PDGF acted as an activator of the HSC and sorafenib inhibits the activation by blocking the combination of PDGF and PDGF receptor. The supernatant of activated HSCs promoted the proliferation, metastasis, and invasion of HL-7702 and SMMC-7721, as well as epithelial-mesenchymal transition (EMT). Agrin found in the HSC supernatant showed the same effect on SMMC-7721 as to the supernatant of activated LX-2. Furthermore, downregulation of agrin by siRNA could decrease the proliferation, metastasis, and invasion of SMMC-7721, and promote MET. Sorafenib prevented DEN-induced hepatocarcinogenesis and could alleviate the liver inflammation and fibrosis. Sorafenib could improve the liver function of Sprague Dawley rats by decreasing the serum levels of ALT and AST. These results demonstrate thatPDGF is an effective activator of HSC and sorafenib could inhibit the activation. In vivo experiment suggested sorafenib could alleviate the hepatocarcinogenesis mediated through agrin secretion and could be potential candidate for treatment of cirrhosis.Evaluating the process and mechanism of fibrogenesis is essential in hepatocellular carcinoma (HCC), especially in hepatocyte transformation and oncogenic signaling. We evaluated the oncogenic role of agrin secreted by platelet-derived growth factor (PDGF)-induced hepatic stellate cell (HSC) in HCC. Cells were co-cultured to investigate the effect of activated HSC on hepatocytes. Liquid chromatography and protein profiling analysis were used to search the distinct proteins secreted in HSC supernatant. Sprague Dawley rats with Diethylnitrosamine (DEN)-induced HCC were used to simulate human liver cancer and sorafenib was administered to investigate its effect on hepatocarcinogenesis. A paired “two-tailed” Student  For in vitro PDGF receptor expression study, we used a VEGF inhibitor sorafenib with additional PDGF-inhibiting properties, which is one of the drugs approved for the treatment of advanced, non-resectable HCC [The development of cirrhosis and progression to HCC is complex process involving a combination of etiologies. The main risk factor for developing HCC is liver cirrhosis , 2. HCC able HCC .To investigate whether PDGF could activate the HSC directly, we stimulated LX-2 with 20 ng/ml PDGF, with or without preconditioned by 10 μM sorafenib. Alpha-SMA and collagen I (col-I), secreted by activated HSCs, were chosen as markers. The expression of α-SMA and col-I increased significantly after the stimulation with PDGF, which showed no change under the pre-treatment with sorafenib Figure  and 1B, To investigate the effect of activated HSCs, we co-cultured HL-7702 cell line and SMMC-7721 with LX-2 cells. Compared with the control group, the proliferation of HL-7702 and SMMC-7721 increased significantly in the presence of PDGF-activated LX-2 and the cells in the control group remained unchanged Figure . The traBecause PDGF is an activator of LX-2 cells, EMT occurred when hepatocytes were co-cultured with activated LX-2. To study if there were any distinct proteins triggered in the activated LX-2 and quiescent LX-2 stages, we performed liquid chromatography. The chromatograms of the 2 different supernatants are represented in Figure To characterize the functional role of agrin in HSC and HCC cell lines, agrin was also depleted in the HSC cell line LX-2 by transfecting with siRNA pool, by which effective agrin knockdown was achieved Figure . In agriTo further characterize the functional role of agrin in HCC, we depleted agrin in the highly metastatic SMMC-7721 cells by transfecting with a short interfering RNA (siRNA) pool, by which effective agrin knockdown was achieved Figure . In the We next rationalized that restoring agrin should rescue the effects caused by agrin siRNA if these phenotypes are primarily attributable to reduced levels of agrin. Hence, we added 3-μM soluble recombinant agrin in agrin knockdown SMMC-7721 cells. The results indicated that recombinant agrin rescue the metastasis, and invasion ability of SMMC-7721 cells as proved by transwell assays Figure . Compareth week, rats of either group were sacrificed and their livers excised for examining the morphological and macroscopical hepatic changes. Several nodules were found scattered throughout the liver specimens of all DEN-treated animals resembling the texture of macro-nodular cirrhosis. On the contrary, the liver specimens of the rats preconditioned with sorafenib were almost normal. Histological liver lesions induced by DEN are summarized in Table P<.0001), considering if grade 6 and higher were defined as hepatocarcinogenetic  and fibrosis (P =.0003). The levels of both collagen and α-SMA were also significantly reduced in sorafenib-treated rats compared with the control  in favor of tumor cell proliferation . In our We studied the differences between the supernatant of activated HSCs and quiescent HSCs to observe enriched proteins expression and were able to characterize agrin expressed either as membrane protein or secreted in ECM. We further elucidated the mechanistic role of agrin by agrin knockdown using siRNA, which decreased the proliferation, metastasis, and invasion ability of HCC cell line and promoted MET Figure  and 5E. The process of transformation from chronic liver injury to liver fibrosis and to HCC has several chronic inflammatory events that trigger the molecular and cellular mechanisms leading to transformation . To undeThe targets of sorafenib are PDGFR and VEGFR. It is still unclear if the serum levels of PDGF and VEGF would be influenced by sorafenib. To elucidate this, we detected the serum levels of PDGF and VEGF. The results indicated that DEN increased PDGF and VEGF, which in turn contribute to hepatocarcinogenesis. It is observed that sorafenib could not decrease the levels of PDGF and VEGF, but it alleviated the inflammation, fibrosis, and rate of oncogenesis by blocking the connection of PDGF and PDGFR.On contrary, the decreased ALT and AST levels in sorafenib-treated rats indicate the protection to hepatocyte damage, because both of these increase in hepatocyte injury. The ALB level remained unchanged between sorafenib-treated and untreated rats, partly demonstrating that the DEN-induced oncogenesis does not destroy the synthetic ability of hepatocytes. Moreover, agrin level of sorafenib-treated rats was significantly lower than the sorafenib-untreated rats, implicating DEN-induced liver inflammation by activated HSC through agrin secretion. On the other hand, sorafenib inhibited the activation of HSCs, reduced agrin secretion, thereby alleviating fibrosis and HCC, demonstrating the oncogenic role of agrin and protective role of sorafenib in hepatocarcinogenesis.The strength of this study lies in the rationale that this is the first time the role of agrin in HCC with regard to the PDGF-HSC mitogenic pathway has been assessed and has been shown the mechanism underlying the oncogenic role of agrin. However, the nature of the study being a pre-clinical study, which projects the findings on laboratory- and animal-based outcomes may be a limitation. However, the findings may be preliminary and may form the basis of future clinical studies to elucidate the mechanistic approach of oncogenic property exerted by agrin in humans. We suggest that, agrin and PDGF may prove to be the crucial targets for the treatment of liver cirrhosis and HCC, and sorafenib is one such targeted drug that has been already approved for HCC.in vivo and in vitro. This may provide a new possible method for the prevention of HCC.In summary, PDGF can induce HSC activation, which could be inhibited by sorafenib. The main mechanism of the inhibiting the activation of HSC was by inhibition of the combination of PDGF and PDGFR and downregulation of the phosphorylation of ERK. Paracrine of agrin by activated HSC can induce the proliferation, metastasis, invasion, and EMT of HCC cell lines. Sorafenib could inhibit the hepatocarcinogenesis both To investigate the differential expression of proteins between the supernatant of quiescent and activated HSC, LX-2 cells were cultured in two culture bottles with same cell count with serum-free medium to keep LX-2 quiescent. The supernatant was replaced with an equal amount of fresh serum-free medium in 2 days, and 20 ng/mL of PDGF (with or without sorafenib) was added into one bottle for another 24 hours. The supernatant and the cellular proteins of the 2 bottles were collected. The activation of the PDGF-treated LX-2 cells was measured in terms of increased α-smooth muscle actin (SMA) detection. Furthermore, the supernatant of activated LX-2 cells was used to culture HL-7702 or SMMC-7721 cells by mixing with 10% FBS to access the proliferation and wound-healing capability. For protein isolation, the culture medium was centrifuged at a high speed at 4°C and 1-mL supernatant was transferred into a new 1.5-mL Eppendorf. The proteins were enriched by lyophilization.Proteins in the conditional supernatant were extracted and enriched as described above, and the powder in the Eppendorf tubes were re-dissolved in 10-μL tri-distilled water and separated on a 10% SDS-PAGE. The extracted peptides were subjected to liquid chromatography analysis to see the differences in proteins expressed in quiescent and activated LX-2 cells. The metabolite signals were obtained following baseline correction and peak alignment. T-score method was adopted to identify the featured peaks differing between supernatant of activated and quiescent LX-2 cells. Equal amounts of re-dissolved powder were separated on a one-dimensional gradient Bis-Tris gel and digested with trypsin. The samples were analyzed on full-scan mass spectra. Identification and quantification of peptides were performed using mascot version 2.5 cells/well), in serum-free media were seeded in the upper trans well chambers and immortal LX-2 cells in the lower insert. During a 48-hour incubation period at 37°C, 5% CO2, the media, was replenished every 24 hours and the co-culturing was continued for 4 days. The insert was fixed in ethanol and stained with crystal violet. The number of the cells on the lower surface was counted by microscope. Following this, RNA and cellular protein of HL-7702 or SMMC-7721 were extracted and detected. The invasion assay was performed in the same manner as the migration assay except that the upper chamber was precoated with 50 g/well of Matrigel overnight before seeding the cells.HL-7702 or SMMC-7721  was used for reverse transcription. Quantitative real-time PCR was performed using Premix Ex Taq (Takara). Primers used for specific genes are represented in Table The conditional cultured cells were homogenized in extraction buffer and centrifuged at 14,000 g for 20 min to collect the lysate. Eighty micrograms of total protein was separated by SDS-PAGE electrophoresis and then transferred to PVDF Transfer Membranes, which were blocked with 5% nonfat milk dissolved in washing buffer, probed with appropriate primary antibodies against target proteins, and detected by anti-rabbit or anti-mouse secondary antibodies. The signal intensity was analyzed by Image J software. For phosphor-proteins, bands were normalized to their respective total protein levels, whereas for other panels, bands were normalized to a loading control (β-actin)..Cell migration was assessed by wound-healing assays. Confluent SMMC-7721 cells were plated on six-well plates and wounded by