maps of human population . A total of 70 million people are estimated to be at risk of contracting sleeping sickness in Africa . This population is distributed over a surface of one and a half million square kilometres , an area six times that of the United Kingdom . Half of the people and of the areas at risk are found in the Democratic Republic of the Congo .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"disease",
"mapping",
"infectious",
"diseases",
"public",
"health",
"and",
"epidemiology",
"epidemiology",
"infectious",
"disease",
"epidemiology",
"african",
"trypanosomiasis",
"neglected",
"tropical",
"diseases",
"parasitic",
"diseases"
] |
2012
|
Estimating and Mapping the Population at Risk of Sleeping Sickness
|
The Three Gorges Dam ( TGD ) is a remarkable , far-reaching project in China . This study was conducted to assess the impact of TGD on changes in the ecological environment , snail distribution and schistosomiasis transmission in Dongting Lake area . Hydrological data were collected from 12 monitoring sites in Hunan section of Yangtze River before and after TGD was established . Data on snail distribution and human schistosomiasis infection were also collected . Correlation analyses were performed to detect the significance of snail distribution to changes in ecological environmental factors and human schistosomiasis infection . A series of ecological environmental factors have changed in Dongting Lake area following the operation of TGD . Volume of annual runoff discharged into Dongting Lake declined by 20 . 85% . Annual sediment volume discharged into the lake and the mean lake sedimentation rate decreased by 73 . 9% and 32 . 2% , respectively . From 2003 to 2015 , occurrence rate of frames with living snails and mean density of living snails decreased overall by 82 . 43% and 94 . 35% , respectively , with annual decrements being 13 . 49% and 21 . 29% . Moreover , human infection rate of schistosomiasis had decreased from 3 . 38% in 2003 to 0 . 44% in 2015 , with a reduction of 86 . 98% . Correlation analyses showed that mean density of living snails was significantly associated with water level ( r = 0 . 588 , p<0 . 001 ) , as well as the mean elevation range of the bottomland ( r = 0 . 374 , p = 0 . 025 ) and infection rate of schistosomiasis ( r = 0 . 865 , p<0 . 001 ) . Ecological environmental changes caused by the TGD were associated with distribution of snails , and might further affect the transmission and prevalence of schistosomiasis . Risk of schistosomiasis transmission still exists in Dongting Lake area and long-term monitoring is required .
The Three Gorges Dam ( TGD ) is located at the upper reaches of the Yangtze River , and it is a remarkable , comprehensive hydropower project [1] . Sediment discharge and water impoundment at the dam started in 2003 . Following the cofferdam power generation period ( 2003–2005 , water level 135 m ) , initial operation period ( 2006–2007 , water level 156 m ) , and trial impoundment period ( 2008–2010 , water level 185 m ) , the TGD became fully operational in 2010 [2] . Dongting Lake is the first large lake that connects Yangtze River after it outflows TGD , and it is the most important flood control lake in the middle reaches of Yangtze River [3] . Moreover , the Dongting Lake area is one of the most severe schistosomiasis endemic areas in China , as it has vast marshlands which are habitations for Oncomelania hupensis populations , the host of Schistosoma japonicum ( S . japonicum ) [4] . Although remarkable disease control efforts have been constantly conducted in the past decades , schistosomiasis remains endemic in five provinces ( Hunan , Jiangsu , Hubei , Anhui and Jiangxi ) along the Yangtze River [5 , 6] . Currently , 1 . 759×109 m2 of Dongting Lake area is infested with snails , which accounts for 48% of the total snail habitation in China [7] . Risk of schistosomiasis transmission still exists in this area [2 , 8–10] . Large-scale hydro-projects not only affect the natural environment , but may also alter regional climate , and further lead to changes in the epidemic distribution of some infectious and endemic diseases [11 , 12] . Previous cases have shown that emergence or re-emergence of schistosomiasis were often caused by newly-built hydro-projects in endemic areas , such as Aswan Dam in Egypt , Tigay Dam in Ethiopia , Gezira-Managil Dam in Sudan , Manantali Dam in Mali and Danling Dam in China [11–13] . Hence , the potential influence of TGD on the transmission of schistosomiasis in downstream Yangtze River basin aroused heated discussion worldwide [2 , 12 , 14 , 15] . During the past decades , lots of studies have been conducted to forecast or assess the influence of TGD on the distribution of snails and transmission of schistosomiasis , but the conclusions were inconsistent [2 , 4 , 12 , 14 , 16 , 17] . Therefore , this study was conducted to analyze the impact of TGD on the ecological environment , snail distribution and schistosomiasis transmission , respectively . And it aims to provide evidence for the control and elimination of schistosomiasis .
This study was conducted in the Dongting Lake region . The lake is located in the northern part of Hunan province and it encompasses a water surface area of 2681 km2 . In the southeast , the lake is fed by four tributaries ( Xiang , Zi , Yuan , and Li ) in Hunan province . In the north , it collects water from Yangtze River via three outlets ( Songzi , Taiping , and Ouchi ) , and returns outflow into Yangtze River at Chenglingji in Yueyang City [18] . The marshland where snails are distributed were selected as the study sites . A total of 12 monitoring sites , were established for this study . These sites were located on the estuaries of Yangtze River ( Fig 1 ) , including bottomlands outside the embankment and polder ditches inside the protective embankment of Yangtze River ( n = 7 , including water gates of Hongshuigang , Tanzikeng , Sizhiqu , Liuzhiqu , Jingjiangmen , Bei and Aiwei ) , as well as bottomlands on diversion channels ( n = 5 , including Songzi estuary , middle Ouchi branch estuary , Ouchi Tuojiang river estuary , east Ouchi branch estuary and Dongting Lake outlet ) . Global Positioning System information of each monitoring site is presented in S1 Table . Hydrological information was provided by hydraulic department of the government to evaluate the impact of TGD construction and impoundment during the preceding years . Information pertaining to Dongting Lake regarding volumes of runoff and sediment , water level , and topsoil moisture level were also collected . Marshland moisture was recorded annually in March and April from 2003 to 2015 , concurrently with the snail investigation . Nine points were selected for each monitoring site , including four in the corners and four at the midpoints of each line of the frame , as well as one in the middle . A field TDR 300 soil moisture meter ( Spectrum Tech , USA ) was used to measure topsoil moisture of the marshland . Snail data including the total areas surveyed , number of frames , number of living snails and number of infected snails were collected annually in March and April from 2003 to 2015 . For bottomlands outside the embankments , the snail survey was conducted using a traditional random equidistant frame survey method ( 0 . 11 m2-sized frames , 20 meters apart between frames ) [19] . While for areas around canal branches , sub-branches , and ditches inside the embankments , the frames were set at 0 . 5 m × 10 m . Records were also taken about the name of marshland , administrative government , water body , as well as the elevation range of marshland where the survey sites are located . The appearance of cercariae in the crushed snail samples when observed under the microscope indicate that the snail has infection and they are defined as infected snails . Data on human schistosomiasis infection in Hunan province were extracted from the statistical annual report of Hunan Institute of Schistosomiasis Control . Data were collected through county ( city , district , farm ) -based field surveys in epidemic areas . The study scope were inhabitants aged 6–65 years old living in 41 counties ( cities , districts , farms ) of Hunan province . These inhabitants were checked every 1–3 years from 2003 to 2015 , according to different categories of epidemic regions . A database was established using Microsoft Excel . Changes in snail distribution , runoff volume , sediments , water level in Dongting Lake area , and resident infection rate of schistosomiasis in Hunan province , were visually described with statistical tables and line graphs . Changes in trend of occurrence rate of frames with living snail was detected using Chi-square trend test . Correlation analyses were performed to detect whether snail distribution was relevant to changes in ecological environmental factors and human schistosomiasis infection . All p values were two-sided , with a significant level of p ≤ 0 . 05 . Statistical analyses were performed using SPSS version 23 . 0 .
The mean annual runoff volume that drained into Dongting lake from three outlets of Yangtze River was 657 ( 108 m3 ) from 1996 to 2002 . After the construction of TGD in 2003 , water discharged into the lake decreased gradually , and the mean annual runoff volume decreased to 520 ( 108 m3 ) in 2007–2010 , equal to a reduction of 20 . 85% compared to that before the impoundment . Duration of dry-up days in eastern branches of Songzi and Ouchi rivers , and in western branches of Hudu and Ouchi rivers had increased from 92 , 117 , 242 and 117 in 1996–2002 to 199 , 190 , 257 and 152 in 2003–2008 , respectively . Mean annual runoff volume of four tributaries in Hunan Province and Dongting Lake declined from 1874 ( 108 m3 ) and 279 ( 108 m3 ) in 1996–2002 , to 1574 ( 108 m3 ) and 244 ( 108 m3 ) in 2007–2010 , with reduction proportion of 17 . 45% and 14 . 76% , respectively ( Fig 2 ) . During the impoundment period of TGD from mid-September to the end of October , volume of water drained from the mainstream of Yangtze River decreased dramatically , this led to rapid reduction of Dongting Lake water level , then resulted into an advanced dry season in Yangtze River and Dongting Lake . However , water level in Dongting Lake during the dry season ( from December to April ) was found higher than before TGD ( 1992–2002 ) . Thus , by comparing data collected after the establishment of TGD ( 2007–2010 ) with that before it , the mean monthly water level at Chenglingji ( outlet of Dongting Lake ) was reduced by 2 . 04m in October , but increased by 0 . 34m , 1 . 34m , 1 . 52m , 1 . 82m , and 0 . 76m in December , January , February , March and April , respectively ( Fig 3 ) . In the pre-TGD period ( 1996–2002 ) , the mean annual sediment discharge volume of the lake basin was 85 . 43 million tons , and the sedimentation rate was 73 . 7% . Following the TGD project ( 2003–2010 ) , these metrics changed to 22 . 28 million tons and 41 . 5% , respectively . Compared with the data collected before TGD , annual sediment discharge declined by 73 . 9% , 90 . 5% of which was derived from the three outlets and 9 . 5% was from the four tributaries . Moreover , the sedimentation rate of the lake basin declined by 32 . 2% . Table 1 depicts sediment discharge volume and lake basin sedimentation rate in Dongting Lake before and after TGD . The highest and lowest elevation ranges integrally decreased following the establishment of TGD , respectively from 30 . 40–34 . 90 m and 23 . 00–30 . 40 m to 29 . 10–34 . 50 m and 21 . 00–30 . 20 m . Meanwhile , the mean elevation range increased slightly after the operation of TGD , from 26 . 95–31 . 50 m to 28 . 20–32 . 35 m , but the change was not statistically significant . Moreover , mean topsoil moisture of bottomland measured from the survey sites was 46 . 4% ( range: 35 . 50–50 . 10% ) prior to the establishment of TGD , and slightly increased to 47 . 30% ( 41 . 40–53 . 40% ) after the establishment of TGD , but no statistical significance was found . Snail distribution result in the 12 monitoring sites is presented in Table 2 . A total of 12 monitoring sites were surveyed at Dongting Lake , the occurrence rate of frames with living snails ( Oncomelania hupensis ) declined from 26 . 87% in 2003 ( before the TGD project ) to 4 . 72% in 2015 ( after the TGD project ) , with reduction rate being 82 . 43% and annual decrement being 13 . 49% . Detailed data showed that occurrence rates of frames with living snails in bottomlands on diversion channels were relatively high ( 9 . 18–48 . 39% ) , and the rates of that in polder ditches inside the protective embankment of Yangtze River were the lowest ( 0–11 . 65% ) . Following the establishment of TGD , the occurrence rate of frames with living snails decreased gradually with significant linear tendency ( χ2 = 7053 . 05 , p<0 . 001 ) . Similarly , the mean density of living snails declined from 1 . 2140 snail/frame in 2003 to 0 . 0686 snail/frame in 2015 , indicating a reduction rate of 94 . 35% and annual decrements of 21 . 29% . Until 2012 , the mean density of living snails had dropped to zero in the monitoring polder ditches inside the protective embankment of seven sites ( Fig 4A ) . Meanwhile , the mean density of living snails in bottomlands outside the embankment of the same sites and of bottomlands on diversion channels of the other five sites had also dropped to a relatively low level ( Fig 4B and 4C ) . All the sampled living snails were screened to identify infected ones . A total of 190 infected snails were identified from 166 frames with living snails in all the sites monitored in 2003 , of which , 101 frames were from bottomlands outside the embankment of seven sites . Following the establishment of TGD , the occurrence rate of frames with infected snails decreased from 1 . 09% in 2003 to 0 . 05% in 2010 , with a significant linear decreasing tendency ( χ2 = 996 . 81 , p<0 . 001 ) . The corresponding mean density of infected snails has been decreasing and finally reached a low level ( 0 . 0008 snails/ 0 . 11m2 ) in 2007 , then the value became zero in 2011 ( Fig 4A , 4B and 4C ) . Data about human schistosomiasis infection in Dongting Lake area is shown in Table 3 . From 2003 to 2015 , the human schistosomiasis epidemic had been well controlled , a total of 256 villages had been out of the human schistosomiasis infection throughout the Hunan province . More than 5×105 persons were randomly selected to check for schistosomiasis infection each year . The infection rate of schistosomiasis had steady decreased from 3 . 38% in 2003 to 0 . 44% in 2015 , with a reduction rate of 86 . 98% . Based on the stable population , the decreased infection rate of schistosomiasis was due to the decline in the number of schistosomiasis patients , the reduction rate for the latter was 86 . 41% . Correlation analysis was performed between three monitored ecological environmental factors ( water level , mean elevation range , and topsoil moisture ) and occurrence rate of frames with living snails , as well as mean density of living snails ( Table 4 ) . Results suggested that the monitored water level was significantly associated with occurrence rate of frames with living snails ( r = 0 . 509 , p = 0 . 002 ) and the mean density of living snails ( r = 0 . 588 , p<0 . 001 ) . Moreover , mean elevation range of the bottomland was found to be significantly associated with mean density of living snails ( r = 0 . 374 , p = 0 . 025 ) . In addition to these , no other statistical association was found , snail distribution was not significantly associated with the topsoil moisture ( Table 4 ) . However , index on snail distribution were found to be significantly associated with infection rate of schistosomiasis and number of schistosomiasis patients , with the correlation coefficients ranging from 0 . 718 to 0 . 865 ( Table 4 ) .
Based on the collected hydrological data and fixed-point monitoring data , our results confirmed that the TGD project had changed water and sand distribution downstream , impacting the ecological environment , snail distribution and schistosomiasis transmission in Dongting Lake area . Due to the impoundment of TGD , from mid-September to the end of October , volume of water drained from Yangtze River mainstream decreased dramatically . Water level of Dongting Lake decreased rapidly while the volume of water flowing out of the lake increased , this resulted into a reduction in the total amount of lake water , which further exposed the bottomlands and also extended the dry season in this area . During the dry season ( from December to April ) , water level in Dongting Lake was higher than that before the construction of TGD , as a result of the release of water from TGD reservoir , which was used to meet the needs of power generation and shipping . While during the wet season , the TGD could be used as a channel to reduce Dongting Lake water level thereby control flooding of the area . Moreover , the highest elevation and mean topsoil moisture of monitoring bottomlands in Dongting Lake slightly increased following the operation of TGD . This study revealed a significant linear decrease in trend of snail populations in all the 12 sites monitored at Dongting Lake from 2003 to 2015 . The occurrence rate of frames with living snails and mean density of living snails declined overall by 82 . 43% and 94 . 35% , respectively , with annual decrements being 13 . 49% and 21 . 29% , respectively . These results were in accordance with the longitudinal monitoring data of schistosomiasis in Hunan province [20] . Moreover , the reduction differed in different types of bottomlands . The polder ditches used to divert water from Yangtze River to irrigate farmlands inside the protective embankments of Dongting Lake had the largest amount of deduction , followed by bottomlands of Yangtze River in Hunan section , and estuaries of Yangtze River where river water entered the lake . At different stages of the TGD project , decline of snail density in bottomlands of Dongting Lake area varies . Overall decline was highest in impoundment period , snail density decreased drastically in polder ditches during impoundment period and early stage of project operation . During the normal operation period , snail distributions had the smallest changes both in bottomlands and polder ditches . This might be related to variable impacts of the impoundment of TGD on different areas of Dongting Lake , moreover , water level was constantly changing during the operation . Once the water in the Three Gorge Reservoir had reached a certain level , water level of the downstream areas , including Dongting Lake , would keep at a relatively constant level . Ecological environment of snails would then reach homoeostasis and the snail distribution would be equilibrated after dropping to a certain level . Schistosomiasis is a natural environmental disease . Changes in natural environmental conditions have a significant impact on the growth , reproduction and spread of snails , and further impact the transmission of schistosomiasis [21] . At present , studies about environmental factors affecting snail distribution are mainly focused on hydrological characteristics , vegetation , water quality ( especially water eutrophication ) , water temperature , PH value and so on [22–25] . Snail is an amphibian , living in moist , shaded and mixed wetland environment . Previous researches have showed that snail distribution was related to water level ranges , water coverage period , underground water level , and topsoil moisture [24 , 26] . Correlation analyses in our study showed that monitored water level ( r = 0 . 588 , p<0 . 001 ) and mean elevation range of the bottomland ( r = 0 . 374 , p = 0 . 025 ) were significantly associated with mean density of living snails , which was in accordance with the report of Zheng and Wang et al . [26 , 27] . The significant positive correlation between ecological environment changes and snail distribution might be interpreted as follows . Firstly , regulatory effect of TGD project reduced the variation between the highest and lowest water levels of Yangtze River and Dongting Lake . These may further change the microenvironment of snail habitats and curtail the infection to humans and animals [12 , 15 , 24] . Secondly , snail distribution in Dongting Lake area presents a feature of “two lines and three zones” . The “two lines” refers to the upper and lower lines containing living snails , while “three zones” refers to the lower sparse snail zone , dense snail zone , and the upper sparse snail zone . Along with the reduction in annual runoff volume into the lake , the highest water level of Dongting Lake area maintained under the lower sparse snail zone , which resulted in water shortage in snail habitation . As a result , the bottomland was no longer suitable for the growth of reeds , and further affected the survival rate of snails . Thirdly , as a result of the water discharged during the power generation of TGD , the lower sparse snail zone of the eastern Dongting Lake became submerged in spring ( mainly in March and April ) . The advanced submerged snails and their eggs might likely affect their survival and further jeopardize the incubation of eggs , which eventually reduced the snail density . Moreover , some researchers believed that distribution of snails had an obvious seasonal variation , which was shown as two peaks in spring and the end of summer [28] . Water of TGD maintained at a relatively low level in summer and at a high level in winter , leading to a so-called “summer-land , winter-water” eco-hydrologic condition , which was opposite to the suitable environment for snails ( “winter-land , summer-water” ) [15] . Oncomelania is the only intermediate host of Schistosoma japonicum , and plays a critical role in the transmission process , its distribution will directly affect the prevalence of schistosomiasis [29] . Actually , our data had also confirmed the close relationship between snail distribution variation and the changes in human schistosomiasis infection , with the correlation coefficients ranging from 0 . 718 to 0 . 865 . Moreover , our monitored data suggested that human infection rate of schistosomiasis and number of schistosomiasis patients in Dongting Lake area had greatly decreased following the establishment of TGD , till the year 2015 , infection rate of schistosomiasis had dropped to 0 . 44% , which was under 1% as expected [4] . We believe that the remarkable disease control efforts conducted by the government , like extensive use of molluscicides , routine praziquantel treatment , health education programs , might have contributed significantly to the successful reductions in the intensity of living snails and human infection during the past decades [30 , 31] . However , based on our analyses , we believe that a series of ecological environmental changes , including water level , bottomland soil moisture , microenvironment temperature , vegetation distribution and so forth , caused by TGD project can disturb distribution of snails , as well as the transmission and prevalence of schistosomiasis . Given that the impact of TGD on snail distribution and schistosomiasis prevalence in Dongting Lake area is much more complex , prolonged and in-depth studies are needed to address these issues for the effective control of snails in Dongting Lake area and eventually achieving the elimination of schistosomiasis .
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Three Gorges Dam ( TGD ) is a tremendous hydrological project in China , it affects the ecological environment and influences the survival of animals downstream . The Dongting Lake is the first large lake that connects Yangtze River after it flows out of TGD , meanwhile , it accounts for 48% of the total snail habitation in China . Hydrological data from 12 monitoring sites , combined with data on snail distribution and human schistosomiasis infection , were used to assess the impact of TGD on ecological environment changes , snail distribution and schistosomiasis transmission in Dongting Lake area . Based on our analyses , following the establishment of TGD until 2015 , the mean density of living snails in Dongting Lake area was under 5 snails/frame , and human infection rate of schistosomiasis declined to below 0 . 5% . As the impact of TGD on snail distribution and schistosomiasis prevalence in Dongting Lake area is much more complex in the real world , long-term monitoring and in-depth studies are still required .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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2017
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Impact of the Three Gorges project on ecological environment changes and snail distribution in Dongting Lake area
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Defects in cilium and centrosome function result in a spectrum of clinically-related disorders , known as ciliopathies . However , the complex molecular composition of these structures confounds functional dissection of what any individual gene product is doing under normal and disease conditions . As part of an siRNA screen for genes involved in mammalian ciliogenesis , we and others have identified the conserved centrosomal protein Azi1/Cep131 as required for cilia formation , supporting previous Danio rerio and Drosophila melanogaster mutant studies . Acute loss of Azi1 by knock-down in mouse fibroblasts leads to a robust reduction in ciliogenesis , which we rescue by expressing siRNA-resistant Azi1-GFP . Localisation studies show Azi1 localises to centriolar satellites , and traffics along microtubules becoming enriched around the basal body . Azi1 also localises to the transition zone , a structure important for regulating traffic into the ciliary compartment . To study the requirement of Azi1 during development and tissue homeostasis , Azi1 null mice were generated ( Azi1Gt/Gt ) . Surprisingly , Azi1Gt/Gt MEFs have no discernible ciliary phenotype and moreover are resistant to Azi1 siRNA knock-down , demonstrating that a compensation mechanism exists to allow ciliogenesis to proceed despite the lack of Azi1 . Cilia throughout Azi1 null mice are functionally normal , as embryonic patterning and adult homeostasis are grossly unaffected . However , in the highly specialised sperm flagella , the loss of Azi1 is not compensated , leading to striking microtubule-based trafficking defects in both the manchette and the flagella , resulting in male infertility . Our analysis of Azi1 knock-down ( acute loss ) versus gene deletion ( chronic loss ) suggests that Azi1 plays a conserved , but non-essential trafficking role in ciliogenesis . Importantly , our in vivo analysis reveals Azi1 mediates novel trafficking functions necessary for flagellogenesis . Our study highlights the importance of both acute removal of a protein , in addition to mouse knock-out studies , when functionally characterising candidates for human disease .
Centrosomes are conserved animal organelles which function as the major microtubule organising centre ( MTOC ) , and are required for diverse processes including formation of cilia and flagella , intracellular trafficking events , cell polarity and division . Structurally , the centrosome consists of a pair of cylindrical centrioles surrounded by a proteinaceous matrix of pericentriolar material ( PCM ) [1] . Importantly , centrioles replicate only once per cell cycle and are essential for the formation of cilia , key signalling organelles during development and homeostasis . In post-mitotic cells , the centrosome moves to the apical surface where the mother centriole docks with the cell membrane to become a basal body and a template for the axonemal microtubules of the primary cilium . Cilia assembly and function requires diverse trafficking events , including intraflagellar transport ( IFT ) , coordinated by the basal body ‘hub’ , which regulates traffic in and out of the ciliary compartment . Enrichment of key signalling receptors and downstream effectors in cilia allows these structures to function as effective signalling organelles , with unique protein and lipid composition [2] . The transition zone , a highly specialised structure just distal to the basal body , is thought to act as an additional ciliary gate , controlling traffic into and out of the cilium [3] . Many of these aspects of ciliogenesis are highly conserved [4] . Despite these common features , cilia are also structurally and functionally diverse . Cilia play important sensory roles , acting as transducers of developmental signalling pathways , detecting fluid flow , as well as highly specialised sensory receptors [5] . Some cilia are motile , involved in generating fluid flow in the embryonic node , airways , oviduct and brain , as well as in the propulsion of sperm . How the core ciliary assembly programme is modified and elaborated on to account for these species- and cell-specific variations is not well understood [4] . Mutations in conserved cilial and centrosomal genes have been identified in a growing spectrum of clinical disorders , termed ciliopathies , with both distinct and overlapping clinical features including polydactyly , skeletal defects , situs inversus , infertility and neuropathology [6] , [7] . Proteomic and genetic studies in several organisms estimate the molecular composition of cilia/centrosomes to include hundreds to thousands of putative components , many of them unknown [8] , [9] . Functional dissection of the role and requirement of many of these ciliopathy candidates in cilia formation and function are often performed using cell culture [10] , [11] , [12] and zebrafish knock-down models [13] . Mouse mutant models are analysed less often as these are more costly in time and resources to produce . Given the phenotypic complexities of clinical features in ciliopathies [14] , what is the best way to understand the underlying molecular mechanisms for candidate genes in relation to human disease ? Recently , centriolar satellites have reported to be the site of localisation of many ciliopathy proteins , and are involved in their ciliary targeting , including OFD1 ( oral-facia1-digital syndrome 1 ) , BBS4 ( Bardet-Biedl Syndrome 4 ) and CEP290 [15] . Conserved among vertebrates , but not present in arthropods , centriolar satellites are electron dense , multi-protein complexes enriched in the area surrounding the centrosome/basal body [16] , [17] , [18] . These are dynamic structures trafficking along microtubules towards the centrosome utilising dynein motors [16] , [18] . Centriolar satellites have been shown to regulate ciliogenesis and centriole biogenesis , in part by regulating trafficking of proteins to and/or sequestering of proteins away from the centrosome/basal body [19] , [20] , [21] , [22] . Centriolar satellites are defined by pericentriolar material 1 ( PCM1 ) which is a key scaffolding component of centriolar satellites and to date , all centriolar satellite-localised proteins have been shown to interact with PCM1 [17] . However , what the functional significance of vertebrate-specific centriolar satellites to mammalian development is and how they affect the function of highly conserved components in ciliogenesis and centriole biogenesis is unknown . Here , we address the role and requirement of 5-azacytidine induced gene 1 ( Azi1 ) /Cep131 ( MGI:107440 ) , a highly conserved centrosomal protein , in ciliogenesis . We screened a subset of cilia-enriched orthologous candidates from Drosophila melanogaster studies [23] by RNAi to identify genes involved in mammalian ciliogenesis , and identified Azi1/Cep131 . This finding agrees with a previous siRNA screen , which showed a role for the human orthologue , AZI1 , in ciliogenesis [10] . Both Drosophila melanogaster mutants and Danio rerio morphants of Azi1 ( dila/CG1625 and cep131 , respectively ) phenocopy mutations of known ciliary genes [24] , [25] , suggesting Azi1 plays a conserved function in ciliogenesis . AZI1 was described as a centrosomal protein ( Centrosomal protein 131: Cep131 ) in a large scale proteomics screen and this localisation was recently refined to the centriolar satellites [26] , [27] , [28] . The mouse protein is highly expressed in the testes in germ cells during the period of flagellar formation [29] . More recently , additional roles for AZI1 include involvement in genome stability and centriole duplication . Knock-down of AZI1 leads to an increase in double-stranded DNA breaks , indicated by γH2AX staining , as well as a slight increase in cells with extra centrioles [28] , [30] . However , little is known of the in vivo role of mouse Azi1 and its requirement for development . Here we utilise knock-down , localisation and live-imaging techniques , to further investigate the role of Azi1 in mammalian ciliogenesis at the cellular level . To determine the requirement for Azi1 in mouse development , we generated Azi1 null mutant mice and focused on the in vivo role of Azi1 in ciliogenesis and genome stability . Our analysis of Azi1 knock-down ( acute loss ) versus gene deletion ( chronic loss ) suggests that Azi1 plays a conserved , but non-critical trafficking role in ciliogenesis . Importantly , our in vivo analysis reveals Azi1 mediates novel trafficking events necessary for spermiogenesis and male fertility .
Using a set of forty orthologous putative ciliary genes identified as highly expressed in ciliated cell types in Drosophila melanogaster [23] , we carried out an siRNA screen in a mouse fibroblast cell line to identify genes involved in mammalian ciliogenesis . Cilia formation was assayed as the percentage of cells with a cilium , marked by anti-Arl13b , a ciliary membrane marker [31] , and anti-acetylated α-Tubulin , a ciliary axoneme marker , using an automated imaging and image analysis system . We identified Azi1/Cep131 as the top hit for genes involved in cilia formation , with at least two of four siRNAs giving a significant reduction in ciliogenesis across three independent assays ( data not shown ) . This observation is supported by the study of Graser et al . ( 2007 ) , who found a reduction in ciliogenesis on AZI1 knock-down in human hTERT-RPE1 cells [10] . To exclude off-target effects of the siRNAs , we co-transfected a different pool of four siRNAs , specifically targeting the 3′ untranslated region ( UTR ) of only Azi1 , along with a plasmid encoding either GFP or Azi1-GFP . The Azi1-GFP plasmid lacks the 3′ UTR of Azi1 and so is resistant to these siRNA . Transfection of Azi1 3′ UTR siRNA leads to a reduction in Azi1 protein to 10% of wild type levels , which can be partially rescued by co-transfection with Azi1-GFP ( Figure 1A ) . Azi1 knock-down leads to a 50% reduction of transfected cells with cilia ( Figure 1B , D and F ) , similar to that seen with a positive control siRNA targeting Ift88 , a gene essential for ciliogenesis [32] , [33] . Importantly , co-expression of Azi1-GFP rescues the phenotype back to control levels demonstrating that the ciliary phenotype observed upon addition of Azi1 siRNA is not due to off-target effects of the siRNA ( Figure 1C , E and F ) . We conclude that Azi1 is involved in mammalian cilia formation . AZI1 was originally identified as a centrosomal protein ( Cep131 [26] ) . We have spatially refined the localisation of mouse endogenous and GFP-tagged Azi1 to centriolar satellites , marked by anti-PCM1 , which confirms recent human AZI1 immunofluorescence reports ( Figure S1A and G ) [28] , [34] . We identified a further pool of human and mouse Azi1 at the transition zone ( Figures 2A , B and S1B–C ) , indicated by co-staining with anti-polyglutamylated tubulin , which stains the ciliary axoneme and basal body but , importantly , is absent from the transition zone [35] . The transition zone is an area at the base of the cilia involved in regulating traffic into the cilium [3] . Co-staining with anti-NPHP1 , a marker of the transition zone [36] , [37] , confirms this localisation ( Figures 2C , D and S1D ) . This is consistent with the observation that in D . melanogaster ciliated sensory neurons Azi1 homologue dila localises distal to the basal body at the putative transition zone [24] . Recently , CEP290 has also been reported to localise to both centriolar satellites and the transition zone [38] , [39] , [40] , raising the possibility this could be a general trend for centriolar satellite proteins . To test this , we investigated the localisation of PCM1 , the core component of centriolar satellites at the transition zone . Indeed , PCM1 localises to the transition zone of most , but not all cilia , as indicated by the gap between the basal body and axoneme on anti-polyglutamylated tubulin staining ( Figure 2E and F ) . Previous reports had shown OFD1 and PCM1 to similarly localise to the distal portion of basal bodies [15] . Interestingly , the putative functional orthologue of OFD1 , UNC , is also found at the putative transition zone of Drosophila mechanosensory neurons [24] , [41] . Together , this suggests that docking at the transition zone may be a conserved feature of components of mammalian centriolar satellites . We used live imaging of Azi1-GFP to address the dynamics of Azi1 localisation . In interphase cells , centriolar satellites have been proposed to function in dynein motor-dependent , microtubule-based trafficking of proteins to the centrosome [19] , [40] , and it has been shown recently that the pericentriolar localisation of AZI1 is microtubule dependent [28] . Similarly , trafficking of cargo associated with IFT motors along microtubules into the ciliary compartment is selectively regulated in part by the transition zone [42] . To examine Azi1 trafficking more directly we imaged Azi1-GFP movement in live mouse NIH-3T3 cells . Azi1-GFP was observed to traffic along microtubules , co-labelled with Map4-RFP ( Figure 2G ) . Azi1-GFP traffics with a dynamic saltatory motion , with periods of fast movement , for an average distance of 3 . 4 µm ( range 1 . 3–10 . 2 µm ) interspersed with sometimes long stationary periods . Azi1-GFP was observed to move both towards and away from the centrosome , similar to observations of PCM1-GFP [18] ( Movie S1 ) . Although average speeds varied according to direction , 1 . 8±0 . 2 µm/s ( mean ± SEM ) towards the minus end of microtubules at the centrosome , and 1 . 0±0 . 3 µm/s away from the centrosome , they were consistent with speeds observed previously for microtubule-based motors in vivo [43] , [44] . This suggests Azi1 is involved in microtubule-based trafficking to and from the centrosome/basal body . Higher levels of endogenous AZI1 staining are observed around basal bodies and surrounding centriolar satellites of ciliated cells compared to non-ciliated cells ( Figure 2H and I ) . However , total levels of AZI1 do not change upon serum starvation ( Figure 2J ) indicating that under these conditions to induce ciliogenesis , there is a redistribution of AZI1 towards the basal body area . It has been proposed that centriolar satellites act as proteinaceous scaffolds to physically restrict access of proteins to the centrosome/cilium complex [22] . Disruption of core components results in dissolution or dispersal of centriolar satellites , and relocalisation of associated centriolar satellite proteins to the centrosome/basal body [15] , [20] , [22] , [40] . Despite its localisation , Azi1 siRNA knock-down in mouse cells does not alter centriolar satellite integrity as shown by Pcm1 localisation , consistent with recent reports for human AZI1 [28] . This suggests Azi1 is not required for mammalian centriolar satellite integrity nor retention of Pcm1 to these structures ( Figure S1E–H ) . Given the high conservation of Azi1 among ciliates ( Table S1 ) , including arthropods which lack centriolar satellites , together with phenotypic mutant data from diverse organisms [24] , [25] , [45] , [46] , we predicted Azi1 would have a central role in mammalian cilia biology in vivo and thus generated mouse mutants null for Azi1 . Azi1Gt ( CCOG35 ) Wtsi embryonic stem ( ES ) cells , which have a gene trap inserted into intron 2 of Azi1 ( Figure 3A ) , were used to generate Azi1+/Gt ( CCOG35 ) Wtsi mice ( referred to as Azi1Gt/+ throughout this manuscript ) . Azi1Gt/Gt mice are born at sub-Mendelian ratios , with approximately two thirds of the expected numbers of Azi1Gt/Gt mice remaining after weaning ( see Table 1 , P = 0 . 0025 ) . A significant reduction in Azi1Gt/Gt numbers was observed at embryonic day 11 . 5–13 . 5 ( E11 . 5–13 . 5 ) , suggesting roughly a third of mutants are lost before mid-gestation , although further work is needed to determine exactly when this loss occurs ( Table 1 ) . Azi1Gt/Gt mice that are born appear morphologically normal , and are the same weight as wild type littermates ( Figure S5G and H ) . Viable Azi1 mutant mice showed none of the gross abnormalities associated with cilia dysfunction in mice , including failure to thrive , hydrocephalus , situs inversus , and chronic airway infections . Azi1 has several coiled-coil domains as well as a predicted t-SNARE domain ( IPR010989 ) implicated in membrane fusion events during vesicular transport ( Figure S2B ) . The gene trap is predicted to truncate Azi1 after the initial 69 amino acids such that any remaining Azi1 trapped protein in Azi1Gt/Gt mice will lack all the predicted domains in the more highly conserved C terminus ( Figure S2 , Table S1 ) , and hence unlikely to be functional . To confirm that the gene trap eliminated gene expression , we examined Azi1 mRNA expression levels across the gene trap insertion site ( Figure 3B and C ) . No expression across the insertion site was detected in kidneys , ovaries or testes from Azi1Gt/Gt mice by RT-PCR or qRT-PCR ( Figure 3B and C ) , whereas robust expression of the gene trap was detected ( Figure 3B ) . X-Gal staining of E11 . 5 Azi1Gt/+ embryos further confirmed expression of the gene trap β-geo gene , and showed Azi1 expression is ubiquitous during development , with higher expression in tissues with high levels of cilia-dependent developmental signalling such as the limbs , eyes , somite derivatives and brain ( Figure 3D ) . We detected some low level expression of the 3′ end of Azi1 in Azi1Gt/Gt mice; when quantified by qRT-PCR this was less than 2% of wild type levels ( Figure S2C–E ) . Importantly , no Azi1 protein was detected in Azi1Gt/Gt mice when probing with an antibody raised against the C terminal of Azi1 ( Figure 3E and S2B ) , despite a single strong band of the expected size in Azi1+/+ and a band of roughly 50% intensity in Azi1Gt/+ samples . Furthermore , anti-Azi1 immunofluorescence analysis of Azi1Gt/Gt mutant mouse embryonic fibroblasts ( MEFs ) or multiciliated airway epithelial cells detected no signal , despite clear centrosomal/basal body localisation of Azi1 in littermate controls ( Figure 3F–H ) . This suggests that any low level transcription detected at the 3′ end of Azi1 in Azi1Gt/Gt mice is untranslated; indeed there are several predicted untranslated transcripts at the 3′ end of Azi1 ( ENSMUST00000156075 , ENSMUST00000150463 , ENSMUST00000144128 and ENSMUST00000145641 ) . We conclude that Azi1Gt/Gt is a null allele of Azi1 . Primary cilia are required for mammalian development , so the fact most Azi1 null mice survive without any patterning defects suggests Azi1 is not required for mammalian ciliogenesis in vivo . In contrast , transient siRNA knock-down of Azi1 leads to a two-fold reduction in ciliogenesis ( Figure 1 ) . On careful examination of cilia formation in Azi1 null primary MEFs we found normal numbers of cilia , marked by anti-Arl13b ( Figure 4A , F and K ) . Moreover , cilia compartmentalisation appeared normal , with correct distributions of the ciliary membrane protein Arl13b , the IFT-B protein Ift88 , and components of the transition zone Nphp1 and Mks1 ( Figure 4A–D , F–I ) . Post-translational modifications of tubulin also appeared normal: acetylated α-Tubulin is present at the basal body and axoneme , polyglutamylated tubulin is present at the axoneme and basal body but absent from the transition zone , and γ-Tubulin is present at the basal body ( Figure 4A–J ) . Localisation of Rab8 , a small GTPase involved in trafficking to the cilium [21] , whose localisation has been shown to be centriolar satellite-dependent [40] , was also localised normally in Azi1Gt/Gt MEFs ( Figure 4E and J ) . Together , this analysis suggests that Azi1 is dispensable for primary cilia formation and compartmentalisation both in vivo and in primary cells . Given its enrichment at centriolar satellites in mouse and human cells , where Azi1 interacts with Pcm1 ( Figure S1 ) [28] , [34] , we examined formation of centriolar satellites in the absence of Azi1 . Similar to Azi1 knock-down , Pcm1 is correctly localised in Azi1Gt/Gt MEFs ( Figure S3A–C ) , as are centriolar satellite components Cep72 and Cep290 , involved in modulating localisation and composition of centriolar satellites [22] , [40] ( data not shown ) . This confirms Azi1 is not required for centriolar satellite formation , or localisation of key structural components like Pcm1 , nor regulatory components like Cep290 or Cep72 . As centriolar satellites are proposed to have a role in regulating centrosome and centriole biogenesis , and AZI1 knock-down in human cells leads to increased Centrin2-positive foci [17] , [19] , [28] , [47] , we examined the numbers of centrioles ( marked by Centrin2-GFP and anti-Centrin3 ) and centrosomes ( marked by anti-γ Tubulin ) in cells lacking Azi1 ( Figure 4A , F , L–R ) . We found both centrosome and centriole numbers were normal in Azi1Gt/Gt MEFs ( Figure 4A , F and N ) . Multiciliated cells have the ability to assemble hundreds of centrioles through two parallel pathways [48] , [49] , in both of which , protein-rich fibrous granules , akin to centriolar satellites , marked by PCM1 , are found surrounding the elongating centrioles [18] . After assembly in the cytoplasm , these centrioles move apically to dock at the plasma membrane and each extend a ciliary axoneme . Ultrastructural analysis of motile multiciliated epithelia of adult trachea revealed normal numbers and docking of basal bodies and appendage formation in Azi1 null mice ( Figure S3D and G ) . Confirming the immunofluorescent analysis in Azi1 mutant cells , transition zone ultrastructure appeared morphologically normal in Azi1 null mice ( Figure S3E and H ) . In summary , and in contrast with the acute Azi1 knock-down in both human and mouse cells ( Figure 1 ) [10] , [28] , cilia and centriole structure appears grossly normal in Azi1 null cells . The difference in ciliary phenotypes observed with acute Azi1 knock-down versus its chronic absence in Azi1Gt/Gt MEFs is intriguing . To rule out differences between the cell line used for the screen and primary cells , we co-transfected wild-type MEFs with siRNA against Azi1 along with plasmids encoding either GFP or Azi1-GFP and examined cilia formation . Similar to the results obtained in the embryonic fibroblast cell line ( Figure 1 ) , Azi1 knock-down in primary MEFs led to a robust reduction in ciliogenesis which was rescued by the siRNA-resistant Azi1-GFP ( Figure 5A–D and I ) . To rule out any residual Azi1 function in our mutant Azi1Gt/Gt cells , and to further eliminate off-target effects , we transfected Azi1Gt/Gt MEFs with Azi1 siRNA . While our positive control Ift88 siRNA gave a robust reduction in ciliogenesis in Azi1Gt/Gt MEFs , ciliogenesis was unaffected upon Azi1 knock-down ( Figure 5E–I ) , demonstrating that Azi1 null MEFs have compensated for the loss of Azi1 . Discrepancies between the phenotypic severity observed with siRNA knock-down versus genetic deletion has previously been attributed to the acute nature of knock-down , allowing less time for compensation to occur [50] , [51] . We conclude that although Azi1 is involved in cilia formation in mouse , compensation during embryogenesis in the absence of Azi1 allows ciliogenesis to proceed normally in most tissues of Azi1 null mice . A role for cilia/centrosomal proteins in genome stability and DNA damage response pathways has been proposed [52] , and was recently reported for AZI1 [28] , [30] . Unlike the reported AZI1 knock-down [28] , [30] , we saw no increase in the number or intensity of γH2AX foci in Azi1 null MEFs ( Figure S4A , F and K and data not shown ) . To test whether Azi1 mutants were more susceptible to DNA damaging agents , we challenged MEFs with either hydroxyurea ( HU ) or ionising radiation and examined γH2AX foci . No significant difference was observed between genotypes at lower concentrations of HU or upon challenge with ionising radiation , although Azi1 null MEFs were more susceptible to high doses of HU ( 5 mM ) ( Figure S4B–E , and G–K ) . As Staples et al . ( 2012 ) reported an increase in micronuclei in AZI1 depleted U2OS cells [28] , we examined Azi1Gt/Gt mice using a peripheral blood micronuclei assay - a highly sensitive method for detecting in vivo DNA damage [53] . Whilst positive control Mcph1Gt/Gt mice show a marked increase in micronucleated erythrocytes as previously documented ( http://www . sanger . ac . uk/mouseportal/phenotyping/MBGX/micronuclei/ ) [54] , Azi1 null mice show no such increase , suggesting there is no elevation in DNA damage in the peripheral blood of Azi1 null mice ( Figure S4L and M ) . Taken together , there is no gross evidence for chromosomal instability in Azi1 null animals , neither in vivo nor in primary cell culture . Once again it is possible that the differences observed between siRNA knock-down and genetic null of Azi1 are due to compensation for Azi1 loss in the genetic null , as seen for the ciliogenic phenotypes observed . We examined Azi1 null mice carefully for adult-onset ciliopathic phenotypes to determine whether cilia formation and function was normal in all tissues . Azi1 mutant males display complete infertility with no evidence of pregnancy or pups born from more than 25 plugged dams . In contrast , Azi1Gt/Gt female mice have normal litter numbers and sizes ( Figure 6A ) . Azi1Gt/Gt males have reduced testes weight ( Azi1Gt/Gt: 185 mg+/−23 . 9 mg vs . Azi1Gt/+: 127 mg+/−9 . 7 mg , P<0 . 05 , Student's t-test ) corresponding to a drastic reduction in sperm density ( less than 2% of wild type ) ( Figure 6B ) . Male infertility is a common symptom of ciliopathies , often in conjunction with airway dysfunction and late-onset phenotypes including retinal degeneration , kidney cysts and obesity in mouse mutants of cilia genes [55] , [56] , [57] . Immunofluorescent and ultrastructural analysis of postnatal multiciliated airway epithelium revealed mutant cilia to be morphologically normal , consistent with the lack of chronic airway infections in these mice ( Figure 3G–H and S3D–I ) . In aged cohorts of littermates , no signs of retinal degeneration were observed in Azi1 null eyes either by ophthalmoscopic examination ( data not shown ) or histologically at six months ( n = 7 , Figure S5A–C ) . No cysts were observed in Azi1 null kidneys aged 6 months or older ( n = 6 , Figure S5D–F ) , nor was any obesity observed in aged Azi1Gt/Gt mice ( n = 8 at 3 months , n = 5 at 6 months , Figure S5G and H ) . In support of our cellular analysis , these in vivo studies of Azi1 null mice demonstrate that Azi1 is not required for cilia formation or function in general but is required for formation and function of the specialised cilia derivative , the sperm flagella . To determine when spermatogenesis is disrupted in Azi1 mutant mice , we examined periodic acid-Schiff ( PAS ) stained sections of testes . Azi1Gt/Gt mutant testes show a significant reduction in tubule lumen size ( Lumen diameter: 15 . 1+/−1 . 2 µm in Azi1Gt/Gt , compared to 36 . 7+/−1 . 4 µm in Azi1+/+ , mean +/− SEM , P<0 . 001 , Student's t-test , n = 3 ) , with a drastic reduction in the number of sperm flagella visible in the lumens of Azi1Gt/Gt testes ( Figure 6C–L ) . Anti-acetylated α-Tubulin , which marks the flagellar axonemes , reveals that whilst control tubule lumens are filled with sperm flagella , almost none were detected in mutant tubules , and any flagella seen appeared shorter and morphologically abnormal , suggesting Azi1 is necessary for flagella formation ( Figure 6M and N ) . Light microscopy revealed the pre-spermiogenic stages of spermatogenesis , up to Step 7–8 spermatids , appeared to be normal ( Figure 6E–L , O–P ) . However , from Step 9 , spermatid morphology is highly abnormal , with mutant elongating spermatids mislocalised and misorientated within the tubule . In addition to axonemal defects , mutant elongating spermatids also exhibit teratozoospermia , where sperm heads show an abnormal club-shaped nuclear morphology , as opposed to the normal hook-shaped head in wild type elongate spermatids ( Figure 6E–L , P ) . Very few spermatids reach maturity and are successfully passed into the epididymis ( Figure 6B , S6A and B ) . Importantly these phenotypes were observable from the first wave of spermatogenesis ( Figure S6C–J ) . Whereas Azi1Gt/Gt tubules resemble Azi1+/+ tubules at postnatal day 20 ( P20 ) and P25 ( Figure S6C–F ) , by P30 , defects such as lack of flagella and misorientated spermatids displaying teratozoospermia become apparent . These results confirm observations in the adult mutant tubules that defects in spermiogenesis arise as the spermatids begin to elongate ( P25–30 ) ( Figure S6G–J ) . To investigate whether the reduction in mature sperm was due to an increase in spermatid death in Azi1Gt/Gt tubules , we analysed levels of activated Caspase 3a and TUNEL staining , both indicators of apoptosis , in adult mutant tubules . Azi1 mutant tubules showed increases in both activated Caspase 3a staining and TUNEL positive cells ( Figure S6K–P ) . The restricted spatial distribution of activated Caspase 3a positive cells , combined with the cell counts showing a reduction in elongating spermatids ( Figure 6O ) indicate Azi1Gt/Gt spermatids undergo apoptosis from Step 9 onwards . The germline is particularly sensitive to DNA damage and given the suggested role for AZI1 in genome stability we considered whether Azi1 null male infertility is due to an increase in DNA damage . Infertility due to defects in DNA damage response pathways generally presents as an early arrest in spermiogenesis , with spermatocytes not progressing through meiosis [58] , [59] , [60] . This is in contrast to the relatively late arrest in post-meiotic spermatogenesis seen in Azi1 null mice , reminiscent of other ciliopathic mouse models [55] , [56] , [57] , [61] , [62] . To affirm that the arrest in spermatogenesis in Azi1 mutant mice is not due to genome instability , we stained Azi1 null testes with anti-γH2AX . In Azi1 mutant tubules , we observed anti-γH2AX staining comparable to controls , emphasising that there is no increase in DNA double-stranded breaks in Azi1 null testes ( Figure S4N and O ) . Together with the previous in vitro and in vivo data ( Figure S4 ) , this shows there is no increase in DNA damage in the absence of Azi1 under physiological conditions . Cauda or caput epididymides from Azi1 mutant mice only contained debris and degenerating sperm , none of which were motile ( Figure 6B , 7A and B , Movie S2 and S3 , and data not shown ) . Even in the testes , flagella were rarely observed by ultrastructure analysis of mutant tubules , which exhibited drastically reduced lumen diameters filled with vacuolar cells and proteinaceous cell debris , in contrast to the open , flagella filled wild type tubule lumens ( Figure 7C and D ) . The rare flagella that remained showed evidence of abnormal trafficking , with swollen flagellar lumens and ectopic mistrafficked outer dense fibres , occasionally associated with microtubules ( Figure 7E and F , S7 ) . Remaining sperm were morphologically abnormal with flagella of severely reduced length ( Figure 7G–I ) . The processes involved in extension of the mammalian sperm flagella are not well understood , but are thought to involve IFT-mediated trafficking [63] , [64] , [65] . Truncated Azi1 mutant axonemes exhibit abnormal post-translational modifications of microtubules and irregular distributions of IFT trains , as marked by anti-Ift88 ( Figure 7G and H ) . Extended axonemal structures are rarely detected by longitudinal TEM sections of mutant spermatids , instead occasional swollen shortened flagellar remnants with build-up of IFT cargo can be seen ( Figure S7 ) . Together these data suggest disruptions in regulated IFT account for the failure of flagellar axoneme extension . However , some of the abnormalities in Azi1 mutant spermatid development , such as teratozoospermia cannot easily be explained by defects in IFT . These defects are first observed at step 9 , when spermatids undergo a series of complex morphological changes , including nuclear remodelling and formation of the transient microtubule structure of the manchette [66] , [67] . This microtubular “sleeve” structure surrounds the head and is assembled concurrently with the elongation and condensation of the spermatid nucleus , as well as growth of the centrosome-derived axoneme [68] . As in IFT , motor-driven trafficking along this track of microtubules delivers cargo from the Golgi-derived acrosome toward the centrosome and nascent sperm tail , in a process of intramanchette transport ( IMT ) [67] . Abnormal club shaped nuclei were previously observed in mutants with defects in manchette formation and function [69] , [70] . Ultrastructural analysis revealed the manchette to be present in Azi1 mutant spermatids but it often appears kinked , and is occasionally misnucleated away from the spermatid head ( Figure 8A and B , S8A and B ) . Late stage spermatids exhibited abnormal nuclear morphologies , consistent with the histological analyses ( Figure 6 and S6 ) , often with detachment of the acrosome from the nucleus ( Figure S8C–E ) . Formation of the sperm tail involves the migration and modification of a peripheral pair of centrioles to the caudal pole of the nucleus , opposite the acrosome , where they become lodged to form the neck or head-tail coupling apparatus ( HTCA ) . In early spermatid differentiation , modifications to the proximal centriole , lodging of the centrioles into the implantation fossa of the nuclear membrane and formation of the centriolar adjunct appear grossly normal in Azi1 mutant spermatids [71] ( Figure 8C and D ) . A range of HTCA phenotypes are observed in later stage mutant spermatids , including misalignment of HTCA with the nucleus and/or displaced implantation fossa [72] ( Figure 8E and F , S8F ) . Together , these results suggest Azi1 may be required for maturation and functional integrity of the HTCA . To understand these spermiogenic phenotypes observed in Azi1 mutants , we examined Azi1 localisation during sperm development . Azi1 is found at the Golgi-derived acrosome ( Step 10–12 spermatids: Figure 8G ) , then in the centrosome-containing HTCA at the flagellar base in later stage spermatids ( Figure 8I ) . Importantly , no Azi1 staining is detected in any stage of mutant spermatids , confirming specificity of this Azi1 localisation ( Figure 8H and J ) . This dynamic stage-specific redistribution of Azi1 is consistent with Azi1 undergoing IMT , although we failed to detect Azi1 specifically in the manchette , possibly due to dispersal of Azi1 below the detection threshold during IMT . This redistribution of Azi1 to the HTCA is consistent with a role in the maturation and functional integrity of this structure , and is reminiscent of enrichment of Azi1 around basal bodies upon ciliation in somatic cells . We next examined localisation of an IMT cargo , Hook1 , a coiled-coil protein implicated in vesicular transport which is mobilised progressively from the acrosome to HTCA during spermiogenesis [73] , [74] . Post-translational modification of microtubules in the manchette are proposed to determine trafficking events by motors [74] . Manchette microtubules in wild type spermatids , although stabilised , are not labelled by the usual microtubule stabilising modifications , such as acetylation [75] ( Figure 8K–M ) . However , strong anti-acetylated α-Tubulin staining is observed in Hook1-positive Azi1 mutant manchettes at step 9–12 ( Figure 8N and O ) . Subsequently , Hook1 is prematurely lost from Step 14–15 mutant manchettes ( Figure 8M and P ) . These results suggest Azi1 mutant spermatids exhibit altered microtubule dynamics and IMT cargo localisation . While intramanchette transport ( IMT ) is essential for both normal sperm head morphology and flagella formation [66] , [73] , [76] , mouse mutations of components trafficked by IMT , like Hook1 [68] , [69] , [73] and RIM-BP3 [70] , do not block extension of the flagellar axoneme completely . These results suggest that loss of Azi1 disrupts the microtubule-based trafficking of both flagellar-directed IMT , and intraflagellar transport , resulting in both abnormal sperm head morphology together with the lack of flagella in Azi1 mutant spermatids .
We and others have shown transient knock-down of mammalian Azi1 leads to a reduction in ciliogenesis [10] . Importantly , we show this Azi1 knock-down ciliogenesis defect is rescued by overexpressing Azi1-GFP , emphasising the phenotype is “on-target” ( Figure 1 ) . Surprisingly , following genetic deletion of all gene function , cilia develop and function normally in vivo and in primary cells ( Figure 3 , 4 , S3 and S5 ) . Ruling out any sub-detectable “leakiness” of our Azi1 gene-trap , transfection of Azi1 siRNA into Azi1Gt/Gt MEFs does not affect ciliogenesis ( Figure 5 ) , suggesting that compensation for the lack of Azi1 has occurred . It has been previously suggested that acute knock-down of proteins can give a more severe phenotype than long-term deletions due to compensation in vivo [50] , [51] . We present the first demonstration of a lack of phenotype in null cells treated with siRNA against the gene of interest , proving this functional compensation exists . In the absence of Azi1 , mammalian cilia develop and function properly in all mouse tissues except for the developing sperm ( Figure 3 , 4 S3 and S5 ) . Azi1 is not essential for ciliogenesis and any involvement it may have in cilia biology can be compensated for in most tissues , aside from the modified cilia of the sperm flagella . Azi1 null mice are born at sub-Mendelian ratios and a third of mutants appear to be lost before mid-gestation ( Table 1 ) , suggesting that a proportion of mutant embryos may fail to compensate for the loss of Azi1 and die earlier in development . Several ciliopathy mouse models are also born at sub-Mendelian ratios , suggesting stochastic events may affect the requirement for ciliary proteins during embryogenesis [55] , [56] , [57] , [77] . Our study highlights the importance of functional follow-up studies of siRNA data , and cautions against direct extrapolation of siRNA phenotypes to the genetic in vivo phenotype . On the other hand , acute knock-down by siRNA can expose roles for genes that would otherwise be overlooked due to compensation or redundancy in long-term deletion studies in vivo . Azi1 null male mice are infertile , suggesting the loss of Azi1 cannot be compensated for during spermiogenesis . Azi1 null mice exhibit post-meiotic defects in spermatogenesis with misorientation and abnormal morphology of elongating and elongated spermatids , including teratozoospermia , from Step 9 of spermatid development onwards ( Figure 6 and S6 ) . Sperm flagella are mostly absent and any remaining axonemes are truncated and immotile , with swollen flagella lumens and evidence of mistrafficking of IFT components and cargo ( Figure 7 and S7 ) , suggesting Azi1 is required for IFT during the formation of the sperm flagella . Additional roles for ciliary proteins in spermatid development outside the axoneme have been suggested , with parallels drawn between intraflagellar transport ( IFT ) and intramanchette transport ( IMT ) [67] . The abnormal club-shaped sperm head morphology is similar to mutants with defective manchettes [68] , [69] , [70] . Azi1 mutant manchettes are structurally abnormal , and exhibit altered microtubule post-translational modifications , which are implicated in motor-selection for cargo delivery [74] ( Figure 8 and S8 ) . Altered expression of Hook1 , an effector of IMT implicated in vesicular transport [74] , is also observed in these mutant structures , suggesting the process of IMT is disrupted in Azi1 mutants . Many motors and associated proteins , some linked to IFT , have been localised to the manchette and the testicular phenotype of Ift88orpk/orpk hypomorphic mice phenocopies the Azi1-dependent trafficking defects [67] , [74] . Although the role of centriolar satellite or transition zone proteins in spermiogenesis is not well understood , it is tempting to draw parallels between the microtubule-based trafficking of centriolar satellites towards the centrosome/basal body of primary cilia , and the trafficking of proteins along the transient microtubular structure of the manchette to the highly specialised motile sperm flagellum . The dynamic relocalisation of Azi1 from the acrosome to the HTCA during spermatid development suggests Azi1 undergoes IMT , as this is the main transport route between these structures ( Figure 8 ) . Interestingly , components of the dynactin complex , which localise to centriolar satellites and is required for their peri-centrosomal localisation , have also been localised to the manchette [19] , [47] , [74] , [78] . It is a conundrum for the cilia field as to why broadly expressed “core” cilia genes have clinically restricted phenotypes when mutated . This “tissue-specific” requirement is demonstrated by the limited phenotypes of late-onset ciliopathies , and many centriolar satellite and transition zone-specific mouse mutants display tissue specific ciliary phenotypes [56] , [61] , [62] , [77] , [79] , [80] . In the case of the transition zone complex components , Nphp4 and Nphp1 null mutant mice display male infertility without kidney phenotypes , in contrast to the human disease in which patients have severe nephronophthisis [61] , [62] , [81] . This discrepancy could reflect differences in species and/or functional nature of the mutation , but the severity in humans could also reflect the mutational load in other components of the complex [82] , [83] . Importantly primary cilia formation and function appears grossly normal in these mutants , like in our Azi1 null mice , emphasising that compensation for function of key cilia genes is likely to be a recurrent theme given the central importance of primary cilia in mammalian development and patterning . Azi1/Cep131 is a conserved protein found in all ciliates except for nematodes , leading to the suggestion that Azi1 may be involved in cilia motility ( Table S1 ) [46] . Alternatively , Azi1/Cep131 could be required to build a canonical nine-triplet centriole , as nematodes build specialized , non-canonical centrioles . However , an Azi1/Cep131 orthologue is present in the Toxoplasma gondii genome which also build non-canonical centrioles [84] . Furthermore , knock-down of Azi1/Cep131 in planaria does not affect centriole formation , but affects cilia motility [45] . While deletion mutations of dila in D . melanogaster [24] lead to defects in specialised “motile” mechanosenory Type I neurons and sperm , cilia do form with defects in mechanosensory neuronal cilia morphology characteristic of IFT mutants , suggesting defective trafficking . Similarly , Azi1 D . rerio morphants phenocopy IFT morphants with cilia forming but displaying tissue-specific reductions in length [25] . Interestingly , in trypanosomes TzAZI1 localises to the flagellar pocket , a dynamic endo-exocytic organelle implicated in membrane trafficking surrounding the flagellar base , and TzAZI1 RNAi affects flagellar function , as opposed to its formation or maintenance [46] . These studies suggest a conserved trafficking function for Azi1 in regulating ciliary-bound cargo . We confirmed mammalian Azi1 localises to centriolar satellites and provide the first direct observation of Azi1 trafficking along microtubules , both towards and away from the centrosome ( Figure 2G , Supplementary Movie 1 ) , similar to the movement observed for PCM1-GFP [18] . The transport away from the centrosome may involve kinesin motors , and it has been shown that CEP290 , which binds AZI1 , interacts with both Dynactin components and the kinesin motor KIF3a [28] , [79] , supporting the theory that these proteins could undergo bidirectional trafficking along microtubules . Centriolar satellites are thought to spatially restrict centrosomal access of proteins involved in basal body maturation and ciliogenesis [22] . While centriolar satellites and the key centriolar satellite protein , Pcm1 , are not found in Drosophila , tight transcriptional control of dila mRNA limits expression to just before the onset of ciliogenesis where the protein can localise to the PCM [24] . Although the regulatory mechanisms are different , in both mammals and flies , it appears Azi1/dila recruitment is involved in centrosome to basal body maturation . In D . melanogaster , mutations in two other coiled-coil proteins YURI , conserved only among the Drosophila genus [85] , [86] , and UNC , also insect-specific but for which centriolar satellite protein OFD1 is proposed to be a functional orthologue [87] partially phenocopy dila mutants and genetically interact with dila [24] . These proteins are involved in the proper maturation and anchoring of the sperm basal bodies to the nuclear membrane [24] , [86] , [87] . Formation of Drosophila sperm flagella axoneme is unusual in that it is IFT-independent , forming instead in cytoplasmic cysts . Sperm axonemes do form in dila mutant flies , although the sperm display defective HTCA formation . This is reminiscent of the defects we observe in compromised integrity of HTCA in Azi1 null sperm ( Figure 8 and S8 ) , although the requirement for IFT in mammalian sperm axonemal formation may explain the more severe IFT-based flagellar phenotypes observed in the mouse mutant spermatids . Interestingly , similar to Ma et al . ( 2011 ) who showed transition zone localisation for DILA and UNC in D . melanogaster [24] , we show Azi1 and PCM1 also localise to transition zone of primary cilium . While this manuscript was in preparation , OFD1 was also shown to localise to the transition zone [41] . Transition zone and centriolar satellite localisation has previously been described for CEP290 , which was recently shown to interact with AZI1 [28] , [38] , [39] . Our data supports that redistribution of centriolar satellite proteins to the transition zone during ciliogenesis is of functional significance . It is possible that centriolar satellite-associated cargo is transported to the centrosome as it matures into a basal body with a functional transition zone ready to be trafficked into the cilium . Alternatively , Azi1/CEP290 could also have a role in the gating function at the transition zone , regulating protein content in the cilium , accounting for the IFT-like phenotypes reported in dila Drosophila mutants [24] and Cep290 Chlamydomonas mutants [42] . Given that centriolar satellites are scaffolds for controlling activity of ciliopathy-associated proteins [15] , [22] , it will be important to define the composition of these specific sub-complexes that move to the transition zone , and ask whether they are part of the compensation mechanism observed in Azi1 null animals . If true , we propose that these interactions among the centriolar satellite proteins could extend to multi-allelic mutational load , including AZI1 , in a subset of human ciliopathies with diverse clinical presentations beyond male infertility .
ShhLIGHT-II ( ATCC , genetically modified NIH-3T3 ) and NIH-3T3 cells were maintained in DMEM ( Life Technologies ) , hTERT-RPE cells were maintained in DMEM-F12 ( Life Technologies ) , all supplemented with 1 . 5 g/L sodium bicarbonate ( Sigma ) , 10% foetal calf serum , 5×108 U/L penicillin and 11 mM streptomycin at 37°C , 5% CO2 . Early passage MEFs were maintained in Optimem ( Life Technologies ) plus 0 . 5 mM beta-mercaptoethanol ( Sigma ) , 10% foetal calf serum , 5×108 U/L penicillin and 11 mM streptomycin at 37°C , 5% CO2 , 3% O2 . To induce ciliogenesis , serum was removed for 48 hours . ShhLIGHT-II cells were co-transfected with 25 nM Dharmacon OnTarget Plus siRNA and 1 µg/mL plasmid DNA with Dharmafect Duo ( Dharmacon ) , serum was removed after 24 h and samples were analysed 72 hours after transfection . MEFs were co-transfected with 50 nM Dharmacon OnTarget Plus siRNA and 1 . 6 µg/mL plasmid DNA using the Invitrogen Neon , according to the manufacturer's protocol . siRNA sequences used: Ctrl: 5′UGGUUUACAUGUCGACUAA3′; Ift88 #3: 5′-CGGAGAAUGUUGAAUGUUU-3′; Ift88 #4: 5′-GCUUGGAGCUUAUUACAUU-3′; Azi1 3′UTR Pool: Equimolar pool of: 5′-AGACACAGGGCUAAGGGUA-3′ , 5′-CAGCUGUUCUAUAGUAAAA-3′ , 5′-CCCUUGGGAUGACGAGCCA-3′ and 5′-GUGUCCAGGUCACGCUCCA-3′ . For live imaging of Azi1-GFP on Map4-RFP microtubules , NIH-3T3 cells were transduced with 30 particles per cell ( PPC ) of CellLIGHT MAP4-RFP BacMan 2 . 0 ( Life Technologies ) , left for 24 hours then transfected with Azi1-GFP using Lipofectamine 2000 ( Life Technologies ) and imaged 24 hours later . For DNA damage assays , MEFs were challenged with hydroxyurea ( Sigma ) at given concentrations for 18 hours . Alternatively , MEFs were irradiated in culture medium at 2Gy/minute using a Faxitron CellRad cabinet X-ray system ( Faxitron Bioptics ) , cultured for 3 hours and then fixed and analysed . Azi1 was PCR amplified from mouse cDNA and TA cloned into pcDNA6 . 2-C-Em-GFP-GW-TOPO cDNA plasmid ( Life Technologies ) . Centrin2 was PCR amplified from mouse cDNA , adding EcoR1 and Sal1 sites . This was restriction enzyme cloned into pEGFP-N1 ( Clontech ) . Cells or testes were homogenised in cell lysis buffer ( Cell Signaling Technology ) plus 1 mM phenylmethylsulfonyl fluoride ( PMSF ) ( Thermo Scientific ) and Complete Protease Inhibitor Cocktail ( Roche ) , sonicated for 2×30 seconds . Testes extracts were concentrated with Amicon Ultra-0 . 5 mL 30 kDa centrifugal filters ( Millipore ) . Samples were separated on Novex 3–8% Tris Acetate gels ( Life Technologies ) then transferred to Hybond nitrocellulose membrane ( GE Healthcare ) , which were blocked in 5% milk . Membranes were incubated in primary antibodies ( Table S4 ) washed , incubated with horse radish peroxidase ( HRP ) -conjugated secondaries ( Table S5 ) and developed with Amersham ECL-plus western blotting detection system . Densitometry was performed using ImageJ . Cells were fixed in 4% paraformaldehyde/phosphate buffered solution ( PFA/PBS ) for 10 minutes at room temperature , or 4% PFA/PHEM ( 120 mM PIPES , 140 mM HEPES , 20 mM EGTA , 16 mM MgSO4 , pH7 ) for 10 minutes at 37°C . Alternatively , pre-extraction was performed for 30 seconds on ice in 0 . 1 M PIPES pH 6 . 8 , 2 mM EGTA and 1 mM MgSO4 , then cells were fixed in ice cold methanol on ice for 10 minutes . Cells were blocked in 10% donkey serum/0 . 1% Triton-X in Tris buffered solution ( TBS ) . Primary antibodies were added ( Table S4 ) , cells were washed then incubated in Alexa Fluor-conjugated secondaries ( Table S5 ) and slides were mounted using Prolong Gold ( Life Technologies ) . Azi1+/Gt ( CCOG35 ) Wtsi 129Ola embryonic stem cells , which have a gene trap inserted into intron 2 of Azi1 , were ordered from the Mutant Mouse Regional Resource Centre ( MMRRC ) . ES cells were injected into C57BL/6J blastocysts and implanted into a recipient C57BL/6J female . These were backcrossed onto C57BL/6J for at least 5 generations for most analyses . Animals were maintained in SPF environment and studies carried out in accordance with the guidance issued by the Medical Research Council in “Responsibility in the Use of Animals in Medical Research” ( July 1993 ) and licensed by the Home Office under the Animals ( Scientific Procedures ) Act 1986 . Genotyping was performed using gene trap specific primers ( GT Forward: 5′-GGTCCCGAAAACCAAAGAAG-3′ and GT Reverse: 5′-AGTATCGGCCTCAGGAAGATCG-3′ ) and primers to Azi1 intron 2 , spanning the insertion site which fail to amplify in the mutant ( In Forward: 5′-GAGGAACCTGGGTGAGACCT-3′ and In Reverse: 5′-GCAGCAGATCTTTGGTCCAC-3′ ) . Details of primers used for characterisation of the Azi1GT allele by RT-PCR and RT-qPCR are provided in Tables S2 and S3 . Tissue samples were collected , kidneys were fixed in 4% PFA/PBS , testes were fixed in Bouin's fixative , and eyes were fixed in Davidson's fixative according to standard protocols . Tissues were serially dehydrated and embedded in paraffin . Microtome sections of 8 µm thickness were examined histologically via haematoxylin and eosin ( H&E ) or periodic acid-Schiff ( PAS ) staining . For immunofluorescent analysis , paraffin sections were dewaxed and re-hydrated via ethanol series . Antigen retrieval was performed by boiling the sections for 15 minutes in the microwave in citrate buffer . Sections were blocked in 10% donkey serum/0 . 1% Triton-X in PBS and primary antibodies were diluted in 1% donkey serum/PBS ( Table S4 ) . Slides were washed and incubated in Alexafluor conjugated secondary antibodies ( Table S5 ) , washed and mounted in ProLong Gold ( Life technologies ) . For immunohistochemistry , the same procedure was used , with the addition of one step after the re-hydration . Slides were immersed in 3% H2O2 in PBS for 20 minutes to block endogenous peroxidases . Slides were incubated in primary antibody , washed , then incubated in biotin-conjugated secondary antibody ( Vector laboratories ) . This was detected using the Vector ABC kit and Vector NovaRed peroxidase substrate kit . For TUNEL , after dewaxing , sections were incubated in 0 . 25% Triton-X and labelling was performed using Click-IT TUNEL staining kit following manufacturer's instructions ( Life Technologies ) . E11 . 5 Azi1+/Gt embryos were fixed in 4% PFA/PBS for 20 minutes , rinsed in PBS and washed 3 times in detergent buffer ( 0 . 1 M phosphate buffer , 2 mM MgCl2 , 0 . 1% sodium deoxycholate and 0 . 02% NP-40 ( IGEPAL CA-630 ) ) . Embryos were then stained overnight in detergent buffer containing 50 mg/ml X-gal , 5 mM K3 and 5 mM K4 at 37°C , protected from light , washed twice in detergent buffer and post fixed overnight in 4% PFA . Testes , cauda and caput epididymides were dissected into M2 media ( Invitrogen ) . For live imaging , sperm were imaged in M2 media or 1% methyl cellulose ( Sigma ) , in capillary tubes ( Vitrotubes Mountain Leaks ) sealed with Cristaseal ( Hawskley ) . Sperm counts were performed on sperm from the cauda epididymides , diluted in H2O using a haemocytometer , only counting intact sperm ( with both head and tail ) . For fixed samples , either sperm spreads or testes squashes were prepared . For sperm spreads , testes were placed through a 100 µm nylon mesh ( BD Biosciences ) . Sperm from the caput epididymus and testes were then placed on a 20–40% Percoll gradient ( GE Healthcare ) and spun at 3 , 000 g for 10 minutes . The sperm were spread on Poly-D-lysine slides ( BD Biosciences ) and fixed 4% PFA/PBS . Cauda sperm was spread directly on Poly-D-Lysine slides and fixed with 4% PFA/PBS . In all cases , sperm were permeabilised with 0 . 4% Triton-X in PBS and immunofluorescence was performed as described . For testes squashes , tubules were dissected as described [88] , placed on a slide and squashed with a coverslip . Slides were snap frozen in liquid nitrogen , coverslips removed and samples fixed and permeabilised by 10 minutes in −20°C methanol , 30 seconds in acetone and then 15 minutes in 4% PFA/PBS . Immunofluorescence was then performed as described . Nasal brushing was performed as described [89] . Cells were fixed for 30 minutes on ice in 4% PFA , cytospun onto slides , fixed with −20°C methanol for 10 minutes then immunofluorescence was performed as described . Samples were dissected into PBS . Samples were fixed in 2% PFA/2 . 5% gluteraldehyde/0 . 1 M Sodium Cacodylate Buffer pH7 . 4 + 0 . 04% CaCl2 . Testes capsules were removed prior to immersion in fix . After 30 minutes at room temperatures , samples were cut into 1 mm cubes and fixed overnight or longer at 4°C . Tissue was rinsed in 0 . 1 M sodium cacodylate buffer , post-fixed in 1% OsO4 ( Agar Scientific ) for one hour and dehydrated in sequential steps of acetone prior to impregnation in increasing concentrations of resin ( TAAB Lab Equipment ) in acetone followed by 100% , placed in moulds and polymerised at 60°C for 24 hours . Ultrathin sections of 70 nm were subsequently cut using a diamond knife on a Leica EM UC7 ultramicrotome . Sections were stretched with chloroform to eliminate compression and mounted on Pioloform filmed copper grids prior to staining with 1% aqueous uranyl acetate and lead citrate ( Leica ) . They were viewed on a Philips CM100 Compustage Transmission Electron Microscope with images collected using an AMT CCD camera ( Deben ) . Mice were tail tipped and blood was collected using a microhematocrit capillary tube with heparin coating ( Globe Scientific ) into heparin ( Sigma ) . This was fixed in super-chilled methanol . Saline was added and then blood was pelleted at 600 g for 5 minutes . Pellets were treated with RNAseA and anti-CD71primary antibody ( Lifespan Biosciences ) . Cells were incubated on ice for 30 minutes then at room temperature for 30 minutes . Propidium iodide was added and flow cytometry was performed on a FACScalibur ( BD Biosciences ) . Data was analysed using FlowJo software ( v7 . 6 . 1 , Tree Star ) as described by [53] . The initial siRNA screen imaging was carried out on the Olympus ScanR microscope , imaging 16 frames per well . Image analysis , including identification and counting of cells and cilia was performed using the Olympus ScanR Analysis Software . Confocal images were captured with a Nikon A1R confocal microscope , comprising a Nikon Eclipse TiE inverted microscope and four laser modules: 405 ( laser diode ) , 457 , 488 , 514 ( multiline Argon ) 561 ( diode-pumped solid-state ) and 638 nm ( laser diode ) . For live imaging of Azi1-GFP and MAP4-RFP , a Zeiss Axiovert 200 fluorescence microscope was used equipped with 100×/1 . 4 plan apochromat objective ( Carl Zeiss , Welwyn , UK ) , Lambda LS 300W Xenon source with liquid light guide and 10-position excitation , neutral density and emission filterwheels ( Sutter Instrument , Novato , CA ) , ASI PZ2000 3-axis XYZ stage with integrated piezo Z-drive ( Applied Scientific Instrumentation , Eugene , OR ) and a Photometrics Coolsnap HQ2 CCD camera ( Roper Scientific , Tucson , AZ ) . Brightfield images were captured with a Coolsnap HQ CCD camera ( Photometrics Ltd , Tucson , AZ ) on a Zeiss Axioplan II fluorescence microscope with Plan-neofluar objectives ( Carl Zeiss , Welwyn Garden City , UK ) . Colour additive filters ( Andover Corporation , Salem , NH ) installed in a motorised emission filter wheel ( Prior Scientific Instruments , Cambridge , UK ) were used sequentially to collect red , green and blue images that were then superimposed to form a colour image . For live imaging of sperm , a Qimaging Retiga camera running at 30 frames per second ( bin 2×2 half frame ) captured image sequences with a 5× objective at zoom 5 on a Nikon AZ100 macroscope . Still figures show all time points superimposed into one image to depict the movement , or lack thereof , during the movie . Apart from the initial screen , image analysis including intensity profiling was performed in ImageJ . Throughout P<0 . 05 is considered significant . Statistics were carried out in Microsoft Excel or GraphPad Prism ( La Jolla , CA ) and the test used is specified in the text/figure legend .
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Cilia are slender projections from the surface of most mammalian cells and have sensory and sometimes motile functions . They are essential for mammalian development and defects in cilia lead to a group of human diseases , termed ciliopathies , with variable symptoms including embryonic lethality , lung and kidney defects , blindness and infertility . Cilia are complex structures composed of hundreds of components , whose individual functions are poorly understood . We screened for mammalian genes important in building cilia , and identified Azi1/Cep131 , a gene previously shown to be required for cilia formation and function in fish and flies . We show that if we acutely reduce levels of Azi1 in mouse cells , fewer cells form cilia , but if we generate cells chronically lacking all Azi1 , cilia form normally . In addition , mice without any Azi1 are healthy and viable , confirming normal cilia function . However , in these mice , the highly specialised ciliary structure of the sperm tail does not form , resulting in male infertility . We suggest Azi1 has conserved trafficking roles in both primary cilia and the specialised sperm flagella . Abruptly removing Azi1 results in instability causing the existing cilia network to collapse , whereas chronic deletion of Azi1 allows the system to re-equilibrate , and cilia to form normally .
|
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2013
|
Acute Versus Chronic Loss of Mammalian Azi1/Cep131 Results in Distinct Ciliary Phenotypes
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Adult T-cell leukaemia/lymphoma ( ATL ) arises from chronic non-malignant human T lymphotropic virus type-1 ( HTLV-1 ) infection which is characterized by high plasma pro-inflammatory cytokines whereas ATL is characterized by high plasma anti-inflammatory ( IL-10 ) concentrations . The poor prognosis of ATL is partly ascribed to disease-associated immune suppression . ATL cells have a CD4+CCR4+CD26-CD7- immunophenotype but infected cells with this immunophenotype ( ‘ATL-like’ cells ) are also present in non-malignant HTLV-1 infection . We hypothesized that ‘ATL-like’ and ATL cells have distinct cytokine producing capacity and a switch in the cytokines produced occurs during leukemogenesis . Seventeen asymptomatic carriers ( ACs ) , 28 patients with HTLV-1-associated myelopathy ( HAM ) and 28 with ATL were studied . Plasma IL-10 concentration and the absolute frequency of IL-10-producing CD4+ T cells were significantly higher in patients with ATL compared to AC . IL-10-producing ATL cells were significantly more frequent than ‘ATL-like’ cells . The cytokine-producing cells were only a small fraction of ATL cells . Clonality analysis revealed that even in patients with ATL the ATL cells were composed not only of a single dominant clone ( putative ATL cells ) but also tens of non-dominant infected clones ( ‘ATL-like’ cells ) . The frequency of cytokine-producing cells showed a strong inverse correlation with the relative abundance of the largest clone in ATL cells suggesting that the putative ATL cells were cytokine non-producing and that the ‘ATL-like’ cells were the primary cytokine producers . These findings were confirmed by RNAseq with cytokine mRNA expression in ATL cells in patients with ATL ( confirmed to be composed of both putative ATL and ‘ATL-like’ cells by TCR analysis ) significantly lower compared to ‘ATL-like’ cells in patients with non-malignant HTLV-1 infection ( confirmed to be composed of hundreds of non-dominant clones by TCR analysis ) . A significant inverse correlation between the relative abundance of the largest clone and cytokine mRNA expression was also confirmed . Finally , ‘ATL-like’ cells produced less pro- and more anti-inflammatory cytokines than non ‘ATL-like’ CD4+ cells ( which are predominantly HTLV uninfected ) . In summary , HTLV-1 infection of CD4+ T cells is associated with a change in cytokine producing capacity and dominant malignant clonal growth is associated with loss of cytokine producing capacity . Non-dominant clones with ‘ATL-like’ cells contribute to plasma cytokine profile in patients with non-malignant HTLV-1 infection and are also present in patient with ATL .
