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Hehe Z, Minna Z, Qin F, Tielin N, Yi F, Liping F, Fangfang C, Houlin T, Shi W, Maohe Y, Fan L. Application of molecular epidemiology in revealing HIV-1 transmission network and recombination patterns in Tianjin, China. J Med Virol 2024; 96:e29824. [PMID: 39072805 DOI: 10.1002/jmv.29824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 06/24/2024] [Accepted: 07/16/2024] [Indexed: 07/30/2024]
Abstract
Using a comprehensive molecular epidemiological approach, we characterized the transmission dynamics of HIV-1 among the MSM population in Tianjin, China. Our findings revealed that 38.56% (386/1001) of individuals clustered across 109 molecular transmission clusters (TCs), with MSM aged 50 and below being the group most commonly transmitting HIV-1. Among the identified TCs, CRF01_AE predominated, followed by CRF07_BC. Notably, CRF07_BC demonstrated a higher propensity for forming large clusters compared to CRF01_AE. Birth-death skyline analyses of the two largest clusters indicated that the HIV/AIDS transmission may be at a critical point, nearly all had Re approximately 1 by now. A retrospective analysis revealed that the rapid expansion of these large clusters was primarily driven by the introduction of viruses in 2021, highlighting the crucial importance of continuous molecular surveillance in identifying newly emerging high-risk transmission chains and adapting measures to address evolving epidemic dynamics. Furthermore, we detected the transmission of drug-resistant mutations (DRMs) within the TCs, particularly in the CRF07_BC clusters (K103N, Y181C, and K101E) and CRF01_AE clusters (P225H and K219R), emphasizing the importance of monitoring to support the continued efficacy of first-line therapies and pre-exposure prophylaxis (PrEP). Recombination analyses indicated that complex recombinant patterns, associated with increased amino acid variability, could confer adaptive traits to the viruses, potentially providing a competitive advantage in certain host populations or regions. Our study highlights the potential of integrating molecular epidemiological and phylodynamic approaches to inform targeted interventions.
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Affiliation(s)
- Zhao Hehe
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zheng Minna
- Department of HIV/AIDS and STDs Control and Prevention, Tianjin Provincial Center for Disease Control and Prevention, Tianjin, China
- Tianjin Key Laboratory of Pathogenic Microbiology of Infectious Disease, Tianjin, China
| | - Fan Qin
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Ning Tielin
- Department of HIV/AIDS and STDs Control and Prevention, Tianjin Provincial Center for Disease Control and Prevention, Tianjin, China
- Tianjin Key Laboratory of Pathogenic Microbiology of Infectious Disease, Tianjin, China
| | - Feng Yi
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- State Key Laboratory for Infectious Disease Prevention and Control, Beijing, China
| | - Fei Liping
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chen Fangfang
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Tang Houlin
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Wang Shi
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yu Maohe
- Department of HIV/AIDS and STDs Control and Prevention, Tianjin Provincial Center for Disease Control and Prevention, Tianjin, China
- Tianjin Key Laboratory of Pathogenic Microbiology of Infectious Disease, Tianjin, China
| | - Lyu Fan
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China
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Yan J, Zhang W, Luo H, Wang X, Ruan L. Development and validation of a scoring system for the prediction of HIV drug resistance in Hubei province, China. Front Cell Infect Microbiol 2023; 13:1147477. [PMID: 37234779 PMCID: PMC10208424 DOI: 10.3389/fcimb.2023.1147477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 04/28/2023] [Indexed: 05/28/2023] Open
Abstract
Objective The present study aimed to build and validate a new nomogram-based scoring system for the prediction of HIV drug resistance (HIVDR). Design and methods Totally 618 patients with HIV/AIDS were included. The predictive model was created using a retrospective set (N = 427) and internally validated with the remaining cases (N = 191). Multivariable logistic regression analysis was carried out to fit a model using candidate variables selected by Least absolute shrinkage and selection operator (LASSO) regression. The predictive model was first presented as a nomogram, then transformed into a simple and convenient scoring system and tested in the internal validation set. Results The developed scoring system consisted of age (2 points), duration of ART (5 points), treatment adherence (4 points), CD4 T cells (1 point) and HIV viral load (1 point). With a cutoff value of 7.5 points, the AUC, sensitivity, specificity, PLR and NLR values were 0.812, 82.13%, 64.55%, 2.32 and 0.28, respectively, in the training set. The novel scoring system exhibited a favorable diagnostic performance in both the training and validation sets. Conclusion The novel scoring system can be used for individualized prediction of HIVDR patients. It has satisfactory accuracy and good calibration, which is beneficial for clinical practice.