manual scratching with a 200-μL pipette tip. At the specific time points, phase contrast images at specific wound sites were taken.Cells were seeded in 96-well plates, and cell proliferation was measured at 0, 12, 24, 36, and 48 hours by Cell Counting Kit 8 assay according to the manufacturer's instructions. Each experiment included 6 replicates and was repeated 3 times.4 cells per milliliter cells were incubated for 24 hours at 37°C, 5% CO2. At the end of the incubation period, cells invading membrane or Matrigel were washed and stained with 0.05% crystal violet solution and imaged in a bright-field microscope.Cell invasion was determined using the 24-well chambers with 8-mm pore poly carbonate membranes either uncoated (for migration) or precoated with Matrigel Basement Membrane Matrix (for invasion). The chambers were rehydrated in serum-free medium. Complete medium with 700-μl 10% FBS served as chemo-attractant in the bottom chamber and 1 × 10Cells cultured on the slides were washed 3 times with PBS and fixed with 4% paraformaldehyde at 4°C for 30 min. After another wash, the cells were permeabilized in PBS with 0.1% Triton X-100 for 15 min at room temperature and then blocked with 10% donkey serum albumin. Cells were incubated with primary antibodies overnight at 4°C, then with fluorescence labeling using secondary antibody for 1 hour at room temperature followed by incubation with DAPI for 10 min. The slides were mounted with an anti-fluorescence quenching agent and observed under an Olympus fluorescence microscope.A total of 40 Sprague Dawley rats were purchased from the Experimental Animal Center, Chinese Forth Military Medical University, and were divided into 2 groups. Group A received DEN and sorafenib  and Group B received diethylnitrosamine (DEN) for HCC induction. The study protocol was approved by the Institutional Ethics Committee of Xijing Hospital. To address the influence of sorafenib on rats with DEN-induced hepatocarcinogenesis, the rats of both groups were intraperitoneally injected with DEN every 3 days for 12 weeks, starting at 8 weeks of age. Sorafenib was administered intragastrically to the rats in Group A every DEN injection. The rats were sacrificed at 22 weeks of age . For quantitative assessment of fibrosis, sections were stained with Sirius red for collagen content. To define the activation of HSCs, levels of α-SMA were assessed by staining with rabbit anti-α-SMA antibody and visualized with HRP-labeled anti-rabbit antibody.The blood of rats was collected in anti-coagulative tube and centrifuged at 1500 rpm for 20 min after standing for 30 min at room temperature. The serum was added into the serum automatic biochemistry analyzer to detect ALT, AST, ALB, and TBIL. The growth factors PDGF and VEGF as well as the secreted protein agrin were detected using ELISA kits.in vitro experiments, bar charts represent mean values, and error bar indicates SD. For in vivo experiments, error bars represent SEM. A paired “two-tailed” Student t-test and chi-square tests were performed using GraphPad Prism software, and data were considered significant at P <.05.All continuous variables were presented as mean ± standard deviation (SD). For


Non-cases are suspect Ebola Virus Disease (EVD) cases testing negative by EVD RT-PCR after admission to an Ebola Treatment Centre (ETC). Admitting non-cases to an ETC prompts concerns on case- and workload in the ETC, risk for nosocomial EVD infection, and delays in diagnosis and disease-specific treatment. We retrospectively analysed characteristics, outcomes and determinants of death of EVD cases and non-cases admitted to the Conakry ETC in Guinea between 03/2014 and 09/2015. Of the 2362 admitted suspects who underwent full confirmatory PCR testing, 1540 (65.2%) were non-cases; among them 727 needed repeated confirmatory PCR testing resulting in 2.5 days (average) in the ETC isolation ward. Twenty-one patients tested positive on the repeat test, most in a period of flawed sampling for the initial test and none after introduction of PCR confirmation with geneXpert. No readmissions following nosocomial EVD infection were recorded. No combination of symptoms yielded acceptable sensitivity and specificity to allow differentiating confirmed from non-cases. Symptoms as ocular bleeding/redness have high specificity, but limited usefulness as not common. Admission delay and age distribution were not different for both groups. In total, 98 (20.6%) of 475 deaths in the ETC were non-cases. Most died within 24 hours after admission. Living in Conakry (aOR 1.78 (1.08–2.96)) was the strongest risk factor for death. Weeks with higher admission load had lower case fatality among non-cases, probably because more acute (and treatable) illnesses of contacts of known cases were admitted. These findings show high numbers of potentially critically ill non-cases need to be considered when setting up triage and referral of EVD suspect cases. Symptoms and risk factors alone do not allow differentiating the non-cases. Integration of highly-sensitive EVD diagnostic methods with short turnaround time in the triage of peripheral hospitals and dropping the systematic 2nd PCR for symptomatic early presenters could limit delays in access to adapted care of cases and seriously ill non-cases. Whether feasible without compromising outbreak control, and under which conditions, should be further assessed. During the 2014/15 Ebola Virus Disease (EVD) outbreak in West Africa Ebola Treatment Centres (ETC) functioned not only as isolation and care unit for confirmed EVD patients, but also as triage point for any ill person possibly suffering from EVD. After anamnestic screening, patients meeting the EVD suspect case definition established by the Guinean Ministry of Health and World Health Organization see  were admIn Guinea, the Conakry ETC was the main referral centre for the capital region with an estimated population of three million people. Between March 2014 and November 2015 when the last case was discharged, 2565 EVD suspects were admitted and tested by EBOV RT-PCR. In Conakry, even though secondary and tertiary health care facilities have continued to consult and admit patients throughout the outbreak, difficulties in determining the risk of exposure to EVD have resulted in delays in access to appropriate health care . Even afThe characteristics and case fatality of EVD confirmed cases have been widely described ,5. On thBeyond case- and workload, admission of non-cases in an ETC prompts also outcome-related concerns. Non-cases risk contracting a nosocomial EVD infection in the ETC suspect wards, where EVD-positive and negative individuals would be mixed in the same ward while waiting for PCR results. This remained a major concern throughout the outbreak, but so far no such nosocomial infections have been confirmed, even after investigation of patients who were readmitted after first having tested negative for EVD . In addiUsing routinely collected data of all suspect EVD patients admitted to the Conakry ETC in Guinea, we aimed to 1) describe the burden of non-cases in relation to the phase of the outbreak; 2) determine the duration of their stay at the ETC and  subsequent nosocomial infections; and 3) compare characteristics, outcome and risk factors for death in confirmed cases and non-cases, in order to improve the selection, diagnosis and/or care of EVD suspects.th 2014, and September 14th 2015. The Conakry ETC was managed by Médecins sans Frontières and for most of the outbreak located within the Donka University Hospital, the largest health care facility in the country. In July 2015 the ETC was moved to a semi-permanent facility in another area of Conakry, Nongo.Towards the end of the 2014/15 EVD outbreak in Guinea, we conducted a retrospective cohort study of all EVD suspects admitted to the ETC in Conakry between the first admission in the ETC on March 25Patients presenting at the ETC triage were referred either from other health facilities, through follow up of contacts of known EVD cases, or presented spontaneously . Upon arrival, patients were screened by history against the EVD suspect case definition see  by trainEVD infection was confirmed using a quantitative RT-PCR assay to detect viral RNA. Between March 2014 and July 2015 confirmatory testing was carried out by the National Laboratory of Viral Haemorrhagic Fever at Donka University Hospital using Taqman RT-PCR assays on whole blood samples which run 40 cycles  . BetweenIf the RT-PCR test was positive, i.e. viral RNA was detected, the patient was transferred to an isolation ward for confirmed EVD cases. Patients who tested negative were discharged from the ETC, unless symptoms had started less than 72 hours prior to admission. For the latter, EVD was only ruled out after repeat PCR testing 72 hours after symptom onset. We use the term ‘non-cases’ for patients who were admitted as suspect cases in the ETC, but for whom EVD was definitively ruled out by diagnostic PCR.Non-cases, alive at discharge, were sent home or transferred to a regular health care facility. Though guidelines foresaw the active follow-up of the discharged as EVD contact during the incubation period of a possible EVD infection contracted during his/her stay in the isolation ward, this was not always possible due to capacity constraints of the contact tracing teams. Follow-up on outcome of non-cases was limited to the time spent in the ETC while waiting for a definitive negative diagnostic EBOV-PCR. Data on deaths among non-cases which occurred after discharge from the ETC for the same illness episode were not available. For confirmed cases the outcome was documented for the entire course of illness (up to death or cure/discharge).In addition to EVD diagnostic testing, the ETC laboratory also carried out Malaria rapid diagnostic tests .Data were combined from routine case notification forms, patient medical files and laboratory results for all patients admitted during the study period. At triage, standardised notification forms were filled in by trained clinicians, recording history, symptoms upon admission and demographic characteristics. The date of symptom onset, type of referral, the outcome at discharge and the date of discharge were retrieved from copies of the medical files held outside the isolation ward. Outcomes at discharge from the ETC included death, discharged home (i.e. cured for confirmed cases), or referral. The outcome was unknown when a patient decided to leave the ETC before being discharged. For ten cases referred to another ETC  outcomes at discharge of the other ETC were added for the analysis. Referral of non-cases took place only after EVD infection had been excluded as described above.Clinical and demographic characteristics and outcome at ETC discharge are reported as frequencies or medians with range and interquartile range. Differences in dichotomous variables between cases and non-cases were analysed using Pearson’s Chi-squared test or Fisher’s exact test (when less than five cases or non-cases presented the sign). Mann-Whitney U test was used for differences in age and in delay of admission. Sensitivity, specificity, positive and negative predictive value, crude positive and negative likelihood ratios were computed for every symptom or sign at admission. Risk factors for deaths were computed through bivariate and multivariate analysis using unconditional logistic regression in the form of odds ratios (OR). All variables tested in the multivariate model were categorized: age, sex, current residence, type of referral, case load in the ETC , increases in case load  and the delay of admission. 