Human T- lymphotropic virus type-1 ( HTLV-1 ) is a complex delta retrovirus infecting an estimated 10 million individuals worldwide [1] . In the majority , infection leads to a chronic asymptomatic carrier state ( AC ) but 2% to 6% develop adult T-cell leukaemia/lymphoma ( ATL ) and another 3% inflammatory disorders e . g . HTLV-1-associated myelopathy ( HAM ) . The diagnosis of ATL is based on clinical features , morphology ( lymphocytes with characteristic ‘flower cell’ morphology ) , immunophenotyping ( CD3+ , CD4+ , CCR4+ , CD25+ , CD26- and CD7- ) and demonstration of dominant HTLV-1 infected clones [2 , 3] . ATL is classified into four subtypes: smouldering , chronic , acute and lymphoma . Smouldering and chronic ATL are suggested to have an indolent course while acute and lymphoma an aggressive course [3] . Survival with chemotherapy is poor [4–7] due to primary chemo-refractory disease , or early relapse or opportunistic infections . [8–11] . Patients with ATL have high plasma concentrations of anti-inflammatory cytokines e . g . interleukin ( IL ) -10 [12 , 13] . IL-10 is secreted by regulatory CD4+ T cells[14] . CD4+ T cells in patients with ATL have been shown to have high IL-10 expression [13 , 15] . ATL cells express regulatory T cell-associated markers CD25 and FOXP3 [16–19] . This has led to the assumption that ATL cells are a regulatory T cell counterpart and mediate an immunosuppressive clinical state by secreting IL-10 . In contrast HAM is characterized by organ damage due to immune activation , and high concentrations of plasma pro-inflammatory cytokines e . g . interferon γ ( IFNγ ) [20] . HTLV-1 infected cells secrete IFNγ [21 , 22] and directly contribute to the plasma cytokine profile in HAM . These findings suggest a distinct cytokine producing capacity of HTLV-1 infected cells in keeping with clinical state . ATL arises de novo in AC and patients with HTLV-1-associated inflammation such as HAM , hereafter referred to as non-malignant HTLV-1 infection . HTLV-1-infected cells have a CD4+CCR4+CD26- immunophenotype; the loss of CD7 expression by these cells differentiates ATL from non-malignant HTLV-1 infection [23–27] . We and others have shown that ‘ATL-like’ HTLV-1-infected ( CD4+CCR4+CD26-CD7- ) cells are present in patients with non-malignant HTLV infection . We hypothesize that the cytokine producing capacity of ATL and ‘ATL-like’ cells are distinct from each other and are directly responsible for the respective plasma cytokine profile in ATL and non-malignant HTLV-1 infection , and that the change in cytokine producing capacity reflects malignant transformation from non-malignant HTLV-1 infection to ATL . Pro- and anti-inflammatory cytokine were measured in plasma and cells ( intracellular staining and gene expression by mRNA sequencing ) in patients with four different HTLV diagnoses: AC; HAM; indolent ATL; aggressive ATL . Clonality analysis of ATL and ‘ATL-like’ cells was performed and the relationship between plasma , cellular cytokine , clonality and clinical state was studied .
The patient cohort is based at the National Centre for Human Retrovirology ( NCHR ) at St Mary’s Hospital , Paddington , London , UK and the University of Miami School of Medicine , Miami , USA . Diagnosis of HTLV-1 infection , HAM and ATL was made according to World Health Organization criteria . Patients’ samples are collected and stored in a Communicable Diseases Research Tissue Bank approved by the UK National Research Ethics Service ( references 09/H0606/106 and 15/SC/0089 ) . Samples at University of Miami School of Medicines are collected under IRB-approved study "Study of Blood , Tissue and Body Fluids of Viral Associated Malignancies" ( EPROST No . 20030608 ) . Samples are stored under license in accordance with the Human Tissues Act 2004 . Samples were collected prior to any systemic therapy in patient with ATL or immune modulatory treatment in patients with HAM . Baseline demographic and clinical characteristics of the study population are shown in S1 Table . Peripheral blood mononuclear cells ( PBMCs ) and plasma were separated from fresh whole blood by density-gradient centrifugation on Histopaque-1077 ( Sigma-Aldrich , St Louis , USA ) . Plasma was stored at -80°C . PBMCs were harvested from the interface , washed in phosphate buffer saline ( PBS , Sigma-Aldrich ) , cryopreserved in 10% dimethyl sulphoxide ( Sigma-Aldrich ) and 90% heat inactivated foetal calf serum ( FCS ) ( Gibco , Carlsbad , USA ) , and stored in liquid nitrogen until use . Plasma cytokine concentrations of the following nine cytokines/chemokines were measured using sensitive and specific V_PLEX immunoassays according to the manufacturer’s protocol ( Meso Scale Discovery , Gaithersburg , USA ) : Human pro-inflammatory cytokines: interferon gamma ( IFNγ ) , interleukin ( IL ) -2 , IL-6 , IL-7 , IL-17α , tumour necrosis factor-alpha ( TNFα ) ; anti-inflammatory cytokine: IL-10 and chemokines: macrophage-derived chemokine ( MDC ) also known as CCL22 and C-X-C motif chemokine 10 ( CXCL-10 ) also known as IFNγ-induced protein 10 ( IP-10 ) , . For samples with detectable concentrations below the limits of quantification missing values were replaced with 99% of the lowest detectable concentration . Thawed cryopreserved PBMCs were incubated in either complete media ( CM ) comprising 10% heat inactivated FCS in RPMI with L-glutamine plus 1% Penicillin only , CM with phytohemaggulutinin ( PHA , final concentration of 5 μg/mL , Sigma ) or CM with 2% cell activation cocktail ( containing phorbol 12-myristate 13-acetate ( PMA ) and ionomycin , ( BioLegend , San Diego , USA ) ) at 37°C , in 5% CO2 at a concentration of 106 cells/ 50 μL for 6 hours . Brefeldin A ( BioLegend ) was added for the last five hours . All washes were done by suspending cells in PBS containing 1% FCS followed by centrifugation at 600g for 5 minutes twice . PBMCs were washed thrice at the end of incubation and stained sequentially with near infrared fixable viability stain followed by flurochrome conjugated monoclonal antibodies against cell surface markers ( CD3 , CD4 , CD7 , CD8 and CCR4 ) for 30 minutes at room temperature ( RT ) . The cells were then fixed using fixation buffer from FoxP3 / Transcription Factor Staining Buffer Set ( eBioscience , San Diego , USA ) . The fixed cells were washed with permeablization buffer followed by incubation with fluorochrome-conjugated monoclonal antibodies against IL-6 , IL-10 , TNFα and IFNγ for 15 minutes at RT . PBMCs were then washed twice and stored at 4°C in PBS 1% FCS overnight until analysis on Becton Dickinson Fortessa II . A minimum of 20 , 000 events were recorded for analysis . Compensation was computed using BD FACS diva software using single staining of Comp ebeads ( eBioscience ) and checked manually . Fluorescence minus one control ( antibody cocktail containing all antibodies except one ) was used for gating . Data were analysed by Flowjo software . Magnetic cell sorting was performing by negative selection . Thawed PBMCs were incubated with primary biotinylated antibodies ( CD7 , CD26 , CD8 , CD14 , CD15 , CD16 , CD19 , CD36 , CD 56 , CD123 , TCR γ/δ , CD235a [Miltenyi Biotec Ltd . , United Kingdom and Biolegend , USA] ) at 4°C for 10 min and 107 cells/100 μl . All washes were done by suspending cells in 1% BSA ( Sigma ) followed by centrifugation at 600g for 5 minutes twice . Labelled PBMCs were washed and incubated with 30% dilution of streptavidin conjugated microbeads at a final concentration of 107 cells/100 μL . Microbead-conjugated PBMCs were washed and magnetically sorted twice on LS Columns ( Miltenyi Biotec Ltd ) according to the manufacturer’s directions . The flow-through containing CD3+CD4+CD7-CD26-cells , of which 99% were CCR4+ , was washed twice . Of these cells , 104 cells were used to check for purity by flow cytometry and the remainder were used for HTLV-1 proviral load quantification and clonality analysis . Genomic DNA was extracted from PBMCs and sorted CD4+ T cell subsets using QIAamp DNA mini kit ( Qiagen , Hilden , Germany ) . The proviral load was determined by real-time PCR as previously described [28] . Clonality analysis was performed using linker-mediated ( LM ) -PCR , high-throughput sequencing analysis of HTLV-1 integration sites according to the method previously described [29] . Random fragments of 1 μg genomic DNA ( 100 ng of sample DNA mixed with 900 ng of uninfected DNA from Jurkat cells ) were generated by sonication and ligated to custom , partially double-stranded , DNA adaptors . Nested PCR using specific primers was performed to selectively amplify adaptor-ligated DNA fragments abutting the HTLV-1 3’ LTR . The PCR products from each sample had two unique 8bp multiplexing barcodes and were pooled for sequencing . Paired-end 150-base reads were generated on an Illumina MiSeq . The reads were de-multiplexed using the MiSeq reporter . The linker and primer sequences are listed in S2 Table . The in silico analysis was conducted in shell and R environment . Read 1 and Read 2 were aligned to reference human ( hg38 , UCSC ) and HTLV-1 genomes using Bowtie2[30] . The number of unique integration sites ( clones ) and the relative and absolute abundance of each unique integration site ( clonal size ) were calculated as previously described [29] . RNA was extracted from MACS-sorted cells using Qiagen Allprep DNA/RNA column-based extraction kits ( Qiagen ) as per the manufacturer’s instructions . RNA-seq was performed on DNase-treated samples using TruSeq Stranded mRNA Library Prep Kit ( Illumina , United States ) as per the manufacturer’s instructions . All sorted cells had the highest RNA quality ( > 100 ng RNA and RNA quality score ≥ 8 ) . All sequencing was performed using 50 nucleotide paired-end reads on an Illumina HiSeq 4000 instrument at the Imperial biomedical research centre ( BRC ) genomics facility , London , United Kingdom . Statistical analysis was performed using Graphpad Prism software . The significance of difference in continuous variables between multiple patient groups was determined by a Kruskal-Wallis test with Dunn post-test analysis . The significance of difference in continuous variables between two cell subsets was determined using a Wilcoxon signed-rank test . The significance of difference in contingency variables was determined by a chi-squared test . Differences were considered statistically significant if p <0 . 05 . The correlation between two continuous variables was determined by a non-parametric Spearman test . The Spearman correlation was considered significant for p<0 . 05 and showing a trend if the p value was between 0 . 05 and 0 . 1 . A classification tree was constructed to identify the hierarchical organisation of plasma cytokine concentration in ACs , patients with HAM and patients with ATL . The classification tree was produced using a Recursive Partitioning and Regression Trees ( RPART ) analysis in R . The network analysis was performed using NodeXL . In the analysis , absolute CD3+ , CD4+ and CD8+ cell counts and PBMC PVL were used as cellular variables , and plasma cytokine concentrations were used as cytokine variables . The cellular and cytokine variables were used as edges of the network while the Spearman correlates between nodes were used as vertices . Vertices with at least a significant trend on Spearman correlation ( p <0 . 1 ) were included . The layout was performed using Harel-Koren Fast multiscale .
The relative and absolute frequencies of CD3+ , CD4+ T cells and HTLV-1 PVL in PBMCs were significantly higher in patients with ATL compared to ACs or patients with HAM ( Table 1 ) . Patients with HAM had significantly higher plasma concentrations of IFNγ , CXCL10 , IL-2 and IL-17 ( pro-inflammatory cytokines ) compared to ACs and patients with ATL ( Fig 1A–1D ) . Patients with ATL and patients with HAM had significantly higher plasma concentrations of the anti-inflammatory cytokine IL-10 compared to ACs ( Fig 1E ) . There was no significant difference in plasma concentrations of IL-6 , IL-7 , CCL22 and TNFα between the three patient groups ( Fig 1F–1I ) . The median plasma concentrations of IL-6 and TNFα in all three patient groups were near the upper limit of the manufacturer’s normal human range while those of CCL22 , IL-2 and IL-7 were near the lower limit . Patients with aggressive ATL had significantly higher TNFα , IL-6 and IL-10 plasma concentrations than those with indolent ATL ( Fig 2A–2C ) . There was no significant difference in plasma concentrations of IFNγ , CXCL10 , CCL22 , IL-2 and IL-7 between ATL subtypes as shown in S1 Fig . The plasma chemokine and cytokine concentrations , of four patients with HAM who developed de novo aggressive ATL and two patients with indolent ATL who progressed to aggressive ATL , were measured at aggressive ATL diagnosis and at 3–12 months earlier . Plasma TNFα , IL-6 and IL-10 concentrations were significantly higher at diagnosis , with median increases of 1 . 9-fold; 3 . 7-fold and 7 . 1-fold respectively ( Fig 2D ) . There was a large variance in changes of plasma IFNγ concentrations . In summary , patients with HAM have the highest concentrations of pro-inflammatory cytokine and ACs had the lowest IL-10 plasma concentrations . Patients with aggressive ATL had higher plasma concentrations of TNFα , IL-6 and IL-10 compared to patients with indolent ATL . These higher plasma concentrations did not precede malignant progression . Network analysis was performed to better understand the interaction between cellular and plasma immune markers . The absolute frequency of CD3+ , CD4+ T cells and PVL were significantly positively correlated with each other in patients with HAM or ATL ( Fig 3A and 3B ) . There were also significant positive correlations between the plasma concentrations of TNFα , IL-6 and IL-10 in all three diagnostic groups ( Fig 3A–3C ) . The plasma concentration of IL-10 correlated significantly with IFNγ in patients with ATL and to a lesser extent with both IFNγ and IL-17 in patients with HAM . A classification tree analysis was performed on plasma cytokine concentrations to identify the cytokine profile which best differentiated AC , HAM and ATL states . A plasma IL-10 concentration < 0 . 16 pg/mL identified AC with a 64 . 7% sensitivity and 73% specificity as shown in Fig 3D . A plasma IL-17 concentration <1 pg/mL or if the IL-17 concentration was >1 pg/mL an IL-10 concentration of >0 . 8 pg/mL identified ATL with 78 . 5% sensitivity and 85% specificity whilst an IL-17 concentration of ≥ 1 pg/mL with an IL-10 concentration between 0 . 16 and 0 . 8 pg/mL identified HAM with 82 . 1% sensitivity and a specificity of 71 . 8% . In summary , a positive correlation between the plasma concentrations of specified pro- and anti-inflammatory cytokines was present in all three HTLV-1 patient groups . Together , the plasma concentrations of IL-10 and IL-17 discriminated between the clinical states associated with HTLV-1 infection . To identify the cellular source of the cytokines identified in plasma , the cytokine producing capacity of monocytes , CD4+ and CD8+ T cells was studied . Intracellular cytokine staining for TNFα , IFNγ , IL-6 and IL-10 was performed in 10 ACs , 11 patients with HAM and 10 with ATL ( four with indolent and six with aggressive ATL ) . The gating strategy to detect cytokine producing cells is shown in S2 Fig . The relative and absolute frequencies of cells secreting each cytokine are shown as percentages and cell count per litre . Although there was no difference in the relative frequency of IL-10+ CD4+ cells ( Fig 4E ) their absolute frequency was higher in patients with ATL compared to AC and HAM ( Fig 4A ) . The relative and absolute frequency of IL-6+ CD4+ cells did not differ by disease state ( Fig 4B–4F ) . The absolute frequencies of TNFα+ CD4+ T-cells were the same in each group ( Fig 4C ) but TNF+ cells made up a significantly lower percentage of all CD4+ T cells in patients with ATL compared to non-malignant HTLV-1 infection ( Fig 4G ) . Finally , although the absolute frequency of CD4+ T-cells secreting IFNγ was increased in ATL ( Fig 4D ) , the relative frequency of these cells was significantly lower in patients with ATL than in ACs and patients with HAM ( Fig 4H ) . In patients with ATL the median relative frequencies of TNFα , IFNγ , IL-6 and IL-10 secreting CD4+ cells were 5% , 1 . 7% , 0 . 6% and 0 . 3% respectively . The frequencies of TNFα , IFNγ , IL-6 and IL-10-secreting CD8+ cells and monocytes ( except IFNγ , which is not secreted by monocytes ) did not differ between diagnostic groups as shown in S3 Fig . In summary , although the absolute frequency of the cytokine-producing CD4+ T-cells was greater in patients with ATL these make up only a minority of their CD4+ T cells suggesting that ATL cells are in general not secreting these cytokines . CD4+ T cells are the dominant reservoir of infected cells in both non-malignant HTLV-1 infection and ATL [23 , 35] . The infected cells are derived from thousands of non-dominant clones in non-malignant HTLV-1 infection and from a dominant clone on a polyclonal background of non-dominant clones in ATL [29 , 36 , 37] . To further characterise the ATL and ‘ATL-like’ cells , their cytokine producing capability was determined . ATL cells have a CD4+CCR4+CD26-CD7- immunophenotype and ‘ATL-like’ cells are present in non-malignant HTLV-1 infection . The CD4+CCR4+CD7- immunophenotype was used to study the cytokine producing capacity of ATL cells as >99% of CD4+CCR4+CD7- cells were also CD26- . The relative ( Fig 5A ) and absolute frequencies ( Fig 5B ) of CD4+CCR4+CD7- T cells were significantly higher in patients with ATL compared to non-malignant HTLV-1 infection whilst there was no difference in the absolute frequency of non-CCR4+CD7- CD4+ T cells ( Fig 5B ) . HTLV-1 PVL significantly and positively correlated with the relative ( rho = 0 . 87 , p<0 . 0001 ) and absolute ( rho = 0 . 88 , p<0 . 0002 ) frequencies of CD4+CCR4+CD7- cells but not the absolute frequency of non-CCR4+CD7- CD4+ T cells ( rho = 0 . 20 , p = 0 . 27 ) . This confirms CD4+CCR4+CD7- cells as marker of ATL and ‘ATL-like’ HTLV-1 infected cells in patients with ATL and non-malignant HTLV-1 infection respectively . HTLV-1 infection leads to an absolute increase in ‘ATL-like’ cells in patients with non-malignant HTLV-1 infection . The relative and absolute frequency of ATL cells was actually higher than non-ATL CD4+T cells in patients with ATL but there was no difference between ‘ATL-like’ and non- ‘ATL-like’ CD4+ T cells in non-malignant HTLV-1 infection . The absolute frequency of IL-10-producing CD4+CCR4+CD7- cells in patients with ATL was significantly higher compared to ACs and HAM ( Fig 6A ) . The relative frequency of IL-10-producing CD4+CCR4+CD7- cells in patients with ATL was lower compared to AC and HAM ( Fig 6E ) . The absolute frequency of IL-6-producing CD4+CCR4+CD7- cells in patients with ATL was higher compared to ACs and patients with HAM ( Fig 6B ) . The relative frequency of IL-6-producing CD4+CCR4+CD7- cells in patients with ATL was low compared to AC and patients with HAM ( Fig 6F ) . The absolute frequency of CD4+CCR4+CD7- T cells producing TNFα ( Fig 6C ) or IFNγ ( Fig 6D ) was significantly higher in patients with ATL compared to ACs whilst there was a trend when compared to patients with HAM ( p = 0 . 11 and p = 0 . 10 respectively ) . However , the relative frequencies of TNFα and IFNγ producing CD4+CCR4+CD7- cells were significantly lower in patients with ATL compared to ACs and HAM as shown in Fig 6G and 6H . The median relative frequencies of TNFα , IFNγ , IL-6 and IL-10 secreting CD4+CCR4+CD7- T cells in patients with ATL were 3 . 3% , 1 . 7% , 0 . 2% and 0 . 3% respectively . The CD4+ T cells which are not CCR4+CD7- are a mixture of predominantly uninfected ( 82% ) and infected cells ( 18% ) [23] . The uninfected non-CCR4+CD7- CD4+T cells are a mix of naïve and memory CD4+T cells . CD4+naïve T cells have been shown to be depleted in HTLV-1 infection . In addition , naïve CD4+T cells produce less cytokine than memory T cells suggesting that the cytokine producing capacity of non-CCR4+CD7- CD4+T cells is mainly derived from uninfected memory T cells . The absolute and relative frequencies of pro- inflammatory cytokine ( TNFα , IFNγ and IL-6 ) producing CCR4+CD7- cells ( i . e . ‘ATL-like’ infected cells ) was lower than in non-CCR4+CD7- CD4+ T cells ( i . e . predominantly HTLV-1 uninfected memory cells , Fig 6F–6H ) in AC and patients with HAM . The absolute and relative frequencies of CD4+CCR4+CD7- cells producing the anti-inflammatory cytokine IL-10 was higher compared to non-CCR4+CD7- CD4+T cells in ACs and patients with HAM as shown in Fig 6A–6H . The relative frequency of pro-inflammatory cytokine producing CD4+CCR4+CD7- cells was lower than non-CCR4+CD7- CD4+T cells whilst the absolute frequency of IL-10 secretion was higher in patients with ATL . The absolute and relative frequencies of TNFα , IFNγ , IL-6 and IL-10 secreting non-CCR4+CD7- CD4+cells did not differ significantly between the three clinical groups . In summary , patients with non-malignant HTLV-1 infection have an increased frequency of CD4+CCR4+CD7- ( ‘ATL-like’ infected cells ) which correlates strongly with PVL . There is a further increase in these cells in patients with ATL . These cells are capable of producing pro- and anti-inflammatory cytokines . However , CD4+CCR4+CD7- cells had lower pro- and higher anti-inflammatory cytokine producing capacity compared to non-CCR4+CD7- CD4+T cells ( predominantly uninfected cells ) in non-malignant HTLV-1 infection . The absolute frequencies of not only IL-10 cytokine secreting CD4+CCR4+CD7- cells but also TNFα , IFNγ and IL-6 secretors were higher in ATL compared to non-malignant HTLV-1 infection . However , the cytokine producing cells made up only a tiny fraction of CD4+CCR4+CD7- T cells in patients with ATL . This suggests that at some point in the transformation of ‘‘ATL-like’ to ‘ATL’ cells CD4+CCR4+CD7- lose their cytokine producing capacity and that they are not the source of the plasma cytokines observed in ATL . The question remains whether the cytokine-secreting ‘ATL’ cells are a sub-population of the malignant clone or ‘ATL-like’ cells . Patients with ATL have a putative dominant clone on a polyclonal background . The dominant cell population in ATL is CD4+CCR4+CD7- . In order to determine the likely clonal origin of cytokine secreting CD4+CCR4+CD7- T cells clonality analysis was performed within sorted CD4+CCR4+CD7- T cells in four patients with aggressive ATL . In these patients , the CD4+CCR4+CD7- cells population had a median of 41 clones with the largest clone contributing a median 88% of the HTLV-1 infection burden as shown in Table 2 . Thus , CD4+CCR4+CD7- cells in patients with ATL are derived not exclusively from a single dominant clone ( putative ATL cells ) but also from tens of non-dominant infected clones i . e . ‘ATL-like’ cells . There was a perfect negative correlation ( rho = -0 . 99 , p<0 . 0001 ) between the relative frequencies of cytokine secreting cells and the relative abundance of the largest clone further supporting the suggestion that ‘ATL-like’ infected cells from non-dominant clones are cytokine producing whilst the cells from the dominant clone secrete little or no cytokines in patients with ATL . In order to determine the global and confirm the deferential cytokine profile of CD4+CCR4+CD7- cells in patients with non-malignant HTLV-1 infection and ATL , we studied the expression of cytokines , chemokines and their receptors by mRNA sequencing of sorted CD4+CCR4+CD7- T cells in eight patients with non-malignant ( four AC and four patients with HAM ) and eight with ATL .
There is an absolute increase in ‘ATL-like’ cells made up of non-dominant infected clones in patients with non-malignant HTLV-1 infection ( AC and patients with HAM ) . There is a further expansion of these ‘ATL-like’ cells in ATL along with the presence of ATL cells . The ‘ATL-like’ cells from non-dominant clones have a distinct cytokine producing pattern and contribute directly to the plasma cytokine profile in both non-malignant HTLV-1 infection and possibly in ATL . The ‘ATL-like’ cells of the dominant clone ( the putative ATL cells ) possess little or no cytokines producing capability . There was progressive loss of pro-inflammatory cytokine producing capacity from non-ATL ( uninfected ) through ‘ATL-like’ ( infected ) to ATL ( malignant ) cells and we hypothesise that this represents stages in the transformation process .
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Human T-cell lymphotropic virus type-1 ( HTLV-1 ) infection of CD4+ T cells is associated with a change in their cytokine producing capacity and is responsible for the different plasma cytokine profiles in patients with adult T-cell leukaemia/Lymphoma ( ATL ) and non-malignant HTLV-1 infection . Dominant malignant clonal growth of the infected CD4+ T cells is associated with loss of cytokine producing capacity . ACs , patients with HAM and patients with ATL have a common cytokine cluster with positive correlations between pro- ( TNFα and IL-6 ) and anti- ( IL-10 ) inflammatory cytokines . Plasma IL-10 was higher in the HAM and ATL states compared to AC whilst there was no difference in pro-inflammatory cytokines . Patients with HAM have raised plasma concentrations of IFNγ , IL-10 and IL-17 suggesting a complex interaction between these cytokine in HAM which was not seen in ATL . Aggressive ATL is associated with raised plasma concentrations of pro- and anti-inflammatory cytokines compared to indolent ATL . This cytokine profile did not precede or predict aggressive ATL . The ‘ATL-like’ infected cells in ACs and in patients with HAM have lower pro- and higher anti-inflammatory cytokine secretion than non- ‘ATL-like’ cells which are predominantly HTLV-1 uninfected . Putative ATL cells have little or no cytokine producing capacity . ‘ATL-like’ infected cells from non-dominant infected clones were present not only in patients with non-malignant HTLV-1 infection but also ATL . ‘ATL-like’ cells have cytokine producing capacity and contribute to plasma cytokine profile in patients with non-malignant HTLV-1 infection and possibly also in ATL .
|
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2018
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Switching and loss of cellular cytokine producing capacity characterize in vivo viral infection and malignant transformation in human T- lymphotropic virus type 1 infection
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Mating-type switching in fission yeast results from gene conversions of the active mat1 locus by heterochromatic donors . mat1 is preferentially converted by mat2-P in M cells and by mat3-M in P cells . Here , we report that donor choice is governed by two portable recombination enhancers capable of promoting use of their adjacent cassette even when they are transposed to an ectopic location within the mat2-mat3 heterochromatic domain . Cells whose silent cassettes are swapped to mat2-M mat3-P switch mating-type poorly due to a defect in directionality but cells whose recombination enhancers were transposed together with the cassette contents switched like wild type . Trans-acting mutations that impair directionality affected the wild-type and swapped cassettes in identical ways when the recombination enhancers were transposed together with their cognate cassette , showing essential regulatory steps occur through the recombination enhancers . Our observations lead to a model where heterochromatin biases competitions between the two recombination enhancers to achieve directionality .
Fission yeast cells switch mating type by directed recombination events where the information in the expressed mat1 locus is replaced with information copied from one of two silent loci , mat2 or mat3 ( reviewed in [1] ) . The system allows investigating multiple facets of recombination , including effects of chromatin structure on recombination and mechanisms of donor choice: how is a particular DNA template selected for recombination when several are available in a cell ? The mat1 , mat2 and mat3 loci are linked in the mating-type region ( Figure 1 ) . mat1 determines the mating type of the cell by expressing two divergent regulatory genes , Pi and Pc in P cells ( mat1-P allele ) , Mi and Mc in M cells ( mat1-M allele; [2] ) . Silent information for the P and M mating types is stored at respectively mat2 ∼17 kb centromere-distal to mat1 , and mat3 ∼29 kb centromere-distal to mat1 [3]–[5] . The mating-type specific information at mat1 , mat2 and mat3 is flanked by short homology boxes , the centromere-distal H1 box and the centromere-proximal H2 box [2] . Other elements are specific for mat2 and mat3 [2] , [6] , [7] ( Figure 1 ) . mat2 and mat3 are furthermore embedded in a ∼20 kb heterochromatic domain that spans the mat2-mat3 interval and extends on both sides to inverted repeat boundaries [8] , [9] . This domain has been studied extensively . It provides one of the best characterized model systems for how heterochromatic regions can be established and maintained . In this domain , histones are hypoacetylated , histone H3 is methylated at lysine 9 ( H3K9me ) in an RNA interference-dependent manner , and chromodomain proteins of the HP1 family are associated with the modified histones [8] , [10]–[15] . The HP1-like chromodomain protein Swi6 interacts with numerous protein complexes believed to modulate heterochromatin formation , gene silencing and recombination , in ways that remain to a large extent undefined in particular regarding roles in recombination [14] , [16]–[19] . Interconversions of the mat1 locus between mat1-P and mat1-M lead to mating-type switching ( reviewed in [1] ) . The conversions are coupled to DNA replication which reaches mat1 from a centromere-distal origin [20] , [21] . Switching is initiated by the introduction of a strand-specific imprint in the lagging strand , resulting from the incorporation of two ribonucleotides or a nick between the mat1 H1 homology box and the mating-type specific information [20] , –[28] . In the following rounds of DNA replication , the imprint is placed again on the chromatid made by lagging-strand synthesis , generating a lineage of imprinted , switchable cells [24] , [29] . While lagging-strand synthesis propagates the imprinted mat1 locus in this lineage , leading-strand synthesis produces switched progeny ( Figure 1B ) . At each division , leading-strand synthesis proceeds through the mat1 H1 homology box and stops at the imprint creating a single-ended double-strand break ( DSB ) or other recombinogenic molecule with a free 3′end [25] , [30] . The free 3′end invades the H1 box of one of the silent loci which is then used instead of mat1 as template for leading-strand synthesis [29] , [31] . This heals the break . Resolution of the recombination intermediate occurs within the H2 homology box with the help of the Swi4/8 and Swi9/10 gene products , producing a switched mat1 locus [5] , [32]–[36] . The newly-switched mat1 locus does not carry an imprint hence it does not switch at the following S phase , however the chromatid made by lagging-strand synthesis acquires an imprint and starts a new lineage of switchable cells . A choice of information is made in all switchable cells such that either mat2-P or mat3-M is used as donor to replicate and convert mat1 . At this step , mat2-P and mat3-M are not picked at random . Switchable mat1-M cells preferentially use mat2-P whereas switchable mat1-P cells use mat3-M [23] , [37] , [38] . Coupled with the mechanism of switching outlined above , these directed choices produce a reproducible pattern of mating-type switching where ( 1 ) one out of four grand-daughters of a newly-switched cell has a high probability of having a switched mating-type ( 80–90%; one-in-four rule ) and ( 2 ) once a cell becomes switchable the probability of recurrent switches in its lineage is very high ( 80–90%; recurrent-switching rule; Figure 1C ) . Previous studies have revealed the importance of donor location and chromatin structure in donor choice [39]–[42] . Strains in which the mat2 and mat3 contents were swapped from mat2-P mat3-M ( h90 configuration ) to mat2-M mat3-P ( h09 configuration ) switch inefficiently to the opposite mating-type [39] . Mutations in the chromodomain protein Swi6 , the H3K9 methyltransferase Clr4 , or the Clr4-complex subunits Clr7 and Clr8 have opposite effects on switching in the h09 and h90 mating-type regions indicating chromatin structure favors use of mat2 in M cells and use of mat3 in P cells [39] , [42] . The phenotypes of these mutants suggest that unproductive homologous switching occurs in h09 cells where mat1-M is converted by mat2-M and mat1-P by mat3-P instead of the productive heterologous switching occurring in h90 cells . The strand exchange taking place at H1 is likely to be a determining step in donor choice . The Swi2 and Swi5 proteins are believed to facilitate this step together with Rad22 , the fission yeast RAD52 homolog [43] and Rhp51 , the S . pombe RecA homolog [44] , [45] . The imprint , detected as a chromosomal fragile site , is formed at mat1 in swi2 and swi5 mutants but a subsequent step in the conversion process fails [5] . Consistent with this step being strand-invasion at the donor loci Swi2 interacts physically with both Swi5 and Rhp51 [17] and Swi5 facilitates Rhp51-mediated strand exchange in vitro [46]–[49] . Combined with the observation that Swi2 interacts with Swi6 [17] , the properties of Swi2 and Swi5 place these factors close to the point where donor selection takes place . A model for the directionality of mating-type switching combining effects of chromatin structure and targeted recruitment of recombination proteins was proposed in [41] ( Figure 1; referred to as 2004 model below ) . In this model , the search for a donor starts at mat2 . If the Swi2/Swi5 recombination-promoting complex ( RPC ) is encountered at mat2 , mat2 is used to convert mat1 . If RPC is not at mat2 , the search proceeds to mat3 and mat3 , which is constitutively associated with RPC , is used to convert mat1 . The constitutive association of mat3 and RPC observed in both P and M cells is proposed to occur through a DNA element named SRE [41] that we will call SRE3 below to reflect its proximity to mat3 . RPC is localized at SRE3 in P cells – ensuring that mat3 is used in P cells - but spreads from SRE3 all the way to mat2 in M cells – ensuring that mat2 is used in M cells . Spreading of RPC from SRE3 to mat2 requires Swi6 . A recent addition to the model proposes that the spreading is driven by a greater abundance of the Swi2 and Swi5 proteins in M cells resulting from the positive regulation of the swi2 and swi5 genes by the M-specific transcription factor Mc [50] . Alternatively , Mc might stimulate the production of a shorter form of Swi2 expressed in P cells through alternative promoter usage [51] . The directionality model summarized above [41] provides a framework for investigations of mating-type switching , although several critical steps in it have no documented mechanism . One unexplained feature is that the search for a donor should start at mat2 . This step is important because it accounts for mat2 being used in M cells when Swi2/Swi5 is present at both mat2-P and mat3-M . The model proposes that a higher-order chromatin structure helps choosing mat2 by placing it near mat1 but how this occurs remains unknown . Another aspect of the model that has not been documented experimentally is the physical interaction between SRE3 and Swi2 . This is also a crucial element because the model is centered on SRE3 being the sole entry point for Swi2 in the mating-type region , accounting for the observation that Swi2 was not detected in the mating-type region of SRE3Δ strains by ChIP [41] . Here , we report further investigations on the directionality of mating-type switching bearing on these and other points . Our results challenge the notions that SRE3 is the sole entry point for Swi2 , that Swi2 reaches mat2 by spreading from SRE3 , and that the search for a donor starts at mat2 . Instead , our results show that directionality requires two recombination enhancers , SRE2 and SRE3 , whose ability to stimulate recombination in a cell-type specific manner is not tied to a specific location in the mating-type region . We present evidence that directionality results from competitions between SRE2 and SRE3 , governed by cell-type specific chromatin structures .
The SRE3 element was described as the entry point at which the Swi2/Swi5 complex associates with the mating-type region [41] . Following this initial association , proposed to take place in both cell types , Swi2 could remain at SRE3 in P cells or spread to mat2 in M cells . Support for this mechanism is provided by ChIP experiments that failed to detect Swi2 anywhere in the mating-type region in strains lacking SRE3 [41] . We examined the model further through a simple genetic test . If the directed association of Swi2 with the mating-type region is abolished in SRE3Δ cells the mating-type bias in SRE3Δ cells should not be altered by deletion of swi2 . In S . pombe , the efficiency of mating-type switching can be estimated by staining sporulated colonies with iodine vapors . Efficiently-switching strains produce colonies that are stained darkly by iodine vapors because they contain many spores while poorly-switching strains produce lightly-stained colonies [52] . The predominant mating-type in cell populations can be further determined by quantifying the content of mat1 by Southern blot or PCR . In addition , we developed here a reporter system in which M cell express YFP and P cells express CFP allowing typing individual cells with a fluorescence microscope ( Figure 1D ) . Sporulated SRE3Δ colonies were stained lightly by iodine and colonies did not stain at their junctions indicating preferential use of one donor ( Figure 2B ) . Southern blotting , competitive PCR , and fluorescent typing all showed that SRE3Δ cells contain predominantly the mat1-P allele ( P∶M = 82∶18 by Southern blot; P∶M = 88∶12 by cell count; Figure 2; competitive PCR not shown ) . The SRE3Δ strain used for these analyses was made in our laboratory [7] hence these results confirm the observations in [41] with an independent strain and support the conclusion of these authors that mat2-P is the preferred donor in SRE3Δ cells . We assessed the effect of deleting swi2 in both wild-type h90 cells and SRE3Δ cells ( Figure 2 ) . Iodine staining indicated that deletion of swi2 severely affected switching efficiency in both backgrounds: h90 swi2Δ and SRE3Δ swi2Δ cells formed streaky colonies staining at their junctions showing that cells are predominantly of the M mating-type in some colonies and predominantly of the P mating type in other colonies ( Figure 2B ) . We measured P∶M ratios in nine independent cultures of respectively h90 swi2+; h90 swi2Δ; SRE3Δ swi2+; and SRE3Δ swi2Δ cells by Southern blot ( Figure 2C ) . While all h90 swi2+ cultures had balanced P∶M ratios and all SRE3Δ swi2+ cultures were predominantly of the P mating-type , large fluctuations in P∶M ratios were observed in h90 swi2Δ and SRE3Δ swi2Δ cultures . The strong phenotypic variability observed in h90 swi2Δ cultures disagrees with the report [41] that h90 swi2Δ cells contain predominantly mat1-P and that the switching pattern of h90 swi2Δ cells is indistinguishable from switching in h90 SRE3Δ cells . Further , the clear phenotypic differences we observed between SRE3Δ swi2+ ( P≫M in all colonies; 81% P cells averaged over 9 cultures ) and SRE3Δ swi2Δ strains ( variegated phenotype; 40% P cells averaged over 9 cultures ) is not predicted in [41] . Similarly , we observed culture-to-culture variations with , if any , a bias towards M cells in h90 swi5Δ cultures ( 72% M cells averaged over 9 cultures; Figure S1 ) in contrast to [50] who found that h90 swi5Δ cells are predominantly P . As for the deletion of swi2+ , deletion of swi5+ abrogated the preferential use of mat2-P in SRE3Δ cells ( Figure S1 ) . We conclude that the RPC is necessary for the efficient and preferential choice of mat2-P in SRE3Δ cells . Since this represents a situation where RPC cannot reach mat2-P by spreading from SRE3 , this result does not support the spreading model and suggests instead that other DNA element ( s ) or factors attract Swi2 and Swi5 independently of SRE3 . While systematically introducing deletions in the mating-type region we found that a set of nested deletions on the centromere-distal side of mat2-P affected switching , defining a ∼500 bp element adjacent to the H1 box , SRE2 . Deletion of SRE2 caused a pronounced switching defect ( Figure 3 ) . Sporulated SRE2Δ colonies were stained very lightly by iodine vapors and they did not stain at their junctions; a Southern blot testing mat1 content in nine independent cultures indicated a large predominance of M cells in all cultures; and the existence of a strong bias towards M cells was also supported by fluorescence microscopy ( Figure 3 ) . Identical phenotypes were obtained when SRE2Δ colonies were seeded from P or M spores confirming efficient asymmetric switching favoring mat3-M ( data not shown ) . The location of SRE2 relative to mat2 is similar to the location of SRE3 relative to mat3 but no extensive sequence similarities were noted between SRE2 and SRE3 . Both elements are A-T rich ( 75% for SRE2 and 72% for SRE3 over 492 bp ) . The authors of a recent study [51] noticed like us that a deletion adjacent to mat2-P prevented efficient use of mat2-P however the study did not characterize the element further . Several observations reported below argue against deletion of SRE2 simply preventing use of mat2 as a donor . They suggest instead that SRE2 regulates donor choice . As for the strains examined above , deleting swi2+ affected switching in SRE2Δ cells . Two types of sporulated colonies were observed following iodine staining , light colonies with a few dark streaks containing mostly M cells , and more rare darker colonies containing a greater proportion of P cells ( Figure 3; 80% M cell averaged over nine colonies ) . Deletion of swi5 produced a similar phenotype ( 77% M cell averaged over nine colonies; Figure S1; Southern blot quantifications are summarized in Figure S2 . ) . These phenotypes are consistent with mat3-M remaining a preferred donor in SRE2Δ cells even when RPC is not present in the cells . This again fails to support the 2004 model , where mat2-P is the preferred donor when RPC is not present due to higher-order chromatin structure . Alternatively , SRE2 might be responsible for the higher-order structure postulated by the model . We investigated the association of Swi2 with parts of the mating-type region by ChIP ( Figure S3 ) . In unswitchable mat1-M cells , where mat2-P is perhaps poised for switching , Swi2 was detected at mat2-P and at SRE2 as previously reported [41] . In our experiments , Swi2 was also detected at these locations in SRE3Δ cells consistent with an SRE3-independent mode of recruitment to the mating-type region . This recruitment appeared facilitated by SRE2 since the association of Swi2 with mat2 was reduced in SRE2Δ cells ( primer pairs 44 , 46 and ‘SRE2Δ’ in M cells , Figure S3 ) . A deletion reducing the use of a donor cassette is not on its own evidence that the deletion removed a directionality element . We explored the possibility that SRE2 and SRE3 are genuine directionality elements by engineering h09 cells . The donor loci are mat2-M mat3-P in the h09 mating-type region [39] . The cassette contents are precisely exchanged between the H2 and H1 homology boxes placing mat2-M near SRE2 and mat3-P near SRE3 . This arrangement results in inefficient switching to the opposite mating-type ( [39]; Figure 4 ) . The h09 strain provides a useful tool to study directionality since it allows designing experiments in which the tested outcome is improved switching rather than loss of switching . We tested whether swapping SRE2 and SRE3 in h09 cells improved heterologous switching . Figure 4 shows that h09 cells with swapped elements switched mating-type very efficiently and produced populations with equal proportions of P and M cells . Their sporulated colonies were undistinguishable from h90 colonies . Their mat1 content examined by Southern blot was evenly balanced and fluorescence microscopy confirmed equal proportions of P and M cells in colonies ( Figure 4 ) . Conversely h90 cells with swapped SRE elements switched mating-type poorly , produced mainly mat1-M cells as h09 cells with unswapped elements do , and formed colonies very similar to h09 colonies ( Figure 4 ) . Together these experiments show that the PSRE2 MSRE3 combination ( whether mat2-PSRE2 mat3-MSRE3 in wild-type h90 cells with native elements or mat2-MSRE3 mat3-PSRE2 in h09 cells with swapped elements ) leads to balanced use of the two cassettes while the PSRE3 MSRE2 combination ( whether mat2-MSRE2 mat3-PSRE3 in h09 cells with native elements or mat2-PSRE3 mat3-MSRE2 in h90 cells with swapped elements ) leads to inefficient heterologous switching . We conclude from these observations that SRE2 and SRE3 behave as directionality elements responsible for the balanced heterologous switching observed in h90 cells . P cells select the cassette adjacent to SRE3 while M cells select the cassette adjacent to SRE2 and SRE2 and SRE3 can both be recognized ectopically when their location relative to mat1 has been swapped . Should SRE2 and SRE3 be the sole determinants of directionality and should their action be fully symmetrical , h09 cells with native SRE elements and h90 cells with swapped SRE elements would engage in futile cycles where mat1-P selects mat3-PSRE3 ( in h09 ) or mat2-PSRE3 ( in h90 cells with swapped elements ) and mat1-M selects mat2-MSRE2 ( in h09 ) or mat3-MSRE2 ( in h90 cells with swapped elements; Figure 4 ) . Two types of colonies would be formed , one type containing predominantly P cells , the other predominantly M cells . This is not what is observed . Both h09 cells with native elements and h90 cells with swapped elements form populations where M cells predominate ( Figure 4 ) indicating preferential choice of the cassette adjacent to SRE2 . The fact that the bias is towards the MSRE2 cassette in both cases even though the MSRE2 cassette occupies different locations in the two strains shows that a preponderant cause for the bias is location independent . The mechanisms responsible for directionality are likely to fail occasionally , allowing the ‘wrong’ donor to be selected . We reasoned that a small error rate would not have a strong impact on the overall composition of h90 cell populations , but the same error rate could have more profound consequences in h09 populations because the mistakes would lead to changes in mating-type that would subsequently be stably propagated through homologous switching . We modeled a situation where P cells use predominantly SRE3 to select a donor for switching , while M cells select predominantly SRE2 ( Figure 4 ) . We allowed a low occurrence of mistakes in both cell types , where P cells occasionally use SRE2 ( 20% of attempted switches ) while M cells use SRE3 more rarely ( 10% of attempted switches ) . As expected such a bias leads to an accumulation of M cells in both h09 cells with native elements and h90 cells with swapped elements supporting the hypothesis that aberrant choices contribute to the preponderance of M cells in these strains . We note that in addition to SRE2 being more promiscuous than SRE3 , the cassette content in the MSRE2 combination might facilitate use of MSRE2 over PSRE3 in P cells . As a way of testing the extent to which P cells can use SRE2 we replaced SRE3 with SRE2 ( mat2-PSRE2 mat3-MSRE2 strain referred to as 2×SRE2 ) . The 2×SRE2 strain switched mating-type efficiently as judged from its dark iodine staining and balanced ratio of P and M cells ( Figure 5 ) . 2×SRE2 populations contained 48% P cells according to Southern blot , 56% P cells according to microscopy . The phenotype of the 2×SRE2 strain shows that P cells are proficient in the use of the SRE2 element in mat3-MSRE2 otherwise P cells would accumulate in the population of 2×SRE2 cells . To illustrate this further SRE3Δ colonies are shown near the 2×SRE2 strain for comparison in Figure 5 . SRE2 at mat3-M considerably improves heterologous switching showing that P cells use SRE2 . Even though 2×SRE2 cells switch mating-type efficiently , mating-type selectivity in 2×SRE2 is not as in wild-type leading us to propose that donor choice is randomized in 2×SRE2 rather than directional . A differential behavior of 2×SRE2 and wild-type mating-type region is shown for example in the next section where h90 swi6Δ and 2×SRE2 swi6Δ strains clearly differ from each other . Similarly we replaced SRE2 with SRE3 ( mat2-PSRE3 mat3-MSRE3 strain referred to as 2×SRE3 ) . The 2×SRE3 strain produced a mixture of P and M cells which shows that M cells can use SRE3 , however with a bias towards M cells ( Figure 5 ) . 2×SRE3 populations contained 23% P cells as estimated from Southern blot , 25% estimated from microscopy . Together with the results presented above for the 2×SRE2 strain these ratios indicate that M cells are not as proficient at using mat2-PSRE3 as P cells are at using mat3-MSRE2 . In summary SRE2 can stimulate recombination of donor loci with mat1 efficiently not only in M cells but also in P cells whereas SRE3 is more active in P cells than in M cells . The ability of each element to function in both cell types shows that these elements are not strictly dependent on cell-type-specific factors to stimulate recombination . A remarkable aspect of mating-type switching is that the donor loci are in heterochromatin . We asked whether and how the ability of the SRE elements to stimulate recombination was affected by heterochromatin through epistasis analyses using cells lacking the chromodomain protein Swi6 . Deletion of swi6+ in h09 or h90 cells with native or swapped elements radically altered donor choice ( compare Figure 4 and 6 ) . Populations of h90 cells or h09 cells with swapped elements went from balanced P∶M ratios ( 49% and 50% P resp . ) to containing predominantly M cells ( 87% and 84% resp . ) . Conversely the M bias in populations of h09 cells or h90 cells with swapped elements was abrogated by swi6Δ . In all cases , the changes reflected that use of the cassette adjacent to SRE2 was greatly decreased in favor of the cassette adjacent to SRE3 following deletion of swi6+ , as indicated in Figure 6 . These phenotypes show that Swi6 biases donor choice towards the cassette controlled by SRE2 , or away from the cassette controlled by SRE3 , whether the cassette contains the P or M information , and whether it is located at mat2 or mat3 . Reduced selection of SRE2 in h90 swi6Δ cells depended on the presence of SRE3 in the same cells . No change in preferred donor was observed in h90 SRE3Δ cells following deletion of swi6+ , SRE2 kept being used ( compare SRE3Δ in Figure 2 to SRE3Δ swi6Δ in Figure 6; mat1-P predominates in both ) . This indicated that SRE2 could stimulate recombination at mat2-PSRE2 in the absence of Swi6 when SRE3 was not present . Very inefficient switching in SRE2Δ SRE3Δ swi6Δ cells confirmed that the selection of mat2-PSRE2 in SRE3Δ swi6Δ cells depended on SRE2 ( Figure 6; inefficient switching in the SRE2Δ SRE3Δ swi6Δ strain produces colonies staining at their junctions and large fluctuations in P/M ratios ) . Similarly , use of mat3-MSRE3 in SRE2Δ swi6Δ cells required SRE3 ( compare SRE2Δ swi6Δ with SRE2Δ SRE3Δ swi6Δ in Figure 6 ) . In summary these phenotypes show that both SRE2 and SRE3 can stimulate recombination in the absence of Swi6 . Competitions between the two enhancers drive donor selection both in the absence and presence of Swi6 . In the absence of Swi6 SRE3 is preferred over SRE2 . When present , Swi6 biases donor selection towards SRE2 .
Cells in which the silent-cassette contents are swapped ( h09 ) switch mating-type inefficiently , indicating cells fail to choose heterologous donors when the donors are not at their endogenous location [39] . Here , we find that a crucial aspect of donor location is proximity of the donors to their respective recombination enhancers , SRE2 and SRE3 . The determining role of SRE2 and SRE3 in donor selection was revealed by the high efficiency of switching in h09 cells when the SRE elements were swapped concomitantly with the contents of mat2 and mat3 ( Figure 4 ) . Heterologous donors could be found efficiently even when they were not at their endogenous location , provided the coupling with their cognate recombination enhancers was maintained . The fact that h09 cells with swapped SRE elements switch well has strong implications for the 2004 model . The 2004 model is a two-component model integrating effects of donor positioning relative to mat1 ( in the model the recombinogenic DSB at mat1 encounters mat2 before it encounters mat3 ) and presence of RPC ( the first RPC-associated donor encountered is used; Figure 1 ) . In h09 cells with swapped elements a search starting at mat2 would encounter SRE3 first . SRE3 being the proposed nucleation site for RPC , constitutively associated with RPC in both cell types , mat2-MSRE3 should be selected preferentially in both cell types which is clearly not the case . Our observations show instead that M cells choose SRE2 and P cells choose SRE3 when these elements are present , independently of their location . One way of reconciling the portability of the SRE elements with the 2004 model is to propose that SRE2 is responsible both for the higher-order chromatin structure that brings mat2 close to mat1 in this model and also for directing the spreading of Swi2 away from SRE3 in M cells . While such roles for SRE2 should be envisioned and tested , other observations we made suggest that Swi2 does not reach mat2 by spreading from SRE3 . ChIP experiments reported in previous publications have detected large , cell-type specific variations in the association of RPC with the mating-type region [41] , [50] . RPC was detected over the entire mat2-mat3 interval in M cells but the association was restricted to SRE3 in P cells [41] . In cells lacking SRE3 , RPC was not detected at all [41] . While these strikingly differential associations hint to some relevance for directionality , how the associations lead to the selection of a specific donor is not straightforward . RPC associations do not on their own determine which cassette will be used since the association of RPC with SRE3 is cell-type independent . Here , we found that RPC catalyzes switching even in situations where RPC was not previously detected by ChIP [41] and in the absence of SRE3 . In our experiments , the pronounced bias towards the P mating-type displayed by SRE3Δ cells was abolished in SRE3Δ swi2Δ cells and in SRE3Δ swi5Δ cells , showing RPC is necessary for the preferential use of mat2-P in SRE3Δ cells ( Figure 2 and Figure S1 ) . Not only is this epistatic relationship not predicted by the 2004 model – the model predicts that the SRE3Δ swi2Δ double mutant should switch like SRE3Δ – but the 2004 model specifically relies on swi2Δ and SRE3Δ cells having identical phenotypes , which is also contradicted by our results ( Figure 2 ) . Based on our genetic evidence we suggest that ChIP has failed to detect interactions between Swi2 and the mating-type region that are relevant to directionality . Difficulties in detecting the association of Swi2 with the mating-type region might be due to the fact that Swi2 is not an abundant protein , that relevant interactions occur in a short window of the cell cycle , or to the fact that ChIP experiments have been conducted in switching-defective cells lacking elements at mat1 that might participate in directionality as indicated in [24] . Unlike [41] , we observed that in M cells Swi2 remained associated with mat2-P and SRE2 in the absence of SRE3 ( Figure S3 ) . A core feature in the 2004 model is that Swi2 spreads from SRE3 to mat2 in P cells . Spreading of a protein along the chromatin fiber can be difficult to distinguish from other mechanisms that might lead to the same final associations . Binding at multiple sites might give the appearance of spreading from one of the sites . Here , we suggest that Swi2 does not have to spread from SRE3 to facilitate switching at SRE2 . We observed that both SRE2 and SRE3 can stimulate recombination in the absence of Swi6 . While populations of SRE2Δ swi6Δ cells were predominantly M and populations of SRE3Δ swi6Δ cells were predominantly P these biases were lost in populations of SRE2Δ SRE3Δ swi6Δ cells ( Figure 6C–D ) showing SRE3 stimulates recombination with mat3-M in SRE2Δ swi6Δ cells and SRE2 stimulates recombination with mat2-P in SRE3Δ swi6Δ cells . We furthermore observed that competitions between SRE2 and SRE3 take place in swi6Δ cells when both elements are present . While SRE3Δ swi6Δ populations were predominantly P ( Figure 6C–D ) , reflecting choice of SRE2 , h90 swi6Δ populations were predominantly M ( Figure 6A–B ) , reflecting choice of SRE3 , from which we conclude that SRE3 outcompetes SRE2 in h90 swi6Δ cells . The switching phenotypes of h09 swi6Δ; h09 with swapped elements swi6Δ; and h90 with swapped elements swi6Δ cells all show that SRE3 is preferred over SRE2 in swi6Δ cells when both elements are present ( Figure 6A–B ) . Swi6 exerts major effects on mating-type switching through SRE2 and SRE3 . Comparing Figure 4 and Figure 6A–B shows that Swi6 tilts the relative efficiency of the two elements , allowing SRE2 to be preferred over SRE3 in M cells . We suggest that this effect is key to directionality . Several lines of evidence have established that heterochromatin differs in the mating-type region of P and M cells making heterochromatin a good candidate for providing cell-type specificity in mating-type switching . Ectopic reporters are more strongly repressed in M cells than in P cells , whether the reporters are near mat2 or mat3 [7] , [53] , ( G . Thon unpublished data ) and consistently more Swi6 is detected over the entire mat2-mat3 region in M cells than in P cells [8] . These differences between P and M cells are likely to reflect differential associations of various protein complexes over the entire mating-type region , including but not limited to Swi6 , Swi2 and Swi5 [14] , [16]–[19] , [54] . Global changes over the entire region would account for our observation that the effects of Swi6 on SRE2 and SRE3 were independent of donor location ( Figure 6 ) . The model for the directionality of switching outlined below proposes that differences in the chromatin structure of P and M cells determine which recombination enhancer is used in each cell type . We propose a simple model for the directionality of mating-type switching that takes into account our observations ( Figure 7 ) . This model is an alternative to models where the recombination enhancers favor cell-type specific interactions between the donor loci and mat1 through DNA looping but it is not incompatible with looping models . In the proposed model SRE2 and SRE3 compete to capture the free DNA end generated at mat1 . When Swi6 and associated factors are in comparatively low abundance in the mating-type region as is the case in P cells , SRE3 stimulates recombination at its adjacent H1 homology box more efficiently than SRE2 . When Swi6 and associated factors are in greater abundance in the mating-type region , as is the case in M cells , SRE2 is more efficient than SRE3 . Several mechanisms can be envisioned for how SRE2 and SRE3 might facilitate strand invasion at their adjacent H1 box in a chromatin-dependent manner . SRE2 and SRE3 might have an intrinsic ability to facilitate D-loop formation as suggested by their low melting temperature ( predicted from 72–75% AT content and data not shown ) . Indeed , evidence has been presented that SRE2 can form a heteroduplex with DNA adjacent to the mat1 H1 box [33] . Swi6 could modulate the ability of SRE2 and SRE3 to stimulate strand-invasion at H1 through changes in chromatin structure . Swi6 binds to nucleosomes methylated at H3K9 and it oligomerizes . The association of Swi6 with chromatin per se might constrain the topology of DNA around H1 and the SRE elements in a way that would alter D-loop induction by the SRE elements and depend on the concentration of Swi6 . Other , non mutually-exclusive effects of SRE2 and SRE3 could be through the positioning of nucleosomes . Swi6 might induce the local sliding or displacement of nucleosomes through one of its associated ATP-dependent chromatin remodeling complexes ( RSC , Ino80 , FACT; [18] , [19] ) thereby altering the ability of a recombination enhancer to increase H1 accessibility . Finally , direct interactions might take place between the recombination enhancers and recombination factors such as Swi2 as suggested in the case of Swi2 and SRE3 [41] . Directionality would occur if SRE3 had a higher affinity for Swi2 than SRE2 but a lower peak efficiency than SRE2 when stimulating recombination in the context of heterochromatin . At low concentration of Swi2 , SRE3 but not SRE2 would be associated with Swi2 , promoting invasion of its adjacent cassette . At high concentrations of Swi2 , SRE2 would not only be associated with Swi2 but it would use its associated Swi2 more efficiently than SRE3 , leading to preferred choice of SRE2 over SRE3 . Low association of Swi6 and Swi2 in the mating-type region of P cells would promote invasion of the SRE3-adjacent cassette . High association of Swi6 and Swi2 in the mating-type region of M cells would promote invasion of the SRE2-adjacent cassette . How pre-existing chromatin structures affect recombination and DSB repair is poorly understood in spite of a great relevance for the maintenance of genome integrity in all eukaryotes . Competitions between donors for gene conversions [55] , [56] and regional , cell-type specific , control of recombination [57]–[59] were observed in S . cerevisiae similar to what we observed here . Indeed , much of our knowledge on the effects of chromatin on recombination was acquired using S . cerevisiae [59]–[61] . Our characterization of the fission yeast SRE elements opens the field for further in vivo and in vitro studies of recombination regulation in other chromatin contexts .
Standard procedures were used to construct and examine S . pombe strains . The details of the strain constructions , Southern blots and microscopy are presented in Text S1 ( Extended experimental procedures ) . Strain genotypes are listed in Table S1 and oligonucleotide sequences in Table S2 .
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The state of chromatin , heterochromatin or euchromatin , affects homologous recombination in eukaryotes . We study mating-type switching in fission yeast to learn how recombination is regulated in heterochromatin . Fission yeast exists as two mating-types , P or M , determined by the allele present at the expressed mat1 locus . Genetic information for the P and M mating-types is stored in two silent heterochromatic cassettes , mat2-P and mat3-M . Cells can switch mating-type by a replication-coupled recombination event where one of the silent cassettes is used as donor to convert mat1 . Mating-type switching occurs in a directional manner where mat2-P is a preferred donor in M cells and mat3-M is preferred in P cells . In this study , we investigated factors responsible for these directed recombination events . We found that two portable recombination enhancers within the heterochromatic region compete with each other and direct recombination in a cell-type specific manner . We also found that heterochromatin plays an important role in directionality by biasing competitions between the two enhancers . Our findings suggest a new model for directed recombination in a heterochromatic domain and open the field for further studies of recombination regulation in other chromatin contexts .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
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Two Portable Recombination Enhancers Direct Donor Choice in Fission Yeast Heterochromatin
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Strongyloides stercoralis is a neglected soil-transmitted helminth species , and there is a lack of parasitologic and epidemiologic data pertaining to this parasite in China and elsewhere . We studied the local occurrence of S . stercoralis in a village in Yunnan province , China , and comparatively assessed the performance of different diagnostic methods . Multiple stool samples from a random population sample were subjected to the Kato-Katz method , an ether-concentration technique , the Koga agar plate method , and the Baermann technique . Among 180 participants who submitted at least 2 stool samples , we found a S . stercoralis prevalence of 11 . 7% . Males had a significantly higher prevalence than females ( 18 . 3% versus 6 . 1% , p = 0 . 011 ) , and infections were absent in individuals <15 years of age . Infections were only detected by the Baermann ( highest sensitivity ) and the Koga agar plate method , but neither with the Kato-Katz nor an ether-concentration technique . The examination of 3 stool samples rather than a single one resulted in the detection of 62% and 100% more infections when employing the Koga agar plate and the Baermann technique , respectively . The use of a mathematical model revealed a ‘true’ S . stercoralis prevalence in the current setting of up to 16 . 3% . We conclude that S . stercoralis is endemic in the southern part of Yunnan province and that differential diagnosis and integrated control of intestinal helminth infections needs more pointed emphasis in rural China .
Soil-transmitted helminthiases are caused by infections with intestinal nematodes , of which Ascaris lumbricoides , Trichuris trichiura and the hookworms ( Ancylostoma duodenale and Necator americanus ) are the most widespread species [1–3] . Collectively , these soil-transmitted helminths affect over 1 billion people and cause a huge public-health burden; yet , soil-transmitted helminthiases are so-called neglected tropical diseases [4] . Strongyloides stercoralis is another and even more neglected soil-transmitted helminth , although an estimated 30–100 million people are infected worldwide [2] . An infection with S . stercoralis occurs transcutaneously and can be perpetuated over long periods by autoinfection [5 , 6] . Clinical signs of S . stercoralis-infected immunocompetent people can be inconspicuous or even absent , but hyperinfection involving the gastrointestinal and pulmonary system is possible . Potentially fatal disseminated infections are seen in immunocompromised individuals , for example , as a result of immunosuppressive drugs or following human T-cell lymphotropic virus type 1 ( HTLV-1 ) infection [6–8] . S . stercoralis is endemic in tropical and temperate zones but accurate information on the geographic distribution and the global burden of strongyloidiasis is lacking . An important underlying reason is that one of the most widely used diagnostic approaches in helminth epidemiology , i . e . , the Kato-Katz method [9] , fails to detect S . stercoralis . Moreover , microscopic examination of direct fecal smears , often used in endemic settings , has a low sensitivity [10 , 11] . More sensitive diagnostic approaches for detection of S . stercoralis larvae include the Koga agar plate method [12] and the Baermann technique [13] . Their sensitivity can be further increased by examining multiple stool samples [14] . In East Asia and Thailand in particular , the epidemiology of S . stercoralis has been studied in some detail . In different investigations carried out among schoolchildren and adults in northern and central Thailand , prevalences ranging between 2 . 3% and 28 . 9% were found [15–19] . S . stercoralis has also been investigated in other Asian countries , including Japan [20] , but there is a paucity of epidemiologic data and comparison of different diagnostic methods from China . This can be illustrated by consulting the PubMed database ( http://www . pubmed . gov ) where the following search strategy “strongyloides OR strongyloidiasis AND China” resulted in only 6 hits; 3 case reports , 1 study on animal strongyloidiasis , 1 global review , and 1 old publication that looked at single and multiple species parasitic infections among 15 , 952 Chinese using direct-smear examinations [21] ( accessed on 29 June 2007 ) . Here , we report findings from a cross-sectional parasitologic and questionnaire survey carried out in a random population sample in a rural setting of southern Yunnan province , China . We investigated the occurrence of S . stercoralis by screening multiple stool samples from the same individuals and comparatively assessed the performance of different diagnostic methods .
The study was carried out in Nongyang village , located in Menghai county , Xishuangbanna prefecture , Yunnan province , China ( 21 . 81° N latitude and 100 . 35° E longitude ) . The village was selected because ( i ) the hookworm prevalence in this area is known to be high ( used as a proxy for the likely occurrence of S . stercoralis , as both species have the same way of transmission ) , and ( ii ) it is readily accessible by project car to assure a rapid transfer of stool samples to the nearby laboratory . Details of the study village and the population sample have been presented elsewhere [22] . In brief , the village is inhabited by members of the Bulang ethnic group , and is situated 20 km southwest of the town of Menghai in a hilly area at an elevation of 1350 m above sea level . The economy of the village is governed by the surrounding tea and sugar cane plantations , other sources of income than farming are not available . Pigs and poultry are the most common domestic animals , others include dogs and buffaloes . Whilst all houses have untreated tap water originating from a nearby river , there are no household-based sanitation facilities . A single community latrine serves the entire population , but it is not consistently used . The village authorities were informed about the study , and a copy of the village family registry , containing basic demographic information , was obtained . According to the village family registry , there were some 150 households . Families with odd registration numbers ( n = 78 ) were contacted in batches of 20–30 families per week , and all members were invited to participate in the survey . The aim and procedures of the study were explained , and an informed consent sheet was signed by the head of the household or a designated literate substitute . Pre-tested individual and household questionnaires were administered to obtain demographic ( age , sex , education attainment ) , behavioral ( wearing shoes , food consumption , personal hygiene , health care seeking ) and occupational data , as well as information about the living conditions ( household asset ownership , house type , sanitation infrastructure , domestic animals ) . Next , pre-labeled plastic containers for stool sample collection were handed out to all participants and their ability to recognize their names was checked . Each morning , filled containers were collected and replaced by empty ones for stool collection on the following day . This procedure was repeated with the goal to obtain 3 stool samples from each individual . The stool samples were stored at ambient temperature and transferred to the laboratory within 2 hours post-collection . They were processed by the Kato-Katz technique [9] , the Baermann method [13] and the Koga agar plate procedure [12] . In addition , one sub-sample per study participant was stored in sodium acetate-acetic acid-formaline ( SAF ) solution , forwarded to a reference laboratory in Switzerland , and processed there by an ether-concentration method for the examination of helminth eggs and intestinal protozoa [23] . All tests were performed according to standard operating procedures and carried out or initiated within 12 hours after sample collection . Specifically , a single Kato-Katz thick smear was prepared from each stool sample and examined within 1 hour of preparation . Helminth eggs were counted separately to obtain parasite-specific infection intensity estimates . For the Baermann test , an apricot-sized stool sample was placed on a gauze-lined mesh in a glass funnel equipped with a rubber tube and a clamp , covered with deionised water and illuminated from below with a bulb . After 2 hours , the lowest 50 ml of the liquid were drained , centrifuged and the sediment examined under a microscope for S . stercoralis larvae ( L1-stage ) . The Koga agar plates were freshly prepared once per week and kept at 4°C in humid conditions pending utilization . A hazelnut-sized stool sample was placed in the middle of the plate and the covered plates were incubated in a humid chamber for 2 days at 28°C . All plates were rinsed with 12 ml SAF solution , the eluent centrifuged and the sediment examined under a microscope . Recovered larvae were differentiated to distinguish S . stercoralis L3 larvae from hookworm larvae . Samples were considered positive if larval or adult S . stercoralis were observed . Questionnaire data were entered in EpiData version 3 . 0 ( EpiData Association; Odense , Denmark ) and statistical analyses were carried out in STATA version 9 . 2 ( StataCorp . ; College Station , USA ) . Prevalence estimates for S . stercoralis according to the Koga agar plate and the Baermann methods were calculated by means of a mathematical model presented and used elsewhere [24 , 25] . Based on the relative frequency of single and repeated positive test results among the multiple stool samples submitted by the participants , the model extrapolates a ‘true’ prevalence and calculates additional test characteristics for a given method . At completion of the study , free treatment with compound mebendazole ( i . e . , mebendazole 100 mg/tablet plus levamisole hydrochloride 25 mg/tablet; 2 tablets per day for 3 consecutive days ) was offered to all inhabitants of the village by staff of the local parasite control station . The institutional review boards of the National Institute for Parasitic Diseases ( Shanghai , China ) and the Swiss Tropical Institute ( Basel , Switzerland ) approved the study . As mentioned before , written informed consent was sought from household heads or appropriate literate substitutes .
In total , 283 individuals from 71 families participated in the survey ( average family size: 4 . 0 people; range: 1–8 ) . At least 1 stool sample of sufficient quantity to perform the various diagnostic tests was available from 234 individuals ( 82 . 7% ) . Two or 3 samples were submitted by 180 individuals ( 63 . 6% ) and subsequent analyses were performed on this cohort . There were 98 females ( 54 . 4% ) and the age of the participants ranged from 4 to 84 years . Among those aged 15 years and above , 92 . 0% were farmers , the others were students . The illiteracy rate in the same age group was 67 . 2% . The majority of those aged 14 years and below attended school ( 58 . 5% ) , whereas the remaining individuals were either pre-school children ( 26 . 8% ) or had never attended school . Fourteen different parasite species were identified , 7 helminths and 7 intestinal protozoa . Very high prevalences of A . lumbricoides ( 93 . 3% ) , T . trichiura ( 88 . 9% ) and hookworms ( 87 . 8% ) were found . Here , we focus on the S . stercoralis results . Stool examination utilizing the Koga agar plate and the Baermann technique resulted in the identification of 19 and 21 S . stercoralis infections , respectively . As summarized in Table 1 , all S . stercoralis infections detected by the Koga agar plate method were also diagnosed by the Baermann technique , whereas 2 infections were identified by the latter method only . Thus , the observed infection prevalence of S . stercoralis , according to Baermann was 11 . 7% . The Kato-Katz method and the ether-concentration technique on SAF-conserved stool specimens failed to identify even a single infection with S . stercoralis . Table 2 shows that the prevalence of S . stercoralis was significantly higher among males than females ( 18 . 3% versus 6 . 1% , χ2 = 6 . 42 , degrees of freedom ( df ) = 1 , p = 0 . 011 ) and increased with age , albeit not significantly ( χ2 = 8 . 70 , df = 4 , p = 0 . 069 ) . No infections were found among participants <15 years , whereas the highest prevalence was recorded in those aged 15–24 years ( 19 . 6% ) . S . stercoralis infections were not found among students of any age . No additional risk factors for a S . stercoralis infection could be identified . Neither protective measures against infection , such as wearing shoes ( odds ratio ( OR ) = 0 . 64 , p = 0 . 516 ) , nor hygiene behavior , e . g . , hand washing before eating ( OR = 1 . 03 , p = 0 . 963 ) or after defecation ( OR = 1 . 23 , p = 0 . 671 ) , willingness to see a doctor in case of illness ( OR = 2 . 91 , p = 0 . 310 ) or presence of domestic animals ( e . g . , dogs; OR = 1 . 88 , p = 0 . 267 ) were associated with infection status . Indicators of the diagnostic performance of the Koga agar plate and the Baermann methods , in relation to different sampling efforts , are presented in Table 3 . The examination of 3 stool samples , rather than a single one , resulted in a significant increase in the number of infections detected by either method . The observed S . stercoralis prevalence increased from 7 . 3% to 11 . 7% when using the Koga agar plate method ( an increase of 62% ) , and from 7 . 0% to 14 . 0% in the case of the Baermann method ( an increase of 100% ) . Whilst using Koga agar plates , larvae were detected with equal frequencies in only 1 , 2 or all 3 stool samples from infected individuals , the Baermann method often failed to detect larvae in multiple samples from the same person . Using the results of the Koga agar plate method and a mathematical model developed by Marti and Koella [24] , we estimated a ‘true’ S . stercoralis prevalence of 12 . 3% . The corresponding value for the Baermann technique was 16 . 3% . The probability of correctly identifying infected individuals by analyzing single stool samples was estimated at 0 . 63 and 0 . 48 for the Koga agar plate and the Baermann technique , respectively . Table 4 shows the effect of the sampling effort for multiple stool sample collection on the observed prevalence and the influence of the available stool quantity on the completeness of the diagnostic results . Three Koga agar plate tests could be performed for 70 . 5% of the 254 participants who submitted at least 1 sufficiently-large stool sample . The higher requirements of the Baermann method regarding the available stool quantity are reflected in the lower number of tests . Only 236 participants had at least one Baermann result , whereas 129 ( 54 . 7% ) submitted 3 large enough stool samples . One S . stercoralis infection was identified by the Koga agar plate method among those participants who submitted stool samples of insufficient quantity to concurrently perform the Baermann test . Combined , the Koga agar plate and the Baermann technique identified 30 S . stercoralis infections among 254 individuals who submitted at least 1 stool sample of sufficient quantity to perform at least the Koga agar plate test , resulting in an observed prevalence of 11 . 8% .
There is a paucity of parasitologic and epidemiologic investigations pertaining to S . stercoralis in China , and to our knowledge the performance of different diagnostic approaches has never been assessed in this setting . We carried out an in-depth study in a random population sample from a small village in Yunnan province in the south-western part of China . The collection of multiple stool samples and their screening by the Koga agar plate and the Baermann techniques revealed a prevalence of S . stercoralis of 11 . 7% . It is conceivable that the observed prevalence still underestimates the ‘true’ prevalence , which is justified on the following grounds . First , in the absence of a diagnostic ‘gold’ standard , it is not possible to determine how often larvae failed to emigrate from the stool sample , or actually resided on the surface of the agar plate , but were not recovered . With regard to the Baermann technique , it is possible that some larvae had not yet reached the water , or settled to the ground of the funnel when the water was drained after 2 hours of exposure to light . Second , a recent study carried out in rural Malawi showed that a delay of 3 hours or more between evacuation of stool specimens by humans and processing/examining of stool samples in the laboratory resulted in a considerably decreased sensitivity of hookworm diagnosis [26] . Hence , there is concern that delays in stool processing might also negatively influence the sensitivity of diagnosing other helminth infections , including S . stercoralis . Future studies should investigate the effect of time from stool evacuation to laboratory examination with an emphasis on S . stercoralis . Third , a mathematical model [24] predicted a considerably higher prevalence of S . stercoralis when compared to the results of 3 stool specimens subjected to either the Koga agar plate or the Baermann technique . The application of other diagnostic methods , such as the charcoal coproculture method , which includes a culture step before harvesting the larvae by the Baermann method , and serology , might detect additional infections . Yet , based on our previous experience , we are confident that the approach taken in the current study ( multiple stool samples and different diagnostic methods ) detected S . stercoralis infections with a high sensitivity . Nonetheless , serological methods suitable to also identify very light infections should be used in future studies to further investigate the conspicuous absence of infections among children . On the other hand , the collection of stool samples over several days under limited supervision by our research team bears the risk of mixing up collection containers at the household level . This would result in the attribution of samples from one infected person to different household members who might not be infected , thus inflating the prevalence . We are confident that this issue did not distort our data , as we provided detailed explanations to all study participants about the importance of stool collection using the designated containers , and checked the ability of at least one household member to recognize each name on the pre-lab containers . Moreover , the age and sex distribution of S . stercoralis infections matched the previously presented epidemiologic patterns from neighboring countries . The 21 infections diagnosed by the Baermann approach originated from 18 families , suggesting that mis-attribution was certainly not a major issue . We also assume that the participation of only 63 . 6% of the eligible villagers did not affect the representativeness of the sample since the age and sex distribution of these 180 individuals was similar to the remaining 103 people who failed to provide at least 2 stool samples of sufficient quantity . Concerning the recovery of larvae from the agar plates , an attempt was made to first visually inspect the plate for larval tracks and characteristic signs of fungal and bacterial growth , but the high prevalence of hookworm larvae necessitated the recovery of the actual larvae for microscopic examination . In some cases signs of larval activity were noted , but no larvae could be recovered . Contrarily , it was shown that larvae can be present even if no signs of their activity can be detected on the surface of the agar plate [12] . We are not aware of previous community-based studies focusing on S . stercoralis in Yunnan province . The overall prevalence of S . stercoralis ( 11 . 7% ) is similar to reports from northern Thailand [17] . Interestingly , southern Yunnan shares some eco-epidemiologic characteristics with northern Thailand , such as the climate , land use patterns and ethnic background . Moreover , in both settings , the prevalence of infection was significantly higher in males than in females [15 , 17] , and increased with age , with the peak prevalence observed in adolescents and young adults [18] . Similar sex and age patterns were also reported from Laos [27] . However , in Laos and Thailand , infections were also found among children , whereas in the current study , infections were confined to individuals aged 15 years and above . These findings might point to age- and gender-specific occupational risk factors , e . g . , different behavioral patterns related to agricultural activities . The absence of infections among children suggests that the main transmission sites are outside the core village , despite the precarious sanitary conditions with 86 . 5% of the participants reporting not using the single community latrine available in the entire village . Possibly as a result of the rather uniform educational , occupational and behavioral population characteristics , we were unable to identify additional risk factors for infection . It is commonly assumed that even if multiple stool samples are available , no single diagnostic technique can detect all S . stercoralis infections . Different methods are therefore employed for the parasitological diagnosis of this helminth but they are often poorly standardized and their performance has rarely been assessed comparatively . In one of the few available studies that compared the diagnostic performance between the Koga agar plate and the Baermann method , the former technique was superior to the Baermann technique [28] . In the present study , however , the Baermann technique identified ‘all’ infections , whereas the Koga agar plate method failed to do so in 3 cases when considering all individuals who provided at least 1 stool sample of sufficient quantity ( Table 4 ) . Even taking into account the somewhat lower sensitivity of the Koga agar plate method , this technique still has advantages in field-based epidemiologic surveys . First , it allows the analysis of small stool samples , thereby reducing the number of participants who have to be excluded from the analysis due to insufficient amounts of stool , as was the case in the current study ( note the total numbers of Koga agar plate and Baermann technique test results in Table 4 ) . Second , the Koga agar plate technique also detects hookworm infections , thus allowing for concurrent diagnosis of both parasites [29] . Previous studies have shown that formaline-ether concentration methods were able to detect S . stercoralis infections , but compared to the Baermann and Koga agar plate methods , their sensitivity was considerably lower [10 , 16] , [30] . The low sensitivity of direct fecal smears and the Kato-Katz method for diagnosis of S . stercoralis is also well known [11] . Over the past decades , profound demographic , ecologic and socio-economic changes have occurred across China [31 , 32] , and the health system underwent significant reforms [33] . These changes also resulted in an increased availability and use of sophisticated medical techniques , including immunomodulatory drugs and organ transplantation . Consequently , it must be assumed that the immunocompromised population is expanding . Previous research has indicated that this population group is at high risk of severe disease when concurrently infected with S . stercoralis . Nevertheless , the obvious importance of S . stercoralis for public-health has yet to prompt new research into the epidemiology and control of this neglected helminth infection in China and elsewhere . In this connection , the importance of differential diagnosis of soil-transmitted helminth infections must be emphasized , particularly in view of the large-scale administration of albendazole and/or mebendazole that usually show good efficacy against A . lumbricoides and hookworms ( only moderate efficacy against T . trichiura ) , but commonly fail to clear S . stercoralis [1] . We have launched additional studies with the objective of enhancing our understanding of the epidemiologic situation of S . stercoralis in adjacent parts of Yunnan province with different environmental , socio-economic and ethnic characteristics , and will also investigate current and future treatment options . Finally , we encourage other groups who focus their research on helminths , not to neglect S . stercoralis any longer .
|
An estimated 30 million to 100 million people are infected with the parasitic worm Strongyloides stercoralis , the causative agent of strongyloidiasis , and yet this is a neglected tropical disease . The diagnosis of this parasite requires specialized techniques ( e . g . Baermann and Koga agar plate method ) , but these are rarely employed in epidemiologic studies . We assessed the occurrence of S . stercoralis in a rural part of southern Yunnan province , China , and compared different diagnostic methods . At least two stool samples were obtained from 180 randomly selected individuals , and examined with four diagnostic approaches , including the Koga agar plate and the Baermann technique . Twenty-one individuals were infected with S . stercoralis ( prevalence: 11 . 7% ) . Males were more often infected than females ( 18 . 3% versus 6 . 1% , p = 0 . 011 ) . Infections were absent in children below the age of 15 years . The Baermann technique showed a higher sensitivity than the Koga agar plate method , and the examination of multiple stool samples improved the diagnostic performances of both methods . The use of a mathematical model suggested a ‘true’ S . stercoralis prevalence of 16 . 3% . There is a need to further study the epidemiology of strongyloidiasis in other parts of China , and control measures are required in settings with high prevalences as observed in this area .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"public",
"health",
"and",
"epidemiology/epidemiology",
"infectious",
"diseases/helminth",
"infections",
"public",
"health",
"and",
"epidemiology/screening",
"infectious",
"diseases/epidemiology",
"and",
"control",
"of",
"infectious",
"diseases"
] |
2007
|
Occurrence of Strongyloides stercoralis in Yunnan Province, China, and Comparison of Diagnostic Methods
|
Translation termination is a highly controlled process in the cell . In Saccharomyces cerevisiae , various regulatory factors employ genetic and epigenetic mechanisms to control this process . We used a quantitative dual luciferase reporter assay to demonstrate a difference in translation termination efficiency between two different yeast strains , BY4724 and RM11-1a . We then used a recently developed linkage mapping technique , extreme QTL mapping ( X-QTL ) , to show that this difference is largely explained by a coding polymorphism in TRM10 ( which encodes a tRNA–methylating enzyme ) and a regulatory polymorphism in SUP45 ( which encodes one of the yeast translation termination factors ) . BY and RM carry variants of TRM10 and SUP45 with opposite effects on translation termination efficiency . These variants are common among 63 diverse S . cerevisiae strains and are in strong linkage disequilibrium with each other . This observation suggests that selection may have favored allelic combinations of the two genes that maintain an intermediate level of translation termination efficiency . Our results also provide genetic evidence for a new role of Trm10p in translation termination efficiency .
