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Funding Information: The research conducted by D.M.H. and N.H. in this study was funded by the Fonds Wetenschappelijk Onder-zoek—Vlaanderen (Research Foundation—Flanders; FWO, http://www.fwo.be/en/) (Grant Agreements G0E8416N and G0B2317N). The research done by J.D.S. and A.M.V. in this study has been supported in part by Grants G.0692.14 and G0B2317N, funded by the FWO, Belgium. P.J.K.L. was supported By a PhD Grant of the FWO (1S31916N), and is currently funded via a post-doctoral fellowship (1242021N) of the FWO. WD was supported by a postdoctoral fellowship from FWO (12L5816N). Research done by V.M. in this study has been completed as part of the ELTE Thematic Excellence Programme 2020 supported by the National Research, Development and Innovation Office (TKP2020-IKA-05). The authors gratefully acknowledge support from the FWO Scientific Research Community on Network Statistics for Sexually Transmitted Diseases Epidemiology. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation—Flanders (FWO) and the Flemish Government—department EWI. Publisher Copyright: © 2021, The Author(s). Model comparisons have been widely used to guide intervention strategies to control infectious diseases. Agreement between different models is crucial for providing robust evidence for policy-makers because differences in model properties can influence their predictions. In this study, we compared models implemented by two individual-based model simulators for HIV epidemiology in a heterosexual population with Herpes simplex virus type-2 (HSV-2). For each model simulator, we constructed four models, starting from a simplified basic model and stepwise including more model complexity. For the resulting eight models, the predictions of the impact of behavioural interventions on the HIV epidemic in Yaoundé-Cameroon were compared. The results show that differences in model assumptions and model complexity can influence the size of the predicted impact of the intervention, as well as the predicted qualitative behaviour of the HIV epidemic after the intervention. These differences in predictions of an intervention were also observed for two models that agreed in their predictions of the HIV epidemic in the absence of that intervention. Without additional data, it is impossible to determine which of these two models is the most reliable. These findings highlight the importance of making more data available for the calibration and validation of epidemiological models. publishersversion published

The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof., Author summary The generation and serial intervals are epidemiological quantities used to describe and predict an ongoing epidemic outbreak. These quantities are related to the contact pattern of individuals, since infection events can take place if infectious and susceptible individuals have a contact. Therefore, intervention measures that reduce the interactions between members of the population are expected to affect both the realized generation and serial intervals. For the current COVID-19 pandemic unprecedented interventions have been adopted worldwide, e.g. strict lockdown, isolation and quarantine, which influence the realized value of generation and serial intervals. The extent of the effect thereof depends on the efficacy of the control measure in place, on the relationship between symptom onset and infectiousness and on the proportion of infectious individuals that can be detected. To get more insight on this, we present an investigation that highlights the effect of quarantine and isolation on realized generation and serial intervals. In particular, we show that not only their variances but also their mean values can differ, suggesting that the use of the mean serial interval as a proxy for the mean generation time can lead to biased estimates of epidemiological quantities.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causesthe infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformaticstools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection,understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to getinsight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for theroutine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemicand evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets anddevelopment of therapeutic strategies. For each tool, we briefly describe its use case and how it advances researchspecifically for SARS-CoV-2. Fil: Hufsky, Franziska. Friedrich Schiller University Jena; Alemania Fil: Lamkiewicz, Kevin. Friedrich Schiller University Jena; Alemania Fil: Almeida, Alexandre. the Wellcome Sanger Institute; Reino Unido Fil: Aouacheria, Abdel. Centre National de la Recherche Scientifique; Francia Fil: Arighi, Cecilia. Biocuration and Literature Access at PIR; Estados Unidos Fil: Bateman, Alex. European Bioinformatics Institute. Head of Protein Sequence Resources; Reino Unido Fil: Baumbach, Jan. Universitat Technical Zu Munich; Alemania Fil: Beerenwinkel, Niko. Universitat Technical Zu Munich; Alemania Fil: Brandt, Christian. Jena University Hospital; Alemania Fil: Cacciabue, Marco Polo Domingo. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Chuguransky, Sara Rocío. European Bioinformatics Institute; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Drechsel, Oliver. Robert Koch-Institute; Alemania Fil: Finn, Robert D.. Biocurator for Pfam and InterPro databases; Reino Unido Fil: Fritz, Adrian. Helmholtz Centre for Infection Research; Alemania Fil: Fuchs, Stephan. Robert Koch-Institute; Alemania Fil: Hattab, Georges. University Marburg; Alemania Fil: Hauschild, Anne Christin. University Marburg; Alemania Fil: Heider, Dominik. University Marburg; Alemania Fil: Hoffmann, Marie. Freie Universität Berlin; Alemania Fil: Hölzer, Martin. Friedrich Schiller University Jena; Alemania Fil: Hoops, Stefan. University of Virginia; Estados Unidos Fil: Kaderali, Lars. University Medicine Greifswald; Alemania Fil: Kalvari, Ioanna. European Bioinformatics Institute; Reino Unido Fil: von Kleist, Max. Robert Koch-Institute; Alemania Fil: Kmiecinski, Renó. Robert Koch-Institute; Alemania Fil: Kühnert, Denise. Max Planck Institute for the Science of Human History; Alemania Fil: Lasso, Gorka. Albert Einstein College of Medicine; Estados Unidos Fil: Libin, Pieter. Hasselt University; Bélgica Fil: List, Markus. Universitat Technical Zu Munich; Alemania Fil: Löchel, Hannah F.. University Marburg; Alemania

