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Nascimento FF, Mehta SR, Little SJ, Volz EM. Assessing transmission attribution risk from simulated sequencing data in HIV molecular epidemiology. AIDS 2024; 38:865-873. [PMID: 38126363 PMCID: PMC10994139 DOI: 10.1097/qad.0000000000003820] [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: 03/07/2023] [Revised: 12/08/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND HIV molecular epidemiology (ME) is the analysis of sequence data together with individual-level clinical, demographic, and behavioral data to understand HIV epidemiology. The use of ME has raised concerns regarding identification of the putative source in direct transmission events. This could result in harm ranging from stigma to criminal prosecution in some jurisdictions. Here we assessed the risks of ME using simulated HIV genetic sequencing data. METHODS We simulated social networks of men-who-have-sex-with-men, calibrating the simulations to data from San Diego. We used these networks to simulate consensus and next-generation sequence (NGS) data to evaluate the risks of identifying direct transmissions using different HIV sequence lengths, and population sampling depths. To identify the source of transmissions, we calculated infector probability and used phyloscanner software for the analysis of consensus and NGS data, respectively. RESULTS Consensus sequence analyses showed that the risk of correctly inferring the source (direct transmission) within identified transmission pairs was very small and independent of sampling depth. Alternatively, NGS analyses showed that identification of the source of a transmission was very accurate, but only for 6.5% of inferred pairs. False positive transmissions were also observed, where one or more unobserved intermediaries were present when compared to the true network. CONCLUSION Source attribution using consensus sequences rarely infers direct transmission pairs with high confidence but is still useful for population studies. In contrast, source attribution using NGS data was much more accurate in identifying direct transmission pairs, but for only a small percentage of transmission pairs analyzed.
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Affiliation(s)
- Fabrícia F. Nascimento
- MRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Sanjay R. Mehta
- Division of Infectious Diseases, University of California San Diego, San Diego, CA, USA
| | - Susan J. Little
- Division of Infectious Diseases, University of California San Diego, San Diego, CA, USA
| | - Erik M. Volz
- MRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK
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Tordoff DM, Minalga B, Trejo A, Shook A, Kerani RP, Herbeck JT. Lessons learned from community engagement regarding phylodynamic research with molecular HIV surveillance data. J Int AIDS Soc 2023; 26 Suppl 1:e26111. [PMID: 37408448 PMCID: PMC10323319 DOI: 10.1002/jia2.26111] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 05/09/2023] [Indexed: 07/07/2023] Open
Abstract
INTRODUCTION The widespread implementation of molecular HIV surveillance (MHS) has resulted in an increased discussion about the ethical, human rights and public health implications of MHS. We narrate our process of pausing our research that uses data collected through MHS in response to these growing concerns and summarize the key lessons we learned through conversations with community members. METHODS The original study aimed to describe HIV transmission patterns by age and race/ethnicity among men who have sex with men in King County, Washington, by applying probabilistic phylodynamic modelling methods to HIV-1 pol gene sequences collected through MHS. In September 2020, we paused the publication of this research to conduct community engagement: we held two public-facing online presentations, met with a national community coalition that included representatives of networks of people living with HIV, and invited two members of this coalition to provide feedback on our manuscript. During each of these meetings, we shared a brief presentation of our methods and findings and explicitly solicited feedback on the perceived public health benefit and potential harm of our analyses and results. RESULTS Some community concerns about MHS in public health practice also apply to research using MHS data, namely those related to informed consent, inference of transmission directionality and criminalization. Other critiques were specific to our research study and included feedback about the use of phylogenetic analyses to study assortativity by race/ethnicity and the importance of considering the broader context of stigma and structural racism. We ultimately decided the potential harms of publishing our study-perpetuating racialized stigma about men who have sex with men and eroding the trust between phylogenetics researchers and communities of people living with HIV-outweighed the potential benefits. CONCLUSIONS HIV phylogenetics research using data collected through MHS data is a powerful scientific technology with the potential to benefit and harm communities of people living with HIV. Addressing criminalization and including people living with HIV in decision-making processes have the potential to meaningfully address community concerns and strengthen the ethical justification for using MHS data in both research and public health practice. We close with specific opportunities for action and advocacy by researchers.
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Affiliation(s)
- Diana M. Tordoff
- Department of EpidemiologyUniversity of WashingtonSeattleWashingtonUSA
| | - Brian Minalga
- Fred Hutch, Office of HIV/AIDS Network CoordinationSeattleWashingtonUSA
| | - Alfredo Trejo
- Department of Political ScienceUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Alic Shook
- Seattle University, College of NursingSeattleWashingtonUSA
- Seattle Children's Center for Pediatric Nursing ResearchSeattleWashingtonUSA
| | - Roxanne P. Kerani
- Department of EpidemiologyUniversity of WashingtonSeattleWashingtonUSA
- Public Health – Seattle & King County, HIV/STD ProgramSeattleWashingtonUSA
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Joshua T. Herbeck
- Department of Global HealthUniversity of WashingtonSeattleWashingtonUSA
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3
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Rich SN, Richards V, Mavian C, Rife Magalis B, Grubaugh N, Rasmussen SA, Dellicour S, Vrancken B, Carrington C, Fisk-Hoffman R, Danso-Odei D, Chacreton D, Shapiro J, Seraphin MN, Hepp C, Black A, Dennis A, Trovão NS, Vandamme AM, Rasmussen A, Lauzardo M, Dean N, Salemi M, Prosperi M. Application of Phylodynamic Tools to Inform the Public Health Response to COVID-19: Qualitative Analysis of Expert Opinions. JMIR Form Res 2023; 7:e39409. [PMID: 36848460 PMCID: PMC10131930 DOI: 10.2196/39409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 11/26/2022] [Accepted: 12/27/2022] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND In the wake of the SARS-CoV-2 pandemic, scientists have scrambled to collect and analyze SARS-CoV-2 genomic data to inform public health responses to COVID-19 in real time. Open source phylogenetic and data visualization platforms for monitoring SARS-CoV-2 genomic epidemiology have rapidly gained popularity for their ability to illuminate spatial-temporal transmission patterns worldwide. However, the utility of such tools to inform public health decision-making for COVID-19 in real time remains to be explored. OBJECTIVE The aim of this study is to convene experts in public health, infectious diseases, virology, and bioinformatics-many of whom were actively engaged in the COVID-19 response-to discuss and report on the application of phylodynamic tools to inform pandemic responses. METHODS In total, 4 focus groups (FGs) occurred between June 2020 and June 2021, covering both the pre- and postvariant strain emergence and vaccination eras of the ongoing COVID-19 crisis. Participants included national and international academic and government researchers, clinicians, public health practitioners, and other stakeholders recruited through purposive and convenience sampling by the study team. Open-ended questions were developed to prompt discussion. FGs I and II concentrated on phylodynamics for the public health practitioner, while FGs III and IV discussed the methodological nuances of phylodynamic inference. Two FGs per topic area to increase data saturation. An iterative, thematic qualitative framework was used for data analysis. RESULTS We invited 41 experts to the FGs, and 23 (56%) agreed to participate. Across all the FG sessions, 15 (65%) of the participants were female, 17 (74%) were White, and 5 (22%) were Black. Participants were described as molecular epidemiologists (MEs; n=9, 39%), clinician-researchers (n=3, 13%), infectious disease experts (IDs; n=4, 17%), and public health professionals at the local (PHs; n=4, 17%), state (n=2, 9%), and federal (n=1, 4%) levels. They represented multiple countries in Europe, the United States, and the Caribbean. Nine major themes arose from the discussions: (1) translational/implementation science, (2) precision public health, (3) fundamental unknowns, (4) proper scientific communication, (5) methods of epidemiological investigation, (6) sampling bias, (7) interoperability standards, (8) academic/public health partnerships, and (9) resources. Collectively, participants felt that successful uptake of phylodynamic tools to inform the public health response relies on the strength of academic and public health partnerships. They called for interoperability standards in sequence data sharing, urged careful reporting to prevent misinterpretations, imagined that public health responses could be tailored to specific variants, and cited resource issues that would need to be addressed by policy makers in future outbreaks. CONCLUSIONS This study is the first to detail the viewpoints of public health practitioners and molecular epidemiology experts on the use of viral genomic data to inform the response to the COVID-19 pandemic. The data gathered during this study provide important information from experts to help streamline the functionality and use of phylodynamic tools for pandemic responses.