Translational fidelity is essential for functional integrity of the cell . Efficient termination is an important aspect of translational fidelity , and a multitude of mechanisms participate in this highly regulated process . Translation termination in eukaryotes is mediated by two termination factors , eRF1 and eRF3 [1] . eRF1 , encoded by SUP45 in Saccharomyces cerevisiae , recognizes all three stop codons ( UAG , UAA , and UGA ) and facilitates release of the nascent polypeptide chain from the translational machinery [2] , [3] . GTPase activity of eRF3 , encoded by SUP35 in S . cerevisiae , is required to couple the recognition of translation termination signals by eRF1 to efficient polypeptide chain release [4] . In a successful translation termination event , termination factors efficiently recognize stop codons . However , in certain instances , transfer RNAs ( tRNAs ) outcompete termination factors in stop codon recognition . The resulting misincorporation of an amino acid into the nascent peptide is known as translational readthrough . Therefore , during translation , any event that directly or indirectly makes a tRNA more likely to bind to a stop codon increases readthrough . It is widely accepted that the efficiency of translation termination is modulated by both cis- and trans-acting factors [5] . In S . cerevisiae , the sequence surrounding the stop codon has been shown to play a major role in translation termination efficiency [6] , [7] . Several trans factors have also been shown to affect translation termination , either directly through contacts with release factors or indirectly , as demonstrated by genetic experiments ( reviewed in [8] ) . Moreover , recent studies of translation termination in S . cerevisiae have revealed genetic and epigenetic regulatory mechanisms that may enable controlled readthrough of stop codons , which can have significant effects on cellular processes such as mRNA degradation and , in some cases , can confer a beneficial phenotype to the cell [9] . The most studied example of such a mechanism is [PSI+] , the prion conformation of the Sup35 protein , which can have pleiotropic effects on growth that vary among different yeast strains [10] . Although our knowledge of translation termination has grown in the past few decades , one can envision that many factors that modulate this complex process remain to be discovered . Natural genetic variation provides a framework for finding such factors . Linkage analysis has been successfully used to find the genetic basis of complex phenotypes in yeast at the cellular level , including growth in different chemical environments [11] , sporulation efficiency [12] and growth at high temperatures [13] , as well as phenotypes at the molecular level , such as genome-wide mRNA expression levels [14] , [15] . Here , we employed linkage analysis to study translation termination efficiency . We used extreme QTL mapping ( X-QTL ) [16] to find the genetic basis for the observed difference in readthrough between two S . cerevisiae strains , RM11-1a ( a wine strain hereafter referred to as RM ) and BY4724 ( a laboratory strain hereafter referred to as BY ) . We show that a coding polymorphism in TRM10 , which encodes a tRNA-methylating enzyme with an unknown physiological role in the cell [17] , affects readthrough in yeast . Moreover , we show that cis-regulatory variation that alters the expression level of SUP45 is another factor involved in translation termination efficiency variation between BY and RM . These two yeast strains carry alleles of TRM10 and SUP45 with opposing effects on readthrough . The BY and RM alleles of both TRM10 and SUP45 are common in a diverse collection of S . cerevisiae strains [18] and are in significant linkage disequilibrium ( LD ) , suggesting that readthrough may be subject to stabilizing selection .
In order to measure readthrough in the two parent strains , we took advantage of a dual luciferase reporter system [19] . This system uses tandem Renilla and firefly luciferase genes that are separated by a single in-frame stop codon . The activity of the firefly luciferase , encoded by the distal open reading frame , provides a quantitative measure of the readthrough of the stop codon that separates the two open reading frames . The activity of the Renilla luciferase , encoded by the proximal open reading frame , serves as an internal control for mRNA abundance . Thus , the relative abundance of these light-emitting proteins measures the efficiency of translation termination . Here , we used two separate reporters; one with UGA ( stop codon ) and one with CGA ( sense codon ) separating the Renilla and firefly open reading frames . For each strain , we calculated the readthrough as the ratio of firefly to Renilla luciferase activity in the presence of the stop codon , normalized by the observed ratio for the sense codon constructs ( Table 1 ) . We found that the readthrough in RM is higher than in BY . We used X-QTL to examine the genetic basis of the readthrough difference between BY and RM in a large pool of segregants from a cross between these strains . In order to be able to select those segregants in the tails of the distribution of readthrough , we constructed a GFP reporter with a UGA stop codon inserted at the beginning of the GFP coding sequence and integrated it into the genomes of BY and RM ( Materials and Methods ) . We also integrated an intact GFP reporter ( without the stop codon ) in the same position . Then , we transferred these reporters into BY and RM strains with suitable markers for X-QTL ( Materials and Methods ) . For each strain , we calculated readthrough as the ratio of the GFP signal in the presence of the stop codon to the GFP signal in the absence of the stop codon . We showed that readthrough measured using the GFP reporter is in agreement with readthrough measured using the dual luciferase assay for both BY and RM ( Table S1 ) . To map the genetic basis of the readthrough difference , we harvested a MATa pool from a sporulation culture of BY×RM diploid hybrids containing the GFP reporter and sorted out segregants from the two extremes of the readthrough distribution by fluorescence-activated cell sorting ( FACS ) . We selected the top 1% of the segregating population ( high GFP signal ) and the bottom 1% of the segregating population ( low GFP signal ) . Samples from both selected pools as well as a sample of the whole ( unselected ) population were subsequently genotyped as previously described [16] . Comparisons of the high and low segregant pools to the whole population showed allele frequency differences on chromosome XV , at the same position but in opposite directions in the two selected tails ( Figure 1A ) . The directions of the skew suggested that carrying a BY allele in this region results in increased readthrough , whereas carrying the RM allele results in a decrease in readthrough . Based on functional annotations available in the Saccharomyces Genome Database and sequence comparison between BY and RM for the genes in this region ( Figure S1 ) , we selected TRM10 as a candidate for further investigation . TRM10 encodes a tRNA modifying enzyme , which methylates the N-1 position of guanosine-9 in ten tRNAs in yeast [17] . Comparison of the coding sequence of TRM10 between BY and RM showed eight single nucleotide polymorphisms ( SNPs ) between the two yeast strains , among which five are nonsynonymous substitutions . In order to test the causality of TRM10 polymorphisms for the observed peak on chromosome XV , we made allele replacement strains in both BY and RM ( replacing TRM10 with the version from the other strain ) and measured readthrough in the newly made strains using the dual luciferase assay . Results of this experiment showed that replacing TRM10 in each strain with the alternative allele of this gene changed readthrough in the direction predicted from the X-QTL results . RM-TRM10BY showed higher readthrough than RM . BY-TRM10RM showed lower readthrough than BY ( Figure 1B ) . These results showed that the effect of the TRM10 coding polymorphism on readthrough is in the opposite direction from the difference observed in the parent strains; swapping TRM10 increased the difference in readthrough between BY and RM . This observation suggested the presence of other polymorphic factor ( s ) that influence translation termination efficiency . To further analyze the relationship between the Trm10p tRNA methylation activity and translation termination efficiency , we made complete TRM10 deletions in both genetic backgrounds . Readthrough measurements using the dual luciferase assay showed that deleting TRM10 in both BY and RM increases readthrough , which provides further evidence for the role of Trm10p tRNA modification in translation termination efficiency ( Figure 1C ) . Moreover , these results suggest that BY carries a partial loss of function allele of TRM10 , because the BY allele of TRM10 is associated with higher readthrough . To identify the causal polymorphism , we also made strains with TRM10L78Q and TRM10S139P single amino acid changes in the RM background using site-directed mutagenesis . These two polymorphisms were chosen based on fungal protein sequence alignment from the Saccharomyces Genome Database , which showed that these residues are highly conserved in different yeast species . Readthrough measurements in RM-TRM10L78Q and RM-TRM10S139P identified the serine to proline substitution at position 139 as the causal polymorphism ( Figure 1D ) . To determine whether TRM10 is the sole factor explaining the observed allele frequency skew on chromosome XV , we carried out X-QTL with segregants from a cross between BY and RM- TRM10BY ( i . e . TRM10 was no longer polymorphic , with both parent strains carrying the BY allele ) . The pool selected from the high tail of the readthrough distribution showed higher average GFP signal relative to the high tail in the original BY×RM cross ( data not shown ) , and no allele frequency skew was observed in the TRM10 region on chromosome XV in either selected pool ( Figure 2A ) . X-QTL results from the cross with both parent strains carrying the BY allele of TRM10 showed a new region of allele frequency skew on chromosome II ( Figure 2A ) . Our ability to detect this locus was improved by the overall increase in the GFP signal resulting from the increased readthrough conferred by the BY allele of TRM10 . The direction of the skew on chromosome II was in the direction expected from the difference between the parent strains: the RM allele at this locus was enriched in the high-readthrough pool and depleted from the low-readthrough pool . This locus ( Figure S2 ) contains SUP45 , which encodes the yeast translation termination factor responsible for stop codon recognition . Sequence comparison between BY and RM showed six synonymous SNPs in the coding sequence and four nucleotide substitutions and two indels in the 400-base pair upstream noncoding region . We previously showed that expression level of SUP45 is lower in RM ( relative expression level = 0 . 00333±0 . 0406 ) than in BY ( relative expression level = 0 . 315±0 . 0415 ) [20] . This study also showed , using an independent panel of 109 segregants , that the expression level difference mapped to the location of the SUP45 gene . These findings strongly suggested that the observed allele frequency skew on chromosome II is due to cis-regulatory polymorphism that alters the expression level of SUP45 between BY and RM . To test this hypothesis , we made SUP45 allele replacement strains in BY and RM , in both TRM10-wild type and TRM10-swapped backgrounds . In the first set of replacements , we swapped only the SUP45 coding sequence . In the second set , we replaced the SUP45 coding sequence as well as the 400-base pair upstream region . We used the dual luciferase assay to measure readthrough in the newly made strains . In both TRM10-wild type and TRM10-swapped strains , replacing the SUP45 coding sequence along with the upstream region had a significant effect on readthrough ( Figure 2B and 2C ) , whereas replacing just the coding sequence of SUP45 did not have a significant effect on readthrough ( Figure S3 ) . These results support the role of SUP45 expression level in translation termination efficiency . When we swapped both TRM10 ( coding sequence ) and SUP45 ( coding and upstream sequence ) in each parent strain with the alleles of these genes from the other ( donor ) strain , translation termination efficiency changed to the level of the donor strain ( Figure 2D ) . These data demonstrate that the polymorphisms in these two genes explain the difference in translation termination efficiency between BY and RM . We also performed X-QTL in BY×RM-SUP45BY ( coding and upstream regions ) , as well as in BY×RM-TRM10BY ( coding region ) -SUP45BY ( coding and upstream regions ) . X-QTL with the segregant pool fixed for the BY allele of SUP45 showed the expected allele frequency skew in the region of chromosome XV containing TRM10 ( Figure 3A ) . X-QTL with the segregant pool fixed for the BY alleles of both TRM10 and SUP45 showed no significant allele frequency skews anywhere in the genome ( Figure 3B ) , providing further evidence that these two genes explain most of the difference in translation termination efficiency between BY and RM . We used a previously published polymorphism survey [18] to determine the frequencies of the BY and RM alleles of TRM10 and SUP45 among 63 diverse S . cerevisiae strains isolated from a broad range of sources . We found that both alleles of both genes are common among these S . cerevisiae strains , with allele frequencies of 0 . 46 and 0 . 44 for the BY alleles of TRM10 and SUP45 , respectively . This observation shows that these variants are not restricted to strains adapted to the laboratory environment , but rather represent naturally occurring polymorphisms in the species . Strains which carry the BY allele of TRM10 include laboratory strains , Sake strains , clinical isolates and oak strains . Most of these strains also carry the BY allele of SUP45 ( Table S2 ) . The SUP45BY-TRM10BY and SUP45RM-TRM10RM haplotypes are present in S . cerevisiae strains more frequently than expected based on random association ( 76% vs . 50% ) . These data suggested that TRM10 and SUP45 might be in linkage disequilibrium ( LD ) . We confirmed the presence of strong LD between these genes by calculating D' and r2 between SUP45 and TRM10 and obtained values of 0 . 537 and 0 . 266 , respectively . LD could arise in part due to the population structure present in S . cerevisiae [18] . To test whether the observed LD is significantly higher than what is expected solely due to structure , we used polymorphisms from 63 diverse S . cerevisiae [18] strains and calculated LD between 1000 randomly chosen SNP pairs from different chromosomes . We observed nine pairs out of 1000 with higher r2 than the TRM10-SUP45 pair ( Figure S4 ) , which showed that LD between TRM10 and SUP45 SNPs is significantly higher than what is expected due to structure alone ( p-value = 0 . 009 ) . We obtained similar results for D' ( p-value = 0 . 011 ) . This non-random association of alleles suggests a functional association between the two genes . The two overrepresented haplotypes carry alleles with opposing effects on readthrough . The BY allele of TRM10 results in higher readthrough , whereas the BY allele of SUP45 results in lower readthrough; conversely , the RM allele of TRM10 results in lower readthrough whereas the RM allele of SUP45 results in higher readthrough . These two combinations keep readthrough within the range ∼0 . 3–0 . 5% , above that of the SUP45BY-TRM10RM combination ( ∼0 . 2% ) and below that of the SUP45RM-TRM10BY combination ( ∼0 . 8% ) . The fact that the two allelic combinations with intermediate readthrough are overrepresented suggests that readthrough may be subject to stabilizing selection .
We have shown that a difference in translation termination efficiency between two yeast strains is explained by polymorphisms in two genes , TRM10 and SUP45 . TRM10 encodes a tRNA-modifying enzyme , which methylates the N-1 position of guanosine-9 in some tRNAs [17] . In the S . cerevisiae , m1G is found at position 9 in 10 out of 34 tRNAs . Despite the evolutionary conservation of this modification and the conservation of the protein [17] , a cellular role for m1G9 modification has not been reported . Previous studies have not found an obvious growth defect in yeast cells lacking Trm10p under standard growth conditions in rich or minimal media . Recently , a temperature-sensitive phenotype was reported in yeast cells lacking Trm10p in the presence of 5- fluorouracil [21] . This study showed that 5- fluorouracil targets tRNA-modifying enzymes and therefore reduces a number of tRNA modifications . Loss of most of the tRNA modifications in the presence of 5- fluorouracil , in combination with loss of the m1G9 modification in the absence of Trm10p , resulted in destabilization of hypomodified tRNAs , which explained the growth defect and suggested a role for m1G9 modification in tRNA stability . Here , we provide genetic evidence supporting a role for m1G9 modification in translation termination efficiency . We showed that a serine to proline substitution at position 139 of Trm10p contributes to the readthrough difference between BY and RM . One of the Trm10p substrates is the tryptophan tRNA , which decodes UGG , one of the closest codons to UGA , the opal stop codon in the dual luciferase and GFP reporters used here . According to the Pfam domain annotation , the Trm10p tRNA methylating domain spans residues 104 to 276 . A serine to proline substitution at position 139 may decrease Trm10p tRNA methylation activity . Loss of this modification may increase the ability of near cognate tRNAs ( such as the tryptophan tRNA ) to outcompete SUP45p in UGA stop codon recognition , resulting in increased readthrough . These data support a role of tRNA methylation by Trm10p in efficient translation termination . We also showed that the other factor involved in the translation termination efficiency difference between BY and RM is a regulatory polymorphism that alters the expression level of SUP45 . Sup45p is the yeast translation termination factor responsible for stop codon recognition . It was previously reported that reducing the cellular level of Sup45p decreases the efficiency of translation termination [22] . When the cellular level of Sup45p is low , for example as a result of a lower expression level of SUP45 , near cognate tRNAs are more likely to outcompete Sup45p in stop codon recognition , which in turn increases readthrough . BY alleles of both TRM10 and SUP45 are common among S . cerevisiae strains . This observation shows that these alleles are not restricted to strains adapted to the laboratory environment , but rather represent naturally occurring polymorphisms . Despite the fact that these two genes reside on different chromosomes and are thus not physically linked , we found strong and significant linkage disequilibrium ( LD ) between these two genes . This non-random allelic association provides evidence that natural selection has favored the SUP45BY-TRM10BY and SUP45RM-TRM10RM haplotypes over the others . The preferred haplotypes consist of alleles with opposing effects on readthrough , and thus strains that carry them should exhibit intermediate readthrough relative to the other two haplotypes . This observation suggests that readthrough may be subject to stabilizing selection . High readthrough may be disadvantageous due to production of too many inappropriately extended proteins . On the other hand , keeping readthrough from dropping too low may protect yeast mRNAs whose translation requires the suppression of leaky stop codons [23] .
Cultures were grown in minimal medium containing 0 . 67% ( w/v ) yeast nitrogen base without amino acids ( Difco ) containing 2% ( w/v ) glucose ( SMD ) or 2% galactose ( SMGal ) or 4% raffinose ( SMRaf ) , as specified . Additional nutritional supplements or drugs were added as required . YPD plates were made as described [24] . For sporulation , SPO++ was used ( http://www . genomics . princeton . edu/dunham/sporulationdissection . htm ) . To make a readthrough GFP reporter , a 78-nucleotide region from the ade1-14 allele [25] was inserted in-frame with GFP coding region into HindIII site ( 6th codon ) of pGAL-GFP plasmid ( kindly provided by James Broach ) . The insertion was confirmed by sequencing . To include a selectable marker , NATMX cassette was inserted into a NotI site downstream of GFP coding sequence in pGAL-GFP-ade1-14 plasmid . To integrate this GFP reporter in yeast genome , the region containing pGAL-GFP-ade1-14-NATMX was amplified with primers with 40 base pairs of homology to regions upstream and downstream of YDL242W ( an open-reading frame unlikely to encode a protein , based on available experimental and comparative sequence data from http://www . yeastgenome . org/ ) . This PCR product was then used in transformation of BY4724 [26] and RM11-1a [14] . Insertion of the GFP reporter inside YDL242W was then confirmed by PCR . We then transferred this GFP reporter into strains with suitable genetic markers . To do so , we crossed these new BY and RM strains with GFP reporter into BY MATα can1Δ::STE2pr-SpHIS5 lyp1Δ his3Δ1 and RM MATα AMN1BY his3Δ0::NatMX ho::HphMX [16] , respectively . After sporulating the obtained diploids and genotyping the dissected tetrads , we selected BY Matα his3Δ1 lyp1Δ can1Δ::STE2prSpHIS5 YDL242W::pGAL-GFP-ade1-14-NATMX and RM Mata AMN1BY his3Δ::NATMX , YDL242W::pGAL-GFP-ade1-14-NATMX . TRM10 and SUP45 replacement strains were generated by a two-step replacement method [27] . TRM10 was replaced with URA3-KanMX cassette from pCORE in BY4724 and RM11-1a generating trm10Δ::URA3-KanMX knockout strains . TRM10 alleles from the donor strains were amplified by PCR with approximately 200-bp overlapping sequence and introduced into recipient strains to replace URA3-KanMX cassette . For SUP45 replacement URA3-KanMX cassette from pCORE was inserted downstream of SUP45 coding sequence in recipient strains . pCORE cassette was then replaced by the PCR-amplified SUP45 allele from donor strains . Allele replacements were confirmed by sequencing . The RM TRM10 and SUP45 sequences were obtained from the whole genome-sequencing project at the Broad Institute ( http://www . broad . mit . edu/annotation/genome/saccharomyces_cerevisiae/Home . html ) . All sequencing was done using standard dideoxy methods . Dual luciferase assay was performed as explained before [19] . Dual luciferase reporter plasmids were kindly provided by David Bedwell . Plasmids with the stop codon ( pDB691 ) or the sense codon ( pDB690 ) were transformed into the indicated yeast strains , and transformants were selected on SMD drop-out plates lacking uracil . Transformed strains were grown in liquid SMD medium to a cell density of 0 . 5–0 . 7 A600 units/mL as measured using Synergy 2 Multi-Mode Microplate Reader ( BioTek Instruments ) . The luciferase assay was performed using the Dual-Luciferase Reporter Assay System ( Promega; E1910 ) . Approximately 104 yeast cells from each strain expressing the indicated dual luciferase reporter were lysed using 100 µL of Passive Lysis Buffer in a 96-well plate ( Costar; 3370 ) . Two microliters of the lysate were added to 10 µL of the Luciferase Assay Reagent II in an opaque 96-well plate ( Costar; 3614 ) . Relative luminescence units ( RLUs ) produced by firefly luciferase activity were then measured for 10 seconds using Synergy 2 Multi-Mode Microplate Reader ( BioTek Instruments ) . 10 µL of Stop&Glo buffer was then added to quench the firefly activity and activate the Renilla luciferase activity . RLUs were again measured for 10 seconds to determine the Renilla luciferase activity . Negative controls that contained all the reaction components except cell lysates were used to determine the background for each luciferase reaction and were subtracted from the experimental values obtained . Percent readthrough is expressed as the mean ± the standard deviation of values obtained from at least eight independent dual luciferase assay including at least four biological replicates . All X-QTL experiments were done in duplicates . MATa haploid segregants from the indicated cross were selected as explained before [16] . To create the segregating pool , a single colony of the diploid progenitor was inoculated into 5 mL YPD and grown to stationary phase . The diploid culture was spun down and the supernatant was decanted . The diploid pellet was then resuspended in 50 mL SPO++ sporulation medium . The sporulation was kept at room temperature ( ∼22°C ) with shaking and monitored for the fraction of diploids that had sporulated . Once more than 50% of the diploids had sporulated , 10 mL of the sporulation were spun down and then the supernatant was decanted . The pellet was resuspended in 2 mL water . 600 µL β-glucoronidase ( Sigma; G7770 ) were added to the preparation , and the mixture was incubated at 30°C for one hour . Water was added to the sample so that the total volume was 20 mL . The spore preparation was spread onto SMD + canavanine/thialysine plates ( Sigma; C9758 for canavanine ( L-canavanine sulphate salt ) ; A2636 for thialysine ( S- ( 2-aminoethyl ) - L-cysteine hydrochloride ) ) , with 100 µL of sample going onto each plate . The plates were incubated at 30°C for two days . Then 10 mL of water were poured onto each plate and a sterile spreader was used to remove the segregants from the plate . The cell mixtures from each plate were then pipetted off the plates into a container . The pool was spun down and the water decanted . Haploid segregants were then inoculated into liquid SMRaf plus canavanine medium at a concentration of ∼1×107 cells mL−1 . The cells were grown for approximately two generations to a density of ∼2×107 cells mL−1 . To induce the GAL promoter , cells were spun down and then were transferred to liquid SMGal plus canavanine with a density of ∼2×106 cells mL−1and were incubated in 30°C while shaking on a rotary shaker at 200 rpm for four hours . Cells were then sorted using BD FACS Vantage SE , collecting 20 , 000 cells from top 1% ( High GFP ) and bottom 1% ( Low GFP ) of the whole population . High GFP , Low GFP and a sample of the whole population were then plated on YPD plates and were incubated at 30°C for two days . DNA was extracted from the grown cells using Genomic-tip 100/G columns ( Qiagen; 10243 ) . DNA was labeled using the BioPrime Array CGH Genomic Labeling Module ( Invitrogen; 18095-012 ) with the sample being labeled with Cy3 dUTP and the reference being labeled with Cy5 dUTP . We used a BY/RM diploid as the reference for all hybridizations . Labeled samples were then hybridized onto the allele-specific genotyping microarray with isothermal probes that assays ∼18 , 000 single nucleotide polymorphisms ( SNPs ) between BY and RM [16] . Hybridization intensities were extracted and normalized using the rank invariant method in the Agilent Feature Extraction software package . For a given SNP , the difference in the log10 ratios of BY and RM-specific probes on a single array ( or log10 intensity difference ) was computed . Background allele frequency changes that occur during pool construction were removed from the data for top 1% ( High GFP ) and bottom 1% ( Low GFP ) selection by subtracting the log10 intensity difference obtained for the whole ( unselected ) population from the log10 intensity difference observed in the High and Low GFP selections . These steps were conducted in R ( http://www . r-project . org/ ) .
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Translation , the process of protein synthesis from messenger RNA ( mRNA ) , cannot be successfully completed without proper termination . The ends of the mRNA coding regions are marked by one of the three stop codons , which are recognized by termination factors rather than by the transfer RNAs ( tRNAs ) that match amino acids to the corresponding codons . Like most biological processes , translation termination is not perfect . Occasionally , tRNAs bind to stop codons , resulting in polypeptides with additional amino acids beyond the normal stop position—a phenomenon known as readthrough . Perturbations that affect the balance between termination factors and tRNAs will change readthrough . Here we demonstrate the effect of two perturbations on translation termination efficiency in the context of natural genetic variation . We show that a difference in readthrough between a laboratory and a vineyard strain of yeast is largely due to two genetic variants . One variant affects the expression level of a key translation termination factor; the other modifies the activity of a tRNA–methylating enzyme . We also show that natural selection has favored an intermediate level of readthrough .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology"
] |
2011
|
Variants in SUP45 and TRM10 Underlie Natural Variation in Translation Termination Efficiency in Saccharomyces cerevisiae
|
The key trigger for Hebbian synaptic plasticity is influx of Ca2+ into postsynaptic dendritic spines . The magnitude of [Ca2+] increase caused by NMDA-receptor ( NMDAR ) and voltage-gated Ca2+ -channel ( VGCC ) activation is thought to determine both the amplitude and direction of synaptic plasticity by differential activation of Ca2+ -sensitive enzymes such as calmodulin . Ca2+ influx is negatively regulated by Ca2+ -activated K+ channels ( SK-channels ) which are in turn inhibited by neuromodulators such as acetylcholine . However , the precise mechanisms by which SK-channels control the induction of synaptic plasticity remain unclear . Using a 3-dimensional model of Ca2+ and calmodulin dynamics within an idealised , but biophysically-plausible , dendritic spine , we show that SK-channels regulate calmodulin activation specifically during neuron-firing patterns associated with induction of spike timing-dependent plasticity . SK-channel activation and the subsequent reduction in Ca2+ influx through NMDARs and L-type VGCCs results in an order of magnitude decrease in calmodulin ( CaM ) activation , providing a mechanism for the effective gating of synaptic plasticity induction . This provides a common mechanism for the regulation of synaptic plasticity by neuromodulators .
Associative learning is underpinned by Hebbian synaptic plasticity at glutamatergic synapses . Spike timing-dependent plasticity ( STDP ) is the classical manifestation of Hebbian plasticity where temporally correlated pre- and post-synaptic activity induces NMDA receptor ( NMDAR ) - and Ca2+ -dependent changes in synaptic strength [1–3] . The calcium control hypothesis states that the direction of synaptic plasticity is determined by the amplitude of postsynaptic [Ca2+] transients; moderate elevations of [Ca2+] result in long-term depression ( LTD ) , whereas higher levels of [Ca2+] lead to long-term potentiation ( LTP ) [4–6] . However , due to high spine-neck electrical resistance [7 , 8] , large amplitude [Ca2+] transients can be elicited by presynaptic stimulation in the absence of postsynaptic spiking without inducing synaptic plasticity [7 , 9] raising a question mark over the validity of the calcium hypothesis . Furthermore , the requirement for Ca2+ influx through voltage-gated Ca2+ channels ( VGCCs ) and temporally precise postsynaptic spiking [9–11] highlights the importance of spatiotemporal patterning of [Ca2+] within the postsynaptic spine for induction of STDP , although the mechanisms for this remain obscure . The expression of Hebbian synaptic-plasticity is dependent on activation of Ca2+ /calmodulin-activated kinase II ( CaMKII ) and therefore on the transduction of Ca2+ signals by calmodulin ( CaM ) [12 , 13] . Interestingly , activation of CaM by Ca2+ does not follow a linear relationship because CaM activation requires Ca2+ binding to sites at both the C- and N-terminals of CaM , which have different affinities and kinetics for Ca2+ binding . The C-terminal lobe is high affinity with slow binding kinetics whilst the N-terminal lobe is low affinity with fast binding kinetics [14] . This ensures that CaM activation by Ca2+ within spines will depend on the spatiotemporal pattern of [Ca2+] transients . In turn , spatiotemporal [Ca2+] distributions are determined by endogenous Ca2+ buffering within spines which restrict Ca2+ to localized micro- or nano-domains [15] . Further control of spine Ca2+ dynamics is provided by small-conductance Ca2+ -activated K+ channels ( SK-channels ) located in the spine membrane [16 , 17] . SK-channels are activated by Ca2+ influx through NMDARs and VGCCs . The resulting hyperpolarization and inhibition of these voltage-dependent channels constitutes a negative regulatory feedback mechanism on Ca2+ influx . Conversely , inhibition of SK-channels relieves the negative regulation resulting in greater spine depolarization and Ca2+ influx which facilitates the induction of LTP [16 , 18] and enhances performance in spatial memory tasks [19] . It is therefore predicted that SK-channels regulate spine Ca2+ dynamics which at the nanodomain-level control CaM activity and the induction of synaptic plasticity . These signaling nanodomains are beyond the resolution of currently available microscopy techniques therefore to test the predicted nonlinear-feedback effects of SK-channels on spine Ca2+ and CaM we developed a 3-dimensional , deterministic reaction-diffusion model within a biophysically plausible dendritic spine calibrated to experimentally recorded global spine-Ca2+ transients from CA1 pyramidal neurons in the hippocampus . Using this model we qualitatively addressed the effect of the SK-channel interactions with spine Ca2+ and CaM , and considered the implications for synaptic plasticity induction . We find that during STDP , SK-channels are activated in response to a priming Ca2+ stimulus from either NMDARs or VGCCs . The subsequent reduction in Ca2+ influx through NMDARs and L-type VGCCs results in a order of magnitude decrease in CaM activation providing a mechanism for the effective gating of synaptic plasticity induction .
Solutions to the spine Ca2+ diffusion model were obtained using finite-element methods implemented in COMSOL Multiphysics 4 . 2a using LiveLink for MATLAB [20 , 21] . Full model equations are given in the Supporting Information . Model parameter-values were constrained using experimental data from electrophysiological and Ca2+ imaging studies , performed on hippocampal CA1 dendritic spines where possible ( S1 Table ) . For spine morphology , we used a solution domain represented by the union of a sphere for the head , and a cylinder for the spine neck . Volume boundaries outside of channel cluster regions had zero-flux condition . Channel cluster boundary regions had time-varying Ca2+ flux conditions determined by a Hodgkin-Huxley-type model formulation . Spine head and neck dimensions were matched to the median of reported CA1 spine size-distributions [8] . The boundary of the spine was discretized into triangular elements and the volume within the boundary was discretized into tetrahedral elements . Predefined mesh parameter values were set to Normal ( maximum and minimum element size 0 . 1μm and 0 . 02μm , respectively ) in the interior of the spine , and Extremely Fine ( maximum and minimum element size 0 . 02μm and 0 . 0002 μm , respectively ) within 50nm of Ca2+ sources . The time-dependent solver used implicit time-stepping backward differentiation formula ( BDF ) scheme ( maximum BDF order 2 ) with adaptive time-step ( maximum allowable step , 10μs ) . Scaled absolute tolerance for solution variables was 0 . 001 . Each ion-channel cluster was represented by a circular region ( diameter 1nm ) on the boundary . Ca2+ flux boundary conditions for NMDAR and VGCC clusters varied in time according to the current densities calculated by the model . Ion channels included were; AMPARs ( total maximal conductance , gA = 60 pS ) , NMDARs ( gN = 160 pS ) , T-type ( gCaT = 0 . 23 pS ) and L-type VGCCs ( gCaL = 0 . 9 pS ) , and SK-channels ( gSK = 25 pS ) . AMPAR and NMDAR open probabilities were modeled using a glutamate binding-scheme [22] . The NMDAR open probability also incorporated Mg2+-unblock voltage-dependence . Activation and inactivation gating-variable steady-states for T-type and L-type VGCCs were modeled using equation form described in [23] . The SK-current was governed by a Hill function which was dependent on local [Ca2+] to the SK-channel cluster . EPSPs were simulated by representing synaptic-cleft glutamate concentration immediately after presynaptic stimulus as a step-pulse of 1mM amplitude with duration 1ms . bAPs were simulated by instantaneous rise of membrane potential to maximal depolarization ( 67mV ) , followed by double exponential-decay to resting potential . Diffusing Ca2+ was buffered by a low binding-capacity endogenous fixed-buffer ( EFB ) ( binding ratio of 20 [24] ) and a mobile buffer ( unless otherwise stated , 100μM calbindin , binding ratio ≈ 250 , DB = 20 μm2s−1 ) . In simulations involving calmodulin , the simple mobile-buffer model ( Eq . 16 ) was replaced with a cooperative binding model of Ca2+ to the two calmodulin lobes [25] ( S1 Fig ) . Unless otherwise stated , initial calmodulin concentration was set to 100μM ( effective binding ratio ≈ 35 ) and diffusivity to 20 μm2s−1 . Spine Ca2+ -extrusion mechanisms , including membrane pumps , exchangers and uptake into intracellular Ca2+-stores , were uniformly modeled throughout the spine volume by a single term , linearly-dependent on local [Ca2+] . Parameter values , constrained by Ca2+ -imaging experiments , were tuned ( in simulations using mobile-buffer binding kinetics for the relevant Ca2+ -indicator dye ) by matching changes in model-predicted levels of Ca2+ -bound mobile buffer to changes in measured Ca2+ dye fluorescence .
To test the hypothesis that spatiotemporal variation of [Ca2+] in postsynaptic dendritic spines is an important factor governing synaptic plasticity , we built a 3-dimensional , deterministic model of [Ca2+] dynamics within a single dendritic spine ( Fig 1 ) . Our approach is suited to Ca2+ simulation where Ca2+ influx at the boundary is dependent on multiple highly non-linear interactions evolving on multiple time scales and involving membrane potential and the [Ca2+] within spine nanodomains . Therefore stochastic effects were not included in the model . The biophysical properties of the modelled spine were estimated from experimental data and , where necessary , tuned by comparison to experimental spine Ca2+ responses to single bAPs or EPSPs ( S1 Table ) . The primary sources of Ca2+ influx in dendritic spines are NMDARs and VGCCs [26 , 27] . NMDAR ion-channels tend to cluster at the post-synaptic density ( PSD ) and VGCCs are more evenly distributed in the spine membrane but are rarely found within 60nm of the PSD [28] . Due to this separation , and to facilitate our analysis of SK-channel coupling to these channels , we considered NMDARs and VGCCs as two distinct channel clusters in our model ( Fig 1A ) . Within the VGCC cluster we included two subtypes of VGCC; low-voltage activated ( LVA ) T-type VGCCs and high-voltage activated ( HVA ) L-type VGCCs . SK-channels co-locate with both NMDARs and VGCCs [17 , 29 , 30] so whilst the locations of the NMDAR and VGCC channel clusters remained fixed for all simulations , we varied the position of the SK-channel cluster in some simulations in order to investigate the effects of SK-channel location relative to the main Ca2+ sources . Another potential source of Ca2+ in dendritic spines is IP3-mediated or calcium-induced calcium release ( CICR ) from internal calcium stores . However , for CA1 spines , evidence suggests a limited role for calcium stores [31] ( but see [32] ) , and so in line with similar modeling studies of spines at this synapse we have omitted calcium stores from our model [33 , 34] . We characterized the model using the canonical STDP protocol for LTP—an EPSP followed by a bAP with 10ms delay ( EPSP-bAP ) . Application of this protocol rapidly established a [Ca2+] gradient across the spine head with highest [Ca2+] near the Ca2+ channel clusters ( Fig 1B ) . [Ca2+] did not equilibrate across the spine volume due to the rapid extrusion of Ca2+ ( extrusion rate , γ = 5000s−1 ) ( Fig 1B ) . The spatial extent of the [Ca2+] signal varied in accordance with the time course of the Ca2+ current ( Fig 1B and 1C ) . EPSP magnitude was ∼25mV , in agreement with reported estimates for local spine-depolarizations [7 , 35] , and was large enough to generate Ca2+ currents through both NMDARs and LVA VGCCs [17] , causing a transient increase in nanodomain [Ca2+] ( Fig 1C ) . The bAP generated a distinct spiked-component of the VGCC-nanodomain [Ca2+] transient by activating the HVA VGCCs , and also caused a second peak in the NMDAR-nanodomain [Ca2+] transient by enhancing the NMDAR conductance via voltage-dependent Mg2+-unblock ( Fig 1C ) . The global , or volume-averaged , spine [Ca2+] was an order of magnitude smaller in amplitude and temporally-smoothed in comparison to the nanodomain [Ca2+] time profiles . The global spine [Ca2+] peaked at around 0 . 15μM , within the range of experimental estimates [24] . [Ca2+] -signal magnitude , rise and decay time , and spatial extent are perturbed by Ca2+ buffer changes [36] . When Ca2+ buffers with fast binding rates are present in high concentrations , large [Ca2+] signals are restricted to channel nanodomains [15] . Modeling studies examining steady-state solutions to the Ca2+ reaction-diffusion system have shown that at distances where the mean diffusion time for Ca2+ ( from source ) is less than the mean reaction time with Ca2+ buffers , endogenous buffers do not modify nanodomain [Ca2+] [37] . We investigated these effects , under transient boundary-flux conditions , by simulating three different stimuli across three Ca2+-buffering scenarios: 1 ) no buffer present , 2 ) endogenous fixed buffer ( EFB ) present , and , 3 ) both EFB and a mobile buffer ( 100μM calbindin ) present , the latter representing the most physiologically realistic scenario ( Figs 2 and S2 ) . Addition of endogenous buffers reduced the amplitude of the global [Ca2+] signal across all stimuli , but the relative changes in peak [Ca2+] were dependent on the stimulation type ( Fig 2 ) . [Ca2+] -transients with fast kinetics—such as the spiked bAP-elicited global [Ca2+] transient—were almost eliminated by temporal smoothing due to endogenous Ca2+ buffers . This filtering was much less effective in the NMDAR and VGCC nanodomains ( Fig 2 ) due to the small time window for buffers to act on the diffusing Ca2+ in this domain [37 , 38] . This was true for a range of endogenous Ca2+ buffers with physiologically plausible Ca2+ -binding kinetics and affinities ( S3 Fig ) . From the perspective of the global domain , endogenous Ca2+ buffers act as a low-pass filter on [Ca2+] signals , whereas in channel nanodomains , [Ca2+] signals more faithfully track the time course of that channel-cluster’s Ca2+-current . SK-channels are activated by Ca2+ influx through either NMDARs or VGCCs . The coupling strength of an SK-channel to a Ca2+ source in the spine , is determined by their distance and Ca2+ buffering [16 , 39] . Therefore , we next tested the dependence of SK-channel activation ( and the resulting regulation of Ca2+ influx ) on SK-channel location and on Ca2+ buffering during three different stimuli . Initially , we fixed the SK-channel cluster location at a 50nm distance from the NMDAR cluster . For all stimuli , Ca2+ current was unaffected by changes to Ca2+ buffering , however , as shown previously ( Fig 2 ) , peak global [Ca2+] was reduced ( Fig 3A ) . This confirmed that the sensitivity of the [Ca2+] transient to Ca2+ buffering condition was due to the direct action of buffers on Ca2+ rather than a more complex interaction involving buffers and [Ca2+] local to the SK-channel cluster . SK-channel to Ca2+ source coupling distances of <∼100nm produced robust SK-channel activation during an EPSP regardless of buffering condition ( Fig 3B ) , assuming SK-channel half-activation , Ks = 0 . 33μM [40] . Since EPSPs are less effective at activating VGCCs than NMDARs , the buffer-insensitive coupling distance for SK-channels and VGCCs was smaller ( ∼70nm ) ( Fig 3B ) . The reverse was true when a single bAP was considered ( S4 Fig ) . These data show that , when SK-channels are tightly coupled to a Ca2+ source , Ca2+ buffering has limited effect on their activation . Next , we varied the position of the SK-channel cluster , defining its angular coordinate , θ , as a free parameter ( Fig 4A ) . For all stimulation protocols , peak SK-channel activation was greatest when the SK-channel cluster was located near to an active Ca2+ source , and substantially reduced at increased coupling distances and Ca2+ buffering capacities ( Fig 4B ) . During EPSP or bAP stimuli , peak global [Ca2+] was only weakly dependent on SK-channel to Ca2+ source coupling distance ( Fig 4B ) . However , we observed a large inhibition of [Ca2+] transients evoked by the combined EPSP-bAP stimulation even though peak SK-activation was similar to that of single EPSP or bAP stimuli . This apparently contradictory result could be resolved by closer inspection of the global [Ca2+] transient . This showed that the effect of SK-channel activation was largely restricted to the [Ca2+] response to the bAP occurring after the EPSP ( Fig 4C ) . This effect , and the weak influence of SK-channel activation on single EPSP or bAP stimulus , can be explained by the speed of SK-channel activation ( τs ∼ 6ms ) [40] , which is slow relative to NMDAR and VGCC activation during unitary stimuli ( Fig 3A ) . During the compound EPSP-bAP protocol , the initial priming stimulus , the EPSP , activates SK-channels that are spatially coupled to either Ca2+ source . The SK-channel activation time constant is too slow to influence the initial EPSP-elicited Ca2+ current , but for stimuli that occur shortly after the priming EPSP—in this case a bAP with 10ms delay—SK-activation has a significant influence over [Ca2+] levels . Furthermore , this priming effect on SK-channels was largely insensitive to the Ca2+ buffering capacity ( Fig 4B ) . These results demonstrate that SK-channel activation has a powerful influence over [Ca2+] transients elicited by combinations of stimuli , but not for [Ca2+] transients elicited by unitary stimuli . These results also suggest that SK-channels only influence global [Ca2+] when they are situated within ∼200nm of a Ca2+ source ( Fig 4B ) , therefore , for SK-channels to have any function in Ca2+ -dependent synaptic-plasticity , they must be organized such that they are in close proximity to a Ca2+ source . CaM activation by spine Ca2+ signals is required for the expression of LTP . CaM is fully activated when its four Ca2+ binding sites are occupied . Two of these sites are found on its N-lobe and two on its C-lobe , each lobe demonstrating different affinities and kinetics [14 , 25] . This leads to a highly non-linear relationship between [Ca2+] and activated CaM that potentially explains discrepancies between the calcium hypothesis for the induction of synaptic plasticity and experimental data [41] . To test this possibility , we next asked the following two questions: Do plasticity-inducing firing patterns ( such as the canonical LTP-inducing EPSP-bAP ) result in higher global concentrations of activated CaM in the spine ? Do plasticity-inducing protocols produce CaM signals that exhibit particular spatial or time-dependent signatures which more benign firing patterns ( such as unitary stimuli ) do not ? In order to address these questions we investigated the spatiotemporal patterns of Ca2+ -bound CaM concentration ( [CaCaM] ) generated in the spine in response to both unitary and compound stimuli . In particular , we focused on a STDP induction protocol—a single EPSP followed by two bAPs at 10ms intervals ( EPSP-2bAPs ) —which is associated with robust LTP at the Schaffer collateral synapse in CA1 of the hippocampus [42 , 43] . We were also interested to see how the CaM N- and C- lobes , with their distinct binding properties , responded to the different stimuli . To achieve this , we analyzed the responses of three different types of CaCaM species in the spine: total fully-activated CaCaM with all four Ca2+ -binding sites occupied; total CaCaM with fully-occupied N-lobe; and total CaCaM with fully-occupied C-lobe . In these simulations , fixed and mobile buffers were included and SK-channels were spatially-coupled to the NMDAR cluster ( coupling distance of 50nm ) . [CaCaM] and levels of fully-occupied CaM N-lobe demonstrated a strong spatial dependence with highest levels found in the immediate vicinity of the Ca2+ channel clusters ( Figs 5 , S5 and S6 ) . This was due to the low affinity of the CaM N-lobe for Ca2+ . The spatial dependence of the fully-occupied , high-affinity C-lobe was much weaker than that shown by the N-lobe . The C-lobe’s high affinity for Ca2+ resulted in a bound-state time-period sufficient for diffusion to equilibrate fully-occupied C-lobe levels across the spine ( Figs 5 and S5 ) [25] . Thus , the diffusion of partially activated CaCaM ( i . e . , C-lobe occupied CaM ) provides a means of communication between spine Ca2+ signaling nanodomains . Globally , [Ca2+] signals were effectively integrated by the level of Ca2+ -occupied CaM C-lobe , whereas the level of Ca2+ -occupied CaM N-lobe—which has a lower affinity but much faster forward-binding rate than the CaM C-lobe—tracked the [Ca2+] signal in time ( Figs 5 and S5 ) . Fully-activated [CaCaM] amplitude and time integral were largest for the EPSP-2bAPs induction protocol ( Figs 5 and S5 ) . Furthermore , following the initial EPSP , subsequent [CaCaM] peaks increased in amplitude with the number of postsynaptic stimuli . From this we conclude that the distinct Ca2+-binding properties of the two CaM lobes give the CaM molecule the properties of a sophisticated Ca2+-sensor , suited to detecting the precise timing and number of post-synaptic spikes following an EPSP , as well as providing a communication channel between Ca2+ signalling nanodomains . SK-channel inhibition facilitates learning and spatial memory formation [44] , and modulates the induction of synaptic plasticity [19] . Since CaM activation is the first step in the signaling-cascades that lead to LTP , we investigated the effect of blocking SK-channels on [CaCaM] in dendritic spines during different stimuli . We examined the effect across multiple induction protocols ( S8 Fig ) but in particular focused on two STDP induction protocols , EPSP-2bAPs and bAP-EPSP ( Fig 6 ) . During the EPSP-2bAPs protocol , blockade of SK-channels increased membrane potential depolarization leading to larger NMDAR and VGCC Ca2+ currents ( Fig 6A ) . In particular , during the two bAPs SK-channel blockade substantially increased the peak VGCC Ca2+ -current by specifically boosting HVA or L-type VGCC activation . ( Fig 6A; HVA VGCC current increased by 222% following SK blockade ) . Most dramatically , SK-channel blockade resulted in a 17-fold increase in the peak global [CaCaM] signal . [CaCaM] signal amplitudes in the NMDAR and VGCC nanodomains were 5- and 24-fold higher respectively during SK blockade . [CaCaM] sensitivity to SK blockade was most pronounced in the VGCC nanodomain since SK-channel blockade enhanced bAP amplitudes over the threshold for HVA L-type VGCC activation; an effective switch causing a brief but large influx of Ca2+ into the VGCC nanodomain ( Fig 6A ) . The increase in [CaCaM] with SK-channel blockade was a consistent observation across a range of HVA VGCC conductances ( S9 Fig ) indicating the qualitative effects of SK-channel blockade did not depend on the degree of VGCC clustering . We also investigated the sensitivity of [CaCaM] to SK-channel blockade during other compound stimuli including the canonical LTP and LTD STDP protocols ( EPSP-bAP and bAP-EPSP respectively ) as well as two EPSPs or two bAPs separated by 10ms ( 2EPSPs and 2bAPs respectively ) ( S8 Fig ) . For stimulation protocols that started with a bAP ( 2bAPs or bAP-EPSP ) , [CaCaM] response was very small and largely insensitive to SK-channel blockade . [CaCaM] during the 2EPSPs protocol was sensitive to SK-channel blockade but [CaCaM] was still low in comparison to the LTP inducing EPSP-2bAPs protocol . Interestingly , although SK-channel blockade enhanced [CaCaM] in response to EPSP-bAP stimulation ( peak global [CaCaM] increased by 7-fold ) the enhancement was much greater for EPSP-2bAPs stimulation ( global peak [CaCaM] increased 17-fold ) ( Figs 6B and S8 ) thereby providing a mechanism for the requirement of bursts of bAPs at Schaffer collateral synapses in the hippocampus during STDP . Our results strongly support the conclusion that SK-channel inhibition has a powerful modulatory effect on [CaCaM] during neural firing patterns that are associated with LTP .
Our analyses show that SK-channels may play a central regulatory role in the induction of Hebbian synaptic-plasticity . 3-dimensional modeling of spine Ca2+ and CaM dynamics revealed that NMDAR-coupled SK-channels potentially modulate activated [CaCaM] up to 17-fold during STDP induction protocols associated with LTP , indicating that SK-channels effectively ‘gate’ the induction of STDP . Our results suggest that the mechanism behind this is ‘priming’ of SK-channels during EPSPs , by NMDAR activation , which effectively silence L-type VGCCs during subsequent bAPs . L-type VGCCs then provide a crucial Ca2+ signal for CaM ( and therefore CaMKII ) activation when SK-channels are inhibited . We have also shown that , in tandem , the distinctive Ca2+-binding properties of the CaM N- and C-lobes give the CaM molecule some sophisticated Ca2+ sensing properties . 3-dimensional modelling improves the biological plausibility and validity of our analyses . There is strong evidence to suggest that Ca2+ signal location in spines is an important determinant for synaptic plasticity , or—at the very least—that the amplitude and temporal characteristics of the spine Ca2+ signal is not sufficient to explain all plasticity observations . Many studies of spine Ca2+ signalling have concluded that the spatial localisation of Ca2+ sources is important for the ultimate biological role of the Ca2+ signal ( see [45] for a review ) . It is essential to use a 3-dimensional model in order to allow for resolving spatially the Ca2+ sources and thus investigating this possibility . We also wished to investigate the effect of SK-channel location relative to NMDAR and VGCC Ca2+ sources , which again entails using a 3-dimensional spatial model . 3-dimensional modelling has been used successfully before to investigate the Ca2+/CaM interaction in the spine [33 , 46] . Several of our findings depend on the spatial restriction of Ca2+/CaCaM signals ( for example the difference in CaCaM activation in response to the relative timing of pre- and post-synaptic activation ) showing that 3-dimensional modelling is capable of revealing roles of Ca2+ signalling that would be missed using a non-spatial model . Endogenous fixed and mobile Ca2+ buffers reduce the amplitude of global [Ca2+] signals whereas nanodomain [Ca2+] signals are less affected . In particular , during bAPs , fast [Ca2+] spikes are absent from the global signal due to Ca2+ buffering . Thus , endogenous Ca2+ buffers act much like a low-pass filter for global [Ca2+] signals [33] . This filtering becomes less pronounced as the [Ca2+] signal readout location approaches a Ca2+ source meaning that the endogenous Ca2+ buffers will have a limited effect on CaM activation when CaM is located within channel nanodomains . Previous theoretical work using 3-dimensional simulation of Ca2+ signalling has demonstrated that CaM activation at nanodomain is affected by its diffusion [46] . We have tested this in our model by varying the diffusivity of CaM . CaM activation in the nanodomain varies by degree with CaM diffusivity as predicted by Naoki et al . [46] ( see S7A Fig ) . However , these simulations also reveal that the main conclusion of the paper , i . e . the modulation of CaM activation by SK channels , is still applicable across the range of diffusivities tested ( see S7B & S7C Fig ) . Endogenous Ca2+ buffering prevents the crosstalk of [Ca2+] signals between NMDA and VGCC nanodomains , however , communication between nanodomains could be enabled by C-lobe occupied CaM which disperses almost uniformly throughout the spine head [25] . Because of the dependence of CaM activation on nanodomain Ca2+ signalling , spine morphology is not predicted to have a major impact on CaM activation although this remains to be tested . In the classic Hebbian stimulation for LTP induction—an EPSP followed by a bAP—EPSP-elicited Ca2+ influx will create a pool of C-lobe occupied CaM that diffuses into VGCC nanodomains ready for the N-lobe to be populated by subsequent high influx of Ca2+ through HVA L-type VGCCs . Interestingly , the reverse sequence of stimuli is not so effective at generating fully-activated [CaCaM] because bAPs preferentially generate N-lobe occupied CaM which unbinds Ca2+ rapidly and therefore is not capable of integrating over compound stimuli [34 , 47] . The importance of L-type VGCCs for CaM activation is supported by the observation that CaMKII activation is abolished during blockade of L-type VGCCs [48] . Given the highly effective endogenous buffering of Ca2+ , if SK-channels are to influence spine membrane potential and Ca2+ influx , then they must be spatially coupled to a Ca2+ source ( Fig 4A ) . SK-channels have been reported to be co-located with both VGCCs and NMDARs in dendritic spines [17 , 29 , 39] . Our data suggest SK-channels are equally activated during EPSPs when coupled to NMDARs or LVA VGCCs since the EPSP depolarization is sufficient to activate T-type VGCCs . Hence , SK-channels coupled to LVA VGCCs will be ‘primed’ by EPSPs in much the same way as those coupled to NMDARs . An important property of SK-channels is their relatively slow activation ( τ∼ 6ms ) [40] which leads to the different [Ca2+] responses to unitary and compound stimuli . SK-channel activation following a single bAP is too slow for the inhibitory current to influence the fast-activating HVA L-type VGCC Ca2+ current . However , if a bAP occurs shortly after an initial ‘priming’ stimulus , then the SK-current effectively silences the L-type Ca2+ channels by repolarizing the membrane potential below the threshold voltage for L-type VGCC activation . Interestingly , fully-activated [CaCaM] signals were insensitive to SK-channel blockade for induction protocols not associated with plasticity ( S8 Fig ) . On the other hand , for protocols involving an EPSP followed by 1 or more bAPs ( the form of the classic STDP LTP-inducing protocols ) , SK-channel blockade enhanced fully-activated [CaCaM] by an order of magnitude . This suggests a potential gating mechanism for the induction of STDP , which could be potentially tested using a FRET based CAMKII sensor [48] . Strong clustering of VGCCs will increase the local influx of Ca2+ thereby saturating endogenous buffers , which will have an effect on other Ca2+ interactions . Strong vs . weak clustering of channels could be viewed as effectively altering the VGCC ‘cluster’ conductance . We probe the effect of varying VGCC cluster conductance on the activation of CaCaM ( see S9A Fig ) and find that across the conductance range tested , dramatic changes in [CaCaM] activation are still seen with SK-blockade ( see S9B Fig ) . This is due to the steep sigmoidal dependence of L-type VGCC activation on membrane voltage , which is independent of VGCC conductance . We have also simulated the case where the VGCC conductance is uniform over the spine surface . The effect of changing the VGCC distribution from clustered to uniform is to reduce the global levels of [CaCaM] in both SK-channel conditions although there is still substantial [CaCaM] when VGCCs are distributed uniformly ( S10 Fig ) . As expected , in the VGCC nanodomain ( which is defined in both conditions as 20nm radial distance from the VGCC Ca2+ source in the cluster condition ) , the [CaCaM] signal is greatly reduced by changing the spatial distribution of the VGCC conductance . In a sense , when the VGCC current density is uniform over the entire surface of the spine , there is no VGCC nanodomain . In the case of uniform distribution of VGCCs the main conclusion of the paper , that SK-channels are powerful modulators of CaM activation still holds . SK-channels are negatively regulated by neuromodulators such as acetylcholine and noradrenaline [18 , 49 , 50] and by metabotropic glutamate receptors [9] . Furthermore , LTP is facilitated by activating M1 muscarinic or metabotropic receptors respectively , which both inhibit SK-channels leading to enhanced NMDAR activation during STDP induction [9 , 18] . Our results propose an additional mechanism by which neuromodulators that inhibit SK-channels could gate LTP by unsilencing the L-type VGCC [Ca2+] signal during STDP LTP induction protocols leading to greatly enhanced CaM ( and therefore CaMKII ) activation . Thus , SK-channels may provide a common mechanism by which multiple neuromodulators can gate Hebbian plasticity enabling the switching of neuronal networks , such as the hippocampus , between LTP-competent and LTP-incompetent configurations .
|
Hebbian or associative plasticity is triggered by postsynaptic Ca2+ influx which activates calmodulin and CaMKII . The influx of Ca2+ through voltage-dependent NMDA receptors and Ca2+ channels is regulated by Ca2+ -activated K+ channels ( SK-channels ) providing negative feedback regulation of postsynaptic [Ca2+] . Using 3-dimensional modeling of Ca2+ and calmodulin dynamics within dendritic spines we show that the non-linear relationship between Ca2+ influx and calmodulin activation endows SK-channels with the ability to “gate” calmodulin activation and therefore the induction of Hebbian synaptic plasticity . Since SK-channels are inhibited by several neuromodulator receptors including acetylcholine and noradrenaline , the gating of synaptic plasticity by SK-channels could represent a common mechanism by which neuromodulators control the induction of synaptic plasticity .
|
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2016
|
Control of Ca2+ Influx and Calmodulin Activation by SK-Channels in Dendritic Spines
|
Buruli ulcer ( BU ) caused by Mycobacterium ulcerans is effectively treated with rifampicin and streptomycin for 8 weeks but some lesions take several months to heal . We have shown previously that some slowly healing lesions contain mycolactone suggesting continuing infection after antibiotic therapy . Now we have determined how rapidly combined M . ulcerans 16S rRNA reverse transcriptase / IS2404 qPCR assay ( 16S rRNA ) became negative during antibiotic treatment and investigated its influence on healing . Fine needle aspirates and swab samples were obtained for culture , acid fast bacilli ( AFB ) and detection of M . ulcerans 16S rRNA and IS2404 by qPCR ( 16S rRNA ) from patients with IS2404 PCR confirmed BU at baseline , during antibiotic and after treatment . Patients were followed up at 2 weekly intervals to determine the rate of healing . The Kaplan-Meier survival analysis was used to analyse the time to clearance of M . ulcerans 16S rRNA and the influence of persistent M ulcerans 16S rRNA on time to healing . The Mann Whitney test was used to compare the bacillary load at baseline in patients with or without viable organisms at week 4 , and to analyse rate of healing at week 4 in relation to detection of viable organisms . Out of 129 patients , 16S rRNA was detected in 65% of lesions at baseline . The M . ulcerans 16S rRNA remained positive in 78% of patients with unhealed lesions at 4 weeks , 52% at 8 weeks , 23% at 12 weeks and 10% at week 16 . The median time to clearance of M . ulcerans 16S rRNA was 12 weeks . BU lesions with positive 16S rRNA after antibiotic treatment had significantly higher bacterial load at baseline , longer healing time and lower healing rate at week 4 compared with those in which 16S rRNA was not detected at baseline or had become undetectable by week 4 . Current antibiotic therapy for BU is highly successful in most patients but it may be possible to abbreviate treatment to 4 weeks in patients with a low initial bacterial load . On the other hand persistent infection contributes to slow healing in patients with a high bacterial load at baseline , some of whom may need antibiotic treatment extended beyond 8 weeks . Bacterial load was estimated from a single sample taken at baseline . A better estimate could be made by taking multiple samples or biopsies but this was not ethically acceptable .
Buruli ulcer is a neglected tropical disease caused by infection with Mycobacterium ulcerans ( Mu ) which is common in rural parts of West African countries including Ghana [1] . It causes large , disfiguring skin ulcers mainly in children aged 5 to 15 years although any age can be affected [2] . The initial lesion is a subcutaneous painless nodule tethered to the skin or an intradermal plaque sometimes associated with oedema . These enlarge over a period of days to weeks and ulcerate in the centre . Ulcers are painless and have a necrotic base and irregular , undermined edges . There is surrounding oedema in about 10% of cases . Ulcers enlarge progressively and may cover the whole of a limb or the trunk if left untreated but the patient remains systemically well unless secondary bacterial infection occurs [3] [4] [5] . The mode of transmission remains unknown[5 , 6]but there have been major advances in understanding the mechanism of disease since the establishment of the WHO Buruli ulcer initiative in 1998 together with improved diagnosis and clinical management . Treatment of Buruli ulcer has changed considerably since 2004 with the introduction of antibiotics as an alternative to surgery . It has now been established that the combination of rifampicin and streptomycin administered daily for 8 weeks is effective in healing all forms of lesion caused by Mu disease and this has reduced the recurrence rate from 6–47% after surgery to 0–2% after antibiotic treatment [6 , 7] . This treatment can be administered by community health nurses and admission to hospital is rarely necessary except when skin grafting is needed . The current duration of antibiotic therapy ( 8 weeks ) was based on observations in patients with early Mu lesions which were excised after treatment for 2 , 4 , 8 or 12 weeks . All lesions remained culture positive after 2 weeks but thereafter all were culture negative [3] . Thus it is likely that a shorter course of treatment may be successful in some patients which would be highly desirable , not least because streptomycin has to be injected intramuscularly . This is supported by recent experience of treating M ulcerans disease in Australia with antibiotic durations of less than 8 weeks suggesting that successful outcomes may be achieved in selected patients [8] . In spite of the success of rifampicin and streptomycin treatment for 8 weeks some lesions take much longer than others to heal despite having appeared identical before treatment . Available data from various studies suggest that healing of up to two thirds of patients occurs within 25 weeks from the start of treatment [9–11] . One reason for slow healing may be that active infection persists despite antibiotic treatment for 8 weeks . In our recent study of BU treated with rifampicin and streptomycin for 8 weeks , persistent infection with M . ulcerans was shown by positive cultures in some lesions 4 weeks after completion of antibiotic treatment despite full adherence to therapy . Furthermore mycolactone , the toxin produced by M . ulcerans , was detected in lesions which were culture negative as well as in culture positive samples , suggesting that it is a more sensitive marker for the presence of viable organisms [12] . However it is not known how long mycolactone can remain in human BU lesions after M . ulcerans is killed and it is vital to establish how often infection persists after a standard course of antibiotic treatment . Reverse transcriptase assays targeting ribosomal or messenger RNA have been applied successfully for the rapid detection of viable mycobacteria in clinical samples from patients with tuberculosis , leprosy and recently Buruli ulcer [13] [14] [15] and as a surrogate for response to chemotherapy in tuberculosis [13] . With respect to Buruli ulcer , the assay is fast , 100% specific for M . ulcerans and highly sensitive with an analytical sensitivity of 6 templates of the targeted 16S rRNA . The excellent performance on clinical samples makes this tool highly promising for monitoring the therapeutic response with the goal of optimizing the duration of antimycobacterial treatment [15] . The aim of the present study was to determine how rapidly combined M . ulcerans 16S rRNA reverse transcriptase / IS2404 qPCR assay ( hereafter referred to as 16S rRNA ) became negative during antibiotic treatment and to relate this to the rate of healing .
In the period from June 2013 to June 2015 , patients more than 5 years of age with suspected Buruli ulcer and subsequent confirmation by M . ulcerans IS2404 dry reagent based ( DRB ) PCR presenting to treatment clinics at the Tepa Government Hospital , Nkawie-Toase Government Hospital , Dunkwa Government Hospital and Agogo Presbyterian Hospital were screened for inclusion . Patients who had already been under antimycobacterial treatment at the time of study initiation were excluded . Demographic data were collected using standard BU01 forms from the WHO together with a careful history to establish when lesions were first observed and their type . The dimensions of lesions were documented with Silhouette ( ARANZ Medical , Christchurch , New Zealand ) a 3-dimensional imaging and documentation system together with digital photographs . The Silhouette camera captures an image of the wound , a tracing of the wound boundary is generated and the wound dimensions including the area , depth and volume are automatically calculated . For oedematous lesions , only digital photographs were obtained . Patients were reviewed at 2 weekly intervals during standard antibiotic treatment and monthly thereafter with further recordings of clinical data as routinely conducted for all BU patients until complete healing . These measurements enabled calculation of healing rate at week 4 and predicted healing time in relation to lesion size and type . Rate of healing in mm per week was calculated by subtracting the mean diameter of the lesion in millimeters determined at week 4 from that determined at week 0 and dividing this result by 4 . Mean diameter was the mean of the maximum diameter and the largest diameter at right angles to that [16] . Two fine needle aspirates ( FNA ) or swabs samples were collected from skin lesions to confirm the diagnosis of Buruli ulcer by microscopy and conventional IS2404 DRB PCR . An additional sample for culture and another for the 16S rRNA reverse transcriptase/IS2404 qPCR assay ( 16S rRNA ) were collected at baseline and during ( week 4 and 8 ) or after treatment ( week 12 and 16 ) from unhealed lesions , immediately placed in either 500μl PANTA media or 500μl RNA protect respectively on site . Human GAPDH mRNA assay was performed on the samples in the RNA protect to assess the stability of the RNA in the solution ( Qiagen , UK ) . All routine laboratory tests were conducted at Kumasi Centre for Collaborative Research in Tropical Medicine ( KCCR ) immediately upon arrival of samples . Prior to the study a human GAPDH mRNA reverse transcriptase qPCR was established and validated at the Department for Infectious Diseases and Tropical Medicine ( DITM ) of the University Hospital of the Ludwig-Maximilians-University ( LMU ) in Munich , Germany . During the study all molecular assays were conducted at the KCCR by trained laboratory staff supervised by Kwame Nkrumah University of Science and Technology ( KNUST ) staff . Whole genome DNA and whole transcriptome RNA were extracted at the KCCR immediately on arrival of samples in RNA protect and subjected to the M . ulcerans 16S rRNA assay [15] . For laboratory confirmation of Buruli ulcer disease , AFB microscopy , IS2404 dry reagent based ( DRB ) -PCR and cultures were performed . IS2404 qPCR were performed by well established methods as previously described [17][18] [15] . IS2404 qPCR was also performed on all samples . A final diagnosis of Buruli ulcer was based on IS2404 DRB-PCR and qPCR results which were the most sensitive tests . FNA and swab samples were transported from study site to the KCCR stabilized in 500 μl RNA protect ( Qiagen , UK ) . Whole transcriptome RNA and whole genome DNA were extracted from the same clinical sample . The RNA and DNA isolation was carried out within 5 hours of sample collection using the AllPrep DNA/RNA Micro kit ( Qiagen , UK ) as previously described with minor modification[15] . Here , homogenizing was carried out with the QiaShredder ( Qiagen , UK ) according to the manufacturers instruction in a biosafety cabinet . 12 μl RNA extracts were immediately reverse transcribed whilst 50 μl DNA extracts obtained were stored at 4–8°C ( short-term ) or -20°C ( long-term ) . To remove potentially contaminating genomic DNA ( gDNA ) from the M . ulcerans whole transcriptome RNA extracted , 2 μl DNA wipe out buffer ( Qiagen , UK ) was added to 12 μl of the total RNA extracts , incubated for 5 min at 42°C and the reaction was terminated by incubating at 95°C for 3 min . 2 μl gDNA free M . ulcerans whole transcriptome RNA extracted was included as a wipe out control . The remaining M . ulcerans whole transcriptome RNA was then reverse transcribed into cDNA using QuantiTect Reverse transcription kit ( Qiagen , UK ) according to the manufacturer’s instructions as described elsewhere[15] . The cDNA samples were stored at -20°C until further processing . “To exclude false negative 16S rRNA RT qPCR results ( e . g . due to RNA degradation during sample transport or RNA extraction procedures ) , the cDNA prepared as described above was subjected to qPCR for detection of the human glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) mRNA ( S1 Protocol ) [18] . The performance of the GAPDH mRNA reverse transcriptase qPCR is provided as supplementary material ( S2 Protocol ) . All whole transcriptome RNA extracts from Buruli ulcer patients tested positive when subjected to GAPDH mRNA RT qPCR at baseline . The cDNA was then subjected to 16S rRNA qPCR and DNA to IS2404 qPCR to increase the specificity for M . ulcerans and quantification of the bacterial load as previously described [15] . Quantitative PCR of IS2404 ( DNA ) , and 16S rRNA ( cDNA ) targets were carried out at 95°C for 15 min , and then 40 cycles of 95°C for 15 sec and 60°C for 60 sec in a BioRad CFX 96 real time PCR detection system ( BIORAD , Singapore ) . Each run included negative extraction controls , negative “no template” controls , negative gDNA wipe-out controls ( 16S rRNA qPCR only ) , inhibition controls ( exogenous IPC ) and positive controls . Ten fold serial dilutions of known amounts of a plasmid standard of IS2404 ( 99 bp ) and 16S rRNA ( 147 bp ) ( Eurofins MWG Operon , Ebersberg , Germany ) were included with PCR amplification for preparation of a standard curve . M . ulcerans bacillary loads in original clinical samples were calculated based on threshold cycle values per template of IS2404 qPCR ( standard curve method ) adjusted to the whole amount of DNA extract and the known copy number of 207 IS2404 copies per M . ulcerans genome on average . The raw data generated from the study was entered in Microsoft Excel ( Microsoft Corporation , Redmond , WA ) and analyzed using Graphpad Prism version 5 . 0 ( GraphPad Software , Inc . , La Jolla , CA ) and Microsoft Excel ( Microsoft Corporation ) . The Kaplan-Meier survival analysis ( log rank test ) was used to determine the time to clearance of M . ulcerans 16S rRNA , as well as to determine the influence of persistent M ulcerans 16S rRNA on time to healing . This approach was used to offset bias due to patient censoring for not showing up at study time points . Mann Whitney test was used to compare the bacillary load at baseline in patients with presence or absence of viable organisms at week 4 , and also to analyse rate of healing at week 4 in relation to detection of viable organisms . Mann Whitney test were used due to variable distribution of data . Fisher’s exact test was used to compare positive results of 16S rRNA assay with culture due to small sample size . P value < 0 . 05 was considered statistically significant in all the analyses . All statistical tests were two-tailed . Verbal and written informed consent was obtained from all eligible participants , and from parents or legal representatives of participants aged 18 years or younger . Ethical approval was obtained from the Committee of Human Research Publication and Ethics , School of Medical Sciences , Kwame Nkrumah University of Science and Technology , Kumasi , Ghana ( CHRPE/AP/229/12 ) .
Of 150 patients presenting to treatment centers with clinically suspected Buruli ulcer , M . ulcerans infection was confirmed by IS2404 PCR in 129 cases ( Table 1 ) : in 104 out of these by gel-based DRB PCR and qPCR , and for the remaining 25 cases by IS2404 qPCR only . Fifty seven ( 44% ) had pre-ulcerative lesions and 16 ( 12% ) had lesions larger than 15 cm in maximum diameter ( category III ) . There were 8 lesions with oedema , 4 of which were pre-ulcerative . Out of 129 IS2404 PCR positive patients , direct smears for the detection of AFB were available for 125 patients ( 96 . 9% ) and 50 ( 40% ) tested positive . Samples were taken for culture from 129 patients of which 44 ( 34% ) were positive . Positive results for M . ulcerans 16S rRNA were obtained in 84 out of 129 patients ( 65% ) at baseline ( Table 2 ) . Although the sensitivity of 16S rRNA was substantially higher than that for culture ( 34% ) , 2 of 38 samples yielding a positive culture had negative 16SrRNA , presumably as a result of sampling error . After initiation of antibiotic therapy , M . ulcerans 16S rRNA was detected in 78% of patients with unhealed lesions at 4 weeks , 52% at 8 weeks , 23% at 12 weeks , and 10% at week 16 ( Fig 1 ) . Of 15 patients censored at week 16 when sampling ended , 3 had positive M . ulcerans 16S rRNA but in 12 patients a sample could not be obtained . Thus despite antibiotic treatment for 8 weeks , positive 16S rRNA was still detected in 52% lesions sampled at week 8 and the median for detection of M . ulcerans by Kaplan-Meier curve analysis was 12 weeks ( 95% CI 8–16 ) . The number of patients whose lesions yielded a positive M . ulcerans culture decreased to 24% at week 4 , 5% at week 8 and none by week 16 . M . ulcerans was detected by culture for a median time of 4 weeks ( 95% CI 4–6 ) ( S1 Table ) . Before antibiotic treatment , 28 patient lesions in which M . ulcerans 16S rRNA was negative and 27 patients with detectable M . ulcerans 16S rRNA at baseline but subsequently undetectable after 4 weeks of antibiotic treatment had a significantly lower bacterial load based on qPCR for IS2404 ( p = 0 . 003; Mann Whitney ) ( Fig 2 ) , than those of 74 patients with detectable 16S rRNA at week 4 or later . Patients with positive 16S rRNA at week 4 had a 3 . 7-fold increase ( 95% CI 2 . 43–5 . 04 ) in the time to complete healing of Buruli ulcer lesions compared to those with negative 16S rRNA result at week 4 ( Fig 3 ) . This was not attributable to lesion size at baseline because there was no significant difference in initial size of patient lesions with or without detectable 16S rRNA at week 4 ( p = 0 . 0798 , Mann Whitney ) . Fig 4 shows that the rate of wound healing ( ROH ) determined at week 4 was higher for patients with undetectable 16S rRNA at week 4 [2 . 4 ( 0 . 8 to 6 . 2 ) mm/week; median ( interquartile range ) ] compared to those with positive 16S rRNA at week 4 [0 . 3 ( -2 . 0 to 3 . 3 ) mm/week] ( p = 0 . 0003 , Mann Whitney ) .
Simultaneous detection of 16S rRNA and IS2404 by qPCR has been shown to be a specific marker for the presence of viable M . ulcerans in human tissue [15] . In this study , we have investigated the time taken for the 16S rRNA assay to become negative during antibiotic treatment for 8 weeks . The assay detected viable bacteria in 65% of samples taken from patients proven to have Buruli ulcer by PCR for IS2404 . Since these samples were from untreated patients , they should all have been M . ulcerans 16S rRNA positive . One possible explanation for false negatives would be loss of mRNA during transport to the laboratory so we measured concurrent detection of human GAPDH mRNA . This was positive showing that mRNA was present in the 16S rRNA negative samples . Sampling error is the most likely explanation for the false negatives which is not surprising since the volume of FNA samples is less than 50 μl and M . ulcerans is not evenly distributed within lesions [19] . We found that there was a relationship between bacterial load measured by qPCR for IS2404 and the result of the 16S rRNA assay; bacterial load was significantly lower in samples with negative 16S rRNA . Thus the combination of low bacterial load and a less sensitive 16S rRNA assay may also account for false negatives . The 16S rRNA assay was more sensitive than culture for M . ulcerans as shown in Table 2; negative 16S rRNA with positive culture was detected in only 2 patient lesions whereas negative culture with positive 16S rRNA was found in 48 lesions . At week 4 , 20 of 129 ( 16% ) lesions had healed and 22% of unhealed lesions had no detectable viable M . ulcerans ( 16S rRNA ) in the lesion ( Fig 1 ) . If these patients could be identified before or during the early stages of treatment it is possible that the course of antibiotics could be shortened substantially with considerable benefit to patients as well as a reduction in the cost of management . The recommendation that patients receive treatment for 8 weeks was derived from the finding that early lesions excised after 2 weeks antibiotic treatment were still culture positive but those excised after 4 weeks were all negative [3] . The 16S rRNA assay is more sensitive than culture as shown in the present study and if lesions could be shown to be 16S rRNA negative at 4 weeks it would be justified to abbreviate the course of antibiotics . This would need to be assessed by a clinical trial , using the currently recommended combination of clarithromycin and rifampicin . Evidence for shorter treatment for selected patients is supported by recent data from Australia where complete healing was achieved after 14 to 28 days of antibiotics in selected patients but most of the patients had received early surgical treatment in addition to antibiotics and the study was retrospective [8] . The cost and skill requirement for the 16S rRNA assay limits its routine use in most countries where Buruli ulcer is endemic but it may be possible to predict rapid responders in other ways . This is the subject of ongoing studies . The healing rate was faster over the first 4 weeks in patients who had cleared active infection by that time ( Fig 3 ) . Also the time to complete healing was significantly longer in patients with persistent infection independently of the initial lesion size . There has been speculation about why some lesions heal slower than others despite appearing clinically comparable before treatment and the findings from this study suggest that persistent infection is an important contributing factor . Furthermore several observations imply that the initial bacterial load may determine the time to total clearance of viable bacteria from BU lesions . A crude estimate of bacterial load was made by quantifying the number of copies of IS2404 using qPCR . A better estimate could be made by taking multiple samples or biopsies but this was not considered ethically acceptable . Given the limitations of the data it is not surprising that there was not a significant correlation between initial bacterial load and the time for which viable bacteria remained detectable but Fig 1 illustrates that they are probably related since the bacterial load in lesions with negative M . ulcerans 16S rRNA at week 0 was significantly lower than that in all other groups . At the end of the standard 8 week period of antibiotic treatment 52% of lesions were 16S rRNA positive ( Fig 1 ) raising the question whether antibiotic treatment should be prolonged for a selected subgroup of patients . We have found positive M . ulcerans culture in 2 patients who had fully complied with treatment for 8 weeks in an earlier study[12] . The finding that healing was delayed in this group compared with those with negative 16S rRNA supports the idea of continuing antibiotics , perhaps for a further 4 weeks but against this is the fact that all the lesions healed eventually without further antibiotic treatment . There is also the difficulty of identifying such lesions except within the context of a research study since this assay is relatively expensive and labor intensive for routine use . At present a judgment would have to be made on purely clinical grounds . The presence of detectable M . ulcerans 16S rRNA after chemotherapy with rifampicin and streptomycin may be indicative sometimes of a persistent altered physiological state of M . ulcerans such that it can reactivate to cause recurrent disease later . An analogous situation arises when M . tuberculosis is treated with rifampicin or pyrazinamide . Subpopulations consisting of dormant or semi-dormant , antibiotic tolerant persisters survive longest during chemotherapy and are difficult to kill with any new antibacterial drug . They are thought to be responsible for the prolonged period required for effective chemotherapy in tuberculosis [20–22] . In human M . ulcerans disease , lesions with persistent viable organisms still go on to heal , albeit slowly , presumably due to immune clearance of the organism whereas in tuberculosis , residual viable organisms invariably cause disease . In BU , as mycolactone concentration decreases in lesions during antibiotic therapy [12] , IFN-gamma levels [23] increase possibly due to M . ulcerans antigens interacting normally with the immune system . The slow clearance of these organisms may however explain the slow healing of some of these wounds due to the inhibition of vital wound healing factors by mycolactone . It is not known whether antibiotic tolerant persisters cause relapse in M . ulcerans disease but current evidence does not support this . Recurrent M . ulcerans disease was fairly common before the antibiotic era when 6–47% of patients experienced relapse after surgical treatment alone , [24] [25] probably because there were residual M . ulcerans in apparently healthy tissue at resection margins [26] . However , since observed antibiotic therapy was introduced , reported series have shown relapse rates below 2% [7 , 9] . Individuals with a deeply compromised immune system such as those co-infected with HIV are at risk of relapse or overwhelming disseminated disease but this is more likely due to the need for a competent immune response to clear infection [27] [28] . That the presence of M . ulcerans 16S rRNA indicates persistence of viable organisms in the tissue is supported by our previous findings that mycolactone can be detected in some patients after they finish antibiotics as can positive cultures for M . ulcerans [12] . The presence of mycolactone , the toxin secreted by M . ulcerans , probably indicates that viable organisms are still extant but the pharmacokinetics of mycolactone are not known and it could persist after killing of the organism . Mycolactone is a powerful inhibitor of many growth factors and if it persists in a Buruli ulcer it is likely to retard healing [29] . Further investigations are ongoing to identify lesions containing the toxin after the end of treatment in the present study . However further work is also needed to determine if there is an association between M . ulcerans 16S rRNA and mRNA detection suggestive of transcriptional activity which would indicate that the organisms are in a replicative state . In conclusion this study has demonstrated that current antibiotic therapy for BU disease is highly successful in most patients but it may be possible to abbreviate the treatment to 4 weeks in patients with a low initial bacterial load . On the other hand evidence has been presented that persistent infection contributes to slow healing in other patients , probably those with a high bacterial load , who may need antibiotics for longer than 8 weeks .
|
Buruli ulcer ( BU ) caused by Mycobacterium ulcerans is effectively treated with rifampicin and streptomycin for 8 weeks but some lesions take several months to heal . We have shown previously that some slowly healing lesions contain the M . ulcerans toxin , mycolactone , suggesting continuing infection after completion of antibiotic therapy . In the present study we have determined how soon M . ulcerans was killed during antibiotic treatment using the M . ulcerans 16S rRNA assay combined with qPCR for IS2404 to detect live bacilli in clinical samples and investigated its influence on healing . This assay is more sensitive than culture for the organism . Using samples collected from one hundred and twenty-nine BU patients prior to antibiotic treatment , viable organisms were detected by culture in 34% but the 16S rRNA assay was positive in 65% . The 16S rRNA remained positive in 78% of patients with unhealed lesions at 4 weeks , 52% at 8 weeks , 23% at 12 weeks , and 10% at week 16 . Lesions with positive 16S rRNA after antibiotic treatment also contained a higher number of bacteria at baseline , had a lower rate of healing at week 4 and took a longer time to heal compared with those in which the organism was undetectable at baseline or by week 4 . Positive 16S rRNA was less likely in ulcerative compared with nodular forms of disease 4 weeks after antibiotic treatment . It may be possible to shorten the treatment to 4 weeks in patients with low numbers of bacteria at baseline . Since persistent infection appears to contribute to slow healing , some patients with a high bacterial load at baseline may need antibiotic treatment for longer than 8 weeks .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
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] |
2017
|
Clearance of viable Mycobacterium ulcerans from Buruli ulcer lesions during antibiotic treatment as determined by combined 16S rRNA reverse transcriptase /IS 2404 qPCR assay
|
Error-driven learning rules have received considerable attention because of their close relationships to both optimal theory and neurobiological mechanisms . However , basic forms of these rules are effective under only a restricted set of conditions in which the environment is stable . Recent studies have defined optimal solutions to learning problems in more general , potentially unstable , environments , but the relevance of these complex mathematical solutions to how the brain solves these problems remains unclear . Here , we show that one such Bayesian solution can be approximated by a computationally straightforward mixture of simple error-driven ‘Delta’ rules . This simpler model can make effective inferences in a dynamic environment and matches human performance on a predictive-inference task using a mixture of a small number of Delta rules . This model represents an important conceptual advance in our understanding of how the brain can use relatively simple computations to make nearly optimal inferences in a dynamic world .