Migration is associated with HIV-1 vulnerability. Objectives: To identify long-term trends in HIV-1 molecular epidemiology and antiretroviral drug resistance (ARV) among migrants followed up in Portugal Methods: 5177 patients were included between 2001 and 2017. Rega, Scuel, Comet, and jPHMM algorithms were used for subtyping. Transmitted drug resistance (TDR) and Acquired drug resistance (ADR) were defined as the presence of surveillance drug resistance mutations (SDRMs) and as mutations of the IAS-USA 2015 algorithm, respectively. Statistical analyses were performed. Results: HIV-1 subtypes infecting migrants were consistent with the ones prevailing in their countries of origin. Over time, overall TDR significantly increased and specifically for Non-nucleoside reverse transcriptase inhibitor (NNRTIs) andNucleoside reverse transcriptase inhibitor (NRTIs). TDR was higher in patients from Mozambique. Country of origin Mozambique and subtype B were independently associated with TDR. Overall, ADR significantly decreased over time and specifically for NRTIs and Protease Inhibitors (PIs). Age, subtype B, and viral load were independently associated with ADR. Conclusions: HIV-1 molecular epidemiology in migrants suggests high levels of connectivity with their country of origin. The increasing levels of TDR in migrants could indicate an increase also in their countries of origin, where more efficient surveillance should occur., SCOPUS: ar.j, info:eu-repo/semantics/published

Introduction: Treatment for All recommendations have allowed access to antiretroviral (ARV) treatment for an increasing number of patients. This minimizes the transmission of infection but can potentiate the risk of transmitted (TDR) and acquired drug resistance (ADR). Objective: To study the trends of TDR and ADR in patients followed up in Portuguese hospitals between 2001 and 2017. Methods: In total, 11,911 patients of the Portuguese REGA database were included. TDR was defined as the presence of one or more surveillance drug resistance mutation according to the WHO surveillance list. Genotypic resistance to ARV was evaluated with Stanford HIVdb v7.0. Patterns of TDR, ADR and the prevalence of mutations over time were analyzed using logistic regression. Results and Discussion: The prevalence of TDR increased from 7.9% in 2003 to 13.1% in 2017 (p &lt, 0.001). This was due to a significant increase in both resistance to nucleotide reverse transcriptase inhibitors (NRTIs) and non-nucleotide reverse transcriptase inhibitors (NNRTIs), from 5.6% to 6.7% (p = 0.002) and 2.9% to 8.9% (p &lt, 0.001), respectively. TDR was associated with infection with subtype B, and with lower viral load levels (p &lt, 0.05). The prevalence of ADR declined from 86.6% in 2001 to 51.0% in 2017 (p &lt, 0.001), caused by decreasing drug resistance to all antiretroviral (ARV) classes (p &lt, 0.001). Conclusions: While ADR has been decreasing since 2001, TDR has been increasing, reaching a value of 13.1% by the end of 2017. It is urgently necessary to develop public health programs to monitor the levels and patterns of TDR in newly diagnosed patients.