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Affiliation(s)
- Jisong Yan
- Department of Respiratory and Critical Care Medicine, Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Hubei Clinical Research Center for Infectious Diseases, Wuhan Research Center for Communicable Disease Diagnosis and Treatment, Chinese Academy of Medical Sciences, Joint Laboratory of Infectious Diseases and Health, Wuhan Institute of Virology and Wuhan Jinyintan Hospital, Chinese Academy of Sciences, Wuhan, Hubei, China
| | - Wenyuan Zhang
- Department of Infectious Diseases, Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Hubei Clinical Research Center for Infectious Diseases, Wuhan Research Center for Communicable Disease Diagnosis and Treatment, Chinese Academy of Medical Sciences, Joint Laboratory of Infectious Diseases and Health, Wuhan Institute of Virology and Wuhan Jinyintan Hospital, Chinese Academy of Sciences, Wuhan, Hubei, China
| | - Hong Luo
- Department of Respiratory and Critical Care Medicine, Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Hubei Clinical Research Center for Infectious Diseases, Wuhan Research Center for Communicable Disease Diagnosis and Treatment, Chinese Academy of Medical Sciences, Joint Laboratory of Infectious Diseases and Health, Wuhan Institute of Virology and Wuhan Jinyintan Hospital, Chinese Academy of Sciences, Wuhan, Hubei, China
| | - Xianguang Wang
- Department of Respiratory and Critical Care Medicine, Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Hubei Clinical Research Center for Infectious Diseases, Wuhan Research Center for Communicable Disease Diagnosis and Treatment, Chinese Academy of Medical Sciences, Joint Laboratory of Infectious Diseases and Health, Wuhan Institute of Virology and Wuhan Jinyintan Hospital, Chinese Academy of Sciences, Wuhan, Hubei, China
| | - Lianguo Ruan
- Department of Infectious Diseases, Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Hubei Clinical Research Center for Infectious Diseases, Wuhan Research Center for Communicable Disease Diagnosis and Treatment, Chinese Academy of Medical Sciences, Joint Laboratory of Infectious Diseases and Health, Wuhan Institute of Virology and Wuhan Jinyintan Hospital, Chinese Academy of Sciences, Wuhan, Hubei, China
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Beckwith CG, Min S, Manne A, Novitsky V, Howison M, Liu T, Kuo I, Kurth A, Bazerman L, Agopian A, Kantor R. HIV Drug Resistance and Transmission Networks Among a Justice-Involved Population at the Time of Community Reentry in Washington, D.C. AIDS Res Hum Retroviruses 2021; 37:903-912. [PMID: 33896212 PMCID: PMC8716515 DOI: 10.1089/aid.2020.0267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Justice-involved (JI) populations bear a disproportionate burden of HIV infection and are at risk of poor treatment outcomes. Drug resistance prevalence and emergence, and phylogenetic inference of transmission networks, understudied in vulnerable JI populations, can inform care and prevention interventions, particularly around the critical community reentry period. We analyzed banked blood specimens from CARE+ Corrections study participants in Washington, D.C. (DC) across three time points and conducted HIV drug resistance testing using next-generation sequencing (NGS) at 20% and 5% thresholds to identify prevalent and evolving resistance during community reentry. Phylogenetic analysis was used to identify molecular clusters within participants, and in an extended analysis between participants and publicly available DC sequences. HIV sequence data from 54 participants (99 specimens) were analyzed. The prevalence of transmitted drug resistance was 14% at both thresholds, and acquired drug resistance was 47% at 20%, and 57% at 5% NGS thresholds, respectively. The overall prevalence of drug resistance was 43% at 20%, and 52% at 5% NGS thresholds, respectively. Among 34 participants sampled longitudinally, 21%–35% accumulated 10–17 new resistance mutations during a mean 4.3 months. In phylogenetic analysis within the JI population, 11% were found in three molecular clusters. The extended phylogenetic analysis identified 46% of participants in 22 clusters, of which 21 also included publicly-available DC sequences, and one JI-only unique dyad. This is the first study to identify a high prevalence of HIV drug resistance and its accumulation in a JI population during community reentry and suggests phylogenetic integration of this population into the non-JI DC HIV community. These data support the need for new, effective, and timely interventions to improve HIV treatment during this vulnerable period, and for JI populations to be included in broader surveillance and prevention efforts.