Decisions are often guided by beliefs about the probability and utility of potential outcomes . These beliefs are learned through past experiences that , in stable environments , can be used to generate accurate predictions . However , in dynamic environments , changes can occur that render past experiences irrelevant for predicting future outcomes . For example , after a change in government , historical tax rates may no longer be a reliable predictor of future tax rates . Thus , an important challenge faced by a decision-maker is to identify and respond to environmental change-points , corresponding to when previous beliefs should be abandoned and new beliefs should be formed . A toy example of such a situation is shown in figure 1A , where we plot the price of a fictional stock over time . In this example , the stock price on a given day ( red dots ) is generated by sampling from a Gaussian distribution with variance $1 and a mean ( dashed black line ) that starts at $10 before changing abruptly to $20 at a change-point , perhaps caused by the favorable resolution of a court case . A trader only sees the stock price and not the underlying mean but has to make predictions about the stock price on the next day . One common strategy for computing this prediction is based on the Delta rule: ( 1 ) According to this rule , an observation , , is used to update an existing prediction , , based on the learning rate , and the prediction error , . Despite its simplicity , this learning rule can provide effective solutions to a wide range of machine-learning problems [1] , [2] . In certain forms , it can also account for numerous behavioral findings that are thought to depend on prediction-error signals represented in brainstem dopaminergic neurons , their inputs from the lateral habenula , and their targets in the basal ganglia and the anterior cingulate cortex [3]–[15] . Unfortunately , this rule does not perform particularly well in the presence of change-points . We illustrate this problem with a toy example in figure 1B and C . In panel B , we plot the predictions of this model for the toy data set when is set to 0 . 2 . In this case , the algorithm does an excellent job of computing the mean stock value before the change-point . However , it takes a long time to adjust its predictions after the change-point , undervaluing the stock for several days . In figure 1C , we plot the predictions of the model when . In this case , the model responds rapidly to the change-point but has larger errors during periods of stability . One way around this problem is to dynamically update the learning rate on a trial-by-trial basis between zero , indicating that no weight is given to the last observed outcome , and one , indicating that the prediction is equal to the last outcome [16] , [17] . During periods of stability , a decreasing learning rate can match the current belief to the average outcome . After change-points , a high learning rate shifts beliefs away from historical data and towards more recent , and more relevant , outcomes . These adaptive dynamics are captured by Bayesian ideal-observer models that determine the rate of learning based on the statistics of change-points and the observed data [18]–[20] . An example of the behavior of the Bayesian model is shown in figure 1D . In this case , the model uses a low learning rate in periods of stability to make predictions that are very close to the mean , then changes to a high learning rate after a change-point to adapt more quickly to the new circumstances . Recent experimental work has shown that human subjects adaptively adjust learning rates in dynamic environments in a manner that is qualitatively consistent with these algorithms [16] , [17] , [21] . However , it is unlikely that subjects are basing these adjustments on a direct neural implementation of the Bayesian algorithms , which are complex and computationally demanding . Thus , in this paper we ask two questions: 1 ) Is there a simpler , general algorithm capable of adaptively adjusting its learning rate in the presence of change-points ? And 2 ) Does the new model better explain human behavioral data than either the full Bayesian model or a simple Delta rule ? We address these questions by developing a simple approximation to the full Bayesian model . In contrast to earlier work that used a single Delta rule with an adaptive learning rate [17] , [21] , our model uses a mixture of biologically plausible Delta rules , each with its own , fixed learning rate , to adapt its behavior in the presence of change-points . We show that the model provides a better match to human performance than the other models . We conclude with a discussion of the biological plausibility of our model , which we propose as a general model of human learning .
Human subject protocols were approved by the University of Pennsylvania internal review board . Informed consent was given by all participants prior to taking part in the study . To familiarize readers with change-point processes and the Bayesian model , we first review these topics in some detail and then turn our attention to the reduced model . In this paper we are concerned with data generated from change-point processes . An example of such a process generating Gaussian data is given in figure 2 . We start by defining a hazard rate , , that in the general case can be variable over time but for our purposes is assumed to be constant . Change-point locations are then generated by sampling from a Bernoulli distribution with this hazard rate , such that the probability of a change-point occurring at time is ( figure 2A ) . In between change-points , in periods we term ‘epochs , ’ the generative parameters of the data are constant . Within each epoch , the values of the generative parameters , , are sampled from a prior distribution , for some hyper-parameters and that will be described in more detail in the following sections . For the Gaussian example , is simply the mean of the Gaussian at each time point . We generate this mean for each epoch ( figure 2B ) by sampling from the prior distribution shown in figure 2C . Finally , we sample the data points at each time , from the generative distribution ( figure 2D and E ) . The goal of the full Bayesian model [18] , [19] is to make accurate predictions in the presence of change-points . This model infers the predictive distribution , , over the next data point , , given the data observed up to time , . In the case where the change-point locations are known , computing the predictive distribution is straightforward . In particular , because the parameters of the generative distribution are resampled independently at a change-point ( more technically , the change-points separate the data into product partitions [22] ) only data seen since the last change-point are relevant for predicting the future . Therefore , if we define the run-length at time , , as the number of time steps since the last change-point , we can write ( 2 ) where we have introduced the shorthand to denote the predictive distribution given the last time points . Assuming that our generative distribution is parameterized by parameters , then is straightforward to write down ( at least formally ) as the marginal over ( 3 ) where is the inferred distribution over given the last time points , and is the likelihood of the data given the generative parameters . When the change-point locations are unknown the situation is more complex . In particular we need to compute a probability distribution over all possible values for the run-length given the observed data . This distribution is called the run-length distribution . Once we have the run-length distribution , we can compute the predictive distribution in the following way . First we compute the expected run-length on the next trial , ; i . e . , ( 4 ) where the sum is over all possible values of the run-length at time and is the change-point prior that describes the dynamics of the run-length over time . In particular , because the run-length either increases by one , with probability in between change-points , or decreases to zero , with probability at a change-point , the change-point prior , , takes the following form ( 5 ) Given the distribution , we can then compute the predictive distribution of the data on the next trial , in the following manner , ( 6 ) where the sum is over all possible values of the run-length at time . All that then remains is to compute the run-length distribution itself , which can be done recursively using Bayes' rule ( 7 ) Substituting in the form of the change-point prior for we get ( 8 ) Thus for each value of the run-length , all but two of the of the terms in equation 7 vanish and the algorithm has complexity of computations per timestep . Unfortunately , although this is a substantial improvement compared to complexity of a more naïve change-point model , this computation is still quite demanding . In principle , the total number of run-lengths we must consider is infinite , because we must allow for the possibility that a change-point occurred at any time in the past . In practice , however , it is usual to introduce a maximum run-length , , and define the change-point prior here to be ( 9 ) With this procedure , the complexity of the computation is bounded but still can remain dauntingly high . Despite the elegance of the full Bayesian algorithm , it is complex , requiring a memory of a large number ( ) of different run-lengths , which , in the worst case , is equivalent to keeping track of all the past data . Thus , it seems an unlikely model of human cognition , and a key question is whether comparable predictive performance can be achieved with a simpler , more biologically plausible algorithm . Here we introduce an approximation to the full model that addresses these issues . First we reduce the model's complexity by removing nodes from the update graph ( Figure 3 ) . Then we transform the update equation for into a Delta-rule update equation in which the sufficient statistic on each node updates independently of the other nodes . The resulting algorithm is a biologically plausible mixture of Delta-rules that is able to flexibly adapt its overall learning rate in the presence of change-points and whose performance is comparable with that of the full Bayesian model at a fraction of the computational cost . Below we derive new update equations for the sufficient statistics and the weights of each new node for this reduced model . To more easily distinguish the full and reduced models , we use to denote run-length in the reduced model and to denote run-length in the full model . Thus , the reduced model has nodes , where node has run-length . The set of run-lengths , , are ordered such that . Unlike the full model , the run-lengths in the reduced model can take on non-integer values , which allows greater flexibility . The first step in our approximation is to remove nodes from the update graph . This step reduces the memory demands of the algorithm but also requires us to change the update rule for the sufficient statistic and the form of the change-point prior . Consider a node with run-length . In the full Bayesian model , the sufficient statistic for this node would be ( 20 ) Note that this form of the update relies on having computed , which is the sufficient statistic at run length . In the full Bayesian model , this procedure is straightforward because all possible run-lengths are represented . In contrast , the reduced model includes only a subset of possible run-lengths , and thus a node with run-length will not exist for some values of . Therefore , the reduced model must include a new method for updating the sufficient statistic and a new form of the change-point prior . We first note that another way of writing the update for is as ( 21 ) This sliding-window update equation depends only on information available at node and thus does not rely on knowing the sufficient statistic at node . However , this update also has a high memory demand because , to update the sliding window , we have to subtract , which we can only do if we keep track of the previous data points on each node . In our model , we remove the dependence on , and hence the additional memory demands , by taking the average of equation 21 . This procedure leads to a memoryless ( yet approximate ) form of the update equation for each node . In particular , if we take the average of equation 21 with respect to , we have ( 22 ) where we have introduced as the Delta-rule's approximation to the mean sufficient statistic and ( 23 ) as the mean of the node . Dividing equation 21 by gives us the following form of the update for the mean ( 24 ) Note that this equation for the update of is a Delta rule , just like equation 1 , with a fixed learning rate , . Thus , the reduced model simply has to keep track of for each node and update it using only the most recent data point . This form of update rule also allows us to interpret non-integer values of the run-length , , in terms of changes in the learning rate of the Delta rule on a continuum . In figure 4 we show the effect of this approximation on the extent to which past data points are used to compute the mean of each node . The sliding window rule computes the average across the last data points , ignoring all previous data . In contrast , the Delta rule computes a weighted average using an exponential that decays over time , which tends to slightly under-emphasize the contributions of recent data and over-emphasize the contributions of distant data relative to the sliding window . Reducing the number of nodes in the model also requires us to change how we update the weights of each node . In particular the update for the weights , , is given as ( 25 ) This equation is similar to equation 7 but differs in the number of run-lengths available . Crucially , this difference requires an adjustment to the change-point prior . The adjusted prior should approximate the full change-point prior ( Eq . 5 ) as closely as possible . Recall that the full prior captures the fact that the run-length either decreases to zero if there is a change-point ( with prior probability ) or increases by one if there is no change-point ( with prior probability ) . To see how to compute this adjusted prior in the reduced model , we first decompose the change-point prior into two terms corresponding to the possibility that a change-point will occur or not; i . e . , ( 26 ) where is the probability that the run-length is given that there was a change-point and that the previous run-length was . Similarly is the probability that the run-length is given that the previous run-length was and there was not a change-point . The change-point case is straightforward , because a change-point always results in a transition to the shortest run-length; i . e . , is zero , except when when it takes value 1 . The no change-point case , however , is more difficult . In the full model the run-length increases by 1 when there is no change-point , thus we would like to have ( 27 ) However , because the nodes have variable spacing in the reduced model , this form is not possible as there may be no node with a run-length . We thus seek an approximation such that the prior defines an average increase in run-length of 1 if there is not a change-point . That is , we require ( 28 ) For we can match this expectation exactly by setting ( 29 ) For we approximate using ( 30 ) In this case we do not match the expected increase in run-length . For the final node , , it is impossible to transition to a longer run-length and so we simply have a self transition with probability 1; i . e . , ( 31 ) Taken together with equation 26 , equations 29 , 30 and 31 define the change-point prior in the reduced model . Like the full Bayesian model , our reduced model also has a graphical interpretation . Again each node , , keeps track of two quantities: 1 ) the mean , computed according to equation 24 , and 2 ) the weight . As in the full model , the weights are computed by passing messages along the edge of the graph . However , the structure of the graph is slightly different , with no increasing message being sent by node and an extra ‘self’ message from to itself . The increasing message has weight ( 32 ) the self message has weight ( 33 ) and the change-point message has weight ( 34 ) Finally the new weight for each node is computed by summing all of the incoming messages to implement equation 25 .
First we consider the simplest cases of one and two nodes with Gaussian data . These cases have particularly simple update rules , and their output is easy to understand . We then consider the more general case of many nodes to show how the reduced model retains many of the useful properties of the full model , such as keeping track of an approximate run-length distribution and being able to handle different kinds of data . Here we derive an approximate , but analytic , expression for the average discrepancy between the predictions made by the reduced model and the ground truth generative parameters . We then use this result to compute approximately optimal node arrangements for a variety of conditions and investigate how the error varies as a function of the parameters in the model . In this section , we ask how well our model describes human behavior by fitting versions of the model to behavioral data from a predictive-inference task [24] . Briefly , in this task , 30 human subjects ( 19 female , 11 male ) were shown a sequence of numbers between 0 and 300 that were generated by a Gaussian change-point process . This process had a mean that was randomly sampled at every change-point and a standard deviation that was constant ( set to either 5 or 10 ) for blocks of 200 trials . Samples were constrained to be between 0 and 300 by keeping the generative means away from these bounds ( the generative means were sampled from uniform distribution [from 40 to 260] ) and resampling the small fraction of samples outside of this range until they lay within the range . The hazard rate was set at 0 . 1 except for the first three trials following a change-point , in which case the hazard rate was zero . The subjects were required to predict the next number in the sequence and obtained more reward the closer their predictions were to the actual outcome . In particular , subjects were required to minimize the mean absolute error between prediction and outcome , which we denote . Because prediction errors depended substantially on the specific sequence of numbers generated for the given session , the exact conversion between error and monetary reward was computed by comparing performance with two benchmarks: a lower benchmark ( LB ) and an higher benchmark ( HB ) . The LB was computed as the mean absolute difference between sequential generated numbers . The HB was the mean difference between mean of the generative distribution on the previous trial and the generated number . Payout was then computed as follows: ( 50 ) A benefit of this task design is that the effective learning rates used by subjects on a trial-by-trial basis can be computed in terms of their predictions following each observed outcome , using the relationships in equation 1 . Our previous studies indicated that these learning rates varied systematically as a function of properties of the generative process , including its standard deviation and the occurrence of change-points [17] , [24] . To better understand the computational basis for these behavioral findings , we compared five different inference models: the full Bayesian model ( ‘full’ ) , the reduced model with 1 to 3 nodes and the approximately Bayesian model of Nassar et al [17] . The Nassar et al model instantiates an alternative hypothesis to the mixture of fixed Delta rules by using a single Delta rule with a single , adaptive learning rate to approximate Bayesian inference . On each trial , each of these models , , produces a prediction about the location of the next data point . To simulate the effects of decision noise , we assume that the subjects' reported predictions , , are subject to noise , such that ( 51 ) where is sampled from a Gaussian distribution with mean 0 and standard deviation that we fit as a free parameter for all models . In addition to this noise parameter , we fit the following free parameters for each model: The full model and the model of Nassar et al . have a hazard rate as their only other parameter , the one-node model has a single learning rate and the remaining models with nodes ( ) have a hazard rate as well as the learning rates . Our fits identified the model parameters that maximized the log likelihood of the observed human predictions , , given each of the models , , which is given by ( 52 ) We used the maximum likelihood value to approximate the log Bayesian evidence , for each model using the standard Bayesian information criterion ( BIC ) approximation [25] , which takes into account the different numbers of parameters in the different models; i . e . , ( 53 ) where is the number of free parameters in model . Models were then compared at the group level using the Bayesian method of Stephan et al . [26] . Briefly , this method aggregates the evidence from each of the models for each of the subjects to estimate two measures of model fit . The first , which we refer to as the ‘model probability’ , is an estimate of how likely it is that a given model generated the data from a randomly chosen subject . The second , termed the ‘exceedance probability’ , is the probability that one model is more likely than any of the others to have generated the behavior of all of the subjects . An important question when interpreting the model fits is the extent to which the different models are identifiable using these analyses . In particular we are interested in the extent to which different models can be separated on the basis of their behavior and the accuracy with which the parameters of each model can be fit . The question of model identifiability is addressed in figure 10 , where we plot two confusion matrices showing the model probability ( A ) and the exceedance probability ( B ) for simulated data . These matrices were generated using simulations that matched the human-subjects experiments , with the same values of the observed stimuli , the same number of trials per experiment and the same parameter settings as found by fitting the human data . Ideally , both confusion matrices should be the identity matrix , indicating that data fit to model is always generated by model and never by any other model ( e . g . , [27] ) . However , because of noise in the data and the limited number of trials in the experiment , it is often the case that not all of the models are completely separable . In the present case , there is good separation for the Nassar et al . , full , 1-node , and 2-node models and reasonable separation between the 3-node model and others . When we extended this analysis to include 4- and 5-node models , we found that they were indistinguishable from the 3-node model . Thus , these models are not included in our analyses , and we consider the ‘3-node model’ to represent a model with 3 or more nodes . Note that the confusion matrix showing the exceedance probability ( figure 10B ) is closer to diagonal than the model probability confusion matrix ( figure 10A ) . This result reflects the fact that exceedance probability is computed at the group level ( i . e . , that all the simulated data sets were generated by model M ) , whereas model probability computes the chance that any given simulation is best by model . To address the question of parameter estimability , we computed correlations between the simulated parameters and the parameter values recovered by the fitting procedure for each of the models . There was strong correspondence between the simulated and fit parameter values for all of the models and all correlations were significant ( see supplementary table S1 ) . The 3-node model most effectively describes the human data ( Figure 11 ) , producing slightly better fits than the model of Nassar et al . at the group level . Figure 11A shows model probability , the estimated probability that any given subject is best fit by each of the models . This measure showed a slight preference for the 3-node model over the model of Nassar et al . Figure 11B shows the exceedance probability for each of the models , the probability that each of the models best fits the data at the group level . Because this measure aggregates across the group it magnifies the differences between the models and showed a clearer preference for the 3-node model . Table 1 reports the means of the corresponding fit parameters for each of the models ( see also supplementary figure S1 for plots of the full distributions of the fit parameters ) . Consistent with the optimal parameters derived in the previous section ( figure 9E ) , for the 2- and 3-node models , the learning rate of the 1st node is close to one ( mean ∼0 . 95 ) .
To address this question , we derived an approximation to the Bayesian model based on a mixture of Delta rules , each implemented in a separate ‘node’ of a connected graph . In this reduced model , each Delta rule has its own , fixed learning rate . The overall prediction is generated by computing a weighted sum of the predictions from each node . Because only a small number of nodes are required , the model is substantially less complex than the full Bayesian model . Qualitatively , the outputs of the reduced and full Bayesian models share many features , including the ability to quickly increase the learning rate following a change-point and reduce it during periods of stability . These features were apparent for the reduced model even with a small number of ( 2 or 3 ) nodes . Thus , effective solutions to change-point problems can be achieved with minimal computational cost . For future work , it would be interesting to consider other generative distributions , such as a Gaussian with unknown mean and variance or multidimensional data ( e . g . , multidimensional Gaussians ) to better assess the generality of this solution . In principle , these extensions should be straightforward to deal with in the current model , which would simply require the sufficient statistic to be a vector instead of a scalar . Another obvious extension would be to consider generative parameters that drift over time ( perhaps in addition to abrupt changes at change-points ) or a hazard rate that changes as a function of run-length and/or time . To address this question , we used a model-based analysis of human behavior on a prediction task with change-points . The reduced model fit the behavioral data better than either the full Bayesian model or a single learning-rate Delta rule . Our fits also suggest that a three-node model can in many cases be sufficient to explain human performance on the task . However , our experiment did not have the power to distinguish models with more that three nodes . Thus , although the results imply that the three-node model is better than the other models we tested , we cannot rule out the possibility that humans use significantly more that three learning rates . Despite this qualification , it is an intriguing idea that the brain might use just a handful of learning rates . Our theoretical analysis suggests that this scheme would yield only a small cost in performance for the variety of different problems considered here . In this regard , our model can be seen as complementary to recent work showing that in many probabilistic-inference problems faced by humans [28] and pigeons [29] , as few as just one sample from the posterior can be enough to generate good solutions . It is also interesting to note that , for models with more than one node , the fastest learning rate was always close to one . Such a high learning rate corresponds to a Delta rule that does not integrate any information over time and simply uses the last outcome to form a prediction . This qualitative difference in the behavior of the fastest node could indicate a very different underlying process such as working memory for the last trial as is proposed in [30] , [31] . One situation in which many nodes would be advantageous is the case in which the hazard rate changes as a function of run-length . In this case , only having a few run-lengths available would be problematic , because the changing hazard rate would be difficult to represent . Experiments designed to measure the effects of variable hazard rates on the ability to make predictions might therefore be able to distinguish whether multiple Delta rules are indeed present . The question of biological plausibility is always difficult to answer in computational neuroscience . This difficulty is especially true when the focus of the model is at the algorithmic level and is not directly tied to a specific neural architecture , like in this study . Nevertheless , one useful approach to help guide an answer to this question is to associate key components of the algorithm to known neurobiological mechanisms . Here we support the biological plausibility of our reduced model by showing that signatures of all the elements necessary to implement it have been observed in neural data . In the reduced model , the update of each node uses a simple Delta rule with a fixed learning rate . The ‘Delta’ of such an update rule corresponds to a prediction error , correlates of which have been found throughout the brain , including notably brainstem dopaminergic neurons and their targets , and have been used extensively to model behavioral data [3]–[15] . More recently , several studies have also shown evidence for representations of different learning rates , as required by the model . Human subjects performing a statistical-learning task used a pair of learning rates , one fast and one slow , that were associated with BOLD activity in two different brain areas , with the hippocampus responsible for slow learning and the striatum for fast learning [32] . A related fMRI study showed different temporal integration in one network of brain areas including the amygdala versus another , more sensory network [33] . Complementary work at the neural level found a reservoir of many different learning rates in three brain regions ( anterior cingulate cortex , dorsolateral prefrontal cortex , and the lateral intraparietal area ) of monkeys performing a competitive game [34] . Likewise , neural correlates of different learning rates have been identified in each of the ventral tegmental area and habenula [35] . Finally , outside of the reward system , other fMRI studies using scrambled movies have found evidence for temporal receptive fields of increasingly long time scales ( equivalent to decreasingly small learning rates ) up the sensory processing hierarchy [36] . Applied to our model , these results suggest that each node is implemented in a distinct , although not necessarily anatomically separated , population of neurons . For our task and the above-referenced studies , in which trials last on the order of seconds , we speculate that the mean of a node is encoded in persistent firing of neurons . Alternatively , for tasks requiring learning over longer timescales , other mechanisms such as changes in synaptic weights might play key roles in these computations . Our model also depends on the run-length distribution , . Functionally , this distribution serves as a weighting function , determining how each of the different nodes ( corresponding to different run lengths ) contributes to the final prediction . In this regard , the run-length distribution can be thought of as an attentional filter , similar to mechanisms of spatial or feature-based attention , evident in multiple brain regions that enhance the output of certain signals and suppress others . For longer timescales , this kind of weighting process might have analogies to certain mechanisms of perceptual decision-making that involve the readout of appropriate sensory neurons [37] . Intriguingly , these readout mechanisms are thought to be shaped by experience – governed by a Delta-rule learning process – to ultimately enhance the most reliable sensory outputs and suppress the others [38] , [39] . We speculate that a similar process might help select , from a reservoir of nodes with different learning rates , those that can most effectively solve a particular task . The brain must also solve another challenge to directly implement the run-length distribution in our model . In particular , the update equation for the weights ( Eq . 25 ) includes a constant of proportionality that serves to normalize the probability distribution . On a computer , ensuring that the run-length distribution is normalized is relatively straightforward: after the update we just divide by the sum of the node weights . In the brain , this procedure requires some kind of global divisive normalization among all areas coding different nodes . While such divisive normalization is thought to occur in the brain [40] , it may be more difficult to implement over different brain regions that are far apart .
|
The ability to make accurate predictions is important to thrive in a dynamic world . Many predictions , like those made by a stock picker , are based , at least in part , on historical data thought also to reflect future trends . However , when unexpected changes occur , like an abrupt change in the value of a company that affects its stock price , the past can become irrelevant and we must rapidly update our beliefs . Previous research has shown that , under certain conditions , human predictions are similar to those of mathematical , ideal-observer models that make accurate predictions in the presence of change-points . Despite this progress , these models require superhuman feats of memory and computation and thus are unlikely to be implemented directly in the brain . In this work , we address this conundrum by developing an approximation to the ideal-observer model that drastically reduces the computational load with only a minimal cost in performance . We show that this model better explains human behavior than other models , including the optimal model , and suggest it as a biologically plausible model for learning and prediction .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"learning",
"cognitive",
"neuroscience",
"psychology",
"social",
"and",
"behavioral",
"sciences",
"cognitive",
"psychology",
"decision",
"making",
"biology",
"neuroscience",
"learning",
"and",
"memory"
] |
2013
|
A Mixture of Delta-Rules Approximation to Bayesian Inference in Change-Point Problems
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There is an interesting overlap of function in a wide range of organisms between genes that modulate the stress responses and those that regulate aging phenotypes and , in some cases , lifespan . We have therefore screened mutagenized zebrafish embryos for the altered expression of a stress biomarker , senescence-associated β-galactosidase ( SA-β-gal ) in our current study . We validated the use of embryonic SA-β-gal production as a screening tool by analyzing a collection of retrovirus-insertional mutants . From a pool of 306 such mutants , we identified 11 candidates that showed higher embryonic SA-β-gal activity , two of which were selected for further study . One of these mutants is null for a homologue of Drosophila spinster , a gene known to regulate lifespan in flies , whereas the other harbors a mutation in a homologue of the human telomeric repeat binding factor 2 ( terf2 ) gene , which plays roles in telomere protection and telomere-length regulation . Although the homozygous spinster and terf2 mutants are embryonic lethal , heterozygous adult fish are viable and show an accelerated appearance of aging symptoms including lipofuscin accumulation , which is another biomarker , and shorter lifespan . We next used the same SA-β-gal assay to screen chemically mutagenized zebrafish , each of which was heterozygous for lesions in multiple genes , under the sensitizing conditions of oxidative stress . We obtained eight additional mutants from this screen that , when bred to homozygosity , showed enhanced SA-β-gal activity even in the absence of stress , and further displayed embryonic neural and muscular degenerative phenotypes . Adult fish that are heterozygous for these mutations also showed the premature expression of aging biomarkers and the accelerated onset of aging phenotypes . Our current strategy of mutant screening for a senescence-associated biomarker in zebrafish embryos may thus prove to be a useful new tool for the genetic dissection of vertebrate stress response and senescence mechanisms .
Chronic oxidative stress has been shown to reduce lifespan in many species and lead to accelerated aging [1]–[3] . It has also been reported that oxidative stress is involved in neurodegeneration , sarcopenia and other muscle wasting conditions , which are accompanied by multiple aging symptoms [4]–[6] . Reactive oxygen species ( ROS ) are generated during normal cellular metabolism , primarily as a result of inefficiencies in the electron transport chain during mitochondrial respiration . Optimally localized levels of ROS serve functionally in the activation of some signal transduction pathways . However , ROS can also cause damaging chemical modifications of macromolecules such as proteins , lipids and DNA , which can in turn contribute to the progression of neurological diseases and neuromuscular disorders including Huntington's disease , Parkinson's disease , Alzheimer's disease , amyotrophic lateral sclerosis , and ataxia telangiectasia [4] , [7] . The genetic regulation of the stress and damage response pathways in vertebrates may be more complex than that seen in simple model organisms such as Drosophila and C . elegans . However , a strong case can be made for repeating the genetic screens performed in these lower organisms , in a vertebrate model , to identify genes regulating oxidative stress . Such analyses have the potential to identify candidate genes related to multiple stress- and age-associated diseases in humans . However , due to the challenges of performing large-scale forward genetic screens in mice , it would be of considerable benefit if the high-throughput screening technology used in simpler organisms ( i . e . , invertebrates ) can be adapted for use in zebrafish ( Danio rerio ) , a vertebrate model in which forward genetic screens are routinely performed [8]–[10] . The zebrafish is inexpensive to maintain and has favorable characteristics for experimentation such as a high fecundity , rapid external development , embryonic translucence , and ease of genetic manipulation . In addition , the sequence of the zebrafish genome , while not yet completely annotated , has already revealed a high degree of similarity between fish and human genes . Thus far , we and two other groups have mainly contributed to establish important baseline information validating the use of zebrafish as a valuable model for aging studies [11]–[16] . We have extensively searched for various biomarkers of aging in zebrafish [17] . However , to faithfully monitor the wide-ranging in vivo effects of several stresses on senescence and aging in zebrafish in a high-throughput manner , we required a reliable and easily applicable biomarker that robustly indicates presence of oxidative stress during embryonic development as well as symptoms of aging in adults . One obvious candidate was senescence-associated β-galactosidase ( SA-β-gal ) , a marker of cellular senescence in vitro as well as of organismal aging in vertebrates [16] , [18]–[21] . Importantly , genes known to cause embryonic senescence can be detected by SA-β-gal in mice [22] , [23] . Mounting evidence suggests that the identity of SA-β-gal is in fact the well characterized lysosomal β-galactosidase enzyme , which is most active at a much lower pH , but has some minimal activity at pH 6 . 0 where it can be detected when abundant [24] , [25] . The cellular lysosomal content increases in aging cells due to the accumulation of non-degradable intracellular macromolecules and organelles in autophagic vacuoles [26] . Thus , lysosomal β-galactosidase induction could represent a general adaptive response to cellular senescence . Oxidized protein and lipid by-products that cannot be degraded by lysosomal hydrolases nor be exocytosed accumulate over time in post-mitotic cells , and are not diluted by cell division . One such by-product is lipofuscin , also known as “age pigment” [27] . Lipofuscin is composed of cross-linked protein and lipid residues [28] , [29] and is generated by iron-catalyzed oxidative processes as well as by the incomplete degradation of damaged mitochondria [30] , [31] . It has previously been demonstrated that both oxidative stress and aging promote lipofuscin accumulation [32] . In our current study , we demonstrate that the level of SA-β-gal is elevated in zebrafish embryos exposed to acute but sub-lethal levels of oxidative stress as well as in aged adults . We then present two genetic screens for mutants in stress responses that might also display altered aging phenotypes . We first examined a collection of retrovirus-mediated insertional mutants for the embryonic induction of SA-β-gal . The screening of these mutants for modified SA-β-gal activity could be performed relatively quickly in the absence of an extrinsic insult and stress , since the homozygous embryos can be identified by their morphology . We found from our results that , of the 306 insertional mutations we examined , at least 11 scored as having significantly elevated SA-β-gal production levels in homozygous mutant embryos , two of which we present in further detail herein . The mutation which resulted in the highest SA-β-gal levels caused an inactivation in a gene encoding the zebrafish homologue of the Drosophila spinster , which is responsible for regulation of aging and lifespan in flies and has been implicated in a lysosomal storage function [33] , [34] . One of the other mutants inactivated the telomeric repeat binding factor a ( terfa ) gene , a zebrafish homologue of the telomeric repeat binding factor 2 ( terf2 ) gene , which plays prominent roles in telomere protection and telomere-length regulation [35] , [36] . For our second screen , we developed a new zebrafish mutant screening protocol based upon N-ethyl-N-nitrosourea ( ENU ) chemical mutagenesis . We performed a sensitized dominant screen in the zebrafish to detect mutations in the heterozygous state by using a chemical sensitizer ( rather than a genetic sensitizer ) . In our pilot screen using this methodology , we obtained eight mutants in two complementation groups that showed altered SA-β-gal activity in response to oxidative stress . Importantly , adult fish that were heterozygous for several of these mutations also showed premature expression of aging markers/phenotypes , and a shorter lifespan . Our new screening strategy using a senescence-associated biomarker during the embryonic stages in zebrafish provides a new tool for the genetic dissection of vertebrate stress responses and aging mechanisms . Moreover , our initial results strongly suggest that genetic lesions in certain early developmental mechanisms lead to late adult-onset phenotypes with age .
To further characterize aging in the adult zebrafish , we have previously examined several potential biological and biochemical markers , including regenerative competence and assays for the oxidative damage of proteins , lipids and DNA [16] , [17] , [19] . The most reliable and readily detectable age-dependent marker was determined to be a histochemical assay for SA-β-gal activity , which can be quantitatively applied to whole adult zebrafish using X-gal as a substrate at pH 6 . 0 [16] . In our current experiments , staining for SA-β-gal was found to increase in the skin of zebrafish with age throughout their lifespan ( n = 139 ) ( Figure 1A , B ) , as was previously reported in both humans and zebrafish [16] , [18] , [19] . To quantitatively examine SA-β-gal levels in vivo , we generated high-resolution digital images that enabled us to select stained pixels using image analysis software and to then calculate the percentage of stained pixels out of the net total in each case ( Figure S1 ) . Unlike other markers that tended to vary discontinuously with age , we found that SA-β-gal activity increases linearly with age in adult fish ranging in age from 5 to 57 months ( Figure 1C ) . We hoped to avoid the need to screen for aging mutants using actual lifespan analyses if we instead screened embryos for mutations that alter the expression of aging markers in response to oxidative stress [17] . For such an approach to succeed , we surmised that the chosen biomarker must respond both to aging in adults and to stress responses during embryonic development . Hence , we tested whether the SA-β-gal assay would also respond to oxidative stress in embryos treated with ROS such as hydrogen peroxide ( H2O2 ) or tert-butyl hydroperoxide ( BHP ) ( Figure 2A–D ) . Several different doses of these peroxides were used over a developmentally long period to better simulate long-term chronic oxidative stress . An experimental endpoint at 6 days post fertilization ( dpf ) was chosen to avoid potential spurious effects of caloric restriction or other nutritional deficiencies , as this is the point in larval development at which the supportive yolk has been consumed and the fish begin to eat and rely on oral intake nutrition . The LD50 values for H2O2 and BHP were measured at approximately 300 µM and 1 mM , respectively , under these assay conditions . At sub-lethal doses of peroxides , SA-β-gal levels increased in a roughly linear fashion with increasing concentrations of H2O2 and BHP to the maximum tolerated doses of 150 µM and 500 µM , respectively . Compared with the untreated controls ( n = 50 ) ( Figure 2A ) , zebrafish embryos treated with either 150 µM of hydrogen peroxide ( n = 50 ) or 500 µM of BHP ( n = 50 ) displayed an approximately 3-fold increase in SA-β-gal staining intensity following six days of development ( Figure 2B–D ) . These results suggested that SA-β-gal-based screens of chemically or genetically stressed embryos could indeed be used to identify senescence-related mutants in zebrafish . To test whether the induction of SA-β-gal activity in zebrafish embryos that have been exposed to oxidative stress occurs in a similar manner to that reported in other organisms , we performed genetic manipulations of a ROS detoxification enzyme in vivo . A number of studies in a variety of species have shown that both catalase and glutathione-peroxidase are responsible for antioxidant protection by limiting the accumulation of hydrogen peroxide [2] , [3] , [37] , [38] . To ascertain the potential importance of catalase in protecting zebrafish embryos from oxidative stress-induced senescence , we altered the expression levels of this enzyme in stressed embryos and measured the effects of this upon SA-β-gal activity . Embryos overexpressing zebrafish catalase were generated by the injection of 300 pg of mRNA encoding this enzyme at the one-cell stage ( n = 50 ) . This resulted in a reduction in both hydrogen peroxide- and BHP-induced SA-β-gal activity , compared with control GFP mRNA injections ( n = 50 ) ( Figure 2E and data not shown ) . BHP was used as the oxidative agent throughout the later stages of this study as it is more stable than hydrogen peroxide and produces less variable stress responses . The most dramatic rescue effects were observed when intermediate concentrations of BHP were used in these catalase experiments . We additionally tested if a reduction in the catalase expression levels would enhance the induction of SA-β-gal activity by oxidative stress . To this end , we injected embryos with an antisense morpholino oligonucleotide ( MO ) targeting zebrafish catalase and , indeed , observed a marked enhancement in their susceptibility to elevated SA-β-gal activity following exposure to oxidative stress ( Figure 2F ) . The greatest effects were again observed when intermediate concentrations of BHP were used . These results confirmed that the manipulation of a single gene can modulate the SA-β-gal activity levels induced by oxidative stress in zebrafish embryos and prompted us to pursue a genetic screening project to uncover potential aging mutants . We hypothesized that a loss-of-function ( or even partial loss-of-function/decrease-of-function ) mutation in certain genes may induce specific stress conditions in mutant embryos . To identify potential aging mutants , we first screened for mutants with an altered production of the stress response marker SA-β-gal in an established zebrafish mutant collection generated by retrovirus-insertional mutagenesis [39] . Currently , the Hopkins' insertional mutant collection contains more than 500 recessive mutants with morphological embryonic phenotypes , which include mutations in 335 different identified genes [10] , [40] , [41] . We screened unstressed homozygous embryos derived from incrossed heterozygotes from 306 of these lines for SA-β-gal expression at 3 . 5–5 dpf , depending upon the onset of the morphological phenotype . In general , the levels of SA-β-gal seen in the homozygous mutants were low , with only 11 mutants clearly scoring robustly higher than wild-type background activity ( Figure 3; Figures S3 and S4 ) ( Table 1 ) . It should be noted also that since all of the 306 mutations screened are ultimately homozygous lethal , these data indicate that SA-β-gal production is not a general result of embryonic death . Figure S4 shows several examples of embryonic lethal mutants whose SA-β-gal levels are no higher than ( or indistinguishable from ) their wild-type siblings despite varying amounts of cell death . Similarly , the cloche ( clo ) mutant , which has no circulatory system did not show detectable SA-β-gal induction above background activity ( n = 54 ) ( Figure 3C ) . However , for 11 of the lines , the mutant embryos showed significantly stronger SA-β-gal staining than their wild-type siblings . For example , as shown in Figure 3D , an insertional mutation in the atp6v1h gene encoding the V1 subunit H component of vacuolar ATPase ( v-ATPase ) , a multi-subunit enzyme that mediates the acidification of eukaryotic intracellular organelles , is one of the 11 mutants identified that showed robust levels of SA-β-gal induction ( n = 45 ) . We chose to study two out of 11 of the insertional mutants in more detail based upon previous knowledge about the mutated genes in other organisms . Of these insertional mutants , the highest SA-β-gal activity was found to be associated with an insertion in the gene denoted “not really started” ( nrs ) ( currently denoted as zebrafish spinster homolog 1 , spns1 ) ( hi891 ) ( nrs mutant , n = 135; wild type , n = 185 ) ( Figure 3B ) [42] . The nrsm/m homozygotes die by 4 dpf and show a substantial accumulation of an opaque substance in the yolk ( Figure 3E , indicated by a black arrow in the left lower panel ) . Furthermore , the nrs gene has been identified as the zebrafish homologue 1 ( spns1 ) of the Drosophila spinster ( spin ) gene . A Drosophila partial loss-of-function ( hypomorphic ) mutant for the spinster gene accumulates lipofuscin granules in the central nervous system , accompanied by neurodegeneration and abnormal ovary development . Notably , Drosophila hypomorphic spin mutants also have a shortened lifespan [33] . The other mutant which we focused on exhibited an insertion in the “telomere repeat binding factor a” ( terfa ) gene . The mutant lines hi3678 ( n = 155 ) and hi1182 ( n = 120 ) ( Figure 4A , lower image for hi3678 and Figure S2B , lower right panels for hi1182 ) had significantly higher SA-β-gal activity compared with wild-type controls ( n = 100 for each ) ( Figure 4A , upper image; Figure S2B , lower left panel ) . The terfa gene is a zebrafish homologue of the human terf2 gene which encodes the telomeric repeat binding factor 2 protein ( TRF2 ) . Due to the varied nomenclature for terfa in other species , we denote the zebrafish gene as terf2 hereafter . TRF2 has an essential role in telomere end protection and t-loop formation [35] , [43] , [44] . Moreover , the disruption of endogenous TRF2 function in human cells by expressing dominant-negative forms of this protein markedly increases the rate of telomere end-to-end fusions and cellular senescence [36] . A deletion of the terf2 gene in mouse embryonic fibroblasts also results in a senescence-like arrest and SA-β-gal induction [45] . Hence , the SA-β-gal induction that we see in our zebrafish terf2 mutant embryos is consistent with the established biological role of this gene and the results of previous studies in other organisms . Telomeres of homozygous terf2 embryos were visualized by cross mating with transgenic fish expressing a green fluorescence protein ( GFP ) -tagged human TRF1/Pin2 fusion protein [46] . While many telomere speckles were evident in the wild-type background ( n = 20 ) ( Figure 4B , upper left panel ) , we observed enlarged telomere speckles and abnormal nuclear shapes in terf2m/m fish embryos ( n = 24 ) ( Figure 4B , lower left and right panels ) , which are likely to reflect telomere end-to-end fusions and impaired chromosome integrity . Moreover , homozygous terf2m/m mutant zebrafish embryos showed aggressive neurodegenerative phenotypes in the eye , brain , and spinal cord ( n = 32 ) ( Figure 4C , right panel; Figure 7W–Y ) , compared with wild type ( n = 10 ) ( Figure 4C , left panel; Figure 7T–V ) . In contrast to normal retinal development in wild-type embryos ( n = 5 ) ( Figure 4D , left panel ) , embryonic retinas stained with phalloidin in order to visualize actin filaments in plexiform layers revealed obvious structural defects in terf2m/m mutants ( n = 10 ) ( Figure 4D , right panel ) . Neurodegeneration in the retina was also detected histologically by performing Fluoro-Jade B staining of terf2m/m embryos ( n = 10 ) ( Figure 4E , right panel ) , compared with normal wild-type retinas ( n = 5 ) ( Figure 4E , left panel ) . Significantly , the neural phenotypes associated with a terf2 mutation appear to be consistent with the recent observations of mammalian TRF2 function reported in neural cells in vitro [47] , [48] . While we did not examine other mutant lines at this level of detail , it was clear that some of the other insertional mutants with elevated SA-β-gal levels also exhibited widespread cell death in the central nervous system and eyes ( Table 1 ) . To further examine the SA-β-gal induction caused by disruptions of the nrs and terf2 genes , we knocked down the translation of their respective mRNAs using MOs that target the start codon of each gene . Injection of a nrs MO at the single- or two-cell stage resulted in an exact phenocopy of the nrs mutant embryos which manifested obvious yolk opacity . Upon SA-β-gal staining , an extremely high level of induction was observed in the nrs morphants ( n = 116 of 120; 97% ) , identical to the SA-β-gal levels in the nrs mutants ( Figure S2A ) . In contrast , the control embryos did not show any significant SA-β-gal activity ( n = 150 ) . We also injected a terf2 MO into zebrafish embryos , and observed robust SA-β-gal induction ( n = 191 of 200; 95 . 5% ) , and a relatively moderate morphological phenotype similar to the terfahi1182/hi1182 allele , that has a weaker phenotype than the insertional mutant ( terfahi3678/hi3678 ) used above ( Figure S2B ) . This is consistent with the higher residual levels of terf2 mRNA in the morphants and in the hi1182 mutants harboring an insertion in the first intron of the terf2 gene which allows for the production of some wild type transcript , in contrast to the hi3678 allele that has the insertion in the first exon of the terf2 gene ( http://web . mit . edu/hopkins/insertion20sites/1182 . htm ) . We additionally exposed nrs and terf2 mutant embryos to oxidative stress by BHP treatment . The homozygous terf2m/m ( terfahi3678/hi3678 ) mutants ( n = 50 ) , but not the heterozygotes , clearly show enhanced induction of SA-β-gal activity with a more severe morphology in the eyes and heads ( Figure 4F ) , whereas no significant difference was observed in the nrs mutant animals of either homozygous or heterozygous backgrounds ( data not shown ) . Taken together , the outcomes from our current screen of 306 lines from the Hopkins' insertional mutant collection serve as a proof of concept for our strategy and the first success in our novel approach to identify potential aging-related genes by examining the senescence-associated biomarker in zebrafish embryos . Having established that SA-β-gal is induced by oxidative stress caused by BHP treatment , we next performed a new screen for ENU mutant zebrafish which displayed phenotypic alterations arising from genetic mutations in stress response mechanisms . We crossed individual F1 mutant males with wild-type females to produce clutches of F2 embryos , each of which was heterozygous at many loci . By using BHP as a chemical sensitizer , we hoped to identify heterozygous mutants with an altered response to oxidative stress; that is we expected the chemical sensitizer to induce haploinsufficiency in many of the potential target genes . We have denoted this methodology ‘CASH’ ( Chemically Assisted Screening in Heterozygotes ) . We treated 50 embryos from the resulting clutches with 350 µM BHP from 6 hours post fertilization ( hpf ) to 6 dpf . In each clutch of embryos , half of the clutch would be heterozygous for any mutant allele , and half would be wild type for that allele . Thus , an F1 male carrying a mutation that alters sensitivity to oxidative stress would produce clutches wherein half the embryos show altered induction of SA-β-gal activity ( Figure 5A ) . We divided SA-β-gal staining intensity in the F2 embryos into discrete quantitative ranges and measured how many embryos fell into each staining intensity range . When we performed this analysis on wild-type embryos , the result was a tight Gaussian distribution ( Figure 5B ) . When we looked at our candidate mutant clutches , in most cases their staining distributions appeared similar to wild type . However , occasionally , nearly half of the embryos were darker than the others and the distributions appeared abnormal ( see a dotted red line throughout Figure 5B , 5D , and 5F ) . The F1 fathers of these clutches were potential carriers of mutations that either enhanced SA-β-gal activity . These are what we will refer to as ‘Class 1’ mutant candidates ( Figure 5C and 5D; n = 35 for this tested candidate; P<0 . 01 , Student t-test ) . We also observed clutches of another class of mutants in which approximately half of the embryos showed a clear morphological abnormality in the presence of BHP ( Figure 5E and 5F; n = 45 for this tested candidate; P<0 . 001 , Student t-test ) . However , when we repeated these outcrosses without exogenous oxidative stress , the resultant clutches appeared morphologically normal . These clutches comprise what we will refer to as ‘Class 2’ mutant candidates . We performed an initial screen in 150 F1 mutagenized genomes , and obtained 8 candidate mutants that bred in a consistent recessive Mendelian fashion through to the F4 generation . Six of the mutants were from Class 1 and two were from Class 2 . When we incrossed F2 siblings ( n>30 ) of each peroxide-sensitive mutant ( psm ) line , we observed the homozygous phenotypes seen in Figure 6 in about 25% of the embryos of each clutch from about 1 out of every 4 incrosses . This is consistent with the expected Mendelian segregation of a recessive trait . For each psm mutant , over 2 generations and at least 20 fish per generation the fish that transmitted the heterozygous psm phenotype with high SA-β-gal activity also transmitted the recessive morphological phenotype . Seven of the phenotypes were very similar ( psm2 , 5 , 6 , 8 , 9 , 10 , 11 ) , with evidence of a moderate dorsal curvature of the trunk , and showed pronounced levels of cell death which was clearly apparent in the brain beginning at 48 hpf ( n = 50 for each mutant ) ( five of these were Class 1 mutants and two were Class 2 mutants ) . These homozygous mutations were embryonic lethal , with death occurring at around 6 dpf . The remaining psm7 mutant ( Class 1 ) developed a minor protrusion of the jaw and had an opaque yolk that first became apparent at 4 dpf ( n = 50 ) , subsequently dying at around 7 dpf . We proceeded to examine the homozygous phenotype of each psm mutation more closely . All F3 homozygous embryos ( n = 50 ) with abnormal phenotypes were also found to have higher SA-β-gal staining than the wild-type controls ( n = 20 ) in the absence of any exogenous oxidative stress ( Figure S5 ) . Punctate SA-β-gal staining was seen throughout the central nervous system in each of the seven mutants we identified ( psm2 , 5 , 6 , 8 , 9 , 10 , 11 ) , which showed brain abnormalities and dorsal curvatures ( n = 25 for each mutant ) ( Figure 7B , 7E and 7F; psm6 mutant is shown ) . Acridine orange ( AO ) , which stains dying cells , produced very intense signals throughout the neural tube and brain tissues of these mutants between 36 and 72 hpf ( n = 50 for each mutant at each time point ) , indicating massive cell death ( Figure 7M and 7O; psm6 ) . In vivo staining of the neurodegenerative mutants at 3 . 5 dpf with dichlorofluorescein diacetate ( DCFH-DA ) , an indicator of ROS , revealed the presence of high levels of ROS in the neural tube , specifically in the dorsal half ( n = 20 for each mutant ) ( Figure 7S; psm6 ) . Interestingly , these phenomena were also true of homozygous terf2m/m mutant embryos that showed high levels of ROS in the neural tube at 3 . 5 dpf ( Figure 7Y ) , and demonstrated positive AO-staining indicating cell death also at 2 dpf ( Figure 7X ) . Histological analysis of these embryos at 2 and 3 dpf indicated evident abnormalities around the regions of the brain , neural tube and eyes where the accumulation of neuronal cell death products ( data not shown ) . At 4 and 5 dpf , further histological examinations revealed that the brain and neural tubes of the psm mutants were considerably smaller than those of wild types and contained fewer neuronal nuclei ( Figures 7P; wild type and 7Q; psm6 ) . The mutant showing yolk opacity ( psm7 ) also showed mottled SA-β-gal staining throughout the muscle in the trunk ( n = 71 ) ( Figure 7C and 7G ) . Histological sections of mutant animals at 4 dpf revealed the absence of muscle fibers throughout the myotomes ( n = 35 ) ( Figure 7I ) , suggesting muscular degeneration . However , this phenotype does not appear to be the result of defective muscle fiber attachment as reported in another dystrophy-like mutant ( Bassett et al . , 2003 ) , since whole-mount in situ histochemical analysis of dystrophin expression appeared to be normal ( n = 20 ) ( Figure S6 ) , indicating that the fiber loss is not likely related to dystrophin-mediated fiber adhesion . In addition , DCFH-DA staining of the psm7 homozygotes at 3 . 5 dpf showed high ROS production in many individual muscle fibers ( n = 45 ) ( Figure 7K ) . It is noteworthy that all of the seven neurodegenerative mutants ( psm2 , 5 , 6 , 8 , 9 , 10 , 11 ) were in the same complementation group while the mutant showing muscle degeneration ( psm7 ) was not linked to any of the neural phenotype mutants ( Table S1 ) . Moreover , each of the psm mutants complemented both the nrs and terf2 mutations ( Table S1 ) . Thus it is possible that this pilot ENU screen has recovered mutations in only 2 genes , each of which are distinct from the nrs and terf2 genes . We wondered whether there might be long-term ( ‘aging’ ) effects of heterozygosity for the genes identified in our screens stemming from the associated embryonic alterations in senescence markers/phenotypes . We thus measured the premature aging marker levels and pathohistological phenotypes in heterozygous fish as they aged . Two of the 5 tested heterozygous psm mutant lines ( psm6 , n = 8 and psm9 , n = 8 ) showed significantly higher levels of SA-β-gal activity in the skin at just 1 . 5 years of age ( 18 months ) compared with their wild-type siblings ( n = 10 for each group ) ( Figure 8A ) . In contrast , a heterozygous nrsm/+ mutant ( n = 12 ) showed only a modest increase in skin SA-β-gal activity at 2 years but showed a high induction of SA-β-gal at 3 . 3 years of age ( 40 months ) , compared with wild-type siblings ( n = 15 ) ( Figure 8A ) . In addition to SA-β-gal activity , lipofuscin is often considered to be a hallmark of aging , showing an accumulation rate that correlates with longevity in some tissues [31] , [49] . We reported previously that wild-type adult zebrafish are refractory to the accumulation of lipofuscin in muscle by 2 years of age [19] . In our current study we found that there is still no detectable lipofuscin accumulation in wild-type sibling fish ( n = 15 ) by 2 . 8 years ( 33 months ) of age , but that heterozygous nrsm/+ mutant fish ( n = 10 ) had accumulated a great deal of lipofuscin in the skeletal muscle by this time ( Figure 8H ) . On the other hand , in terms of the liver histology of the wild-type zebrafish , lipofuscin accrual changed dramatically with age ( Figure S7A ) , similar to that reported in mice [50] . Notably also , we found in our current analyses that male psm6 ( n = 10 ) and psm7 ( n = 10 ) heterozygous animals , as well as the male nrsm/+ ( n = 8 ) heterozygotes , accumulated lipofuscin in the liver at an early age compared with their wild-type siblings ( n = 10 for each group ) ( Figure 8B–G ) . Although heterozygous nrsm/+ and psm7m/+ mutant fish did not display significantly increased SA-β-gal levels at 25 months ( 2 . 1 years ) and 18 months ( 1 . 5 years ) of age , respectively ( Figure 8A ) , both of these mutants did show increased lipofuscin accumulation at these ages ( nrs , n = 9 and psm7 , n = 8 ) ( Figure 8E , G ) . This indicated that different aging phenotypes may occur independently or that liver lipofuscin buildup may be an earlier or more sensitive indicator of aging in these mutants . Taken together , these results suggest that our newly identified mutants show a robust early-onset expression of aging biomarkers that normally only manifest in much older wild-type animals . A striking phenotype associated with the terf2m/m homozygous mutant embryos was observed in the central nervous system , including the eye ( retina ) and brain , as shown in Figure 4C–E and Figure 7W–Y . Given the degenerative phenotype seen in embryonic terf2m/m retinas ( Figure 4C–E , right panels ) , we examined histological sections of heterozygous adult mutants . Histological sections of mutant ( n = 23 ) and wild-type sibling ( n = 15 ) retinas were analyzed at various ages to determine the cellular basis for the observed age-dependent retinal defects . We observed structural abnormalities , principally retinal cell degeneration , in most aged mutant retinas with drusen-like autofluorescent accumulations around the retinal pigment epithelium ( RPE ) . This degeneration was not uniform over the retinas but tended to be patchy . Areas of rods ( rod outer segments ) degeneration were interspersed between areas where significant numbers of rods remained . Cones ( cone outer segments ) generally were better preserved than the rods , but areas of cone degeneration were also noted . The observed degeneration was progressive with age . In animals older than 12 months , degeneration was usually seen only in the central-most regions of the retina . By 21 months of age , however , degeneration was observed across much of the retina , although the far peripheral regions tended to be spared . Importantly , in the wild-type zebrafish retinas , these degenerative changes appeared dramatically with advancing age ( Figure S7B ) . Representative histological sections from a 23-month old wild-type sibling fish and an age-matched heterozygous terf2m/+ mutant fish were compared and are shown in Figure 8I . In zebrafish , the photoreceptors are typically tiered so that in the light-adapted retina , the rods are positioned more distally than the cones . In the 23-month old terf2m/+ retina , the peripheral regions were found to be similar to control retinas , but obvious abnormalities were observed throughout the central retina . In some areas only the rods were affected , i . e . , the rod outer segments were disorganized and reduced in length , and autofluorescent accruals in the RPE were increased in number and size ( Figure 8I , right panel in terf2m/+ ) . In other areas , the rods were severely degraded but the cones appeared relatively normal . In yet other areas , both the cones and rods were affected , i . e . , both were sometimes disorganized and reduced in length ( data not shown ) . Moreover , the inner plexiform layers of the retina usually looked thinner in affected animals , and occasionally some patchy thinning of the inner nuclear layer and empty spaces were also observed ( Figure 8I , left panel in terf2m/+ ) , suggesting some loss of inner retinal neurons . To investigate whether there might be loss of elements other than photoreceptors in the mutant zebrafish retinas , we measured the thickness of various retinal layers centrally in control ( n = 6 ) and mutant ( n = 8 ) animals at 23 months of age ( Table S2 ) , and also in control ( n = 6 ) and mutant ( n = 6 ) animals at 30 months of age ( data not shown ) . The retinas of mutant animals were clearly thinner than those of the wild-type fish ( Figure 8I ) . Much of this change in thickness was accounted for by a decrease in the thickness of the photoreceptor layer and inner plexiform layer . Finally , we obtained Kaplan-Meier survival curves for some of our mutant fish . The oldest fish that were heterozygous for psm mutations in our stocks at the time of writing had not reached their maximum lifespan ( wild-type zebrafish have a maximum lifespan of roughly 5 years [51] ) , so we have not yet been able to determine the effects of these mutations upon overall lifespan . We have , however , maintained populations of heterozygous nrs and terf2 mutant fish and their wild-type siblings until death , and conducted observational studies on their lifespan . We scored the cohorts of nrsm/+ fish ( no genders identified; n = 148 ) in comparison with their wild-type siblings ( no genders identified; n = 256 ) , and found significant decreases in the lifespan of the heterozygous mutants ( P<0 . 0001 , log rank test ) ( Figure 9A ) . Moreover , the male terf2m/+ fish cohorts ( n = 96 ) also manifested a shorter lifespan compared with that of their wild-type male siblings ( n = 79 ) ( P<0 . 0001 , log rank test ) ( Figure 9B ) . In contrast , neither the heterozygous terf2 mutant females nor their wild-type female siblings had reached the end point of their lifespan during the period of our current experiment . These lifespan analyses of the two mutations identified by embryonic senescence phenotypes suggest that screening for the embryonic appearance of ‘aging biomarkers’ may in some cases at least , predict a role for specific genes in the organismal aging process .
The goal of our current study was to test the hypothesis that mutations which enhance the appearance of embryonic stress markers might result in degenerative or even aging phenotypes in adults . We have presented the results of two different screens for potential aging mutants in this report , both utilizing SA-β-gal production in the zebrafish embryo as a key part of the screening process . We first screened a collection of retrovirus-mediated insertional mutants for elevated SA-β-gal production in unstressed homozygous embryos . Notably , this collection of insertional mutants was isolated based on the requirement for homozygous developmental phenotypes . Each of the two mutants out of the 11 candidates from this first screen included a lesion in the nrs gene , the zebrafish homologue of the Drosophila spinster gene a known regulator of aging in flies [33] , or the terf2 gene , which plays roles in telomere protection and telomere-length regulation as a component of the telosome/shelterin complex [35] . In the second screen , using an oxidant agent as a sensitizer , we have isolated a series of mutants , denoted psm mutants , which showed elevated SA-β-gal expression in the stressed heterozygous state and additionally in an unstressed homozygous state of embryos . Sensitized screens are much less labor and time intensive than traditional screens for recessive phenotypes in homozygous mutant embryos . Importantly , heterozygous mutant fish from both screens showed elevated expression of aging biomarkers in relatively younger ages . Thus , mutants from both screens showed degenerative phenotypes in homozygous embryos during early development and in heterozygous adults with age . Notably , the heterozygous animals in some cases also appeared to die at earlier ages than controls . It is possible that the two screens revealed some different classes of mutants . For instance neither of the two insertional mutants which we studied in detail showed statistically significant alterations in their sensitivity to oxidative stress as heterozygotes ( data not shown ) . The heterogeneity of the observed phenotypes in mutants from the 2 screens persisted in the adult heterozygotes . Both nrs and psm mutations showed hastened lipofuscin accrual in the livers of young adult heterozygotes , whereas two psm mutants ( psm6 and psm9 ) showed enhanced SA-β-gal production in relatively young adults ( 1 . 5 y ) compared with their wild-type siblings , a trait shown only in older nrsm/+ fish ( 3 . 3 y ) . However , since we examined mutants for only two genes in each screen in any detail , it is premature to derive substantial conclusions at this stage . The accelerated production of lipofuscin in the liver is a phenotype that was found to be common to the adult zebrafish from both screens . Lipofuscin accumulation is a hallmark of aging in many organisms from worms to mammals [32] , [52] . Tissues that are traditionally thought to be sensitive to lipofuscin accrual in mammals include the brain , and both skeletal and cardiac muscle . However , previous studies from our laboratory have shown that there is no clear accumulation of lipofuscin inclusions with age in wild-type zebrafish skeletal muscle cells and cardiac myocytes , at least up to 2 . 5 years of age [13] , [19] ( unpublished observations ) . In contrast , age-related changes in liver structure and lipofuscin accumulation have been demonstrated in male mice [50] . We have also observed premature lipofuscin accrual in the adult liver of our heterozygous male psm mutants as well as in male nrsm/+ heterozygotes . It is widely believed that oxidative damage processes underlie sources of lipofuscin production , and that its accumulation may have multiple negative effects , including a further increase in sensitivity to many stress-induced damage responses [32] . Since we observed no obvious response to exogenous ( extrinsic ) oxidative stress by BHP in nrs mutant embryos , we suspect that differences in intrinsic energy metabolism may underlie lipofuscin appearance in the nrs mutants , although this clearly requires further investigations . In the case of flies , it has also been shown that mutations in the spinster gene cause enhanced lipofuscin production [33] , though the precise underlying mechanism also remains unknown . Therefore , parallel studies of this gene product in other organismal aging model systems would be very desirable to provide a more conclusive insight into its mechanistic roles . There is cumulative evidence to date to suggest that early onset neuronal degeneration phenotypes in homozygous zebrafish mutants are predictive of a late-onset visual impairment in the corresponding heterozygous animals [53]–[56] . Intriguingly , in heterozygous terf2m/+ mutant adult fish , drusen-like autofluorescent accumulation ( presumably caused by lipofuscin accrual ) is more obvious in the RPE compared with age-matched siblings . In contrast to other tissues , ocular lipofuscin has been identified as N-retinylidene-N-retinylethanolamine ( A2E ) . A2E is a quaternary amine and retinoid by-product of the visual cycle and causes the accumulation of free and esterified cholesterol in RPE cells [57] , [58] . Although endogenously produced A2E in the RPE has been associated with macular degeneration , the precise mechanisms are unclear . Therefore , the involvement of the telomeric factor TRF2 in the mechanism of the RPE lipofuscin accrual might provide intriguing new insights into potential novel strategies for the prevention and treatment of neurodegenerative disorders . Alternatively , telomere-associated proteins might be involved in neural differentiation , as homozygous terf2m/m mutant embryos show an aggressive neurodegenerative phenotype in both the eye ( retina ) and brain . In this regard , divergent molecular and physiological responses to telomere dysfunction in mitotic neural stem/precursor cells and postmitotic neurons appear to regulate the differentiation and survival of neurons as well as RPE cells [47] , [48] . Notably , both terf2 ( males ) and nrs ( gender not identified ) heterozygous mutant had shorter lifespans in comparison with their control wild-type siblings , suggesting that partial loss-of-function/decrease-of-function in these genes may have systemic effects on physiological aging rather than the organ-specific tissue aging on which we focused in the current study . In addition , there may be gender-dependent differences in longevities and mortality rates in zebrafish when we look at the survival curves of wild-type siblings of terf2 and nrs mutants . Male terf2 heterozygous mutant fish and their wild-type male siblings were kept isolated from females except for the occasional matings performed in our current study . Heterozygous terf2 mutant females and their wild-type female siblings had not reached the end point of their lifespan at the time when the male fish all died out . On the other hand , heterozygous nrs mutant males and females basically co-habited throughout their life , as did their wild-type siblings . When they died , sex determination was therefore not possible due to rapid decomposition ( autolysis ) in water . Although further studies are definitely needed , social and environmental factors between genders might affect zebrafish lifespan in addition to their genetic background . Any understanding of the mechanisms by which the psm genes regulates the functions of age accompanied with degenerative phenotypes will require positional cloning of the mutated genes . The psm mutants may also be useful for studying signal transduction pathways and related biological processes in addition to both stress responses and aging . The fact that these mutations regulate embryonic tissue-specific responses to oxidative stress is also interesting per se . It will be of considerable interest to see if these mutations lie in genes already associated with oxidant responses , such as antioxidant genes or genes that maintain mitochondrial functions . It is also possible that the psm mutants which showed neuronal phenotypes may have value as new models for specific disorders such as Huntington's disease , Parkinson's disease , Alzheimer's disease , amyotrophic lateral sclerosis , and ataxia telangiectasia , because several lines of evidence suggest that oxidative stress is associated with the development of these neurodegenerative diseases [59]–[63] . In addition , oxidative stress has been shown to play a role in sarcopenia and other muscle wasting conditions [5] , [6] , where the psm7 mutant may be involved . Since we have already shown an embryonic haplosensitivity of the psm mutants to oxidative stress by the very nature of the CASH strategy , we are currently further investigating the possibility that more of these mutants may show accelerated onset of aging in the adult heterozygotes , expanding their utility as models of stress-associated pathophysiological aging and degenerative disease . In the future , we hope to further utilize our technology to identify a real “suppressor” type mutant which would display enhanced embryonic stress resistance and , perhaps , a longer healthy lifespan ( ‘health span’ ) . Alternatively , it might also be possible to isolate a ‘revertant’ from the background of an accelerated aging mutant , which would restore the original normal phenotype by means of a suppressor mutation . In summary , our current study has demonstrated for the first time in vertebrates that it is possible to obtain mutations that alter adult aging markers/phenotypes and lifespans by screening mutagenized and sensitized embryos for the extemporal expression of the aging biomarker . It is our hope that this novel tactic of screening for aging biomarkers in zebrafish embryos may open a new avenue for the future genetic dissection of vertebrate aging mechanisms .
Zebrafish of the wild-type strain and mutants were maintained with a 14:10 h light/dark cycle and fed living brine shrimp twice per day . Brine shrimp was given using 1 mL pipettes at an amount of about 0 . 75 mL per 20 fish . Flake food was also given a few days per week semiquantitatively according to the number of fish in the tanks . A continuously cycling Aquatic Habitats™ system was used to maintain water quality ( Apopka , FL , USA ) which completely replaces the water in each tank every 6–10 min . Each tank is a baffle/tank system that ebbs water in a circular motion to ensure flushing and water turnover . Ultraviolet ( UV ) sterilizers ( 110 , 000 microwatt-s cm−2 ) were employed to disinfect the water and prevent the spread of disease in the recirculating system . The water temperature was maintained at 28±0 . 5°C . The system continuously circulated water from the tanks through Siporax™ strainers , through a fiber mechanical filtration system , and finally into a chamber containing foam filters and activated carbon inserts . Water quality was tested daily for chlorine , ammonia , pH , nitrate , and conductivity under real-time computer monitoring with alarms to signal potential fluctuations . The general health of each fish was observed on a daily basis throughout fish life to monitor longevity , and abnormal looking or acting fish were quarantined into isolated tanks unconnected from the general circulation . The water in these quarantined tanks was treated with methylene blue ( 0 . 0001–0 . 001% ) . If and when fish recovered , they were returned to their original tanks on the general circulation . All animals showing signs of infectious or parasitic disease that are not alleviated by a 7-day incubation with methylene blue in water were euthanized in a beaker containing tricaine ( approximately final 0 . 5% in water ) . Animals exhibiting overt tumors or extreme morbidity were also euthanized . Embryos were collected by natural spawning , raised in 10% Hank's saline with or without 0 . 003% 1-phenyl-2-thiourea ( PTU ) ( embryo media ) and staged according to Kimmel et al . [64] . All embryos were incubated at 28 . 5°C during development . Data processing and statistical analyses were performed using Microsoft Excel and Statistical Package for the Social Sciences ( SPSS ) version 14 . 0 , which were used to generate each of the scatter plots , tables , and graphs shown in the text , performing statistical tests where appropriate . Additional statistical analyses were performed at the Department of Biostatistics and Computational Biology , Dana-Farber Cancer Institute . These analyses included survival estimates using the method of Kaplan and Meier , and comparison of survival between mutant fish and their wild type controls using the log rank test . Zebrafish adults and embryos were fixed in 4% paraformaldehyde in phosphate buffered saline ( PBS ) at 4°C ( for 3 days in adults and overnight in embryos ) , and then washed 3 times for 1 h in PBS-pH 7 . 4 and for a further 1 h in PBS-pH 6 . 0 at 4°C . Staining was performed overnight at 37°C in 5 mM potassium ferrocyanide , 5 mM potassium ferricyanide , 2 mM MgCl2 , and 1 mg/ml X-gal in PBS adjusted to pH 6 . 0 . All animals were photographed under the same conditions using reflected light under a dissecting microscope . SA-β-gal activity in each animal was quantitated using a selection tool in Adobe Photoshop for a color range that was chosen by 25 additive blue color selections of regions that showed visually positive SA-β-gal staining . For analyses of embryos , these regions were selected in each embryo proper only and not in the yolk in order to eliminate variability due to differences in initial yolk volume and yolk consumption over time . Since the yolk stains much more intense blue for SA-β-gal at all stages of development than any other embryonic tissues , even under conditions of high oxidative stress , it was desirable to eliminate this as a source of variability . Following pixel selection , a fuzziness setting of 14 was used , and the chosen pixel number was calculated using the image histogram calculation . For adult zebrafish analyses , the trunk area for colorimetric quantitation was chosen by selection of the area between the operculum and the dorsal and anal fins ( Figure S1 ) . The cloning of zebrafish catalase cDNA was performed using the SuperScript One-Step RT-PCR kit with Platinum Taq ( Invitrogen ) according to the manufacturer's instructions , by using the forward primer 5′-TTTGCCTCGTGTTTTGTCAC-3′ and reverse primer; 5′-GGAGTCAGTGTTGCATTTGCT-3′ . These primers were designed using the flanking regions of the zebrafish Ensembl sequence for the predicted human catalase homolog . The full-length cDNA was then cloned into the pCS2+ vector . The resulting plasmid was linearized by digestion at a restriction site immediately after the poly-A signal , and capped mRNAs were transcribed in vitro using the mMachine Kit ( Ambion Inc . ) following the manufacturer's instructions . The stated amount ( 300 pg ) of mRNA were injected into one-cell stage zebrafish embryos using a gas driven microinjector ( Medical Systems Corp . ) . Knockdown of zebrafish catalase was performed by injection of 8 ng antisense morpholino oligonucleotide ( MO ) ( Gene-Tools , LLC ) with the sequence 5′-TCGACTTTTCTCTGTCGTCTGCCAT-3′ or a control morpholino 5′-CCTCTTACCTCAGTTACAATTTATA-3′ . Knockdown of zebrafish nrs/spns1 and terf2/terfa was performed also by injections of MOs ( 8 ng ) containing the sequences 5′-ATCTGCTTGTGACATCACTGCTGGA-3′ and 5′-GGTTCGCAGGGTTTGTCGCTCATTC-3′ , respectively . Heterozygous incrosses of each mutant line were performed , and the resulting embryos were raised in 10% Hank's saline at 28 . 5°C . The embryos were fixed at either 3 . 5 dpf or at least 18 h before the occurrence of embryonic lethality after 3 . 5 dpf for lines whose the homozygotes are known to die . SA-β-gal staining was performed as above , and positive candidates were determined by correlating high SA-β-gal activity with the presence of a homozygous mutant phenotype . Since some of mutants needed to be distinguished by their pigmentation patterns , the entire screening of 306 mutants was performed in these embryos without PTU treatment . Embryos of selected 11 candidates were also processed for single-embryo SA-β-gal quantitation with PTU treatment as described above . ENU mutagenesis was performed as previously described [65] . Briefly , regularly bred 1-year old *AB strain adult zebrafish males were treated with 3 mM N-ethyl-N-nitrosourea at 20°C for 1 h once a week for 3 times . From five to seven weeks after the final treatment , mutagenized males were outcrossed to wild-type *AB females and F1 progeny were raised to adulthood . F1 mutagenized males were outcrossed with three wild-type *AB females to maximize the resulting clutch size . 50 embryos from the resultant clutches were then raised from 6 hpf to 6 dpf in 10% Hank's Saline with 0 . 003% 1-phenyl-2-thiourea ( PTU ) and 350 µM tert-butyl hydroperoxide ( BHP ) . The media was refreshed every 48 h . Embryos were processed for single-embryo SA-β-gal quantitation as described above . Clutches showing either obvious phenotypic abnormalities in 50% of the embryos , or having staining standard deviations of at least 1 . 5 times that of wild-type clutches in both of two independent breedings , were considered to be a positive hit . Positive hit F1 males were subsequently outcrossed with wild-type *AB females and the resulting F2 generation was raised to adulthood . F3 embryos derived from these F2 sibling incrosses in each hit family were assessed for their phenotype and SA-β-gal activity levels both in the presence and absence of oxidative stress . For the in vivo detection of cell death , live 2-day old embryos were incubated in 2 µg/ml acridine orange ( AO ) ( Sigma ) in embryo media in the dark for 30 min and washed three times for 5 min in fresh embryo media . Fluorescence was then observed under a 488 nm wavelength excitation . For in vivo ROS detection , live 2–4-day old embryos ( 2–4 dpf ) were incubated in 5 µM 2′ , 7′-dichlorofluorescein diacetate ( DCFH-DA ) ( Sigma ) for 20 min at 28 . 5°C and washed three times for 5 min with embryo media . Fluorescence was again observed under a 488 nm wavelength excitation . For Fluoro-Jade B histochemical analysis of 2 dpf embryos , adjacent sections were stained using the standard Fluoro-Jade staining procedure as described previously [66] . Embryos and adult tissue samples were fixed in 4% paraformaldehyde in PBS for 48 h at 4°C . Samples were dehydrated in ethanol and infiltrated in JB-4 resin following the manufacturer's instructions ( Polysciences Inc . ) . Specimens were then sectioned at 5 µm using a Jung Supercut 2065 microtome . Histological hematoxylin-eosin ( H&E ) staining of the sections was subsequently carried out using standard protocols . After digestion with diastase , periodic acid Schiff's ( PAS ) staining was performed as follows: sections were heat-adhered to slides at 70°C for 5 min and placed in the following solutions at room temperature; 1% periodic acid for 5 min , several water changes over 5 min , Schiff's reagent for 30 min , 0 . 5% sodium metabisulfite in 1% concentrated HCl 3×2 min , and several water changes for 10 min each .
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By performing genetic mutant screens using senescence-associated biomarkers , we show that the zebrafish is a tractable model system for the study of aging . In vertebrate organisms , it has not previously been possible to carry out systematic screens for genes that are important for stress responses and aging in an unbiased way . However , such vertebrate models are of considerable importance , given the provocative evidence of common biochemical and functional pathways modulating stress responses and lifespan as well as aging in a wide range of organisms . Our present study has successfully employed a colorimetric high-throughput method using a senescence-associated β-galactosidase-based assay to screen for mutations that alter the stress responses in zebrafish embryos , in the hope that these might represent potential aging mutants . Subsequently , the mutations identified by embryonic senescence have indeed displayed adult aging-related phenotypes in zebrafish . Hence , our method for the identification of mutant zebrafish has the immediate potential to accelerate the discovery of novel genes and new functions relevant for our understanding of aging processes in vertebrates . Such knowledge will be essential for the ultimate development of pharmacological , nutritional , and behavioral interventions for the amelioration of oxidative stress- and age-associated diseases and disabilities in humans .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"evolutionary",
"biology/animal",
"genetics",
"geriatrics/geriatric",
"ophthalmology",
"ophthalmology/retinal",
"disorders",
"developmental",
"biology/aging",
"genetics",
"and",
"genomics/disease",
"models"
] |
2008
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The Identification of Zebrafish Mutants Showing Alterations in Senescence-Associated Biomarkers
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Distinguishing arboviral infections from bacterial causes of febrile illness is of great importance for clinical management . The Infection Manager System ( IMS ) is a novel diagnostic algorithm equipped on a Sysmex hematology analyzer that evaluates the host response using novel techniques that quantify cellular activation and cell membrane composition . The aim of this study was to train and validate the IMS to differentiate between arboviral and common bacterial infections in Southeast Asia and compare its performance against C-reactive protein ( CRP ) and procalcitonin ( PCT ) . 600 adult Indonesian patients with acute febrile illness were enrolled in a prospective cohort study and analyzed using a structured diagnostic protocol . The IMS was first trained on the first 200 patients and subsequently validated using the complete cohort . A definite infectious etiology could be determined in 190 of 463 evaluable patients ( 41% ) , including 89 arboviral infections ( 81 dengue and 8 chikungunya ) , 94 bacterial infections ( 26 murine typhus , 16 salmonellosis , 6 leptospirosis and 46 cosmopolitan bacterial infections ) , 3 concomitant arboviral-bacterial infections , and 4 malaria infections . The IMS detected inflammation in all but two participants . The sensitivity , specificity , positive predictive value ( PPV ) , and negative predictive value ( NPV ) of the IMS for arboviral infections were 69 . 7% , 97 . 9% , 96 . 9% , and 77 . 3% , respectively , and for bacterial infections 77 . 7% , 93 . 3% , 92 . 4% , and 79 . 8% . Inflammation remained unclassified in 19 . 1% and 22 . 5% of patients with a proven bacterial or arboviral infection . When cases of unclassified inflammation were grouped in the bacterial etiology group , the NPV for bacterial infection was 95 . 5% . IMS performed comparable to CRP and outperformed PCT in this cohort . The IMS is an automated , easy to use , novel diagnostic tool that allows rapid differentiation between common causes of febrile illness in Southeast Asia .
Arboviruses and bacterial infections such as salmonellosis , leptospirosis , and rickettsiosis are common causes of acute febrile illness in tropical and subtropical countries [1–3] . Discriminating between these infections is of great importance to triage patients in need of antibiotics or monitoring for dengue complications . In daily practice , dengue and bacterial infections are often diagnosed on clinical grounds and many patients are prescribed antibiotics without laboratory confirmation of a bacterial infection . Confirmatory microbiological tests , including blood cultures , serology , molecular tests , and antigen- or antibody-based rapid tests are frequently unavailable and suffer from important diagnostic limitations . An alternative for pathogen-specific diagnostic tests is the assessment of the host immune response , using biomarkers such as C-reactive protein ( CRP ) or procalcitonin ( PCT ) [4 , 5] . Disease-specific changes in circulating blood cells may also be helpful , for example , leukopenia and thrombocytopenia support a diagnosis of dengue [6] . The discriminatory performance of cell numbers alone is , however , insufficient for clinical decision-making . A promising development is the ability to measure phenotypic changes in blood cells by automated hematology analyzers . For example , activated leukocytes contain more lipid rafts in their cell membrane and altered intracellular DNA/RNA levels [7] which can be quantified using specific reagents and distinct fluorescence patterns [8 , 9] . Based on the principle that different infections evoke different patterns in blood cell number and phenotype , a diagnostic algorithm called the Infection Manager System ( IMS ) , was developed for use on Sysmex hematology analyzers . The IMS indicates whether an inflammatory response is present and whether an arboviral , bacterial , or malarial origin is suspected . The aim of our present study was to enroll adult patients with common causes of undifferentiated fever in Southeast Asia in order to train and evaluate the diagnostic performance of the IMS for these infections , as well as to compare the diagnostic performance against CRP and PCT .