95% confidence intervals and p-values were computed using Likelihood ratio tests. Statistical analyses were performed using R and Stata 12 .Only routinely collected programme data were collected, anonymized, and analysed. The Ebola intervention and Conakry ETC were a joint project of the Ministry of Health of Guinea and Médecins Sans Frontières. The study fulfilled the exemption criteria set by the Ethics Review Board (ERB) of Médecins Sans Frontières (MSF), Geneva, Switzerland.Between 25 March 2014 and 14 September 2015, 2390 individuals were admitted as suspect EVD cases to the Conakry ETC. 2372 admitted patients underwent confirmatory testing. 822 (34.8%) were diagnosed with EVD, either after a single RNA positive EBOV RT-PCR test (n = 801) or after a second test at least 72 hours after symptom onset (n = 21). 1540 (65.2%) admitted suspects tested negative by RT-PCR and were designated as non-cases, following single (n = 813) or repeated (n = 727) negative RT-PCR. 18 patients chose to leave the ETC before any confirmatory testing and 10 underwent an initial PCR but evaded before a second test could confirm a diagnosis . Also 31The largest number of admissions to the ETC was seen in the last weeks of December 2014, with a peak of 92 admissions in epidemiological week 51, including 52 EVD confirmed cases, 39 non-cases and one unknown case. During the study period, a median of 9 confirmed cases (IQR 2–18) and 19 non-cases (IQR 13–29) were admitted per week. Admissions of confirmed cases outnumbered those of non-cases only at the start of the outbreak in March 2014 and when case-loads were highest in December 2014 . Over th31.7% (748/2362) of all EVD suspects or 47.2% (727/1540) of the non-cases underwent repeated diagnostic RT-PCR testing and therefore stayed longer than one day in the suspect isolation ward. Among the 822 confirmed EVD cases, 21 only got confirmed after that second RT-PCR test, thus yielding false negative initial PCR results. However, 14 of the 21 false negative first PCR tests occurred between January 28 and February 10 2015, when wrong sampling tubes (heparin instead of EDTA) were used. The remaining 7 false negative initial PCR tests all occurred before the incident. Two of those during one day in the beginning of the outbreak, and three occurred in the week with the highest caseload in December 2014. No false negative initial PCR results occurred after February 2015, on a total of 154 confirmed cases and thus none after introducing the Xpert Ebola Assay in May 2015, on 45 confirmed cases. Non-cases who needed only one PCR stayed on average 0.80 days , but those who needed a second PCR test to exclude EVD had to stay on average 2.49 days .Four  non-cases were readmitted. Three of them were also PCR negative at the second admission as suspect case. One patient who had tested PCR negative in the weeks with the tube incident, was readmitted a week after leaving the ETC, tested positive and died 6 days later.The age distribution among cases and non-cases was similar, though there were slightly more young children among the non-cases. Significantly more non-cases were male . The proportion residing in the capital region Conakry at the time of admission was higher for non-cases (67.9%) than for cases .The median delay between onset of symptoms and admission was similar. The longest delay recorded between onset of symptoms and admission for a case was 29 days, whereas a delay before admission of more than a month occurred in 6 non-cases.Generalised fatigue and fever were the most common symptoms upon admission in confirmed and non-cases. Nausea or vomiting, diarrhoea, fatigue, loss of appetite, swallowing problems, joint aches, hiccups, unexplained bleeding and ocular redness/bleeding were less frequent among non-cases. Abdominal pain, headache, breathing problems, unexplained bleeding other than conjunctival bleeding, sore throat and coma were more frequent among non-cases .The clinical criteria of the suspect EVD case definition, thus not considering contact history  and 98 (6.4%) died during their stay in the ETC, thus non-cases accounted for 20.6% (98/475) of all deaths in the ETC. The median length of stay in the ETC between admission and death was 4 days  for confirmed cases. Most  non-cases died on the day of admission, 33 during the 2is known .The monthly trend of case fatality rate of cases and of non-cases is plotted against the total number of admissions in the ETC in Little attention has been given to the non-cases, suspect cases that tested negative for EVD, despite serious challenges such as 1) the possibility of nosocomial EVD infection during their stay in the isolation ward, 2) additional workload in the ETC when a large number of non-cases also require care and blood sampling, 3) missed opportunities for emergency care for non-cases in need of intensive care for another condition, 4) the stressful experiences the concerned patients underwent during their stay in the ETC and 5) the difficult and delayed access to regular health care facilities when EVD can only be excluded in an ETC.Our study highlights the importance of considering the non-cases when designing referral, diagnosis and care of EVD suspects. Almost two thirds of admitted EVD suspects were non-cases and one in five deaths occurring in the ETC was a non-case dying from another condition than EVD before RT-PCR results were available. Other ETCs have also reported high proportions of non-cases, ranging between 33 and 58% ,8,11, alETC inpatient mortality among non-cases in rural Liberian and Sierra Leonean ETCs was 5.4% and 4.7% respectively , both slIn future EVD outbreaks, the current set-up requiring testing through RT-PCR after referral to a centralised isolation ward may continue to compromise diagnosis and care of non-cases and interfere and delay care of confirmed cases. Even with PCR confirmatory testing yielding results within 2 hours , hours tWe observed moreover that using the point-of-care Xpert RT-PCR Ebola Assay no EVD cases were missed when only a single confirmatory test would have been carried out. In the Conakry ETC most false negative initial PCR results, all before February 2015, were related to errors in the sampling tubes used. Of the remaining false negatives five out of seven occurred clustered over a few days, also possibly suggesting a quality problem. The Xpert RT-PCR Ebola Assay has been proven to be at least as performant as several common laboratory-based assays, with no false negative results reported in studies on whole blood samples –14. AddiA case report of a false negative test of an asymptomatic high risk contact in Monrovia in September 2014  argued fThe one third of non-cases who lived outside Conakry had better chances for survival than those from Conakry. We assumed that referrals of severely ill non-EVD patients from the largest health care facilities in Conakry had a higher chance of dying, but our hypothesis was not confirmed from the limited records on the type of referral of suspect cases. Non-cases referred from further districts were more often contacts of known EVD cases referred by surveillance teams, and therefore in better health than critically ill patients referred from health care facilities.Previous studies of confirmed cases found that younger age below 5 years) or older age (over 50 years) are determinants for higher mortality among confirmed EVD cases ,5,16,17, years orThe number of admissions of confirmed cases started to increase from August 2014, reaching its peak in December 2014 when bed capacity was fully reached and work load for the ETC staff was overwhelming. However, this was not mirrored by an increased case fatality rate among cases and non-cases during these weeks. Paradoxically a decreased case fatality among non-cases was seen in weeks with increasing admissions (from 7.9 to 2.9%). One would have expected that the extra work load could have a detrimental effect on case fatality during the busiest weeks or weeks with large increases in the number of admissions. Hypothetically, this might be due to severely ill non-cases with severe conditions being referred especially at times when the risk of nosocomial infection in the ETC is considered low, whereas in weeks with increased numbers of admissions proportionally more contacts of known EVD cases with acute illnesses are referred, and they have better chances of survival.Although much feared, we found no cases of nosocomial EVD infection contracted while staying in the isolation ward for EVD confirmatory testing. Only one patient out of 1436 discharged or transferred non cases was readmitted and tested positive after the second admission. This patients’ initial negative result was false negative following an error with the blood sampling tubes.There are a number of important limitations to this study. We had no final illness-episode outcomes available for 256 (16.6%) of the 1540 discharged non-cases, who were transferred to regular health facilities after EVD infection was excluded, which limits our case fatality findings. We could only analyse deaths up to the moment of transfer from the ETC, probably underestimating the total number of deaths among non-cases. More detail on diagnosis and causes of death of non-cases may have provided more insight on how and what proportion of deaths among non-cases could have been averted. Another limitation is that other possible drivers for the large proportion of non-cases could not be assessed: When the population started to gain confidence in the ETC, patients for whom no care options were available elsewhere may have started seeking care in the ETC; During times with higher epidemic intensity health care facilities demanded ill patients to first get tested in the ETC.Our findings on non-cases in Conakry, studied during most of the West African Ebola outbreak, highlight the importance of considering non-cases when setting up triage and referral of EVD suspect cases. Centralising triage at the ETCs comes at a cost: the majority of admissions are non-cases in need of treatment for other conditions, and even though non-cases were only admitted for a maximum of three days until EVD confirmation, mortality may have been different when these patients would have been immediately admitted to a hospital where stabilization and critical care, as well as diagnose-specific examinations and treatment can be practiced without the limits of full-barrier care.No combination of symptoms with sufficient sensitivity and specificity to differentiate EVD cases from non-cases can be proposed from our data. Other options to consider to overcome delays in access to adapted care for cases and non-cases are 1) the integration of highly sensitive EVD diagnostic tests with short turnaround time in the triage at peripheral hospitals, or 2) speeding up the diagnostic timeframe by dropping the second confirmatory EVD test for those presenting at the ETC and health care facilities less than 72 hours after symptom onset. This latter strategy should be backed up by a sound contact tracing and follow-up system. Both strategies need further research to ensure feasibility and that outbreak control is not compromised.