Simulations are widely used to provide expectations and predictive distributions under known conditions against which to compare empirical data. Such simulations are also invaluable for testing and comparing the behaviour and power of inference methods. We describe SANTA-SIM, a software package to simulate the evolution of a population of gene sequences forwards through time. It models the underlying biological processes as discrete components: replication, recombination, point mutations, insertion-deletions, and selection under various fitness models and population size dynamics. The software is designed to be intuitive to work with for a wide range of users and executable in a cross-platform manner. ispartof: VIRUS EVOLUTION vol:5 issue:1 ispartof: location:England status: published

Following the onset of the ongoing COVID-19 pandemic throughout the world, a large fraction of the global population is or has been under strict measures of physical distancing and quarantine, with many countries being in partial or full lockdown. These measures are imposed in order to reduce the spread of the disease and to lift the pressure on healthcare systems. Estimating the impact of such interventions as well as monitoring the gradual relaxing of these stringent measures is quintessential to understand how resurgence of the COVID-19 epidemic can be controlled for in the future. In this paper we use a stochastic age-structured discrete time compartmental model to describe the transmission of COVID-19 in Belgium. Our model explicitly accounts for age-structure by integrating data on social contacts to (i) assess the impact of the lockdown as implemented on March 13, 2020 on the number of new hospitalizations in Belgium; (ii) conduct a scenario analysis estimating the impact of possible exit strategies on potential future COVID-19 waves. More specifically, the aforementioned model is fitted to hospital admission data, data on the daily number of COVID-19 deaths and serial serological survey data informing the (sero)prevalence of the disease in the population while relying on a Bayesian MCMC approach. Our age-structured stochastic model describes the observed outbreak data well, both in terms of hospitalizations as well as COVID-19 related deaths in the Belgian population. Despite an extensive exploration of various projections for the future course of the epidemic, based on the impact of adherence to measures of physical distancing and a potential increase in contacts as a result of the relaxation of the stringent lockdown measures, a lot of uncertainty remains about the evolution of the epidemic in the next months. We thank several researchers from the SIMID COVID-19 consortium from the University of Antwerp and Hasselt University for numerous constructive discussions and meetings. SA and NH gratefully acknowledge support from the Research Foundation Flanders (FWO), Belgium (RESTORE project — G0G2920N). This work also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (PC and NH, grant number 682540 – TransMID project, NH, PB grant number 101003688 – EpiPose project). The authors are also very grateful for access to the data from the Belgian Scientific Institute for Public Health, Sciensano, and from the Vaccine & Infectious Disease Institute (VaxInfectio), University of Antwerp. LW and PL gratefully acknowledge funding from the Research Foundation Flanders, Belgium (postdoctoral fellowships 1234620N and 1242021N). We acknowledge support from the Antwerp Study Centre for Infectious Diseases (ASCID). The resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the FWO and the Flemish Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. All authors contributed to the final version of the paper and approved the final version of the manuscript.