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Affiliation(s)
- Shannan N Rich
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
- Department of Epidemiology, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Veronica Richards
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
- Department of Epidemiology, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Carla Mavian
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Brittany Rife Magalis
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Nathan Grubaugh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, United States
| | - Sonja A Rasmussen
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
- Department of Epidemiology, College of Medicine, University of Florida, Gainesville, FL, United States
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Simon Dellicour
- Spatial Epidemiology Lab, Université Libre de Bruxelles, Bruxelles, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Bruxelles, Belgium
| | - Bram Vrancken
- Spatial Epidemiology Lab, Université Libre de Bruxelles, Bruxelles, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Bruxelles, Belgium
| | - Christine Carrington
- Department of Preclinical Sciences, University of the West Indies, St Augustine, Trinidad and Tobago
| | - Rebecca Fisk-Hoffman
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
- Department of Epidemiology, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Demi Danso-Odei
- Florida Department of Health in Alachua County, Gainesville, FL, United States
| | - Daniel Chacreton
- Division of Disease Control and Health Protection, Florida Department of Health, Tallahassee, FL, United States
| | - Jerne Shapiro
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
- Department of Epidemiology, College of Medicine, University of Florida, Gainesville, FL, United States
- Florida Department of Health in Alachua County, Gainesville, FL, United States
| | - Marie Nancy Seraphin
- Division of Infectious Diseases and Global Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Crystal Hepp
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, United States
- School of Informatics, Computing, and Cyber Systems, College of Engineering, Informatics, and Applied Sciences, Northern Arizona University, Flagstaff, AZ, United States
- Pathogen and Microbiome Division, Translational Genomics Research Institute, Flagstaff, AZ, United States
| | - Allison Black
- Chan Zuckerberg Initiative, Redwood City, CA, United States
| | - Ann Dennis
- Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Nídia Sequeira Trovão
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, United States
| | - Anne-Mieke Vandamme
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
- Center for Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Angela Rasmussen
- Vaccine and Infectious Disease Organization, University of Saskatchewan, Saskatoon, SK, Canada
| | - Michael Lauzardo
- Division of Infectious Diseases and Global Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Natalie Dean
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
- Department of Biostatistics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Marco Salemi
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Mattia Prosperi
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
- Department of Epidemiology, College of Medicine, University of Florida, Gainesville, FL, United States
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Characterization of HIV-1 Transmission Clusters Inferred from the Brazilian Nationwide Genotyping Service Database. Viruses 2022; 14:v14122768. [PMID: 36560771 PMCID: PMC9783618 DOI: 10.3390/v14122768] [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: 10/05/2022] [Revised: 11/23/2022] [Accepted: 11/28/2022] [Indexed: 12/14/2022] Open
Abstract
The study of HIV-1 transmission networks inferred from viral genetic data can be used to clarify important factors about the dynamics of HIV-1 transmission, such as network growth rate and demographic composition. In Brazil, HIV transmission has been stable since the early 2000s and the study of transmission clusters can provide valuable data to understand the drivers of virus spread. In this work, we analyzed a nation-wide database of approximately 53,000 HIV-1 nucleotide pol sequences sampled from genotyped patients from 2008-2017. Phylogenetic trees were reconstructed for the HIV-1 subtypes B, C and F1 in Brazil and transmission clusters were inferred by applying genetic distances thresholds of 1.5%, 3.0% and 4.5%, as well as high (>0.9) cluster statistical support. An odds ratio test revealed that young men (15-24 years) and individuals with more years of education presented higher odds to cluster. The assortativity coefficient revealed that individuals with similar demographic features tended to cluster together, with emphasis on features, such as place of residence and age. We also observed that assortativity weakens as the genetic distance threshold increases. Our results indicate that the phylogenetic clusters identified here are likely representative of the contact networks that shape HIV transmission, and this is a valuable tool even in sites with low sampling density, such as Brazil.
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Optimized phylogenetic clustering of HIV-1 sequence data for public health applications. PLoS Comput Biol 2022; 18:e1010745. [PMID: 36449514 PMCID: PMC9744331 DOI: 10.1371/journal.pcbi.1010745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 12/12/2022] [Accepted: 11/17/2022] [Indexed: 12/02/2022] Open
Abstract
Clusters of genetically similar infections suggest rapid transmission and may indicate priorities for public health action or reveal underlying epidemiological processes. However, clusters often require user-defined thresholds and are sensitive to non-epidemiological factors, such as non-random sampling. Consequently the ideal threshold for public health applications varies substantially across settings. Here, we show a method which selects optimal thresholds for phylogenetic (subset tree) clustering based on population. We evaluated this method on HIV-1 pol datasets (n = 14, 221 sequences) from four sites in USA (Tennessee, Washington), Canada (Northern Alberta) and China (Beijing). Clusters were defined by tips descending from an ancestral node (with a minimum bootstrap support of 95%) through a series of branches, each with a length below a given threshold. Next, we used pplacer to graft new cases to the fixed tree by maximum likelihood. We evaluated the effect of varying branch-length thresholds on cluster growth as a count outcome by fitting two Poisson regression models: a null model that predicts growth from cluster size, and an alternative model that includes mean collection date as an additional covariate. The alternative model was favoured by AIC across most thresholds, with optimal (greatest difference in AIC) thresholds ranging 0.007-0.013 across sites. The range of optimal thresholds was more variable when re-sampling 80% of the data by location (IQR 0.008 - 0.016, n = 100 replicates). Our results use prospective phylogenetic cluster growth and suggest that there is more variation in effective thresholds for public health than those typically used in clustering studies.
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Ragonnet-Cronin M, Hayford C, D’Aquila R, Ma F, Ward C, Benbow N, Wertheim JO. Forecasting HIV-1 Genetic Cluster Growth in Illinois,United States. J Acquir Immune Defic Syndr 2022; 89:49-55. [PMID: 34878434 PMCID: PMC8667185 DOI: 10.1097/qai.0000000000002821] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 09/08/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND HIV intervention activities directed toward both those most likely to transmit and their HIV-negative partners have the potential to substantially disrupt HIV transmission. Using HIV sequence data to construct molecular transmission clusters can reveal individuals whose viruses are connected. The utility of various cluster prioritization schemes measuring cluster growth have been demonstrated using surveillance data in New York City and across the United States, by the Centers for Disease Control and Prevention (CDC). METHODS We examined clustering and cluster growth prioritization schemes using Illinois HIV sequence data that include cases from Chicago, a large urban center with high HIV prevalence, to compare their ability to predict future cluster growth. RESULTS We found that past cluster growth was a far better predictor of future cluster growth than cluster membership alone but found no substantive difference between the schemes used by CDC and the relative cluster growth scheme previously used in New York City (NYC). Focusing on individuals selected simultaneously by both the CDC and the NYC schemes did not provide additional improvements. CONCLUSION Growth-based prioritization schemes can easily be automated in HIV surveillance tools and can be used by health departments to identify and respond to clusters where HIV transmission may be actively occurring.
