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Goldberg EE, Lundgren EJ, Romero-Severson EO, Leitner T. Inferring Viral Transmission Time from Phylogenies for Known Transmission Pairs. Mol Biol Evol 2024; 41:msad282. [PMID: 38149995 PMCID: PMC10776241 DOI: 10.1093/molbev/msad282] [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: 09/12/2023] [Revised: 12/15/2023] [Accepted: 12/21/2023] [Indexed: 12/28/2023] Open
Abstract
When the time of an HIV transmission event is unknown, methods to identify it from virus genetic data can reveal the circumstances that enable transmission. We developed a single-parameter Markov model to infer transmission time from an HIV phylogeny constructed of multiple virus sequences from people in a transmission pair. Our method finds the statistical support for transmission occurring in different possible time slices. We compared our time-slice model results to previously described methods: a tree-based logical transmission interval, a simple parsimony-like rules-based method, and a more complex coalescent model. Across simulations with multiple transmitted lineages, different transmission times relative to the source's infection, and different sampling times relative to transmission, we found that overall our time-slice model provided accurate and narrower estimates of the time of transmission. We also identified situations when transmission time or direction was difficult to estimate by any method, particularly when transmission occurred long after the source was infected and when sampling occurred long after transmission. Applying our model to real HIV transmission pairs showed some agreement with facts known from the case investigations. We also found, however, that uncertainty on the inferred transmission time was driven more by uncertainty from time calibration of the phylogeny than from the model inference itself. Encouragingly, comparable performance of the Markov time-slice model and the coalescent model-which make use of different information within a tree-suggests that a new method remains to be described that will make full use of the topology and node times for improved transmission time inference.
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Affiliation(s)
- Emma E Goldberg
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Erik J Lundgren
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | | | - Thomas Leitner
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
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2
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Goldberg EE, Lundgren EJ, Romero-Severson EO, Leitner T. Inferring viral transmission time from phylogenies for known transmission pairs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.12.557404. [PMID: 37745490 PMCID: PMC10515827 DOI: 10.1101/2023.09.12.557404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
When the time of an HIV transmission event is unknown, methods to identify it from virus genetic data can reveal the circumstances that enable transmission. We developed a single-parameter Markov model to infer transmission time from an HIV phylogeny constructed of multiple virus sequences from people in a transmission pair. Our method finds the statistical support for transmission occurring in different possible time slices. We compared our time-slice model results to previously-described methods: a tree-based logical transmission interval, a simple parsimony-like rules-based method, and a more complex coalescent model. Across simulations with multiple transmitted lineages, different transmission times relative to the source's infection, and different sampling times relative to transmission, we found that overall our time-slice model provided accurate and narrower estimates of the time of transmission. We also identified situations when transmission time or direction was difficult to estimate by any method, particularly when transmission occurred long after the source was infected and when sampling occurred long after transmission. Applying our model to real HIV transmission pairs showed some agreement with facts known from the case investigations. We also found, however, that uncertainty on the inferred transmission time was driven more by uncertainty from time-calibration of the phylogeny than from the model inference itself. Encouragingly, comparable performance of the Markov time-slice model and the coalescent model-which make use of different information within a tree-suggests that a new method remains to be described that will make full use of the topology and node times for improved transmission time inference.