In recent years, an increasing number of outbreaks of Dengue, Chikungunya and Zika viruses have been reported in Asia and the Americas. Monitoring virus genotype diversity is crucial to understand the emergence and spread of outbreaks, both aspects that are vital to develop effective prevention and treatment strategies. Hence, we developed an efficient method to classify virus sequences with respect to their species and sub-species (i.e. serotype and/or genotype). This tool provides an easy-to-use software implementation of this new method and was validated on a large dataset assessing the classification performance with respect to whole-genome sequences and partial-genome sequences. Available online: http://krisp.org.za/tools.php., SCOPUS: ar.j, info:eu-repo/semantics/published

Dengue virus (DENV) is estimated to cause 390 million infections per year worldwide. A quarter of these infections manifest clinically and are associated with a morbidity and mortality that put a significant burden on the affected regions. Reports of increased frequency, intensity, and extended geographical range of outbreaks highlight the virus’s ongoing global spread. Persistent transmission in endemic areas and the emergence in territories formerly devoid of transmission have shaped DENV’s current genetic diversity and divergence. This genetic layout is hierarchically organized in serotypes, genotypes, and sub-genotypic clades. While serotypes are well defined, the genotype nomenclature and classification system lack consistency, which complicates a broader analysis of their clinical and epidemiological characteristics. We identify five key challenges: (1) Currently, there is no formal definition of a DENV genotype; (2) Two different nomenclature systems are used in parallel, which causes significant confusion; (3) A standardized classification procedure is lacking so far; (4) No formal definition of sub-genotypic clades is in place; (5) There is no consensus on how to report antigenic diversity. Therefore, we believe that the time is right to re-evaluate DENV genetic diversity in an essential effort to provide harmonization across DENV studies., SCOPUS: ar.j, info:eu-repo/semantics/published

Treatment with pan-genotypic direct-acting antivirals, targeting different viral proteins, is the best option for clearing hepatitis C virus (HCV) infection in chronically infected patients. However, the diversity of the HCV genome is a major obstacle for the development of antiviral drugs, vaccines, and genotyping assays. In this large-scale analysis, genome-wide diversity and selective pressure was mapped, focusing on positions important for treatment, drug resistance, and resistance testing. A dataset of 1415 full-genome sequences, including genotypes 1–6 from the Los Alamos database, was analyzed. In 44% of all full-genome positions, the consensus amino acid was different for at least one genotype. Focusing on positions sharing the same consensus amino acid in all genotypes revealed that only 15% was defined as pan-genotypic highly conserved (¥99% amino acid identity) and an additional 24% as pan-genotypic conserved (¥95%). Despite its large genetic diversity, across all genotypes, codon positions were rarely identified to be positively selected (0.23%–0.46%) and predominantly found to be under negative selective pressure, suggesting mainly neutral evolution. For NS3, NS5A, and NS5B, respectively, 40% (6/15), 33% (3/9), and 14% (2/14) of the resistance-related positions harbored as consensus the amino acid variant related to resistance, potentially impeding treatment. For example, the NS3 variant 80K, conferring resistance to simeprevir used for treatment of HCV1 infected patients, was present in 39.3% of the HCV1a strains and 0.25% of HCV1b strains. Both NS5A variants 28M and 30S, known to be associated with resistance to the pan-genotypic drug daclatasvir, were found in a significant proportion of HCV4 strains (10.7%). NS5B variant 556G, known to confer resistance to non-nucleoside inhibitor dasabuvir, was observed in 8.4% of the HCV1b strains. Given the large HCV genetic diversity, sequencing efforts for resistance testing purposes may need to be genotype-specific or geographically tailored. ispartof: Viruses vol:7 issue:9 pages:5018-5039 ispartof: location:Switzerland status: published

Background Portugal has one of the most severe HIV-1 epidemic in Western Europe. Two subtypes circulate in parallel since the beginning of the epidemic. Comparing their transmission patterns and its association with transmitted drug resistance (TDR) is important to pinpoint transmission hotspots and to develop evidence-based treatment guidelines. Methods 3599 HIV-1 naive patients collected between 2001 and 2014 were included in the study. Sequences obtained from drug resistance testing were used for subtyping, TDR determination and transmission clusters (TC) analyses. Results Subtype B transmission was significantly associated with young males, while subtype G was associated with older heterosexuals. Young males infected with subtype B were more likely to be included in TC. Consistently, a decreasing trend of prevalence and transmission of subtype G in Portuguese originated people was observed. Active TCs were associated with subtype B-infected males residing in Lisbon. TDR was significantly different when comparing subtypes B (10.8% [9.5-12.2]) and G (7.6% [6.4-9.0]) (p=0.001). Discussion TC analyses shows that the subtype B epidemic is active and fueled by young male patients residing in Lisbon and that transmission of subtype G in Portugal is decreasing. Despite similar treatment rates for both subtypes in Portugal, TDR is different between subtypes.