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Affiliation(s)
- Manon Ragonnet-Cronin
- Department of Medicine, University of California San Diego, San Diego, USA
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Christina Hayford
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Richard D’Aquila
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Fangchao Ma
- Illinois Department of Public Health, Chicago, USA
| | - Cheryl Ward
- Illinois Department of Public Health, Chicago, USA
| | - Nanette Benbow
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Joel O. Wertheim
- Department of Medicine, University of California San Diego, San Diego, USA
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Ragonnet-Cronin M, Benbow N, Hayford C, Poortinga K, Ma F, Forgione LA, Sheng Z, Hu YW, Torian LV, Wertheim JO. Sorting by Race/Ethnicity Across HIV Genetic Transmission Networks in Three Major Metropolitan Areas in the United States. AIDS Res Hum Retroviruses 2021; 37:784-792. [PMID: 33349132 PMCID: PMC8573809 DOI: 10.1089/aid.2020.0145] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
An important component underlying the disparity in HIV risk between race/ethnic groups is the preferential transmission between individuals in the same group. We sought to quantify transmission between different race/ethnicity groups and measure racial assortativity in HIV transmission networks in major metropolitan areas in the United States. We reconstructed HIV molecular transmission networks from viral sequences collected as part of HIV surveillance in New York City, Los Angeles County, and Cook County, Illinois. We calculated assortativity (the tendency for individuals to link to others with similar characteristics) across the network for three candidate characteristics: transmission risk, age at diagnosis, and race/ethnicity. We then compared assortativity between race/ethnicity groups. Finally, for each race/ethnicity pair, we performed network permutations to test whether the number of links observed differed from that expected if individuals were sorting at random. Transmission networks in all three jurisdictions were more assortative by race/ethnicity than by transmission risk or age at diagnosis. Despite the different race/ethnicity proportions in each metropolitan area and lower proportions of clustering among African Americans than other race/ethnicities, African Americans were the group most likely to have transmission partners of the same race/ethnicity. This high level of assortativity should be considered in the design of HIV intervention and prevention strategies.
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Affiliation(s)
- Manon Ragonnet-Cronin
- Department of Medicine, University of California, San Diego, California, USA
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Nanette Benbow
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, Illinois, USA
| | - Christina Hayford
- Third Coast Center for AIDS Research, Northwestern University, Chicago, Illinois, USA
| | - Kathleen Poortinga
- Division of HIV and STD Programs, Los Angeles County Department of Public Health, Los Angeles, California, USA
| | - Fangchao Ma
- HIV/AIDS Section, Illinois Department of Public Health, Chicago, Illinois, USA
| | - Lisa A. Forgione
- HIV Epidemiology and Field Services Program, Bureau of HIV Prevention and Control, New York City Department of Health and Mental Hygiene, New York City, New York, USA
| | - Zhijuan Sheng
- Division of HIV and STD Programs, Los Angeles County Department of Public Health, Los Angeles, California, USA
| | - Yunyin W. Hu
- Division of HIV and STD Programs, Los Angeles County Department of Public Health, Los Angeles, California, USA
| | - Lucia V. Torian
- HIV Epidemiology and Field Services Program, Bureau of HIV Prevention and Control, New York City Department of Health and Mental Hygiene, New York City, New York, USA
| | - Joel O. Wertheim
- Department of Medicine, University of California, San Diego, California, USA
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Abstract
OBJECTIVE We investigated the duration of HIV transmission clusters. DESIGN Fifty-four individuals newly infected at enrollment in the ALIVE cohort were included, all of whom had sequences at an intake visit (T1) and from a second (T2) and/or a third (T3) follow-up visit, median 2.9 and 5.4 years later, respectively. METHODS Sequences were generated using the 454 DNA sequencing platform for portions of HIV pol and env (HXB2 positions 2717-3230; 7941-8264). Genetic distances were calculated using tn93 and sequences were clustered over a range of thresholds (1--5%) using HIV-TRACE. Analyses were performed separately for individuals with pol sequences for T1 + T2 (n = 40, 'Set 1') and T1 + T3 (n = 25; 'Set 2'), and env sequences for T1 + T2 (n = 47, 'Set 1'), and T1 + T3 (n = 30; 'Set 2'). RESULTS For pol, with one exception, a single cluster contained more than 75% of samples at all thresholds, and cluster composition was at least 90% concordant between time points/thresholds. For env, two major clusters (A and B) were observed at T1 and T2/T3, although cluster composition concordance between time points/thresholds was low (<60%) at lower thresholds for both sets 1 and 2. In addition, several individuals were included in clusters at T2/T3, although not at T1. CONCLUSION Caution should be used in applying a single threshold in population studies where seroconversion dates are unknown. However, the retention of some clusters even after 5 + years is evidence for the robustness of the clustering approach in general.
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Wertheim JO, Panneer N, France AM, Saduvala N, Oster AM. Incident infection in high-priority HIV molecular transmission clusters in the United States. AIDS 2020; 34:1187-1193. [PMID: 32287065 PMCID: PMC8580737 DOI: 10.1097/qad.0000000000002531] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To identify correlates of incident HIV infection in rapidly growing HIV molecular clusters. DESIGN Phylogenetic analysis of HIV public health surveillance data. METHODS High-priority HIV genetic transmission clusters with evidence of rapid growth in 2012 (i.e. clusters with a pairwise genetic distance ≤0.005 substitutions/site and at least three cases diagnosed in 2012) were identified using HIV-TRACE. Then, we investigated cluster growth, defined as HIV cases diagnosed in the following 5 years that were genetically linked to these clusters. For clusters that grew during the follow-up period, Bayesian molecular clock phylogenetic inference was performed to identify clusters with evidence of incident HIV infection (as opposed to diagnosis of previously infected cases) during this follow-up period. RESULTS Of the 116 rapidly growing clusters identified, 73 (63%) had phylogenetic evidence for an incident HIV case during the 5-year follow-up period. Correlates of an incident HIV case arising in clusters included a greater number of diagnosed but virally unsuppressed cases in 2012, a greater number of inferred undiagnosed cases in the cluster in 2012, and a younger time of most recent common ancestor for the cluster. CONCLUSION These findings suggest that incident infections in rapidly growing clusters originate equally from diagnosed but unsuppressed cases and undiagnosed infections. These results highlight the importance of promoting retention in care and viral suppression as well as partner notification and other case-finding activities when investigating and intervening on high-priority molecular transmission clusters.
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Affiliation(s)
- Joel O. Wertheim
- Department of Medicine, University of California, San Diego, CA, USA
| | - Nivedha Panneer
- Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Anne Marie France
- Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | - Alexandra M. Oster
- Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA
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10
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Understanding disclosed and cryptic HIV transmission risk via genetic analysis: what are we missing and when does it matter? Curr Opin HIV AIDS 2020; 14:205-212. [PMID: 30946142 DOI: 10.1097/coh.0000000000000537] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
PURPOSE OF REVIEW To discuss the recent HIV phylogenetic analyses examining HIV transmission patterns among and within risk groups. RECENT FINDINGS Phylodynamic analysis has recently been applied to multiple HIV outbreaks among people who inject drugs to determine whether HIV transmission is ongoing. Large-scale analyses of datasets of HIV sequences collected for drug-resistance testing provide population-level insights into transmission patterns. One focus across world regions has been to investigate whether age-disparity is a driver of HIV transmission. In sub-Saharan Africa, researchers have examined transmission between heterosexuals and MSM and between high prevalence fishing communities and inland communities. In the US and the UK, cryptic risk groups such as nondisclosed MSM and the partners of transgender women are increasingly being uncovered based on their position in densely sampled molecular transmission networks. SUMMARY Analysis of HIV genetic sequence can resolve viral transmission patterns between risk groups at unprecedented scales and levels of detail. Future research should focus on understanding the effect of missing data on inferences and the biases of different methods. Uncovering groups and patterns obscured from traditional epidemiolocal analyses is exciting but should not compromise the privacy of the groups in question.