High-throughput sequencing of the small RNA transcriptome of locust reveals differences in post-transcriptional regulation between solitary and swarming phases and provides insights into the evolution of insect small RNAs. All the reports on insect small RNAs come from holometabolous insects whose genome sequence data are available. Therefore, study of hemimetabolous insect small RNAs could provide more insights into evolution and function of small RNAs in insects. The locust is an important, economically harmful hemimetabolous insect. Its phase changes, as a phenotypic plasticity, result from differential gene expression potentially regulated at both the post-transcriptional level, mediated by small RNAs, and the transcriptional level.Here, using high-throughput sequencing, we characterize the small RNA transcriptome in the locust. We identified 50 conserved microRNA families by similarity searching against miRBase, and a maximum of 185 potential locust-specific microRNA family candidates were identified using our newly developed method independent of locust genome sequence. We also demonstrate conservation of microRNA*, and evolutionary analysis of locust microRNAs indicates that the generation of miRNAs in locusts is concentrated along three phylogenetic tree branches: bilaterians, coelomates, and insects. Our study identified thousands of endogenous small interfering RNAs, some of which were of transposon origin, and also detected many Piwi-interacting RNA-like small RNAs. Comparison of small RNA expression patterns of the two phases showed that longer small RNAs were expressed more abundantly in the solitary phase and that each category of small RNAs exhibited different expression profiles between the two phases.The abundance of small RNAs in the locust might indicate a long evolutionary history of post-transcriptional gene expression regulation, and differential expression of small RNAs between the two phases might further disclose the molecular mechanism of phase changes. Regulation of gene expression can occur at both transcriptional and post-transcriptional levels. In recent years, the discovery of numerous small RNAs has increased interest in post-transcriptional gene expression regulation during development and other biological processes. Small RNAs include several kinds of short non-coding RNAs, such as microRNA (miRNA), small interfering RNA (siRNA), and Piwi-associated RNA (piRNA), which all regulate gene expression at the post-transcriptional level. Typically, miRNAs are approximately 22 nucleotide small-RNA sequences  that plaInsects comprise the largest group of metazoans, and previous studies have shown that small RNAs are involved in a significant number of biological processes in them . Many smLocusta migratoria) is a typical hemimetabolous insect within the family Acrididae and is a worldwide, highly prevalent agricultural pest causing hundreds of millions of dollars worth of damage every year. The locust has also been used in research as a model organism for the study of developmental, physiological, immune, and neural pathways, as well as others . We obtained 1,566,242 reads from the gregarious library and 1,949,248 reads from the solitary library after discarding the empty adapters. Generally, length distribution of small RNAs in two phase libraries is different . Although in most cases the key 'seed' site of the miRNA is nucleotides 2-8 [D. melanogaster miR-79 (dme-miR-79) has been validated as being at nucleotides 1-8 [We identified 55 miRNA sequences, belonging to 50 families , in the migratory locust by BLAST against the miRBase v11.0 . Most ofides 2-8 ,19, the ides 1-8 , which iFor lmi-miR-10, much like lmi-miR-79, the mature sequence in the locust has an additional nucleotide at the 5' end, in this case a uridine , which is the same as the miR-10 of non-insect organisms. Previous studies have demonstrated that miR-10 in both species that do and do not have an extra U have similar targets . AlthougD. melanogaster miRNA*  in the locust library, indicating conservation of these miRNAs* between the locust and the fruit fly.Although mature miRNA and miRNA* (the miRNA:miRNA* duplex) are complementary, their base-pairing is imperfect in the presence of compensatory substitutions , and the miRNA* is generally less stable than the mature miRNA . AnalysiTo test whether the locust miRNA* and their corresponding mature miRNA sequences came from the same precursors, we used a PCR-based method to confirm the relationship between the miRNA and its miRNA*. If the miRNA and its miRNA* came from the same precursor, we should be able to amplify 55-70 bp fragments from the genomic DNA. As expected, we amplified 55-70 bp products from all the miRNAs with the exception of mir-iab-4 Figure , and theWe used the sequences of the amplified products of the conserved miRNA precursors to predict their secondary structure using mfold ,23, and Since the locust and fruit fly separated about 350 million years ago , it is sIn an attempt to discover locust-specific miRNA families, we integrated the data from the locust small RNA libraries we created with those of the locust EST database ,14. ThisWe obtained 185 miRNA duplex-like pairs . Reads of the most abundant miRNAs are about 10,000-fold higher than those of the scarce miRNAs. Such extreme variation can provide some basic insight into the function of these miRNAs. The most abundant miRNA is mir-1, which had approximately 163,143 reads in the gregarious library and 135,794 in the solitary library. As a muscle-specific miRNA , mir-1 ip < 1.0 × 10-6).As miRNA abundance is linked to the extent of conservation ,20, consciboulot gene of fruit fly, which encodes an actin binding protein and plays a major role in axonal growth during Drosophila brain metamorphosis [In animals, although miRNAs have been shown to repress the expression of their targets by binding to sequences in the 3' untranslated region (UTR) in most cases ,30, bothorphosis .Drosophila gene pale has significant differences in its expression levels between the two phases . We found that the 3' UTR sequence of locust pale contains a target site of lmi-miR-133 ; see Materials and methods; Figure Drosophila species also have conserved target sites of miR-133 in the 3' UTR sequences of the pale gene [pale at the post-transcriptional level. Therefore, miR-133 may contribute to the different expression of pale between the gregarious and solitary phases (see Discussion).We also found that some unigenes that had significant differences at the expression level between the gregarious and solitary phases were targeted by miRNAs. Although these genes may be regulated at the transcriptional level, it is possible that miRNAs play roles in regulating their expression. For example, microarray results in our lab show that the locust homolog of the ale gene ,20,32  and some appear to be much younger . Such age differences indicate that there is an ongoing process of miRNA evolution and it is possible that the insect lineage gave birth to the insect-specific miRNAs. Previous work in volution .Although the 50 miRNA families in the locust are highly conserved throughout widely divergent animal taxa, there are lineage-specific sequence substitutions in most of these families that are present in both vertebrates and insects. Based on their characteristic sequences in different lineages, we divided these families into five categories Table ; in doinDespite the short sequences of mature miRNAs, the major clades are well separated due to substitutions in categories II to IV Figure , indicatWe found that 26,519 reads matched the sense strand of ESTs and 11,596 reads matched the antisense strand ,14 in thThe proportion of endo-siRNAs in the small RNA libraries of locust is much lower than that of miRNAs Figure . HoweverAbout 20%  of the small RNAs with a perfect match with ESTs were derived from transposons. Previous research has shown that transposons can generate two kinds of small RNAs: endo-siRNA and piRNA [There are a variety of transposons that could generate small RNAs regardless of whether they are siRNAs or piRNAs , which may indicate the presence of a broad range of small RNAs for silencing these selfish genetic elements. Analysis of the transposons we observed indicated that long interspersed elements (LINEs) were the dominant class producing small RNAs . CR1 and RTE-BovB are the dominant subtypes generating small RNAs . As more transposon sequence information in the locust becomes available, we expect there will be additional transposon-derived small RNAs identified, which will give greater understanding of the impact of these elements on genome evolution of the locust and related species.The rest of the sequences in the locust small RNA libraries remained unannotated. Most of the unannotated sequences in the gregarious library were 22 and 23 nucleotides long and commonly began with uracil . We expected that these were miRNAs missed in our search process, and thus suspected that these 22- and 23-mer RNAs included additional locust-specific miRNAs. In order to identify the potential miRNA candidates in the remaining 22- and 23-nucleotide sequences, we analyzed their 5' ends to determine whether they were similar in features to the miRNA 5' terminus  that generally began with uracil, especially in the solitary library . Their features indicated that these 26-29-mer small RNAs might be of the piRNA class of small RNAs. Therefore, we analyzed the sequences of these small RNAs to look for the presence of an adenine at position 10, a common feature of piRNAs [Small RNAs in the gregarious library were enriched for lengths of 22-23 nucleotides, a typical length for animal miRNAs, and those in the solitary library were enriched for lengths of 26-29 nucleotides and 22-23 nucleotides Figure . For smamir-276, mir-125, mir-1, and let-7) in the gregarious phase were shown to be expressed 1.94-fold, 1.87-fold, 1.5-fold, and 1.5-fold as much as in the solitary phase, respectively  in order to make a comparison between the gregarious and solitary small RNA libraries. Almost each kind of small RNA, including miRNAs, endo-siRNAs, and piRNA-like small RNAs, had some differences in expression level between the two phases. Seventeen conserved and 84 predicted locust-specific miRNAs differed by ≥ 1.5-fold between the two phases. Four examples of conserved miRNAs  small RNAs in the solitary phase were much higher than in the gregarious phase . The findings here provide additional support for the feasibility and reliability of our method. We believe that more than 100 of our predicted locust-specific miRNAs would be canonical.In addition to the PCR-based method of validation, we also assessed the reliability of our miRNA prediction method by using Although the principle of our method, based on features of miRNA biogenesis, coincides with that of miRDeep , our metIt is difficult to predict miRNA targets in animals because the detailed mechanism of interaction between miRNA and its target transcripts is not clear, although several bioinformatic tools have been developed, such as miRanda. None of the available computational methods can predict miRNA targets accurately and they all give results with higher false positive rates ,32,35. Mpale gene, a potential target of lmi-miR-133  and the phenomena of phase changes are both linked with metamorphic processes, we think that the two miRNAs and the phenotype of phase changes may be related. mir-1 and mir-315 also have different expression levels (data on mir-315 expression levels is not shown). mir-1 is a muscle-specific miRNA [mir-315 is a potent activator of Wingless signaling in Drosophila [mir-1 and mir-315.We found 17 conserved miRNAs to have different expression levels between the gregarious and solitary phases. These miRNAs may be involved in gene expression regulation at the post-transcriptional level during phase transition. Especially, we are most interested in 5 of the 17 miRNAs that are expressed differentially in the two phases. s Figure , althougosophila ,43. Phasic miRNA , and mirosophila . BecauseBased on our analysis of the small RNA expression levels in gregarious and solitary locusts, we believe that some small RNAs that regulate the expression of protein coding genes in the two phases must be involved in the process of phase changes. It is possible that we could provide insight into the phase changes and find new approaches to control the locust plagues throughout the world by small RNAs.High-throughput sequencing provides a good chance for us to study small RNAs in the