Co-infections with HIV and HCV are very frequent among people who inject drugs (PWID). However, very few studies comparatively reconstructed the transmission patterns of both viruses in the same population. We have recruited 117 co-infected PWID during a recent HIV outbreak in Romania. Phylogenetic analyses were performed on HIV and HCV sequences in order to characterize and compare transmission dynamics of the two viruses. Three large HIV clusters (2 subtype F1 and one CRF14_BG) and thirteen smaller HCV transmission networks (genotypes 1a, 1b, 3a, 4a and 4d) were identified. Eighty (65%) patients were both in HIV and HCV transmission chains and 70 of those shared the same HIV and HCV cluster with at least one other patient. Molecular clock analysis indicated that all identified HIV clusters originated around 2006, while the origin of the different HCV clusters ranged between 1980 (genotype 1b) and 2011 (genotypes 3a and 4d). HCV infection preceded HIV infection in 80.3% of cases. Coincidental transmission of HIV and HCV was estimated to be rather low (19.65%) and associated with an outbreak among PWID during detention in the same penitentiary. This study has reconstructed and compared the dispersion of these two viruses in a PWID population. © 2017 Paraschiv et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Despite significant progress in hepatitis C (HCV) treatment, global viral eradication remains a challenge. An in-depth map of its genome diversity within the context of structural and immunological constraints could contribute to the design of pan-genotypic antivirals and preventive vaccines. For such analyses, extensive information is only available for the highly prevalent HCV genotypes (GT) 1a and 1b. Using 647 GT1a and 408 GT1b full-genome sequences obtained from the Los Alamos database, we found that respectively 3 per cent and 82 per cent of all codon positions are under positive and negative selective pressure, suggesting variation mainly accumulates due to random genetic drift. An association between conservation and both structured RNA and secondary protein structures confirmed the important role of structural elements at nucleotide and at amino acid level. Remarkably, CD8+ T-cell epitopes in HCV GT1a were significantly more conserved, while at the same time containing more sites under positive selection. Similarly, CD4+ T-cell epitopes were significantly more conserved in both HCV subtypes, but under less positive selective pressure in GT1b and more negative selective pressure in GT1a. In contrast, B-cell epitopes in both subtypes were less conserved and under less stringent negative selection. These findings argue against immune selective pressure as the main force of between-host diversifying evolution. Despite its high variability, HCV is under strict evolutionary constraints, most probably to keep its genes and proteins functional during the replication cycle. These are encouraging findings for vaccine and drug design, which could consider these newly established genetic diversity profiles. ispartof: VIRUS EVOLUTION vol:2 issue:2 ispartof: location:England status: published

Major advances have revolutionized the HCV antiviral treatment field, with interferon-free combinations of direct-acting antivirals (DAAs) resulting into success rates of more than 90% for all HCV genotypes. Nevertheless, viral eradication at a global level stills remains challenging, stimulating the continued search for new affordable pan-genotypic drugs. To overcome selection of drug resistant variants, targeting host proteins can be an attractive mechanism of action. Alisporivir (Debio 025) is a potent pan-genotypic host-targeting antiviral agent, acting on cyclophilin A, which is necessary for HCV replication. The efficacy and safety of three different oral doses of alisporivir in combination with pegylated interferon-α2a given over a period of four weeks, was investigated in a randomized, double-blind and placebo-controlled phase IIa clinical trial, in 90 treatment-naïve subjects infected with chronic hepatitis C, wherefrom 58 HCV1b samples were selected for genetic sequencing purposes. Sequencing results were used to study the HCV genome for amino acid changes potentially related with selective pressure and resistance to alisporivir. By comparing baseline and on-treatment sequences, a large variation in proportion of amino acid changes was detected in all treatment arms. The NS5A variant D320E, which was previously identified during in vitro resistance selection and resulted in 3.6-fold reduced alisporivir susceptibility, emerged in two subjects in the alisporivir monotherapy arm. However, emergence of D320E appeared to be associated only with concurrent viral load rebound in one subject with 0.8 log10 IU/ml increase in HCV RNA. In general, for all datasets, low numbers of positions under positive selective pressure were observed, with no significant differences between naïve and treated sequences. Additionally, incomplete sequence information for some of the 22 patients and the low number of individuals per treatment arm, is limiting the power to assess the association of alisporivir or interferon treatment with the observed amino acid changes. publisher: Elsevier articletitle: HCV1b genome evolution under selective pressure of the cyclophilin inhibitor alisporivir during the DEB-025-HCV-203 phase II clinical trial journaltitle: Infection, Genetics and Evolution articlelink: http://dx.doi.org/10.1016/j.meegid.2016.06.050 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved. ispartof: Infection, Genetics and Evolution vol:44 pages:169-181 ispartof: location:Netherlands status: published