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Affiliation(s)
- Emma E. Goldberg
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos NM, USA
| | - Erik J. Lundgren
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos NM, USA
| | | | - Thomas Leitner
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos NM, USA
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3
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Chao E, Chato C, Vender R, Olabode AS, Ferreira RC, Poon AFY. Molecular source attribution. PLoS Comput Biol 2022; 18:e1010649. [PMID: 36395093 PMCID: PMC9671344 DOI: 10.1371/journal.pcbi.1010649] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Elisa Chao
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
| | - Connor Chato
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
| | - Reid Vender
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
- School of Medicine, Queen’s University, Kingston, Ontario, Canada
| | - Abayomi S. Olabode
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
| | - Roux-Cil Ferreira
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
| | - Art F. Y. Poon
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
- * E-mail:
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4
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Lundgren E, Romero-Severson E, Albert J, Leitner T. Combining biomarker and virus phylogenetic models improves HIV-1 epidemiological source identification. PLoS Comput Biol 2022; 18:e1009741. [PMID: 36026480 PMCID: PMC9455879 DOI: 10.1371/journal.pcbi.1009741] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 09/08/2022] [Accepted: 08/02/2022] [Indexed: 01/07/2023] Open
Abstract
To identify and stop active HIV transmission chains new epidemiological techniques are needed. Here, we describe the development of a multi-biomarker augmentation to phylogenetic inference of the underlying transmission history in a local population. HIV biomarkers are measurable biological quantities that have some relationship to the amount of time someone has been infected with HIV. To train our model, we used five biomarkers based on real data from serological assays, HIV sequence data, and target cell counts in longitudinally followed, untreated patients with known infection times. The biomarkers were modeled with a mixed effects framework to allow for patient specific variation and general trends, and fit to patient data using Markov Chain Monte Carlo (MCMC) methods. Subsequently, the density of the unobserved infection time conditional on observed biomarkers were obtained by integrating out the random effects from the model fit. This probabilistic information about infection times was incorporated into the likelihood function for the transmission history and phylogenetic tree reconstruction, informed by the HIV sequence data. To critically test our methodology, we developed a coalescent-based simulation framework that generates phylogenies and biomarkers given a specific or general transmission history. Testing on many epidemiological scenarios showed that biomarker augmented phylogenetics can reach 90% accuracy under idealized situations. Under realistic within-host HIV-1 evolution, involving substantial within-host diversification and frequent transmission of multiple lineages, the average accuracy was at about 50% in transmission clusters involving 5-50 hosts. Realistic biomarker data added on average 16 percentage points over using the phylogeny alone. Using more biomarkers improved the performance. Shorter temporal spacing between transmission events and increased transmission heterogeneity reduced reconstruction accuracy, but larger clusters were not harder to get right. More sequence data per infected host also improved accuracy. We show that the method is robust to incomplete sampling and that adding biomarkers improves reconstructions of real HIV-1 transmission histories. The technology presented here could allow for better prevention programs by providing data for locally informed and tailored strategies.
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Affiliation(s)
- Erik Lundgren
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Ethan Romero-Severson
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Jan Albert
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden
| | - Thomas Leitner
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- * E-mail:
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5
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Abstract
PURPOSE OF REVIEW Within-host diversity complicates transmission models because it recognizes that between-host virus phylogenies are not identical to the transmission history among the infected hosts. This review presents the biological and theoretical foundations for recent development in this field, and shows that modern phylodynamic methods are capable of inferring realistic transmission histories from HIV sequence data. RECENT FINDINGS Transmission of single or multiple genetic variants from a donor's HIV population results in donor-recipient phylogenies with combinations of monophyletic, paraphyletic, and polyphyletic patterns. Large-scale simulations and analyses of many real HIV datasets have established that transmission direction, directness, or common source often can be inferred based on HIV sequence data. Phylodynamic reconstruction of HIV transmissions that include within-host HIV diversity have recently been established and made available in several software packages. SUMMARY Phylodynamic methods that include realistic features of HIV genetic diversification have come of age, significantly improving inference of key epidemiological parameters. This opens the door to more accurate surveillance and better-informed prevention campaigns.
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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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7
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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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Abstract
HIV is one of the fastest evolving organisms known. It evolves about 1 million times faster than its host, humans. Because HIV establishes chronic infections, with continuous evolution, its divergence within a single infected human surpasses the divergence of the entire humanoid history. Yet, it is still the same virus, infecting the same cell types and using the same replication machinery year after year. Hence, one would think that most mutations that HIV accumulates are neutral. But the picture is more complicated than that. HIV evolution is also a clear example of strong positive selection, that is, mutants have a survival advantage. How do these facts come together?