locust, which is an important worldwide pest. This study led to the discovery of a large number of small RNAs in the locust, including miRNAs, endo-siRNAs and piRNA-like small RNAs. Importantly, we have identified 185 potential locust-specific miRNA candidates using the method we developed, although there is no locust genome sequence available. Our method makes it possible to discover more miRNA families in a broader range of species whose genome sequences have not been sequenced. We further show the evolutionary path of miRNAs in the locust, indicating the potential evolutionary mechanism of miRNAs. The function of small RNAs in phase changes of the locust is disclosed in our study. We found significant differences in the expression of small RNAs between the two phases of the locust and target prediction shows that some genes expressed differentially in the two phases are targets of miRNAs, which gives us clues to further discover the mechanisms of phase change in locusts.Locusta migratoria that we fed in our lab. We collected 0-1, 2-3, 4-5, 6-7, 8-9, 10-11, 12-13 and 14-15 day-old embryos cultured at 30°C in clean sand with relative humidity. For the larvae, we collected the whole body except the midgut and pooled them to ensure every instar was present in the sample. We chose to collect adults at eclosion, sexual maturation, post-spawning, and elderly stages separately and then pooled them together. Total RNA was extracted according to the manufacturer's protocol. We examined the quality of RNA using an Agilent 2100 Bioanalyzer.Total RNA was extracted using TRIzol reagent  from mixed-stage  RNA fragments 14-30 bases long were isolated from total RNA by Novex 15% TBE-Urea gel (Invitrogen). Then, a 5' adaptor  was ligated to purified small RNAs followed by purification of ligation products on Novex 15% TBE-Urea gel. The 5' ligation products were then ligated to a 3' adaptor (Illumina) and products with 5' and 3' adaptors were purified from Novex 10% TBE-Urea gel (Invitrogen). Subsequently, these ligation products were reverse transcribed followed by PCR amplification. The amplification products were excised from 6% TBE-Urea gel (Invitrogen). The purified DNA fragments were used for clustering and sequencing by Illumina Genome Analyzer at the Beijing Genomics Institute, Shenzhen.D. melanogaster or other insects  were identified as conserved miRNAs.We discarded bad reads that were the result of incorrect sequencing or were the reads of adaptor contamination that were not ligated to any other sequences. We clustered the remaining reads based on sequence similarity and the dominant reads were analyzed as follows: the reads were analyzed by BLAST against EST database  and FlyBC. elegans, D. melanogaster, and Arabidopsis [We first looked at the high-throughput sequencing data of small RNAs in other species, including bidopsis ,20,38, aBased on the biogenesis features of miRNA Figure , we deveIn order to satisfy the requirement of input sequences analyzed by mfold, we joined the two sequences in each candidate pair using a standard hairpin-forming linker sequence (GCGGGGACGC). Those pairs that met the following conditions were analyzed further: the pairs had a free energy less than or equal to -21 kcal/mol ; the pairs had no bulge bigger than 6 nucleotides and multiple loops. The way of determining the best parameters and of testing this method is described in the Methods in Additional data file 3.We extracted genomic DNA from the fifth instar locust using a Gentra Puregene Tissue Kit  according to the manufacture's protocol. We designed primers for 8 conserved miRNAs and 24 candidate miRNA-miRNA* pairs we predicted based on a dependence of the sequences of the mature miRNA and miRNA* species using Primer Premier 5.0.  Because mature miRNA may come from either arm of the precursor, we designed two pairs of primers for each duplex. Corresponding fragments were amplified by PCR and the length of amplification products was examined on 2.5% agarose gels. Fragments between 55 and 70 nucleotides in length were subcloned into pMD18-T vector  for sequencing analyses.The 23-29 nucleotide long RNAs matching ESTs annotated as transposons were considered as piRNA-like small RNAs. Those small RNAs that perfectly matched EST antisense strands were considered as candidate endo-siRNAs if they were not from annotated transposons. Moreover, we also searched the ESTs for miRNA precursors. Although there were some sequences that perfectly matched EST sense strands, no typical hairpin structure of these ESTs could be identified using mfold. Rather than folding the entire EST sequence, regions of 70 nucleotides, 100 nucleotides and 150 nucleotides on either side of the small RNA sequences were folded.Unigene sequences from the EST database of the locust ,14 were pale gene was obtained by 3' rapid amplification of cDNA ends (RACE) using a SMART RACE cDNA Amplification Kit  with the primer GCGACCTGGACAACTGCAACCACCTCAT according to the manufacturer's protocol.The 3' UTR sequence of the locust endo-siRNA: endogenous siRNA; EST: expressed sequence tag; lmi-miR number: locust miR-number; miRNA: microRNA; piRNA: piwi-associated RNA; RACE: rapid amplification of cDNA ends; siRNA: small interfering RNA; UTR: untranslated region.YW and LK designed the study. YW performed all the bioinformatic analysis except target prediction of miRNAs. SC provided help to analyze piRNA-like small RNAs. PY predicted targets of miRNAs and YW analyzed the prediction results. SC and YW isolated total RNA and carried out PCR experiments. ZM carried out 3' RACE experiments. YW and LK prepared the manuscript. All authors read and approved the final manuscript.Drosophila miRNA data.The following additional data are available with the online version of this paper. Additional data file Locust endo-siRNAs.Click here for fileLocust piRNA-like small RNAs.Click here for fileDrosophila miRNA data.Figure S1 shows alignment of miR-79 and miR-10 of different species. Figure S2 shows expression patterns of locust miRNAs. Figure S3 shows transposon types from which small RNAs are derived in the locust. Figure S4 shows lengths and initial nucleotide distributions of the unannotated small RNA sequences. Table S1 lists the sequences of conserved miRNAs and miRNA*s in the locust. Table S2 lists precursor sequences of the seven conserved miRNAs that have a conserved star sequence. Table S3 lists sequences of predicted locust-specific miRNAs. Table S4 lists the ten most abundant miRNA-like 5'-end small RNAs in the remaining reads after annotation of miRNAs, siRNAs and piRNA-like small RNAs. Table S5 lists endo-siRNAs with different expression levels between the two phases. Table S6 lists piRNA-like small RNAs with different expression levels between the two phases. The Methods show the way to determine the best parameters of our miRNA prediction method and assess the reliability of our method using Click here for file


Molecular studies of microbial diversity have provided many insights into the bacterial communities inhabiting the human body and the environment. A common first step in such studies is a survey of conserved marker genes (primarily 16S rRNA) to characterize the taxonomic composition and diversity of these communities. To date, however, there exists significant variability in analysis methods employed in these studies.Here we provide a critical assessment of current analysis methodologies that cluster sequences into operational taxonomic units (OTUs) and demonstrate that small changes in algorithm parameters can lead to significantly varying results. Our analysis provides strong evidence that the species-level diversity estimates produced using common OTU methodologies are inflated due to overly stringent parameter choices. We further describe an example of how semi-supervised clustering can produce OTUs that are more robust to changes in algorithm parameters.http://fames.jgi-psf.org/.Our results highlight the need for systematic and open evaluation of data analysis methodologies, especially as targeted 16S rRNA diversity studies are increasingly relying on high-throughput sequencing technologies. All data and results from our study are available through the JGI FAMeS website  An evaluation of taxonomic classification through database searches [Microbial diversity within the human body has recently been quantified through 16S rRNA surveys  and metasearches  or othersearches  are beyoThe OTU clustering process typically begins by constructing a multiple alignment (MSA) of the 16S rRNA sequences. The MSA is then used to estimate pairwise distances between individual sequences, expressed as the fraction of nucleotides that have changed as the sequences have evolved from their most recent common ancestor. To accurately reflect evolutionary processes, the distances inferred from the MSA are corrected using one of several models of evolution . The disThe choice of MSA, distance correction, clustering algorithm, and distance threshold vary considerably between studies, and to our knowledge, there has been no rigorous evaluation of the impact of methodological choices on the ecological conclusions of the analysis process. Previously, simulated datasets have been successfully used to evaluate methods for the assembly and binning of metagenomic data . In thisThrough this evaluation framework, we will demonstrate that OTUs are highly sensitive to small changes in the clustering methodology and reveal a surprising observation that reducing the stringency of clustering distance thresholds tends to produce more accurate species-level representations of a community. We will further assess the impact of OTU variability on common ecological measures of diversity and provide an example of how semi-supervised clustering could produce more robust OTU structures by accounting for varying evolutionary rates across the microbial phylogeny.Our simulated dataset comprises 1677 16S rRNA sequences from the RDP database (release 9.57)  requirinIt is important to note that, by choice, these sequences are high quality and belong to relatively well-characterized taxonomic groups. Therefore any results obtained on this highly curated dataset represent an upper bound on the performance that can be achieved when analyzing noisy data from environmental surveys.E. coli O157:H7 str. TW14359 16S rRNA gene.All 1677 sequences were aligned using MUSCLE , ClustalDistance matrices were constructed with DNADIST from the PHYLIP package  using eaThe process described above generated 1242 sets of clusters/OTUs , 749 of which are distinct (i.e. multiple parameter combinations lead to the same set of OTUs).C, is a partition of a set S into disjoint subsets (clusters) where:We employed the Variation of Information (VI) metric  as a meam elements in set S, and we let mi be the number of elements in cluster Ci, then to compute the Variation of Information between two clusterings, we first find the probability that a randomly selected sequence is in a particular cluster, that is, i is the entropy associated with clustering C, defined as:If there are C = {C1, C2, ..., CM}, and D = {D1, D2, ..., DM'}. Then we calculate the joint distribution C and D. The mutual information between the clusterings C and D is then defined to be C and D is defined as the sum of the individual clustering entropies less 2 times the mutual information:Now, suppose we have two clusterings C and D are identical clusterings, then H(C) = H(D) = I, and the VI = 0. The VI distance is a true metric, satisfying symmetry, non-negativity, and the triangle inequality.If In order to provide a reference set of VI distances for known clusters, we measured the VI between the "true" species clustering (i.e. annotated according to the RDP taxonomy) and the annotated phylum, class, order, family, and genus clusterings  in the subtree rooted at c. Collectively, the chosen nodes correspond to a node-cut K, which induces a non-overlapping clustering AK. Let D represent the partial clustering of labeled sequences such that sequences with the same label are grouped together. The VI-cut algorithm finds the AK that minimizes the VI distance to D:VI-cut is a procedure that finds a clustering from a hierarchical tree decomposition n metric . A clustT, an optimal node-cut can be found efficiently using dynamic programming [forbidden nodes. Specifically, any node n with a corresponding diameter ≥ 0.07 was labeled as "forbidden", and we required that the clusters produced by VI-cut do not contain any forbidden nodes. This requirement implicitly restricts the maximum diameter of an