BACKGROUND: Dolutegravir is approved for the treatment of HIV-1 patients exposed to other integrase inhibitors, but the decision to use dolutegravir in this setting should be informed by drug resistance testing. OBJECTIVES: This study determined the extent of disagreement in predicted residual dolutegravir activity after raltegravir use, and identified individual mutational patterns for which uncertainty exists among HIV-1 expert systems. STUDY DESIGN: Mutation patterns were classified in raltegravir signature pathways including positions 143, 148 and 155, and interpreted into clinically informative resistance levels using genotypic drug resistance interpretation systems ANRS v24, HIVdb v7.0 and Rega v9.1.0, and instructions of dolutegravir use as approved by the Food and Drug Administration and the European Medicines Agency. RESULTS: In 216HIV-1 patients failing raltegravir-therapy, 87% patients displayed mutations associated with resistance towards integrase inhibitors. A total of 141 unique mutational patterns were observed, with N155H (25.4%), Q148H (16.2%) and Y143R (8.3%) the most prevalent signature mutations. The Q148 pathway occurred almost exclusively in HIV-1 subtype B viruses. Concordances in predicted dolutegravir susceptibility scores among 5 systems were obtained in 57.8% of patients, and concordant intermediate resistant and concordant resistant scores were only observed in 6.5% and 0.9% of patients, respectively. However, systems individually scored higher levels of dolutegravir intermediate resistance and resistance, ranging from 4.2% to 10.2% and from 14.8% to 22.7% of patients, respectively. A consensus on interpreting the extent of residual activity was lacking in 34.7% of patients and was highly resistance pathway-specific. CONCLUSIONS: Dolutegravir may potentially be effective in the majority of HIV-1 patients failing raltegravir, but concern over the uncertainty in predicted residual activity could withhold clinicians from prescribing dolutegravir during its clinical assessment. info:eu-repo/semantics/publishedVersion

INTRODUCTION: Clinically evaluating genotypic interpretation systems is essential to provide optimal guidance in designing potent individualized HIV-regimens. This study aimed at investigating the ability of the latest Rega algorithm to predict virological response on a short and longer period. MATERIALS METHODS: 9231 treatment changes episodes were extracted from an integrated patient database. The virological response after 8, 24 and 48 weeks was dichotomized to success and failure. Success was defined as a viral load below 50 copies/ml or alternatively, a 2 log decrease from the baseline viral load at 8 weeks. The predictive ability of Rega version 8 was analysed in comparison with that of previous evaluated version Rega 5 and two other algorithms (ANRS v2011.05 and Stanford HIVdb v6.0.11). A logistic model based on the genotypic susceptibility score was used to predict virological response, and additionally, confounding factors were added to the model. Performance of the models was compared using the area under the ROC curve (AUC) and a Wilcoxon signed-rank test. RESULTS: Per unit increase of the GSS reported by Rega 8, the odds on having a successful therapy response on week 8 increased significantly by 81% (OR = 1.81, CI = [1.76-1.86]), on week 24 by 73% (OR = 1.73, CI = [1.69-1.78]) and on week 48 by 85% (OR = 1.85, CI = [1.80-1.91]). No significant differences in AUC were found between the performance of Rega 8 and Rega 5, ANRS v2011.05 and Stanford HIVdb v6.0.11, however Rega 8 had the highest sensitivity: 76.9%, 76.5% and 77.2% on 8, 24 and 48 weeks respectively. Inclusion of additional factors increased the performance significantly. CONCLUSION: Rega 8 is a significant predictor for virological response with a better sensitivity than previously, and with rules for recently approved drugs. Additional variables should be taken into account to ensure an effective regimen. ispartof: PLoS One vol:8 issue:4 ispartof: location:United States status: published