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Affiliation(s)
- Curt G. Beckwith
- Division of Infectious Diseases, The Miriam Hospital, Providence, Rhode Island, USA
- The Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Sugi Min
- The Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Akarsh Manne
- Division of Infectious Diseases, The Miriam Hospital, Providence, Rhode Island, USA
- The Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Vladimir Novitsky
- Division of Infectious Diseases, The Miriam Hospital, Providence, Rhode Island, USA
- The Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Mark Howison
- Research Improving People's Lives, Providence, Rhode Island, USA
| | - Tao Liu
- Department of Biostatistics, Brown University School of Public Health, Providence, Rhode Island, USA
| | - Irene Kuo
- George Washington University Milken Institute School of Public Health, Washington, District of Columbia, USA
| | - Ann Kurth
- Yale University School of Nursing, Orange, Connecticut, USA
| | - Lauri Bazerman
- Division of Infectious Diseases, The Miriam Hospital, Providence, Rhode Island, USA
| | - Anya Agopian
- George Washington University Milken Institute School of Public Health, Washington, District of Columbia, USA
| | - Rami Kantor
- Division of Infectious Diseases, The Miriam Hospital, Providence, Rhode Island, USA
- The Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA
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Blassel L, Zhukova A, Villabona-Arenas CJ, Atkins KE, Hué S, Gascuel O. Drug resistance mutations in HIV: new bioinformatics approaches and challenges. Curr Opin Virol 2021; 51:56-64. [PMID: 34597873 DOI: 10.1016/j.coviro.2021.09.009] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 08/31/2021] [Accepted: 09/13/2021] [Indexed: 12/11/2022]
Abstract
Drug resistance mutations appear in HIV under treatment pressure. Resistant variants can be transmitted to treatment-naive individuals, which can lead to rapid virological failure and can limit treatment options. Consequently, quantifying the prevalence, emergence and transmission of drug resistance is critical to effectively treating patients and to shape health policies. We review recent bioinformatics developments and in particular describe: (1) the machine learning approaches intended to predict and explain the level of resistance of HIV variants from their sequence data; (2) the phylogenetic methods used to survey the emergence and dynamics of resistant HIV transmission clusters; (3) the impact of deep sequencing in studying within-host and between-host genetic diversity of HIV variants, notably regarding minority resistant variants.
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Affiliation(s)
- Luc Blassel
- Unité Bioinformatique Evolutive, Institut Pasteur, Paris, France; Sorbonne Université, Collège Doctoral, Paris, France
| | - Anna Zhukova
- Unité Bioinformatique Evolutive, Institut Pasteur, Paris, France; Hub de Bioinformatique et Biostatistique, Institut Pasteur, Paris, France
| | - Christian J Villabona-Arenas
- Centre for the Mathematical Modelling of Infectious Diseases (CMMID), London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Katherine E Atkins
- Centre for the Mathematical Modelling of Infectious Diseases (CMMID), London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Stéphane Hué
- Centre for the Mathematical Modelling of Infectious Diseases (CMMID), London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Olivier Gascuel
- Institut de Systématique, Evolution, Biodiversité (ISYEB, UMR 7205 - CNRS, Muséum National d'Histoire Naturelle, EPHE, SU, UA), Paris, France.
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5
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Using machine learning and big data to explore the drug resistance landscape in HIV. PLoS Comput Biol 2021; 17:e1008873. [PMID: 34437532 PMCID: PMC8425536 DOI: 10.1371/journal.pcbi.1008873] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 09/08/2021] [Accepted: 08/09/2021] [Indexed: 12/21/2022] Open
Abstract
Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive patients. However, we then consider each mutation individually and cannot hope to study interactions between several mutations. Here, we aim to leverage the ever-growing quantity of high-quality sequence data and machine learning methods to study such interactions (i.e. epistasis), as well as try to find new DRMs. We trained classifiers to discriminate between Reverse Transcriptase Inhibitor (RTI)-experienced and RTI-naive samples on a large HIV-1 reverse transcriptase (RT) sequence dataset from the UK (n ≈ 55, 000), using all observed mutations as binary representation features. To assess the robustness of our findings, our classifiers were evaluated on independent data sets, both from the UK and Africa. Important representation features for each classifier were then extracted as potential DRMs. To find novel DRMs, we repeated this process by removing either features or samples associated to known DRMs. When keeping all known resistance signal, we detected sufficiently prevalent known DRMs, thus validating the approach. When removing features corresponding to known DRMs, our classifiers retained some prediction accuracy, and six new mutations significantly associated with resistance were identified. These six mutations have a low genetic barrier, are correlated to known DRMs, and are spatially close to either the RT active site or the regulatory binding pocket. When removing both known DRM features and sequences containing at least one known DRM, our classifiers lose all prediction accuracy. These results likely indicate that all mutations directly conferring resistance have been found, and that our newly discovered DRMs are accessory or compensatory mutations. Moreover, apart from the accessory nature of the relationships we found, we did not find any significant signal of further, more subtle epistasis combining several mutations which individually do not seem to confer any resistance. Almost all drugs to treat HIV target the Reverse Transcriptase (RT) and Drug resistance mutations (DRMs) appear in HIV under treatment pressure. Resistant strains can be transmitted and limit treatment options at the population level. Classically, multiple statistical testing is used to find DRMs, by comparing virus sequences of treated and naive populations. However, with this method, each mutation is considered individually and we cannot hope to reveal any interaction (epistasis) between them. Here, we used machine learning to discover new DRMs and study potential epistasis effects. We applied this approach to a very large UK dataset comprising ≈ 55, 000 RT sequences. Results robustness was checked on different UK and African datasets. Six new mutations associated to resistance were found. All six have a low genetic barrier and show high correlations with known DRMs. Moreover, all these mutations are close to either the active site or the regulatory binding pocket of RT. Thus, they are good candidates for further wet experiments to establish their role in drug resistance. Importantly, our results indicate that epistasis seems to be limited to the classical scheme where primary DRMs confer resistance and associated mutations modulate the strength of the resistance and/or compensate for the fitness cost induced by DRMs.