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Wertheim JO, Oster AM, Switzer WM, Zhang C, Panneer N, Campbell E, Saduvala N, Johnson JA, Heneine W. Natural selection favoring more transmissible HIV detected in United States molecular transmission network. Nat Commun 2019; 10:5788. [PMID: 31857582 PMCID: PMC6923435 DOI: 10.1038/s41467-019-13723-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 11/22/2019] [Indexed: 01/10/2023] Open
Abstract
HIV molecular epidemiology can identify clusters of individuals with elevated rates of HIV transmission. These variable transmission rates are primarily driven by host risk behavior; however, the effect of viral traits on variable transmission rates is poorly understood. Viral load, the concentration of HIV in blood, is a heritable viral trait that influences HIV infectiousness and disease progression. Here, we reconstruct HIV genetic transmission clusters using data from the United States National HIV Surveillance System and report that viruses in clusters, inferred to be frequently transmitted, have higher viral loads at diagnosis. Further, viral load is higher in people in larger clusters and with increased network connectivity, suggesting that HIV in the United States is experiencing natural selection to be more infectious and virulent. We also observe a concurrent increase in viral load at diagnosis over the last decade. This evolutionary trajectory may be slowed by prevention strategies prioritized toward rapidly growing transmission clusters.
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Affiliation(s)
- Joel O Wertheim
- Department of Medicine, University of California, San Diego, CA, USA.
| | - Alexandra M Oster
- Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - William M Switzer
- Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Chenhua Zhang
- ICF International, Atlanta, GA, USA
- SciMetrika LLC, Atlanta, GA, USA
| | - Nivedha Panneer
- Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Ellsworth Campbell
- Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | - Jeffrey A Johnson
- Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Walid Heneine
- Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA
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12
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Han AX, Parker E, Maurer-Stroh S, Russell CA. Inferring putative transmission clusters with Phydelity. Virus Evol 2019; 5:vez039. [PMID: 31616568 PMCID: PMC6785678 DOI: 10.1093/ve/vez039] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Current phylogenetic clustering approaches for identifying pathogen transmission clusters are limited by their dependency on arbitrarily defined genetic distance thresholds for within-cluster divergence. Incomplete knowledge of a pathogen’s underlying dynamics often reduces the choice of distance threshold to an exploratory, ad hoc exercise that is difficult to standardise across studies. Phydelity is a new tool for the identification of transmission clusters in pathogen phylogenies. It identifies groups of sequences that are more closely related than the ensemble distribution of the phylogeny under a statistically principled and phylogeny-informed framework, without the introduction of arbitrary distance thresholds. Relative to other distance threshold- and model-based methods, Phydelity outputs clusters with higher purity and lower probability of misclassification in simulated phylogenies. Applying Phydelity to empirical datasets of hepatitis B and C virus infections showed that Phydelity identified clusters with better correspondence to individuals that are more likely to be linked by transmission events relative to other widely used non-parametric phylogenetic clustering methods without the need for parameter calibration. Phydelity is generalisable to any pathogen and can be used to identify putative direct transmission events. Phydelity is freely available at https://github.com/alvinxhan/Phydelity.
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Affiliation(s)
- Alvin X Han
- Protein Sequence Analysis Group, Bioinformatics Institute, Agency for Science, Technology and Research (ASTAR), 30 Biopolis Street, 138671 Singapore.,NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore (NUS), 21 Lower Kent Ridge, 119077 Singapore.,Laboratory of Applied Evolutionary Biology, Department of Medical Microbiology, Academic Medical Centre, Meibergdreef 9, 1105 AZ Amsterdam-Zuidoost, The Netherlands
| | - Edyth Parker
- Laboratory of Applied Evolutionary Biology, Department of Medical Microbiology, Academic Medical Centre, Meibergdreef 9, 1105 AZ Amsterdam-Zuidoost, The Netherlands.,Department of Veterinary Medicine, University of Cambridge, Madingley Rd, Cambridge CB3 0ES, UK
| | - Sebastian Maurer-Stroh
- Protein Sequence Analysis Group, Bioinformatics Institute, Agency for Science, Technology and Research (ASTAR), 30 Biopolis Street, 138671 Singapore.,Department of Biological Sciences, National University of Singapore, 16 Science Drive 4, 117558 Singapore
| | - Colin A Russell
- Laboratory of Applied Evolutionary Biology, Department of Medical Microbiology, Academic Medical Centre, Meibergdreef 9, 1105 AZ Amsterdam-Zuidoost, The Netherlands
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13
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Rose R, Hall M, Redd AD, Lamers S, Barbier AE, Porcella SF, Hudelson SE, Piwowar-Manning E, McCauley M, Gamble T, Wilson EA, Kumwenda J, Hosseinipour MC, Hakim JG, Kumarasamy N, Chariyalertsak S, Pilotto JH, Grinsztejn B, Mills LA, Makhema J, Santos BR, Chen YQ, Quinn TC, Fraser C, Cohen MS, Eshleman SH, Laeyendecker O. Phylogenetic Methods Inconsistently Predict the Direction of HIV Transmission Among Heterosexual Pairs in the HPTN 052 Cohort. J Infect Dis 2019; 220:1406-1413. [PMID: 30590741 PMCID: PMC6761953 DOI: 10.1093/infdis/jiy734] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 12/21/2018] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND We evaluated use of phylogenetic methods to predict the direction of human immunodeficiency virus (HIV) transmission. METHODS For 33 pairs of HIV-infected patients (hereafter, "index patients") and their partners who acquired genetically linked HIV infection during the study, samples were collected from partners and index patients close to the time when the partner seroconverted (hereafter, "SC samples"); for 31 pairs, samples collected from the index patient at an earlier time point (hereafter, "early index samples") were also available. Phylogenies were inferred using env next-generation sequences (1 tree per pair/subtype). The direction of transmission (DoT) predicted from each tree was classified as correct or incorrect on the basis of which sequences (those from the index patient or the partner) were closest to the root. DoT was also assessed using maximum parsimony to infer ancestral node states for 100 bootstrap trees. RESULTS DoT was predicted correctly for both single-pair and subtype-specific trees in 22 pairs (67%) by using SC samples and in 23 pairs (74%) by using early index samples. DoT was predicted incorrectly for 4 pairs (15%) by using SC or early index samples. In the bootstrap analysis, DoT was predicted correctly for 18 pairs (55%) by using SC samples and for 24 pairs (73%) by using early index samples. DoT was predicted incorrectly for 7 pairs (21%) by using SC samples and for 4 pairs (13%) by using early index samples. CONCLUSIONS Phylogenetic methods based solely on the tree topology of HIV env sequences, particularly without consideration of phylogenetic uncertainty, may be insufficient for determining DoT.