BACKGROUND: It is estimated that 15 to 20 million people are infected with the human T-cell lymphotropic virus type 1 (HTLV-1). At present, there are more than 2,000 unique HTLV-1 isolate sequences published. A central database to aggregate sequence information from a range of epidemiological aspects including HTLV-1 infections, pathogenesis, origins, and evolutionary dynamics would be useful to scientists and physicians worldwide. Described here, we have developed a database that collects and annotates sequence data and can be accessed through a user-friendly search interface. The HTLV-1 Molecular Epidemiology Database website is available at http://htlv1db.bahia.fiocruz.br/. METHODOLOGY/PRINCIPAL FINDINGS: All data was obtained from publications available at GenBank or through contact with the authors. The database was developed using Apache Webserver 2.1.6 and SGBD MySQL. The webpage interfaces were developed in HTML and sever-side scripting written in PHP. The HTLV-1 Molecular Epidemiology Database is hosted on the Gonçalo Moniz/FIOCRUZ Research Center server. There are currently 2,457 registered sequences with 2,024 (82.37%) of those sequences representing unique isolates. Of these sequences, 803 (39.67%) contain information about clinical status (TSP/HAM, 17.19%; ATL, 7.41%; asymptomatic, 12.89%; other diseases, 2.17%; and no information, 60.32%). Further, 7.26% of sequences contain information on patient gender while 5.23% of sequences provide the age of the patient. CONCLUSIONS/SIGNIFICANCE: The HTLV-1 Molecular Epidemiology Database retrieves and stores annotated HTLV-1 proviral sequences from clinical, epidemiological, and geographical studies. The collected sequences and related information are now accessible on a publically available and user-friendly website. This open-access database will support clinical research and vaccine development related to viral genotype.

Scientific development had generated a great amount of data that should be stored and managed, in a way, that the users have access to the complete data of interest. The amount of HTLV-1 not related data that had been generated warranties the designing of a database that would be able of put together a greater number of virus data: as epidemiological and environmental aspects, to give support to the inferences about infection, pathogenesis, origin and, principally evolutionary dynamics. All the data was obtained from papers available at GenBank or through contact with the authors. All collected data was organized using the PostgreSQL, and are accessed and managed through the RegaDB: a Viral Data and Analysis Management Environment. We, currently, have 1,972 registered sequences which 23.5% are complete genome and 76.5% are partial genome (Sequences corresponding to LTR, gag, env and pX regions). In relation to clinical status we have collected information for 42.70% of the sequences, and 39.54% of them were obtained from viral isolates of TSP/HAM individuals, while 21.14%, 33.01% and 6.31% of them, were obtained from ATL patients, asymptomatic and other infections, respectively. We also have reports of gender and age in 12.38% and 9.33% of the sequences, respectively. Nowadays, we are dedicating ourselves to the preparation of an interface to permit the access of exterior users, and to the test of the tool utility, and finally to the web publication.