A prospective cohort study was conducted between July 2014 and February 2016 in three hospitals ( Hasan Sadikin University Hospital , Salamun General Hospital , and Cibabat General Hospital ) and two primary care outpatient clinics , all located in Greater Bandung , the capital of the West Java province in Indonesia . Patients aged 14 years and above presenting an acute febrile illness and clinical suspicion of an arboviral infection , salmonellosis , leptospirosis , rickettsiosis , or any other common bacterial infection were enrolled . Exclusion criteria included pregnancy and the suspicion of a chronic infection , such as tuberculosis or HIV , and severe concomitant conditions like dialysis , autoimmune diseases , or malignancies . The sample size of 600 individuals was based on the assumption that a proven or probable bacterial or arboviral infection could be diagnosed in 50% of enrolled patients and that enteric fever , leptospirosis , or rickettsiosis could be diagnosed in approximately 20% ( n = 30 ) of subjects with a proven or probable bacterial infection . To test how often the IMS flags an inflammatory response in healthy adults , the trained IMS was also tested in a cohort of healthy Dutch adults , derived from a well-established prospective population-based study , incorporating 13 , 432 individuals from the north of the Netherlands ( www . lifelines . nl ) . The first selection of patients was done by treating physicians at the participating health facilities on the basis of clinical features and routine additional examinations . Demographic data , medical history , physical examination , results of laboratory and radiology tests , and suspected diagnosis were recorded in a standardized electronic study case report form . All admitted patients were followed up three days after enrolment to evaluate the clinical picture and perform additional diagnostic tests on indication . A policlinic visit was planned with the same purpose between days 7–14 after enrolment day . Non-admitted patients were followed up twice: first appointment between 2–7 days after enrolment , a second appointment within one week thereafter . Fig 1 summarizes the study flow and diagnostic procedures . Blood was drawn at inclusion in all patients for immediate hemocytometry and microbiological testing . EDTA plasma , serum , and whole blood were stored at -80°C for additional microbiological tests . Initial microbiological tests were performed at the discretion of the treating physician . These included the performance of blood cultures in patients with a suspected bacterial sepsis or enteric fever , pus cultures in case of an abscess , and dengue NS1 rapid test or serological tests for suspected dengue , enteric fever , or leptospirosis . Radiological examinations such as a chest X-ray were performed on indication . Next , stored blood of all enrolled subjects was tested using the following diagnostic algorithm: dengue diagnostics were performed using a dengue NS1 antigen rapid diagnostic test ( RDT ) , and if negative , paired dengue IgM and IgG serology and dengue PCR . Furthermore , RDTs or serology were done on all samples for chikungunya IgM , Salmonella IgM ( Tubex® ) , and Leptospira IgM ( Panbio® ) . In case of a positive chikungunya IgM , Salmonella IgM score ≥4 or a positive Leptospira IgM , specific serum or whole blood PCRs for these pathogens were performed . The remaining cases without a proven diagnosis were tested for Rickettsia typhi IgM and IgG , followed by a specific R . typhi real-time PCR in case of a positive result . The following case definitions were used: a proven dengue virus infection was defined as: i ) positive result of NS1 RDT or dengue PCR , or ii ) seroconversion of anti-dengue IgM and/or IgG , or iii ) fourfold or greater increase of anti-dengue IgG titers in convalescent serum . Chikungunya or Leptospirosis were proven when the PCR was positive . Salmonellosis was proven when Salmonella spp . were isolated from blood culture or when the whole blood Salmonella PCR was positive . Murine typhus was proven when there was seroconversion or a four-fold increase in IgM or IgG R . typhi titer or a positive PCR on the buffy coat . A proven cosmopolitan bacterial infection was defined as isolation of a pathogenic pathogen from blood culture or other sterile location , or by a combination of clinical features and results of radiology , for example in case of pneumonia . Malaria was proven if Plasmodium parasites were detected on a blood smear . In case no proven diagnosis was obtained , two experienced clinicians ( AvdV and QdM ) graded the remainder of the cases as probable or possible arboviral or bacterial infection without any further sub-classification or as fever from unknown origin . Grading was done using all clinical data and additional investigations , but without results of IMS and CRP or PCT . Hemocytometry was done on EDTA blood within 4 hours using Sysmex XN-1000 , Sysmex XN-550 , and a regular Sysmex XE-5000 analyzer . Details of the performed microbiological tests and the CRP and PCT measurements are given in S1 Table . The IMS is based on novel techniques that quantify cellular activation and cell membrane composition using distinct fluorescence and surfactant reagents that target RNA , DNA , and bioactive lipids , respectively [8–10] . The IMS algorithm is given in S1 Fig . The IMS first flags whether an inflammatory response is detected and if so , whether it fits a bacterial , ( arbo ) viral , or malarial origin or cannot be classified and designated as an unspecified inflammatory response . When no inflammatory reaction is noticed , no message is given . The sponsor was not involved in data acquisition , including results of hemocytometry or microbiological assays . Employees of the sponsor were involved in the training of the IMS algorithm using the first 200 enrolled cases with the goal to further optimize the IMS performance . For this training , the sponsor had access to clinical information , results from microbiology and radiology examinations , and the tentative cause of the febrile illness as classified by the clinical study team . Results of PCRs and CRP/PCT were not yet available at that time . Next , the final version of the IMS was tested on all evaluable cases with employees of the sponsor classifying all enrolled patients into: no sign of inflammation , or suspected arboviral , bacterial , malarial , or unspecified inflammation . For this classification , the sponsor was blinded to all clinical data , results from additional tests and the final classification by the study team of the cause of the febrile illness . Whereas the IMS classification was performed by the sponsor in this feasibility study , the intention is to create an analyzer that directly reports the IMS classification after measurement of the blood sample without requiring data to be sent to another site for analysis . For CRP and PCT the following cut-off levels were evaluated in predicting a bacterial etiology of fever: for CRP >20 mg/L and >40 mg/L and for PCT >0 . 5 ng/mL and >2 . 0 ng/mL plasma levels upon admission , respectively [2] . For additional analyses , a special group named ‘antibiotics’ , was created , containing individuals who were flagged as either bacterial or unspecified inflammation by the IMS , as antibiotics may be indicated in these cases . Patients with a proven concomitant arboviral-bacterial infection were also classified as bacterial infection . Descriptive statistics were conducted for all variables . Differences in hematology parameters between groups were analyzed using Wilcoxon rank sum test in case of two groups and Kruskal-Wallis test in case of more than two groups . All statistical analyses were performed using R ( R Core Team ( 2016 ) ) . All procedures followed were in accordance with the ethical standards of the Helsinki Declaration . All study participants provided written informed consent . In patients aged 14–18 years , a parent or guardian provided informed consent with written assent by the child . The study protocol was approved by the Ethics Committee of Hasan Sadikin General Hospital ( LB . 02 . 01/C02/515/I/2015 , LB . 02 . 01/C02/2352/II/2016 ) .
A total of 600 patients were enrolled . A total number of 137 patients were subsequently excluded because of missing data , mostly because of insufficient follow-up while no proven diagnosis was made . From the remaining 463 subjects , 342 patients could be classified as having a proven , probable , or possible arboviral , bacterial , combined arboviral-bacterial , or malaria infection ( Fig 1 ) . A total number of 89 individuals had a proven arboviral infection: 81 cases with dengue , based on a positive result of a dengue NS1 antigen test ( n = 68 ) , IgM dengue seroconversion ( n = 9 ) , or dengue PCR ( n = 4 ) and eight cases with chikungunya . Three patients with IgM dengue seroconversion also had a bacteremia ( two Salmonella spp . and one Staphylococcus aureus ) . A total of 94 patients had a proven bacterial infection: murine typhus ( n = 26 ) , salmonellosis ( n = 16 ) , leptospirosis ( n = 6 ) and cosmopolitan bacterial infections , including bacteremia ( n = 13 ) , community-acquired pneumonia ( n = 15 ) , skin or soft tissue infection ( n = 11 ) , urinary tract infection ( n = 5 ) and single cases of puerperal infection and peritonitis . A total number of 121 patients were classified as unknown origin of infection . Baseline characteristics of participants with a proven infection are summarized in Table 1; characteristics of participants with proven or probable infections are given in S2 Table . In total , 82% of the enrolled patients were hospitalized and ten patients died during hospitalization , all from the proven bacterial group . Fig 2 and Fig 3 show the results of a selection of novel leukocyte parameters per infection or aggregated in arboviral or bacterial infections . Whereas there was a large overlap in the number of activated neutrophils ( Neut-RI ) and monocytes ( Re-Mono ) across the different infections , dengue was characterized by a marked increase in AS-Lymph and Re-Lymph , which are considered to represent plasma cells and reactive lymphocytes , respectively . In contrast , chikungunya was not associated with increased AS-Lymph or Re-Lymph . Participants with the intracellular bacterial infections salmonellosis and murine typhus also had significantly higher Re-Lymph than those with other bacterial infections ( salmonellosis vs . leptospirosis P = 0 . 006; salmonellosis vs . cosmopolitan bacterial infection P< 0 . 0001; murine typhus vs . leptospirosis P = 0 . 007; murine typhus vs . cosmopolitan bacterial infections P< 0 . 0001 ) . Table 2 summarizes the diagnostic performance of the IMS . An inflammatory response was flagged in all but two cases; one case of dengue in whom the dengue diagnosis was based on IgM seroconversion , and one patient with salmonellosis . Overall , the sensitivity , specificity , positive predictive value ( PPV ) , and negative predictive value ( NPV ) of the IMS for arboviral infections were 69 . 7% , 97 . 9% , 96 . 9% and 77 . 3% , respectively , and for bacterial infections 77 . 7% , 93 . 3% , 92 . 4% and 79 . 8% . Inflammation remained unclassified in 19 . 1% and 22 . 5% of patients with a proven bacterial or arboviral infection , respectively . Importantly , six out of seven ( 86% ) cases with proven chikungunya were classified as unspecified inflammation . Similarly , a relatively high proportion of cases with murine typhus were either classified as unspecified inflammation ( 27% ) or arboviral inflammation ( 8% ) . None of the other proven or probable bacterial infections were classified as arboviral . The three cases with a combined arboviral-bacterial infection were all flagged as bacterial infection . One of four malaria cases was not correctly flagged as being malaria . Fig 4A shows CRP and PCT plasma levels at study enrolment per infection , and Fig 4B provides these levels for cases aggregated in proven or proven/probable bacterial or arboviral etiology . In the proven cases , a bacterial etiology was associated with significantly higher CRP and PCT levels than a proven arboviral etiology with median ( IQR ) CRP levels of 110mg/L ( 52-192mg/L ) vs . 11mg/L ( 5-23mg/L; P<0 . 0001 ) and PCT levels of 2 . 6ng/mL ( 0 . 8–7 . 5ng/mL ) and 0 . 4ng/mL ( 0 . 2–0 . 7ng/mL; P<0 . 0001 ) , respectively ( Table 1 and Fig 4A ) . Table 3 summarizes the diagnostic performance of the IMS compared with CRP and PCT . A special category , named ‘antibiotics’ , was created for the IMS result , containing individuals who were flagged as either bacterial or unspecified inflammation by the IMS , as antibiotics may be indicated in these . In total , 88% and 84% of bacterial cases had CRP levels above the pre-defined cut-offs of >20mg/L or >40mg/L , respectively , whereas 81% and 54% had PCT levels >0 . 5ng/mL or >2 . 0ng/mL , respectively . For the arboviral group , 72% and 91% of cases had CRP levels below these cut-offs and 55% and 93% PCT levels below these cut-offs , respectively . The optimal CRP plasma level cut-off to distinguish between a bacterial and viral etiology was 36 . 6 mg/L ( sensitivity 85 . 1% with specificity 91 . 0%; area under the receiver operating characteristic ( ROC ) curve 0 . 92 ) and for PCT 0 . 96ng/mL ( sensitivity 72 . 3%; specificity 83 . 1%; area under the ROC curve 0 . 81 ) . Overall , CRP with a cut-off of 40mg/L had a somewhat higher sensitivity for bacterial infections than the IMS with a somewhat lower specificity . Using the ‘antibiotics’ classification in IMS shifted the balance to a higher sensitivity and higher NPV , but lower specificity compared with CRP . PCT performed less well than either the IMS or CRP . Finally , we determined how frequently the IMS flags an inflammatory response in healthy individuals . A total of 13 , 432 Dutch subjects were available from the lifelines cohort that had no sign or symptoms of illness or abnormality on routine laboratory examination and in whom IMS data were accessible as well . The IMS indicated an unspecified inflammatory response in five participants .
The main finding of the present study is that a novel diagnostic algorithm operating on an automated Sysmex hematology analyzer , called the IMS , is capable of confirming the presence of an infection in Indonesian adults presenting with an acute febrile illness and discriminate arboviral from bacterial infections . The IMS is based on the principle that pathogens induce specific changes in the number and phenotype of circulating blood cells and that these changes can differentiate viral from bacterial infections . The idea that algorithms incorporating novel blood count parameters may be used as decision tools for antibiotic therapy is supported by recent studies in febrile children [9] and ICU patients [11 , 12] . In resource-limited countries , costly and expertise-reliant diagnostic assays cannot be performed routinely . The IMS has the advantage that it operates on a standard hematology analyzer with results being available within a few minutes at an affordable price . In health facilities with a hematology analyzer , the IMS holds promise as an alternative for pathogen-specific RDTs or host biomarker tests , and as a tool for a more targeted use of pathogen-specific diagnostic assays . In addition , in patients with dengue , daily hemocytometry is advised to monitor platelet and leukocyte counts . This offers a unique opportunity to combine diagnostics with clinical monitoring . The arboviral group in our study mainly comprised of dengue cases . Dengue is the most common arboviral infection with more than one third of the world's population living in areas at risk for infection [13] . Dengue was characterized by increases in antibody synthesizing ( AS-Lymph ) and reactive lymphocytes ( Re-Lymph ) , in combination with thrombocytopenia and a high immature platelet fraction . Polyclonal plasmacytosis has previously been reported to be a feature of dengue infections [14 , 15] . In chikungunya cases , elevations in AS-Lymph and Re-Lymph were not observed and 86% of chikungunya infections were classified as ‘unspecified inflammation’ . The diagnostic performance of the IMS for viral infections other than dengue , including common respiratory infections and other arboviruses such as Zika , therefore awaits to be determined . Bacterial infections were also aggregated into one group because of relatively low numbers per group . Interestingly , Salmonella spp . and R . typhi are intracellular growing bacteria and infections with these pathogens elicited a distinct pattern with a significantly higher Re-Lymph . Therefore , our data suggest that the IMS also has the potential to differentiate among specific subtypes of bacterial infections . IMS classified a substantial number of infections as ‘unspecified’ inflammation . Because antimicrobial therapy may still be warranted in conditions flagged as unspecified inflammation , a category ‘antibiotics’ was created . The NPV of the IMS for the ‘antibiotics’ category was high ( 95 . 5% ) , suggesting that the IMS holds promise to improve the correct use of antibiotics as well as antimicrobial stewardship in these settings . Dengue-bacterial co-infections are probably underestimated and withholding antibiotics may have severe consequences [16] . Fortunately , in the three patients with a proven double infection in our study , the IMS scored all as bacterial infections . The IMS can also provide an indication on the presence of malaria , but novel techniques using laser technologies and reagents specifically designed for malaria detection using Sysmex analyzers are currently under clinical evaluation ( ClinicalTrials . gov Identifier: NCT02669823 ) . Overall , the trained IMS performed comparable to CRP with the latter having a slightly higher sensitivity but lower specificity to diagnose bacterial infections . Including cases with unclassified inflammation in the bacterial etiology group ( ‘antibiotics’ category ) , the balance shifted to a higher sensitivity , but lower specificity . Cut-offs for clinical decision making depend on the clinical setting . So far , only a few studies have reported CRP or PCT levels in tropical infections [2 , 17] . Our findings are comparable to those by Wangrangsimakul et al . who also found a CRP level of 36mg/L as the optimal cut-off level to distinguish between bacterial and viral causes of undifferentiated fever in Thailand [2] . We enrolled patients suspected of having specific infections that are very common throughout much of Southeast Asia ( e . g . dengue , enteric fever , leptospirosis , murine typhus ) and our findings are therefore most likely applicable to areas outside Indonesia . The performance of the IMS in areas with a different infection epidemiology is currently unknown . Results of a diagnostic study investigating the performance of the IMS in Sub-Saharan Africa are expected in the coming year ( ClinicalTrials . gov , NCT02669823 ) . The IMS software operates on routine hematology analyzers ( Sysmex XN series ) and results are provided within one minute . The costs associated with the assay are expected to be in the range of a regular full blood count . A full blood count is among the most commonly performed laboratory tests–also in resource-poor areas in Asia–and introduction of the IMS algorithm is especially promising for the workup of febrile patients in larger healthcare facilities where hemocytometry analyzers are already in routine use , but which lack facilities for more specialized microbiological assays . Limitations of the present study are that proof of infection , using microbiology or imaging studies , was obtained in only 35% of cases . Our results do not however differ very much from other similar studies in low-income settings [18 , 19] . Secondly , we used stringent microbiological criteria . Despite our efforts to include as much ‘tropical’ infections as possible , the total number of proven tropical bacterial infections remained limited . In line with other studies , we also found that murine typhus is an important and often unrecognized infection [2 , 20 , 21] . Thirdly , our study did not include consecutive febrile patients , but limited selection to those patients suspected of having a specific type of infection in order to train the IMS algorithm . This , together with the stringent microbiological criteria , may have led to selection bias , e . g . dengue patients of whom the majority had a positive NS1 antigen test . Confirmatory validation studies enrolling consecutive febrile patients are therefore required . Lastly , a cohort of healthy Dutch instead of Indonesian individuals was used to test how frequently the trained IMS indicates inflammation in absence of an infection . Inclusion of a large control population from the same demography would have been preferred , because factors such as ethnicity and living conditions may influence hematological reference ranges . Nonetheless , earlier data showed that reference ranges on Sysmex analyzers in a Dutch and Asian ( Indian ) population of healthy adults were fairly similar [22 , 23] , suggesting that important differences in IMS performance are not expected . Age-related differences in reference ranges are bigger , especially between children below the age of six years and adults . Our study did not include children and it is important to emphasize that the IMS first needs validation in children as well as other healthy and patient populations in other areas before it can be introduced on a routine basis . In conclusion , the IMS is a promising novel diagnostic algorithm that can be equipped on a standard hematology analyzer and can be used to triage patients in need of antibiotics or monitoring for dengue complications .
|
Distinguishing arboviral infections , such as dengue , from bacterial causes of febrile illness is of great importance for clinical management and antimicrobial stewardship . In resource-limited countries , costly and expertise-reliant diagnostic assays cannot be performed routinely . The Infection Manager Software ( IMS ) is a novel diagnostic algorithm equipped on an automated Sysmex hematology analyzer , making use of the principle that different infections evoke different changes in blood cell number and cell phenotype . In a cohort of adult Indonesian patients presenting to hospital with an arboviral and/or bacterial infection , we first trained and subsequently evaluated the diagnostic performance of the IMS to distinguish common causes of acute febrile illness . The authors show that the IMS has a reasonable sensitivity for detection of arboviral and bacterial infections and a high specificity . In comparison with the commonly used biomarkers C-reactive protein ( CRP ) and procalcitonin ( PCT ) , the performance of the IMS was comparable to CRP and better than PCT . The authors conclude that the IMS is a novel , automated , easy to use diagnostic tool that allows rapid differentiation between common causes of febrile illness in Southeast Asia .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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"antimicrobials",
"medicine",
"and",
"health",
"sciences",
"pathology",
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"chikungunya",
"infection",
"drugs",
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"tropical",
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"salmonellosis",
"bacterial",
"diseases",
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"malaria"
] |
2019
|
A novel diagnostic algorithm equipped on an automated hematology analyzer to differentiate between common causes of febrile illness in Southeast Asia
|
We review the leaky competing accumulator model for two-alternative forced-choice decisions with cued responses , and propose extensions to account for the influence of unequal rewards . Assuming that stimulus information is integrated until the cue to respond arrives and that firing rates of stimulus-selective neurons remain well within physiological bounds , the model reduces to an Ornstein-Uhlenbeck ( OU ) process that yields explicit expressions for the psychometric function that describes accuracy . From these we compute strategies that optimize the rewards expected over blocks of trials administered with mixed difficulty and reward contingencies . The psychometric function is characterized by two parameters: its midpoint slope , which quantifies a subject's ability to extract signal from noise , and its shift , which measures the bias applied to account for unequal rewards . We fit these to data from two monkeys performing the moving dots task with mixed coherences and reward schedules . We find that their behaviors averaged over multiple sessions are close to optimal , with shifts erring in the direction of smaller penalties . We propose two methods for biasing the OU process to produce such shifts .
There is increasing evidence from in vivo recordings in monkeys that oculomotor decision making in the brain mimics a drift-diffusion ( DD ) process , with neural activity rising to a threshold before movement initiation [1]–[4] . In one well-studied task , monkeys are trained to decide the direction of motion of a field of randomly moving dots , a fraction of which move coherently in one of two possible target directions ( T1 or T2 ) , and to indicate their choice with a saccadic eye movement [5]–[7] . Varying the coherence level modulates the task difficulty , thereby influencing accuracy . This paper addresses ongoing experiments on the motion discrimination task , but unlike most previous studies in which correct choices of either alternative are equally rewarded , the experiment is run under four conditions . Rewards may be high for both alternatives , low for both , high for T1 and low for T2 , or low for T1 and high for T2 . This design allows us to study the interaction between bottom-up ( stimulus driven ) and top-down ( expectation driven ) influences in a simple decision process . A second distinction with much previous work is that reponses are delivered following a cue , rather than given freely . We idealize this as an interrogation protocol ( cf . [8] ) , in which accumulated information is assessed at the time of the cue rather than when it passes a threshold , and we model the accumulation by an Ornstein-Uhlenbeck ( OU ) process . Closely related work on human decision making is reported in [9] , [10] . Consistent with random walk and diffusion processes [4] , [11]–[15] , neural activity in brain areas involved in preparing eye movements , including the lateral intraparietal area ( LIP ) , frontal eye field and superior colliculus [7] , [16]–[18] , exhibits an accumulation over time of the motion evidence represented in the middle temporal area ( MT ) of extrastriate visual cortex . Under free response conditions , firing rates in area LIP reach a threshold level just prior to the saccade [19] . Further strengthening the connection , it has recently been shown that models of LIP using heterogeneous pools of spiking neurons can reproduce key features of this accumulation process [20] , [21] , and that the averaged activities of sub-populations selective for the target directions behave much like the two units of the leaky competing accumulator ( LCA ) model of Usher and McClelland [22] . In turn , under suitable constraints , the LCA can be reduced to a one-dimensional OU process: a generalization of the simpler DD process [8] , [23] , [24] . This allows us to obtain explicit expressions for psychometric functions ( PMFs ) that describe accuracy in terms of model and experimental parameters , and to predict how they should be shifted to maximize expected returns in case of unequal rewards . The goals of this work are to show that PMFs derived from the OU model describe animal data well , that they can accommodate reward information and allow optimal performance to be predicted analytically , and finally , to compare animal behaviors with those predictions . Analyzing data from two monkeys , we find that , when faced with unequal rewards , both animals bias their PMFs in the appropriate directions , but by amounts larger than the optimal shifts . However , in doing so they respectively sacrifice less than 1% and 2% of their expected maximum rewards , for all coherence conditions , based on their signal-discrimination abilities ( sensitivities ) , averaged over all session of trials . They achieve this in spite of significant variability from session to session , across which the parameters that describe their sensitivity to stimuli and reward biases show little correlation with the relationships that optimality theory predicts . This paper extends a recent study that describes fits of behavioral data from monkeys learning the moving dots task , which also shows that DD and OU processes can provide good descriptions of psychometric functions ( PMFs ) [25] . A related study of humans and mice performing a task that requires time estimation [26] shows that those subjects also approached optimal behavior . The paper is organised as follows . After reviewing experimental procedures in the Methods section , we describe the LCA model and its reduction to OU and DD processes , propose simple models for the influence of biased rewards , and display examples of the resulting psychometric functions . The Results section contains the optimality analysis , followed by fits of the theory to data from two animals and assessments of their performances . A discussion closes the paper .
To motivate the theoretical developments that follow , we start by briefly describing the experiment . More details will be provided , along with reports of electrophysiological data , in a subsequent publication . We now describe a simple model for two-alternative forced-choice ( 2AFC ) tasks . Several other models are reviewed in [8] , along with the relations among them and conditions under which they can be reduced to OU and DD processes . The model yields explicit expressions that predict psychometric functions and that reveal how these functions depend upon parameters describing the stimulus discriminability and reward priors . While optimality analyses can be conducted using fitted PMFs such as sigmoidal functions , our derivation links the behavioral data to underlying neural mechanisms .
Given a fixed slope , we now ask what is the shift in the PMF that maximizes expected rewards in the case that the two alternatives are unequally rewarded . How much should the subject weight the reward information relative to that in the stimulus , in order to make optimal use of both ? Here we perform fits of accuracy data collected for a discrete set of coherences , namely , under the four reward schedules described under Experimental paradigm . As noted there , T was not tested with the lowest coherences and ±3% . Data from the two monkeys ( A and T ) are analyzed separately . While each coherence is presented with equal probability , their spacing increases with , so that the majority of trials occurs in the center of the range around , unlike the case of uniformly-distributed coherences . This will play a subtle role when we compare optimal shifts for the two animals .
We reduce a leaky competing accumulator model to an Ornstein-Uhlenbeck ( OU ) process , and therefrom derive a cumulative normal psychometric function ( PMF ) that describes how accuracy depends upon coherence ( signal-to-noise ratio ) in a two-alternative forced-choice task with cued responses . The key parameters in the PMF are its slope at 50% accuracy , which quantifies a subject's sensitivity to the stimulus , and its shift: the coherence at which 50% accuracy is realised . We compute analytical expressions describing optimal shifts that maximize expected rewards for given slopes and reward ratios . We find that this PMF can fit behavioral data from two monkeys performing a motion discrimination task remarkably well . The resulting slopes and shifts show that , faced with mixed coherences , while both animals “overshift” for unequal rewards , they nonetheless garner 98–99% of their maximum possible rewards ( Figure 8 ) , and they achieve this in spite of significant variability in sensitivity and shifts from session to session . The linear OU process has the advantages of simplicity and it yields an explicit expression for the PMF , but it only approximates the dynamics of the decision process . Nonlinear drift-diffusion processes can also be derived from multi-dimensional models containing individual spiking neurons or neural pools [21] , [41] , but the Kolmogorov equations analogous to Eq . ( 6 ) cannot generally be solved and explicit expressions for PMFs are not available . Such more accurate models ( with additional parameters ) might provide better fits to data than the cumulative normal of Eq . ( 11 ) , although the free response data presented in [41] indicates that there is little difference between linear and nonlinear models in fit quality per se . Nonlinear models do , however , better represent limiting neural behavior at high and low spike rates . We also propose two simple methods by which the OU process could be biased by reward expectations , in order to produce such shifts . The first requires a biased starting point for evidence accumulation , the second assumes a continuing bias to the drift rate that enters the OU process prior to and throughout the stimulus viewing period . In the free response case , with blocked trials and fixed coherence in each block , it is known that the former is optimal [8] , and recent experiments focusing on stimulus proportions confirm that well-practiced human subjects do approximate this [49] . As described under Models of stimuli and reward biasing , the fixed viewing time experiment employed here cannot distinguish among these or other biasing models . Responses gathered for different reward cue and motion periods would enable such distinctions; cf . [25] . Accumulator models have also been proposed for working memory following stimulus offset ( e . g . see [50] for a somatosensory comparison task ) . Addition of such a model and analysis of electrophysiological data throughout the trial , including the variable delay period , may further illuminate the biasing mechanism . Our optimality analysis presumes that the PMF slope ( ) has an upper bound that reflects fundamental limits on sensitivity to the visual stimulus . We then seek the unique shift ( ) that maximizes expected rewards over the given coherence and reward conditions , for a fixed slope . This makes for a well-posed mathematical analysis , but it does not imply that the animal is faced with a given sensitivity and then “chooses” a shift . He might equally well choose a shift and then “accept” a sensitivity that delivers adequate rewards , perhaps by implicitly selecting a weight for the top-down reward information , and then relaxing attention to the stimuli until his reward rate reaches a predetermined level . He may even co-vary these parameters to achieve the same end . This is reminiscent of a robust-satisficing strategy that has been studied in connection with setting speed-accuracy tradeoffs [51] . A related study of optimal decision strategies in two-alternative forced-choice tasks with free responses has shown that decision thresholds can be determined for a pure drift diffusion process that optimize reward rate by setting a speed-accuracy tradeoff [8] . In that work it is necessary to assume that trials are blocked ( e . g . with equal coherences ) , so that conditions remain statistically stationary during each session and one can appeal to optimality of the DD process [43] . In contrast , for cued responses only the accuracy level need be maximized , one need not assume a pure DD process , and optimization can be done in the face of mixed coherences and mixed reward contingencies . As the theory developed above shows , reduction to a one-dimensional process permits explicit calculations of PMFs and optimality conditions , and comparison with data requires only simple two parameter fits . However , the present behavioral data lacks the reaction time distributions that allow fits that could distinguish among multiparamater variants of DD and OU models [15] , [22] , [52] , [53] . We have taken as a utility function the ( normalised ) value of expected rewards , implicitly assuming that two drops of juice are worth twice one drop . Subjective utility may not vary linearly with reward size: for example , at high reward ratios it may rise more slowly and saturate due to satiety . In contrast , if we suppose that two drops of juice are worth 2 . 5 or 3 times as much as one drop , then the shifts of both animals would lie much closer to the optimal curves of Figure 6 ( translate the HL data points horizontally from to 2 . 5 or 3 , and the LH data points from to 0 . 4 or 0 . 33 ) . However , a study of subjective value quantification would require investigation of a broad range of reward ratios . The behavioral data analyzed here were obtained simultaneously with electrophysiological recordings from single neurons in the lateral intraparietal area ( LIP ) of the cerebral cortex , a region that is thought to play a key role in the formation of oculomotor decisions within the central nervous system [7] , [19] , [34] . The results presented in this paper raise important questions for our ongoing analysis of the neurophysiological data . Do decision-related neurons in LIP encode or at least reflect effects of both the reward prior and the coherence of the visual stimuli ? Are the two effects present in the same proportions at the neural level as at the behavioral level ( as quantified in the present paper ) ? Is the effect of reward bias evident as an offset at the start of accumulation of motion information by LIP neurons , or as a gain factor on the accumulation process , or both ? These questions will be addressed in a future publication integrating neurophysiological data with the behavioral results .
|
Decisions are commonly based on multiple sources of information . In a forced choice task , for example , sensory information about the identity of a stimulus may be combined with prior information about the amount of reward associated with each choice . We employed a well-characterized motion discrimination task to examine how animals combine such sources of information and whether they weigh these components so as to harvest rewards optimally . Two monkeys discriminated the direction of motion in a family of noisy random dot stimuli . The animals were informed before each trial whether reward outcomes were equal or unequal for the two alternatives , and if unequal , which alternative promised the larger reward . Predictably , choices were biased toward the larger reward in the unequal reward conditions . We develop a decision-making model that describes the animals' sensitivities to the visual stimulus and permits us to calculate the choice bias that yields optimal reward harvesting . We find that the monkeys' performance is close to optimal; remarkably , the animals garner 98%+ of their maximum possible rewards . This study adds to the growing evidence that animal foraging behavior can approach optimality and provides a rigorous theoretical basis for understanding the computations underlying optimality in this and related tasks .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"neuroscience/behavioral",
"neuroscience",
"mathematics",
"neuroscience/cognitive",
"neuroscience",
"neuroscience/theoretical",
"neuroscience"
] |
2009
|
Can Monkeys Choose Optimally When Faced with Noisy Stimuli and Unequal Rewards?
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Filariae are tissue-invasive nematodes that cause diseases such as elephantiasis and river blindness . The goal of this study was to characterize the role of histamine during Litomosoides sigmodontis infection of BALB/c mice , a murine model of filariasis . Time course studies demonstrated that while expression of histidine decarboxylase mRNA increases throughout 12 weeks of infection , serum levels of histamine exhibit two peaks—one 30 minutes after primary infection and one 8 weeks later . Interestingly , mice treated with fexofenadine , a histamine receptor 1 inhibitor , demonstrated significantly reduced worm burden in infected mice compared to untreated infected controls . Although fexofenadine-treated mice had decreased antigen-specific IgE levels as well as lower splenocyte IL-5 and IFNγ production , they exhibited a greater than fourfold rise in eosinophil numbers at the tissue site where adult L . sigmodontis worms reside . Fexofenadine-mediated clearance of L . sigmodontis worms was dependent on host eosinophils , as fexofenadine did not decrease worm burdens in eosinophil-deficient dblGATA mice . These findings suggest that histamine release induced by tissue invasive helminths may aid parasite survival by diminishing eosinophilic responses . Further , these results raise the possibility that combining H1 receptor inhibitors with current anthelmintics may improve treatment efficacy for filariae and other tissue-invasive helminths .
Filariae are vector-borne tissue-invasive nematodes that infect over 100 million people worldwide and cause the debilitating conditions of river blindness and elephantiasis [1] . A major obstacle to ongoing efforts to control and potentially eradicate these diseases is the limited ability of anti-filarial drugs to kill adult worms , especially when given as single dose treatments . One of the fairly unique aspects of helminth infections , in contrast to infection with most other pathogens , is the induction of histamine release in response to the parasites . Like other helminths , filariae induce the production of antigen-specific IgE , which then sensitizes basophils and mast cells to release histamine in response to parasite antigens . Histamine ( 2-[4-imidazolyl]ethylamine ) is a short-acting biogenic amine that , in addition to having potent acute inflammatory properties , also has numerous immunomodulatory effects on chronic inflammation [2] . Histamine is synthesized by the enzyme histamine decarboxylase ( HDC ) and is either stored in cytoplasmic granules in basophils and mast cells or is immediately released into the periphery [3] . Histamine release from both basophils and mast cells in response to parasite antigen has been observed in numerous studies of helminth infections [2 , 4–8] . Although sensitivity to parasite antigens is primarily dependent on parasite-specific IgE [9] , several helminths can also induce histamine release in the absence of parasite-specific IgE [10] . In this study we investigated the role histamine plays in the immune response to filariae and the effect antihistamine therapy has on filarial worm burdens . Using the Litomosoides sigmodontis/mouse model we observed that administration of fexofenadine , a histamine receptor 1 antagonist ( HR1i ) , reduces adult worm numbers by over 50% . Additionally , clearance of adult worms in HR1i treated mice was found to be primarily eosinophil dependent , as HR1i administration did not enhance worm clearance in eosinophil-deficient infected mice .
All experiments were performed under protocols approved by the Uniformed Services University Institutional Animal Care and Use Committee . Female BALB/c ( NCI , Frederick , MD ) , and BALB/c eosinophil deficient ( ΔdblGATA mice , The Jackson Laboratory , Bar Harbor ME ) , were maintained at the Uniformed Services University with free access to food and water . At study endpoints , all animals were euthanized using carbon dioxide followed by cervical dislocation . Blood was collected by cardiac puncture . For Litomosoides sigmodontis infection , L3-stage larvae ( L3s ) were obtained from infected jirds ( Meriones unguiculatus , TRS labs , Atlanta , GA ) by pleural lavage with RPMI 1640 . 40 L3s were collected and injected subcutaneously ( dorsal neck ) into 6–10 week old mice as previously described [11] . For microfilarial counts , 30 μl of blood was taken and mixed with 1 mL ACK lysing buffer ( Quality Biological ) . To count microfilarial numbers , the lysate was pelleted and assessed for counts microscopically . Fexofenadine HCl , an HR1 antagonist ( HR1i ) , was dissolved in the drinking water at a concentration of 0 . 25 mg/ml for an average daily dosage of 20mg/kg/day . Cimetidine ( Sigma-Aldrich ) , an HR2 antagonist , was prepared by dissolving in hydrochloric acid ( HCl ) and mixed with water . The pH was then adjusted to 7 . 0 with sodium hydroxide ( NaOH ) . The final concentration of cimetidine was 2 . 5 mg/ml in drinking water , for an average daily dosage of 200 mg/kg/day . Drinking water bottles containing antihistamines were changed every other day . Antihistamine activity was confirmed by testing stomach pH at time of euthanasia ( for HR1 antagonists ) and by local anaphylaxis in response to a direct histamine challenge ( for HR2 antagonists ) . Blood was collected at different time points in heparinized plasma separator microfuge tubes ( Starstedt , Nümbrecht , Germany ) . Samples were centrifuged at 15 , 000 X g for 1 . 5 minutes . Histamine in plasma was detected using a commercially available histamine ELISA assay according to the manufacturer’s instructions ( Beckman-Coulter ) . Adult worms were collected from the pleural cavity of infected animals at 8 weeks post infection . Adult worms were fixed overnight in 4% paraformaldehyde and were washed in 70% ethanol prior to histological processing by Histoserv , Inc ( Rockville , MD ) . In brief , the fixed tissue was dehydrated through graded alcohols , cleared in xylene and infiltrated with paraffin . The processed tissue was then embedded in paraffin and sectioned on a microtome at 5 microns . The slides were then deparaffinized in xylene , hydrated through graded alcohols to water then stained with Carazzi’s hematoxylin . Following a water rinse , they were stained with eosin and dehydrated with graded alcohols . The slides were then cleared using xylene and coverslipped with permount . RNA from whole blood was isolated according to the manufacturer’s instructions ( Ambion , Mouse Whole Blood RNA isolation ) . cDNA synthesis was performed using random primers according to the manufacturer’s instructions ( iScript cDNA synthesis kit , BioRad ) . RT-PCR was performed using a murine histidine decarboxylase ( HDC ) gene expression assay following manufacturer’s instructions . Samples were analyzed using an Applied Biosystems 7500 Real-Time PCR system and results calculated as fold change relative to an endogenous 18s rRNA control using the 2-ΔΔ CT method . L . sigmodontis worm antigen ( LsAg ) was prepared as previously described [12] . L3 stage larvae were collected from infected jirds as previously described . For in vitro survival assays , 200 L3s were cultured in 5ml of RPMI 1640 medium supplemented with gentamicin . Cultures were supplemented with 200mM histamine or 1mM Fexofenadine HCl and observed daily for mobility to assess survival . To assess eosinophil numbers at site of adult worm infection , pleural cells were collected by pleural lavage . Red blood cells were lysed using ImmunoLyse kit ( Beckman Coulter ) and then 2 . 0x106cells/mL were permeabilized with BD Permeablization/Wash buffer ( BD Biosciences ) . For analysis , cells were blocked using CD16/CD32 ( soluble FcεR III/II receptor , BD Pharmingen ) and stained for flow cytometry using anti-SiglecF PE , anti-CD11c APC and anti-CD45 FITC ( all from BD Pharmingen ) . Flow cytometry was performed using a BD LSR II system and analyzed with FACSDiVa 6 . 1 software ( BD Biosciences ) . Antibodies for all flow cytometry experiments were titrated prior to use . During analysis , cut-offs for CD45 positivity and Siglec F positivity were determined using the fluorescence minus one approach . To assess levels of eosinophil peroxidase at the site of adult worm infection , pleural fluid was collected by pleural lavage using 1 mL of sterile RMPI . EPO in lavage fluid was assessed by ELISA according to manufacturers instructions ( US Biological Life Sciences ) . Statistical analysis was performed using GraphPad Prism software ( GraphPad Software , San Diego , Ca ) . To determine differences between multiple groups , analysis was performed using Kruskal-Wallis test followed by Dunn multiple comparisons . To determine differences between two un-paired groups , Mann-Whitney analysis was performed . A p value of <0 . 05 was considered significant .
To determine if infection with L . sigmodontis results in detectable histamine release , BALB/c mice were infected with 40 L3 stage larvae by subcutaneous injection and circulating histamine levels assessed at 30 minutes , 1 , 4 , 8 and 12 weeks by competitive ELISA . In this model L3 larvae migrate to the pleural space where they mature to adult worms , start releasing blood-dwelling microfilariae by 7 weeks , and survive for 12–20 weeks . There was a significant peak of circulating histamine observed 30 minutes after injection of L3s and a second , higher peak correlating with the production of microfilariae at 8 weeks of infection ( Fig 1A , p < 0 . 01 for both timepoints when compared to age-matched uninfected controls ) . Of note , histamine was not detected in the blood of mice 30 minutes after injection of vehicle ( sterile RPMI ) or peritoneal lavage fluid ( S1 Fig ) . Using RT-PCR we determined the levels of histidine decarboxylase ( HDC ) from whole blood RNA . In contrast to histamine , blood levels of HDC mRNA increased throughout the 12 weeks , indicating that histamine may be continually synthesized during infection ( Fig 1B ) . Using ELISA , we determined circulating levels of Ls-specific IgE . Ls-specific IgE levels became detectable after 4 weeks of infection ( Fig 1C ) . Development of detectable Ls-specific IgE thus preceded peak histamine levels in the plasma , suggesting peak histamine release may be due to IgE-mediated basophil and mast cell activation . In contrast , the immediate early release of histamine at the 30 minute time point is suggestive of parasite-specific antibody independent activation of basophils and mast cells . We next sought to determine whether histamine plays a role in maintaining worm burdens during primary infection . To test this , BALB/c mice were infected with 40 L3s and treated with HR1 or HR2 antagonists administered in water for the duration of infection . At 8 weeks , mice were euthanized and adult worm burden was determined . Untreated infected mice had a mean recovery of 18 adult worms . Mice treated with HR1 antagonists had a mean recovery of 8 adult worms ( 58 . 1% reduction , p = 0 . 001 ) while mice treated with HR2 antagonists had a mean recovery of 13 worms ( 22 . 5% reduction , p = 0 . 0573 ) ( Fig 2 ) . This data indicates that signaling via HR1 may play a role in long-term survival of L . sigmondontis in the mammalian host . Given that we observed a reduction of adult worms at 8 weeks post-infection in animals treated with an HR1 antagonist , we sought to determine if HR1 antagonism altered the circulating microfilaria load or the male-to-female ratio . HR1i administration did not alter the number of microfilariae circulating in the blood ( S2 Fig ) or the male-to-female ratio of recovered adult worms ( S3 Fig ) . The lack of a decrease in microfilaria burden is not too surprising as microfilaria load does not correlate with adult worm numbers [13] . Due to the observed reduction in adult worm burdens at 8 weeks , we next sought to determine whether there was a particular timepoint during infection when HR1 blockade enhances worm clearance . In a typical course of infection , L3 stage larvae migrate from the subcutaneous tissues to the pleural space from days 1–5 , molt to L4 stage worms by d 11 , and then molt to adult worms between days 24–30 . Mice were treated for 10 , 35 , and 56 days post-infection with HR1 antagonist . At 56 days ( 8 weeks ) , all groups of mice were euthanized and living adult worms collected . Recovered worms that were motile yet had parts that were covered with granulomas were classified as “encased” ( Fig 3A and 3B ) . As previously demonstrated , mice treated with HR1 antagonists for 8 weeks demonstrated a significant reduction in adult worm burden compared to untreated mice ( Fig 3C ) . While there was no difference in total worm burden at the 8 week timepoint between mice treated with HR1 antagonists for 10 days and untreated mice ( mean worm burden of 17 vs 20 ) , mice treated with 10 days of fexofenadine ( HR1i ) had significantly greater numbers of adult worms that were encased in granulomas ( Fig 3D ) at 56 days post infection . The trend towards lower worm burdens with longer fexofenadine treatment courses suggests that H1R blockade enhances worm clearance at numerous stages of worm development . Given the reduction in adult worm burden observed in HR1 treated mice , we next evaluated whether fexofenadine is directly toxic to L . sigmodontis worms and whether exogenous histamine enhances worm survival . To test this , L3 stage worms were cultured in vitro and supplemented daily with 200nM histamine , 10mM of fexofenadine , or media alone and assessed daily for survival . As seen in Fig 4 , there were no observed differences in survival times between L3s supplemented with histamine , L3s supplemented with fexofenadine , and those supplemented with media ( Fig 4 ) . These data indicate that exogenous histamine does not directly enhance worm survival and that fexofenadine is not directly toxic to worm viability . Because fexofenadine did not appear directly toxic to L . sigmodontis worms , we next evaluated whether H1R antagonism alters the immune response that develops during infection . To assess this , humoral and cellular immunological studies were conducted on infected mice treated with 8 weeks of fexofenadine . Both total and LsAg-specific IgE were significantly decreased in mice treated with fexofenadine compared to untreated controls ( Fig 5A and 5B ) . In terms of cellular immune responses , parasite antigen-driven production of IL-5 and IFN-γ from splenocytes was also significantly reduced in fexofenadine treated mice ( Fig 5D and 5E ) , whereas IL-4 production was not ( Fig 5C ) . This suggests that signaling via HR1 may enhance both type 1 and type 2 immune responses . In contrast to the decreases in IgE , IL-5 , and IFN-γ , the cellular infiltrate present in the pleural cavity , the site where adult L . sigmodontis worms reside , was dramatically increased in fexofenadine treated mice . Whereas infection of untreated BALB/c mice resulted in a median of 1 . 7 x 106 cells in the pleural space at study endpoint , fexofenadine-treated mice had 4 . 8 x 106 cells ( p = 0 . 0080 , Fig 6A ) . Flow cytometric analysis revealed that eosinophils comprised over half of the cells in the pleural infiltrate of fexofenadine-treated mice , increasing in numbers from a median of 4 . 7 x 105 cells in untreated infected mice to 2 . 5 x 106 cells fexofenadine-treated infected animals ( p = 0 . 0043 , Fig 6B ) A number of studies utilizing the L . sigmondontis model have demonstrated a significant role for eosinophils in immune-mediated clearance of worms [14 , 15] . As fexofenadine increased eosinophil numbers at the site of adult worm infection , we next tested whether fexofenadine mediated worm clearance is dependent on eosinophils . Eosinophil deficient mice ( ΔdblGATA ) and background control BALB/c mice were infected with L . sigmondontis , treated for 8 weeks with HR1 antagonists , and euthanized at 8 weeks for enumeration of adult worm burden . In contrast to fexofenadine-treated wild type mice , eosinophil deficient mice administered fexofenadine had no reduction in adult worm burden ( mean recovery 18 ) when compared to untreated ΔdblGATA controls ( mean recovery 15 ) or BALB/c background controls ( mean recovery 14 ) ( Fig 7 ) . To further assess the activity of eosinophils in antihistamine mediated worm clearance , BALB/c mice were infected , treated with HR1 antagonists for 8 weeks , and then euthanized . A pleural lavage was performed and ELISA used to detect eosinophil peroxidase ( EPO ) as evidence of eosinophil degranulation . Fexofenadine treated mice demonstrated significantly highler levels of EPO in the lavage fluid ( S4 Fig ) . Taken together , these findings demonstrate that H1R blockade enhances worm clearance through an eosinophil-dependent mechanism .
In this study we found that histamine is released throughout filarial infection , that antihistamine therapy reduced IgE levels and increased eosinophilic responses at the site of infection , and that administration of fexofenadine , a HR1 blocker , enhances clearance of adult worms in an eosinophil-dependent manner . Our first experiment was a time course study of circulating histamine levels to determine the kinetics of histamine release during primary filarial infection . As the t1/2 of histamine in blood is approximately 60s [16 , 17] , blood levels are representative of ongoing histamine release . We found that histamine was released throughout the course of primary L . sigmondontis infection . The 1st peak in circulating histamine occurred 30 minutes post infection in naïve mice . This finding has two important implications . First , as basophils and mast cells are the only cells carrying pre-formed histamine [18] , it suggests that one or both of these cell types are activated within minutes of filaria infection . Early activation of these cells may be important for the shaping of the immune response to tissue invasive helminths . Second , early histamine release represents a non-specific mechanism of mast cell or basophil activation , since specific IgE is not present until weeks after infection . This is consistent with a number of studies that have demonstrated direct activation of basophils by helminth antigens ( reviewed in [10] ) . Timecourse studies next revealed that histamine release in infected mice peaks again at 8 weeks of infection . We speculate that the 8 week peak may be due to basophil and mast cell activation in response to circulating microfilariae , which appear starting 7 weeks post-infection . As detectable LsAg-specific IgE develops by 6 wks post-infection , this activation is likely occurring through IgE . This 2nd peak is then followed by a decrease in circulating histamine , even though histamine decarboxylase message in blood cells increases throughout infection . This data is consistent with previous findings that basophils become hyporesponsive over time , requiring more signal to achieve activation [19] . Therefore , even though histamine synthesis continues throughout the course of infection , basophils and mast-cells are releasing less histamine in the chronic stages of infection . Perhaps the most striking finding of this study is the significant reduction of adult worm burden at 8 weeks in mice treated with a HR1 antagonist . To determine the timing of worm clearance , infected mice were treated for 10 , 35 , or 56 days with fexofenadine and assessed for adult worm burden at 8 weeks . We found that the longer mice were treated with fexofenadine the greater the reduction in adult worm burden at 8 weeks and that treatment with fexofenadine resulted in a significant increase in encased worms in all groups . Previous studies have suggested a role for histamine in early larval invasion into the lymphatics [20] . Taken together these data indicate that that HR1 blockade enhances worm clearance at numerous stages of development . As in vitro studies revealed that fexofenadine is not directly toxic to Litomosoides sigmodontis , we next evaluated whether fexofenadine augments immune responses directed against the parasite . Although helminth-specific IgE , IL-5 and IFNγ responses were all decreased in fexofenadine treated mice , eosinophil numbers at the site of worm infection were significantly elevated . Experiments with eosinophil deficient ΔdblGATA mice demonstrated that eosinophils were required for fexofenadine-mediated helminth clearance . In contrast to fexofenadine treated wild type mice , which had 80% fewer adult worms than wild type controls , fexofenadine treated ΔdblGATA mice exhibited no decrease in worm numbers . These results are consistent with prior studies suggesting eosinophils are key effector cells against helminths [21–23] . Studies utilizing the L . sigmondontis model have shown that mice deficient in eosinophil peroxidase or major basic protein , key eosinophil granule proteins , have significantly higher filarial worm burdens than wild type controls [14] . Prior studies [24] have shown that mice deficient in IL-5 produce neutrophilic rather than predominantly eosinophilic granulomas around L . sigmodontis worms . In our study , IL-5 production was likely not the driving mechanism for larger eosinophilic granulomas in the setting of H1R blockade as splenocytes from fexofenadine-treated mice demonstrated less IL-5 production than splenocytes from untreated infected animals . Together , the results of these papers and this study suggest that IL-5 is required for eosinophilic granuloma formation , and that fexofenadine enhances this process . There are multiple mechanisms by which HR1 blockade may have enhanced eosinophil responses in this study . One possibility is that HR1 blockade may have enhanced eosinophil survival . Data from one in vitro study suggests histamine signaling reverses IL-5 afforded eosinophil survival [25] . A second hypothesis to explain increased eosinophil numbers at the site of worm infection is enhancement of eosinophil chemotaxis by blockade of HR1 signaling . Histamine is a known chemoattractant molecule for eosinophils [26–28] , and the recently discovered [29] histamine receptor 4 ( HR4 ) has been demonstrated to play a significant role in eosinophil chemotaxis and activation [30–35] . As such , it is possible that blockade of histamine signaling through HR1 enhances the effects of histamine through HR4 . Alternatively , HR1 blockade may indirectly enhance eosinophil chemotaxis by increasing production of eosinophil chemotaxins or augmenting eosinophil sensitivity to such agents . Of note , dblGATA mice are deficient in basophils as well as eosinophils . Thus , it is possible that worm clearance in fexofenadine-treated mice is due to the action of basophils rather than eosinophils . However , as we have previously found that depletion of basophils does not alter adult worm numbers ( [36] , [37] ) , and as eosinophils are known to have the ability to kill adult filarial worms [14 , 38] , we believe it is most likely that worm clearance induced by fexofenadine is through enhancement of eosinophil numbers at the site of infection . One of the most interesting findings of this study is the observation that fexofenadine treatment caused significant reductions in circulating IgE levels and splenocyte production of IL-5 and IFNγ as well as increased numbers of eosinophils at the site of infection . While we can only speculate on the mechanisms underlying these apparently contrasting findings , we expect that it may be related 1 ) to the concentrations of histamine locally ( at the site of infection ) vs systemically , and 2 ) to unknown effects of histamine on the function of various immune effector cells . Another possibility is that decreased IgE levels and IL-5 production may have been due to the decreased adult worm burdens observed in fexofenadine-treated mice . The concentrations of histamine at different body sites during infection and the effects histamine has type 2 responses from B cells , T cells , macrophages , and dendritic cells will be the focus of future investigations . Another possibility is that decreased IgE levels and IL-5 production may have been due to the decreased adult worm burdens observed in fexofenadine-treated mice . These findings demonstrate that histamine , in addition to its immediate proinflammatory effects , also functions to shape the immune response to helminth infections . The exact mechanisms by which this occurs are not yet clear . Histamine is known to alter the immunological function of a variety of cell types , including epithelial cells , granulocytes , T-cells , B-cells , and dendritic cells [3 , 39–41] . Investigations combining HR1 deficient mice with airway hyperresponsiveness models are mixed [42–45] . Whereas one showed decreases in type 2 cytokines , no changes in IFNγ , decreased IgE levels , and increased blood eosinophil numbers [42] , another showed increases in type 2 cytokines , decreased IFNγ , and decreased bronchoalveolar lavage eosinophil numbers [43] . We believe the differences in these studies demonstrate the complex role histamine plays in shaping immune responses . The exact effects of histamine are likely dependent not only on the cell types involved , but also on the cytokine environment in which histamine is acting and on the repertoire of histamine receptors displayed by individual cells . The results of our study may have some important clinical ramifications . Currently there is a worldwide effort to control and potentially eradicate lymphatic filariasis and onchocerciasis by repeated mass drug administration ( MDA ) of anti-filarial medications , especially diethylcarbamazine ( DEC ) [44] . A major factor limiting success of MDA is the inability of anti-filarial drugs to kill adult worms when given as a short course [45] . Since antifilarial medications primarily clear microfilariae , ongoing mass drug administration programs require repeated administration of antifilarial agents s for years until natural death of adult worms occurs . [46 , 47] . One of the interesting aspects of DEC therapy is that DEC does not appear sufficient on its own to kill filarial worms . Numerous studies have shown that DEC-mediated clearance of filariae is dependent in large part on the host immune response [48 , 49] . Since we have shown that fexofenadine can augment immune clearance of adult filarial worms , we hypothesize that addition of fexofenadine to DEC or other antifilarial medications may result in better adult worm eradication than current regimens . Discovering a short course therapy that can successfully eliminate adult filarial worms would greatly increase our ability to control and eradicate filarial infections . Further elucidating the mechanisms by which fexofenadine decreases adult worm burdens , and investigating whether combining fexofenadine with current antifilarial medications enhances adult worm clearance , will be the focus of future studies .
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Filariae are tissue-invasive parasitic roundworms that infect over 100 million people worldwide and cause debilitating conditions such as river blindness and elephantiasis . One of the major factors limiting our ability to eliminate these infections is the lack of drugs that kill adult worms when given as a short course therapy . Additionally , the mechanisms by which adult worms are cleared from infected individuals remains poorly understood . In this study , we demonstrate that treatment of infected mice with fexofenadine , an inhibitor of histamine receptor 1 , significantly reduces adult worm numbers through a mechanism dependent on host eosinophils . These findings suggest that histamine release induced by parasitic worms may aid parasite survival by decreasing eosinophilic responses . Further , as antihistamines are generally safe medications , these results raise the possibility that antihistamine therapy may be useful either alone , or potentially in combination with other antifilarial medications such as diethylcarbamazine ( DEC ) , to eliminate adult filarial worms from infected individuals .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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Histamine 1 Receptor Blockade Enhances Eosinophil-Mediated Clearance of Adult Filarial Worms
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This study was conducted in Bangladeshi patients in an outpatient setting to support registration of Paromomycin Intramuscular Injection ( PMIM ) as a low-cost treatment option in Bangladesh . This Phase IIIb , open-label , multi-center , single-arm trial assessed the efficacy and safety of PMIM administered at 11 mg/kg ( paromomycin base ) intramuscularly once daily for 21 consecutive days to children and adults with VL in a rural outpatient setting in Bangladesh . Patients ≥5 and ≤55 years were eligible if they had signs and symptoms of VL ( intermittent fever , weight loss/decreased appetite , and enlarged spleen ) , positive rK39 test , and were living in VL-endemic areas . Compliance was the percentage of enrolled patients who received 21 daily injections over no more than 22 days . Efficacy was evaluated by initial clinical response , defined as resolution of fever and reduction of splenomegaly at end of treatment , and final clinical response , defined as the absence of new clinical signs and symptoms of VL 6 months after end of treatment . Safety was assessed by evaluation of adverse events . A total of 120 subjects ( 49% pediatric ) were enrolled . Treatment compliance was 98 . 3% . Initial clinical response in the Intent-to-Treat population was 98 . 3% , and final clinical response 6 months after end of treatment was 94 . 2% . Of the 119 subjects who received ≥1 dose of PMIM , 28 . 6% reported at least one adverse event . Injection site pain was the most commonly reported adverse event . Reversible renal impairment and/or hearing loss were reported in 2 subjects . PMIM was an effective and safe treatment for VL in Bangladesh . The short treatment duration and lower cost of PMIM compared with other treatment options may make this drug a preferred treatment to be investigated as part of a combination therapy regimen . This study supports the registration of PMIM for use in government health facilities in Bangladesh . ClinicalTrials . gov identifier: NCT01328457
Visceral leishmaniasis ( VL ) , also known as kala-azar , remains one of the most neglected diseases in Bangladesh with an estimated 65 million people at risk of the disease [1] . Widely available , safe , and affordable therapies for visceral leishmaniasis are needed . Although VL was nearly eliminated in Bangladesh during the Malaria Eradication Programme of 1961–1970 [2] , the country experienced an epidemic in the mid-2000s ( 9 , 379 cases reported in 2006 by the Ministry of Health and Family Welfare [MoHFW] ) [3] . More recently , the number of VL cases reported by MoHFW has declined annually from 4 , 932 cases in 2007 to 3 , 300 cases in 2011 [3 , MoHFW personal communication] . Although the decline in reported VL cases is promising , sustained efforts are needed to meet the Kala-Azar Elimination Program target of <1 case annually per 10 , 000 population at the upazila level by 2015 . VL mostly occurs in the poorest of the poor , whose poverty is further impacted by lost productivity when a family member is affected . Cases are treated for free in the Upazila Health Complexes , which are the government-run primary health care centers situated in the upazila ( sub-district ) . Patients are referred to tertiary hospitals when they need evaluation for treatment failure , relapse , or other complications . In Bangladesh , the transmission of VL is purely anthroponotic . Therefore , breaking the human reservoir host-vector cycle requires timely diagnosis and treatment of VL patients , a decrease in the human host reservoir , and rigorous reduction in vector contact with humans . The MoHFW of Bangladesh has instituted a kala-azar elimination program , including diagnosis and adequate treatment of all cases of VL , disease surveillance , and vector surveillance and control . Although sodium stibogluconate , miltefosine , and paromomycin are included in the National Essential Drug List for VL , only miltefosine is currently registered in Bangladesh . Paromomycin is a low-cost drug that was demonstrated to be efficacious and generally safe and well tolerated for the treatment of VL in a Phase III trial conducted in endemic areas of India [4] . Based on results of the Phase III trial in India , paromomycin IM injection ( PMIM ) was approved for the treatment of VL in August 2006 by the Drugs Controller General of India and was included in the World Health Organization ( WHO ) Essential Medicines List in 2007 . A subsequent Phase IV trial in India revealed a similarly high efficacy and safety profile coupled with excellent treatment compliance in a rural outpatient setting [5] . A clinical trial on different treatment regimens for VL completed in four East African countries showed that the combination of sodium stibogluconate ( SSG ) and PMIM administered over 17 days had efficacy and safety comparable with a 30-day course of SSG alone [6] . Thus , adding PMIM to the treatment regimen lowered both the cost and duration of treatment . This Phase IIIb study was designed to confirm the effectiveness of PMIM to treat VL in Bangladeshi patients in an outpatient setting to support registration of this low-cost treatment option in Bangladesh .
This study was conducted in accordance with the Bengal Drugs Rules , 1946 ( as amended in 1952 ) , the Drugs Act 1940 of the People's Republic of Bangladesh ( as modified in 1964 ) , the Declaration of Helsinki ( adopted at the 59 World Medical Association General Assembly , Seoul , October 2008 ) , and International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use—Good Clinical Practice ( ICH-GCP ) . The research protocol ( see Protocol S1 ) was approved by the Research Review Committee and the Ethical Review Committee of International Centre for Diarrhoeal Disease Research , Bangladesh ( icddr , b ) ; the Director General Health Services ( DGHS ) , Bangladesh . icddr , b , also appointed an independent Data Safety Monitoring Board to monitor safety during the trial . The trial is registered at ClinicalTrials . gov ( identifier: NCT01328457 ) . Written informed consent was obtained in accordance with ICH-GCP , the Declaration of Helsinki , laws and regulations of Bangladesh and all applicable regulatory requirements . If a patient was younger than 18 years old , his/her legal representative ( a parent or legal guardian ) signed the informed consent for the patient . Assent from the participating minor was also obtained . For illiterate patients , the entire informed consent procedure was witnessed by an impartial , literate person who attested ( with his/her dated signature ) that the patient gave informed consent voluntarily and that the information provided was a complete and accurate representation of the informed consent form . The Ethical Review Committee allowed illiterate patients to provide a thumbprint on the informed consent document . The written informed consent was signed and dated by subject/parents/legal guardian/witness as well as the investigator prior to any study-related procedure . Participation was voluntary , and patients could withdraw from the trial at any time without further obligation . Rescue medication was available to patients who discontinued treatment with PMIM or who were considered a treatment failure . This Phase IIIb , open-label , multi-center , single-arm trial was designed to assess the effectiveness and safety of PMIM ( Gland Pharma Ltd . , Hyderabad , India ) administered at a dose of 11 mg/kg ( paromomycin as the base ) intramuscularly once daily for 21 consecutive days to children and adults with VL in rural Bangladesh . The planned sample size of 120 patients with VL was not based on statistical calculations but was considered a sufficient size to determine effectiveness and safety of PMIM in the Bangladeshi population based on high efficacy rates reported in large-scale studies of PMIM in Indian populations [4 , 5] . The study was conducted between January 2011 and June 2012 at two health complexes in Mymensingh District , Bangladesh . Because this was the first time that paromomycin was used in Bangladesh , the study team ensured supply of all equipment and materials . All clinical staff involved in the study received training on ICH GCP , study protocol activities including consent procedures , universal safety precautions , administration of PMIM , and management and reporting of adverse events . Patients who were ≥5 years ( weighing at least 5 kg ) and ≤55 years old were eligible if they met the following case definition criteria: ( 1 ) living in the VL-endemic areas in Bangladesh , ( 2 ) signs and symptoms of VL including history of intermittent fever for at least 2 weeks , history of weight loss and/or decrease in appetite , and enlarged spleen , ( 3 ) a positive serological rK39 test . For patients who had a prior history of VL ( and , therefore , anticipated positive rK39 results independent of current VL infection status ) , the clinical signs and symptoms and VL-endemic residency ultimately determined eligibility . Patients had to be clinically stable and able to maintain adequate hydration . Exclusion criteria included pregnancy or lactation; active tuberculosis or taking antituberculosis medications; previous treatment with PMIM; clinically significant anemia; current or history of clinically significant renal or hepatic dysfunction; serum creatinine above the upper limit of normal range; proteinuria; history of hepatitis B or C or HIV positive; history of hearing loss; significant coexisting disease; any history of VL or treatment for VL; history of hypersensitivity to aminoglycosides or sulfite; and concomitant use of other aminoglycosides , nephrotoxic and ototoxic drugs , or immunosuppressive drugs . Enrolled patients received PMIM administered at a dose of 11 mg/kg ( paromomycin as the base; dosing based on screening body weight ) intramuscularly into the gluteus muscle once daily for 21 consecutive days ( or over 22 days if one day was missed ) . Treatment and study assessments were conducted at the study center on an outpatient basis; however , patients who did not have access to local lodging could have been admitted to the study center facility . Safety was assessed throughout the study . Patients were assessed for initial clinical response on the last day of treatment ( Day 21/22; end of treatment [EOT] ) ; they were instructed to return to the study center if they had any symptoms of relapse , adverse event , or hearing loss within 30 days after EOT . Patients were not required to return for follow-up , but were encouraged to return if problems arose during this 30-day window . Six months after EOT ( approximately Study Day 202 ) , patients were asked to return for an assessment of final clinical response . Patients could be removed from treatment if they experienced a life-threatening adverse event considered related to PMIM; reported hearing loss , tinnitus , or other unexplained auditory or vestibular symptoms; needed treatment with an immunosuppressant or a medication with ototoxic and/or nephrotoxic potential; or treatment failure ( no improvement in disease severity after ≥14 days of PMIM treatment ) .
One hundred fifty-three children and adults were screened , of which 120 patients were enrolled and included in the intent-to-treat ( ITT ) population ( Fig 1 ) . A total of 117 subjects completed the study as per protocol ( 1 subject withdrew consent before receiving study drug and was excluded from the Safety population; 1 subject had a serious adverse event [SAE] , and 1 subject died [described below in Safety Results] ) . Of the 120 enrolled subjects , 58% were male and 49% were pediatric ( Table 1 ) . The mean ( ± standard deviation ) age was 19 . 4 ± 11 . 55 years ( median 18; range 5–50 ) . A total of 118/120 subjects ( 98 . 3% ) were compliant with treatment ( 21 daily injections over no more than 22 days ) . The initial clinical response rate at EOT in the ITT population was 98 . 3% ( 95% confidence interval [CI] 96 . 0–100 . 6 ) ( Table 2 ) . Nearly all pediatric ( 58/59; 98 . 3% ) and adult ( 60/61; 98 . 4% ) subjects achieved initial clinical response . All males ( 70/70 ) and 96 . 0% ( 48/50 ) of females achieved initial clinical response . The final clinical response rate 6 months after EOT was 94 . 2% ( 95% CI 90 . 0–98 . 4 ) ; 94 . 9% in pediatric ( 56/59 ) and 93 . 4% in adult ( 57/59 ) subgroups; and 94 . 3% ( 66/70 ) in males and 94 . 0% ( 47/50 ) in females . Of 119 subjects who received at least one dose of study drug , 118 subjects received 21 consecutive daily doses of the study drug , and one subject ( adult female ) had an SAE ( ear infection requiring antibiotics ) and was discontinued from study drug after receiving 4 doses of PMIM . Adverse events are summarized in Table 3 and detailed in Table 4 . Three severe adverse events were reported in two subjects who also had serious adverse events as described below . Four subjects had 6 serious adverse events during the study ( tinnitus , hearing impairment , and renal impairment all in one subject; and peptic ulcer , ear infection , and tetanus each in one subject ) . Peptic ulcer considered unrelated to study drug was reported in a pediatric male after EOT . Ear infection associated with slight to mild hearing loss considered possibly related to study drug was reported in an adult female after 4 doses of PMIM; the subject was withdrawn from study drug per protocol and improved after antibiotic treatment . Tinnitus , hearing impairment , and renal impairment , all considered possibly related to study drug , were reported in one adult male after EOT . The male subject also had a life-threatening adverse event of anemia , which is an expected manifestation of impaired renal function . The subject was treated , reported feeling better , and did not request further follow-up . Tetanus associated with severe malnutrition was reported after EOT in an adult female who subsequently died 22 days after EOT after being “lost against medical advice . ” Both the tetanus and malnutrition were considered severe , but neither event was considered related to study drug . No pregnancies were reported during the study treatment period . Pregnancy was reported in one female during the follow-up period . The offspring was born healthy , and a hearing test conducted on the infant at 1 . 5 months of age confirmed reaction to sound . An Otoscopy and Oto-Acoustic Emission test to determine function of the middle and inner ear was conducted at 3 months of age and confirmed normal hearing function .
In a Phase III randomized , controlled , open-label clinical trial in 667 children and adults in India , PMIM ( 11 mg/kg/day [base] administered intramuscularly for 21 days ) was determined to be a safe and effective for treatment of VL [4] . PMIM was subsequently registered for the treatment of VL in India in 2006 and was included in the WHO Essential Medicines List in 2007 . In the absence of safe , effective and affordable treatment options , VL continues to remain an important parasitic disease in Bangladesh , and safe , effective , affordable treatments are needed . Results of this open-label , multi-center , single-arm , Phase IIIb clinical trial show the efficacy , safety , and compliance of the same regimen of PMIM ( 11 mg paromomycin base/kg/day for 21 days ) for treatment of VL in rural areas of Bangladesh and support the use of PMIM as part of a combination therapy regimen . High and consistent response rates were observed across the initial ( end of treatment ) and final ( 6 months after end of treatment ) assessments in this study . The final clinical response rate ( 94 . 2% ) is similar to the final clinical response rate observed in the Phase III ( 94 . 2% final response ) and Phase IV ( 94 . 6% final response ) studies of PMIM in India [4 , 5] . The compliance rate of 98 . 3% in this study is similar to the 98% compliance rate in the Phase IV study conducted in 506 children and adults in an outpatient setting in India [5] . These high compliance rates in rural outpatient treatment settings further supports the use of PMIM in rural endemic areas of Bangladesh . The evaluation based on clinical outcome at 6 months was planned for several reasons . This study was conducted with limited resources and was designed similar to a phase 4 study to generate evidence that PMIM works in Bangladesh similar to India , where the etiological agent and patient populations are thought to be very similar . The 6-month observation after treatment was the only practically feasible way to assess a cure in this study because splenic aspiration or other invasive procedures were not appropriate to conduct at the clinical study centers . Adverse events were reported more frequently in females versus males and slightly more frequently in adults than in pediatrics ( Tables 3 and 4 ) . Few adverse events were reported by more than 1 subject . Injection site pain , which is an expected side effect of PMIM administration , was the most commonly reported adverse event . There was no involvement of the scitic nerve , which was found in cases of severe malaria treated with IM quinine in Africa [7] . Paromomycin belongs to the aminoglycoside class of agents , which are known to cause nephrotoxicity and ototoxicity . Patients with clinically significant renal dysfunction or history of hearing loss were excluded from this study . Nephrotoxicity and ototoxicity were not formally studied in this trial . During the study , 2 subjects reported adverse events related to hearing loss after end of treatment: moderate ear pruritus in an adult female and moderate hearing impairment and tinnitus in an adult male who also had moderate renal impairment . All of these events were reversible . In conclusion , PMIM was found to be an effective and safe treatment for VL in Bangladesh . Compliance was high ( 98 . 3% ) in this rural outpatient setting in Bangladesh . The short treatment duration and lower cost compared with other treatment options may make PMIM a preferred drug to include as part of a combination therapy regimen for VL patients in Bangladesh . Use of PMIM as part of combination therapy may reduce duration of treatment and reduce the chances of drug resistance [8] and is in line with recommendations by the Regional Technical Advisory Group of WHO for South East Region . This study supports the registration of PMIM for use in government health facilities in Bangladesh .
|
Effective and safe therapies for visceral leishmaniasis ( VL ) , also known as kala-azar , at an affordable cost are urgently needed in Bangladesh . Although sodium stibogluconate , miltefosine , and paromomycin are included in the National Essential Drug List for VL , only miltefosine is currently registered in Bangladesh . Paromomycin is a low-cost drug that was demonstrated to be an efficacious and safe treatment for VL in endemic areas of India , which led to approval of paromomycin IM injection ( PMIM ) for the treatment of VL in India in 2006 and inclusion of PMIM in the WHO Essential Medicines List in 2007 . A subsequent Phase IV trial in India revealed a similarly high efficacy and safety profile coupled with high treatment compliance in a rural outpatient setting . We confirmed the effectiveness , safety , and high compliance of PMIM when used in an outpatient setting in Bangladesh . This study supports the registration of PMIM as a low-cost treatment option for VL in Bangladesh .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Effectiveness Study of Paromomycin IM Injection (PMIM) for the Treatment of Visceral Leishmaniasis (VL) in Bangladesh
|
The nature of the neural codes for pitch and loudness , two basic auditory attributes , has been a key question in neuroscience for over century . A currently widespread view is that sound intensity ( subjectively , loudness ) is encoded in spike rates , whereas sound frequency ( subjectively , pitch ) is encoded in precise spike timing . Here , using information-theoretic analyses , we show that the spike rates of a population of virtual neural units with frequency-tuning and spike-count correlation characteristics similar to those measured in the primary auditory cortex of primates , contain sufficient statistical information to account for the smallest frequency-discrimination thresholds measured in human listeners . The same population , and the same spike-rate code , can also account for the intensity-discrimination thresholds of humans . These results demonstrate the viability of a unified rate-based cortical population code for both sound frequency ( pitch ) and sound intensity ( loudness ) , and thus suggest a resolution to a long-standing puzzle in auditory neuroscience .
The nature of the neural code for perception is a fundamental question in neuroscience 1–5 . In auditory neuroscience , the search for the neural code for pitch—an essential perceptual attribute of sound classes such as music and speech—has attracted considerable interest [6]–[9] . Two main types of neural codes for pitch have been offered: “timing” codes , which rely on fine spike-timing information [10] , and “rate” codes , which involve spike rates computed over relatively long time windows—typically , a few hundred milliseconds [11] . Timing codes can carry considerably more information than rate codes [12] , and the spike times of auditory-nerve fibers have been found to contain more information than needed to account for human listeners' ability to discriminate very small changes in frequency [11] , [13] , [14] . However , temporal coding degrades rapidly beyond the auditory nerve , making spike timing a less viable code at higher levels of neural processing . Indeed , in the primary auditory cortex , single units cannot precisely follow frequencies higher than a few hundred Hertz [15]–[17] – more than an order of magnitude below the upper limit of accurate pitch perception in humans [18]–[20] . Although studies in non-human animals found no deficits in pure-tone intensity or frequency discrimination following bilateral ablation of auditory cortex , substantial deficits in pure-tone frequency ( pitch ) and intensity ( loudness ) discrimination have been observed in human patients with cortical lesions [21] , [22] , suggesting that the auditory cortex plays an important role in those two perceptual abilities . It seems likely , therefore , that any timing code for frequency in the auditory nerve is transformed into a cortical rate-place code . However , it is not known whether the information contained in the spike counts of a population of cortical neurons is sufficient to account for the very fine frequency-discrimination thresholds of human listeners . A cortical rate-place code for frequency discrimination faces two major obstacles: relatively broad receptive fields [23] , implying poor resolution of small frequency differences by single units , and correlated spike counts [24] , [25] , which can severely limit the benefit of pooling information across multiple units [26]–[29] . Here , we examine the properties of a population of virtual neurons with frequency-tuning and spike-count correlation characteristics similar to those measured in the primary auditory cortex of primates . We determine that statistically optimal decoding of the information contained in the spike rates of these neurons can account quantitatively for the remarkable ability of trained human listeners to discriminate sound frequency . In addition , we show that the same cortical population code is also consistent with psychophysical data concerning another fundamental auditory ability: intensity discrimination . These results demonstrate the viability of a cortical rate code for both frequency and intensity discrimination , thus providing a possible resolution for a long-standing puzzle in auditory neuroscience .
Figure 1A shows frequency tuning curves ( spike-rate versus stimulus frequency ) for an array of virtual frequency-selective neurons with best frequencies ( BFs ) equally spaced on a logarithmic scale spanning a 1-octave range centered on 1 kHz . For illustration purposes , tuning curves are plotted for a small subset of units ( n = 6 ) and a limited BF range , but the results described below are based on a larger number of units ( n = 1700 ) and a wider BF range ( 2 octaves ) . A key characteristic of neural tuning curves is their sharpness . A common measure of sharpness is the “quality factor” ( Q ) , which is obtained by dividing the BF of the unit by a measure of tuning , in this case the width of the tuning curve at half of the peak spiking rate . The sharpness of the simulated units was adjusted to yield Q values consistent with those measured in the primary auditory cortex of primates , which have been found to equal 12 on average for sharply tuned units , and 3 . 7 on average for non-sharply tuned units [23] . Since sharp tuning is generally beneficial for frequency discrimination , in the context of this study we were interested primarily in discrimination performance based on the outputs of sharply tuned units . Thus , unless indicated otherwise , Q was set to 12 . The tuning curves illustrated in Figure 1A reflect this choice . Figure 1B shows simulated spike counts for this population of virtual neurons in response to a 1000 Hz , 50 dB SPL pure tone with a duration of 1 s . The spike counts were modeled as integer-valued random draws from a multivariate Gaussian distribution in which the variance of the spike counts for a given unit was equal to the unit's mean spike count—as is the case for Poisson-distributed spike counts . The covariance between the spike rates of two different units was either set to zero , reflecting an assumption of complete statistical independence between units , or to the product of the geometric mean spike rate and the spike-count correlation coefficient—consistent with the facts that , and for all i , where COV ( Ci , Cj ) , ρi , j , V ( Ci ) , V ( Cj ) , E ( Ci ) , and E ( Cj ) denote the covariance , correlation , variances , and expected values of the spike counts of units i and j , respectively . The latter covariance structure is consistent with neurophysiological data , which show decreasing spike-count correlations between pairs of cortical units as the distance between the units increases , and the overlap between their receptive fields decreases [24] , [25] , [30]–[32] . In the context of this article , the phrase “spike-count correlations” refers specifically to covariations in the spike counts of different units across multiple presentations of the same stimulus . Such correlations , also known as “noise correlations , ” should not be confused with correlations between the spike counts of different units across different stimuli , which are traditionally referred to as “signal correlations” [33] . The resulting covariance and correlation matrices are shown in Figs . 1C and 1D , respectively . The correlation matrix was scaled so that the spike-count correlation coefficient ( or , equivalently , the expected value of the correlation between the spike counts ) of two units , ρi , j , where i and j indicate different units , was maximally equal to ρ . Unless indicated otherwise , ρ was set to 0 . 25 . This value was chosen based on recent findings , which indicate that such a value is not atypical for proximal cortical neurons [32] , especially for output layers [34] . Even though higher discrimination performance might be achieved based on the response of cortical input layers [34] , we reasoned that the properties of output layers of the primary auditory cortex were more relevant than those of other cortical layers for predicting the discrimination performance for a read-out mechanism located beyond the primary auditory cortex . Figure 2A shows mean population responses evoked by two sequentially presented pure tones with slightly different frequencies: 1000 and 1001 . 68 Hz . The frequency difference , 1 . 68 Hz , corresponds approximately to the mean frequency-discrimination threshold ( corresponding to a d′ of 1 ) at 1000 Hz [35] . Note that the difference between the spike rates ( r ) evoked by the two tones ( Figure 2B , black curve ) is quite small relative to the variability of the spike counts ( Figure 1B ) : across the entire population of neurons ( n = 1700 ) , the largest single-unit signal-to-noise ratio ( SNR ) —computed as the difference in spike rates evoked by the two stimuli ( Δr ) divided by the square root of the spike rate evoked by the first stimulus [5]—was equal to 0 . 12 . An SNR of 0 . 12 corresponds approximately to only 53% correct in a two-interval two-alternative forced-choice ( 2I2AFC ) discrimination task [36] , where chance performance is 50% correct . This leads to the question of how many units an optimal observer must pool spike-count information from in order to obtain the same performance as trained human listeners in this task , and with these stimuli , i . e . , a d′ of 1 , or 76% correct in a 2I2AFC experiment . For statistically independent units with a constant spike-count covariance matrix , , where SNRi is the SNR ( as defined above ) for unit i . Therefore , if all the units in the population had uncorrelated spike counts and the same BF and tuning curve as the most informative unit ( i . e . , the unit for which SNR was the highest ) , combining spike-count information from ∼70 units would be sufficient to obtain a d′ of 1 . In the presence of spike-count correlations , and of a stimulus-dependent covariance matrix , the relationship between overall performance ( d′ ) and the single-unit SNRs ( SNRi ) is more complex , but d′ can still be evaluated based on the Fisher information ( see Methods ) [37] . The Fisher information is inversely related to the Cramér-Rao lower bound on the variance of an estimator , which places a limit on the precision with which a quantity can be estimated using any decoding scheme ( linear or nonlinear ) [38] , [39]; it is often used to quantify the best decoding performance that can be achieved based on the information contained in the responses of a population of neurons [27]–[29] , [37] . Using this approach , we found that , for a population of units with tuning curves and spike-count correlations as illustrated in Figure 1 , a d′ of 1 was reached when the number of units in the population ( with BFs spread evenly across the two-octave BF range ) , was set to 1700 , which corresponds to a density of 850 units/octave . With no spike-count correlation ( ρ = 0 ) , a density of 300 units/octave was sufficient to obtain a d′ of 1 . 0 . Even if only 25% of units in primary auditory cortex are sharply tuned [23] , a density of 850 sharply tuned units per octave implies an overall neuronal density of 3400 units per octave; this number is well within the range of physiologically realistic neuronal densities for the output layer of primary auditory cortex [40] . To gain insight into the effective contribution of each unit in the population to the overall performance , we computed the product of the square-root of the Fisher information for each unit and the frequency difference between the two stimuli ( 1 . 68 Hz ) , and plotted the resulting measure , d′/unit , as a function of BF . This was done for ρ = 0 . 25 ( Figure 2B , solid green curve ) and for ρ = 0 ( Figure 2B , dashed green curve ) . Units with BFs more than ½ octave below , or above , the reference stimulus frequency ( 1 kHz ) contributed very little to the overall discrimination performance . In fact , for ρ = 0 . 25 , only 130 ( ∼8% of the 1700 ) units had a d′/unit larger than half of the d′/unit of the “best” ( i . e . , most informative ) unit . Almost all of these units had BFs located within a frequency range of 2 semitones ( 12% , or 1/6th of an octave ) centered on the reference-stimulus frequency ( 1 kHz ) . Our finding that a larger pool size is needed to reach the same performance ( d′ = 1 ) in the presence than in the absence of spike-count correlations is consistent with previous findings [26] . A simple explanation for the detrimental impact of spike-count correlations on stimulus discrimination performance is that they limit an observer's ability to “average out” neural noise without simultaneously canceling the signal . The “cost” of spike-count correlations on discrimination performance is apparent in the difference between the areas under the solid and dashed green curves ( Figure 2B ) —the square root of the sum of the squared d′/unit values across all units , which is equal to d′ , was ∼70% larger for ρ = 0 than for ρ = 0 . 25 . The “dip” at 1 kHz in the d′/unit curves stems from the fact that , for units with a BF close to 1 kHz , the difference between the spike rates evoked by the two stimuli ( Δr , black curve ) was close to zero . The other dips , which are apparent in the dashed green curve , reflect the combined influence of the two factors that determine the Fisher information for each unit , namely , the change in spike rate ( Δr ) and the change in the spike-count covariance matrix ( see Methods ) . Intuitively , d′/unit values close to zero indicate units whose spike counts convey little information beyond that already provided by other units , once spike-count correlations are taken into account . Figure 2C shows the mean population responses evoked by two tones having the same frequency ( 1000 Hz ) , but a different intensity ( 50 dB SPL versus 51 . 22 dB SPL ) . The intensity difference between the two stimuli ( 1 . 22 dB ) was selected to represent the difference that corresponds to human discrimination sensitivity d′ of 1 , based on data in the psychoacoustic literature [41] . We determined the change in spike rate needed to obtain a d′ of 1 using the same correlation coefficient ( ρ = 0 . 25 ) and pool size ( n = 1700 ) , which were found earlier to yield a d′ of 1 for the frequency-discrimination task . We found that a change in spike rate of slightly less than 1 spike/s ( namely , 0 . 94 spikes/s ) was sufficient . A spike-rate change of 0 . 94 spikes/s for a 1 . 22 dB change in sound intensity translates to a change of approximately 15 spikes/s for a 20-dB change in intensity . This value is consistent with example rate-level functions for neurons in primary auditory cortex in the literature , which typically show increases of 10 to 20 spike/s as the intensity of a tone at BF increases from 40 to 60 dB SPL [42] . Thus , it is possible to account for performance in the intensity-discrimination task using the same pool of sharply tuned units as assumed for the frequency-discrimination task with the same coarse rate-based neural code . Note that the maximal change in spike rate ( across all units ) corresponding to the discrimination threshold was larger ( by a factor of 2 . 5 ) for the intensity-discrimination task than for the frequency-discrimination task—compare the heights of the black curves in Figs . 2B and 2D . This outcome underscores the fact that equally discriminable stimulus differences need not correspond to equal differences in spike rates . It can be understood by considering the impact of spike-count correlations on the discrimination of frequency or intensity changes for a population containing only two units . Figure 3 shows equal-probability contours of probability distributions for spike counts ( or single-trial estimates of spike-rates ) evoked by tones differing in frequency ( Figure 3A ) or in intensity ( Figure 3B ) , for two units , i and j . In this example , unit i , whose spike rates are plotted on the x-axis , has a BF below the reference stimulus frequency ( 1 kHz ) , while unit j , whose spike rates are plotted on the y-axis , has a BF above that frequency . When the frequency of the stimulus is increased , the spike rate of unit i decreases while that of unit j increases ( Figure 3A ) . By contrast , when the intensity of the stimulus is increased , the spike rates of both units increase simultaneously ( Figure 3B ) . Note that , for illustration purposes , the mean magnitude of the stimulus-induced changes in spike rate is the same for the two units and the two tasks . Under these circumstances , positive spike-count correlations , which are reflected in elongated contours along the major diagonal , lead to a smaller overlap between the two distributions for the frequency change than for the intensity change . Since the error rate of the optimal observer is directly related to the overlap between the spike-count probability distributions , for this case , positive correlations have a more dramatic impact on intensity-discrimination performance than on frequency-discrimination performance .
