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      Increase in HIV-1-transmitted drug resistance among ART-naïve youths at the China-Myanmar border during 2009 ~ 2017

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          Abstract

          Background

          HIV-transmitted drug resistance (TDR) is found in antiretroviral therapy (ART)-naïve populations infected with HIV-1 with TDR mutations and is important for guiding future first- and second-line ART regimens. We investigated TDR and its effect on CD4 count in ART-naïve youths from the China-Myanmar border near the Golden Triangle to better understand TDR and effectively guide ART.

          Methods

          From 2009 to 2017, 10,832 HIV-1 infected individuals were newly reported along the Dehong border of China, 573 ART-naïve youths (16 ~ 25 y) were enrolled. CD4 counts were obtained from whole blood samples. HIV pol gene sequences were amplified from RNA extracted from plasma. The Stanford REGA program and jpHMM recombination prediction tool were used to determine genotypes. TDR mutations (TDRMs) were analyzed using the Stanford Calibrated Population Resistance tool.

          Results

          The most common infection route was heterosexuals (70.51%), followed by people who inject drugs (PWID, 19.20%) and men who have sex with men (MSM) (8.90%). The distribution of HIV genotypes mainly included the unique recombinant form (URF) (44.08%), 38.68% were CRFs, 13.24% were subtype C and 4.04% were subtype B. The prevalence of TDR increased significantly from 2009 to 2017 (3.48 to 9.48%) in ART-naïve youths (4.00 to 13.16% in Burmese subjects, 3.33 to 5.93% in Chinese subjects), and the resistance to non-nucleoside reverse transcriptase inhibitors (NNRTIs), nucleoside and nucleotide reverse transcriptase inhibitors (NRTIs), and protease inhibitors (PIs) were 3.49, 2.62, and 0.52%, respectively. Most (94.40%, n = 34) of HIV-1-infected patients with TDRM had mutation that conferred resistance to a single drug class. The most common mutations Y181I/C and K103N, were found in 7 and 9 youths, respectively. The mean CD4 count was significantly lower among individuals with TDRMs (373/mm 3 vs. 496/mm 3, p = 0.013).

          Conclusions

          The increase in the prevalence of HIV-1 TDR increase and a low CD4 count of patients with TDRMs in the China-Myanmar border suggests the need for considering drug resistance before initiating ART in HIV recombination hotspots.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12879-021-05794-5.

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          Most cited references36

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          RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

          Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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            Web resources for HIV type 1 genotypic-resistance test interpretation.

            Interpreting the results of plasma human immunodeficiency virus type 1 (HIV-1) genotypic drug-resistance tests is one of the most difficult tasks facing clinicians caring for HIV-1-infected patients. There are many drug-resistance mutations, and they arise in complex patterns that cause varying levels of drug resistance. In addition, HIV-1 exists in vivo as a virus population containing many genomic variants. Genotypic-resistance testing detects the drug-resistance mutations present in the most common plasma virus variants but may not detect drug-resistance mutations present in minor virus variants. Therefore, interpretation systems are necessary to determine the phenotypic and clinical significance of drug-resistance mutations found in a patient's plasma virus population. We describe the scientific principles of HIV-1 genotypic-resistance test interpretation and the most commonly used Web-based resources for clinicians ordering genotypic drug-resistance tests.
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              Automated subtyping of HIV-1 genetic sequences for clinical and surveillance purposes: performance evaluation of the new REGA version 3 and seven other tools.

              To investigate differences in pathogenesis, diagnosis and resistance pathways between HIV-1 subtypes, an accurate subtyping tool for large datasets is needed. We aimed to evaluate the performance of automated subtyping tools to classify the different subtypes and circulating recombinant forms using pol, the most sequenced region in clinical practice. We also present the upgraded version 3 of the Rega HIV subtyping tool (REGAv3). HIV-1 pol sequences (PR+RT) for 4674 patients retrieved from the Portuguese HIV Drug Resistance Database, and 1872 pol sequences trimmed from full-length genomes retrieved from the Los Alamos database were classified with statistical-based tools such as COMET, jpHMM and STAR; similarity-based tools such as NCBI and Stanford; and phylogenetic-based tools such as REGA version 2 (REGAv2), REGAv3, and SCUEAL. The performance of these tools, for pol, and for PR and RT separately, was compared in terms of reproducibility, sensitivity and specificity with respect to the gold standard which was manual phylogenetic analysis of the pol region. The sensitivity and specificity for subtypes B and C was more than 96% for seven tools, but was variable for other subtypes such as A, D, F and G. With regard to the most common circulating recombinant forms (CRFs), the sensitivity and specificity for CRF01_AE was ~99% with statistical-based tools, with phylogenetic-based tools and with Stanford, one of the similarity based tools. CRF02_AG was correctly identified for more than 96% by COMET, REGAv3, Stanford and STAR. All the tools reached a specificity of more than 97% for most of the subtypes and the two main CRFs (CRF01_AE and CRF02_AG). Other CRFs were identified only by COMET, REGAv2, REGAv3, and SCUEAL and with variable sensitivity. When analyzing sequences for PR and RT separately, the performance for PR was generally lower and variable between the tools. Similarity and statistical-based tools were 100% reproducible, but this was lower for phylogenetic-based tools such as REGA (~99%) and SCUEAL (~96%). REGAv3 had an improved performance for subtype B and CRF02_AG compared to REGAv2 and is now able to also identify all epidemiologically relevant CRFs. In general the best performing tools, in alphabetical order, were COMET, jpHMM, REGAv3, and SCUEAL when analyzing pure subtypes in the pol region, and COMET and REGAv3 when analyzing most of the CRFs. Based on this study, we recommend to confirm subtyping with 2 well performing tools, and be cautious with the interpretation of short sequences. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.
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                Author and article information

                Contributors
                mjhan@chinaaids.cn
                mal@chinaaids.cn
                Journal
                BMC Infect Dis
                BMC Infect Dis
                BMC Infectious Diseases
                BioMed Central (London )
                1471-2334
                21 January 2021
                21 January 2021
                2021
                : 21
                : 93
                Affiliations
                [1 ]GRID grid.508379.0, ISNI 0000 0004 1756 6326, State Key Laboratory of Infectious Disease Prevention and Control, National Center for AIDS/STD Control and Prevention, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Chinese Center for Disease Control and Prevention, ; 155 Changbai Road, Changping District, Beijing, 102206 China
                [2 ]GRID grid.508395.2, Institute for AIDS/STD Control and Prevention, Yunnan Center for Disease Control and Prevention, ; No. 158, Dongsi Street, Xishan District, Kunming, 650022 Yunnan Province China
                [3 ]Dehong Dai and Jingpo Autonomous Prefecture Center for Disease Control and Prevention, Mangshi, 678400 China
                Article
                5794
                10.1186/s12879-021-05794-5
                7818912
                33478415
                24a7dec9-ddc6-40bf-9e49-e874076968ee
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 19 September 2020
                : 12 January 2021
                Funding
                Funded by: National Science and Technology Major Project
                Award ID: 2018ZX10101002-004-003
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81871694
                Award ID: 81773447
                Award ID: 81561128006
                Award Recipient :
                Funded by: National Major Project of the State Key Laboratory of Infectious Diseases Prevention and Control
                Award ID: 2011SKLID102
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                Infectious disease & Microbiology
                hiv,transmitted drug resistance,cd4 count,hotspots
                Infectious disease & Microbiology
                hiv, transmitted drug resistance, cd4 count, hotspots

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