OTU to 7% divergence. To incorporate forbidden nodes into the VI-cut algorithm, we first ran the standard VI-cut algorithm. Any cluster that contained a forbidden node n was then sub-divided into non-overlapping clusters by identifying subtrees dominated by n that do not contain any forbidden nodes.Although there are exponentially many possible node-cuts in gramming . As descTo isolate the individual impact of each component in an OTU methodology, the 200 methodologies resulting in the lowest VI distance from the annotated species clustering were analyzed using multi-way analysis of variance (ANOVA) considering four factors: multiple sequence alignment, evolutionary distance correction, clustering algorithm, and distance threshold.F = 0.002, P = 0.998), implying that such corrections are roughly equivalent within the short evolutionary time frame defining a microbial species. We extended this comparison to include the Olsen distance correction in ARB [The results of our analysis . In terms of clustering strategy, furthest neighbor resulted in the best agreement with the annotated species structure of our environment . Large variation in the OTU content is observed even when we fix the similarity threshold to 0.01 (approximately strain-level) - the number of OTUs ranges from 79 to 248 at this similarity level, depending on the choice of MSA or clustering strategy. Surprisingly, the OTU clustering closest to the annotated species clustering was obtained using a similarity threshold of 0.05 - a value larger than the cutoffs usually used to approximate the species-level composition of an environment (0.01-0.03 ). In tert Figure .Even the combination of analysis parameters with the lowest VI distance  led to an overestimate of the number of species in our sample, resulting in 56 OTUs. This result highlights a fundamental limitation of hierarchical clustering strategies for 16S rRNA analysis - only 42 of the 49 species present in our sample corresponded to a homogeneous sub-tree within the best hierarchical clustering of the data. The remaining 7 species cannot be correctly clustered irrespective of the similarity threshold chosen.The results presented above highlight a wide variation in the OTU structure as we explore the parameters of the analysis process. To determine whether such variation is also present in the methodologies used in practice, we compared three analysis methodologies that performed well in our combinatorial search to several methodologies reported in published literature. The results shown in Table SChao1) [SAce) [H) index [SAce = 57, SChao1 = 67, and H = 3.41. SAce and SChao1 estimates for the computed OTU clusterings ranged from 52 to 427 and 84 to 466 phylotypes, respectively, while Shannon diversity indices ranged from 3.04 to 4.66   and ACE ) [SAce)  richnessH) index  are measTo improve phylogenetic analyses, researchers often remove hypervariable segments of MSAs either manually or using a filter such as LaneMask ,31. We eOur study has so far made the assumption that one of the primary goals of a 16S analysis pipeline is to estimate the composition of an environment at a pre-specified taxonomic level (e.g. species). As demonstrated by our results, the OTU methodologies proposed in the literature fail to achieve this goal, generally overestimating the number of species. Even by systematically evaluating various settings for the parameters of the analysis process, we could not obtain perfect concordance between the OTU structure and the species composition of the environment. This is in part due to the fact that the concept of "species" is born out of gross morphological and phenotypic traits of microorganisms, and therefore cannot be precisely mapped to fine-scale molecular measurements. Furthermore, the rate of evolution varies across the tree of life, making it unrealistic to rely on a single distance threshold.VI-cut [As an alternative, we investigated the use of a semi-supervised clustering method to adaptively select a set of local distance thresholds that lead to OTUs that better fit the species composition of the environment. Specifically, we employed VI-cut , a clustWe applied VI-cut to our data by simulating partial taxonomic knowledge of the dataset. For each MSA and the optimal distance correction  fail to capture the underlying species composition of an environment and are frequently too stringent, producing inflated estimates of diversity. This result is in large part due to the fact that the overly-general biological definition of a species cannot be directly mapped at the fine-scale resolution provided by molecular-level observations. In addition, diversity estimates are highly sensitive to the abundance of rare members of a community and, thus, can be easily confused by the "noise" caused by sequencing errors, transient organisms, or "naked" DNA not originating from one of the members of the community. For all of these reasons, aggregate diversity measures do not necessarily correlate with the biological functions performed by members of a community, and must be augmented by additional, more specific, measurements of the community structure .While the community simulated in our study was dominated by Proteobacteria, a significant proportion of the data belongs to other microbial phyla, thus the general results of our study likely hold in most other datasets. Furthermore, the organisms present in our samples are sufficiently distant at the 16S rRNA level to allow unambiguous clustering once a distance threshold was selected, i.e. the results we obtained reflect actual characteristics of 16S rRNA data rather than artifacts of the analysis process.ad hoc similarity thresholds in an attempt to match poorly-defined taxonomic labels. Semi-supervised procedures such as VI-cut can generate accurate clusterings while only requiring high-quality labels for only a small subset of the sequences.Our simulated samples were composed of a small number of high-quality sequences with exceptional length (>800 bp) - a relatively simple challenge compared to current datasets generated by pyrosequencing (Roche/454). 16S surveys employing pyrosequencing technologies generate considerably larger datasets (millions of sequences per sample), comprising reads of shorter lengths (~100-400 bp). The results we describe for long reads will only be exacerbated in the context of short-read data. Further, these data present several new computational challenges for OTU clustering including the need for faster alignment algorithms, the computation and storage of evolutionary distances , and the selection of distance thresholds appropriate for sequences with significantly less phylogenetic information than the Sanger-based reads used in our study. The analysis of the new sequencing data require the development of robust methods for OTU clustering, as well as rigorous validation of analysis methods. We finally demonstrated that a semi-supervised clustering approach (VI-cut) can significantly improve analysis quality, highlighting the potential for semi-supervised clustering approaches to fill the gap between the two extreme classes of approaches commonly used in analyzing 16S rRNA datasets: fully-supervised database searches that rely on expensive highly-curated datasets; and fully unsupervised OTU clustering procedures that use ad hoc choice of analysis parameters, in particular the selection of different distance thresholds, complicates cross-study comparisons and even basic descriptions of diversity. As 16S rRNA surveys are increasingly applied in a clinical setting [Finally, it is important to observe that clustering 16S rRNA sequences into a set of OTUs is a valuable analysis tool even if the resulting OTUs do not correlate with pre-defined taxonomic entities. However, the  setting ,32-35 (ehttp://fames.jgi-psf.org - a repository for metagenomic analysis benchmarks [http://www.cbcb.umd.edu/VICut/.The data used in this study have been deposited in the FAMeS online database nchmarks . SoftwarOTU: operational taxonomic unit; rRNA: ribosomal ribonucleic acid; VI: Variation of Information; MSA: multiple sequence alignment; RDP: Ribosomal Database Project; ANOVA: analysis of variance;JRW and SN ran the experiments and analyzed the data. M-RG provided computational expertise. JW, SN, NN, M-RG, CK and MP wrote the manuscript. All authors read and approved the final manuscript.Variation of information distances of high-level taxonomic clusterings from the annotated species clustering. To give the reader some intuition about the VI distance metric, we computed VI distances between the annotated species-level clustering and other clusterings based on phylum, class, order, family, and genus annotations. This file contains a table of these reference distances.Click here for file


The identification of virus replication is an important step towards the understanding of the pathogenesis process of viruses in their respective hosts. In the present study, we developed a strand-specific RT-PCR-based method for analysis of Deformed Wing Virus (DWV) replication in honey bees and in honey bee parasitic mites, Varroa Destructor.For years, the understanding of the pathogenetic mechanisms that underlie honey bee viral diseases has been severely hindered because of the lack of a cell culture system for virus propagation. As a result, it is very imperative to develop new methods that would permit the The results shows that the method developed in our study allows reliable identification of the virus replication and solves the problem of falsely-primed cDNA amplifications that commonly exists in the current system. Using TaqMan real-time quantitative RT-PCR incorporated with biotinylated primers and magnetic beads purification step, we characterized the replication and tissue tropism of DWV infection in honey bees. We provide evidence for DWV replication in the tissues of wings, head, thorax, legs, hemolymph, and gut of honey bees and also in Varroa mites.The strategy reported in the present study forms a model system for studying bee virus replication, pathogenesis and immunity. This study should be a significant contribution to the goal of achieving a better understanding of virus pathogenesis in honey bees and to the design of appropriate control measures for bee populations at risk to virus infections. Apis mellifera, the most economically valuable pollinator of agricultural and horticultural crops worldwide. In the U.S. alone, the honey bee has an annual market value exceeding 14.6 billion dollars producing honey and other hive products [in vitro.The viruses pose a serious threat to the health and well-being of the honey bee, products ,3. Recenproducts , investiVarroa destructor), a potential vector of DWV, was also investigated.Recent advances in molecular technology have greatly expanded our ability to detect and elucidate the molecular events associated with virus infections and pathogenesis. With the current molecular technology, complete genomes of several honey bee viruses have been sequenced and analyzed -14. UsinThe strand specificity of conventional RT-PCR was evaluated. As shown in Figure In this study, Tagged RT-PCR was evaluated for its specificity for amplification of both positive and negative-strand RNA from bees with deformed wings using four combinations of primers. Tagged RT-PCR assay was based on the generation of cDNAs by the primer containing a tag and further amplification of cDNA by a tag-primer and a primer complementary to the synthesized cDNA. The result showed that cDNAs generated by either tag-forward or tag-reverse primers were consistently amplified by subsequent PCR amplification, regardless of whether a pair of primers or only a single primer was used for PCR amplification. As shown in Figure In order to achieve highly strand-specific detection of RNA for DWV, strand-specific RT-PCR was conducted using a biotinylated primer for cDNA generation and magnetic separation to purify synthesized cDNA prior to PCR amplification. As shown in Figure in vitro transcribed from the pCR2.1 TA cloning vector against corresponding CT value. As shown in Figure 3 pg to 1 fg and the corresponding CT values within the concentration range. The detection limit of positive and negative-strand DWV RNAs were the same. The lowest limit of detection with qRT-PCR for DWV was 1 fg per reaction for both positive and negative-strand RNA. The RNA concentration below 1 fg could not be amplified in the reaction.Further, the strand-specific TaqMan real-time qRT-PCR incorporated with biotinylated primer and magnetic separation