Historically , auditory researchers have found it difficult to account for both frequency discrimination and intensity discrimination within the same framework , or using the same neural code . This is in part because the changes in spike rate—or the changes in “excitation patterns” in psychoacoustical models— corresponding to threshold are usually smaller for a frequency-discrimination task than for an intensity-discrimination task [11] , [13] , [43] , [44] . This has led to the view that intensity discrimination relies on spike-rate information whereas frequency discrimination requires fine spike-timing information—at least , for frequencies lower than about 8 kHz . One of the strongest arguments supporting this view stemmed from comparisons of discrimination thresholds measured in human listeners with predictions obtained using observer models that operate on spike-count information only , or use fine spike timing [13] . However , these models have traditionally been based on neural responses at the level of the auditory nerve , which contain a wealth of precise temporal information . The findings described above suggest a different conclusion at the level of the auditory cortex , where neurons are unable to accurately phase-lock to frequencies higher than , at most , a few hundred Hertz . We find that the spike rates of a realistically small population of units , with frequency-tuning and response-correlation characteristics similar to those observed in the primary auditory cortex of primates , contain enough statistical information to account for the smallest frequency-discrimination thresholds measured in human listeners—slightly less than 0 . 2% , or ∼2 Hz at 1 kHz . Any viable rate-based population code for frequency ( or pitch ) discrimination must overcome two major limitations . The first limitation stems from the width of neural tuning curves: even the most sharply tuned units in the primary auditory cortex of primates have relatively wide receptive fields , with bandwidths ( measured at half the peak spike-rate ) of approximately 8% of the unit's best frequency ( BF ) [23] . One consequence of such wide receptive fields is that the change in spike rate produced by a small ( e . g . , 0 . 2% ) change in stimulus frequency is very small relative to the neural noise , i . e . , the random variability in spike counts . In principle , the detectability of small spike-rate differences can be enhanced by pooling information across many neural units . However , previous work in theoretical neuroscience has indicated that the benefit of pooling spike-count information across multiple units can be drastically limited if the pooled spike counts are correlated [26]–[29] , [37] . The spike counts of cortical neurons are correlated [24]–[26] , [30] , [32]–[34] . Thus , it was unclear a priori whether a population code based solely on spike-rate information in auditory cortex could support the remarkably fine frequency-discrimination performance of humans . The results described above offer a positive answer to this question . They show that , contrary to popular belief , a cortical rate-place code can provide sufficient information to account for human behavior in the dimensions of both frequency and intensity , using reasonable assumptions relating to unit density , unit tuning , and inter-unit correlations . As with any modeling study , the conclusions of this work depend on the assumptions of the underlying model . In particular , our estimates of the number of units needed to achieve a given level of behavioral discrimination performance rely on the assumption that downstream neurons use the information contained in the spike counts of the population optimally in a statistical ( maximum-likelihood ) sense . It remains to be determined whether neural networks in the auditory cortex can achieve , or even approach , this optimum . If they cannot , the estimated numbers of neurons needed to explain the behavioral performance of human listeners in the frequency- and intensity-discrimination task would be under-estimates . Importantly , however , increasing the assumed population size would not necessarily alter our main conclusion , according to which the behavioral thresholds for these two tasks can , at least in theory , be accounted for using the same population and same type of ( spike-rate ) code . Another assumption on which our conclusions may depend relates to the strength of spike-count correlations and its relationship with other characteristics , such as the BFs and frequency-tuning widths of the units . Our choice of correlation structure for the virtual population was based in part on neurophysiological data [24] , [25] , and in part on theoretical and simplicity considerations . Lastly , the conclusions of this study are subject to the limitations of Fisher information as a measure of optimum decoding performance for neural populations [e . g . , 45] . Our finding that a cortical population code operating solely on spike-count information can account for frequency-discrimination performance in humans has important implications for the search of neural correlates of frequency ( pitch ) perception in humans . For example , while explanations for the dependence of frequency-discrimination thresholds on stimulus parameters such as frequency , intensity , and duration , have so far focused almost exclusively on peripheral ( i . e . , cochlear and auditory-nerve ) response properties , our approach provides a method for examining the role of central factors , such as variations in neuronal density [46] , [47] or in spike-count correlations across BFs at the cortical level , in determining behavioral discrimination thresholds .
Responses of a population of frequency-selective cortical units were simulated as follows . The spike rate ( in spikes/s ) for unit i ( i = 1 , … , n ) in response to a tone of frequency , f , and intensity , l , was computed as , ( 1 ) where re ( l ) and rs denote the stimulus-evoked spike rate at BF and the spontaneous spike rate , respectively , and hi ( f ) represents the frequency-tuning function , ( 2 ) in which φi denotes the BF ( in Hz ) of unit i , and the sharpness parameter , αi , was adjusted to yield a quality factor , Q , consistent with that of single units in the primary auditory cortex of primates [23] . This function is sometimes referred to as the “rounded exponential” ( roex ) function , and has been used to model psychophysical auditory-filter shapes [48] as well as neural frequency-tuning curves in the primary auditory cortex of primates [49] . The spontaneous rate , rs , was set to 0 . 1 spikes/s and the evoked rate , re , for a 50 dB SPL pure tone having a frequency equal to the BF of the unit was set to15 spikes/s . These numbers are consistent with neurophysiological data [42] . Other physiologically realistic values for these parameters ( e . g . , rs = 1 and re = 10 or 20 ) were also tested and led to qualitatively similar conclusions . Spike counts were simulated by drawing samples from a multivariate Gaussian probability density function with mean vector , r ( f , l ) = [r1 ( f , l ) , … , rn ( f , l ) ] , and covariance matrix , V ( f , l ) , ( 3 ) where ○ denotes the Hadamard ( entrywise ) matrix product . Consistent with neurophysiological data indicating that spike-count correlations for neuron pairs in primary auditory cortex tend to decrease with increasing BF distance and decreasing receptive-field overlap between the units [24] , [25] , the spike-rate correlation matrix , C , was defined as , ( 4 ) where δi , j = 1 for i = j and δi , j = 0 for i≠j , and H = [h1 ( φ ) , … , hn ( φ ) ] , in which the elements of each n-vector , hi ( φ ) , were equal to evaluated at f = φj , j = 1 , … , n , and . To obtain integer-valued spike counts , samples from the multivariate Gaussian probability density function were rounded to the nearest unit . Neglecting the effect of rounding , the highest frequency-discrimination performance , d′f , that can be obtained using the information contained in the spike counts of a population of units with characteristics as described above can be determined as [42] , [47] , ( 5 ) where Δf denotes the frequency difference between the two tones being discriminated , and I ( f ) denotes the Fisher information for frequency , ( 6 ) where Tr denotes the trace operator . The partial derivative of the rate vector with respect to frequency , , can be determined based on the preceding equations . Analogous equations were used to compute performance for an intensity-discrimination task . To compute the partial derivative of the rate vector with respect to sound intensity , , we assumed that the spike rate varied linearly with the stimulus intensity ( in dB ) , and adjusted the constant of proportionality between these two variables to yield the desired performance ( d′ = 1 ) for the intensity-discrimination task . Unit-specific measures of performance , d′/unit , were computed as , ( 7 ) with , ( 8 ) where x can be either f ( frequency ) or l ( intensity ) , [ . ]i denotes the ith element of its ( vector ) argument , and Diagi[ . ] denotes the ith element of the diagonal of its argument . The stimulus difference , Δx , was set to the discrimination threshold ( corresponding to d′ = 1 ) for the considered task: 1 . 68 Hz for frequency discrimination , and 1 . 22 dB for intensity discrimination .
|
A widely held view among auditory scientists is that the neural code for sound intensity ( or loudness ) involves temporally coarse spike-rate information , whereas the code for sound frequency ( or pitch ) requires more fine-grained and precise spike timing information . One problem with this view is that neurons in auditory cortex do not produce precisely time-locked responses to higher frequencies within the pitch range , suggesting that a transformation to a rate code must occur . However , because cortical neurons exhibit relatively broad tuning to frequency and correlated spike counts , it is unclear whether a cortical population code based on spike rates alone can support the remarkably precise pitch-discrimination ability of humans . Here we show that a relatively small population of virtual neurons with frequency-tuning and spike-count correlation characteristics consistent with those of actual neurons in the primary auditory cortex of primates , can account for both the smallest frequency- and intensity-discrimination thresholds measured behaviorally in humans . These results suggest a resolution to a long-standing puzzle in auditory neuroscience .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2013
|
Auditory Frequency and Intensity Discrimination Explained Using a Cortical Population Rate Code
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Target selection is the first and pivotal step in drug discovery . An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent . We collected a set of 332 targets that succeeded or failed in phase III clinical trials , and explored whether Omic features describing the target genes could predict clinical success . We obtained features from the recently published comprehensive resource: Harmonizome . Nineteen features appeared to be significantly correlated with phase III clinical trial outcomes , but only 4 passed validation schemes that used bootstrapping or modified permutation tests to assess feature robustness and generalizability while accounting for target class selection bias . We also used classifiers to perform multivariate feature selection and found that classifiers with a single feature performed as well in cross-validation as classifiers with more features ( AUROC = 0 . 57 and AUPR = 0 . 81 ) . The two predominantly selected features were mean mRNA expression across tissues and standard deviation of expression across tissues , where successful targets tended to have lower mean expression and higher expression variance than failed targets . This finding supports the conventional wisdom that it is favorable for a target to be present in the tissue ( s ) affected by a disease and absent from other tissues . Overall , our results suggest that it is feasible to construct a model integrating interpretable target features to inform target selection . We anticipate deeper insights and better models in the future , as researchers can reuse the data we have provided to improve methods for handling sample biases and learn more informative features . Code , documentation , and data for this study have been deposited on GitHub at https://github . com/arouillard/omic-features-successful-targets .
More than half of drug candidates that advance beyond phase I clinical trials fail due to lack of efficacy [1 , 2] . One possible explanation for these failures is sub-optimal target selection [3] . Many factors must be considered when selecting a target for drug discovery [4 , 5] . Intrinsic factors include the likelihood of the target to be tractable ( can the target’s activity be altered by a compound , antibody , or other drug modality ? ) , safe ( will altering the target’s activity cause serious adverse events ? ) , and efficacious ( will altering the target’s activity provide significant benefit to patients ? ) . Extrinsic factors include the availability of investigational reagents and disease models for preclinical target validation , whether biomarkers are known for measuring target engagement or therapeutic effect , the duration and complexity of clinical trials required to prove safety and efficacy , and the unmet need of patients with diseases that might be treated by modulating the target . Over the past decade , technologies have matured enabling high-throughput genome- , transcriptome- , and proteome-wide profiling of cells and tissues in normal , disease , and experimentally perturbed states . In parallel , researchers have made substantial progress curating or text-mining biomedical literature to extract and organize information about genes and proteins , such as molecular functions and signaling pathways , into structured datasets . Taken together , both efforts have given rise to a vast amount of primary , curated , and text-mined data about genes and proteins , which are stored in online repositories and amenable to computational analysis [6 , 7] . To improve the success rate of drug discovery projects , researchers have investigated whether any features of genes or proteins are useful for target selection . These computational studies can be categorized according to whether the researchers were trying to predict tractability [8 , 9] , safety [10–13] , efficacy ( no publications to our knowledge ) , or overall success ( alternatively termed “drug target likeness” ) [8 , 13–26] . Closely related efforts include disease gene prediction , where the goal is to predict genes mechanistically involved in a given disease [27–32] , and disease target prediction , where the goal is to predict genes that would make successful drug targets for a given disease [33–35] . To our knowledge , we report the first screen for features of genes or proteins that distinguish targets of approved drugs from targets of drug candidates that failed in clinical trials . In contrast , related prior studies have searched for features that distinguish targets of approved drugs from the rest of the genome ( or a representative subset ) [13 , 15–25] . Using the remainder of the genome for comparison has been useful for finding features enriched among successful targets , but it is uncertain whether these features are specific to successful targets or are enriched among targets of failed drug candidates as well . Our study aims to fill this knowledge gap by directly testing for features that separate targets by clinical outcome , expanding the scope of prior studies that have investigated how genetic disease associations [36] and publication trends [37] of targets correlate with clinical outcome . Our work has five additional innovative characteristics . First , we included only targets of drugs that are presumed to be selective ( no documented polypharmacology ) to reduce ambiguity in assigning clinical trial outcomes to targets . Second , we included only phase III failures to enrich for target efficacy failures , as opposed to safety and target engagement failures , which are more common in phase I and phase II [2] . Third , we excluded targets of assets only indicated for cancer , as studies have observed that features of successful targets for cancer differ from features of successful targets for other indications [22 , 23] , moreover , cancer trials fail more frequently than trials for other indications [2] . Fourth , we interrogated a diverse and comprehensive set of features , over 150 , 000 features from 67 datasets covering 16 feature types , whereas prior studies have examined only features derived from protein sequence [16–18 , 24 , 25] , protein-protein interactions [13 , 15 , 18–23] , Gene Ontology terms [13 , 15 , 16] , and gene expression profiles [15 , 19 , 21 , 25] . Fifth , because targets of drugs and drug candidates do not constitute a random sample of the genome , we implemented a suite of tests to assess the robustness and generalizability of features identified as significantly separating successes from failures in the biased sample . A handful of the initial 150 , 000+ features passed our tests for robustness and generalizability to new targets or target classes . Interestingly , these features were predominantly derived from gene expression datasets . Notably , two significant features were discovered repeatedly in multiple datasets: successful targets tended to have lower mean mRNA expression across tissues and higher expression variance than failed targets . We also trained a classifier to predict phase III success probabilities for untested targets ( no phase III clinical trial outcomes reported for drug candidates that selectively modulate these targets ) . We identified 943 targets with sufficiently unfavorable expression characteristics to be predicted twice as likely to fail in phase III clinical trials as past phase III targets . Furthermore , we identified 2 , 700 , 856 target pairs predicted with 99% consistency to have a 2-fold difference in success probability . Such pairwise comparisons may be useful for prioritizing short lists of targets under consideration for a therapeutic program . We conclude this paper with a discussion of the biases and limitations faced when attempting to analyze , model , or interpret data on clinical trial outcomes .
We extracted phase III clinical trial outcomes reported in Pharmaprojects [38] for drug candidates reported to be selective ( single documented target ) and tested as treatments for non-cancer diseases . We grouped the outcomes by target , scored targets with at least one approved drug as successful ( NS = 259 ) , and scored targets with no approved drugs and at least one documented phase III failure as failed ( NF = 72 ) ( S1 Table ) . The target success rate ( 77% ) appears to be inflated relative to typically reported phase III success rates ( 58% ) [2] because we scored targets by their best outcome across multiple trials . We obtained target features from the Harmonizome [39] , a recently published collection of features of genes and proteins extracted from over 100 Omics datasets . We limited our analysis to 67 datasets that are in the public domain or GSK had independently licensed ( Table 1 ) . Each dataset in the Harmonizome is organized into a matrix with genes labeling the rows and features such as diseases , phenotypes , tissues , and pathways labeling the columns . We included the mean and standard deviation calculated along the rows of each dataset as additional target features . These summary statistics provide potentially useful and interpretable information about targets , such as how many pathway associations a target has or how variable a target’s expression is across tissues . The datasets contained a total of 174 , 228 features covering 16 feature types ( Table 1 ) . We restricted our analysis to 44 , 092 features that had at least three non-zero values for targets assigned a phase III outcome . Many datasets had strong correlations among their features . To reduce feature redundancy and avoid excessive multiple hypothesis testing while maintaining interpretability of features , we replaced each group of highly correlated features with the group mean feature and assigned it a representative label ( Fig 1 , S2 Table ) . The number of features shrunk to 28 , 562 after reducing redundancy . We performed permutation tests [40 , 41] on the remaining 28 , 562 target features to find features with a significant difference between the successful and failed targets , and we corrected p-values for multiple hypothesis testing using the Benjamini-Yekutieli method [42] ( Fig 1 , S2 Table ) . We used permutation testing to apply the same significance testing method to all features , since they had heterogeneous data distributions . We detected 19 features correlated with clinical outcome at a within-dataset false discovery rate of 0 . 05 ( Table 2 ) . The significant features were derived from 7 datasets , of which 6 datasets were gene expression atlases: Allen Brain Atlas adult human brain tissues [43 , 44] , Allen Brain Atlas adult mouse brain tissues [43 , 45] , BioGPS human cell types and tissues [46–48] , BioGPS mouse cell types and tissues [46–48] , Genotype-Tissue Expression Project ( GTEx ) human tissues [49 , 50] , and Human Protein Atlas ( HPA ) human tissues [51] . The remaining dataset , TISSUES [52] , was an integration of experimental gene and protein tissue expression evidence from multiple sources . Two correlations were significant in multiple datasets: successful targets tended to have lower mean expression across tissues and higher expression variance than failed targets . Because targets of drugs and drug candidates do not constitute a random sample of the genome , features that separate successful targets from failed targets in our sample may perform poorly as genome-wide predictors of success versus failure . We performed three analyses to address this issue ( Fig 1 ) . Statistical significance did not guarantee the remaining features would be useful in practice for discriminating between successes and failures . To test their utility , we trained a classifier to predict target success or failure , using cross-validation to select a model type ( Random Forest or logistic regression ) and a subset of features useful for prediction . Because we used all targets with phase III outcomes for the feature selection procedure described above , simply using the final set of features to train a classifier on the same data would yield overly optimistic performance , even with cross-validation . Therefore , we implemented a nested cross-validation routine to perform both feature selection and model selection [58] .
We searched over 150 , 000 target features from 67 datasets covering 16 feature types for predictors of target success or failure in phase III clinical trials ( Table 1 , Fig 1 ) . We found several features significantly correlated with phase III outcome , robust to re-sampling , and generalizable across target classes ( Table 2 ) . To assess the usefulness of such features , we implemented a nested cross-validation routine to select features , train a classifier to predict the probability a target will succeed in phase III clinical trials , and estimate the stability and generalization performance of the model ( Figs 2 and 3 , Tables 3 , 4 and 5 ) . Ultimately , we found two features useful for predicting success or failure of targets in phase III clinical trials . Successful targets tended to have low mean mRNA expression across tissues and high standard deviation of mRNA expression across tissues ( Fig 3F ) . These features were significant in multiple gene expression datasets , which increased our confidence that their relationship to phase III outcome was real , at least for the targets in our sample , which included only targets of selective drugs indicated for non-cancer diseases . One interpretation of why the gene expression features were predictive of phase III outcome is that they are informative of the specificity of a target’s expression across tissues . A target with tissue specific expression would have a high standard deviation relative to its mean expression level . Tissue specific expression has been proposed by us and others as a favorable target characteristic in the past [4 , 14 , 60–62] , but the hypothesis had not been evaluated empirically using examples of targets that have succeeded or failed in clinical trials . For a given disease , if a target is expressed primarily in the disease tissue , it is considered more likely that a drug will be able to exert a therapeutic effect on the disease tissue while avoiding adverse effects on other tissues . Additionally , specific expression of a target in the tissue affected by a disease could be an indicator that dysfunction of the target truly causes the disease . The distribution of the success and failure examples in feature space ( Fig 3F ) partially supports the hypothesis that tissue specific expression is a favorable target feature . Successes were enriched among targets with low mean expression and high standard deviation of expression ( tissue specific expression ) , and failures were enriched among targets with high mean expression and low standard deviation of expression ( ubiquitous expression ) . However , it does not hold in general that , at any given mean expression level , targets with high standard deviation of expression tend to be more successful than targets with low standard deviation of expression . To further investigate the relationship between these features and phase III clinical trial outcomes , we re-ran the entire modeling pipeline ( Fig 2 ) with gene expression entropy , a feature explicitly quantifying specificity of gene expression across tissues [21] , appended to each tissue expression dataset ( S1 Text ) . Model performance was unchanged ( S1 Fig ) ; gene expression entropy across tissues became the dominant selected feature , appearing in 610 models over 1000 train-test cycles; and mean gene expression across tissues remained an important feature , appearing in 381 models ( S6 Table ) . To find concrete examples illustrating when tissue expression may be predictive of clinical trial outcomes , we pulled additional information from the Pharmaprojects database about targets at the two extremes of tissue expression ( tissue specific or ubiquitous ) . We found examples of: 1 ) successful tissue specific targets where the target is specifically expressed in the tissue affected by the disease ( Table 6 ) , 2 ) failed tissue specific targets with plausible explanations for failure despite tissue specific expression ( Table 7 ) , 3 ) failed ubiquitously expressed targets ( Table 8 ) , and 4 ) successful ubiquitously expressed targets with plausible explanations for success despite ubiquitous expression ( Table 9 ) . Our results encourage further investigation of the relationship between tissue specific expression and clinical trial outcomes . Deeper insight may be gleaned from analysis of clinical trial outcomes of target-indication pairs using gene expression features explicitly designed to quantify specificity of a target’s expression in the tissue ( s ) affected by the disease treated in each clinical trial . Latent factors ( variables unaccounted for in this analysis ) could confound relationships between target features and phase III outcomes . For example , diseases pursued vary from target to target , and a target’s expression across tissues may be irrelevant for diseases where drugs can be delivered locally or for Mendelian loss-of-function diseases where treatment requires systemic replacement of a missing or defective protein . Also , clinical trial failure rates vary across disease classes [2] . Although we excluded targets of cancer therapeutics from our analysis , we otherwise did not control for disease class as a confounding explanatory factor . Modalities ( e . g . small molecule , antibody , antisense oligonucleotide , gene therapy , or protein replacement ) and directions ( e . g . activation or inhibition ) of target modulation also vary from target to target and could be confounding explanatory factors or alter the dependency between target features and outcomes . The potential issues described above are symptoms of the fact that our analysis ( and any analysis of clinical trial outcomes ) attempts to draw conclusions from a small ( 331 targets with only 72 failures ) and biased sample [63 , 64] . The large uncertainty in the performance of the classifier across 200 repetitions of 5-fold cross-validation is evidence of the difficulty in finding robust signal in such a small dataset ( Fig 3 ) . For example , in the region where the model predicts highest probability of success ( low mean expression and high standard deviation of expression ) , there are no failed phase III targets ( Fig 3F ) , which is why the median PPV rises nearly to 1 ( Fig 3C ) , but targets with phase III outcomes sparsely populate this region , so the PPV varies widely depending upon how targets happen to fall into training and testing sets during cross-validation . The small sample issue is compounded by latent factors , such as target classes , disease classes , modalities , and directions of target modulation , that are not uniformly represented in the sample . Correlations between target features and clinical trial outcomes likely depend on these factors , but attempts to stratify , match , or otherwise control for these factors are limited by the sample size . ( The number of combinations of target class , disease class , modality , and direction of modulation exceeds the sample size . ) We employed several tests to build confidence that our findings generalize across target classes , but did not address other latent factors . Consequently , we cannot be sure that conclusions drawn from this study apply equally to targets modulated in any direction , by any means , to treat any disease . For specific cases , expert knowledge and common sense should be relied upon to determine whether conclusions from this study ( or similar studies ) are relevant . Another limitation is selection bias [63 , 64] . Targets of drugs are not randomly selected from the genome and cannot be considered representative of the population of all possible targets . Likewise , diseases treated by drugs are not randomly chosen; therefore , phase III clinical trial outcomes for each target cannot be considered representative of the population of all possible outcomes . Although we implemented tests to build confidence that our findings can generalize to new targets and new target classes , ultimately , no matter how we dissect the sample , a degree of uncertainty will always remain about the relevance of any findings for new targets that lack a representative counterpart in the sample . Additionally , data processing and modeling decisions have introduced bias into the analysis . For example , we restricted the analysis to phase III clinical trial outcomes because failures in phase III are more likely to be due to lack of target efficacy than failures in earlier phases , but factors unrelated to target efficacy still could explain many of the phase III failures , such as poor target engagement , poorly defined clinical trial endpoints , and a poorly defined patient population . Also , we scored each target as successful or failed by its best outcome in all applicable ( selective drug , non-cancer indication ) phase III clinical trials . This approach ignores nuances . A target that succeeded in one trial and failed in all others is treated as equally successful as a target that succeeded in all trials . Also , the outcome of a target tested in a single trial is treated as equally certain as the outcome of a target tested in multiple trials . Representing target outcomes as success rates or probabilities may provide better signal for discovering features predictive of outcomes . Another decision was to use datasets of features as we found them , rather than trying to reason about useful features that could be derived from the original data . Because of the breadth of data we interrogated , the effort and expertise necessary to hand engineer features equally well across all datasets exceeded our resources . Others have had success hand engineering features for similar applications in the past , particularly with respect to computing topological properties of targets in protein-protein interaction networks [18 , 20 , 21] . This analysis could benefit from such efforts , potentially changing a dataset or feature type from yielding no target features correlated with phase III outcomes to yielding one or several useful features [22] . On a related point , because we placed a priority on discovering interpretable features , we performed dimensionality reduction by averaging groups of highly correlated features and concatenating their ( usually semantically related ) labels . Dimensionality reduction by principal components analysis [65] or by training a deep auto-encoder [66] could yield more useful features , albeit at the expense of interpretability . We also employed a stringent univariate feature selection step ( Fig 2 , Step 2 ) to bias our analysis toward yielding a simple and interpretable model . In doing so , we diminished the chance of the multivariate feature selection step ( Fig 2 , Step 4 ) finding highly predictive combinations of features that individually were insignificantly predictive . We addressed this concern by re-running the entire modeling pipeline ( Fig 2 ) with the threshold for the univariate feature selection step made less stringent by eliminating the multiple hypothesis testing correction and accepting features with nominal p-values less than 0 . 05 ( S2 Text ) . This allowed hundreds of features to pass through to the multivariate feature selection step ( Random Forest with incremental feature elimination ) and ultimately dozens of features ( median of 73 ) were selected for each of the final models in the 1000 train-test cycles ( S7 Table ) . Despite this increase in number of features , the mean expression and standard deviation of expression features were still robustly selected , appearing in 958 and 745 models , respectively , and the models had a median AUROC of 0 . 56 and AUPRC of 0 . 75 , performing no better than the simple models ( S2 Fig ) . This finding suggests that our sample size was not large enough to robustly select predictive combinations of features from a large pool of candidate features [67 , 68] . We cannot stress enough the importance of taking care not to draw broad conclusions from our study , particularly with respect to the apparent dearth of features predictive of target success or failure . We examined only a specific slice of clinical trial outcomes ( phase III trials of selective drugs indicated for non-cancer diseases ) summarized in a particular way ( net outcome per target , as opposed to outcome per target-indication pair ) . Failure of a feature to be significant in our analysis should not be taken to mean it has no bearing on target selection . For example , prior studies have quantitatively shown that genetic evidence of disease association ( s ) is a favorable target characteristic [3 , 36] , but we did not find a significant correlation between genetic evidence and target success in phase III clinical trials . Our finding is consistent with the work of Nelson et al . [36] , who investigated the correlation between genetic evidence and drug development outcomes at all phases and found a significant correlation overall and at all phases of development except phase III . As a way of checking our work , we applied our methods to test for features that differ between targets of approved drugs and the remainder of the druggable genome ( instead of targets of phase III failures ) , and we recovered the finding of Nelson et al . that targets of approved drugs have significantly more genetic evidence than the remainder of the druggable genome ( S8 Table ) . This example serves as a reminder to be cognizant of the domain of applicability of research findings . Though we believe we have performed a rigorous and useful analysis , we have shed light on only a small piece of a large and complex puzzle . Advances in machine learning enable and embolden us to create potentially powerful predictive models for target selection . However , as described in the limitations , scarce training data are available , the data are far from ideal , and we must be cautious about building models with biased data and interpreting their predictions . For example , many features that appeared to be significantly correlated with phase III clinical trial outcomes in our primary analysis did not hold up when we accounted for target class selection bias . This study highlights the need for both domain knowledge and modeling expertise to tackle such challenging problems .
Our analysis revealed several features that significantly separated targets of approved drugs from targets of drug candidates that failed in phase III clinical trials . This suggested that it is feasible to construct a model integrating multiple interpretable target features derived from Omics datasets to inform target selection . Only features derived from tissue expression datasets were promising predictors of success versus failure in phase III , specifically , mean mRNA expression and standard deviation of expression across tissues . Although these features were significant at a false discovery rate cut-off of 0 . 05 , their effect sizes were too small to be useful for classification of the majority of untested targets , however , even a two-fold improvement in target quality can dramatically increase R&D productivity [69] . We identified 943 targets predicted to be twice as likely to fail in phase III clinical trials as past phase III targets , and , therefore , should be flagged as having unfavorable expression characteristics . We also identified 2 , 700 , 856 target pairs predicted with 99% consistency to have a 2-fold difference in success probability , which could be useful for prioritizing short lists of targets with attractive disease relevance . It should be noted that our analysis was not designed or powered to show that specific datasets or data types have no bearing on target selection . There are many reasons why a dataset may not have yielded any significant features in our analysis . In particular , data processing and filtering choices could determine whether or not a dataset or data type has predictive value . Also , latent factors , such as target classes , disease classes , modalities , and directions of target modulation , could confound or alter the dependency between target features and clinical trial outcomes . Finally , although we implemented tests to ensure robustness and generalizability of the target features significantly correlated with phase III outcomes , selection bias in the sample of targets available for analysis is a non-negligible limitation of this study and others of its kind . Nevertheless , we are encouraged by our results and anticipate deeper insights and better models in the future , as researchers improve methods for handling sample biases and learn more informative features .
Our goals in performing dimensionality reduction were to identify groups of highly correlated features , avoid excessive multiple hypothesis testing , and maintain interpretability of features . For each dataset , we computed pair-wise feature correlations ( r ) using the Spearman correlation coefficient [72–74] for quantitative , filled-in datasets , and the cosine coefficient [73 , 74] for sparse or categorical datasets . We thresholded the correlation matrix at r2 = 0 . 5 ( for the Spearman correlation coefficient , this corresponds to one feature explaining 50% of the variance of another feature , and for the cosine coefficient , this corresponds to one feature being aligned within 45 degrees of another feature ) and ordered the features by decreasing number of correlated features . We created a group for the first feature and its correlated features . If the dataset mean was included in the group , we replaced the group of features with the dataset mean . Otherwise , we replaced the group of features with the group mean and assigned it the label of the first feature ( to indicate that the feature represents the average of features correlated with the first feature ) , while also retaining a list of the labels of all features included in the group . We continued through the list of features , repeating the grouping process as described for the first feature , except excluding features already assigned to a group from being assigned to a second group . We performed permutation tests [40 , 41] to find features with a significant difference between successful and failed targets . We used permutation testing in order to apply the same significance testing method to all features . The features in our collection had heterogeneous shapes of their distributions and varying degrees of sparsity , and therefore no single parametric test would be appropriate for all features . Furthermore , individual features frequently violated assumptions required for parametric tests , such as normality for the t-test ( for continuous-valued features ) or having at least five observations in each entry of the contingency table for the Chi-squared test ( for categorical features ) . For each feature , we performed 105 success/failure label permutations to obtain a null distribution for the difference between the means of successful and failed targets , and then calculated an empirical two-tailed p-value as the fraction of permutations that yielded a difference between means at least as extreme as the actual observed difference . We used the Benjamini-Yekutieli method [42] to correct for multiple hypothesis testing within each dataset and accepted features with corrected p-values less than 0 . 05 as significantly correlated with phase III clinical trial outcomes , thus controlling the false discovery rate at 0 . 05 within each dataset . We trained a classifier to predict target success or failure in phase III clinical trials , using a procedure like the above for initial feature selection , then using cross-validation to select a model type ( Random Forest or logistic regression ) and subset of features useful for prediction . We used an outer cross-validation loop with 5-folds repeated 200 times , yielding a total of 1000 train-test cycles , to estimate the generalization performance and stability of the feature selection and model selection procedure [58] . Each train-test cycle had five steps: 1 ) splitting examples into training and testing sets , 2 ) univariate feature selection on the training data , 3 ) aggregation of significant features from different datasets into a single feature matrix , 4 ) model selection and model-based ( multivariate ) feature selection on the training data , and 5 ) evaluation of the classifier on the test data . Computational analyses were written in Python 3 . 4 . 5 and have the following package dependencies: Fastcluster 1 . 1 . 20 , Matplotlib 1 . 5 . 1 , Numpy 1 . 11 . 3 , Requests 2 . 13 . 0 , Scikit-learn 0 . 18 . 1 , Scipy 0 . 18 . 1 , and Statsmodels 0 . 6 . 1 . Code , documentation , and data have been deposited on GitHub at https://github . com/arouillard/omic-features-successful-targets .
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Drug discovery often begins with a hypothesis that changing the abundance or activity of a target—a biological molecule , usually a protein—will cure a disease or ameliorate its symptoms . Whether a target hypothesis translates into a successful therapy depends in part on the characteristics of the target , but it is not completely understood which target characteristics are important for success . We sought to answer this question with a supervised machine learning approach . We obtained outcomes of target hypotheses tested in clinical trials , scoring targets as successful or failed , and then obtained thousands of features ( i . e . properties or characteristics ) of targets from dozens of biological datasets . We statistically tested which features differed between successful and failed targets , and built a computational model that used these features to predict success or failure of targets in clinical trials . We found that successful targets tended to have more variable mRNA abundance from tissue to tissue and lower average abundance across tissues than failed targets . Thus , it is probably favorable for a target to be present in the tissue ( s ) affected by a disease and absent from other tissues . Our work demonstrates the feasibility of predicting clinical trial outcomes from target features .
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[
"Abstract",
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"Methods"
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2018
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Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets
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The 146-kDa Pasteurella multocida toxin ( PMT ) is the main virulence factor to induce P . multocida-associated progressive atrophic rhinitis in various animals . PMT leads to a destruction of nasal turbinate bones implicating an effect of the toxin on osteoblasts and/or osteoclasts . The toxin induces constitutive activation of Gα proteins of the Gq/11- , G12/13- and Gi-family by deamidating an essential glutamine residue . To study the PMT effect on bone cells , we used primary osteoblasts derived from rat calvariae and stromal ST-2 cells as differentiation model . As marker of functional osteoblasts the expression and activity of alkaline phosphatase , formation of mineralization nodules or expression of specific transcription factors as osterix was determined . Here , we show that the toxin inhibits differentiation and/or function of osteoblasts by activation of Gαq/11 . Subsequently , Gαq/11 activates RhoA via p63RhoGEF , which specifically interacts with Gαq/11 but not with other G proteins like Gα12/13 and Gαi . Activated RhoA transactivates the mitogen-activated protein ( MAP ) kinase cascade via Rho kinase , involving Ras , MEK and ERK , resulting in inhibition of osteoblast differentiation . PMT-induced inhibition of differentiation was selective for the osteoblast lineage as adipocyte-like differentiation of ST-2 cells was not hampered . The present work provides novel insights , how the bacterial toxin PMT can control osteoblastic development by activating heterotrimeric G proteins of the Gαq/11-family and is a molecular pathogenetic basis for understanding the role of the toxin in bone loss during progressive atrophic rhinitis induced by Pasteurella multocida .
Bone tissue is a common target for bacterial infections . Diseases like caries , periodontitis or osteomyelitis are due to infections by Streptococcus mutans , Actinobacillus actinomycetemcomitans or Staphylococcus aureus inter alia . The mechanism of bacterial-induced bone damage may be caused by factors released from pathogens , which interact with bone matrix or affect bone cells , or by bacteria which directly invade bone cells to initiate pathological changes [1] . One of the skeleton affecting bacteria is Pasteurella multocida , which causes various diseases in men and an`imals . As a commensal P . multocida is found mainly in the nasal/pharyngeal space of domesticated and wild animals and is frequently isolated from cat and dog bites [2] . P . multocida is directly or as a supportive factor connected to several diseases like haemorrhagic septicaemia in hoofed animals , avian cholera or snuffles in rabbits [3] . In the case of the economically important progressive atrophic rhinitis P . multocida has a central role [4] . Atrophic rhinitis is characterized by drastic degeneration of nasal turbinate bones , leading to a shortening and/or twisting of the snout accompanied by growth retardation of young pigs . Besides domesticated pigs , rabbits , wild pigs and cattle show atrophic rhinitis-like symptoms [3] . The causative agent of atrophic rhinitis is Pasteurella multocida toxin ( PMT ) , which is produced by capsular type D and some type A strains [5] . Inoculation of PMT alone is sufficient to generate all symptoms of atrophic rhinitis in animals [6] . Bone tissue is constantly rebuilt by the action of osteoblasts and osteoclasts [7] . Accordingly , analysis of nasal turbinates in atrophic rhinitis disclosed effects of PMT on both types of cells . Besides bone resorption areas , a depletion of osteoblasts was reported [8] . In in vitro models the toxin inhibits osteoblastic differentiation and stimulates the differentiation of osteoclasts [9]–[11] . Moreover , a PMT-induced activation of RhoA seems to be important for the blockade of osteoblast differentiation [12] . Notably , PMT induces bone destruction but exhibits no obvious cytotoxicity [3] , [13] . Up to date a detailed analysis of PMT-activated signaling pathways in osteoblasts was hampered by the fact that the intracellular substrate of the toxin was unknown . Recently , we identified the molecular mechanism of PMT . The toxin stimulates heterotrimeric G protein signaling . In the switch II region of the α-subunit of heterotrimeric G proteins , PMT deamidates a specific Gln residue , which is involved in GTP hydrolysis [14] , [15] . Once the α-subunits are deamidated , they have a constitutive active phenotype . PMT targets α-subunits of the Gαq/11- , Gα12/13- and Gαi-family [16]–[20] . A consequence is the activation of multiple signal transduction pathways , leading in a cell type specific manner to strong mitogenicity , anti-apoptotic effects or restructuring of the cytoskeleton [21] , [22] . Differentiation and activity of osteoblasts and osteoclasts are tightly regulated . Osteoblast differentiation is stimulated by various factors like BMP , PTH or growth factors as IGF or TGF , acting on different types of receptors [23] . In addition , previous studies showed that various heterotrimeric G proteins and G protein-coupled receptors are involved in the regulation of osteoblast differentiation . Thereby , Gαs and Gαi signaling appear to control differentiation of bone cells in an opposite manner [24] , [25] . The opposing effects of Gαs and Gαi on osteoblasts depend at least partly on the regulation of adenylyl cyclase [26] . Furthermore , it was shown that a constitutive active mutant of Gαq blocked differentiation of osteoblasts . Transgenic mice , expressing this mutant in osteoblast progenitors , developed osteopenia [27] . Also the mitogen-activated protein kinase ( MAPK ) pathway contributes to bone development . However , the data available are inconsistent , because studies provide evidence for positive as well as for negative effects on bone cell development upon MAPK activation [28]–[31] . Elucidation of the molecular mechanism of PMT and recent progress in the understanding of bone cell development prompted us to analyze the effects of PMT on osteoblasts and osteoblast precursors in more detail . Here we present evidence that PMT controls the differentiation of osteoblasts by constitutive activation of Gαq/11 , subsequent stimulation of RhoA/Rock pathway via p63RhoGEF and transactivation of MAPK cascade .
To study the effects of PMT on osteoblast differentiation , we used the established cell culture model of ST-2 cells . ST-2 cells are characterized by their potency to differentiate into osteoblast or adipocyte lineages . Thus , in respect to differentiation , the cells are comparable to osteoblast progenitor cells . The primary effect of PMT is the activation of heterotrimeric G proteins by deamidation of a conserved glutamine . Recently , a monoclonal antibody was described , which selectively recognizes the PMT-deamidated G proteins but not the unaffected ones [32] . By using this antibody , we could demonstrate that the toxin-induced deamidation takes place also in ST-2 cells ( Fig . 1A ) . Thus , we studied osteoblastic differentiation by measuring the activity of alkaline phosphatase ( ALP ) after 10 d , which is an early marker of osteoblasts and indicates proper differentiation . As shown in Figure 1B , PMT but not the catalytically inactive mutant PMTC1165S inhibited the osteoblast formation . Moreover , as determined by the concentration dependent blockade of ALP activity , the toxin was found to be a strong inhibitor of osteoblastic differentiation . 10 pM PMT nearly abolished osteoblastic differentiation ( Fig . 1C ) . Additionally , osteoblastic differentiation can be visualized by using the phosphatase substrate enzyme labeled fluorescence ( ELF ) 97 , which specifically stains ALP . Control cells showed strong staining with ELF97 , indicating proper osteoblast differentiation , whereas no alkaline phosphatase staining was visible in PMT-treated cells ( Fig . 1D ) . To exclude that PMT induces degradation of ALP , we quantified mRNA levels . In PMT-treated cells ALP expression levels decreased as compared to cells solely cultured in osteoblastic differentiation medium . In contrast , PMTC1165S exhibited no effect on mRNA levels ( Fig . 1E ) . Because ST-2 cells possess the potency to differentiate to diverse lineages , we tested whether PMT also affects differentiation into the adipocyte lineage . Induction of adipocyte differentiation was monitored by the formation of lipid vacuoles . ST-2 cells were incubated for 6 d with adipocyte differentiation medium and lipid droplets were visualized by Oil Red O staining . As shown in Figure 2A , coincubation with PMT did not impair adipocyte differentiation . In contrast , PMT but not PMTC1165S , even increased lipid droplet formation compared to the induction by differentiation medium alone ( Fig . 2B ) . The increase in Oil Red O staining could be due to more intense staining or more cells showing staining . To address this question we counted positive- and un-stained cells . PMT treatment significantly increased the total cell number ( adipocyte differentiation medium ( adm ) 183 , ±21 SD; adm+PMT 255 , ±20 SD , p<0 . 01 , n = 8 ) and the relative portion of Oil Red O-positive cells ( adm 30 . 5% , ±4% SD; adm+PMT 38 . 0% , ±3 . 8% SD , p<0 . 01 , n = 8 ) . Adipocytes are characterized by the expression of several marker proteins , such as peroxisome proliferator-activated receptor γ ( PPARγ ) or CCAAT/enhancer-binding family of proteins ( C/EBPα ) . Congruently , quantification of mRNA expression levels of those markers showed no inhibitory effects of PMT ( Fig . 2C ) . To exclude the possibility that PMT is not able to activate heterotrimeric G proteins under adipogenic conditions , we verified G protein deamidation in an immunoblot . PMT but not PMTC1165S led to deamidation under these conditions ( Fig . 2D ) . A system of primary osteoblasts from calvariae of neonatal rats was used to strengthen our studies . Also these primary osteoblasts were directly targeted by PMT as shown by deamidation of heterotrimeric G proteins ( Fig . 3A ) . For functional studies , primary osteoblasts were cultivated for 4–10 d and ALP activity was determined by the ALP activity assay or the ELF97 staining , respectively . As found for the differentiation of ST-2 cells to the osteoblastic lineage , PMT inhibited ALP activity of primary osteoblasts at picomolar concentrations ( Fig . 3B ) . In the PMT-treated primary osteoblasts no ALP staining was detectable with ELF97 ( Fig . 3C ) . In accordance with the diminished ALP activity , also mRNA levels of ALP were reduced by PMT . Specific transcription factors , e . g . runt-related transcription factor 2 ( Runx2 ) or osterix ( SP7 ) regulate osteoblast differentiation . qPCR analysis of SP7 and Runx2 expression revealed that PMT treatment significantly reduced the expression of SP7 but not Runx2 in primary osteoblasts ( Fig . 3D ) . The formation of mineralization nodules is another marker for osteoblasts . Also mineralization nodules , detected by van Kossa staining , were nearly abolished in PMT-treated samples ( Fig . 3E ) . The small GTPase RhoA is one of the downstream effectors of PMT-activated heterotrimeric G proteins [17] . Furthermore , it was shown that RhoA is involved in inhibition of osteoblastic differentiation [12] . Therefore , we tested whether the direct activation of RhoA is sufficient to block osteoblastogenesis . To this end , we utilized cytotoxic necrotizing factor ( CNF ) y from Yersinia pseudotuberculosis , which is a specific activator of RhoA [33] . PMT and CNFy activate RhoA as shown by effector pulldown of activated RhoA ( Fig . 4A ) . Treatment of ST-2 cells with increasing concentration of CNFy blocked osteoblastogenesis as measured by the activity of the ALP ( Fig . 4B ) . RhoA activates several effector proteins like the Rho-associated , coiled-coil containing protein kinase ( Rock ) . Pharmacological inhibition of Rock with Y27632 abrogated the inhibitory effect of PMT on osteoblast differentiation ( Fig . 4C ) . In ST-2 cells Y27632 not only reversed the effect of PMT but also increased osteoblastic differentiation . This pro-osteoblastic effect of Y27632 was already previously described [34] and might be due to the reduction of RhoA/Rock signaling below the basal activity level . Our results demonstrate that active RhoA/Rock-signaling inhibits osteoblastogenesis . In turn blockade of RhoA/Rock impairs the PMT effect on differentiation . The small GTPase RhoA can be stimulated by members of the Gαq/11- and Gα12/13-family of heterotrimeric G proteins . Via different Rho guanine nucleotide exchange factors ( GEF ) like p115RhoGEF or p63RhoGEF the heterotrimeric G proteins are linked to RhoA [35] , [36] . To determine the Gαq/11 dependent portion of PMT-induced RhoA activity , we utilized a specific inhibitor of Gαq/11 signaling , YM-254890 [37] . In the presence of YM-254890 the PMT-stimulated RhoA activity was strongly diminished ( Fig . 4D ) . The pivotal role of Gαq/11 in PMT-induced RhoA activity prompted us to study the effect of Gαq/11 inhibition on osteoblast differentiation . Blockade of PMT-induced Gαq/11 activation by YM-254890 abrogated the toxins effect on ALP activity and expression in ST-2 cells ( Fig . 4E ) . Congruently , in primary osteoblasts pharmacological inhibition of RhoA/Rock or Gαq/11 prevent the PMT effect on osteoblast differentiation as measured by ALP activity ( Fig . 4C/E ) . A specific RhoGEF protein , which couples Gαq/11 to RhoA , is p63RhoGEF [36] . This RhoGEF is not ubiquitously expressed [38] , however , we detected its expression in ST-2 cells and primary osteoblasts . Next , we addressed the question whether p63RhoGEF is involved in PMT-induced inhibition of osteoblastogenesis . To this end , we depleted endogenous p63RhoGEF by sh-RNA , using an adenoviral transfection system [39] . As shown in Fig . 5 A/B , sh-p63RhoGEF effectively reduced the endogenous content of p63RhoGEF in stromal ST-2 cells and rat primary osteoblasts . Both types of p63RhoGEF-knockdown cells exhibited declined RhoA activity as compared to control cells after PMT treatment ( Fig . 5C/D ) . These results indicated that a predominant portion of PMT-induced RhoA activity depends on the Gαq/11-p63RhoGEF axis . Moreover , we analyzed the ability of PMT to inhibit osteoblast function in primary osteoblasts after p63RhoGEF-knockdown . In control cells PMT inhibited ALP activity as measured by the ELF97 staining . In contrast , in p63RhoGEF-knockdown cells the inhibitory effect of PMT on osteoblastogenesis was significantly abrogated as shown by strong activity of ALP ( Fig . 5E/F ) . By this approach we confirmed that the Gαq/11-p63RhoGEF-RhoA axis is of major importance for PMT-dependent inhibition of osteoblast differentiation . The mitogen-activated protein kinase ( MAPK ) pathway has been implicated in regulation of osteogenesis [40] . Therefore , we tested whether PMT stimulates the MAPK pathway in osteoblasts by measuring phosphorylation of the extracellular signal-regulated kinase ( ERK ) 1/2 . To this end serum-starved stromal ST-2 cells were incubated with PMT and ERK activity was determined by an immunoblot approach . PMT strongly enhanced ERK activity as shown in Fig . 6A . Additionally , we studied the effect of the specific RhoA activator CNFy . Similar to PMT treatment , the RhoA activation by CNFy increased ERK phosphorylation . Next , we asked whether PMT-induced MAPK activation is involved in the inhibition of osteoblast differentiation . The MEK-1 inhibitor PD98059 effectively abrogated the PMT effect on osteoblastogenesis in ST-2 cells and primary osteoblasts as measured by ALP activity . PD98059 also blocked PMT-induced ERK phosphorylation ( Fig . 6B/C ) . The MAPK/ERK pathway depends on the small GTPase Ras , which is the major switch between MAPK cascade and the growth hormone receptor . We addressed the question , whether the PMT-dependent MAPK activation depends on Ras . To this end , we utilized TpeL ( toxin perfringens large ) , a toxin produced by Clostridium perfringens . TpeL mono-O-GlcNAcylates specifically Ras and inhibits thereby Ras signaling and Ras-Raf interaction [41] . However , at higher concentrations TpeL is also known to affect Rac1 . Therefore , we confirmed that in ST-2 cells and at utilized concentrations TpeL only acts on Ras but not on Rac1 ( Protocol S1 and Figure S1 ) . For further experiments a Ras-selective concentration of TpeL was used . Preincubation of ST-2 cells with TpeL inhibited PMT- and CNFy-induced ERK phosphorylation , demonstrating that MAPK activation by PMT and CNFy depends on functional Ras ( Fig . 6A ) . This result prompted us to study , whether the activation of MAPK depends directly on PMT-induced RhoA activity . Y27632 , a known inhibitor of the RhoA-regulated Rock I and II , impaired ERK phosphorylation in ST-2 cells and primary osteoblasts ( Fig . 6C ) . Additionally , the Gαq/11- induced MAPK activation was studied by using the inhibitor YM-254890 and the p63RhoGEF knockdown cells . YM-254890 abolished PMT-induced ERK phosphorylation . Most interestingly , also the knockdown of p63RhoGEF decreased PMT-induced MAPK signaling in stromal ST-2 cells ( Fig . 6D ) . Both results implicate a Gαq/11 dependent transactivation of the MAPK cascade via the p63RhoGEF-RhoA-Rock axis . This signaling pathway negatively controls osteoblastogenesis .
Atrophic rhinitis is characterized by increased bone resorption by osteoclasts and a lack of bone regeneration by osteoblasts [8] , [42] . In this study we focused on the effect of the causative agent of atrophic rhinitis , PMT , on osteoblast differentiation and activity . Osteoblasts develop from mesenchymal stem cells [43] . These stem cells are multipotent cells , having the potential to differentiate amongst others to adipocytes , osteoblasts or chondrocytes [44] . Therefore , we utilized stromal ST-2 cells derived from murine bone marrow as a cell culture model [45] . Like primary mesenchymal stem cells , ST-2 cells give rise to different cell lineages like adipocytes [46] , osteoblasts [47] , or hematopoiesis supporting cells [48] . Under osteogenic conditions ST-2 cells differentiate into osteoblasts . This can be determined by the measurement of various specific markers of osteoblasts like alkaline phosphatase activity . We found that PMT is able to inhibit osteogenic differentiation of stromal cells as measured by the activity and mRNA expression of the alkaline phosphatase . This effect completely depended on the catalytic action of PMT as the inactive mutant ( PMTC1165S ) exhibited no inhibitory effect on osteoblastic differentiation of ST-2 cells . The high potency of PMT is reflected by the low concentrations sufficient for impairment of cell differentiation . Because of the high potency and effectiveness of PMT to block osteogenic differentiation , we were prompted to test , whether the toxin inhibits any kind of differentiation in stromal ST-2 cells; e . g . osteogenic and/or adipogenic differentiation . Therefore , we cultured stromal ST-2 in a medium forcing adipogenesis . Interestingly , PMT was not able to reduce differentiation of the adipocyte lineage . In line with these findings , the toxin was not able to reduce the mRNA levels of PPARγ and C/EBPα , which typically increase during adipogenesis of ST-2 cells . However , PMT even increased Oil Red O staining . Whether the observed rise in cell number completely accounts for increased Oil Red O staining or the increased expression of transcription factors like C/EBPα is additionally involved , should be clarified in further studies . If the toxin even enhances adipocyte development should also be addressed in a subsequent work . The data indicate that PMT is not a general inhibitor of any type of differentiation but a specific inhibitor of osteoblastic but not adipogenic differentiation in stromal cells like ST-2 . In another cell culture model PMT inhibited adipogenesis [49] . However , a different cell type ( NIH3T3-L1 ) was used , which is hardly comparable with ST-2 cells . Primary osteoblasts from newborn rat calvariae were used to verify the results obtained with the cell culture model ST-2 . Martineau-Doizè and colleagues previously demonstrated that PMT-challenged rats develop atrophic rhinitis like symptoms [50] . Therefore , our model system of primary osteoblasts from rat calvariae should provide insights into the pathogenesis under veterinarian conditions . As observed for ST-2 cell differentiation , PMT strongly impaired the osteogenic development of primary osteoblasts as measured by early markers ( ALP ) or late markers of osteoblastogenesis ( mineralization nodules ) . Osteoblast development is under the control of specific transcription factors . For example RUNX2 and osterix ( SP7 ) are well known regulators of osteogenic differentiation , RUNX2 as an early transcription factor and SP7 as a late one [44] . PMT induced a significant down regulation of the transcription factor SP7 , indicating that not only expression of osteoblast specific proteins , like ALP , is down regulated , but also master regulators of osteoblast differentiation are affected by PMT . However , PMT affected SP7 much stronger as compared to RUNX2 . This might be due to the differentiation status of primary osteoblasts , which already undergo differentiation . Therefore , the late transcription factor SP7 might be more active and can be more efficiently regulated by PMT . PMT reportedly affects primary osteoblasts and osteoclasts in various in vitro systems . Mulan and coworkers performed studies with co-cultures of osteoblasts and osteoclasts . They discussed the question whether both osteoblasts and osteoclasts are directly affected by PMT or whether the toxin targets one cell type to induce effects in the other cell type in a paracrine manner [51] . To unambiguously clarify that PMT directly acts on Gα proteins in osteoblasts , we utilized a monoclonal antibody , which selectively detects a PMT-induced deamidation [32] . This toxin-induced modification of Gα subunits was observed in osteoblastic cells indicating a direct action of PMT . Although PMT did not impair adipocytic differentiation of ST-2 cells , we could also verify the toxin activity under adipocytic conditions . Therefore , we conclude that PMT specifically inhibits the osteoblastic differentiation of stromal ST-2 cells but not adipocytic differentiation , although G proteins are deamidated under both conditions . To analyze the signal transduction pathway of PMT-induced blockade of osteoblastogenesis , the role of Rho proteins was studied . RhoA is a common effector of Gα12/13 and Gαq/11 and PMT was shown to stimulate RhoA via both G protein families [17] , [52] . Because previous studies gave evidence for an important role of RhoA in PMT-induced blockade of osteoblast differentiation [12] , we started to investigate the pathway utilized by PMT to induce RhoA activation . We observed an increased RhoA activity , after treatment of ST-2 cells and primary osteoblasts with PMT . Interestingly , a specific inhibitor of Gαq/11 , YM-254890 [37] , inhibited toxin-induced RhoA activation . These results indicate that in the cells studied , RhoA stimulation is predominantly dependent on Gαq/11 but not on Gα12/13 . In a next step we studied the effect of Gαq/11 inhibition on PMT-dependent blockade of osteoblast differentiation . Gαq/11 inhibition by YM-254890 abolished the inhibitory effect of PMT on the expression of osteoblast markers ( e . g . , alkaline phosphatase ) during osteoblast differentiation . These results confirmed the important role of Gαq/11 in PMT-induced effects . Moreover , our results are in line with the studies of Ogata et al . showing that ectopical expression of a constitutive active mutant of Gαq impairs differentiation and induces osteopenia [27] , [53] . In addition , inhibition of Rock abrogated the effects of PMT on osteoblast differentiation in stromal ST-2 cells and primary osteoblasts reconfirming the pivotal role of active RhoA/Rock as a negative regulator of osteoblastic differentiation [12] , [34] . Because RhoA and Gαq/11 play pivotal roles in PMT-induced osteoblast impairment , we were encouraged to elucidate the missing link between these signaling factors . Heterotrimeric G proteins activate RhoA via RhoGEF proteins , e . g . p115RhoGEF ( Gα12/13 ) or LARG ( Gα12/13 , Gαq/11 ) [35] , [54] , [55] . Moreover , p63RhoGEF specifically couples Gαq/11 but not Gα12/13 , to RhoA activation [36] . By immunoblot analysis we detected expression of several splice variants of p63RhoGEF in stromal ST-2 cells and primary osteoblasts . To clarify the involvement of p63RhoGEF in PMT-induced inhibition of osteoblastogenesis , we utilized an adenoviral shRNA knockdown of p63RhoGEF . p63RhoGEF knockdown in osteoblasts dramatically diminished PMT-induced RhoA activity . These findings indicate that a major portion of RhoA activation depends on the Gαq/11-p63RhoGEF axis . Therefore the Gαα12/13-induced RhoA activation via other RhoGEFs may represent only a minor part of entire RhoA activity . Finally , the inhibitory effect of PMT on osteoblastogenesis in primary osteoblasts was compared to p63RhoGEF knockdown cells . Primary osteoblasts depleted for p63RhoGEF were not affected by PMT , whereas differentiation of control cells was inhibited by PMT intoxication . These results strongly suggest that Gαq/11 activity is inhibitory to osteoblast differentiation . Furthermore , RhoA stimulation due to the Gαq/11-specific p63RhoGEF is sufficient for this effect . The contribution of MAPK signaling in osteoblast differentiation is under discussion [40] . Inhibition of MAPK cascade at the level of growth factor receptor , MEK-1 or ERK leads to an increased differentiation of osteoblasts in different models as primary calvaria-derived osteoblasts or in pre-osteoblastic cell lines [28] , [29] . It is known that PMT induces mitogenic signaling via MAPK-pathway in various cell lines as rat fibroblasts or HEK293 cells [13] , [21] . Therefore , we analyzed the effect of PMT on the MAPK pathway in osteoblastic cells by measuring ERK phosphorylation . PMT was found to be a strong activator of MAPK signaling in ST-2 cells and primary osteoblasts . In line with our observation , it is described that MAPK activation blocks osteoblast differentiation . Congruently , inhibition of PMT-induced ERK phosphorylation abrogated the toxin's effect on osteoblastogenesis . Utilizing various approaches , we examined the PMT-utilized pathway to stimulate MAPK signaling in ST-2 cells and primary osteoblasts . Inhibition of Gαq/11 , Rock and knockdown of p63RhoGEF blocked the toxin-induced ERK phosphorylation . This indicates , that PMT-activated Gαq/11 leads via p63RhoGEF , RhoA and Rock to a transactivation of the MAPK cascade . Inhibition of Ras by TpeL toxin , which inactivates Ras by GlcNAcetylation [41] , supported the hypothesis that the transactivation of the MAPK cascade by PMT depends on functional Ras . Moreover , direct activation of RhoA by CNFy was sufficient to transduce MAPK activation in a Ras dependent manner . However , it is loosely understood how Rho/Rock signaling leads to Ras-dependent MAPK activation . It was suggested that actin dynamics provide a link between Rock and Ras activity [56] . Previously , RhoA-induced MAPK signaling has been implicated in osteoblastogenesis [40] . More recently , a genome wide analysis revealed the GEF Trio responsible for sustained MAPK pathway activation [57] . Trio contains a primary Rac-specific and a secondary Rho-specific GEF domain , of which the latter one is highly homologous to p63RhoGEF and can be activated by Gαq/11 [58] . Here , in osteoblastic cells , we identified p63RhoGEF as a Gq effector , involved in MAPK transactivation via RhoA . p63RhoGEF exhibits a restricted tissue distribution [38] and may represent a specific RhoGEF in osteoblastic cells , important for Gαq/11 dependent signaling . Thus , we suggest that PMT affects pre-/osteoblasts by activating the Gαq/11-p63RhoGEF-RhoA axis . This leads to transactivation of the MAPK pathway resulting in inhibition of the osteoblastogenesis . Besides Gαq/11 PMT activates α-subunits of the Gα12/13 and Gαi family [16] . Also these G proteins are associated with proliferative signaling , i . e . MAPK pathway stimulation [59] , [60] . However , in the tested osteoblastic cells , stromal ST-2 cells and rat calvaria-derived primary osteoblasts the PMT-activated Gαq/11 pathway apparently prevails . The pivotal clinical symptom of atrophic rhinitis is the atrophy of nasal turbinate bones . Over the last decades it was discovered that infections with P . multocida and/or Bordetella bronchiseptica give rise to atrophic rhinitis [61] . The virulence factors of B . bronchseptica and P . multocida are dermonecrotic toxin ( DNT ) and PMT , respectively . DNT activates small GTPases of the Rho family like RhoA , Rac and Cdc42 by deamidation or polyamination and impairs osteoblastogenesis [61] , [62] . Our results with the specific RhoA activator CNFy strengthen the hypothesis that DNT-induced activation of the small GTPase RhoA is the key for the observed effects on bone cells . However , combined effects of DNT , directly acting on Rho GTPases , and PMT , activating Rho GTPases via Gαq/11-p63RhoGEF , might account for the exacerbation of the disease in the case of coinfection . A single infection with B . bronchiseptica causes only moderate bone loss whereas coinfection with P . multocida induces more drastic effects called progressive atrophic rhinitis [61] . Moreover , de Jong and Nielson recognized P . multocida as the causative agent of progressive atrophic rhinitis without any coinfection necessary [4] . This drastic degradation of bone tissue might be explained by the synergy of the PMT-induced effects on bone cells . On the one hand , PMT specifically inhibits osteoblastic differentiation and function and , therefore , hinders new bone formation . For this purpose , PMT utilizes a distinct signaling pathway , which we present in this work . On the other hand , PMT stimulates osteoclast activity and induces thereby a reduction of bone mass [61] . Whether the effect of PMT on osteoclast activity and/or differentiation is direct or indirect is under discussion [51] . For example , osteoclast differentiation is regulated by osteoblast-derived factors . E . g . receptor activator of NF-κB ligand ( RANKL ) is a positive osteoblast-derived factor for osteoclastogenesis; whereas osteoprotegerin ( OPG ) is a negative regulator [63] . Therefore , PMT should at least indirectly affect osteoclast development by targeting osteoblasts . In further studies it would be of interest to clarify the effect of PMT on osteoclasts and osteocytes , which also participate in bone tissue renewal . Besides this obvious impact on bone tissue , the inhibition of osteoblastogenesis by PMT might result in a further pathogenetic advantage for P . multocida as a strong functional interaction of bone and the immune system takes place [64] . Recently , the involvement of osteoblasts in B cell differentiation was demonstrated [65] , [66] . Additionally , it is known that PMT is a poor immunogene . Pigs suffering from atrophic rhinitis do not develop protective or specific immune response [67] , [68] . Moreover , colonization of piglets with toxigenic but not with non-toxigenic strains of P . multocida reduces serum IgA and IgG response to ovalbumin [69] , [70] . Whether the inhibition of osteoblast differentiation via the Gαq/11-p63RhoGEF axis is associated with these previously described immune modulatory effects of PMT should be analyzed in further studies . This would present an important function of PMT in addition to the manifest destruction of bone tissue . In summary , our findings indicate that PMT-induced Gαq/11 activation impairs osteoblastogenesis via a RhoA – MAPK pathway ( Fig . 7 ) . In pre-/osteoblasts , Gαq/11 and RhoA/Rock is linked by p63RhoGEF . PMT-induced RhoA/Rock activation leads to a Ras-dependent transactivation of the MAPK cascade , which is responsible for inhibition of osteoblastogenesis . Thus , the bacterial toxin PMT regulates the osteoblastic cell fate in a heterotrimeric G protein dependent manner .
PCR primers were from Apara ( Denzlingen , Germany ) . All other reagents were of analytical grade and purchased from commercial sources . Murine stromal ST-2 cells were obtained from the Leibniz Institute DSMZ ( German Collection of Microorganisms and Cell Cultures ) and cultivated in RPMI 1640 supplemented with 10% FCS . ST-2 cells were incubated to differentiate into adipocytes or osteoblasts in the corresponding induction medium for 10 days . The adipocyte differentiation medium ( adm ) consisted of RPMI 1640 ( Gibco ) supplemented with 10 µg/mL insulin ( Sigma ) and 40 ng/mL dexamethasone ( Sigma ) . The osteoblast differentiation medium ( odm ) consisted of RPMI 1640 ( Gibco ) supplemented with ascorbic acid ( 284 µM ) and β-glycerophosphate ( 200 µM ) . Medium including supplements was changed every two days . Primary osteoblasts were isolated from 2–3 day old rats using collagenase digestion [71] . The crushed skulls were incubated at 37°C in RPMI 1640 Medium containing 0 . 1% collagenase P for 15 min per digestion cycle . Extracted cells were harvested and resuspended in RPMI 1640 Medium containing 10% FCS . Fractions 2 to 5 were pooled and used for further experiments . After 3 days cultures were trypsinized and seeded for further experiments . Cell lysates were prepared as follows: Cells were grown to confluency and serum starved overnight . After 30 min preincubation with the indicated inhibitors the cells were incubated with PMT ( 1 nM , 5 h ) . Thereafter cells were lysed in RIPA buffer ( 50 mM Hepes , pH 7 . 4 , 150 mM NaCl , 5 mM MgCl2 , 1 mM EDTA , 1% Nonidet P-40 , 0 . 5% ( w/v ) deoxycholate and 0 . 1% ( w/v ) SDS ) , containing complete protease inhibitor ( Roche ) and phosphatase inhibitor cocktail 2 and 3 ( Sigma ) . Protein concentrations of the lysates were determined by Bradford measurement . Lysates with equalized amounts of protein were used for immunoblot analysis . PMTwt and the catalytically inactive mutant PMTC1165S were expressed and purified as described previously [72] . To perform the alkaline phosphatase assay cells were cultured for 10 ( ST-2 ) or 4 ( primary osteoblasts ) days in odm . To measure alkaline phosphatase activity the cells were washed with PBS and then incubated with ALP assay solution ( 8 mM p-nitrophenylphosphate-6 H2O ( Sigma ) , 12 mM MgCl2 , 0 . 1 mM ZnCl2 and 100 mM glycine-NaOH , pH 10 . 5 ) for 10 min at 37°C . The reaction was stopped by the addition of 200 mM NaOH . The absorption was determined at 405 nm . Alkaline phosphatase staining: On coverslips cultivated cells were fixed in 4% PFA for 30 min . To remove the PFA cells were washed twice with PBS . To stain the alkaline phosphatase the cells were incubated with ALP assay solution containing 5% ( v/v ) ELF97 ( Life Techn . ) in the absence of light for 15 min at 25°C . Again cells were washed twice with PBS and incubated for 1 h with a 0 . 02% SYTO Green/PBS solution . Cells were mounted with Mowiol 4–88 ( Carl-Roth ) . van Kossa staining: Cells were stained as described by Mukherjee et . al 2008 [73] . In brief , cells were fixed , washed and incubated with a 5% silver nitrate solution for 30 min . After washing the cells with water the microscopic pictures were obtained . Oil Red O staining: To detect lipid droplets , cells were stained with Oil Red O . Therefore , the cells were fixed in PFA and incubated with a saturated Oil Red O solution . Unbound Oil Red O was washed out with 70% ( v/v ) EtOH . To quantify the amount of Oil Red O bound to lipid droplets , the dye was extracted with a 4% ( v/v ) Nonidet P-40/isopropanol solution . The absorption was measured at 520 nm . Total RNA was extracted from either ST-2 cells or primary osteoblasts with the RNeasy Mini Kit ( Qiagen ) . cDNA was prepared using the QuantiTect Reverse Transcription Kit ( Qiagen ) . All Kits were used following the manufacturer's manual . Quantitative PCR was performed using GoTaq qPCR Master Mix ( Promega ) . The expression levels of the ribosomal Protein S29 ( mouse ) or HPRT ( rat ) were used as an internal control and fold changes were calculated using the ΔΔCt method . Values are shown as 2−ΔΔCt . The following primer pairs were used for analysis: mS29: forward-ATGGGTCACCAGCAGCTCTA , reverse-AGCCTATGTCCTTCGCGTACT , mALP: forward-AATGAGGTCACATCCATCCTG , reverse-CACCCGAGTGGTAGTCACAA , mPPARγ : forward-AAGACAACGGACAAATCACCA , reverse-GGGGGTGATATGTTTGAACTTG , mC/EBPα: forward-AAACAACGCAACGTGGAGA , reverse-GCGGTCATTGTCACTGGTC , rHPRT: forward-GACCGGTTCTGTCATGTCG , reverse-ACCTGGTTCATCATCACTAATCAC , rALP: forward-GCACAACATCAAGGACATCG , reverse-TCAGTTCTGTTCTTGGGGTACAT , rRUNX2: forward-CCACAGAGCTATTAAAGTGACAGTG , reverse-AACAAACTAGGTTTAGAGTCATCAAGC , rSP7: forward-CGTCCTCTCTGCTTGAGGAA , reverse-TGGAGCCACCAAACTTGC For immunoblot analysis proteins were subjected to SDS-polyacrylamide gel electrophoresis and transferred onto polyvinylidene difluoride-membrane . Anti RhoA-antibody ( sc-418 ( 26C4 ) ) was purchased from Santa CruzBiotech ( Heidelberg , Germany ) , anti p63RhoGEF-antibody ( 51004 ) from Proteintech , anti pERK-antibody ( 4370S ) from New England Biolabs , anti tubulin-antibody ( T9026 ) from Sigma Aldrich and anti p84-antibody ( ab487 ) from Abcam . Deamidation specific antibody anti-Gαq Q209E ( 3G3 ) was kindly provided by Dr . Y . Horiguchi ( Osaka University , Japan ) [32] . Enhanced chemiluminescent detection reagent ( 100 mM Tris-HCl , pH 8 . 0 , 1 mM luminol ( Fluka ) , 0 . 2 mM p-coumaric acid , 3 mM H2O2 ) was used to detect binding of the second horseradish peroxidase-coupled antibody with the imaging system LAS-3000 ( Fujifilm ) . Quantifications of immunoblots were done using MultiGauge software . To detect the levels of activated RhoA a Rhotekin pulldown assay was performed as described previously [17] . In brief , cells were lysed after 4 h of treatment with indicated compounds . Rhotekin-coupled beads were incubated with the lysates for 1 h at 4°C . The amount of bound and therefore active RhoA was analyzed by immunoblot analysis . For adenoviral infection cells were seeded and directly supplemented with sh-virus suspension and 8 µg/mL polybrene . A specific p63RhoGEF shRNA was used and as control a specific GFP shRNA [39] . One day after infection cells were starved for 24 h in fresh medium containing sh-virus . Cells were cultured in 10% FCS for two more days for a maximal knockdown efficacy . On day four after viral infection of the cells the described assays were performed . Results are presented as means ± S . E . Significance was assessed by paired Student's t test . p values<0 . 05 were considered statistically significant ( * = p<0 . 05; ** = p<0 . 01; ns , not significant ) . Multiple group comparisons were analyzed by ANOVA followed by Student's t test . All animal experiments were performed in compliance with the German animal protection law ( TierSchG ) . The animals were housed and handled in accordance with good animal practice as defined by FELASA ( www . felasa . eu/guidelines . php ) and the national animal welfare body GV-SOLAS ( www . gv-solas . de ) . The animal welfare committees of the universities of Freiburg as well as the local authorities ( Regierungspräsidium Freiburg , license X-09/31S ) approved all animal experiments .
|
Pasteurella multocida causes as a facultative pathogen various diseases in men and animals . One induced syndrome is atrophic rhinitis , which is a form of osteopenia , mainly characterized by facial distortion due to degradation of nasal turbinate bones . Strains , which especially affect bone tissue , produce the protein toxin P . multocida toxin ( PMT ) . Importantly , PMT alone is capable to induce all symptoms of atrophic rhinitis . To cause osteopenia PMT influences the development and/or activity of specialized bone cells like osteoblasts and osteoclasts . Recently , we could identify the molecular mechanism of PMT . The toxin constitutively activates certain heterotrimeric G proteins by deamidation . Here , we studied the effect of PMT on the differentiation of osteoblasts . We demonstrate the direct action of PMT on osteoblasts and osteoblast-like cells and as a consequence inhibition of osteoblastic differentiation . Moreover , we revealed the underlying signal transduction pathway to impair proper osteoblast development . We show that PMT activates small GTPases in a Gαq/11 dependent manner via a non-ubiquitously expressed RhoGEF . In turn the mitogen-activated protein kinase pathway is transactivated leading to inhibition of osteoblastogenesis . Our findings present a mechanism how PMT hijacks host cell signaling pathways to hinder osteoblast development , which contributes to the syndrome of atrophic rhinitis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"mitogenic",
"signaling",
"host-pathogen",
"interaction",
"microbiology",
"g-protein",
"signaling",
"pasteurellosis",
"bacterial",
"diseases",
"gtpase",
"signaling",
"mapk",
"signaling",
"cascades",
"bacterial",
"pathogens",
"erk",
"signaling",
"cascade",
"infectious",
"diseases",
"signaling",
"in",
"cellular",
"processes",
"pasteurella",
"multocida",
"ras",
"signaling",
"biology",
"pathogenesis",
"signal",
"transduction",
"gram",
"negative",
"molecular",
"cell",
"biology",
"signaling",
"cascades"
] |
2013
|
Pasteurella Multocida Toxin Prevents Osteoblast Differentiation by Transactivation of the MAP-Kinase Cascade via the Gαq/11 - p63RhoGEF - RhoA Axis
|
Loss of gut integrity is linked to various human diseases including inflammatory bowel disease . However , the mechanisms that lead to loss of barrier function remain poorly understood . Using D . melanogaster , we demonstrate that dietary restriction ( DR ) slows the age-related decline in intestinal integrity by enhancing enterocyte cellular fitness through up-regulation of dMyc in the intestinal epithelium . Reduction of dMyc in enterocytes induced cell death , which leads to increased gut permeability and reduced lifespan upon DR . Genetic mosaic and epistasis analyses suggest that cell competition , whereby neighboring cells eliminate unfit cells by apoptosis , mediates cell death in enterocytes with reduced levels of dMyc . We observed that enterocyte apoptosis was necessary for the increased gut permeability and shortened lifespan upon loss of dMyc . Furthermore , moderate activation of dMyc in the post-mitotic enteroblasts and enterocytes was sufficient to extend health-span on rich nutrient diets . We propose that dMyc acts as a barometer of enterocyte cell fitness impacting intestinal barrier function in response to changes in diet and age .
The intestinal epithelium forms a permeable barrier that segregates the internal and external environments , allowing for the absorption of nutrients , but at the same time keeping toxic substances and pathogens from entering the body . The intestine also manages the interaction between the host and the gut microbiome . Increasing gut permeability is one of the risk factors for developing inflammatory bowel disease ( IBD , including ulcerative colitis and Crohn's disease ) [1] and contributes to systemic immune activation , which promotes the progression of chronic inflammation [2] , a known risk factor for aging and some age-related diseases [3 , 4] . Several mechanisms have been postulated to influence intestinal permeability . These include changes in the microbiota , luminal secretion of mucins and anti-microbial peptides ( AMPs ) , and tight junction proteins [2 , 5] . However , the role of intestinal cell turnover in modulating intestinal permeability remains underexplored . Removal of intestinal cells by apoptosis , a type of programmed cell death , is an active process that is used to eliminate unwanted cells which are then replaced by dividing intestinal stem cells . Increased intestinal apoptosis and gut barrier dysfunction have been linked to multiple diseases , including necrotizing enterocolitis ( NEC ) , IBD , intestinal cancer , and HIV infection [6–8] . However , the causal link between intestinal apoptosis and gut barrier function remains to be established . Disruption of intestinal homeostasis is one of the hallmarks of aging in both vertebrate and invertebrate species [9–11] . There are several similarities between the mammalian and Drosophila gut architecture , making flies an attractive model to study gut barrier disorders [12–15] . The intestinal epithelium in Drosophila is comprised of intestinal stem cells ( ISC ) , enteroblasts ( EB ) , enterocytes ( EC ) , and enteroendocrine cells ( EE ) . Loss of intestinal barrier function leads to the increased systemic production of AMPs , which are regulated by Toll and Immune deficiency ( IMD ) innate immune pathways [16 , 17] . Loss of intestinal barrier function is also associated with increased mortality in aging flies [18 , 19] and is likely a consequence of age-associated inflammation . Dietary restriction ( DR ) , which is the reduction of specific nutrients without causing malnutrition , is a robust environmental intervention that slows aging and age-related diseases in a diverse set of species including yeast , worms , fruit flies and rodents [20–23] . In D . melanogaster , DR imposed by reduction of yeast in the diet not only extends lifespan [24 , 25] but is also able to slow the age-related decline in gut integrity [19 , 26] . Similarly in the mouse gut , calorie restriction ( CR ) is known to alter epithelial structure and function , including villi length , crypt depth and cell turnover [27–29] . Thus , studying the mechanisms by which DR improves gut integrity has great significance for understanding aging and certain intestinal disorders where nutrition is a risk factor . We demonstrate that the rate of enterocyte apoptosis in the rich nutrient conditions can be attenuated by DR in Drosophila melanogaster . We hypothesize that nutrient-dependent as well as age-related increase in apoptosis in enterocytes holds the key to understanding phenomena like gut inflammation , gut permeability , and aging . We show that a rich diet reduces intestinal cellular fitness due to the reduction of dMyc expression and enhances cell competition-mediated cell death . Cell competition is defined as short-range elimination of unfit cells ( loser cells ) by apoptosis when confronted by fitter neighboring cells ( winner cells ) , which has been shown to occur in the fly intestine [30 , 31] . In the Drosophila larval wing disc , relative dMyc expression levels have been shown to determine loser and winner cells . Wing disc cells with lower dMyc expression levels compared to neighboring cells become loser cells and are eliminated by apoptosis [32] . Importantly , this Myc-dependent cell competition mechanism is conserved in mammalian embryos [33 , 34] . Myc-high naive cells remove Myc-low differentiating cells to maintain the purity of the pluripotent cell pool [35] . However , cell competition and the role of dMyc in intestinal post-mitotic cells has not been described before . We have identified a critical role for diet-dependent modulation of dMyc in regulating the age-related cellular fitness in ECs . Furthermore , we show that dMyc-dependent regulation of intestinal cell death is crucial for intestinal barrier function and organismal survival . Our findings highlight the importance of understanding mechanisms that balance intestinal apoptosis with repair upon aging and dietary modulation , which are likely to play a significant role in age-related diseases and various intestinal disorders .