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Affiliation(s)
| | - Matthew Hall
- Big Data Institute, University of Oxford, United Kingdom
| | - Andrew D Redd
- Laboratory of Immunoregulation, Division of Intramural Research, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Baltimore, Maryland
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | | | | | - Stephen F Porcella
- Genomics Unit, Research Technologies Section, Rocky Mountain Laboratories, Division of Intramural Research, NIAID, NIH, Hamilton, Montana
| | - Sarah E Hudelson
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | | | - Marybeth McCauley
- Science Facilitation Department, Durham, Chapel Hill, North Carolina
| | - Theresa Gamble
- Science Facilitation Department, Durham, Chapel Hill, North Carolina
| | - Ethan A Wilson
- Vaccine and Infectious Disease Science Division, Fred Hutchinson Cancer Research Institute, Seattle, Washington
| | | | - Mina C Hosseinipour
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | | | | | | | - Jose H Pilotto
- Hospital Geral de Nova Iguaçu, Rio de Janeiro, Brazil
- Laboratorio de AIDS e Imunologia Molecular (IOC/Fiocruz), Rio de Janeiro, Brazil
| | - Beatriz Grinsztejn
- Instituto Nacional de Infectologia Evandro Chagas-INI-Fiocruz, Rio de Janeiro, Brazil
| | - Lisa A Mills
- Centers for Disease Control and Prevention (CDC) Division of HIV/AIDS Prevention/KEMRI–CDC Research and Public Health Collaboration HIV Research Branch, Kisumu, Kenya
| | | | - Breno R Santos
- Servico de Infectologia, Hospital Nossa Senhora da Conceicao/GHC, Porto Alegre, Brazil
| | - Ying Q Chen
- Vaccine and Infectious Disease Science Division, Fred Hutchinson Cancer Research Institute, Seattle, Washington
| | - Thomas C Quinn
- Laboratory of Immunoregulation, Division of Intramural Research, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Baltimore, Maryland
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Myron S Cohen
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Susan H Eshleman
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Oliver Laeyendecker
- Laboratory of Immunoregulation, Division of Intramural Research, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Baltimore, Maryland
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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14
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Volz EM, Le Vu S, Ratmann O, Tostevin A, Dunn D, Orkin C, O'Shea S, Delpech V, Brown A, Gill N, Fraser C. Molecular Epidemiology of HIV-1 Subtype B Reveals Heterogeneous Transmission Risk: Implications for Intervention and Control. J Infect Dis 2019; 217:1522-1529. [PMID: 29506269 PMCID: PMC5913615 DOI: 10.1093/infdis/jiy044] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 01/22/2018] [Indexed: 11/25/2022] Open
Abstract
Background The impact of HIV pre-exposure prophylaxis (PrEP) depends on infections averted by protecting vulnerable individuals as well as infections averted by preventing transmission by those who would have been infected if not receiving PrEP. Analysis of HIV phylogenies reveals risk factors for transmission, which we examine as potential criteria for allocating PrEP. Methods We analyzed 6912 HIV-1 partial pol sequences from men who have sex with men (MSM) in the United Kingdom combined with global reference sequences and patient-level metadata. Population genetic models were developed that adjust for stage of infection, global migration of HIV lineages, and changing incidence of infection through time. Models were extended to simulate the effects of providing susceptible MSM with PrEP. Results We found that young age <25 years confers higher risk of HIV transmission (relative risk = 2.52 [95% confidence interval, 2.32–2.73]) and that young MSM are more likely to transmit to one another than expected by chance. Simulated interventions indicate that 4-fold more infections can be averted over 5 years by focusing PrEP on young MSM. Conclusions Concentrating PrEP doses on young individuals can avert more infections than random allocation.
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Affiliation(s)
- Erik M Volz
- Department of Infectious Disease Epidemiology and the National Institute for Health Research Health Protection Research Unit on Modeling Methodology, Imperial College London
| | - Stephane Le Vu
- Department of Infectious Disease Epidemiology and the National Institute for Health Research Health Protection Research Unit on Modeling Methodology, Imperial College London
| | - Oliver Ratmann
- Department of Infectious Disease Epidemiology and the National Institute for Health Research Health Protection Research Unit on Modeling Methodology, Imperial College London
| | - Anna Tostevin
- Institute for Global Health, University College London
| | - David Dunn
- Institute for Global Health, University College London
| | | | - Siobhan O'Shea
- Infection Sciences, Viapath Analytics, Guy's and St Thomas' NHS Foundation Trust, London
| | | | | | | | - Christophe Fraser
- Li Ka Shing Centre for Health Information and Discovery, Oxford University, United Kingdom
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15
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Le Vu S, Ratmann O, Delpech V, Brown AE, Gill ON, Tostevin A, Dunn D, Fraser C, Volz EM. HIV-1 Transmission Patterns in Men Who Have Sex with Men: Insights from Genetic Source Attribution Analysis. AIDS Res Hum Retroviruses 2019; 35:805-813. [PMID: 31280593 PMCID: PMC6735327 DOI: 10.1089/aid.2018.0236] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Near 60% of new HIV infections in the United Kingdom are estimated to occur in men who have sex with men (MSM). Age-disassortative partnerships in MSM have been suggested to spread the HIV epidemics in many Western developed countries and to contribute to ethnic disparities in infection rates. Understanding these mixing patterns in transmission can help to determine which groups are at a greater risk and guide public health interventions. We analyzed combined epidemiological data and viral sequences from MSM diagnosed with HIV at the national level. We applied a phylodynamic source attribution model to infer patterns of transmission between groups of patients. From pair probabilities of transmission between 14,603 MSM patients, we found that potential transmitters of HIV subtype B were on average 8 months older than recipients. We also found a moderate overall assortativity of transmission by ethnic group and a stronger assortativity by region. Our findings suggest that there is only a modest net flow of transmissions from older to young MSM in subtype B epidemics and that young MSM, both for Black or White groups, are more likely to be infected by one another than expected in a sexual network with random mixing.
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Affiliation(s)
- Stéphane Le Vu
- Department of Infectious Disease Epidemiology, National Institute for Health Research Health Protection Research Unit on Modeling Methodology, Imperial College London, London, United Kingdom
| | - Oliver Ratmann
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Valerie Delpech
- HIV and STI Department of Public Health England's Center for Infectious Disease Surveillance and Control, London, United Kingdom
| | - Alison E. Brown
- HIV and STI Department of Public Health England's Center for Infectious Disease Surveillance and Control, London, United Kingdom
| | - O. Noel Gill
- HIV and STI Department of Public Health England's Center for Infectious Disease Surveillance and Control, London, United Kingdom
| | - Anna Tostevin
- Institute for Global Health, University College London, London, United Kingdom
| | - David Dunn
- Institute for Global Health, University College London, London, United Kingdom
| | - Christophe Fraser
- Nuffield Department of Medicine, Big Data Institute, Li Ka Shing Center for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Erik M. Volz
- Department of Infectious Disease Epidemiology, National Institute for Health Research Health Protection Research Unit on Modeling Methodology, Imperial College London, London, United Kingdom
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16
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Kosakovsky Pond SL, Weaver S, Leigh Brown AJ, Wertheim JO. HIV-TRACE (TRAnsmission Cluster Engine): a Tool for Large Scale Molecular Epidemiology of HIV-1 and Other Rapidly Evolving Pathogens. Mol Biol Evol 2019; 35:1812-1819. [PMID: 29401317 DOI: 10.1093/molbev/msy016] [Citation(s) in RCA: 162] [Impact Index Per Article: 32.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
In modern applications of molecular epidemiology, genetic sequence data are routinely used to identify clusters of transmission in rapidly evolving pathogens, most notably HIV-1. Traditional 'shoe-leather' epidemiology infers transmission clusters by tracing chains of partners sharing epidemiological connections (e.g., sexual contact). Here, we present a computational tool for identifying a molecular transmission analog of such clusters: HIV-TRACE (TRAnsmission Cluster Engine). HIV-TRACE implements an approach inspired by traditional epidemiology, by identifying chains of partners whose viral genetic relatedness imply direct or indirect epidemiological connections. Molecular transmission clusters are constructed using codon-aware pairwise alignment to a reference sequence followed by pairwise genetic distance estimation among all sequences. This approach is computationally tractable and is capable of identifying HIV-1 transmission clusters in large surveillance databases comprising tens or hundreds of thousands of sequences in near real time, that is, on the order of minutes to hours. HIV-TRACE is available at www.hivtrace.org and from www.github.com/veg/hivtrace, along with the accompanying result visualization module from www.github.com/veg/hivtrace-viz. Importantly, the approach underlying HIV-TRACE is not limited to the study of HIV-1 and can be applied to study outbreaks and epidemics of other rapidly evolving pathogens.