was carried out for quantification of DWV replication in host tissues and parasitic mites of honey bees. To ensure an accurate quantification as well as the highest sensitivity of the assay, a standard curve was first established by plotting seven 10-fold dilutions of DWV-specific RNA 4 times higher in the wings (p < 0.0001), 27.8 times higher in the legs (p < 0.0001), 19.8 times higher in the head (p = 0.0015), 107.1 times higher in the thorax (p = 0.0008)  and descended down in order of wings, hemolymph, legs, gut, head, abdomen, and thorax Figure . Meanwhi) Figure .3 times more abundant than negative-strand DWV RNA in the virus-infected bees (p = 0.007). Positive-strand DWV RNA was detected in all tissues of bees with symptomatic or asymptomatic infections even though the average concentration of positive-strand viral RNA in tissues of bees with wing deformity was significantly higher than tissues of bees with normal wings; 8 × 102 times more in the hemolymph, 103 times more in the wings, 90 times more in the legs, 29.8 times more in the head, 1.8 × 103 times more in the thorax, 15.2 times more in the gut (p < 0.0001) and 29.8 times more in the abdomen (p < 0.0001)  Figure .Using the same methodology, negative-strand RNA of DWV was detected in 81% (17/21) while the positive-strand RNA of DWV was found in 95.2% (20/21) of the Varroa mites tested. Quantification of positive and negative-strand in the Varroa mites showed no significant difference (p = 0.07) between the titers of the negative-strand and positive-strand RNAs as seen in the honey bees (p < 0.05).Replication is a key step in successful virus infections. The replication strategies of positive-strand RNA viruses share remarkable similarities: all replicate and express their genomes through negative-strand RNA intermediates that are used as templates for the production of positive-strand progeny RNAs packaged in new virion particles . Therefoin vitro RNA replication assay for honey bee viruses, we first evaluated the existing methods, including conventional RT-PCR and Tagged RT-PCR for specificity of strand-specific detection of DWV RNA. The results showed that DWV RNA could be amplified by conventional RT-PCR without any primer for reverse transcription. The reason for nonspecific cDNA synthesis by conventional RT-PCR could be attributed to different events, including false-priming by antigenomic viral RNA or cellular RNAs, as well as self primering due to the secondary structure at the 5'UTR of viral RNA during reverse transcription, as earlier reports suggested [In an attempt to develop an uggested ,25-27. Tuggested ,29. Howeuggested . The eviIn order to circumvent the problems of false priming and contamination of residue primers from RT, it is worthwhile to develop improved procedures for analysis of virus replication. We report here the development of a novel strand-specific RT-PCR coupled with a biotinylated primer for reverse transcription and purification of biotinylated cDNA with magnetic beads. Using biotinylated oligonucleotide primers during the reverse transcription can lead to subsequent synthesis of biotinylated cDNAs, which have a high binding affinity to the streptavidin-coated magnetic beads. The purification step of streptavidin magnetic beads-cDNA complex, ensures the capture of only biotinylated cDNAs. The disappearance of a positive signal for non-strand-specific amplification of magnetic-bead purified biotinylated cDNA in our study suggests that the assay developed is a significant modification of conventional or Tagged RT-PCR. The purification of biotinylated cDNA with magnetic beads is a key step in assuring the strand-specific detection and elimination of the residues of RNA, non-incorporated RT primers as well as enzymes and salts that would interfere with subsequent PCR amplification.While quantitative detection of DWV infection and tissue tropism of DWV in the host were previously reported ,31, therThe observation that different tissues showed distinct kinetics of DWV replication, together with the fact that differences in the titers of positive-strand DWV RNAs were not significant among tissues examined, suggested that DWV had a tropism to certain host tissues in the replication. Among seven tissues examined, the most abundant amount of negative-strand DWV RNA was detected in the wings, which suggests that the wings are likely the predominant tissue site of DWV replication. Our earlier studies ,31 demonCompared to the titer of positive-strand DWV RNA, the relatively low amount of negative-strand DWV RNA implies that a regulatory mechanism may exist to facilitate the viral replication. These findings would stimulate further investigation to unveil the regulatory mechanisms that honey bees use to control the pattern of replication. Both positive and negative-strand RNAs of DWV were also detected in Varroa mites (data not shown) using the magnetic beads methodology. This finding is in agreement with preliminary works ,23 and sIn sum, strand-specific detection and quantification of DWV RNA were achieved using qRT-PCR incorporated with biotinylated primers and purification of biotinylated cDNA with magnetic beads. The elucidation of DWV replication profiles in honey bees would have broad implications for future development of therapeutic strategies for viral diseases. The assay developed in this study represents a useful tool to study not only replication of honey bee viruses but also other single-stranded RNA viruses.in vivo using the honey bee as a model. This permitted the specific identification of an important honey bee virus (DWV) replication sites in the honey bee body and it's quantification. The screening between symptomatic and asymptomatic bees using this technology had permitted the identification of the digestive tract and abdomen as a critical replication site in the course of symptomatic infection.We conclude that qRT-PCR incorporated with biotinylated primers and purification of biotinylated cDNAs with magnetic beads is a strong approach to specific detection and quantification of a virus genome and anti-genome Varroa mites and maintained in the USDA-ARS Bee Research Laboratory backyard apiary, Beltsville, MD. Bees intended for tissue dissection were kept alive inside of containers with the cap loosened to allow airflow before dissection. Otherwise, bees were immediately stored in -80°C freezer for subsequent RNA extraction.Individual adult worker bees, with and without deformed wings, and individual Varroa mites, were collected from honey bee colonies that were left untreated for Each live bee (N = 18) was fixed on the wax top of a dissecting dish with insect pins. Under a dissecting microscope, hemolymph was collected with a micropipette by making a small hole at the joint area between the wing and body with a needle to make it bleed. Following hemolymph collection, the gut was carefully pulled out with forceps. The remaining body including wings, legs, head, thorax and abdomen were cut apart with scissors and collected individually. To prevent possible contamination with hemolymph, all tissues were thoroughly rinsed once with 1× PBS and twice with nuclease-free water. All the tissue samples were subjected to RNA extraction.pro, Amersham Biosciences). RNA samples were stored at -80°C prior to molecular detection for viruses.Collected tissues from bees and Varroa mites (n = 21) were individually homogenized in TRIzol Reagent, a solution of guanidine isothiocyanate and phenol, for RNA extraction . After the addition of chloroform to remove proteins, lipids and DNA, the upper aqueous phase containing RNA was removed to a new microcentrifuge tube, precipitated with isopropanol, and the resulting pellet was dissolved in diethyl-pyrocarbonate (DEPC)-treated water with the addition of 1 μl of RNaseOUT a ribonuclease inhibitor . The concentration of total RNA was determined by measuring the absorption at 260 nm and the purity of RNA was estimated by the absorbance ratio of 260 nm/280 nm using a spectrophotometer with a 50 μl ultramicrovolume cell holder  was used for RT-PCR following the manufacturer's instructions. Primers used in the study were a pair of DWV specific primers as reported before . In ordeTagged RT-PCRs were performed under the same thermal cycling conditions as the conventional RT-PCR described above, except that the primers used in the study were modified by adding a 15-bp long sequence tag . The seqIn order to increase strand specificity, the regular primers or tagged-primers were replaced by biotinylated-primers for reverse transcription. The biotinylated-primers were synthesized by Invitrogen. After the reverse transcription reaction, cDNAs generated by either biotinylated forward or biotinylated reverse primers were magnetically purified by the Dynabeads kilobaseBINDER M-280 kit  following the manufacturer's recommendations. Magnetic beads coated with a monolayer of streptavidin were added to the RT reaction mixture containing the biotinylated cDNAs. The reaction mixture was incubated at room temperature for 3 hours in a roller incubator to allow immobilization of the biotinylated cDNA onto the magnetic beads. The Dynabeads/DNA-complex were washed twice in washing solution and once in distilled water and resuspended in PBS buffer. After releasing immobilized Biotinylated cDNAs from magnetic beads, PCR amplification was conducted for each sample under the same conditions as described above. The cDNAs that were generated by biotinylated primers and directly subjected to PCR amplification without magnetic bead purification were used as a control.TaqMan real-time quantitative RT-PCR (qRT-PCR) incorporated with biotinylated-primers and magnetic bead purification was performed for quantification of negative and positive-strands of DWV using the Stratagene Mx3000P spectrofluorometric thermal cycler operated by MxPro qPCR software. The virus levels were quantified based on the value of the cycle threshold (Ct), which represents the number of cycles needed to generate a fluorescent signal above a predefined threshold and is inversely proportional to the concentration of the initial target that has been amplified. The house keeping gene, β-actin, was employed as an endogenous control for normalization of the quantification. The sequence information of primers and probes for both DWV and β-actin are the same as described before [in vitro transcription using the Megascript T7 kit  in order to generate positive and negative DWV-specific ssRNAs and to construct a new standard curve for sensitive analysis and absolute virus quantification. Seven 10-fold serial dilutions of positive or negative  in vitro transcribed RNA were subjected to reverse transcription with biotinylated primers followed by magnetic bead purification and qPCR amplification. Each sample was run in triplicate for statistical purposes. The standard curve was established by plotting the initial quantities of the 10-fold serial diluted RNA against the corresponding threshold value (CT).Purified DWV specific fragments were incorporated into a pCR2.1 TA cloning vector  following the manufacturer's protocol. Recombinant plasmid DNA, containing DWV fragment in both directions, was purified using the Plasmid Mini Prep Kit  and used as a template for The specificity of the RT-PCR assay was confirmed by sequencing analysis. RT-PCR bands were excised from a low-melting-temperature agarose gel  and purified using the Wizard PCR Prep DNA purification system . The nucleotide sequences of the RT-PCR fragments were determined from both forward and reverse directions to confirm the specificity of the DWV amplification. The sequence data of each virus fragment were analyzed using the BLAST server at the National Center for Biotechnology Information, NIH.The Fisher's Least Significant Difference (LSD) Comparison of Means Test was used to analyze for significant differences of positive and negative-strand DWV titers among different tissues of the bees. The results are expressed as mean ± SD. Differences were considered statistically significant if p value < 0.05.DWV: Deformed Wing Virus; RT-PCR: Retro transcription-Polymerase chain reaction; CCD; Colony Collapse Disorder; CPE: Cytopathic effect.The authors declare that they have no competing interests.HFB conceived the research, performed the experiments, and wrote the manuscript. YPC developed the conceptual aspects of the work and wrote and edited the manuscript. All authors participated in data collection and read and approved the final manuscript.Mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.