Heterozygous dMyc mutant flies display a lifespan extension [36] . Furthermore , reduced expression of Myc also extends lifespan and slows the onset of age-related pathologies like osteoporosis , cardiac fibrosis , and immunosenescence in mice , suggesting conserved effects of Myc on aging [37] . While these findings have shown that reduced Myc levels are beneficial , a recent report demonstrated that DR increases dMyc protein abundance and boosts the innate immune response [38] . Hence , dMyc may have different effects in specific tissues under different nutrient conditions . We first examined if modulation of dMyc in different tissues is necessary for nutrient-dependent lifespan changes . We imposed DR and ad libitum ( AL ) conditions on adult D . melanogaster using diets that differed only in the yeast content ( 0 . 5% and 5% yeast extract in the media for DR and AL diets respectively ) [39–41] . Surprisingly , EB/EC-specific knockdown of dMyc during the adult stage using a drug ( RU486 ) -inducible Gal4 driver ( 5966-GS Gal4; referred to as 5966-GS ) reduced the maximal DR-mediated lifespan extension . EB/EC-specific dMyc knockdown reduced lifespan by 32% in flies on DR compared to control flies not administered RU486 . However , EB/EC-specific dMyc knockdown in flies on AL resulted in a slight lifespan reduction ( 19% ) ( Figs 1A and 4D , S2 and S3 Tables ) . Two independent RNAi strains confirmed dMyc’s effect on lifespan ( Figs 1A , S1A and S1B ) . We used the Cox proportional-hazards model to statistically determine the gene diet-interaction in influencing lifespan upon inhibition of dMyc . The gene-diet interaction terms was highly significant ( p < 0 . 0001 ) ( S4 Table ) . Similar results were also observed upon knockdown with an EC-specific driver , Np-1-Gal4 ( S1C Fig and S2 Table ) . To achieve adult-specific dMyc knockdown and avoid developmental defects by the loss of dMyc , we used the temperature sensitive Gal4 suppressor , Gal80ts in combination with NP-1- Gal4 [42] . We also observed that dMyc knockdown using Act5C-GS , which targets multiple tissues including the intestine also prevented the maximal lifespan extension by DR ( S1D and S1E Fig and S2 Table ) . Compared to dMyc knockdown using a 5966-GS driver , DR-dependent lifespan extension was not altered when dMyc expression was inhibited in both the ISCs and EBs ( using 5961-GS ) . Similarly , dMyc knockdown in the fat body ( using S1106-GS ) failed to affect DR-dependent lifespan extension ( Fig 1B–1D , S2 and S3 Tables ) . These results suggest temporal importance for the effect of dMyc expression on lifespan . Although reduction of dMyc in the whole body from the developmental stage extends lifespan , an inhibition of dMyc in the ECs in the adult stage significantly diminishes the benefit of DR on lifespan . Gut barrier dysfunction has been associated with increased mortality in flies [18 , 19] . Therefore , we examined gut integrity in EB/EC-specific dMyc knockdown flies using the Smurf assay . In this assay , flies are fed a blue food dye that normally does not cross the intestinal barrier . However , flies that display a loss of gut integrity turn blue and are thus termed , ‘Smurfs’ [19 , 43] . Consistent with a previous report [19] , the number of Smurf flies increased with age . Control populations ( without RU486 ) reared under DR conditions exhibited reduced percentage of Smurfs compared to AL conditions ( Fig 2A ) . DR diet also delayed the age-related induction of the antimicrobial peptide , Diptericin , in the fat body and gut of w1118 flies ( S2B and S2C Fig ) which suggests a reduction in systemic inflammation . EB/EC-specific dMyc knockdown , on the other hand , resulted in a significant increase in the percentage of Smurfs upon DR ( Fig 2A ) . Diptericin expression was increased in flies with EB/EC-specific dMyc knockdown at a middle age on AL condition , but showed increased expression at a later age upon DR ( Figs 2B and S2D ) . These results suggest that dMyc in ECs is necessary to maintain the gut barrier function . A dynamic equilibrium exists between damaged enterocytes and ISC proliferation to maintain intestinal homeostasis [44] . Damaged enterocytes undergo apoptosis and release the interleukin-6-like cytokine , upd3 , to enhance ISC proliferation and initiate intestinal repair [44] . Thus , we examined whether dMyc knockdown in the ECs alters intestinal homeostasis by modulating secretion of upd3 from ECs , to induce proliferation of ISCs . We observed increased expression of upd3 in 21-day old dMyc knockdown flies under both AL and DR conditions ( Fig 2C ) . EB/EC-specific dMyc knockdown upon DR also resulted in a significant increase in ISC proliferation as measured by the mitotic cell proliferation marker phospho-histone H3 , in 21-day old flies ( Fig 2D ) . However , no significant change in ISC proliferation on AL diet was observed ( Fig 2D ) , presumably because a threshold for activating ISC proliferation in the AL conditions is higher than that of DR . These data suggest that dMyc in ECs influences diet-dependent changes in intestinal permeability as well as ISC proliferation , thus impacting function and homeostasis of the intestinal epithelium . Next , we investigated whether the increase in ISC proliferation upon loss of dMyc using the 5966-GS driver is a result of increased cell death in the intestine . We performed acridine orange fluorescence staining and the TUNEL assay to assess apoptosis [45] . Consistent with the low appearance of Smurf flies upon DR ( Fig 2A ) , control flies reared on DR showed fewer numbers of apoptotic cells in the gut compared to flies on AL ( Figs 3A , 3A’ and S3A ) . Knockdown of dMyc in the gut using 5966-GS significantly increased both the number of TUNEL and acridine orange positive cells upon DR ( Fig 3A , 3A’ and S3A ) . Similar to the ISC proliferation data ( Fig 2E ) , we did not observe a significant difference in the number of apoptotic cells in EB/EC-specific dMyc knockdown flies on the AL diet , despite these flies expressing high levels of upd3 ( Fig 2C ) . In Drosophila , activated JNK signaling induces apoptotic cell death through induction of the pro-apoptotic gene hid [46 , 47] . dMyc knockdown on both diets resulted in an upregulation of the JNK targets , hid and puckered when examined in 21-day old flies ( Figs 3B and S3B ) . We also confirmed the activation of JNK signaling upon EB/EC-specific dMyc knockdown using the pucE69 reporter strain ( puc-lacZ ) [48] at day 21 of age ( S3C and S3C’ Fig ) . When we inhibited JNK signaling in the EB/EC-specific dMyc knockdown background , we found it failed to rescue the reduction of lifespan . ( S3D Fig and S2 Table ) . We also tested the inhibition of JNK signaling alone using 5966-GS . JNK inhibition in EBs/ECs strongly reduced lifespan on both AL and DR conditions ( S3E Fig and S2 Table ) . These results argue that several downstream pathways are likely to be important to explain the dMyc knockdown phenotypes and that JNK is one of them but not sufficient for rescuing the lifespan . Additionally , JNK signaling in the EBs/ECs is required to optimally enhance fly survival . Together , these data suggest that dMyc plays a crucial role in modulating the diet-dependent changes in intestinal cell death with age . To verify whether induction of cell death in the EBs/ECs is sufficient to increase the number of flies with intestinal permeability , we ectopically induced apoptosis in EBs/ECs and measured gut permeability . Overexpressing the pro-apoptotic gene reaper in the enterocytes using 5966-GS increased the number of Smurf flies ( Fig 3C ) . Next , we investigated whether gut dysfunction caused by loss of dMyc is due to increased apoptosis . We induced p35 ( a universal apoptosis inhibitor ) in the dMyc-RNAi background . p35 overexpression significantly reduced the number of Smurf flies ( Fig 3D ) and ISC proliferation ( Fig 3E ) in both AL and DR conditions . Furthermore , p35 overexpression was able to partially rescue the lifespan reduction seen in 5966-GS-specific dMyc knockdown flies under both nutrient conditions ( Fig 3F , S2 and S3 Tables ) . We also observed similar results by inhibition of dronc ( an initiator caspase-9 ortholog ) in the dMyc-RNAi background ( S4A , S4B and S4C Fig ) . These data support the notion that enterocyte cell death is necessary to cause intestinal permeability and reduce lifespan upon inhibition of dMyc . Intestinal apoptosis has been linked with changes in microbiome composition in D . melanogaster . Inhibition of the intestinal homeobox gene caudal leads to overexpression of AMPs . This overexpression results in gut epithelial cell apoptosis , which is mediated by the microbiome [49] . Thus , we examined whether the gut microbiota contributes to dMyc mediated cell death in the ECs . We reared EB/EC-specific dMyc knockdown flies on antibiotic diets after eclosion , to address the role of intestinal bacteria in modulating cell death . We observed that antibiotic treatment is sufficient to reduce fat body-specific expression of Diptericin in flies at 21 days on both AL and DR diets ( Fig 4A ) . Importantly , antibiotic treatment did not rescue the up-regulation of apoptosis indicator genes ( puc , hid and upd3 ) in the gut ( Fig 4B–4D ) , or the ISC hyper-proliferation in EB/EC-specific dMyc knockdown flies ( Fig 4E ) . Furthermore , antibiotics failed to rescue gut integrity in EB/EC-specific dMyc knockdown flies ( Fig 4F ) . However , antibiotic treatment was sufficient to partially extend the lifespan in dMyc knockdown flies on both DR and AL diets ( Fig 4G ) . These results suggest that increased apoptosis in dMyc knockdown flies is not dependent on the influence of gut bacteria; however , the reduction of bacterial load in the gut can diminish mortality in these conditions . These data are consistent with the idea that EC cell death compromises gut barrier function and thus exposes the internal tissues to infiltration by bacteria or bacterial antigens , resulting in systemic inflammation and increased mortality . To further investigate the mechanisms by which dMyc regulates EC fate , we quantified dMyc mRNA expression in the gut under AL and DR conditions . Flies fed on AL diet showed an age-dependent reduction in the expression of dMyc mRNA in the intestine but not the fat body ( Figs 5A and S5A ) . Notably , this age-dependent decrease in dMyc expression was attenuated upon DR ( Fig 5A ) . This age-dependent downregulation of dMyc mRNA cannot be explained by the number of ECs , because DR flies showed higher dMyc mRNA at old age after normalizing by expression level of Pdm1 , an EC-specific marker [50] ( S5B Fig ) . We also observed that dMyc expression in the posterior midgut was maintained upon DR until old age in dMyc:GFP-tagged flies [36] ( Fig 5B and 5B’ ) . Age-related reduction of dMyc expression is consistent with the increased apoptosis observed in the flies in AL conditions compared to the DR diet ( Fig 3A ) . As dMyc-deficient cells have been shown to be removed from the larval imaginal disc by cell competition [51] , we hypothesized that the same phenomenon occurred in dMyc-deficient enterocytes . To investigate this hypothesis , we created genetic mosaic dMyc knockdown EBs/ECs in the adult intestine . We utilized the CoinFLP-Gal4 system [52] , which contains transcriptional STOP cassette in between canonical FRT sites and FRT3 sites . Act5C-Gal4 is expressed when recombination occurs between canonical FRT sites but not in the FRT3 sites . We induced these two types of recombination events only in the post-mitotic intestinal cells , the EBs and ECs , by temporal activation of UAS-FLP for 24 hours under the control of 5966-GS , in young flies upon DR diet . Thus , we named this the 5966-GS: Coin-Flip-out system ( S5C and S5D Fig ) . Since this system carries the UAS-EGFP transgene , flip-out EBs/ECs are GFP positive , which allows one to monitor the turnover of post-mitotic intestinal cells . We found that the area of dMyc knockdown flip-out cells was significantly reduced at 7 days after flip-out event ( AFO ) compared to that at 48 hours AFO upon DR ( Fig 5C and 5C’ ) . In contrast , we did not observe a significant difference in the area of the GFP-positive cell between 48 hours and 7 days AFO in the WT flip-out cells ( Fig 5C and 5C’ ) . Elimination of dMyc knockdown flip-out cells is not DR-specific , as we also observed similar results on AL conditions ( S5E and S5E’ Fig ) , suggesting that importance of dMyc on EC health . Next , we asked whether loss of dMyc flip-out cells is caused by cell death using SYTOX orange nucleic acid staining . SYTOX orange was incorporated into the nucleus of GFP-positive dMyc flip-out cells at 48 hours AFO upon DR while WT flip-out cells did not show the staining with SYTOX ( Fig 5D ) . Furthermore , inhibition of apoptosis by overexpressing Drosophila inhibitor of apoptosis 1 ( DIAP1 ) in dMyc flip-out cells was sufficient to inhibit elimination of dMyc knockdown cells ( Fig 5C and 5C’ ) . Thus , the loss of dMyc in the intestine leads to a reduction in cellular fitness and eliminates cells by apoptosis . We also found that wild-type ISCs respond to the loss of dMyc by enhancing ISC proliferation , which was quantified using a phospho-histone H3 antibody ( Fig 5E and 5E’ ) . Finally , we examined whether EB/EC-specific overexpression of dMyc is sufficient to increase fly survival . Indeed , overexpressing dMyc in EBs/ECs was sufficient to extend lifespan on AL diets , but it reduced lifespan on the DR diet ( S6A Fig ) . In order to overcome this detrimental effect , we activated dMyc expression in the middle of life by feeding RU486 from 21 days of age . Activation of dMyc in EBs/ECs later in life was able to delay the onset of death on AL diets , but it slightly reduced maximum lifespan on AL . There was a lack of a significant effect on the lifespan under DR conditions ( Figs 6A and S6B ) . In order to get a modest activation of dMyc in the EB/ECs , we fed RU486 to flies only 2 days a week during the entirety of adult life . We found that intermittent dMyc overexpression in the EBs/ECs extended health-span on AL and did not show any detrimental effect on fly survival compared to control flies ( Figs 6B and S6C ) . These data support our notion that slightly enhanced dMyc levels in the ECs extend lifespan , however , excess levels of dMyc appears to have a detrimental effect on flies .
The intestine is subject to continuous cellular turnover . To maintain gut homeostasis , both proliferative and regenerative capacities have to be regulated . Previous studies have demonstrated that upon exposure to acute stresses there is an increase in EC apoptosis which induces ISC proliferation to enhance repair of the intestine [53] . We hypothesize that enterocytes undergo cell death in response to cellular stresses imposed by overnutrition and age , which are then repaired by the elimination of damaged cells and their eventual replacement by differentiated ISCs . Clonal analysis revealed that gut turnover in protein-poor conditions is slower than turnover in protein-rich conditions in Drosophila [54] . Consistently , we found that ISC proliferation is significantly decreased upon DR while apoptosis is reduced ( Figs 2D & 3A ) . These results suggest that gut turnover in DR conditions is slower than AL conditions due to the reduced nutrient stress of DR . A number of labs have previously shown an age-related increase in ISC proliferation [10 , 55] . Our data suggest that the increases in ISC proliferation are a compensatory mechanism for the age-related increase in enterocyte apoptosis . The gastrointestinal tract forms an excellent ecological niche for a wide variety of commensal microbes that live in proximity with the mucosal epithelial barrier [56] . Age-related intestinal barrier dysfunction is linked to microbiota dysbiosis [18] . In our experiments , antibiotic treatment partially rescued the lifespan but did not rescue the intestinal cell death-associated phenotypes in EB/EC-specific dMyc knockdown flies ( Fig 4 ) . This suggests that dMyc knockdown-mediated apoptosis and cytokine expression are not due to microbiota changes . The commensal microbial ecosystem contributes to the maintenance of the overall intestinal barrier architecture by regulating the organization of epithelial tight junctions lining mucosal surfaces [56] . Tight junctions are complexes that seal adjacent epithelial cells and prevent the trafficking of elements across the gut epithelial barrier [57] . Intestinal barrier permeability has been shown to increase with age in mammals [11 , 58 , 59] , perhaps due to differential expression of tight junction proteins ( such as occludins and claudins ) [5] . In Drosophila , epithelial cell integrity is regulated by an apical protein complex composed of septate junctions , which are the Drosophila analog to tight and adherence junctions [60] . Recent reports have described that gut barrier dysfunction is tightly associated with a reduction of cell junction components [18 , 26] . Therefore , we investigated whether dMyc regulates the expression of the septate junction protein , Discs large ( Dlg ) . Although we observed that EC/EB-specific dMyc knockdown alters epithelial cell integrity , we were still able to detect Dlg expression in 5966-GS-specific dMyc knockdown flies on both diets ( S7 Fig ) . These data suggest that dMyc does not maintain the gut barrier function through modulation of cell junction proteins . We show that dMyc expression declines in an age-dependent manner , especially in the gut , in rich-nutrient conditions ( Fig 5A and 5B ) . Furthermore , we show that loss of intestinal dMyc is detrimental and leads to increased cell death and intestinal permeability . Thus , dMyc may act as a barometer of fitness in adult enterocytes . Previous studies have shown that reduction of Myc in the whole body has beneficial effects on lifespan in mice and flies [36 , 37] , but here we show that inhibition of dMyc in the ECs during the adult stage reduces lifespan . In Drosophila , Myc has diverse tissue-specific effects [61–63] . One possibility to explain these contradictory results is that developmental or tissue-specific knockdown of dMyc may have beneficial effects in adult life , while knockdown of dMyc in the gut is detrimental . Consistent with this notion , a previous study has shown that DR-dependent increases in dMyc abundance improves immune response and resistance against pathogenic bacteria infection in adult flies [38] . Hence , the role of dMyc in different tissues may be altered by dietary composition and age . A recent study revealed that cell competition contributes to healthy aging and tissue homeostasis by eliminating unfit cells , especially during development [64] . Our study suggests that enterocytes in the adult aging intestine maintain homeostasis through cell competition . Our study provides a major role for dMyc-mediated cell competition in the adult intestine upon dietary shifts which influences intestinal permeability and lifespan ( Fig 7 ) . Loss of dMyc with age or under high nutrient stress could alter the balance of cellular fitness and induce cell death by cell competition to eliminate unfit cells . On the other hand , reduced gut turnover upon DR may compromise the replacement of dying cells upon dMyc knockdown , leading to gut barrier dysfunction and abrogation of lifespan extension . Cell competition in the intestinal stem cell compartment in the fly has been shown to be mediated by the JNK and JAK-STAT pathways [30] . We find induction of JNK and the JAK-STAT ligand Upd3 in animals with intestine-specific knockdown of dMyc ( Figs 2 and S3 ) . However , the inhibition JNK signaling fails to rescue the lifespan reduction observed in dMyc knockdown flies . ( S3D Fig ) . Multiple mechanisms are likely at play to regulate the cell death upon inhibition of dMyc . It is possible that the innate immunity system modulates cell competition in the gut because Toll-related receptors ( TRRs ) /NFkB signaling contributes to loser cell elimination during Drosophila development [65] . Further analysis is needed to unveil the role of the innate immunity pathway on cell competition during aging and dietary shifts . Loss of intestinal homeostasis is associated with many diseases , including IBD , autoimmune diseases , chronic inflammation , cancer , obesity , and diabetes [66 , 67] . The intestine is a highly metabolically active tissue which is exposed to the environment and has to adapt to various dietary changes as well as the microbial environment . Thus , the rate of damage accumulating in the aging gut is likely to be significantly higher than in other tissues . Our study demonstrates a critical role for Myc in diet- and age-induced changes in gut homeostasis . We hypothesize that Myc and pathways regulating cell competition are possible targets for therapeutic interventions against a range of age-related and inflammatory diseases .
Flies were reared on standard laboratory diet ( Caltech food recipe; 8 . 6% Cornmeal , 1 . 6% Yeast , 5% Sucrose , 0 . 46% Agar , 1% Acid mix ) [41 , 68] . Emerged adults were transferred within 3–5 days to yeast extract diet ( 8 . 6% Cornmeal 5% Sucrose , 0 . 46% Agar , 1% Acid mix , and variable concentrations of yeast extract ) . The AL diet contains 5% yeast extract while the DR diet has 0 . 5% yeast extract . For Gene-Switch Gal4 drivers , RU486 was dissolved in 95% ethanol and was used at a final concentration of 100 μM ( the media is then referred to as '+RU486' ) . The control AL or DR diet contained the same volume of 95% ethanol and is referred to as '–RU486' . Lifespan analysis was followed as described previously [41] . For antibiotic treatment , 50 μg/ml each of kanamycin sulfate , ampicillin sodium , tetracycline hydrochloride , and erythromycin were mixed with autoclaved diets . Survival curves were created using the product-limit method of Kaplan and Meier . The log-rank ( Mantel-Cox ) test was used to evaluate differences between survivals and determine P values . We used the Prism software package ( GraphPad Software ) to carry out the statistical analysis and to determine lifespan values . We analyzed the significance of the interaction between two variables in several of the survival outcomes and determine P values using Cox proportional hazards analysis implemented in the R package 'survival' . The following strains were obtained from the Bloomington stock center: UAS-dMyc RNAi TRiP-1 ( 25784 ) , UAS-dMyc RNAi TRiP-2 ( 36123 ) , UAS-dMyc ( 9674 ) , dMyc-GFP . FPTB ( 38633 ) , UAS-dronc RNAi ( 32963 ) and CoinFLP-Gal4; UAS-EGFP ( 58751 ) . UAS-dMyc RNAi ( v2947 ) was obtained from the Vienna Drosophila RNAi Center . Total RNA was extracted from 12 female guts , 8 female fat bodies ( fly abdomen ) or 5 female whole flies using Quick-RNA MiniPrep Kit ( Zymo Research ) . cDNA was synthesized using QuantiTect Reverse Transcription Kit ( QIAGEN ) . 1 μg of total RNA was used per sample . qPCR reaction was performed in duplicate on each of 3 independent biological replicates using SensiFAST SYBR No-ROX Kit ( BIOLINE ) . Error bars indicate SD . Samples were normalized with an endogenous control , ribosomal protein 49 ( rp49 ) . The primer sets used for qPCR are summarized in S1 Table . Flies were dissected in PEM ( 100 mM Pipes , 2mM EGTA and 1 mM MgSO4 ) . Dissected guts were fixed with 4% formaldehyde in PEM for 45 minutes . Samples were washed for 10 minutes three times with PEM then incubated with 1% NP40/PEM for 30 minutes . Samples were washed for 10 minutes three times with TBS-TB ( 20 mM Tris-HCl , 130 mM NaCl , 1 mM EDTA , 0 . 1% Triton X-100 and 0 . 2% BSA ) and blocking was performed with 5% goat serum in TBS-TB for 2 hours at room temperature . Samples were incubated with primary antibody overnight at 4°C , were then washed for 10 minutes three times with TBS-TB , and incubated with secondary antibody for 2 hours at room temperature . Nuclei were stained using DAPI . Samples were mounted with Mowiol mounting buffer and analyzed by confocal microscope ( Zeiss: LSM780 ) and fluorescence microscope ( KEYENCE: BZ-X710 ) . The following antibodies were used in this study: anti-rabbit GFP ( Life technologies: 1/500 ) , anti-rabbit phospho-histone H3 ( Millipore: 1/1 , 000 ) , anti-rabbit β-galactosidase ( MP: 1/500 ) , anti-mouse Dlg ( DSHB: 1/50 ) , anti-rabbit Alexa fluor 488 ( Life technologies: 1/500 ) , anti-mouse Alexa fluor 488 ( Life technologies: 1/500 ) and anti-rabbit Alexa fluor 555 ( Life technologies: 1/500 ) . Apoptotic cells were detected using the ApopTag Red In Situ Apoptosis Detection Kit ( Millipore: S7165 ) . Flies were dissected in PEM . Dissected guts were fixed with 4% formaldehyde in PEM for 45 minutes . Then we followed the manufacture’s protocol . Nuclei were stained using DAPI . Samples were mounted with Mowiol mounting buffer and analyzed by the fluorescence microscope ( KEYENCE: BZ-X710 ) . Dissected guts were incubated with acridine orange ( Sigma: 5 μg/ml ) and Hoechst 33342 ( Life technologies: 10 μg/ml ) in PBS for 5 minutes at room temperature . Samples were rinsed with PBS twice , then mounted with PBS and immediately analyzed by microscope ( Olympus: BX51 ) . Dissected guts were incubated with SYTOX Orange Nucleic Acid Stain ( Invitrogen: 1 μM ) and Hoechst 33342 ( Invitrogen: 10 μg/ml ) in PEM for 10 minutes at room temperature . Samples were rinsed with PEM twice , then mounted with PEM and immediately analyzed by microscope ( KEYENCE: BZ-X710 ) . Smurf assay was adapted as described [19] . Female flies were fed either AL or DR diets before the assay . Flies were placed in an empty vial containing a piece of 2 . 0 cm x 4 . 0 cm filter paper . 350 μl of blue dye solution , 2 . 5% blue dye ( FD&C #1 ) in 5% sucrose , was used to wet the paper as feeding medium . Flies were maintained with feeding paper for 24 hours at 25°C .
|
Dietary restriction ( DR ) is a robust environmental method to slow aging and age-related diseases in diverse organisms . Age-related disruption of gut integrity has been observed in both mammals and fruit flies and is a determinant of lifespan . In Drosophila , DR is able to slow the age-related decline in gut integrity . Although commensal dysbiosis has been proposed as a leading cause of gut barrier dysfunction , antibiotic treatment does not prevent the age-related increase in gut permeability . We identify that an intrinsic mechanism regulates gut barrier function through regulation of enterocyte apoptosis by ‘cell competition’ . We show DR up-regulates dMyc expression in the gut which enhances enterocyte cellular fitness , prevents the age-related decline in gut integrity , and contributes to DR-induced lifespan extension . Conversely , on a rich diet , inhibition of dMyc in the enterocytes leads to cell death that enhances gut permeability and leads to systemic inflammation and shortened lifespan .
|
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2018
|
Dietary restriction improves intestinal cellular fitness to enhance gut barrier function and lifespan in D. melanogaster
|
Although local eradication is routinely attempted following introduction of disease into a new region , failure is commonplace . Epidemiological principles governing the design of successful control are not well-understood . We analyse factors underlying the effectiveness of reactive eradication of localised outbreaks of invading plant disease , using citrus canker in Florida as a case study , although our results are largely generic , and apply to other plant pathogens ( as we show via our second case study , citrus greening ) . We demonstrate how to optimise control via removal of hosts surrounding detected infection ( i . e . localised culling ) using a spatially-explicit , stochastic epidemiological model . We show how to define optimal culling strategies that take account of stochasticity in disease spread , and how the effectiveness of disease control depends on epidemiological parameters determining pathogen infectivity , symptom emergence and spread , the initial level of infection , and the logistics and implementation of detection and control . We also consider how optimal culling strategies are conditioned on the levels of risk acceptance/aversion of decision makers , and show how to extend the analyses to account for potential larger-scale impacts of a small-scale outbreak . Control of local outbreaks by culling can be very effective , particularly when started quickly , but the optimum strategy and its performance are strongly dependent on epidemiological parameters ( particularly those controlling dispersal and the extent of any cryptic infection , i . e . infectious hosts prior to symptoms ) , the logistics of detection and control , and the level of local and global risk that is deemed to be acceptable . A version of the model we developed to illustrate our methodology and results to an audience of stakeholders , including policy makers , regulators and growers , is available online as an interactive , user-friendly interface at http://www . webidemics . com/ . This version of our model allows the complex epidemiological principles that underlie our results to be communicated to a non-specialist audience .
Impacts of invading pathogens can be extremely severe , and so understanding how controls can be optimised is imperative [1] . We focus here on plant disease , motivated by the serious and potentially irreparable ecological damage that can follow introductions of plant pathogens into natural host populations [2] , and the obvious food security and economic implications of epidemics in crops [3–5] . Increased global trade and travel mean the risk of introduction of exotic pathogens can only reasonably be expected to increase [6] , which in turn indicates control of invasive plant disease is likely to remain important for many years to come . We target optimising reactive eradication of small-scale outbreaks of an invading plant pathogen [7–10] occurring in regions extending from 1-10km . We concentrate on how cryptic infection ( i . e . infectivity without symptoms ) , the inherent stochasticity of epidemics , and uncertainties in the parameters controlling disease spread affect the performance of control via local removal of plant hosts in the vicinity of detected infection . Effectively controlling in this fashion is extremely challenging , requiring an estimate of how far the epidemic has spread ahead of visibly infected regions . Nevertheless , recent modelling studies of a number of plant pathogens have shown how , in principle , control by local removal of susceptible hosts can be effective in managing plant disease [11–13] , even when disease spreads non-locally via complex contact networks [14–18] . A consensus has begun to emerge that this type of control can be successful , albeit with the beguilingly simple proviso that there is a need to “match the scale of control with the intrinsic scale of the epidemic” [19] . The obvious problem is that the appropriate scale is very difficult to define , and depends in a complex fashion on the interplay between the epidemiology of the plant-pathogen interaction , the spatial distribution of susceptible hosts , the implementation and logistics of detection and control , and the current state of the epidemic . The quantitative detail of how these factors affect the nature of the optimal control strategy and how effectively it performs is extremely complex , and general principles remain ill-understood . However , identifying such general principles is clearly relevant to the control of all plant diseases . Here we use mathematical modelling to investigate epidemiological principles underlying successful control . We consider a range of strategies for management of a newly invading plant pathogen , and identify optimal control scenarios that minimise the “epidemic impact”; we define this to be the total of both the number of hosts lost to disease and healthy hosts removed by control . We show the importance of allowing for the inherent stochasticity of epidemics in comparing control scenarios by analysing the empirical distribution of epidemic impacts for fixed values of epidemiological and control parameters . In particular , we show how the optimal control strategy changes when we take account of different levels of risk aversion [20] . We further analyse the effects of critical epidemiological and logistic factors on disease control . The following are considered: the rate and spatial scale of disease spread , the initial level of infection , the ability to detect disease , the length of time infection remains cryptic , the frequency of surveying and any notice period or other delay before removal . We also show how accounting for the risk of export of inoculum outside of the region of immediate interest leads to optimum control strategies that differ from those derived by focusing solely on local impacts [21 , 22] . Finally we consider how control is adversely affected if pathogen spread occurs via a thick-tailed dispersal kernel , although we demonstrate that even then an optimal control strategy can still be defined . Although the issues we investigate are generic , we illustrate our work in the context of an economically important system for which eradication by reactive culling was attempted: citrus canker ( caused by the bacterium Xanthomonas citri pv . citri ) on commercial and residential citrus in Florida . The United States government spent over one billion dollars on survey , control and compensation costs during an eradication campaign that ran from 1996 to 2006 [9] ( see S1 Text for further details ) . Aside from the attraction of choosing a prominent and controversial real-world example to frame our analyses , a major motivation for using this system as our case-study is an extremely detailed data set on disease spread in five uncontrolled sites in the Miami region , originally collected by the United States Department of Agriculture [23] . These data have allowed parsimonious , stochastic , spatially-explicit , epidemiological models to be fitted to the spread of citrus canker that track the disease status of individual host plants [11 , 12 , 24 , 25] , and here we use a flexible extension of these models to analyse the effectiveness of the control scenarios we consider . We also take advantage of recent work fitting this type of model to huanglongbing disease ( also known as HLB or citrus greening ) [26] , a disease of citrus vectored by psyllids and that is caused by Candidatus Liberibacter spp . bacteria , in order to demonstrate the flexibility of our methods and the generality of the underlying principles . A principal challenge in using epidemiological models to inform policy is rendering the assumptions and outputs of models in forms that can be readily understood and interrogated by policy makers [27] . What is needed is a tool to allow epidemiological and disease control scenarios to be explored by regulatory decision makers . This would not only help ensure appropriate action is taken , but would also help ensure factors affecting the success ( and the risk of failure ) of a preferred control strategy are understood by those who have to make and justify decisions . Epidemiologists have typically adopted a “black-box” approach , in which the analytical process is hidden and where only the resulting control recommendations are delivered . This lack of transparency makes it difficult and in some cases impossible for stakeholders affected by control to question the scientific basis of decision making , leading to controversy and even to less effective control [28] . Accordingly we introduce a user-friendly interface to our model , enabling the effects on control performance of changes to disease spread and control parameters to be explored , which as we demonstrate here can include applying the model to different pathosystems . By allowing for an ensemble of simulation runs with identical parameters , this ‘front-end’ also allows stochastic variability to be visualised . The front-end is available online at http://www . webidemics . com/ ( Webidemics is a backronym: ( WEB ) -based ( I ) nteractive ( D ) emonstration of ( E ) pidemiological ( M ) odelling ( I ) nforming ( C ) ontrol ( S ) trategies ) . The Webidemics interface demonstrates the challenges inherent in optimising control strategies that account for cryptic infection , stochasticity and uncertainty in parameter values .
Our model readily accommodates an arbitrarily complex host landscape , from regular geometrical patterns typical of agricultural and horticultural crops , to random and clustered patterns typical of plant hosts in natural environments . With that generality in mind , we illustrate the approach for a random host landscape typical of urban Florida , with 2000 trees randomly distributed on a 2km x 2km square at a plausible density for dooryard citrus ( i . e . trees in residential gardens ) [12 , 25] . We note our Webidemics interface also accommodates a citrus grove ( the colloquial term for what is also referred to as a “citrus orchard” ) , with 2016 hosts in two adjacent blocks , planted in rows 10m apart and with a 5m within-row host spacing , reflecting standard practice in the U . S . citrus industry . For brevity all results presented in this paper for citrus canker correspond to the random host landscape ( note , however , that our application of the model to HLB considers disease spread through the citrus grove host landscape ) . We use a spatially-explicit , stochastic , individual-based , compartmental model to represent the spread of a plant pathogen through a population of N hosts ( Fig 1a ) . Hosts are categorised by disease status: ( S ) usceptible hosts are uninfected; ( E ) xposed hosts are latently infected , and so are neither symptomatic nor infectious; ( C ) ryptic hosts are infectious but still asymptomatic; ( D ) etectable hosts are symptomatic but not infectious; ( I ) nfected hosts are both infectious and symptomatic; and ( R ) emoved hosts are epidemiologically inert , either because of disease-induced death or because the host has been removed by any control effort . The Webidemics interface allows the timing of the cryptic and detectable classes to be exchanged , with visible symptoms either preceding or following the onset of infectiousness , and therefore can represent any of the SECI , SEDI , SECIR and SEDIR epidemic models [19] . Here , motivated by the biology of citrus canker [24 , 29 , 30] , we concentrate exclusively on the SECI and SECIR variants of the model , in which detectable symptoms strictly follow infectiousness . This is the case in which control is most difficult , since it is hampered by invisible cryptic infection . We note that , although citrus canker does not itself directly kill host plants , accounting for control requires there to be a removed compartment in the model , as does allowing the user of our Webidemics interface to apply the model to host-pathosystems for which there is disease-induced host death . In the absence of control , the E to I and C/D to I transitions occur at fixed rates γ and σ , and so waiting times in these compartments are exponentially distributed , with means 1/γ and 1/σ , respectively ( see Table 1 ) . This assumption could of course readily be relaxed to allow for other distributions of waiting times [31] . In the SECIR and SEDIR variants of the model , the rate of disease-induced death is μ , again with an exponentially distributed transition time ( mean 1/μ ) . For any given host plant , the rate of the S to E ( susceptible to exposed ) transition depends on the status of other hosts and the suitability of the environment for infection . In particular , if host i is susceptible at time t then it becomes latently infected ( i . e . transitions to the E compartment ) at rate ϕi ( t ) =ω ( t ) ( ε+β∑jK ( dji;α ) ) , ( 1 ) where the summation runs over all infectious hosts , j , and where host j is at distance dji from host i . The underlying maximal rates of primary and secondary infection are ε and β , respectively , and ω ( t ) ≤1 parameterises any time-variation in environmental suitability for infection ( see below ) . The dispersal kernel , K ( d;α ) , reflects the probability that an infectious host causes infection of a susceptible host at distance d , and is governed by scale parameter α . To allow robustness to the form of dispersal to be explored , we consider two contrasting kernels: the thin-tailed exponential kernel , K ( d;α ) = A exp ( –d/α ) , and the thick-tailed Cauchy kernel , K ( d;α ) = A/ ( 1+ ( d/α ) 2 ) . In each case the normalising constant A is set via ( 1/N ) ∑i∑j≠iK ( dij;α ) =1 . We assume that the host landscape is surveyed for disease at regular intervals Ts , starting at time t = T0 , and on each survey any symptomatic ( i . e . class D or I ) hosts are independently detected with probability p . Detected hosts are flagged for subsequent removal , together with any other hosts ( irrespective of disease status ) within a pre-determined distance L of each detected focus . In practice , removal actually occurs after a variable time delay , and we assume this is normally distributed with specified mean ( Tc ) and standard deviation ( σc ) . This allows for logistic delay ( s ) in control , including notice periods to allow for legal challenges , or delays in deployment of requisite equipment and/or manpower . A truncated normal distribution is used to ensure that all delays are positive and so that removal occurs strictly after detection . Removed hosts are not replanted in our model , in keeping with the original practice for citrus canker in Florida . We model environmental suitability for pathogen spread via a discrete-time Markov chain with two states , ( S ) uitable and ( U ) nsuitable ( Fig 1b ) . This controls ω ( t ) in ( Equation 1 ) , with ω ( t ) = ωs = 1 for state S and ω ( t ) = ωu ≤ 1 for state U . The value ωu is therefore a measure of the relative unsuitability of state U . A probabilistic transition between states potentially occurs every Tw units of time . The probability of entering either state then depends only upon the current state , with p ( U | S ) = η and p ( S | U ) = ρ . At equilibrium , the probability of the environment being suitable ( i . e . in state S ) is π=ρη+ρ , and so the mean value of ω ( t ) is π + ( 1–π ) ωu . The initial state is chosen randomly according to π , ensuring that the equilibrium properties of the chain control its statistics . Markov chains offer a parsimonious approximation to environmental dynamics in a number of contexts [32] , and a similar two-state formalism has previously been used to model infection rates of plant pathogens [33] . An advantage of a Markov chain model is that it simply requires information on threshold conditions , for example temperature and humidity , that favour or inhibit infection , and these are likely to be known for many plant pathogens or can at least be quantified relatively easily . Although we acknowledge more extensive information concerning these drivers is in fact already well-known for citrus canker [29] , in general this obviates the need to derive costly functional forms for relationships between propagule production and environmental driving variables . By default , however , in illustrating the use of the model we restrict our attention to the case in which ωu = ωs = 1 . Here we use the citrus canker system as a case study to illustrate general principles underlying the effectiveness of control . Parameters can readily be adapted via our front-end interface to reflect other pathosystems ( cf . S2 Text , which describes the application of our model to HLB , and S1–S3 Figs . , which show the results ) . As defaults we therefore use illustrative parameters informed by the biology [29 , 30] and adapted from previous models of citrus canker [11 , 12 , 24 , 25] ( Table 1 ) to drive our mathematical model . The host population is surveyed every 90 days [30] , symptomatic hosts are detected with probability 0 . 8 , and host removal occurs exactly 60 days after detection [29] . The default cull radius is set to be 75m; this default radius was chosen to emphasise the range of outcomes that is possible for a single control strategy , even when all parameters remain fixed . Epidemics are seeded with two exposed hosts at t = 0 ( a different pair of hosts for each realisation ) . The average latent period is 10d [29] and symptoms take an average of 110d to emerge following infection [11] . The dispersal kernel is exponential , with mean dispersal distance of 40m ( i . e . α = 20m ) , and we take the rate of secondary infection to be β = 0 . 03d-1 ( cf . the values of α = 37m and β = 0 . 036d-1 as used in the analyses of Cook et al . and Parnell et al . [11 , 12 , 24] , after accounting for our normalisation of the dispersal kernel ) . We selected default dispersal scale and infection rate parameters that lead to slightly slower and more spatially-restricted spread in comparison with those in previous analyses . This allows us to present extensive sensitivity analyses to parameters that would be expected to make control more difficult ( e . g . long cryptic periods , lengthy delays between detection and tree removal ) , without optimal controls degenerating to immediate removal of the entire population at the time of the first control for our rather small population of interest . However , we demonstrate the robustness of our results to this slight alteration of the parameters in S3 Text and S4–S6 Figs . , in which we repeat a selection of the analyses using exactly the parameters of Cook et al . [24] as a baseline . In explaining and discussing the practical use of this model with stakeholders , including policy makers , regulators and growers , it became apparent that providing a user-friendly version of the model for presentation was important to allow the inferences to be understood and visualised by non-specialists . We therefore developed an online interface to the model , allowing the results of either a single run or a small ensemble of runs to be explored , and also allowing for the alteration of parameters controlling disease spread and/or control . This Webidemics front-end runs in commonly-used web browsers via the freely-available Adobe Flash Player plug-in . It is an interface to a ‘back-end’ program that runs on a central web server; this is written in C , and is the component that actually performs the model simulations presented in this paper . Implementation of the back-end via Gillespie’s algorithm [34] allows the extensive replication ( many millions of independent runs ) that underlies the analytical results we present . It also allows the user of the front-end to obtain results from an entire ensemble of hundreds of replicate epidemics within a reasonable time . Parameters and results are passed between the front- and back-ends via a Perl CGI ( “Common Gateway Interface” ) wrapper program hosted on the web server .
To examine how control performance depends on the cull radius , we performed 10000 replicate simulations for epidemics spreading according to the default parameters ( cf . Table 1 ) , at control radii ranging from L = 0m to L = 500m ( Fig 2a ) . The epidemic impact , κE ( the total number of hosts lost to disease or control by the time of eradication ) is highly sensitive to the cull radius , L . At small L , the region of cryptic infection surrounding detected trees is underestimated and the disease spreads widely; at large L , many healthy trees are unnecessarily removed . At intermediate cull radii , performance improves markedly , and an optimum radius can be uniquely determined if the objective is phrased in terms of minimising an average of the epidemic impact , κE . For example , median κE is minimised at cull radius L = 159m , with a median of 132 hosts removed from the total of 2000 . The radius L = 159m is therefore optimal in the sense of previous modelling studies [11 , 12 , 24] , and the “intrinsic scale of the epidemic” sensu Gilligan [35] has therefore been identified at this radius . However , focusing solely on average performance ignores elements of the response of epidemic impact ( κE ) to the cull radius ( L ) that may be of practical significance . The distributions shown in the inset to Fig 2 for a selection of radii ( L = 50m , 75m , 100m , 150m , 200m and 400m ) allow the following pair of provisos to this naïve optimum to be identified . In practice optimisation of any control strategy would be driven by parameters estimated for pathogen spread and epidemiology , and these would be subject to error and/or uncertainty . This tends to make the sensitivity of epidemic impact ( κE ) to changes in cull radius identified in i ) ( above ) unworkable , since an optimum strategy ( distribution D ) that skirts so close to failure ( distribution C or more dramatically B ) would almost certainly be difficult to recommend in practice . A pragmatic choice would therefore be to focus on a higher percentile of the distribution of κE when prescribing the control , with the particular percentile selected corresponding to the risk-aversion of the decision-maker . This approach can be formalised , by explicitly considering a risk of failure that is deemed to be acceptable ( Fig 2b ) . In particular , given a notion of an acceptable level of risk ( e . g . at most a 10% chance of κE corresponding to the loss of more than Ω = 20% of hosts ) a range of acceptable cull radii can readily be determined . A workable strategy would then be to select a cull radius near the centre of this range . Such a combination of criteria would lead to a prescribed cull radius of around 225m here ( for the default parameters , p ( Failure at risk = 20% ) < 0 . 1 for 122m < L < 329m ) . For simplicity , we revert to using the median epidemic impact to summarise the efficacy of intervention , although we note the criterion could be readily adjusted as described above to suit different degrees of risk aversion . The optimal cull radius L is surprisingly unresponsive to the initial level of infection , E0 ( Fig 2c ) ; the “correct” radius is virtually unaffected by the epidemic size when control starts . However , the corresponding epidemic impact increases very rapidly , and , for example , when only 2 . 5% of hosts are initially infected ( uniformly at random ) , approximately 80% of hosts would eventually have to be removed before the pathogen was eradicated , even when controlling optimally . This confirms and quantifies the intuition that it is important to act quickly when confronted by a new outbreak , particularly if the initial infections are not clustered in space . The optimum cull radius and the performance when controlling optimally also depend strongly on the scale of pathogen dispersal ( α ) and the rate of secondary infection ( β ) ( Fig 2d and 2e ) . We illustrate typical decisions that must be made by policy-makers by focusing on the effect on control performance of a selection of four parameters that may be changed during the course of an eradication scheme ( Fig 3 ) . These are the average cryptic period , ( 1/σ ) , which may vary depending upon environmental conditions; the probability of detecting a symptomatic host ( p ) , which may vary depending on the experience of the teams of observers; and the interval between successive surveys ( Ts ) and the delay period before culling actually occurs ( Tc ) , which are both controlled by the availability of resources . As 1/σ , Ts or Tc increase , or as p decreases , effective control becomes more challenging , and so the optimal cull radius and epidemic impact at this radius both increase . Particularly striking is that extreme changes to the probability of detection and average cryptic period are required for performance to degrade significantly . The influence of changes to either of these is mitigated by averaging over a large number of hosts: only a single host must become detectable or be detected for control to be initiated locally , and the resulting cull then affects many nearby hosts simultaneously . However , because the survey interval and the notice period both affect all hosts equally , more modest changes affect the success of control to a greater extent . Susceptible hosts will almost always be present outside the area of first detection . However , we have focused on control performance on a small landscape; in this sense we have considered only the “local” impact of the epidemic . In practice “global” impacts ( i . e . on all plants that could possibly become infected , irrespective of location ) would also need attention in designing control strategies . A possible proxy for the risk to the area outside the region of immediate interest is the time taken to eradicate the local epidemic ( “epidemic time” , τE ) , since this sets the duration of possible export of inoculum ( Fig 4a ) . Surprisingly the epidemic time ( τE ) initially increases as the cull radius ( L ) increases , at least for L below the optimum that minimises the local epidemic impact . While such controls fail to keep up with the region of cryptic infection surrounding detected hosts and so do not effectively control the epidemic , a proportion of infected plants is detected and removed on each round of surveying , and this causes the epidemic to spread more slowly ( because infected hosts are being removed and there are fewer susceptible hosts to infect ) . Slower spread then allows the pathogen to persist for longer , since it takes more time for the infection and eventual removal of hosts . For larger control radii , however , we note the epidemic time can be very small; the pathogen is eradicated very quickly , generally within one or two rounds of detection and control . The appropriate balance between local and global impacts is necessarily a pragmatic choice to be made by the decision maker , and it is impossible for us to be too prescriptive . We account for this need for flexibility by introducing a tuneable composite measure of global “epidemic cost” , ΨE , intended to balance the epidemic impact and epidemic time , allowing for different weightings of local vs . global impacts . In particular , we define the normalised epidemic cost via ψE= ( 1-η ) κ^E+ητ^E , where κ^E and τ^E are simply the epidemic impact , κE , and the epidemic time , τE , normalised to a [0 , 1] scale ( this is done by dividing by the maximum of the median values over all cull radii; for κE this is 1980 hosts at L = 0m , whereas for τE it is 3200d at L = 63m ) . The dimensionless weighting parameter η then controls the relative importance assigned to global impacts . In particular , taking η = 0 means only local impacts would be considered , with normalised epidemic cost ψE= κ^E ( i . e . the normalised epidemic impact ) , whereas η = 1 would entirely focus on impacts outside the region of immediate interest , with ψE= τ^E ( i . e . the normalised epidemic time ) . As η is increased , the cull radius that minimises ΨE is increased ( Fig 4b ) : the larger weighting given to global impacts means that it becomes increasingly optimal to control very aggressively to eradicate the local epidemic as quickly as possible , despite the large number of local removals that would then be required . The epidemic time , τE , may be of particular significance to policy makers , since the duration of a control programme will be an important determinant of public opinion . However , the time taken to eradicate the pathogen is of course not the only way of characterising the risk of pathogen spread outside the area that is actively being controlled . The area under the disease progress curve AE=∫t=0τE ( C ( t ) +I ( t ) ) dt , quantifies the total amount of inoculum that would be exported over the course of the entire epidemic ( Fig 4c ) . This can be used to calculate the probability of at least one escape to the region that is not being controlled , pE , via pE=1−exp ( −λAE ) , where λ is a measure of the degree of connectivity between the local and non-local populations of host plants . In principle the connectivity ( λ ) could be determined for any particular landscape structure , although as we show here , the response of pE to the cull radius ( L ) is robust to extremely wide variations in the value of λ ( Fig 4d ) . Assuming that a single escape from the region under active control would be sufficient to initiate a global epidemic , we can then define a variant measure of epidemic cost via ζE= ( 1-δ ) κ^E + δ p^E , where δ controls the importance assigned to local vs . global impacts ( i . e . δ plays the same role in the definition ζE of as does η in the definition of ΨE ) , and where p^E is pE normalised to a [0 , 1] scale ( by dividing by the maximum value of pE , which occurs at L = 0 ) . The response of ζE to the weighting parameter δ is similar to the response of ΨE to η ( cf . Fig 4e , the response to different values of δ when λ = 10-5d-1 ) , and the conclusion that the optimal radius increases with increasing the weighting of global impacts , δ , is robust to all values of the connectivity , λ , we consider , over a range of orders of magnitude ( Fig 4f ) . Again , very extensive controls in the region under active management become optimal when the possible global impacts of disease are judged to be important . Neri et al . [25] recently fitted a model of the type we use here to the dataset on the spread of citrus canker in Miami that we described in the Introduction ( note this is also the dataset used by Cook et al . [24] ) . In common with Cook et al . [24] , Neri et al . [25] found that an exponential dispersal kernel was best-supported by the data . However these authors found only a small difference in model goodness of fit between the exponential and Cauchy kernels . Neri et al . [25] suggest this partial lack of identifiability is driven by the effect of continual primary infection ( i . e . infection from outside the study site , within which disease spread was mapped ) . At large distances from infected hosts , the small and slowly decreasing probability of infection that would be associated with Cauchy dispersal is very difficult to distinguish from the small and effectively unchanging probability that would follow an exponential kernel combined with a constant background rate of primary infection caused by fat-tailed dispersal from one or more distant sources of inoculum , or by anthropomorphic introduction of inoculum on implements , clothing or cuttings . The study sites were relatively small ( <10km2 ) uncontrolled regions embedded within a large ongoing epidemic . A non-zero rate of primary infection from outside was necessary to fit the spread data in these sites , since there was significant ingress of infection from outside each site . Here , since we specifically target an isolated outbreak of emerging plant disease , far from any large source of inoculum , primary infection is not required in the analyses in the current paper . We accordingly set the rate of primary infection to zero throughout our analyses , and default to using an exponential dispersal kernel , for consistency with the fitting of Neri et al . [25] and Cook et al . [24] , together with the previous analyses of Parnell et al . [11 , 12] . It is well known that exponential dispersal leads to epidemics characterised by wavelike spread , whereas thick-tailed dispersal ( exemplified here by the Cauchy kernel ) implies continual production of distant secondary foci with no well-defined epidemic front [36 , 37] . The striking difference in epidemic pattern suggests that effective control via local removal of hosts should be more difficult when there is Cauchy dispersal . For purposes of comparison , we therefore test the effect of fat-tailed dispersal on our analyses At low infection rates , effective control remains possible even with thick-tailed dispersal . For the Cauchy kernel with scale parameter α = 20m and infection rate β = 0 . 007d-1 , good control can be achieved using a cull radius L = 100m ( Fig 5a ) , although the long tail of the epidemic impact distribution ( Inset B ) reveals large epidemics are possible even when controlling optimally . However , successful control requires a small infection rate , and only moderate increases to the infection rate β greatly increase both the minimum median epidemic impact κE and the cull radius , L , at which this optimum κE is achieved ( Fig 5b ) . What responses of the median κE to L and β do not reveal , however , is how quickly the chance of a large epidemic increases as the infection rate goes up . For an infection rate β of only 0 . 01d-1 there is a significant risk of failure of control , as indicated by the extremely variable distribution of epidemic impact for all control radii , even near the optimum cull radius L ~ 300m ( Fig 5c ) . Indeed it is impossible to select a range of radii that leads to at most a 10% chance of losing less than Ω = 20% of hosts for this value of β ( see Fig 5d and contrast with Fig 2c ) , reiterating the relative difficulty of control when there is fat-tailed dispersal . We also note that fat-tailed dispersal would be expected to increase the degree of connectivity between the local and non-local populations of host plants , λ , were we to repeat the analysis associated with Fig 4 using such a dispersal kernel . Taken together these observations suggest that successful control by culling may be difficult for plant pathogens that spread via windborne propagules which are tolerant to desiccation . These include powdery mildews and rust pathogens . Effective control of these pathogens is likely to require large local cull radius and could be expected to have relatively high risks of failure . The Webidemics interface ( http://www . webidemics . com/ ) shows either the results of a single realisation of the model ( Fig 6a ) or summarises an entire ensemble of replicate simulations performed using identical parameters ( Fig 6b ) . When results from a single run are displayed , an animation of disease progress is shown , with hosts colour coded by epidemiological type ( i . e . S , E , C or D , I , R ) , and with any hosts set to be inaccessible for detection denoted by a black cross . Any hosts in compartment R that were removed by disease-induced death are distinguished from those culled by control . The same colour coding is used for the graph showing the number of hosts in each class . In the single run screen , the graph shows the time-dependence in the number of hosts in each class for the realisation being shown . However , in the ensemble of runs screen , the graph tracks the time-dependence of the average number of hosts in each compartment . The left hand panel then shows animated histograms of the numbers of hosts in each compartment over time . Clicking on any bar in a histogram switches back to the single run view , displaying a ( randomly chosen ) realisation from within the original ensemble that had a number of hosts within the range of the chosen histogram entry at the relevant time . Epidemiological , climatic and/or control parameters may be set on either screen , using the three buttons at the bottom right: clicking any of these buttons reveals a pop-up panel allowing parameter values to be set ( Fig 6c ) . When parameters are altered , the displayed results are not updated until a call is made to the back-end to actually run new simulation ( s ) with the new parameters ( this is done by clicking the “Run New Simulation”/“Run New Ensemble” button ) . Changes to parameters that have not yet been followed by a call to the back-end and so are not reflected in the current results are indicated by a colour change of the button from grey to red . Further details of the user-interface are available via its help facility , which includes a full description showing how to use model in practice , designed for first time users . The default cull radius of 75m as used in the front-end was chosen to emphasise how stochasticity can affect the effectiveness of a single control scenario when all parameters are fixed . We show here the results from this scenario , using fixed default epidemiological and control parameters ( see Table 1 ) , as seen by the user of our front-end interface . Control efficacy is extremely variable ( Fig 7a and 7b ) . The realisation shown in Fig 7a leads to fewer than 10% of hosts ( 159 from 2000 ) removed before eradication at 790 days . However the simulation run shown in Fig 7b reveals the risk of a far greater epidemic impact , despite identical parameters controlling disease spread and control . A small proportion of asymptomatic but infectious trees escape control on each round of removal , and this leads to widespread disease . Nearly 90% of hosts ( 1743 out of 2000 ) are eventually removed before the pathogen is fully controlled at t = 4300d . Similar behaviour is easily observable via the interface ( cf . Fig 7c , which shows typical histograms summarising the final state of 500 runs using the default parameterisation ) .
We present a novel stochastic analysis of the control of plant disease , focusing on how the performance of reactive control by localised culling can be optimised . We have also introduced Webidemics , an interactive online tool designed to communicate principles affecting effective control to an audience of stakeholders , including policy makers , regulators , growers and scientists . Default parameterisation of the underlying epidemiological model targets the spread of citrus canker in Florida by adapting the parameters of previous modelling studies , although these defaults can readily be altered to represent other model parameterisations or even pathosystems by the user of our front-end interface . The analysis and the user-friendly interface address and illustrate the challenges posed by cryptic infection , stochasticity and uncertainty in parameter values , and demonstrate how these factors must be accounted for in designing successful disease control strategies . Our key result is to verify that it is indeed possible to optimise control via targeted host removal by matching the “intrinsic scale of the epidemic” [35] , selecting a cull radius that minimises the epidemic impact , κE ( i . e . the total number of hosts lost to disease or control before the pathogen is eradicated ) . However , given particular parameters controlling disease spread and the logistics of control , we have shown how the cull radius that would be selected depends on the percentile of the epidemic impact distribution that is to be optimised over , and therefore on the risk-aversion of the decision-maker . Since costs of disease are typically greater than costs of detection and control , under-control can be more harmful than over-control [22] . This is reflected in the sharp increase in epidemic impact , with even small decreases in the cull radius below the optimum ( Fig 2 ) , a pattern that is largely unresponsive to the values of the parameters ( Fig 3 ) . The pattern also holds for other baseline sets of parameters ( S3 Text ) and also when applying the model to other pathosystems ( S2 Text ) . We have also confirmed the intuition that control should start quickly to be successful; if even a small proportion of hosts is infected when intervention commences , a large epidemic impact appears unavoidable , particularly if the initial infections are not spatially-clustered ( Fig 2c ) . We investigated how the nature of the optimum control strategy is conditioned on a selection of parameters controlling the host-pathogen interaction and the logistics of control . Our results show how success of control depends strongly on the rate and spatial scale of pathogen spread ( Fig 2d and 2e ) . We also showed how factors that act differently at the level of the individual host can have less severe effects as they are altered than those that affect all individuals equally , as a consequence of the former being averaged over the entire population ( Fig 3 ) . While control by local culling can remain viable when there is thick-tailed dispersal , control is only then successful for low infection rates ( Fig 5 ) . Even moderate rates of spread due to higher infection rates ( β ) lead to too many new disease foci when there is fat-tailed dispersal , and , in turn , this means that control is often unsuccessful , with significant risk of large epidemic impacts . This high risk of failure remains hidden if only the median epidemic impact is considered ( Fig 5c ) , re-emphasising the need to examine the full distribution of outcomes when assessing the efficacy of control . Our purpose here was to illustrate and test some key principles underlying the success of control . For this we selected a biologically-plausible set of parameters for citrus canker that allowed extensive sensitivity analyses to factors that are likely to affect the success of control . Our initial analyses focused on the optimal cull radius to eradicate disease in local outbreaks following a small number of primary infections in a spatially restricted ( 2km x 2km ) urban host landscape . Our analyses show that the magnitude of the optimal cullradius depends upon the degree of risk aversion to failure of control , calculated from percentiles for the probability of a given epidemic impact that accounts for the cost of disease and the cost of control . Values for our default parameterisation for citrus canker varied from 104m for the 5th percentile of local epidemic impact to 194m for the 95th percentile , with an optimal radius of 159m when optimising over the median impact ( Fig 2 ) . However , while the responses of epidemic impact to changes in the cull radius , and of the optimum cull radius to different levels of risk aversion , are both robust , the basic estimate of the optimal cull radius can vary widely ( from ~100m to ~500m ) , depending on changes to epidemiological and logistical parameters ( Fig 3 ) and the underlying parameterisation selected for the model ( S3 Text ) . All of these figures relate to local control and are typically smaller than the cull radius of 571 . 9m used by USDA for statewide control of the citrus canker epidemic in Florida from 2002 [29] . One driver of this difference is that our default host landscape uses planting densities typical of residential citrus , whereas extended regions of commercial citrus would lead to faster spread due to higher planting densities . Moreover uncooperative landowners meant that in practice certain trees were inaccessible for pathogen survey , and legal challenges sometimes led to extremely long delays before other trees could be cut down . While both of these may be investigated via the front-end , our default parameters arguably downplay these effects ( e . g . a fixed notice period of 60 days , when in practice legal challenges could lead to delays of many months or even years ) . More significantly , however , these initial estimates do not take account of the risk of infection spreading from the prescribed region of interest to surrounding regions . As we have shown here , to advance from optimisation at local to statewide scales in fact requires proper consideration of the balance between local and global impact of the epidemic ( Fig 4 ) . Work targeting animal epidemics suggests optimal control strategies depend strongly on how local and global impacts are balanced [21 , 22] . We examined this by introducing two variants of the “epidemic cost” , that account for possible infections outside the area of interest via the proxies of local time to eradication or the probability of pathogen escape , but that are flexible enough to allow for different weightings of local vs . global priorities . Strategies giving a high weighting to global performance required extensive controls at the local level ( Fig 4b , 4e and 4f ) . This is because the potential for spread of disease to create a new focus of infection elsewhere is judged to be so harmful that it becomes optimal to use a rather draconian policy in the region under active control , even at the cost of many local removals . While we motivate our analyses using citrus canker , our work can readily be placed in a broader context . The recent emergence of citrus greening or huanglongbing ( HLB , caused by Candidatus Liberibacter spp . bacteria ) , potentially an even more devastating disease [38] , puts control of citrus pathogens firmly back on the scientific and political agenda in the United States . HLB is vectored by psyllids , and although it would be reasonable to assume dispersal of infective vectors declines monotonically with distance from infected plants , it might be expected that changes in psyllid populations over time would add extra complexity to disease dynamics . However , recent work has shown how our underlying model focusing only on disease status and representing disease spread via a time-independent and spatially-isotropic dispersal kernel can be applied to this pathosystem with no change to the fundamental model structure [26] . We recreated a selection of our results for the control of HLB in a citrus grove in S2 Text . We emphasise that using this type of model for HLB means that the activity and population dynamics of the psyllid population are not tracked explicitly , but instead that these factors are included in the dispersal and infection rate parameters of the model . The results concerning principles for control were qualitatively unchanged , although of course the exact detail of the optimum radius and epidemic impact were different reflecting a different pathogen and host topology . Similar models are increasingly used for other plant pathogens at both small [11–13 , 18 , 24–26 , 39–42] and large spatial scales [10 , 16 , 36 , 43–47] . An emerging epidemic to which models are already being applied is sudden oak death ( caused by Phytophthora ramorum ) in the United Kingdom [48]: predictions from a larger-scale stochastic compartmental model are already informing the extent of felling of commercial larch [49] . The United Kingdom government’s response to Chalara ash dieback is also based on predictions from this type of model [50] . Models with static hosts have also been applied to pathogens of agricultural animals , most notably for epidemics of foot and mouth disease . Our particular focus has been a simple control strategy , in which all hosts within a certain distance of detected hosts are removed , and where this distance is fixed in advance . While this corresponds to the approach most often taken in practice , and has definite advantages in terms of ease of implementation and transparency to those affected by control , recent modelling work has examined more elaborate strategies . In particular te Beest et al . [51] consider a complex and time-varying control strategy for an animal disease epidemic spreading through a set of farms that takes account of heterogeneity in the potential risk according to farms’ position and the current state of the epidemic . Our results are also conditioned on the metric used to define the epidemic impact . More complex notions of cost are possible; an obvious extension , for example , would be to include the cost of detection [14 , 15 , 18] . Although our model allows for fluctuations in environmental conditions and we allow the user to set parameters causing the pathogen to be affected by the environment via the front-end , we have not focused on these effects in this paper . We have also not accounted for the additional and significant difficulty in control of novel invasive pathogens for which the parameters controlling spread are themselves ill-characterised . Nor have we considered the effects of any spatial patterning of the host population in terms of , for example , systematic differences in host quality or differential resistance , although we note these spatial patterns would need to be well-characterised in order to be used in the model . We suggest that exploration of these issues , together with assessing and optimising the performance of control scenarios on spatially-extended host landscapes and particularly when there is thick-tailed dispersal , are important challenges . Further work is also underway to augment our theoretical work with interactive user-friendly front-end interfaces , after our extremely positive experiences in using the Webidemics interface to explain and present the ideas underlying our results to an audience of non-specialists .
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Increases in global trade and travel suggest outbreaks of plant disease caused by invasive pathogens will increase in frequency . We use mathematical modelling to show how control of such disease outbreaks can be optimised . Although our methods and analyses are generic , we use the attempted eradication of citrus canker from Florida ( 1996–2006 ) as a case study , and focus upon the performance of reactive culling ( i . e . removal of all host plants within a certain distance of detected infection ) . We show how the cull radius can be optimised , even when there is significant cryptic infection ( i . e . infection without visible symptoms ) . The inherent randomness of disease transmission implies a control strategy can lead to a number of outcomes: the optimal strategy therefore depends on the level of risk that is tolerable . We also consider balancing local vs . global impacts of disease . We show how it can be optimal to control initial outbreaks very extensively , even though this would lead to many local removals , since timely local eradication reduces the risk of a devastating large-scale epidemic . Our model is available as an interactive , user-friendly interface at http://www . webidemics . com/ , intended to illustrate the sometimes counter-intuitive epidemiological principles that underlie successful disease control .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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Optimising and Communicating Options for the Control of Invasive Plant Disease When There Is Epidemiological Uncertainty
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The positive-strand RNA viruses initiate their amplification in the cell from a single genome delivered by virion . This single RNA molecule needs to become involved in replication process before it is recognized and degraded by cellular machinery . In this study , we show that distantly related New World and Old World alphaviruses have independently evolved to utilize different cellular stress granule-related proteins for assembly of complexes , which recruit viral genomic RNA and facilitate formation of viral replication complexes ( vRCs ) . Venezuelan equine encephalitis virus ( VEEV ) utilizes all members of the Fragile X syndrome ( FXR ) family , while chikungunya and Sindbis viruses exploit both members of the G3BP family . Despite being in different families , these proteins share common characteristics , which determine their role in alphavirus replication , namely , the abilities for RNA-binding and for self-assembly into large structures . Both FXR and G3BP proteins interact with virus-specific , repeating amino acid sequences located in the C-termini of hypervariable , intrinsically disordered domains ( HVDs ) of viral nonstructural protein nsP3 . We demonstrate that these host factors orchestrate assembly of vRCs and play key roles in RNA and virus replication . Only knockout of all of the homologs results in either pronounced or complete inhibition of replication of different alphaviruses . The use of multiple homologous proteins with redundant functions mediates highly efficient recruitment of viral RNA into the replication process . This independently evolved acquisition of different families of cellular proteins by the disordered protein fragment to support alphavirus replication suggests that other RNA viruses may utilize a similar mechanism of host factor recruitment for vRC assembly . The use of different host factors by alphavirus species may be one of the important determinants of their pathogenesis .
The Alphavirus genus of the Togaviridae family contains a wide variety of human and animal pathogens . Alphaviruses are broadly distributed on all continents , where they are transmitted between vertebrate hosts by mosquito vectors . The alphavirus genome is a single-stranded RNA of positive polarity . It is approximately 11 . 5 kb in length , mimics the structure of cellular mRNAs and serves as a template for translation of four nonstructural proteins , nsP1-4 . These proteins are initially synthesized as polyprotein precursors P123 and P1234 and then processed into their individual components: nsP1 , nsP2 , nsP3 and nsP4 . This differential processing regulates the synthesis of the negative-strand genome replication intermediate , viral genome and subgenomic RNA ( G RNA and SG RNA ) at different steps of virus replication . The SG RNA is translated into the viral structural proteins: capsid , E2 and E1 , which ultimately package the viral genome into infectious virions [1] . The wide distribution of various alphavirus species into distant geographical areas implies distinctly different evolutionary trajectories , and therefore , unique adaptation to a variety of mosquito vectors and vertebrate hosts . New World ( NW ) alphaviruses are mostly encephalitogenic , while their Old World ( OW ) relatives primarily induce polyarthritis . In terms of genetic sequence , the structural proteins are the least conserved . They possess ~40% sequence identity between the members of the six currently known alphavirus serocomplexes [1] . These differences are believed to primarily determine the specificities of these viruses to both mosquito vectors and their amplifying hosts . The nonstructural proteins demonstrate lower rates of evolution and as a result , ~60–80% levels of identity are observed between members of the different serocomplexes . The nsP1 , nsP2 and nsP4 proteins encode defined enzymatic functions required for genome amplification during alphavirus replication . Thus , the possibility of rapid accumulation of mutations in these genes during viral evolution is restricted . In contrast to other nsPs , the functions of nsP3 are poorly understood . It is co-isolated in complex with other nsPs from alphavirus-infected cells [2–5] , but the exact function of nsP3 beyond its apparent involvement in viral RNA replication , remains to be determined [6 , 7] . The N-terminal fragment of nsP3 contains the macro domain , also referred to as the X-domain , which is homologous to similar domains found in the nonstructural proteins of many other positive-strand RNA viruses , and to some bacterial and cellular proteins [8] . It can bind ADP-ribose , poly ( ADP-ribose ) and RNA and exhibits a low level of adenosine di-phosphoribose 1”-phosphate phosphatase activity [9] . Next is a Zn-binding domain [10] , but its functions remain to be determined . The distinguishing characteristic of the nsP3 protein is the presence of an approximately 200-aa-long C-terminal hypervariable domain ( HVD ) , which displays essentially no sequence identity between the members of different serocomplexes [11] . This domain has no defined secondary structure and is intrinsically disordered . Our studies and those from other teams demonstrated that it can tolerate extended deletions and insertions [12–16] . However , its complete deletion makes most viruses nonviable . Recently , it has become apparent that intrinsically disordered proteins and protein domains play important roles as assembly hubs and can facilitate the formation of numerous macromolecular complexes [17] . Viruses from many different families encode for proteins possessing large disordered domains , suggesting their critical role in viral replication [18] . In this study , we demonstrate that nsP3 HVDs of geographically isolated members of NW and OW alphaviruses have evolved to interact with different sets of cellular proteins . Our experiments were focused on the members of two families of proteins . The HVD of the representative members of the NW encephalitogenic alphaviruses , Venezuelan equine encephalitis virus ( VEEV ) , and the HVDs of the OW arthritogenic alphaviruses , such as Sindbis virus ( SINV ) and chikungunya virus ( CHIKV ) , interact with FXR and G3BP protein family members , respectively . These interactions are mediated by repeating , virus-specific amino-acid sequences located at the C-termini of nsP3 HVDs . FXRs and G3BPs are RNA-binding proteins and major components of many ribonucleoprotein complexes ( RNPs ) including cellular stress granules [19 , 20] . The abilities of these proteins to self-assemble into higher order structures and to efficiently bind RNAs are utilized by alphaviruses in the formation of their replication complexes ( vRCs ) .