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Affiliation(s)
| | - Steven Weaver
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA
| | - Andrew J Leigh Brown
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom
| | - Joel O Wertheim
- Department of Medicine, University of California, San Diego, CA
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17
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Wertheim JO, Chato C, Poon AFY. Comparative analysis of HIV sequences in real time for public health. Curr Opin HIV AIDS 2019; 14:213-220. [PMID: 30882486 DOI: 10.1097/coh.0000000000000539] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
PURPOSE OF REVIEW The purpose of this study is to summarize recent advances in public health applications of comparative methods for HIV-1 sequence analysis in real time, including genetic clustering methods. RECENT FINDINGS Over the past 2 years, several groups have reported the deployment of established genetic clustering methods to guide public health decisions for HIV prevention in 'near real time'. However, it remains unresolved how well the readouts of comparative methods like clusters translate to events that are actionable for public health. A small number of recent studies have begun to elucidate the linkage between clusters and HIV-1 incidence, whereas others continue to refine and develop new comparative methods for such applications. SUMMARY Although the use of established methods to cluster HIV-1 sequence databases has become a widespread activity, there remains a critical gap between clusters and public health value.
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Affiliation(s)
- Joel O Wertheim
- Department of Medicine, University of California, San Diego, California, USA
| | | | - Art F Y Poon
- Department of Pathology and Laboratory Medicine
- Department of Microbiology and Immunology, Western University, London, Ontario, Canada
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18
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Ratmann O, Grabowski MK, Hall M, Golubchik T, Wymant C, Abeler-Dörner L, Bonsall D, Hoppe A, Brown AL, de Oliveira T, Gall A, Kellam P, Pillay D, Kagaayi J, Kigozi G, Quinn TC, Wawer MJ, Laeyendecker O, Serwadda D, Gray RH, Fraser C. Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis. Nat Commun 2019; 10:1411. [PMID: 30926780 PMCID: PMC6441045 DOI: 10.1038/s41467-019-09139-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 02/22/2019] [Indexed: 11/09/2022] Open
Abstract
To prevent new infections with human immunodeficiency virus type 1 (HIV-1) in sub-Saharan Africa, UNAIDS recommends targeting interventions to populations that are at high risk of acquiring and passing on the virus. Yet it is often unclear who and where these 'source' populations are. Here we demonstrate how viral deep-sequencing can be used to reconstruct HIV-1 transmission networks and to infer the direction of transmission in these networks. We are able to deep-sequence virus from a large population-based sample of infected individuals in Rakai District, Uganda, reconstruct partial transmission networks, and infer the direction of transmission within them at an estimated error rate of 16.3% [8.8-28.3%]. With this error rate, deep-sequence phylogenetics cannot be used against individuals in legal contexts, but is sufficiently low for population-level inferences into the sources of epidemic spread. The technique presents new opportunities for characterizing source populations and for targeting of HIV-1 prevention interventions in Africa.
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Affiliation(s)
- Oliver Ratmann
- Department of Mathematics, Imperial College London, London, SW72AZ, UK.
- Department of Infectious Disease, Epidemiology School of Public Health, Imperial College London, London, W21PG, UK.
| | - M Kate Grabowski
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, 21205-2196, USA
- Rakai Health Sciences Program, Entebbe, P.O.Box 49, Uganda
| | - Matthew Hall
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford, OX3 7BN, UK
| | - Tanya Golubchik
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford, OX3 7BN, UK
| | - Chris Wymant
- Department of Infectious Disease, Epidemiology School of Public Health, Imperial College London, London, W21PG, UK
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford, OX3 7BN, UK
| | - Lucie Abeler-Dörner
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford, OX3 7BN, UK
| | - David Bonsall
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford, OX3 7BN, UK
| | - Anne Hoppe
- Division of Infection and Immunity, University College London, London, WC1E 6BT, UK
| | - Andrew Leigh Brown
- School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FF, UK
| | - Tulio de Oliveira
- College of Health Sciences, University of KwaZulu-Natal, Durban, 4041, South Africa
| | - Astrid Gall
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Paul Kellam
- Department of Medicine, Imperial College London, London, W12 0HS, UK
| | - Deenan Pillay
- Division of Infection and Immunity, University College London, London, WC1E 6BT, UK
- Africa Health Research Institute, Private Bag X7, Durban, 4013, South Africa
| | - Joseph Kagaayi
- Rakai Health Sciences Program, Entebbe, P.O.Box 49, Uganda
| | - Godfrey Kigozi
- Rakai Health Sciences Program, Entebbe, P.O.Box 49, Uganda
| | - Thomas C Quinn
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, 21205-2196, USA
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892-9806, USA
| | - Maria J Wawer
- Rakai Health Sciences Program, Entebbe, P.O.Box 49, Uganda
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Oliver Laeyendecker
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, 21205-2196, USA
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892-9806, USA
| | - David Serwadda
- Rakai Health Sciences Program, Entebbe, P.O.Box 49, Uganda
- Makerere University School of Public Health, Kampala, 8HQG+3V, Uganda
| | - Ronald H Gray
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, 21205-2196, USA
- Rakai Health Sciences Program, Entebbe, P.O.Box 49, Uganda
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Christophe Fraser
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford, OX3 7BN, UK
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19
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Jovanović L, Šiljić M, Ćirković V, Salemović D, Pešić-Pavlović I, Todorović M, Ranin J, Jevtović D, Stanojević M. Exploring Evolutionary and Transmission Dynamics of HIV Epidemic in Serbia: Bridging Socio-Demographic With Phylogenetic Approach. Front Microbiol 2019; 10:287. [PMID: 30858834 PMCID: PMC6397891 DOI: 10.3389/fmicb.2019.00287] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2018] [Accepted: 02/04/2019] [Indexed: 12/04/2022] Open
Abstract
Previous molecular studies of Serbian HIV epidemic identified the dominance of subtype B and presence of clusters related HIV-1 transmission, in particular among men who have sex with men (MSM). In order to get a deeper understanding of the complexities of HIV sub-epidemics in Serbia, epidemic trends, temporal origin and phylodynamic characteristics in general population and subpopulations were analyzed by means of mathematical modeling, phylogenetic analysis and latent class analysis (LCA). Fitting of the logistic curve of trends for a cumulative annual number of new HIV cases in 1984–2016, in general population and MSM transmission group, was performed. Both datasets fitted the logistic growth model, showing the early exponential phase of the growth curve. According to the suggested model, in the year 2030, the number of newly diagnosed HIV cases in Serbia will continue to grow, in particular in the MSM transmission group. Further, a detailed phylogenetic analysis was performed on 385 sequences from the period 1997–2015. Identification of transmission clusters, estimation of population growth (Ne), of the effective reproductive number (Re) and time of the most recent common ancestor (tMRCA) were estimated employing Bayesian and maximum likelihood methods. A substantial proportion of 53% of subtype B sequences was found within transmission clusters/network. Phylodynamic analysis revealed Re over one during the whole period investigated, with the steepest slopes and a recent tMRCA for MSM transmission group subtype B clades, in line with a growing trend in the number of transmissions in years approaching the end of the study period. Contrary, heterosexual clades in both studied subtypes – B and C – showed modest growth and stagnation. LCA analysis identified five latent classes, with transmission clusters dominantly present in 2/5 classes, linked to MSM transmission living in the capital city and with the high prevalence of co-infection with HBV and/or other STIs.Presented findings imply that HIV epidemic in Serbia is still in the exponential growth phase, in particular, related to the MSM transmission, with estimated steep growth curve until 2030. The obtained results imply that an average new HIV patient in Serbia is a young man with concomitant sexually transmitted infection.