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6
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Ishikawa SA, Zhukova A, Iwasaki W, Gascuel O. A Fast Likelihood Method to Reconstruct and Visualize Ancestral Scenarios. Mol Biol Evol 2019; 36:2069-2085. [PMID: 31127303 PMCID: PMC6735705 DOI: 10.1093/molbev/msz131] [Citation(s) in RCA: 117] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
The reconstruction of ancestral scenarios is widely used to study the evolution of characters along phylogenetic trees. One commonly uses the marginal posterior probabilities of the character states, or the joint reconstruction of the most likely scenario. However, marginal reconstructions provide users with state probabilities, which are difficult to interpret and visualize, whereas joint reconstructions select a unique state for every tree node and thus do not reflect the uncertainty of inferences. We propose a simple and fast approach, which is in between these two extremes. We use decision-theory concepts (namely, the Brier score) to associate each node in the tree to a set of likely states. A unique state is predicted in tree regions with low uncertainty, whereas several states are predicted in uncertain regions, typically around the tree root. To visualize the results, we cluster the neighboring nodes associated with the same states and use graph visualization tools. The method is implemented in the PastML program and web server. The results on simulated data demonstrate the accuracy and robustness of the approach. PastML was applied to the phylogeography of Dengue serotype 2 (DENV2), and the evolution of drug resistances in a large HIV data set. These analyses took a few minutes and provided convincing results. PastML retrieved the main transmission routes of human DENV2 and showed the uncertainty of the human-sylvatic DENV2 geographic origin. With HIV, the results show that resistance mutations mostly emerge independently under treatment pressure, but resistance clusters are found, corresponding to transmissions among untreated patients.
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Affiliation(s)
- Sohta A Ishikawa
- Unité Bioinformatique Evolutive, Institut Pasteur, C3BI USR 3756 IP & CNRS, Paris, France
- Department of Biological Sciences, The University of Tokyo, Tokyo, Japan
- Evolutionary Genomics of RNA Viruses, Virology Department, Institut Pasteur, Paris, France
| | - Anna Zhukova
- Unité Bioinformatique Evolutive, Institut Pasteur, C3BI USR 3756 IP & CNRS, Paris, France
| | - Wataru Iwasaki
- Department of Biological Sciences, The University of Tokyo, Tokyo, Japan
| | - Olivier Gascuel
- Unité Bioinformatique Evolutive, Institut Pasteur, C3BI USR 3756 IP & CNRS, Paris, France
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Chang JM, Floden EW, Herrero J, Gascuel O, Di Tommaso P, Notredame C. Incorporating alignment uncertainty into Felsenstein's phylogenetic bootstrap to improve its reliability. Bioinformatics 2019; 37:1506-1514. [PMID: 30726875 PMCID: PMC8275982 DOI: 10.1093/bioinformatics/btz082] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 12/12/2018] [Accepted: 02/05/2019] [Indexed: 12/30/2022] Open
Abstract
Motivation Most evolutionary analyses are based on pre-estimated multiple sequence alignment. Wong et al. established the existence of an uncertainty induced by multiple sequence alignment when reconstructing phylogenies. They were able to show that in many cases different aligners produce different phylogenies, with no simple objective criterion sufficient to distinguish among these alternatives. Results We demonstrate that incorporating MSA induced uncertainty into bootstrap sampling can significantly increase correlation between clade correctness and its corresponding bootstrap value. Our procedure involves concatenating several alternative multiple sequence alignments of the same sequences, produced using different commonly used aligners. We then draw bootstrap replicates while favoring columns of the more unique aligner among the concatenated aligners. We named this concatenation and bootstrapping method, Weighted Partial Super Bootstrap (wpSBOOT). We show on three simulated datasets of 16, 32 and 64 tips that our method improves the predictive power of bootstrap values. We also used as a benchmark an empirical collection of 853 1-to-1 orthologous genes from seven yeast species and found wpSBOOT to significantly improve discrimination capacity between topologically correct and incorrect trees. Bootstrap values of wpSBOOT are comparable to similar readouts estimated using a single method. However, for reduced trees by 50% and 95% bootstrap thresholds, wpSBOOT comes out the lowest Type I error (less FP). Availability The automated generation of replicates has been implemented in the T-Coffee package, which is available as open source freeware available from www.tcoffee.org. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jia-Ming Chang