Understanding the interaction between viral proteins and neutralizing antibodies at atomic resolution is hindered by a lack of experimentally solved complexes. Progress in computational docking has led to the prediction of increasingly high-quality model antibody-antigen complexes. The accuracy of atomic-level docking predictions is improved when integrated with experimental information and expert knowledge.Binding affinity data associated with somatic mutations of a rotavirus-specific human adult antibody (RV6-26) are used to filter potential docking orientations of an antibody homology model with respect to the rotavirus VP6 crystal structure. The antibody structure is used to probe the VP6 trimer for candidate interface residues.Three conformational epitopes are proposed. These epitopes are candidate antigenic regions for site-directed mutagenesis of VP6, which will help further elucidate antigenic function. A pseudo-atomic resolution RV6-26 antibody-VP6 complex is proposed consistent with current experimental information.The use of mutagenesis constraints in docking calculations allows for the identification of a small number of alternative arrangements of the antigen-antibody interface. The mutagenesis information from the natural evolution of a neutralizing antibody can be used to discriminate between residue-scale models and create distance constraints for atomic-resolution docking. The integration of binding affinity data or other information with computation may be an advantageous approach to assist peptide engineering or therapeutic antibody design. The Rotavirus (RV) particle is composed of three concentric viral protein (VP) layers. The intermediate layer consists of VP6 , and the potential flexibility of the antibody Fab, which is compose of 227 residues. However, biological knowledge helps to reduce the size of the docking search space. For example, it is known that the lower half of the VP6 is buried inside the RV double-layered particle and, thus, is not accessible to the antibody for binding. Docking predictions are most reliable when prior biological information is incorporated into the modeling process .The diversity of antibodies is due to the six complementarity determining region (CDR) loops, whose flexibility and large number of surface accessible side chains allow the antibody to match a particular antigen epitope. When such induced conformational changes are large, one expects docking predictions to become less accurate if backbone flexibility is not incorporated. However, the large binding affinity of antibody-antigen associations in general, and the RV6-26-VP6 complex in particular, may limit the size of conformational shifts upon complex formation due to the evolutionary advantage of constrained loops for tighter binding ,10. We uRosettaDock has performed well in the blind Critical Assessment of Predicted Interactions (CAPRI) protein-protein docking challenge , includiIn the current paper, our goal is to identify candidate VP6 epitopes of the human neutralizing antibody RV6-26 by defining an antibody orientation filter that combines the distances between interface residues of the docking partners with experimental binding affinity changes due to site-directed mutagenesis of somatic antibody mutations back to germline. Unique to this study is the use of somatic mutations, which occurred during the natural evolution of an adult human antibody. The orientation filter is used to select low-resolution RosettaDock model complexes for refinement. During a low-resolution RosettaDock search, each protein is represented as a backbone with the side chains approximated by their centroids. During a high-resolution search, all side-chain atoms are fully represented, and we use finer rotational and translational sampling as described below. After clustering the refined complexes, the RV6-26 antibody mutation that was most disruptive for binding following reversion to the germline sequence (Y66S ) acts asab initio molecular modeling for those parts of the antibody that are too variable for homology methods. For all docking runs, we included an alignment of our Fab with antibody binding subsequences of known antibody-antigen complexes, which allows RosettaDock to restrict the antibody from assuming an unlikely orientation of its CDR loops [Since a crystal structure is not available for mAb RV6-26, we built a homology model of the structure using the Web Antibody Modeling (WAM) . WAM useDR loops .Figure H1–46 is the known immunodominant region. Ref. [In the second step, we used a somatic mutation score (described below) based on binding affinity changes measured when naturally occurring RV6-26 antibody mutations were back-mutated to germline. This score was used to filter the 1000 low-resolution RosettaDock decoys. The motivation for using this data as a filter is that affinity is a measure of the evolutionary fitness of each mutation that occurs over the course of antibody evolution. The adult RV6-26 antibody contained 13 somatic mutations within the heavy chain. To identify which of these mutations affected binding to VP6, mutant antibodies were produced corresponding to the reversion of each somatic mutation amino acid back to the germline amino acid. Somatic mutations also occur in the light chain, but we focused on the 13 heavy chain mutations because the Von. Ref.  providesFigure The third profile (TFN) of the alignment in Fig. D of pair-wise distances between the Cα atoms of the 13 residue mutations of the RV6-26 antibody and a collection of VP6 residues from the interface, chosen based on visual inspection of the cryo-EM density. Virus interface residues were selected for the filter from two of the three VP6 chains: B-chain residues 197–213, 253–282, 287–301, 304, and 308; and C-chain residues 157–173, 236–247, and 351–374. The rows of the matrix D correspond to theVP6 residues at the interface and the columns to the antibody mutations highlighted in Fig. D. These distances yield a vector d whose elements are given byFor each complex, we created a matrix whose length is equal to the number of antibody mutations.F of a complex is given by the following sum over all antibody mutations M (M = 13 for RV6-26)The filter score where the contribution from each residue isdc is a distance cutoff in Angstroms that characterizes our measure of closeness between the antibody and antigen. We classify a somatic mutation as "active" if back-mutation to germline has a disruptive effect on binding to the antigen. "Neutral" somatic mutations are non-disruptive when mutated back to germline. In Eq. (3a), an active somatic mutation contributes 1 to the affinity filter score of a complex if this residue is within dc Angstroms of the antigen interface. In our application, we chose a relatively loose cutoff of dc= 12Å to allow all active mutations the possibility of contributing to the score, including ones that may be more distant from the interface. It has been observed that many affinity-maturing mutations in singe chain Fv antibodies correspond to residues that are more distant from the interface [dc. This essentially penalizes neutral somatic mutations that are closer to the interface than the cutoff distance. Of course, it is still possible for non-disruptive mutations to be near the interface, so this soft distance constraint penalizes but does not exclude neutral residues from contacting the antigen. In the final piece (Eq. 3c), all other somatic mutations do not contribute to the filter score of Eq. (2).and nterface ,19. A smIn the third step of Fig. a priori choose the number of clusters in k-medoids; however, this choice is easily validated by visual inspection of the 3D complexes. The candidate binding sites were determined from the final candidate complexes by finding the VP6 residues within a 5Å radius of the RV6-26 Tyr66. This mutation was chosen as a computational probe because it had the largest effect on binding affinity as back-mutation of this residue resulted in an 83-fold decreased rate of dissociation [The resulting high-resolution decoys were ranked according to their full-atom scores. Candidate complexes were determined by an additional round of refinement  following k-medoids (k = 3) clustering of the top Rosetta-scoring complexes. Root-mean-square deviation (RMSD) of the alpha-carbon coordinates was used as the cluster metric, and the cluster location was given by the decoy with the lowest full-atom energy score within the cluster. We used k-medoids clustering because, unlike hierarchical clustering, it does not require linkage assumptions, and it is simpler than mixture model clustering. Unlike model-based clustering, which has the advantage of a statistical model that allows it to estimate the number of clusters, we must Following the creation of 1000 antibody-antigen decoy complexes with the fit into the cryo-EM density map in Ref.  as a staF = 9.0) low-resolution complex in Fig. Starting from the best 45 filtered low-resolution complexes from the top four clusters in Fig. In Fig. The best scoring high-resolution model, shown in Fig. In this study, we proposed an integrative structure-based computational approach to identify the RV VP6 epitope for a human adult antibody. Successful computational docking strategies have been developed that integrate experimental data, such as NMR chemical shift perturbations, residual dipolar couplings, and mutagenesis data ,7. To inThis strategy is unique in its use of naturally occurring somatic mutations of a human antibody as a filter. The fact that all six active antibody residues were within 12Å of the interface Fig.  corrobor5) number of low-resolution complexes to explore the global space of potential interfaces, and then choose an encompassing region around the largest cluster as the VP6 residues near the interface.In the applied result in this paper, the algorithm had the advantage of an initial distance constraint provided by a cryo-EM density. This gave us added confidence in the antigenic-site candidates found by the algorithm, and it provided a starting point for validation of the algorithm on complexes where only binding affinity data is known. Unlike the two candidate complexes (whose antibody tyrosines were further down the VP6 neck in Fig. Affinity changes measured for the reversion of somatic mutations provides information about interface residues but not about specific antigen contacts. Additional feedback with experiment is necessary to unambiguously identify the interface. Thus, binding affinity changes caused by directed mutagenesis of the proposed viral protein residues will be used to eliminate false positive epitopes that we suspect in the less conservative complexes. Once residues from the epitope have been identified through this process, binding affinity changes from VP6 mutagenesis will be used together with the naturally evolved somatic mutations of the RV6-26 antibody that enhance binding affinity (Fig. in silico procedures for the prediction of antibody mutations that produce higher affinity, higher specificity binding to a desired target macromolecule than the natural antibody sequence. Studies involving the in vitro evolution of antibodies have shown that the diversity of structurally stable antibody sequences is much greater than the diversity observed in nature, which suggests that this diversity could be exploited for therapeutic antibody design [in silico-in vitro integrative approach to guide the design or enhancement of therapeutic antibodies.In addition to conformational epitope identification, computational docking that integrates experimental data may lead to more general y design ,23. CompThe authors declare that they have no competing interests.BAM and JM conceived of the computational strategy. BAM carried out the computational analysis and drafted the manuscript. NLK carried out the experimental studies, and JEC oversaw the experimental design. All authors contributed to the draft of the manuscript and helped interpret the results. All authors read and approved the final manuscript.