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Affiliation(s)
- Thomas Leitner
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM
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9
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Ezeonwumelu I, Bártolo I, Martin F, Abecasis A, Campos T, Romero-Severson EO, Leitner T, Taveira N. Accidental Father-to-Son HIV-1 Transmission During the Seroconversion Period. AIDS Res Hum Retroviruses 2018; 34:857-862. [PMID: 30073842 DOI: 10.1089/aid.2018.0060] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
A 4-year-old child born to an HIV-1 seronegative mother was diagnosed with HIV-1, the main risk factor being transmission from the child's father who was seroconverting at the time of the child's birth. In the context of a forensic investigation, we aimed to identify the source of infection of the child and date of the transmission event. Samples were collected from the father and child at two time points about 4 years after the child's birth. Partial segments of three HIV-1 genes (gag, pol, and env) were sequenced and maximum likelihood (ML) and Bayesian methods were used to determine direction and estimate date of transmission. Neutralizing antibodies were determined using a single cycle assay. Bayesian trees displayed a paraphyletic-monophyletic topology in all three genomic regions, with the father's host label at the root, which is consistent with father-to-son transmission. ML trees found similar topologies in gag and pol and a monophyletic-monophyletic topology in env. Analysis of the time of the most recent common ancestor of each HIV-1 gene population indicated that the child was infected shortly after the father. Consistent with the infection history, both father and son developed broad and potent HIV-specific neutralizing antibody responses. In conclusion, the direction of transmission implicated the father as the source of transmission. Transmission occurred during the seroconversion period when the father was unaware of the infection and was likely accidental. This case shows how genetic, phylogenetic, and serological data can contribute for the forensic investigation of HIV transmission.
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Affiliation(s)
- Ifeanyi Ezeonwumelu
- HIV Evolution, Epidemiology and Prevention, Research Institute for Medicines (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, Lisboa, Portugal
- Centro de Investigação Interdisciplinar Egas Moniz (CiiEM), Instituto Universitário Egas Moniz (IUEM), Monte de Caparica, Portugal
| | - Inês Bártolo
- HIV Evolution, Epidemiology and Prevention, Research Institute for Medicines (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, Lisboa, Portugal
| | - Francisco Martin
- HIV Evolution, Epidemiology and Prevention, Research Institute for Medicines (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, Lisboa, Portugal
| | - Ana Abecasis
- Global Health and Tropical Medicine, GHTM, Instituto de Higiene e Medicina Tropical, IHMT, Universidade Nova de Lisboa, UNL, Lisboa, Portugal
| | - Teresa Campos
- Departamento de Pediatria, Hospital Prof. Dr. Fernando Fonseca EPE, Amadora, Portugal
| | - Ethan O. Romero-Severson
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Thomas Leitner
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Nuno Taveira
- HIV Evolution, Epidemiology and Prevention, Research Institute for Medicines (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, Lisboa, Portugal
- Centro de Investigação Interdisciplinar Egas Moniz (CiiEM), Instituto Universitário Egas Moniz (IUEM), Monte de Caparica, Portugal
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10
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Leitner T, Romero-Severson E. Phylogenetic patterns recover known HIV epidemiological relationships and reveal common transmission of multiple variants. Nat Microbiol 2018; 3:983-988. [PMID: 30061758 PMCID: PMC6442454 DOI: 10.1038/s41564-018-0204-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Accepted: 06/22/2018] [Indexed: 11/09/2022]
Abstract
The growth of human immunodeficiency virus (HIV) sequence databases resulting from drug resistance testing has motivated efforts using phylogenetic methods to assess how HIV spreads1-4. Such inference is potentially both powerful and useful for tracking the epidemiology of HIV and the allocation of resources to prevention campaigns. We recently used simulation and a small number of illustrative cases to show that certain phylogenetic patterns are associated with different types of epidemiological linkage5. Our original approach was later generalized for large next-generation sequencing datasets and implemented as a free computational pipeline6. Previous work has claimed that direction and directness of transmission could not be established from phylogeny because one could not be sure that there were no intervening or missing links involved7-9. Here, we address this issue by investigating phylogenetic patterns from 272 previously identified HIV transmission chains with 955 transmission pairs representing diverse geography, risk groups, subtypes, and genomic regions. These HIV transmissions had known linkage based on epidemiological information such as partner studies, mother-to-child transmission, pairs identified by contact tracing, and criminal cases. We show that the resulting phylogeny inferred from real HIV genetic sequences indeed reveals distinct patterns associated with direct transmission contra transmissions from a common source. Thus, our results establish how to interpret phylogenetic trees based on HIV sequences when tracking who-infected-whom, when and how genetic information can be used for improved tracking of HIV spread. We also investigate limitations that stem from limited sampling and genetic time-trends in the donor and recipient HIV populations.