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Evan W Floden
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Javier Herrero
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Olivier Gascuel
- Unité Bioinformatique Evolutive, Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI)-USR 3756 CNRS and Institut Pasteur, Paris, France
| | - Paolo Di Tommaso
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Cedric Notredame
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain
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8
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Grossman Z, Rico SV, Cone K, Shao W, Rehm C, Jones S, Bozzi G, Dean S, Dewar R, Rehman T, Purdy J, Hadigan C, Pau AK, Maldarelli F. Early Presence of HIV-1 Subtype C in Washington, D.C. AIDS Res Hum Retroviruses 2018; 34:680-684. [PMID: 29936863 DOI: 10.1089/aid.2018.0041] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
The presence of non-B HIV subtypes in the USA has been documented during the epidemic, although the timing of early introductions of different subtypes remains uncertain. Subtype C, the most common HIV variant worldwide, was first reported in the USA in 1996-97, after subtype C had expanded greatly in sub-Saharan Africa. In this study, we report a patient with subtype C infection acquired by mother-to-child transmission, born in the USA in 1990 to a Washington, D.C. resident who never traveled outside the USA, demonstrating that subtype C was present in the USA much earlier. Comparative analysis of the sequence from this patient and subtype C sequences in the USA and elsewhere suggest multiple independent introductions of this subtype into the USA have taken place, many of which are traced to sub-Saharan or East Africa. These data indicate subtype C HIV was already present in the USA years earlier than previously reported, and during the early period of subtype C expansion.
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Affiliation(s)
- Zehava Grossman
- HIV Dynamics and Replication Program, NCI, NIH, Frederick, Maryland
- Sackler Faculty of Medicine, School of Public Health, Tel Aviv University, Tel Aviv, Israel
| | - Sheryl-vi Rico
- Intramural Clinical Management & Operations Branch, NIAID, NIH, Bethesda, Maryland
| | - Katherine Cone
- Intramural Clinical Management & Operations Branch, NIAID, NIH, Bethesda, Maryland
- Department of Social Work, NIH Clinical Center, Bethesda, Maryland
| | - Wei Shao
- Leidos Biomedical Research, Inc., Frederick, National Laboratory for Cancer Research, Frederick, Maryland
| | - Catherine Rehm
- Intramural Clinical Management & Operations Branch, NIAID, NIH, Bethesda, Maryland
| | - Sara Jones
- Clinical Research Directorate/Clinical Monitoring Research Program, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, Maryland
| | - Giorgio Bozzi
- HIV Dynamics and Replication Program, NCI, NIH, Frederick, Maryland
| | - Sophie Dean
- HIV Dynamics and Replication Program, NCI, NIH, Frederick, Maryland
| | - Robin Dewar
- Leidos Biomedical Research, Inc., Frederick, National Laboratory for Cancer Research, Frederick, Maryland
| | - Tauseef Rehman
- Leidos Biomedical Research, Inc., Frederick, National Laboratory for Cancer Research, Frederick, Maryland
| | - Julia Purdy
- Critical Care Medicine Department, NIH Clinical Center, NIH, Bethesda, Maryland
| | - Colleen Hadigan
- Intramural Clinical Management & Operations Branch, NIAID, NIH, Bethesda, Maryland
| | - Alice K. Pau
- Intramural Clinical Management & Operations Branch, NIAID, NIH, Bethesda, Maryland
| | - Frank Maldarelli
- HIV Dynamics and Replication Program, NCI, NIH, Frederick, Maryland
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