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Affiliation(s)
- Luka Jovanović
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Marina Šiljić
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Valentina Ćirković
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Dubravka Salemović
- Infectious and Tropical Diseases University Hospital, Clinical Centre of Serbia, Belgrade, Serbia
| | - Ivana Pešić-Pavlović
- Virology Laboratory, Microbiology Department, Clinical Centre of Serbia, Belgrade, Serbia
| | - Marija Todorović
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Jovan Ranin
- Infectious and Tropical Diseases University Hospital, Clinical Centre of Serbia, Belgrade, Serbia
| | - Djordje Jevtović
- Infectious and Tropical Diseases University Hospital, Clinical Centre of Serbia, Belgrade, Serbia
| | - Maja Stanojević
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
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20
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Wertheim JO, Murrell B, Mehta SR, Forgione LA, Kosakovsky Pond SL, Smith DM, Torian LV. Growth of HIV-1 Molecular Transmission Clusters in New York City. J Infect Dis 2018; 218:1943-1953. [PMID: 30010850 PMCID: PMC6217720 DOI: 10.1093/infdis/jiy431] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Accepted: 07/10/2018] [Indexed: 11/12/2022] Open
Abstract
Background HIV-1 genetic sequences can be used to infer viral transmission history and dynamics. Throughout the United States, HIV-1 sequences from drug resistance testing are reported to local public health departments. Methods We investigated whether inferred HIV transmission network dynamics can identify individuals and clusters of individuals most likely to give rise to future HIV cases in a surveillance setting. We used HIV-TRACE, a genetic distance-based clustering tool, to infer molecular transmission clusters from HIV-1 pro/RT sequences from 65736 people in the New York City surveillance registry. Logistic and LASSO regression analyses were used to identify correlates of clustering and cluster growth, respectively. We performed retrospective transmission network analyses to evaluate individual- and cluster-level prioritization schemes for identifying parts of the network most likely to give rise to new cases in the subsequent year. Results Individual-level prioritization schemes predicted network growth better than random targeting. Across the 3600 inferred molecular transmission clusters, previous growth dynamics were superior predictors of future transmission cluster growth compared to individual-level prediction schemes. Cluster-level prioritization schemes considering previous cluster growth relative to cluster size further improved network growth predictions. Conclusions Prevention efforts based on HIV molecular epidemiology may improve public health outcomes in a US surveillance setting.
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Affiliation(s)
| | - Ben Murrell
- Department of Medicine, University of California, San Diego
| | - Sanjay R Mehta
- Department of Medicine, University of California, San Diego
- Veterans Affairs Healthcare System San Diego, California
| | - Lisa A Forgione
- New York City Department of Health and Mental Hygiene, New York
| | | | - Davey M Smith
- Department of Medicine, University of California, San Diego
- Veterans Affairs Healthcare System San Diego, California
| | - Lucia V Torian
- New York City Department of Health and Mental Hygiene, New York
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21
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Bulla I, Spickanll IH, Gromov D, Romero-Severson EO. Sensitivity of joint contagiousness and susceptibility-based dynamic optimal control strategies for HIV prevention. PLoS One 2018; 13:e0204741. [PMID: 30335855 PMCID: PMC6193630 DOI: 10.1371/journal.pone.0204741] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 09/13/2018] [Indexed: 11/24/2022] Open
Abstract
Predicting the population-level effects of an infectious disease intervention that incorporate multiple modes of intervention is complicated by the joint non-linear dynamics of both infection transmission and the intervention itself. In this paper, we consider the sensitivity of Dynamic Optimal Control Profiles (DOCPs) for the optimal joint investment in both a contagiousness and susceptibility-based control of HIV to bio-behavioral, economic, and programmatic assumptions. The DOCP is calculated using recently developed numerical algorithms that allow controls to be represented by a set of piecewise constant functions that maintain a constant yearly budget. Our transmission model assumes multiple stages of HIV infection corresponding to acute and chronic infection and both within- and between-individual behavioral heterogeneity. We parameterize a baseline scenario from a longitudinal study of sexual behavior in MSM and consider sensitivity of the DOCPs to deviations from that baseline scenario. In the baseline scenario, the primary determinant of the dominant control were programmatic factors, regardless of budget. In sensitivity analyses, the qualitative aspects of the optimal control policy were often robust to significant deviation in assumptions regarding transmission dynamics. In addition, we found several conditions in which long-term joint investment in both interventions was optimal. Our results suggest that modeling in the service of decision support for intervention design can improve population-level effects of a limited set of economic resources. We found that economic and programmatic factors were as important as the inherent transmission dynamics in determining population-level intervention effects. Given our finding that the DOCPs were robust to alternative biological and behavioral assumptions it may be possible to identify DOCPs even when the data are not sufficient to identify a transmission model.
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Affiliation(s)
- Ingo Bulla
- Department of Mathematics and Computer Science, University of Greifswald, Greifswald, Germany
| | - Ian H. Spickanll
- Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Dmitry Gromov
- Faculty of Applied Mathematics and Control Processes, Saint Petersburg State University, Saint Petersburg, Russia
| | - Ethan Obie Romero-Severson
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- * E-mail:
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Kusejko K, Kadelka C, Marzel A, Battegay M, Bernasconi E, Calmy A, Cavassini M, Hoffmann M, Böni J, Yerly S, Klimkait T, Perreau M, Rauch A, Günthard HF, Kouyos RD. Inferring the age difference in HIV transmission pairs by applying phylogenetic methods on the HIV transmission network of the Swiss HIV Cohort Study. Virus Evol 2018; 4:vey024. [PMID: 30250751 PMCID: PMC6143731 DOI: 10.1093/ve/vey024] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Age-mixing patterns are of key importance for understanding the dynamics of human
immunodeficiency virus (HIV)-epidemics and target public health interventions. We use the
densely sampled Swiss HIV Cohort Study (SHCS) resistance database to study the age
difference at infection in HIV transmission pairs using phylogenetic methods. In addition,
we investigate whether the mean age difference of pairs in the phylogenetic tree is
influenced by sampling as well as by additional distance thresholds for including pairs.