A better understanding of human immunodeficiency virus type 1 drug-resistance evolution under the selective pressure of combination treatment is important for the design of long-term effective treatment strategies. We applied Bayesian network learning to sequences from patients treated with the reverse transcriptase inhibitor combination of zidovudine (AZT) and lamivudine (3TC) to identify the role of many treatment-selected mutations in the development of resistance. Based on the Bayesian network structure, anin vivofitness landscape was built, reflecting the necessary selective pressure under treatment, to evolve naive sequences to sequences obtained from patients treated with the combination. This landscape, combined with an evolutionary model, was used to predict resistance evolution in longitudinal sequence pairs. In our analysis, mutations 41L, 70R, 184V and 215F/Y were identified as major resistance mutations to the combination of AZT and 3TC, as they were associated directly with treatment experience. The network also suggested a possible role in resistance development for a number of novel mutations. Estimated fitness, using the landscape, correlated significantly within vitroresistance phenotype in genotype–phenotype pairs (R2=0.70). Variation in predicted evolution under selective pressure correlated significantly with observedin vivoevolution during AZT plus 3CT treatment. In conclusion, we confirmed current knowledge on resistance development to the combination of AZT and 3CT, but additional novel mutations were identified. Moreover, a model to predict resistance evolution during AZT and 3CT treatment has been built and validated.

Las epidemias virales emergentes representan una amenaza para la salud pública a nivel mundial. La reciente propagación del virus Zika (ZIKV) en la región del Pacífico y las Américas es particularmente preocupante, dada su asociación con malformaciones congénitas graves. El 1 de febrero de 2016, la Organización Mundial de la Salud (OMS) declaró al ZIKV una emergencia de salud pública de importancia internacional, lo que provocó una respuesta mundial para mejorar nuestra comprensión de la epidemiología y las manifestaciones de la enfermedad del ZIKV. La vigilancia intensificada del ZIKV y el intercambio en tiempo real de nueva evidencia científica coincidieron con una ampliación de la secuenciación del genoma completo del ZIKV con fines de salud pública y diagnóstico [1,2]. Como resultado, la mayoría de los genomas humanos del ZIKV disponibles hasta la fecha se originan en el brote en curso. Si bien esta proliferación de datos genómicos ofrece nuevas oportunidades para la genómica comparativa y evolutiva del ZIKV, demostramos que el rápido avance en la genómica del ZIKV ha resultado en inconsistencias que complican la interpretación, la reproducibilidad y la comparación de los hallazgos de los estudios y entre ellos, particularmente debido a la falta de consenso sobre una anotación genómica de referencia estandarizada y representativa. Los genomas de referencia de ZIKV no coinciden con las cepas de virus muestreadas de la epidemia mundial o no están adecuadamente anotados a nivel de proteína, y la heterogeneidad actual en la metodología del estudio limita en última instancia todo el potencial de la genómica de ZIKV. En esta carta, abordamos la necesidad de curación y anotación estandarizada de los genomas de referencia de ZIKV para guiar a los investigadores y médicos en los análisis genómicos y la traducción de los resultados de la investigación. Emerging viral epidemics pose a threat to public health globally. The recent spread of the Zika virus (ZIKV) across the Pacific region and the Americas is particularly disturbing, given its association with severe birth malformations. On February 1, 2016, the World Health Organization (WHO) declared ZIKV a Public Health Emergency of International Concern, which prompted a global response to improve our understanding of ZIKV epidemiology and disease manifestations. Intensified ZIKV surveillance and real-time sharing of novel scientific evidence coincided with a scaling up of ZIKV whole genome sequencing for public health purposes and diagnostics [1,2]. As a result, most human ZIKV genomes available to date originate from the ongoing outbreak. While this proliferation of genomic data offers new opportunities for comparative and evolutionary genomics of ZIKV, we demonstrate that the rapid advance in ZIKV genomics has resulted in inconsistencies that complicate the interpretation, reproducibility, and comparison of findings from and across studies, particularly due to the lack of consensus on a standardized and representative reference genome annotation. ZIKV reference genomes do not match virus strains sampled from the global epidemic or are not adequately annotated at the protein level, and current heterogeneity in study methodology ultimately limits the full potential of ZIKV genomics. In this letter, we address the need for curation and standardized annotation of ZIKV reference genomes in order to guide researchers and clinicians in genomic analyses and the translation of research findings.