To identify cellular proteins specifically interacting with nsP3 HVDs derived from different alphaviruses , we applied replicon-based expression systems . Our previous studies strongly suggested that VEEV and Sindbis virus ( SINV ) utilize different host factors to build nsP3-specific complexes [16] . Therefore , the VEEV HVD-coding sequence was cloned into the SINV replicon ( SINrep ) as a Flag-GFP-HVDveev fusion under control of the subgenomic promoter . Conversely , the SINV HVD was cloned into a VEEV replicon ( VEErep ) as a similar Flag-GFP-HVDsinv cassette ( Fig 1A ) . BHK-21 cells were infected with the replicons packaged into viral particles . They were collected within 3 h post infection ( PI ) , as soon as GFP expression became visible , before expression of any protein encoded by replicon genomic and subgenomic RNAs reached saturation levels . Flag-GFP-HVD-specific protein complexes were isolated using Flag-specific antibodies and analyzed by mass spectrometry . The list of cellular proteins definitively identified in Flag-GFP-HVD samples , but not detected in the samples generated using control Flag-GFP-expressing replicons , is presented in Fig 1B . G3BP1 , G3BP2 and BIN1 proteins have been previously co-immunoprecipitated with nsP3 from SINV-infected cells [2 , 3 , 5 , 21] . Their co-isolation with Flag-GFP-HVDsinv validated our approach . Recently , interaction of G3BPs with nsP3 proteins of other OW alphaviruses , such as CHIKV and Semliki Forest virus ( SFV ) , has been also demonstrated [22 , 23] . In addition , our strategy identified a previously undetected WDR48 protein , interacting with SINV HVD . In support of our previous observation that nsP3-specific protein complexes formed in VEEV-infected cells differ from those formed during SINV replication [16] , none of the SINV HVD-specific proteins were identified as interacting with the VEEV HVD ( Fig 1B ) . Cellular VEEV HVD-binding proteins were represented by two groups . The first group included all of the members of the FXR protein family , FXR1 , FXR2 and FMR1 . This protein family received its name from FMR1 , the Fragile X mental retardation protein 1 , which is associated with autism and mental retardation [24] . The second group consisted of CAPZA1 , CAPZB and CD2AP , which have been shown to form a complex interacting with actin [25] . Recently , one of the FXR family members , FXR1 has also been detected by mass spectrometry in nsP3 complexes isolated from VEEV TC-83-infected cells [26] . Next , we examined the distribution of FXR proteins in mock- and VEEV-infected cells using FXR1- , FXR2- and FMR1-specific antibodies . As shown in Fig 1C , during VEEV infection of NIH 3T3 cells , FXRs migrate from the cytoplasm and accumulate in the nsP3-containing complexes . Taken together , the accumulated data demonstrate that VEEV-specific nsP3 HVD interacts with all members of the FXR family , while nsP3 HVDs of the OW alphaviruses , such as SINV and CHIKV , interact with both members of the G3BP family , G3BP1 and G3BP2 . During alphavirus infection , these proteins efficiently re-localize into nsP3 complexes . In this study , we focused on comparative analysis of the roles of FXR and G3BP proteins in alphavirus replication and did not yet study functions of other identified host factors . Identification of all of the FXR or G3BP proteins by co-IP with nsP3 HVDs suggested that these family members may have redundant functions in replication of the NW and OW alphaviruses , respectively . Previously , our attempts to evaluate the roles of G3BPs in SINV replication using siRNA- or shRNA-mediated knockdown of both G3bp genes generated inconclusive results . Others groups have also reported contradictory data demonstrating both inhibitory and stimulatory roles of G3BPs in OW alphavirus replication . Cristea et al found that siRNA mediated G3BP1 and G3BP2 knockdown led to statistically significant increase in translation of SINV ns polyprotein , but the positive effect on virus replication was very small [4] . In contrast , Scholte et al found that CHIKV replicated to ~10-fold lower titers in human cells treated with G3BP1- and G3BP2-specific siRNAs [23] and suggested that G3BPs have a pro-viral role in virus replication and likely regulate “switch from translation to genome amplification . ” It is possible that these discrepancies resulted from different residual levels of G3BPs after RNAi-induced knockdown and different alphavirus species used in the experiments . In this study , we utilized CRISPR/Cas9 technology to generate single and double G3bp KO cell lines ( S1B , S1D and S3A Figs ) . Similarly , considering the possible redundancy of FXR protein functions , we generated double and triple Fxr KO cell lines ( S1A and S1C Fig ) . The knockout of both Fxr1 and Fxr2 ( Fxr dKO cells ) had a detectable negative effect on the efficiency of VEEV replication ( Fig 2A ) . Additional knockout of Fmr1 in Fxr tKO cells caused an even more pronounced decrease in viral replication rates both at high ( Fig 2A ) and low MOIs ( S2A Fig ) . At 8 h PI , VEEV TC-83 titers were ~1000-fold lower than those in the parental NIH 3T3 cells ( Fig 2A ) . It formed very large clear plaques on the parental NIH 3T3 cells and only pinpoint plaques on Fxr tKO cells ( Fig 2C ) . The reduction in the rates of infectious virus release correlated with more than 12-h-long delay of CPE development in VEEV TC-83-infected Fxr tKO cells . To rule out the possibility that this effect was specific only to the vaccine strain TC-83 , replication of the wt epizootic VEEV 3908 was evaluated . A similar reduction in infectious titers was detected for both strains in the Fxr tKO cells , compared to NIH 3T3 cells ( S2B Fig ) . Importantly , double and triple knockout of Fxr genes had no detectable impact on replication of the OW alphaviruses , such as SINV and CHIKV ( Fig 2A ) , whose HVDs did not bind FXR proteins in the IP experiments . Collectively , these findings indicate that i ) the negative effect of the Fxr tKO was specific to VEEV , and that ii ) downregulation of VEEV replication was not the result of a possible adverse effect of the triple knockout on cell biology . The knockout of G3bp2 alone ( G3bp2 KO cells ) had no noticeable effect on SINV replication ( Fig 2B and 2D ) . However , the knockout of both genes , G3bp1 and G3bp2 , in G3bp dKO cells strongly reduced SINV replication rates and plaque size ( Fig 2B and 2D ) . Similarly , knockout of G3bp1 alone had almost no effect on CHIKV replication ( S3B Fig ) . However , its replication was less efficient in G3bp2 KO cells , and CHIKV was essentially not viable in a G3bp dKO cell line ( Fig 2B and 2D ) . A small increase in CHIKV infectious titers was reproducibly detected only after 24 h PI ( S4 Fig ) . In contrast to the OW alphaviruses , VEEV replication was not affected in G3bp2 KO and G3bp dKO cells ( Fig 2B and 2D ) . Our results are in agreement with those of the previous study [23] , in which the G3BP2-specific siRNAs caused stronger decrease in CHIKV replication in human cells . Thus , our experiments demonstrate that knockout of G3BP or FXR proteins differentially affected alphavirus replication . The profound negative effects were more pronounced when all family members were no longer expressed in the cells . Next , we evaluated the redundancies of FXR and G3BP family members in alphavirus replication and verified that the negative effects of FXR and G3BP knockout were not caused by cell clone-specific phenomena . We used the Fxr tKO and G3bp dKO cells to develop cell lines , which stably , ectopically expressed individual family members in the absence of endogenous expression of all of the family members . We selected only the clonal cell lines , which demonstrated natural , diffuse cytoplasmic distribution of these proteins and in which the levels of expressed proteins were similar to those in parental NIH 3T3 cells ( S5 Fig ) . G3BP1 and G3BP2 proteins were expressed as fusions with GFP , since we have previously shown that the C-terminal GFP fusion does not affect G3BP function [5] . FXR and G3BP proteins exhibit a high degree of conservation between homologs ( S6 and S7 Figs ) . Accordingly , expression of any single FXR restored VEEV replication to levels comparable to those detected in the parental NIH 3T3 cells ( Fig 3A ) . Similarly , both G3BP1 and G3BP2 alone were capable of supporting CHIKV infection ( Fig 3B ) . Different levels of virus replication in the cell lines may reflect variations in their activities or be also explained by small differences in the levels of protein expression in the generated cell lines . However , the levels of ectopic expression were very similar to those found in parental NIH 3T3 cells ( S5 Fig ) . Both FXRs and G3BPs have several putative RNA-binding domains as well as other domains of unknown function ( Fig 3C and 3D , S6 and S7 Figs ) . To define the domains that are important for virus replication , we designed sets of FXR1-GFP and G3BP1-GFP fusion constructs with deletions of various domains ( Fig 3C and 3D ) and used them to generate stable cell lines in Fxr tKO ( for FXR1 ) and G3bp dKO ( for G3BP1 ) cells . Despite the FXR1 deletion mutants were expressed at higher levels than the full-length protein Fxr tKO cells ( S5E Fig ) , none of them increased VEEV titers , while the full-length FXR1-GFP efficiently supported virus replication ( Fig 3C ) . We failed to select only a cell line stably expressing the FXR1Δ5 mutant . The function ( s ) of G3BP1 was also sensitive to deletions ( Fig 3D and S5E Fig ) , and only the mutant with deletion of the acidic domain ( G3BP1Δ2 ) exhibited strong pro-viral activity . Thus , all of the homologs in FXR or G3BP families have redundant functions , and each of them individually can support VEEV and CHIKV replication , respectively . All of the tested FXR1 domains and all but one of the G3BP1 domains are essential for protein functions in virus replication . The requirement of the RNA-binding domains suggested that these proteins may also interact with viral RNAs . Complete abrogation of CHIKV replication in G3bp dKO cells and significant lag in virus production detected for VEEV and SINV in Fxr tKO and G3bp dKO cells , respectively , suggested that loss of FXRs or G3BPs blocks an early step in virus replication . To further investigate this , we infected G3bp dKO and Fxr tKO cells with VEEV and CHIKV expressing GFP under the control of an additional subgenomic promoter . This allowed us to determine the numbers of infected cells and thus , the efficiency of initiation of viral replication; moreover , the levels of GFP synthesis reflected the levels of RNA replication ( Fig 4A ) [27] . At 4 h PI at an MOI of 10 , almost all NIH 3T3 and G3bp dKO cells were infected with VEEV/GFP ( ~88–98% ) , while only 52% of the Fxr tKO cell , infected with the same virus , became GFP-positive . Importantly , all of the infected Fxr tKO cells demonstrated lower levels of GFP expression , which in case of alphaviruses , is dependent on replication of viral genome and transcription of the SG RNA [27] . Thus , the triple Fxr KO had a negative effect on both initiation of virus replication and RNA replication efficiency . In agreement with these data , VEEV demonstrated more then 10-fold lower efficiency of plaque formation on Fxr tKO than on NIH 3T3 cells . As expected , none of the G3bp dKO cells demonstrated detectable levels of GFP expression upon infection with CHIKV/GFP , and no difference in the numbers of GFP-positive cells or in the intensity of GFP expression was found between infected parental NIH 3T3 and Fxr tKO cells ( Fig 4A ) . To gain further insight into the FXRs’ and G3BPs’ respective functions , we systematically evaluated their possible effects on the different steps of the replication cycle . The double KO of G3bp genes did not affect CHIKV entry and RNA uncoating . The chimeric VEE/CHIKV , which encodes VEEV replication machinery and CHIKV-specific structural proteins , replicated equally efficiently in the parental NIH 3T3 and G3bp dKO cells ( Fig 4B ) . The reciprocal chimeric virus ( CHIK/VEEV ) was not designed for safety reasons , since it would express two genes , capsid and nsP2 , whose products inhibit the innate immune response [28] . Therefore , we directly compared the efficiency of VEEV particle binding to NIH 3T3 and Fxr tKO cells , and assessed particle disassembly . No significant differences were detected in either step . The same numbers of particles were found at the plasma membrane of NIH 3T3 , Fxr dKO and Fxr tKO cells after incubation with VEEV samples at 4°C ( S8A Fig ) . There was no difference in VEEV capsid protein release into the cytoplasm and its subsequent characteristic re-localization to the nuclear membrane following incubation at 37°C ( S8B Fig ) [29–31] . To compare the translation efficiency of VEEV and CHIKV genomes after their release from nucleocapsid in different cell lines , we took advantage of our previously developed chimeric EIL/nLuc/VEEV-based experimental system [29] ( S9 Fig ) . We used this system to package nLuc-encoding mRNA , which had 5’UTRs of VEEV TC-83 or CHIKV genomes , into naturally configured VEEV virions in mosquito cells . Then mosquito cell-derived viral particles were used to infect vertebrate cells . The genome of EIL/nLuc/VEEV is absolutely incapable of replication and transcription of SG RNAs in vertebrate cells . However , the nLuc-encoding RNAs , which are delivered by viral particles , are efficiently translated for 4 h PI . The expressed nLuc activity was dependent on the designed , virus-specific 5’UTRs and thus , mimicked translation of VEEV and CHIKV genomes in the absence of viral replication [29] . In these experiments , translation of the nLuc RNA with a VEEV genome-specific 5’UTR was essentially the same in the NIH 3T3 , G3bp dKO and Fxr tKO cells ( Fig 4C ) . The 5’UTR derived from the CHIKV genome also drove nLuc expression in these cell lines with equal efficiency ( Fig 4C ) . Collectively , these experiments established that virion attachment , entry , RNA uncoating , and translation of virion- delivered G RNA , were not affected in Fxr tKO and G3bp dKO cells . The next steps in the virus life cycle include formation of a few vRCs from incoming G RNA followed by rapid amplification of viral G RNA and nsPs , and subsequent assembly of more vRCs . Therefore , we analyzed the kinetics of G RNA and viral protein accumulation in knockout cells . Triple KO of Fxr genes had a dramatic negative effect on the rates of replication of VEEV genomes and viral protein production . By 10 h PI , VEEV G RNA was present at more than 100-fold lower concentrations in Fxr tKO than in parental NIH 3T3 cells ( Fig 4D ) . The expression of nsP3 was strongly delayed and was detected at 5-fold lower levels in Fxr tKO cells by 8 h PI ( Fig 4E ) . The delay in synthesis of structural proteins ( capsid as example ) was even more dramatic . At 9 h PI , the amount of CHIKV G RNA in G3BP dKO cells remained below the level detected at the onset of the infection in the cell-adsorbed viral particles ( Fig 4D ) . The marked reduction in viral protein and RNA synthesis suggested that the block imposed by lack of FXRs or G3BPs may be at the step of vRC assembly . The first step in formation of the alphavirus vRCs is synthesis of a dsRNA intermediate , which represents a reliable marker for functional vRCs [32] . Staining with dsRNA-specific Abs revealed that by 4 h PI , the NIH 3T3 cells already contained approximately ten-fold as many dsRNA-containing complexes ( 355±108 ) as the Fxr tKO cells ( 31±27 ) ( Fig 4F ) . Moreover , the number of dsRNA-containing complexes in the Fxr tKO cells increased more slowly than in their parental counterparts . The ectopic expression of either FXRs in Fxr tKO cells restored the rates of increase in the numbers of dsRNA complexes , albeit with different efficiencies ( Fig 4G ) . As expected , no dsRNAs were detected in CHIKV infected G3bp dKO cells within the first 8 h PI . Collectively , these data suggested that FXRs and G3BPs play critical roles in RNA replication and vRC formation in VEEV- and CHIKV-infected cells , respectively . The data derived from the experiments described above indicated that FXRs and G3BPs may play a similar role in vRC formation . The principal feature shared by FXRs and G3BPs is the presence of several RNA-binding domains , and deletions of these domains inactivated their ability to support virus replication ( Fig 3C and 3D ) . This suggested that viral RNA may be involved in complex assembly and function . To demonstrate the presence of virus-specific RNAs in the FXR/nsP3 and G3BP/nsP3 complexes , we performed an in situ hybridization with pools of fluorescent oligonucleotides , specific to viral G RNAs . At 6 h PI , large cytoplasmic FXR/nsP3 complexes contained high levels of VEEV G RNA ( Fig 5A for nsP3 and Fig 5B for FXR1 ) . We estimated that in the cells infected with VEEV encoding an nsP3/GFP fusion ( VEEV/nsP3-GFP ) , 99 . 7±0 . 1% ( n cells = 6 ) of the detectable nsP3-GFP fluorescence signal colocalized with the G RNA . However , at this time , only a fraction of G RNA-specific fluorescence was associated with nsP3-GFP ( 10 . 8±5 . 0% , n cells = 6 ) . Similarly , in VEEV-infected Fxr tKO cells with ectopically expressed FXR1-GFP , the entire pool of FXR1-GFP was colocalized with G RNA ( 98 . 5±2 . 0% , n cells = 6 ) , while only a fraction of G RNAs was FXR1-GFP-associated ( 16 . 6±1 . 7% , n cells = 6 ) . The G RNAs outside nsP3-FXR complexes were either distributed as small puncta in cytoplasm or were associated with not yet defined complexes ( Fig 5A , red arrowhead ) , which need further investigation . Likewise , at 6 h PI with CHIKV/nsP3-Cherry , 35 . 3±7 . 4% ( n cells = 6 ) of G RNA colocalized with large cytoplasmic nsP3-Cherry complexes ( Fig 6A ) , while 98 . 0±1 . 5% ( n cells = 6 ) of nsP3-Cherry was associated with G RNA . Thus , large nsP3/FXR or nsP3/G3BP complexes accumulate viral G RNA . However , we have previously reported that these complexes are formed later than the membrane-associated vRCs , do not contain viral dsRNA replication intermediates and are not vRCs . The plasma membrane-associated nsP3 complexes , which are in close proximity to vRCs/dsRNA , are formed within the first 1–2 hours PI [16 , 32] . These complexes are likely to be composed of unprocessed ns polyproteins and , in the case of SIN infection , also contain G3BPs . Therefore , we next evaluated whether at early times PI , the PM-bound , smaller nsP3 complexes are associated with FXRs or G3BPs . Indeed , at 2 h PI , the PM-bound nsP3 strongly colocalized with FXR1 and G3BP1 in VEEV- and CHIKV-infected cells , respectively ( Pearson's coefficients 0 . 91±0 . 01 and 0 . 72±0 . 04; Figs 5C , 5D and 6B ) . We detected a partial overlap between PM-bound nsP3 and dsRNA , which supported the previous findings that nsP3 and dsRNAs are closely located , but are not exactly in the same complex [32] . The quantitative analysis revealed that at this early time PI , 35 . 5±8 . 5% of VEEV nsP3-GFP and 73 . 2±11 . 6% of dsRNA fluorescence signals were closely located ( Fig 5D ) . However , the low Pearson's coefficient value in colocalized volume ( 0 . 28±0 . 12 , n cells = 5 ) indicated the likelihood that PM-bound nsP3 complexes rather overlapped with dsRNA than actually co-localized . Unlike what was observed later in the infection cycle ( Figs 5A and 6A ) , at 2 h PI , almost the entire pool of single-stranded G RNA was found to be associated with PM-bound nsP3 in VEEV/nsP3-GFP-infected cells ( 93 . 2±6 . 1% , Fig 5E ) . However , at this early time point PI , only a fraction of nsP3 was associated with G RNA ( 61 . 5±23 . 3% ) , and G RNA-free nsP3 complexes were readily detectable ( Fig 5E , green arrowhead ) . The PM-bound nsP3 complexes , both those containing and those lacking G RNA , formed strips or patches , which were similar to those we had previously described for SINV nsP3 [32] . As described above , these strips always included dsRNA-containing vRCs , some of which overlapped with nsP3-GFP/G RNA signals ( Fig 5E , pink arrowheads ) . The dsRNA-specific fluorescence signal in the triple complexes often had low intensity and volume , suggesting the presence of short dsRNA , which likely represented dsRNA intermediates in the process of their synthesis , and thus , binding fewer dsRNA-specific Abs . We also observed that the nsP3 complexes in close proximity to dsRNAs typically colocalized with G RNA , while the more distant ones contained no G RNA ( Fig 5E , green arrowhead ) . This pattern suggested that nsP3 complexes positioned close to dsRNA-containing , active vRCs had already bound newly synthesized G RNA . Similar accumulation of G RNA on PM-bound nsP3-G3BP complexes was found in CHIKV infected cells ( Fig 6 ) . Taking together these data demonstrate that both large cytoplasmic and small PM-bound FXR/nsP3 and G3BP/nsP3 complexes accumulate viral G RNA . Importantly , we could distinguish at least three types of membrane-bound complexes: 1 ) complexes containing nsP3 , FXR or G3BP , and G RNA , which represented the majority of early nsP3-containing complexes; 2 ) PM-bound nsP3 complexes lacking G RNA; and 3 ) PM-bound nsP3 complexes containing both ss G RNA and dsRNA . Some of dsRNA-containing complexes contained nsP3 at very low level , and it could only be detected by more sensitive techniques [32] . In spite of the high variability between the HVDs of different alphavirus species , a short homologous sequence , which is often present in two copies , has been identified in the C-termini of many OW alphavirus nsP3 HVDs [22] . Likewise , a distinct aa sequence is located in the same position of HVDs in different VEEV strains [12] . It is also usually present in two copies , particularly in highly pathogenic strains . In biochemical studies , the repeats described for the OW alphaviruses , have been shown to bind directly to G3BPs [22] . Thus , we speculated that the VEEV-specific repeating element binds FXRs , and further explored the function and biological significance of the HVD repeating sequences in VEEV and CHIKV replication . Deletion of the repeat in VEEV nsP3 strongly affected virus replication rates [12] . This reduction was of the same order as that observed for wt VEEV infection of Fxr tKO cells in this study ( Fig 2A ) . However , the repeat deletion did not completely abolish virus growth . This indicated that the HVD fragment located between the conserved N-terminal domains and the repeating elements ( Fig 7A ) can support some level of VEEV replication . This is likely achieved through interaction with other cellular proteins presented in Fig 1B , or possibly with additional , currently unidentified binding partners . Thus , to conclusively dissect the function of the repeating elements in the absence of HVD interactions with other host factors , we utilized a VEEV/mutHVD/GFP mutant [12] . This virus contained the repeating sequence , but the remaining HVD aa sequence was randomized ( Fig 7A ) . It also contained GFP gene under control of additional subgenomic promoter . This mutant demonstrated equally efficient replication in the parental NIH 3T3 and G3bp dKO cells , but was essentially not viable in Fxr tKO cells ( Fig 7A ) . Further deletion of the repeats made VEEV/mutΔ1+2/GFP incapable of replication even in NIH 3T3 cells ( Fig 7A ) . These data demonstrated that i ) VEEV/mutHVD/GFP replication was determined strictly by FXR proteins , and ii ) the presence of the C-terminal HVD repeat was sufficient to drive the FXR-dependent mode of VEEV replication . Next , we replaced the VEEV repeat in VEEV/mutHVD/GFP with a heterologous , CHIKV-specific repeating sequence . This chimeric virus , VEEV/chikvR/GFP replicated efficiently in Fxr tKO cells , but performed poorly in G3bp dKO cells ( Fig 7A ) . This result indicates that replacement of the repeat switched virus replication from an FXR-dependent to G3BP-dependent mode . We have also analyzed colocalization of FXR1 and G3BP2 with membrane-bound nsP3 complexes in the cells infected with wt and mutant viruses containing VEEV- or CHIKV-specific repeating elements ( Fig 7C ) . As expected , nsP3 colocalized with FXR1 , but not with G3BP2 , in cells infected with VEEV repeat-containing viruses , VEEV/GFP and VEEV/mutHVD/GFP . In VEEV/chikvR/GFP-infected cells , nsP3 formed complexes with G3BP2 and no longer co-localized with FXR1 . In the case of CHIKV , one repeating element in CHIKV/Δ1/GFP was sufficient to support virus replication in NIH 3T3 cells ( Fig 7B ) . The deletion of both repeats made the CHIKV/Δ1+2/GFP virus non-viable in any cell line and thus , confirmed that CHIKV replication is critically dependent on the nsP3-G3BP interaction . A single repeat derived from another OW alphavirus , SINV , was also able to support CHIKV/sinvR/GFP replication . This SINV peptide has sequence similarity with the CHIKV-specific repeating element ( Fig 7B ) . As observed in the experiments with KO cell lines ( Fig 2B ) , CHIKV was found to be very sensitive to changes in HVD/G3BP interactions , and both CHIKV/Δ1/GFP and CHIKV/sinR/GFP , which contained only one element of the repeat , reproducibly replicated to 10-100-fold lower titers . The attempt to switch CHIKV replication to FXR-dependent mode was unsuccessful , and CHIKV/veevR/GFP was not viable .
Many viruses encode proteins with long intrinsically disordered regions ( IDRs ) , but the functions of only a small number of these IDRs have been explored [18] . Alphavirus nsP3 proteins have large IDRs , referred to as hypervariable domains ( HVDs ) . Their length varies between 150 to 250 aa , and they exhibit very low identity between different alphaviruses . In this study , we found that VEEV and SINV HVDs interact with different sets of cellular , RNA-binding proteins . However , we have previously reported that VEEV and SINV , containing HVDs derived from heterologous , distantly related alphaviruses , were capable of efficient replication [16] . Taken together the accumulated data suggested that the distinct sets of HVD-binding host factors identified here likely have similar functions in the replication of geographically isolated alphaviruses . FXR and G3BP proteins , which were identified by co-IP to interact with nsP3 HVD of NW and OW alphaviruses , respectively , share a number of common characteristics , such as the presence of several RNA-binding domains and involvement in the formation of ribonucleoprotein complexes ( RNPs ) , including stress granules ( SGs ) . Another common feature of G3BP and FXR proteins is their ability for homo- and hetero-oligomerization [33 , 34] . We have previously isolated G3BPs as components of different nsP3 complexes formed during SINV replication . Importantly , both G3BP1 and G3BP2 were co-isolated at high levels with nsP3 from the membrane fraction of SINV-infected cells , which was enriched with dsRNA-containing , functional vRCs [5] . Similar membrane complexes isolated from mosquito cells included high levels of Rasputin , the insect homolog of G3BPs [5] . These data strongly suggested that G3BPs function in RC formation and viral RNA replication . Later studies demonstrated complex formation between G3BPs and nsP3 proteins of other OW alphaviruses , such as SFV and CHIKV , and this led to a hypothesis that G3BP/nsP3 interaction is a common alphavirus-specific mechanism of interference with SG formation and is beneficial for virus replication [35 , 36] . However , lack of VEEV nsP3 interaction with G3BPs and an absence of experimental evidence that SGs can affect replication of other alphaviruses prompted us to re-examine this hypothesis . The experiments presented here using KO cell lines demonstrated that nsP3 interactions with G3BPs play critical roles in viral RNA replication , and this function is specific for the OW alphaviruses . This group of alphaviruses has evolved to use their nsP3 HVD to re-direct the major SG components , G3BP1 and G3BP2 , for efficient generation of functional vRCs . Conversely , VEEV , a NW alphavirus , has adapted to utilize FXRs , another group of proteins involved in RNP and SG assembly [19 , 37] , to facilitate vRC formation . Importantly , only complete ablation of all of the FXR or G3BP homologs resulted in a deleterious effect on virus replication , and it was consistently observed that ectopic expression of any single homolog efficiently rescued virus infection . This high degree of redundancy is undoubtedly beneficial for replication of alphavirus in different hosts and tissues , in which the sequence and concentration of particular FXR and G3BP family members may vary . This study and previous reports revealed that interaction of FXRs or G3BPs with nsP3 is mediated by the short repeating peptides located in the C-terminus of the NW and OW alphavirus nsP3 HVDs [12 , 16 , 22] . The location of these repeats at the end of a disordered fragment makes them readily accessible for binding and , more importantly , may promote formation of FXR or G3BP oligomers . In the case of VEEV , FXRs are not the only host factors , which mediate vRC assembly and RNA replication . VEEV demonstrated detectable levels of replication in the Fxr tKO cell line . The deletion of both repeats in VEEV nsP3 also failed to completely abrogate virus replication in murine cells , and additional randomization of the HVD fragment upstream of the deleted repeat was required to render the virus nonviable . Similarly , SINV retained the ability to replicate in G3BP dKO cells , albeit with almost 1000-fold lower efficiency . These data suggested another level of redundancy in VEEV and SINV replication mechanism , which is also mediated by HVDs’ interaction with additional host factors . In this study , CHIKV was the only alphavirus that could not replicate at all in the absence of a single type , G3BP/HVD , interaction , achieved by either double KO of G3bps or deletion of both C-terminal HVD repeats . In contrast to other tested alphaviruses , even the RNAi-mediated reduction of G3BP levels in 293 cells caused a detectable decrease in CHIKV replication [23] . However , our data do not rule out a possibility that additional proteins may compensate for G3BPs absence and support CHIKV replication in different cell types . Taken together , the results suggest that the differences in utilizing host factors exist not only between the OW and the NW alphaviruses , but also between different alphavirus clades . The key findings of this study , which begin to elucidate the mechanism of FXR , G3BP and other host protein functions in alphavirus replication , include ( 1 ) an inability of G3BP and FXR to function in RNA replication after deletion of the domains involved in protein-protein interactions or their RNA-binding domains; ( 2 ) identification of several distinct membrane-bound FXR/nsP3 and G3BP/nsP3 complexes , which included those containing and lacking viral G RNA , and complexes containing both viral G RNA and dsRNA replication intermediate; and ( 3 ) a dramatic reduction in the numbers of vRCs and pronounced reduction or abrogation of virus replication in the Fxr tKO and G3bp dKO cells . Our new data allowed us to further expand on the model of alphavirus vRC formation ( Fig 8 ) . After receptor-mediated endocytosis of virions , the released nucleocapsids are uncoated in the cytoplasm via capsid protein binding to the ribosomes [1] . Viral G RNA mimics cellular mRNAs , in which it has a 5’ cap and a 3’ poly ( A ) tail , and , thus , it is immediately translated to produce P123 and/or P1234 . The newly translated viral P1234 polyproteins are partially processed to form P123+nsP4 complexes , which are targeted to the plasma membrane by the membrane-binding ability of nsP1 [11] , with one or a few of them containing viral G RNA . Then the first molecule of dsRNA intermediate is synthesized and isolated into the membrane spherule to begin amplification process . Even at this very early step , G3BP and FXR binding and oligomerization in the case of SINV/CHIKV and VEEV infections , respectively , are prerequisites for RNA replication . These interactions may be required for protection of viral G RNA from degradation during transport of P123+nsP4/G RNA complex to the plasma membrane . In support of this hypothesis , we failed to detect CHIKV vRC formation in G3bp dKO cells at any MOI . Similarly , VEEV infectivity , determined by the efficiency of the initiation of RNA replication , was strongly reduced in Fxr tKO cells . Next , dsRNA begins to function as a template for synthesis of new G RNAs , and the released molecules serve as templates for translation of the next generation of P123+nsP4 complexes ( Fig 8 ) . They form arrays at the plasma membrane of vertebrate cells ( Figs 5 and 6 ) suggesting that translation occurs in close proximity to the primary RNA replication sites . The new complexes also bind FXRs or G3BPs via the C-terminal repeats , and these cellular proteins contribute to formation in the arrays of larger , microscopically detectable complexes through homo- and hetero- oligomerization . The oligomerization likely leads to a strong increase in the number of RNA binding domains and , consequently , in high avidity of these complexes to RNA . The resulting nsP123+nsP4/G3BP- or nsP123+nsP4/FXR-containing structures efficiently recruit the G RNAs , synthesized by the already active vRCs ( Figs 5E and 6D ) , and form pre-vRCs , which contain G RNA , but yet lack dsRNA . Thus , oligomerization of FXRs or G3BPs likely promotes rapid , exponential increase in the number of G RNA-containing pre-vRCs , which are normally detected within first two hours of alphavirus replication . Detection of G RNA-free FXR/nsP3 or G3BP/nsP3 complexes further away from vRCs suggest that they are overproduced , and not all of them become eventually involved in dsRNA synthesis and transformed into active dsRNA-containing vRCs . Thus , after assembly of a primary vRC , newly synthesized G RNAs are arbitrarily directed either to translation or into pre-vRCs for replication . Several previously published observation support the proposed model . For example , alphavirus nsPs can efficiently amplify the defective interfering ( DI ) RNAs and helper RNAs in trans [38 , 39] . NsP1-4 , synthesized in cells by using T7 RNA polymerase-based expression systems can use a variety of RNA templates for dsRNA synthesis [40 , 41] . These RNA templates , supplied in trans do not necessarily require all of the promoter elements , suggesting a high level of nonspecific RNA recruitment [42] , which is likely mediated by cellular RNA-binding proteins , FXRs and G3BPs . However , further transition beyond the dsRNA synthesis stage into the RNA amplification stage certainly depends on the presence of cis-acting promoter elements . Importantly , alphaviruses are prone to nonhomologous recombination , duplication of genomic fragments and even acquisition of cellular sequences [1 , 43 , 44] . This can now be explained by accumulation of different RNAs in the same FXR/nsP3 or G3BP/nsP3 macroscopic PM-bound complexes . It is well accepted that formation of new vRCs is largely completed by 4 h PI due to accumulation of high levels of cytoplasmic nsP2 , which rapidly processes P123 polyproteins [1] . Indeed , we could not detect formation of new PM-bound nsP3 complexes after 2–3 h PI . After this time , mostly large cytoplasmic FXR/nsP3 and G3BP/nsP3 structures , which also contain viral G RNA , but not dsRNA , are formed . These complexes progressively grow in size with time PI [2 , 16] . The dynamics of their evolution and the presence of RNA suggests that their formation is the result of a liquid-liquid demixing due to increasing concentration of the components as has been described for other RNPs [45] . Importantly , the presence of G RNA in the large cytoplasmic FXR/nsP3 and G3BP/nsP3 structures suggests that they do not simply sequester SG-related proteins , as it was previously suggested [22 , 35 , 36] , but have additional functions . Further studies are needed to define the role ( s ) of large cytoplasmic complexes in alphavirus replication or cell metabolism . Many positive-strand RNA viruses encounter the challenge of initiation of replication from the very limited number of G RNA molecules delivered into the cell . Given our discovery that distantly related alphaviruses independently evolve to utilize different cellular RNA-binding proteins for formation of pre-vRCs , it is reasonable to hypothesize that other positive-strand RNA viruses may utilize a similar mechanism for vRC formation . Indeed , interaction of SG-related proteins with viral proteins or G RNAs has been reported for many positive-strand RNA viruses . It has been demonstrated that G3BP1 binds HCV NS5B and the 5’ terminus of the negative-strand RNA , and that siRNA-mediated depletion of G3BP1 strongly affects HCV replication [46] . G3BP1 has been shown to colocalize with viral proteins and G RNA of rubella virus , another member of Togaviridae family [47] . Similarly , G RNAs of several flaviviruses bind TIA1 and large complexes , containing TIA1 and viral proteins were detected in the infected cells [48] . Importantly , G3BPs , FXRs and TIA1/TIAR proteins have several RNA binding domains and form homo- and hetero-oligomers . Interestingly , we found that VEEV nsP3 HVD interacts with several proteins , which are associated with neurodegenerative diseases . The Fragile X mental retardation protein 1 , FMR1 is required for normal cognitive development . Mutations in this proteins or dysregulation of its expression lead to large spectra of neurodegenerative diseases and intellectual disabilities , including fragile X syndrome , autism , Parkinson’s disease and etc . [49 , 50] . Its homologs , FXR1 has been associated with schizophrenia and bipolar disorder in several studies [51 , 52] . Fxr2 knockout mice demonstrated behavior phenotype [53] . The CD2-associated protein , CD2AP , which also binds to VEEV nsP3 HVD ( Fig 1 ) , has been identified as a genetic risk factor for Alzheimer’s disease [54 , 55] . Importantly , it has been shown that CD2AP is involved in supporting blood-brain barrier integrity [56] . Thus , it is reasonable to speculate that interaction of VEEV nsP3 with these proteins may contribute to the encephalitogenic phenotype of VEEV , and this infection may be a risk factor for cognitive disabilities . The practical outcome of this study is the demonstration that CHIKV replication in murine cells completely depends on G3BP/nsP3 interaction . This makes G3BP/nsP3 binding an attractive target for therapeutic intervention against CHIKV infection . Weakening of nsP3/G3BP interaction and , thus , reduction of replication efficiency can be used for virus attenuation in vaccine development . Similarly , targeting of VEEV FXR/nsP3 interaction will strongly attenuate virus replication , and further understanding of the mechanism of this interaction could promote vaccine designing and drug discovery .
NIH 3T3 cells were obtained from the American Type Culture Collection ( Manassas , VA ) . BHK-21 cells were provided by Paul Olivo ( Washington University , St . Louis , MO ) . Cells were maintained in alpha minimum essential medium ( αMEM ) supplemented with 10% fetal bovine serum ( FBS ) and vitamins at 37°C , 5% CO2 . The original plasmids containing the infectious cDNAs of the genomes of the following viruses: experimental vaccine strain of VEEV ( VEEV TC-83 , GenBank accession no . L01443 ) , epizootic strain VEEV 3908 ( GenBank accession no . U55350 ) , laboratory strain SINV Toto1101 ( SINV Toto1101 , [57] ) , vaccine strain CHIKV ( CHIKV-181/25 , GenBank accession no . L37661 ) , chimeric virus VEEV/CHIKV , which encodes VEEV TC-83 replication machinery and CHIKV LaReunion structural genes , EIL/5’TCVEEV-nLuc/VEEV and EIL/5’CHIKV-nLuc/VEEV were described elsewhere [58–63] . EIL/5’TCVEEV-nLuc/VEEV and EIL/5’CHIKV-nLuc/VEEV encode the replication machinery of the insect cell-specific Eilat alphavirus ( EILV ) and structural proteins of VEEV TC-83 . The nLuc sequence was fused with VEEV TC-83 or CHIKV 5’UTRs and the amino-terminal fragment of the corresponding nsP1 , and these cassettes were cloned under the control of an additional subgenomic promoter into the cDNA of the chimeric viral genome [29] . Other plasmids were designed using standard PCR-based techniques . The schematic representations of the modified genomes are shown in the corresponding figures . Plasmids , encoding alphavirus replicons VEErep/Flag-GFP-HVDsinv and SINrep/Flag-GFP-HVDsinv had the VEEV or SINV structural protein genes replaced by Flag-GFP , fused with HVD sequences derived from the indicated alphaviruses . The control replicons SINrep/Flag-GFP and VEErep/Flag-GFP encoded GFP , which was fused only with Flag . G3bp1 , G3bp2 , Frx1 , Frx2 and Fmr1 genes were synthesized by RT-PCR using mRNA isolated from NIH 3T3 or BHK21 cells . These genes were cloned into modified PiggyBac plasmids ( System Bioscience , Inc ) under control of the CMV promoter . These plasmids were designed to express bi-cistronic mRNA , which encodes sequences of interest , and puromycin N-acetyltransferase ( Pac ) or blasticidin S resistance gene under the control of the EMCV IRES . Further modifications , such as fusions with GFP and deletions of the domain-coding sequences were introduced by PCR . Plasmids encoding the genomes of helper constructs , which were used for packaging of replicon RNAs into infectious viral particles , were described elsewhere [39 , 64] . Sequences of the plasmids and details of the cloning procedures can be provided upon request . 2x107 BHK-21 cells were infected with viral particles containing VEErep/Flag-GFP-HVDsinv , SINrep/Flag-GFP-HVDveev , VEErep/Flag-GFP and SINrep/Flag-GFP at an MOI of 20 inf . u/cell . Cells were harvested as soon as GFP expression became detectable by fluorescence microscopy ( 2–3 h PI ) . At this stage of infection , expression of the replicon-encoded nonstructural and SG RNA-encoded proteins is not at saturation level , which is usually achieved by 8 h PI . Cells were pelleted by centrifugation and resuspended in hypotonic buffer ( 10 mM Tris-HCl , pH7 . 5 , 10 mM NaCl , 5 mM MgCl2 ) supplemented with protease and phosphatase inhibitor cocktails ( Sigma , P8340 and P2850 ) . After incubation on ice for 40 min , NaCl concentration was adjusted to 150 mM , and cells were lysed by dropwise addition of NP40 to 1% . The lysates were cleared by centrifugation at 10 , 000 g for 10 min . Supernatants were mixed with magnetic beads with anti-Flag antibodies and incubated on a rotary mixer for 1 h at 4°C or for 2 h at 20°C . Then beads were washed 4 times with 1xNP buffer ( 10 mM Tris-HCl , pH7 . 5 , 150 mM NaCl , 5 mM MgCl2 , 1% NP40 ) and incubated for 10 min at 65°C in 40 μl of the protein loading buffer to elute the proteins . Samples were separated on 4–12% NuPAGE gel ( Invitrogen ) . After staining with Coomassie blue , each lane was cut into 6 pieces , which were used for mass spectrometry . Each gel piece was cut into small pieces and equilibrated in 100 mM ammonium bicarbonate . Then they were reduced , carbidomethylated , dehydrated , and proteins were digested with Trypsin Gold ( Promega , Madison , WI ) as per the manufacturers’ instructions . Following digestion , the resultant peptides were extracted , concentrated , and resolubilized in 0 . 1% formic acid prior to analysis by 1D reverse phase LC-ESI-MS2 . Peptide digests were separated by nanoflow HPLC and directly sprayed into either an Orbitrap Velos Pro hybrid [65] or an LTQ XL mass spectrometer ( Thermo Scientific , San Jose CA ) [66] . In both cases the gradient was set to increase the acetonitrile concentration from 0%-50% in water containing 0 . 1% formic acid . Searches were performed using SEQUEST ( Thermo Scientific , San Jose CA ) with a combined mouse-specific subset of the UniRef100 database , which included sequences of viral proteins used in these experiments . The peptide files were further filtered , grouped by protein , and quantified by spectral counting using Scaffold 4 . 0 ( Proteome Software , Portland , OR ) . The filter cut-off values were set with peptide length >5 AA's , peptide probability >90% C . I . , ≥2 peptides/protein , and protein probabilities set to >99% C . I . The proteins were selected as specifically bound to the HVDs if the total spectra for an experimental set was 5 times more than for the control , and the total number of spectra was 5 or more in both experiments . Guide RNA ( gRNA ) sequences were designed to target close to the initiating codon to prevent synthesis of truncated proteins with unknown functions . Oligonucleotides for gRNA cloning were as follows: Fxr1 ( and Fxr2 ) : CTCCAACGGGGCTTTCTACAgtttt and TGTAGAAAGCCCCGTTGGAGcggtg; Fmr1: CTCCAATGGCGCTTTCTACAgtttt and TGTAGAAAGCGCCATTGGAGcggtg; G3bp2: AAGCTCCCGAGTATTTGCACgtttt and GTGCAAATACTCGGGAGCTTcggtg; G3bp1: GTACTACACTCTGCTGAACCgtttt and GGTTCAGCAGAGTGTAGTACcggtg . Oligonucleotides encoding guide RNA were cloned into linearized the GeneArt CRISPR Nuclease [CD4 Enrichment] vector , which also encodes Cas9 and CD4 genes ( #A21175 , Life Technologies ) . The correct insertion was confirmed by sequencing . The plasmids were transfected into NIH 3T3 cells using TransIT-X20 ( Mirus ) . At two days post transfection , transfected cells expressing the surface CD4 receptor were enriched using the Dynabeads CD4 positive isolation kit ( #11331D , Life Technologies ) . Isolated cells were seeded at different densities to isolate single cell-derived clones . A few cell clones were analyzed for expression of a targeted protein by immunoblot and immunostaining of infected cells . If any positive cells were detected by immunofluorescence , subcloning was repeated . For each targeted gene , 2–3 cloned cell lines demonstrating complete absence of targeted proteins by immunoblot and immunofluorescence , were selected . The presence of mutations in the cellular genome was confirmed by sequencing of the targeted regions . The following primers were used to amplify the targeted region: Fxr 1: CCCTCGCGTTGGAAAGTTTCTAGAATCTCTTCC and CCACCACCTGACACCTCTCCTCG; Fxr2: CCGTTTCCCTCACGGTGGCG and GGGTCAAGACCAAGCTCCAGAAACTCG; Fmr1: GGAGCGTTTCGGTTTCACTTCCGGTGAG and CTCACATCCCACAGCCCGCC; G3bp1: ccacgaattCTGTGTTGAGTTGGCTTAGCACAGTCC and ccacaagcttCCGCAAAACATGGTGAGATCTTATGCTG; G3bp2: ccacggatcCTCAGTTATATATCTAAGAAGATTTATTTTGTGGTATTTTGCAAGG and ccacaagcttGGCACTAAGATATGACATGTTGTTCCTGTTTGC . The amplified PCR fragments were cloned into the pRS plasmid and several clones per cell line were sequenced . The effects of double and triple knock out on virus replication were tested on more than one cell clone . Representative data is presented from a single clone for each KO . Stable knock-in ( KI ) cell lines were generated by transfection of KO cells with PiggyBac-based plasmids encoding the genes of interest and the integrase-encoding helper plasmid ( System Bioscience , Inc ) . After blasticidin or puromycin selection , clones of the KI cells were analyzed for their levels of protein expression by Western blot and the absence of aggregation of the expressed proteins were confirmed by microscopy . Clonal cells demonstrating levels of protein similar to those found in NIH 3T3 cells and lacking nonspecific protein aggregation were used for further experiments . Plasmids containing complete viral genomes , replicons and helper genomes , were purified by ultracentrifugation in CsCl gradients . They were linearized using the unique restriction sites located downstream of the 3’ poly ( A ) tails of the cloned constructs . RNAs were synthesized in vitro using SP6 RNA polymerase in the presence of a cap analog as previously described [67] . The yield and integrity of the RNAs were analyzed by agarose gel electrophoresis under non-denaturing conditions . The transcription mixtures , containing 1 μg of RNAs of viral genomes , were directly used for electroporation into BHK-21 cells [68] . Viruses were harvested at 24 h post electroporation . Infectious titers were determined using a standard plaque assay on BHK-21 cells [68 , 69] . The experiments with epizootic strain VEEV 3908 and corresponding RNA were performed in the BSL3 facility of the UAB SEBLAB according to IBC-approved protocols . In order to package replicons into virus-like particles , replicon and corresponding helper RNAs were mixed and electroporated into BHK-21 cells . The replicon-containing viral particles were collected at 24 h post electroporation . To assess titers , BHK-21 cells in 6-well Costar plates ( 5x105 cells per well ) were infected with different dilutions of packaged replicons . Numbers of GFP-positive cells were determined at 6 h post infection by fluorescence microscopy , and titers were calculated accordingly . For VEErep/Flag-GFP-HVDsinv , SINrep/Flag-GFP-HVDveev , VEErep/Flag-GFP and SINrep/Flag-GFP , infectious titers of packaged replicons were 2–2 . 5x109 inf . u/ml . In order to assess accumulation of CHIKV and VEEV genomic RNAs during infection , cellular total RNAs were isolated using the RNeasy minikit ( Qiagen ) at different times post infection . cDNAs were synthesized using the QuantiTect reverse transcription kit ( Qiagen ) . Quantitative PCR was performed using the SsoFast EvaGreen Supermix ( Bio-Rad ) in a CFX96 real-time PCR detection system ( Bio-Rad ) for 40 cycles . The specificity of the quantitative PCR was confirmed by analyzing the melting temperatures of the amplified products . The efficiency of each pair of primers was determined using the standard curves obtained by performing real-time PCR on 10-fold dilutions of a control sample . The qPCR reactions were performed in parallel with primers specific to β-actin for normalization , and the fold difference in RNA concentration was calculated using the ΔΔCT method . Each qPCR was performed in triplicate , and the means and standard deviations were calculated . The data were normalized to the number of GE ( genome equivalent ) in viral particles adsorbed to the cells , before RNA replication began . The following primers were used VEEVdir: CTGACCTGGAAACTGAGACTATG , VEEVrev: GGCGACTCTAACTCCCTTATTG , CHIKVdir: GGTCAGAGAAAGAACACTAACCT , CHIKVrev: CCTTCTGGATTGACTGGGTATC . The parental NIH 3T3 cells and their KO and KI derivatives were seeded into 6-well Costar plates ( 5x105 cells/well ) and infected at MOIs indicated in the figure legends . After 1 h incubation at 37°C cells were washed twice with PBS , overlaid with 1 ml of complete media and further incubated at 37°C . At the indicated times post infection , media were replaced . Virus titers in the harvested samples were determined by standard plaque assay on BHK-21 cells . Virus growth rates were determined at different MOIs to assess the reproducibility of the data . The most detailed growth curves are presented in the figures . In order to additionally demonstrate reproducibility and the statistical significance of the detected differences , the experiments were additionally repeated three more times for a single time point . For all imaging , experiments cells were seeded in 8-well Ibidi chambers ( 5x103/well ) and incubated overnight at 37°C . Then cells were infected with the indicated viruses in 200 μl for 1h at 37°C . The inocula were replaced with 200 μl of fresh media , and cells were further incubated at 37°C . At the time post infection indicated in the figure legends , cells were fixed with 4% paraformaldehyde , permeabilized and stained with primary and secondary Abs . The following primary antibodies were used: anti-dsRNA mouse monoclonal antibodies ( MAB J2 or MAB K1 , Scicons , Hungary ) , anti-G3BP1 rabbit polyclonal antibodies ( gift from Dr . Richard Lloyd ) , anti-G3BP2 rabbit polyclonal antibodies ( #A302-040 , Epitomics ) , anti-FXR1 rabbit monoclonal antibodies ( #12295 , Cell Signaling ) , anti-FXR2 rabbit monoclonal antibodies ( #7098 , Cell Signaling ) , anti-FMR1 rabbit monoclonal antibodies ( #7104 , Cell Signaling ) , anti-CHIKV nsP3 mouse monoclonal antibodies ( provided by UAB Epitope Recognition & Immunoreagent Core ) , anti-VEEV TC-83 mouse polyclonal antibodies ( gift from Dr . Robert Tesh , UTMB ) , Anti-VEEV nsP3 mouse monoclonal antibodies ( custom produced by UAB Epitope Recognition & Immunoreagent Core [16] ) . Cell nuclei were stained with Hoechst dye . For the in situ hybridization , sets of 48 fluorescently labeled oligonucleotides specific to the genome fragments encoding viral nonstructural proteins , were designed using the Stellaris RNA FISH Probe Design application ( Bioresearch Technologies ) . The probe set for hybridization to the VEEV genome was labeled with Quasar 570 dye . The probe sets specific to the CHIKV genome were labeled with fluorescein or Quasar 670 dyes . Hybridizations were performed according to the probe manufacturer’s instructions and using the manufacturer’s provided reagents ( Bioresearch Technologies ) . After the in situ hybridization , some samples were additionally stained with fluorescent antibodies using the protocol descried above . We found that the anti-dsRNA MAB J2 recognized DNA-RNA hybrids produced as result of hybridization , while the anti-dsRNA MAB K1 specifically recognized dsRNA only . 3D stacks were acquired on a Zeiss LSM700 confocal microscope with a 63X 1 . 4NA PlanApochromat oil objective . The image stacks were deconvoluted by using the measured PSF value in Huygens software ( Scientific Volume Imaging ) , and images were assembled using Imaris software ( Bitplane AG ) . Colocalization parameters were determined using Huygens and Imaris software . The spot function of Imaris was used for presentation and quantitative analysis of i ) the numbers of vRCs identified by staining with anti-dsRNA antibodies , and ii ) virions at the cell surface . For each sample , we acquired 3D images of about 30 randomly selected cells . Viral particle adsorption assay and virus entry and uncoating analysis were performed as described elsewhere [29] . To assess translation efficiency of the specific mRNAs delivered by viral particles , stocks of EIL/5’TCVEEV-nLuc/VEEV and EIL/5’CHIKV-nLuc/VEEV viruses were prepared by electroporation of mosquito C7/10 cells with the in vitro-synthesized RNA . The chimeric viruses replicated in mosquito cells to titers approaching 1010 inf . u/ml , and the released viral particles contained not only viral genomes , but also high levels of the nLuc-encoding SG RNA containing 5’UTRs of interest . 5x105 NIH 3T3 cells and their KO derivatives were infected with C7/10-derived samples of EIL/5’TCVEEV-nLuc/VEEV and EIL/5’CHIKV-nLuc/VEEV , and activity of nLuc , translated from the SG RNA , delivered in viral particles , was measured at 4 h post infection using the Nano-Glo luciferase assay system ( Promega ) . The control samples were derived from cells infected with the same viruses , but incubated in the presence of cycloheximide and puromycin . These samples showed nLuc activity that was lower by a few orders of magnitude . Differences between means were determined using an unpaired Student’s t test unless indicated otherwise and data are presented as a mean±SD . A value of p<0 . 05 was considered to be statistically significant: *p<0 . 05 , **p< 0 . 01 , ***p<0 . 001 , ****p<0 . 0001 , ns–non significant .
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Many viruses encode proteins containing intrinsically disordered domains , whose functions are as yet unknown . Here we show that such a domain ( HVD ) in the alphavirus nsP3 protein orchestrates assembly of viral replication complexes through interaction with RNA-binding cellular factors . Surprisingly , geographically isolated viruses have evolved to utilize different cellular proteins: the nsP3 HVD of Venezuelan equine encephalitis virus ( VEEV ) binds all members of the FXR family , while nsP3 HVDs of Sindbis and chikungunya viruses interact with G3BP proteins . Despite being in different families , G3BPs and FXRs have similar domain organization , and assemble into higher order complexes , such as stress granules . Alphaviruses exploit their abilities for complex self-assembly and RNA binding to build RNA-containing pre-replication complexes . Using CRISPR/Cas9 mediated knockouts , we show that deletion of all homologs strongly affects virus replication , while knockout of a single FXR or G3BP homolog has no or mild effect . Our data suggest that an alphavirus HVD serves as a hub to recruit host factors for replication complex assembly and may determine virus adaptation to distinct cellular environments . Notably , the improved understanding of HVD interactions allows alphavirus replication to be switched from an FXR- to G3BP-dependent mode and opens new possibilities for development of antiviral therapeutics .
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2016
|
New World and Old World Alphaviruses Have Evolved to Exploit Different Components of Stress Granules, FXR and G3BP Proteins, for Assembly of Viral Replication Complexes
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Training can modify the visual system to produce a substantial improvement on perceptual tasks and therefore has applications for treating visual deficits . Visual perceptual learning ( VPL ) is often specific to the trained feature , which gives insight into processes underlying brain plasticity , but limits VPL’s effectiveness in rehabilitation . Under what circumstances VPL transfers to untrained stimuli is poorly understood . Here we report a qualitatively new phenomenon: intrinsic variation in the representation of features determines the transfer of VPL . Orientations around cardinal are represented more reliably than orientations around oblique in V1 , which has been linked to behavioral consequences such as visual search asymmetries . We studied VPL for visual search of near-cardinal or oblique targets among distractors of the other orientation while controlling for other display and task attributes , including task precision , task difficulty , and stimulus exposure . Learning was the same in all training conditions; however , transfer depended on the orientation of the target , with full transfer of learning from near-cardinal to oblique targets but not the reverse . To evaluate the idea that representational reliability was the key difference between the orientations in determining VPL transfer , we created a model that combined orientation-dependent reliability , improvement of reliability with learning , and an optimal search strategy . Modeling suggested that not only search asymmetries but also the asymmetric transfer of VPL depended on preexisting differences between the reliability of near-cardinal and oblique representations . Transfer asymmetries in model behavior also depended on having different learning rates for targets and distractors , such that greater learning for low-reliability distractors facilitated transfer . These findings suggest that training on sensory features with intrinsically low reliability may maximize the generalizability of learning in complex visual environments .
Training in fundamental visual perceptual tasks can lead to substantial improvement , a phenomenon known as Visual Perceptual Learning ( VPL ) , which is associated with adult brain plasticity . VPL has powerful real-word applications [1–3] including improving the vision of adults with cortical blindness [4] , amblyopia [5–7] and presbyopia [8] . VPL is often specific to the trained feature and location ( reviewed by [9] ) . From a theoretical point of view , specificity can provide important insight into the neuronal mechanisms that underlie VPL . For example , specificity has been taken to imply plasticity in early-stage visual processing ( e . g . , [10 , 11] ) . However , from a practical or clinical viewpoint , specificity can be a major obstacle in the development of effective training protocols , and it is therefore critical to understand the factors that determine VPL specificity and the conditions that lead to transfer . For complete transfer to occur , the visual system needs to apply learning for one stimulus to another stimulus . The ability to generalize improvements across stimuli may be most likely when the representation of the stimuli is intrinsically similar . However , the visual system has intrinsic variations in its representation of different feature values . In particular , the reliability with which different feature values are represented can vary considerably within a feature dimension . For example , the reliability of orientation representation in V1 strongly depends on the orientation value . Cells responding to orientations around cardinal are larger in number and have smaller response variability compared to cells responding to orientations around oblique [12 , 13] . In human V1-V3 , sensory uncertainty estimated from the fMRI BOLD signal is higher near oblique orientations than near cardinal orientations , which correlates with orientation estimation behavior [14] . These studies show a gradual variation in representational reliability as a function of orientation , with higher reliability for orientations closer to cardinal ( especially horizontal ) and lower reliability for more oblique orientations . These intrinsic differences have been linked to substantial behavioral effects unrelated to learning . They explain the advantage that observers have in discriminating orientations around cardinal compared to around oblique [13–16]: the oblique effect [17]; and in detecting oblique targets among cardinal [18–20] or near-cardinal [21] distractors over the reverse: orientation search asymmetry [22] . Explanations of search asymmetry propose that oblique distractors have less reliable representations than cardinal distractors and thus hinder target detection more [19 , 20] . Intrinsic variations in representational reliability are not limited to orientation; for example , stimulus processing also varies across spatial frequency [23] . Thus far , however , no study has directly investigated the effect of these preexisting variations in representational reliability on VPL transfer and specificity . Investigation of VPL has focused instead on the manipulation of task properties . By varying task difficulty [10 , 24] and task precision ( e . g . , orientation difference in a discrimination task; [25] ) , researchers varied the representational precision required to successfully perform the task , and studied its effect on VPL specificity . However , variability in task demands is distinct from initial variability in the underlying representation and may invoke different learning mechanisms . For example , increased specificity in difficult or high-precision tasks has been attributed to changes in the modulated level of representation in the visual processing hierarchy [10 , 24] , whereas intrinsic differences in representational reliability are present within the same hierarchical level . Here , we asked whether variations in representational reliability alone can explain VPL and its specificity and transfer , when task properties such as difficulty and precision are the same . Our results show that near-cardinal and oblique orientations not only yield an orientation search asymmetry [18–22] but also show asymmetric transfer of VPL in visual search . Conversely , task difficulty , which was independently manipulated by varying the stimulus onset asynchrony ( SOA ) between a mask and the search display , did not affect the pattern of transfer . To test the sufficiency of a reliability-based account , we fit a computational model that combines learning-related increases in the reliability of stimulus representations with a Bayesian search strategy based on Ma et al . [26] . This Bayesian search model was well-suited to test our hypothesis , because it explicitly represents orientation reliability . Using an unchanging optimal decision rule , the model accounts for both search and transfer asymmetry via initial differences in near-cardinal and oblique orientation reliability .
To test for learning , we compared the first and the last training days . For all three dependent measurements ( sensitivity , bias and RT ) we conducted a ( 2X2X2 ) three-way mixed design analysis of variance ( ANOVA ) with training effect ( training day 1 vs . 6 ) and SOA ( 35 , 59 , 94 and 129 ms ) as within-observers factors and group ( near-cardinal vs . oblique training ) as a between-observers factor . To test for the transfer of learning for each of the three dependent measurements , we conducted a ( 2X2X2 ) three-way mixed design ANOVA with tests ( color test vs . orientation test ) and SOA ( 35 , 59 , 94 and 129 ms ) as within-observers factors and group ( near-cardinal vs . oblique group ) as a between-observers factor . As Fig 1E reveals , whereas color test performance was very similar to the last day of learning in both groups , orientation test performance was dependent on the group . Because baseline performance ( training day 1 ) for the near-cardinal condition was lower than for the oblique condition , it may be that during the orientation test ( when target and distractor orientations swapped ) specificity was inflated by the baseline difference . In order to control for this possibility , we additionally assessed transfer and specificity by comparing performance ( d′ ) in the orientation transfer test from one group with the baseline performance ( training day 1 ) and trained performance ( training day 6 ) of the other group , such that the orientation condition was the same within each comparison ( Fig 2B ) . First we tested whether transfer performance is higher than baseline , which would indicate that at least some learning partially transferred to the untrained orientation . Two independent sample one-tailed t-tests revealed significant transfer both to near-cardinal and to oblique orientations , t ( 8 ) = 3 . 08 , p = 0 . 007 , Cohen’s d = 1 . 94 , t ( 8 ) = 2 . 01 , p = 0 . 039 , Cohen’s d = 1 . 28 , respectively . Next we tested whether transfer performance is different than trained performance; a difference would indicate specificity , while no difference would indicate full transfer of learning to the untrained orientation . Two independent sample t-tests revealed significant partial specificity following oblique orientation training , t ( 8 ) = 2 . 96 , p = 0 . 018 , Cohen’s d = 2 . 05 , but not following near-cardinal orientation training , t<1 . The same results were obtained when a nonparametric test was used ( S1 Table ) . Thus , learning only partly transferred to the near-cardinal orientation but fully transferred to the oblique orientation . Because we found that VPL specificity and transfer depended on the trained orientation–despite equated task difficulty and task precision–we hypothesized that differences in the representational reliability of near-cardinal and oblique orientations may lead to both search and VPL transfer asymmetries . To investigate this possibility , we used computational modeling . We developed a model that consists of two parts: optimal orientation search [26 , 28] and reliability improvement over the course of learning . The goal was to determine whether orientation reliability and its improvement with learning could explain the behavioral data . We compared four models to test different hypotheses about the role of orientation reliability in learning and transfer in the orientation search task . We tested whether initial reliability differences between near-cardinal and oblique orientations alone ( Reliability model ) , different learning rates for targets and distractors alone ( Learning model ) , both of these factors together ( Reliability-and-Learning model ) , or these factors with independent learning rates for the two groups ( Reliability-Learning-Group model ) best accounted for the data . Detailed descriptions of the models can be found in the Methods , and all model fits are shown in S1 Fig . Model comparison using the AICc metric indicated that initial reliability differences between near-cardinal and oblique orientations were critical to explain the data . The Reliability model ( three parameters , AICc = 10 . 61 ) and the Reliability-and-Learning model ( four parameters , AICc = 13 . 00 ) outperformed the Learning model ( three parameters , AICc = 21 . 05 ) and the Reliability-Learning-Group model ( six parameters , AICc = 24 . 56 ) . When we compared cross-validated r2 , the Reliability-and-Learning model fit the data better than the Reliability model . For the Reliability-and-Learning model , cross-validated r2 was 0 . 81 ( SD 0 . 09 ) , falling within the noise ceiling ( lower and upper bounds , [0 . 75 0 . 84] ) , Model performance was therefore as good as possible given the noise in the data . For the Reliability model , cross-validated r2 was 0 . 70 ( SD 0 . 24 ) , falling below the noise ceiling . To determine whether transfer and specificity in the two best models could be predicted based only on the learning phase , we fit the models to the training days only and predicted the transfer test performance for each group . For the Reliability-and-Learning model , the predicted orientation test performance was similar to the observed performance , namely , transfer in the near-cardinal group and specificity in the oblique group ( Fig 3A , stars ) . The Reliability model predicted more transfer in the oblique group than was observed in the data ( Fig 3A , plus signs ) , similar to its fit to all data points ( S1 Fig ) . The pattern of transfer and specificity therefore did not depend on including the test session data when fitting the model , and the Reliability-and-Learning model better explained transfer behavior . The Reliability-and-Learning model , then , captured the three key features of the data: 1 ) the search asymmetry at baseline , 2 ) the performance improvement with learning , and 3 ) the orientation dependence of VPL specificity and transfer ( Fig 3A ) . Learning in the oblique group maintained the difference in reliability between the near-cardinal and oblique orientations , thereby maintaining the search asymmetry present at baseline and preventing full transfer . Conversely , learning in the near-cardinal group decreased the reliability difference between orientations , effectively overcoming the search asymmetry and allowing similar near-cardinal and oblique performance by the end of training . Fig 3B shows Reliability-and-Learning model estimates of near-cardinal and oblique reliability as a function of training session for each group . The model estimated greater sensory uncertainty ( lower reliability ) for the oblique than for the near-cardinal orientation , consistent with physiological and behavioral findings [12 , 13] . For the best-fitting parameter estimates , the distractor learning rate was 0 . 65 and the target learning rate was 0 . 24 .
Existing models of VPL predict the same level of specificity across the same levels of task-difficulty [24] , task precision [25 , 29] and feature exposure during training [30] . The demonstration that a mere difference in the trained feature value , near-cardinal vs . oblique orientation , determined VPL specificity challenges these views . Supported by computational modeling , we suggest that intrinsic differences in the representational reliabilities of near-cardinal and oblique orientations governed VPL specificity and transfer in orientation search . Our design enabled us to control for the involvement of task-related factors and to assess the effect of representational reliability per se . In both groups the equal orientation difference between targets and distractors ( 30° ) , equated performance controlled by SOA , and identical exposure to the transfer feature insured independence from task precision [25] , difficulty [24] , and feature exposure [30] , respectively . Our analyses confirmed that both learning rate and magnitude were equal for the two groups . In addition , our results cannot be explained in terms of differences in number of difficult trials during training . A larger number of difficult trials during training has been found to increase specificity [31] . This relationship would predict a result opposite to ours: specificity in the near-cardinal group , which was more difficult on average ( across SOAs ) . Thus , stimulus-related properties , rather than task , determined specificity here . The dependence of transfer on the specific orientation value has implications for the investigation and interpretation of VPL transfer and specificity using oriented stimuli . Indeed such stimuli have been commonly used to investigate VPL , including in orientation discrimination tasks ( e . g . , [25 , 30 , 32–34] ) , visual search ( e . g . , [10 , 30 , 35 , 36] ) and texture discrimination tasks ( e . g . , [37–41] ) . Some VPL studies have varied orientation values to manipulate task properties , such as task difficulty , and then linked those task properties to the resulting feature specificity ( e . g . , [10 , 30 , 37] ) . Our study suggests that orientation differences alone can affect the pattern of feature specificity and transfer and therefore should be controlled , particularly in displays with more than one orientation . Researchers have inferred the site of the underlying plasticity in VPL based on specificity and transfer results ( reviewed by [42] ) . Specificity and transfer have been taken to indicate learning in early and late visual areas , respectively [10 , 11 , 24 , 30] . Here we show that preexisting variation in representational reliability , which can occur within the same level of processing , can determine VPL transfer . Our findings , therefore , suggest that specificity and transfer are not always appropriate diagnostic tools for the level of VPL plasticity . Our model combined orientation-dependent reliability , improvement of reliability with learning , and an optimal search strategy . We based the search strategy on the optimal visual search model by Ma et al . [26] , because that model provides a parsimonious explanation of orientation search with minimal parameters . We found that a single change to the model–letting reliability depend on orientation–captured orientation search asymmetry prior to learning . According to the model , the lower reliability of oblique compared to near-cardinal stimuli leads to more uncertainty during the local decision regarding the identity of an item . The disrupting effect of this uncertainty on visual search performance is larger with oblique distractors ( near-cardinal target ) than with near-cardinal distractors ( oblique target ) , simply because there are many distractors but only one possible target in any given display . The improvement of reliability across training days captured the behavioral pattern of both learning and transfer . Importantly , the model uses the same optimal decision rule throughout training and during the transfer tests . Search asymmetry and learning , therefore , could be attributed to variation in sensory reliability only , rather than changes in decision strategy and rule based learning [30] . Comparing alternative versions of the model allowed us to determine which factors were critical to explain the behavioral data . Preexisting differences in reliability were essential–a model without this component failed to fit the data–but independent learning for targets and distractors also improved model performance , particularly in capturing transfer behavior . This result is consistent with a previous study that found independent target and distractor learning in an orientation search task [43 , 44] . Our learning rate estimates correspond well to that study’s finding of about twice as much learning for distractors as for targets [43] . It is therefore the combination of preexisting reliability differences and greater learning for distractors than targets that best explained behavior , in this family of models . Specifically , greater learning for the initially low-reliability oblique distractors eliminated the search asymmetry and enabled full transfer for the near-cardinal group . Our model follows the account that differences between the reliabilities of the cardinal ( or near-cardinal ) and oblique representations cause orientation search asymmetry [18–20] . A key component of these accounts is the ratio of target signal to background noise , which depends on the target and distractor identities [18 , 19 , 45] . Alternative accounts have also been proposed . One influential theory explains visual search asymmetries by considering a map of feature dimensions and their interactions [46] . This theory suggests that targets with larger feature values ( e . g . more oriented , i . e . oblique ) are inherently more detectable than targets with smaller values ( e . g . less oriented , i . e . cardinal ) ( e . g . , [46 , 47] ) . Based on this theory , a neural computational model was developed that explains search asymmetry in terms of a salience map in V1 [48] . However , it is unclear how the elimination of search asymmetry following near-cardinal training could be predicted if search asymmetry arises from inherent feature properties like “more tilted” [46 , 48 , 49] . Moreover , no previous model addresses VPL in orientation search . Analogous to the reliability differences between orientation values represented by our model , neurons responding to oblique orientations have larger tuning curve widths than those responding to near-horizontal orientations in macaque V1 [13] , and there is more cortical area tuned to near-cardinal orientations than to oblique orientations in ferret cortex [12] . Higher sensory uncertainty has also been estimated for oblique compared to near-cardinal orientations in human V1-V3 [14] . The Ma et al . [26] model on which the orientation search component of our model is based has been implemented as a biologically plausible neural network model , strengthening the connection between the physiological literature and our current computational results . Learning was modeled as an increase in the representational reliability of the stimulus orientations . This increase could be implemented either as a reduction of the tuning curve width of V1 or V4 neurons with training [50–53] or as an improvement in readout from the early sensory response [29] . Both mechanisms have been proposed previously for an orientation discrimination task . Our model , therefore , applies VPL principles derived from orientation discrimination tasks to explain VPL for more complex visual search tasks . Our findings are limited to VPL in orientation search , and more study is required to determine whether they generalize to other stimuli and tasks . Our study also does not rule out alternative models for orientation search asymmetry and VPL in visual search , but it shows that a parsimonious optimal decision rule , preexisting differences in orientation reliability , and reliability learning suffice to explain both search and transfer asymmetry . For simplicity our model assumes the same representational reliability for all stimulus locations . However , stimulus reliability can vary as function of eccentricity ( e . g . , [18 , 23] ) and polar angle [54 , 55] . It will be interesting to test the relation between location-dependent feature reliability and VPL transfer and specificity . Researchers have sought to understand the perceptual and neuronal processes that underlie VPL by studying how task demands affect VPL specificity . In the present study we control for task while testing the effect of the intrinsic reliability of feature representations on VPL specificity in visual search . We found a striking difference in VPL transfer depending on the orientation of the trained target , which we interpret as an effect of representational reliability . This interpretation is supported by both previous neurophysiological findings and computational modeling of the present data . We conclude that preexisting variation in the reliability of feature representations within a single level of processing may have a critical effect on VPL transfer and specificity , calling into question the logic that the degree of feature specificity can be used to infer the neural level at which VPL occurs , especially for complex visual displays . A growing body of research demonstrates the potential benefits of VPL in clinical ( e . g . , [4–8 , 56 , 57] ) and professional ( e . g . , [58] ) applications . Our study suggests a testable hypothesis: to increase the generalizability of perceptual learning in real-world applications , efficient training protocols should focus training on low-reliability features–oblique orientations and motion directions [59] , peripheral spatial locations [60] , and so forth–which may limit performance in a variety of natural tasks .
We developed a model that consisted of two parts: optimal search ( based on Ma et al . [26] ) and reliability improvement over the course of learning . We compared alternative versions of the model to determine which parameters were required to explain , in a single fit , the data from both observer groups , including the initial search asymmetry , performance improvement over the course of training , and the transfer asymmetry .
|
Training can modify the visual system to produce improvements on perceptual tasks ( visual perceptual learning ) , which is associated with adult brain plasticity . Visual perceptual learning has important clinical applications: it improves the vision of adults with visual deficits , e . g . amblyopia and cortical blindness , and even presbyopia ( aging eye ) . A critical issue in visual perceptual learning is its specificity to the trained stimulus . Specificity gives insight into the processes underling experience-dependent plasticity but can be an obstacle in the development of efficient rehabilitation protocols . Under what circumstances visual perceptual learning transfers to untrained stimuli is poorly understood . Here we report a qualitatively new phenomenon: specificity in visual search depends on intrinsic variations in the reliability of feature representations; e . g . , vertically oriented lines are represented in V1 with greater reliability than tilted lines . Our data and computational model suggest that training on sensory features with intrinsically low reliability can maximize the generalizability of learning , particularly in complex natural environments in which task performance is limited by low-reliability features . Our study has possible implications for the development of efficient clinical applications of perceptual learning .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"learning",
"medicine",
"and",
"health",
"sciences",
"social",
"sciences",
"neuroscience",
"learning",
"and",
"memory",
"perceptual",
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"cognitive",
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"sensory",
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"sensory",
"perception",
"cognitive",
"science"
] |
2017
|
Feature reliability determines specificity and transfer of perceptual learning in orientation search
|
Twin cohorts provide a unique advantage for investigations of the role of genetics and environment in the etiology of variation in common complex traits by reducing the variance due to environment , age , and cohort differences . The GenomEUtwin ( http://www . genomeutwin . org ) consortium consists of eight twin cohorts ( Australian , Danish , Dutch , Finnish , Italian , Norwegian , Swedish , and United Kingdom ) with the total resource of hundreds of thousands of twin pairs . We performed quantitative trait locus ( QTL ) analysis of one of the most heritable human complex traits , adult stature ( body height ) using genome-wide scans performed for 3 , 817 families ( 8 , 450 individuals ) derived from twin cohorts from Australia , Denmark , Finland , Netherlands , Sweden , and United Kingdom with an approximate ten-centimorgan microsatellite marker map . The marker maps for different studies differed and they were combined and related to the sequence positions using software developed by us , which is publicly available ( https://apps . bioinfo . helsinki . fi/software/cartographer . aspx ) . Variance component linkage analysis was performed with age , sex , and country of origin as covariates . The covariate adjusted heritability was 81% for stature in the pooled dataset . We found evidence for a major QTL for human stature on 8q21 . 3 ( multipoint logarithm of the odds 3 . 28 ) , and suggestive evidence for loci on Chromosomes X , 7 , and 20 . Some evidence of sex heterogeneity was found , however , no obvious female-specific QTLs emerged . Several cohorts contributed to the identified loci , suggesting an evolutionarily old genetic variant having effects on stature in European-based populations . To facilitate the genetic studies of stature we have also set up a website that lists all stature genome scans published and their most significant loci ( http://www . genomeutwin . org/stature_gene_map . htm ) .
Human adult stature ( body height ) has been the target of numerous genetic quantitative trait linkage studies in the past few years [1–17] . Despite high heritability estimates for all populations , based on either twin comparison [18–21] or on actual genetic resemblance in siblings [22] , the results have been disappointing and inconsistent , with reports of quantitative trait loci ( QTLs ) scattered across the genome and rarely replicated . Only loci on Chromosomes 3 , 5 , 6 , and 7 have been implicated in more than one study [14] . It seems that the multifactorial and oligo- or polygenic nature of the trait renders identification of the genes involved quite a formidable task . It is likely that special strategies are needed to tackle the identification of the loci and genes underlying human height . One obvious strategic decision in addressing a quantitative trait regulated by multiple QTLs , each potentially with a minor effect , is to maximize the sample size . Although this could introduce multiple challenges , including an increase in genetic and phenotypic heterogeneity , robust QTL analysis of large cohorts of families is the natural first choice . Based on a meta-analysis of linkage studies , the only factors independently associated with successful locus identification are an increase in the number of individuals studied and a study sample drawn from one ethnic group [23] . Our approach is based on the idea that in addition to maximizing the sample size , since environmental factors are likely to play a significant role in human stature , it would be desirable to minimize environmental noise by sampling relatives of similar age and shared environment , preferably dizygotic twins . We performed QTL analysis of stature using data from genome-wide scans performed for 3 , 817 families ( 8 , 450 individuals , 3 , 301 twin pairs ) collected within GenomEUtwin from Caucasian populations in six different countries: Australia , Denmark , Finland , Netherlands , Sweden , and United Kingdom . This study sample extracts its information from twin pairs sharing early environment throughout the critical period of human growth and although we are pooling sibpairs from various populations , they are all of European origin .
To date , about 20 genome scans have been published that investigate human adult body height [1–19] . Since height and various phenotypic characters are recorded in most studies , it is expected that this field will expand even more in the future . To make access more tractable for researchers in the field , we have set up a Web site that lists all genome scans published and their most significant loci . We searched the MEDLINE and PubMed databases ( http://www . ncbi . nih . gov ) and bibliography reviews ( as of December 2006 ) to identify all eligible studies and have provided the essential data for them as well as the references on the website . The criteria for the eligibility for inclusion on this web site are: ( 1 ) the study is a family-based study utilizing genome-wide markers and quantitative linkage analysis on the adult human height and ( 2 ) the families are unselected for adult height . Figure 3 illustrates all loci from the studies fulfilling the above criteria with LOD score ≥ 2 .
To our knowledge , this is by far the largest genetic linkage study performed regarding human height and on any trait in DZ twins . The nuclear families originated from six different countries of European origin . The method of data pooling used here includes the assumption of some locus homogeneity across the study samples . Silventoinen et al . [19] have shown that there are only minor differences in the genetic architecture of height between these twin cohorts , especially among men . We can see this from our data as well; we obtained evidence for linkage on Chromosomes 8 , 20 , and X , contributed by both sexes and from several cohorts . Since three identified loci , on 8q , 20p , and Xq25 , represent the genome segments earlier identified in family-based studies for stature [19] , they probably are of significance for stature generally . That said , the 7q arm that has been linked to stature in at least three different studies [19] did not show any evidence for linkage on the q arm but rather to 7p , which could of course reflect the same signal , but due to long genetic distance between these loci ( >100cM ) we consider this unlikely . Growth in twins differs to some degree from that in singletons , especially during fetal life . Dizygotic twins share the same intrauterine conditions , albeit with separate placenta and fetal membranes . Twins grow at the same rate as singletons during the first half of pregnancy , but exhibit slowing growth rates in the third trimester of pregnancy , primarily due to space restriction in utero . This results in lower birth weights than in singletons on average , mostly due to shorter gestation time [24] . Twins show a rapid catch-up growth in infancy , exhibit the same general pattern of growth and onset of puberty as singletons , and do not differ from general population in attained height [25] . However , the initially larger twin ( both monozygotic and DZ ) at birth tends to remain larger even at adulthood [26] . This shows that the influence of intrauterine conditions for the adult height is of importance and may introduce some “environmental noise” even into this unique human study sample , which is , to a large extent , harmonized for major environmental effects between sibs . Moreover , DZ twins share not only the pre- but also post-birth environment ( in most cases ) . Here , we also formed a hypothesis that by restricting our analyses to DZ twins only , who share all the early life events , the potential environmental ( nongenetic ) noise affecting growth would be reduced . This would then allow genetic loci components influencing adult stature to be more easily detected , such as the peak ( DZ males , LOD 1 . 47 ) on a previously linked locus to male stature on 1q21 [19] . It is of interest that the previous study by Sammalisto et al . [19] was performed on a genetically and culturally homogenous population of Finns , and that the linked locus was seen in males only , thus representing some analogy to the subcohort showing some evidence for linkage here . The loci on Chromosomes 20 and 21 also show some evidence for linkage seen only in the cohort restricted to DZ twins; however , the results are only suggestive . Although the most evident height differences among humans appear between the sexes , and nutrition and infections are critical factors for final adult height across the global populations [27] , there is ample evidence that autosomal loci also contribute to human growth and adult height . For example , in Turner's syndrome patients , who possess an abnormal number of X chromosomes , correlations for stature are similar to those for non-Turner mothers and daughters [28] . Also , most of the previously suggested QTLs for stature are located on autosomes [14]; however , the main reason for this is that most of the previous studies report only autosomal data . Our most significant finding on Chromosome 8 is not surprising , since suggestive linkage for stature to 8q24 was previously reported by Hirschorn et al . [3] . Interestingly , the 8q21 area contains the gene for Nijmegen breakage syndrome , which is characterized by growth retardation . After birth the growth rate of these children is appropriate , but individuals remain small for their age . However , in light of the large difference in stature between males and females , it would be surprising to find no evidence at all for QTL on a sex chromosome in such a large dataset . The potential role of the X-chromosomal locus was identified through analyses based on data from several cohorts , and this locus would not have been apparent in any stand-alone cohort analysis . This lends support to the pooling strategy used here to identify such minor-to-moderate QTLs . This X-chromosomal locus , Xq25 , has previously shown suggestive linkage to stature in pedigrees of European origin [29] and it harbors several candidate genes for growth . Telomerically from the peak marker , DXS1047 , lies the gene associated with Borjeson-Forssman-Lehmann syndrome , X-linked mental retardation of which one characteristic is moderately short stature [30 , 31] . Two genes associated with the syndrome , the widely expressed plant homeodomain–like finger gene , PHF6 , and fibroblast growth factor 13 , FGF13 , [29] lie within 9 Mb of DXS1047 . Other potential candidate genes on the area are the skeletal muscle LIM protein 1 , SLIM 1 , which shows elevated expression levels in skeletal muscle during postnatal growth , and glypican 3 , GPC3 . Variants in GPC3 cause Simpson-Golabi-Behmel syndrome , a condition characterized by pre- and postnatal overgrowth ( gigantism ) with visceral and skeletal anomalies [32] . GPC3 seems to be involved in the suppression/modulation of growth in the mainly mesodermal tissues and organs and also may interact with the insulin-like growth factor II ( IGF2 ) , thus regulating growth [33] . There are no previous QTLs linked to stature on Chromosome 15 [14] . Here , the only subcohort linked to that locus was the largest , the Australian cohort . This cohort constitutes almost one third of the total sample ( 30 . 8% ) . The linked families were not characteristically different from others in the Australian cohort ( unpublished data ) ; there were no outliers for stature values and rigorous quality control of genotypes did not expose obvious errors in data produced . The Australian cohort is mostly European based , as are the other cohorts , and was earlier found to be comparable to a European population in regard to microsatellite allele [34] . Thus , although other cohorts and the pooled analyses did not lend significant support for linkage to this region , we find it potentially interesting , while being aware that replication is needed to confirm the potential QTL for stature on 15q . The same applies to the locus on 10q for the Dutch sample—this locus has not previously been reported to be linked to stature in other populations , nor was there any evidence for linkage in other subgroups in this study . Part of the Dutch cohort was included in an earlier study on stature , in which the Chromosome 10 peak was also seen [14] . Here , however , the Dutch cohort includes more people and not all the loci in the earlier study [14] are as evident as the one on Chromosome 10 . Also , the Finnish cohort showed some evidence of linkage on previously linked loci for stature in Finnish studies , such as 2q , 4q [14] , and 9q [14] . Obviously , differing information content of the genotyped cohorts could potentially explain these population-specific peaks; however , this is not evident from our data . The genotyping success rates and map densities did not show variations on the above-mentioned population-specific loci ( unpublished data ) . We identified several other potential QTLs , which were not statistically significant , but nevertheless triggered some interest . For example , the loci for which data from many cohorts seem to contribute to and increase the overall LOD score ( 20p and Xq ) might mirror minor loci either needing larger sampling or some kind of dissection of the sample to produce a more homogeneous sample in relation to the locus in question . Loci that have previously been reported to be linked to stature , such as on Chromosomes 8q , 20q , 21p , and Xq25 , might well present a real QTL , but due to multiple testing of genome-wide scans here we cannot apply the more relaxed linkage thresholds that are often used in replication studies , and , thus , they remain suggestive . Several previous loci were not replicated in this study , maybe because stature is a very heterogeneous phenotype , or possibly due to type I or II errors in this or earlier studies . We think that our findings support the essential argument of quantitative genetics , that of the infinitesimal model . From the results we can deduce that , most probably for stature , the ultimate causation of variance components is segregating alleles at many underlying loci , each of which has a very small individual impact on the character in question . One of Fisher's several important contributions to evolutionary theory was to provide a statistical outline for such a complex inheritance model in a Mendelian framework that could account for continuous variation [35] , giving rise to the field of quantitative genetics [36 , 37] . Here , we have combined genome scans using multiallelic markers from a total of 8 , 450 individuals , including 3 , 301 DZ twin pairs , the largest published twin QTL genome scan , with microsatellite genome-wide scan data , and produced evidence for several potential stature QTLs in this Caucasian study sample in a manner confirmed to minimize the nongenetic noise of early environment . However , given the relatively low LOD scores observed in even this sizable study sample , it is quite evident that , although highly heritable , human stature is quite polygenic , probably determined by several minor QTLs , interacting with environment and sex differences and adding up to the phenomenon we know as variation in stature .
GenomEUtwin is a research consortium of 12 partners , including eight twin cohorts from Europe and Australia ( http://www . genomeutwin . org ) , formed to explore genetic influences on common traits using large population and twin cohorts . For these analyses , genome-wide microsatellite scan data were available from six twin cohorts: The Australian Twin Registry [38] , The Danish Twin Registry [39] , The Finnish Twin Cohort [40] , The Netherlands Twin Register [41] , The Swedish Twin Registry [42] , and United Kingdom St . Thomas' UK Adult Twin Registry [43] . While the Australian twin cohort is geographically quite distinct from the others , it does have a mostly European-based population background . The Australian twin registry has self-reported ancestry information on a random subset that is representative of the entire sample . From this subset ( 897 families ) , 99 . 7% are Caucasian . From that , 92 . 5% are from the United Kingdom . The rest are from Europe and the United States . While the twin studies served as the recruitment basis for the participants , additional siblings and/or parents/descendants of the twins were also recruited in some cohorts and included in the analysis set here where available . The total number of families included in the analysis was 3 , 817 , and the number of full DZ sib pairs with genome-wide marker and phenotype data was 3 , 301 . A federated database with open-source code has been created to share the data across the study partners [44 , 45] . Genotypes for the Australian , Dutch , and United Kingdom cohorts , as well as 102 Swedish twins , were produced as described earlier [12 , 46 , 47] . Dutch samples were genotyped by the Mammalian Genotyping Service in Marshfield , and the Molecular Epidemiology Section in Leiden University Medical Centre ( The Netherlands ) . The Finnish subjects , as well as the Danes , were genotyped at the Finnish Genome Center ( using 96 capillary Megabase 1000 sequencers [Amersham Biosciences , http://www1 . gelifesciences . com] ) and partly at Uppsala Rudbeck laboratory ( using Applied Biosystems automated DNA sequencing system , http://www . appliedbiosystems . com ) . A total of 487 Swedish twin pairs were genotyped by deCODE Genetics ( http://www . decode . com ) , using the Applied Biosystems automated DNA sequencing system ( 1000 marker , 4-cM map ) . The program GRR , ( Graphical Representation of Relationships , http://www . sph . umich . edu/csg/abecasis/GRR/index . html ) [48] was used to screen for inconsistencies in familial relationships as well as excluding potential monozygotic twin pairs . Between-group phenotypic comparisons were conducted using the program SAS 8 . 2 ( SAS Institute , http://www . sas . com ) . Measured height was available from the Dutch and United Kingdom cohorts as well as from large parts of the Australian and Danish cohort , while the Swedish and Finnish height data was acquired from questionnaires . Genotypes were checked for Mendelian inconsistencies using the PedCheck program [49] and the program Merlin's [50] genotyping-error option was used for identifying problematic-yet-Mendelian genotypes , which were then excluded from the analyses using Merlin's Pedwipe-program . Having access to raw data of all the genome-wide scans performed , we decided to pool all raw data ( genotypes and phenotypes ) instead of applying meta-analytic strategy . Since the original genome scans differed in their selection of genetic markers , we first harmonized the genetic marker maps using the in-house-developed Cartographer program [14] . Cartographer retrieves the physical location of the markers from the University of California Santa Cruz ( UCSC ) database and orders the markers based on the sequence information . The genetic location of each marker is defined using the published deCODE genetic map [51] , which are also stored in the UCSC database . For markers that were not included in the deCODE genetic map , we used linear interpolation for obtaining estimates of the genetic locations of these markers by using the physical and the genetic locations of the immediately flanking deCODE markers . Also , if the physical location from the UCSC database and the genetic location from the deCODE genetic map for a given marker were in disagreement , we obtained an estimate of the genetic location via interpolation using the nearest flanking deCODE markers that are in agreement with the sequence information . For those markers that were not found in the UCSC database , we retrieved PCR primers from UniSTS and used these to map the marker using UCSC in-silico PCR , and performed the linear interpolation to obtain an estimate of the genetic location as described previously [14] . After harmonization of the maps across the cohorts studied , the cohorts were pooled together such that all identical markers that were genotyped in several populations were renamed for all of them and these renamed markers were located at 0 . 001 recombination fractions from each other . A total of 253 markers were genotyped in all six populations ( 90 in five populations , 50 in four , 142 in three and 238 in two , while 2 , 939 markers were genotyped in only one population ) . To circumvent the problems produced by this partial overlap of markers among the cohorts when interpreting two-point LOD scores , only multipoint linkage results are shown here . All data were then analyzed together using the variance component method in the program Merlin [50] . Genome-wide scan was automized by using the program AUTOGSCAN [52] . People differing in height by more than four standard deviations from the population-and sex-specific mean were excluded from the phenotypic analyses , but their genotypes were included for phase-determination purposes . Age , sex , and country of origin were used as covariates in the variance component analysis . The distribution of height was not notably skewed or kurtotic , so no transformations of any of the variables were necessary . Sexes were also analyzed separately with age and country of origin as covariates . The LOD scores are presented unadjusted for the analyses on these six subcohorts ( total cohort , DZ twins , and the sex-specific analyses ) . No monozygotic twin pairs were identified in the analyses . To estimate empirical p-values for the obtained results , a total of 100 replicates was created and analyzed using Merlin's simulate option . The same analyses that were conducted with the actual data were repeated for each of the simulated genomes . Since any evidence of linkage found in the simulated genomes is due to chance , these simulations allowed us to evaluate the false-positive rate . We determined the empirical genome-wide significance of a given LOD score as the fraction of simulated genome scans in which this LOD score was reached or exceeded . The Wilson confidence intervals were estimated as described in [53] .
The Online Mendelian Inheritance in Man ( OMIM ) database accession numbers for the syndromes discussed in this paper are Borjeson-Forssman-Lehmann syndrome , 301900; Nijmegen breakage syndrome , 251260; and Simpson-Golabi-Behmel syndrome , 312870 .
|
Twin cohorts provide a unique advantage for research of the role of genetics and environment behind common complex traits by reducing the variance due to environment , age , and cohort differences . The GenomEUtwin consortium consists of eight twin cohorts with the total resource of hundreds of thousands of twin pairs ( http://www . genomeutwin . org ) . We performed quantitative family-based genetic linkage analysis for one of the most heritable human complex traits , adult stature ( body height ) , using genome-wide scans derived from twin cohorts from Australia , Denmark , Finland , Netherlands , Sweden , and United Kingdom . Age , sex , and country were adjusted for in the data analyses . Human stature was found to be very heritable across all the cohorts and in the combined dataset . We found evidence for a shared genetic locus accounting for human stature on Chromosome 8 , and suggestive evidence for loci on Chromosomes X , 7 , and 20 . Since twins from several countries contributed to the identified loci , an evolutionarily old genetic variant must influence stature in European-based populations . To facilitate the research in the field we have also set up a website that lists all stature genome scans published and their most significant loci ( http://www . genomeutwin . org/stature_gene_map . htm ) .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"homo",
"(human)",
"genetics",
"and",
"genomics"
] |
2007
|
Combined Genome Scans for Body Stature in 6,602 European Twins: Evidence for Common Caucasian Loci
|
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