HIV-1 pol-sequences of 11,922 SHCS patients and approximately 240,000 Los
Alamos background sequences were used to build a phylogenetic tree. Using this tree, 100
per cent down to 1 per cent of the tips were sampled repeatedly to generate pruned trees
(N = 500 for each sample proportion), of which pairs of SHCS patients
were extracted. The mean of the absolute age differences of the pairs, measured as the
absolute difference of the birth years, was analyzed with respect to this sample
proportion and a distance criterion for inclusion of the pairs. In addition, the
transmission groups men having sex with men (MSM), intravenous drug users (IDU), and
heterosexuals (HET) were analyzed separately. Considering the tree with all 11,922 SHCS
patients, 2,991 pairs could be extracted, with 954 (31.9 per cent) MSM-pairs, 635 (21.2
per cent) HET-pairs, 414 (13.8 per cent) IDU-pairs, and 352 (11.8 per cent) HET/IDU-pairs.
For all transmission groups, the age difference at infection was significantly
(P < 0.001) smaller for pairs in the tree compared with randomly assigned pairs,
meaning that patients of similar age are more likely to be pairs. The mean age difference
in the phylogenetic analysis, using a fixed distance of 0.05, was 9.2, 9.0, 7.3 and
5.6 years for MSM-, HET-, HET/IDU-, and IDU-pairs, respectively. Decreasing the cophenetic
distance threshold from 0.05 to 0.01 significantly decreased the mean age difference.
Similarly, repeated sampling of 100 per cent down to 1 per cent of the tips revealed an
increased age difference at lower sample proportions. HIV-transmission is age-assortative,
but the age difference of transmission pairs detected by phylogenetic analyses depends on
both sampling proportion and distance criterion. The mean age difference decreases when
using more conservative distance thresholds, implying an underestimation of
age-assortativity when using liberal distance criteria. Similarly, overestimation of the
mean age difference occurs for pairs from sparsely sampled trees, as it is often the case
in sub-Saharan Africa.
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Affiliation(s)
- Katharina Kusejko
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zürich, Rämistrasse 100, Zürich, Switzerland.,Institute of Medical Virology, University of Zürich, Winterthurerstrasse 190, Zürich, Switzerland
| | - Claus Kadelka
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zürich, Rämistrasse 100, Zürich, Switzerland.,Institute of Medical Virology, University of Zürich, Winterthurerstrasse 190, Zürich, Switzerland
| | - Alex Marzel
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zürich, Rämistrasse 100, Zürich, Switzerland.,Institute of Medical Virology, University of Zürich, Winterthurerstrasse 190, Zürich, Switzerland
| | - Manuel Battegay
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, Petersgraben 4, CH-4031 Basel; University of Basel, Petersplatz 1, Basel, Switzerland
| | - Enos Bernasconi
- Division of Infectious Diseases, Regional Hospital Lugano, Via Tesserete 46, Lugano, Switzerland
| | - Alexandra Calmy
- Laboratory of Virology and Division of Infectious Diseases, Genève University Hospital, Rue Gabrielle-Perret-Gentil 4, CH-1205 Genève; University of Genève, 24 rue du Général-Dufour, Genève, Switzerland
| | - Matthias Cavassini
- Division of Infectious Diseases, Lausanne University Hospital, Rue du Bugnon 46, Lausanne, Switzerland
| | - Matthias Hoffmann
- Division of Infectious Diseases, Cantonal Hospital St Gallen, Rorschacher Strasse 95, St. Gallen, Switzerland
| | - Jürg Böni
- Institute of Medical Virology, University of Zürich, Winterthurerstrasse 190, Zürich, Switzerland
| | - Sabine Yerly
- Laboratory of Virology and Division of Infectious Diseases, Genève University Hospital, Rue Gabrielle-Perret-Gentil 4, CH-1205 Genève; University of Genève, 24 rue du Général-Dufour, Genève, Switzerland
| | - Thomas Klimkait
- Molecular Virology, Department of Biomedicine, University of Basel, Petersplatz 10, Basel, Switzerland
| | - Matthieu Perreau
- Division of Infectious Diseases, Lausanne University Hospital, Rue du Bugnon 46, Lausanne, Switzerland
| | - Andri Rauch
- Clinic for Infectious Diseases, Bern University Hospital, Freiburgstrasse 18, Bern; University of Bern, Hochschulstrasse 6, CH-3012 Bern, Switzerland
| | - Huldrych F Günthard
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zürich, Rämistrasse 100, Zürich, Switzerland.,Institute of Medical Virology, University of Zürich, Winterthurerstrasse 190, Zürich, Switzerland
| | - Roger D Kouyos
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zürich, Rämistrasse 100, Zürich, Switzerland.,Institute of Medical Virology, University of Zürich, Winterthurerstrasse 190, Zürich, Switzerland
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23
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Sivay MV, Hudelson SE, Wang J, Agyei Y, Hamilton EL, Selin A, Dennis A, Kahn K, Gomez-Olive FX, MacPhail C, Hughes JP, Pettifor A, Eshleman SH, Grabowski MK. HIV-1 diversity among young women in rural South Africa: HPTN 068. PLoS One 2018; 13:e0198999. [PMID: 29975689 PMCID: PMC6033411 DOI: 10.1371/journal.pone.0198999] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 04/21/2018] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND South Africa has one of the highest rates of HIV-1 (HIV) infection world-wide, with the highest rates among young women. We analyzed the molecular epidemiology and evolutionary history of HIV in young women attending high school in rural South Africa. METHODS Samples were obtained from the HPTN 068 randomized controlled trial, which evaluated the effect of cash transfers for school attendance on HIV incidence in women aged 13-20 years (Mpumalanga province, 2011-2015). Plasma samples from HIV-infected participants were analyzed using the ViroSeq HIV-1 Genotyping assay. Phylogenetic analysis was performed using 200 pol gene study sequences and 2,294 subtype C reference sequences from South Africa. Transmission clusters were identified using Cluster Picker and HIV-TRACE, and were characterized using demographic and other epidemiological data. Phylodynamic analyses were performed using the BEAST software. RESULTS The study enrolled 2,533 young women who were followed through their expected high school graduation date (main study); some participants had a post-study assessment (follow-up study). Two-hundred-twelve of 2,533 enrolled young women had HIV infection. HIV pol sequences were obtained for 94% (n = 201/212) of the HIV-infected participants. All but one of the sequences were HIV-1 subtype C; the non-C subtype sequence was excluded from further analysis. Median pairwise genetic distance between the subtype C sequences was 6.4% (IQR: 5.6-7.2). Overall, 26% of study sequences fell into 21 phylogenetic clusters with 2-6 women per cluster. Thirteen (62%) clusters included women who were HIV-infected at enrollment. Clustering was not associated with study arm, demographic or other epidemiological factors. The estimated date of origin of HIV subtype C in the study population was 1958 (95% highest posterior density [HPD]: 1931-1980), and the median estimated substitution rate among study pol sequences was 1.98x10-3 (95% HPD: 1.15x10-3-2.81x10-3) per site per year. CONCLUSIONS Phylogenetic analysis suggests that multiple HIV subtype C sublineages circulate among school age girls in South Africa. There were no substantive differences in the molecular epidemiology of HIV between control and intervention arms in the HPTN 068 trial.
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Affiliation(s)
- Mariya V. Sivay
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Sarah E. Hudelson
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Jing Wang
- Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America
| | - Yaw Agyei
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | | | - Amanda Selin
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Ann Dennis
- Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Kathleen Kahn
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - F. Xavier Gomez-Olive
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Catherine MacPhail
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- School of Health and Society, University of Wollongong, New South Wales, Australia
| | - James P. Hughes
- University of Washington, Seattle, WA, United States of America
| | - Audrey Pettifor
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Susan H. Eshleman
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Mary Kathryn Grabowski
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
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