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Affiliation(s)
- Thomas Leitner
- Theoretical Biology and Biophysics Group, MS K710, Los Alamos National Laboratory, Los Alamos, NM, USA.
| | - Ethan Romero-Severson
- Theoretical Biology and Biophysics Group, MS K710, Los Alamos National Laboratory, Los Alamos, NM, USA
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11
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De Maio N, Worby CJ, Wilson DJ, Stoesser N. Bayesian reconstruction of transmission within outbreaks using genomic variants. PLoS Comput Biol 2018; 14:e1006117. [PMID: 29668677 PMCID: PMC5927459 DOI: 10.1371/journal.pcbi.1006117] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 04/30/2018] [Accepted: 04/03/2018] [Indexed: 01/19/2023] Open
Abstract
Pathogen genome sequencing can reveal details of transmission histories and is a powerful tool in the fight against infectious disease. In particular, within-host pathogen genomic variants identified through heterozygous nucleotide base calls are a potential source of information to identify linked cases and infer direction and time of transmission. However, using such data effectively to model disease transmission presents a number of challenges, including differentiating genuine variants from those observed due to sequencing error, as well as the specification of a realistic model for within-host pathogen population dynamics. Here we propose a new Bayesian approach to transmission inference, BadTrIP (BAyesian epiDemiological TRansmission Inference from Polymorphisms), that explicitly models evolution of pathogen populations in an outbreak, transmission (including transmission bottlenecks), and sequencing error. BadTrIP enables the inference of host-to-host transmission from pathogen sequencing data and epidemiological data. By assuming that genomic variants are unlinked, our method does not require the computationally intensive and unreliable reconstruction of individual haplotypes. Using simulations we show that BadTrIP is robust in most scenarios and can accurately infer transmission events by efficiently combining information from genetic and epidemiological sources; thanks to its realistic model of pathogen evolution and the inclusion of epidemiological data, BadTrIP is also more accurate than existing approaches. BadTrIP is distributed as an open source package (https://bitbucket.org/nicofmay/badtrip) for the phylogenetic software BEAST2. We apply our method to reconstruct transmission history at the early stages of the 2014 Ebola outbreak, showcasing the power of within-host genomic variants to reconstruct transmission events. We present a new tool to reconstruct transmission events within outbreaks. Our approach makes use of pathogen genetic information, notably genetic variants at low frequency within host that are usually discarded, and combines it with epidemiological information of host exposure to infection. This leads to accurate reconstruction of transmission even in cases where abundant within-host pathogen genetic variation and weak transmission bottlenecks (multiple pathogen units colonising a new host at transmission) would otherwise make inference difficult due to the transmission history differing from the pathogen evolution history inferred from pathogen isolets. Also, the use of within-host pathogen genomic variants increases the resolution of the reconstruction of the transmission tree even in scenarios with limited within-outbreak pathogen genetic diversity: within-host pathogen populations that appear identical at the level of consensus sequences can be discriminated using within-host variants. Our Bayesian approach provides a measure of the confidence in different possible transmission histories, and is published as open source software. We show with simulations and with an analysis of the beginning of the 2014 Ebola outbreak that our approach is applicable in many scenarios, improves our understanding of transmission dynamics, and will contribute to finding and limiting sources and routes of transmission, and therefore preventing the spread of infectious disease.
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Affiliation(s)
- Nicola De Maio
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Colin J Worby
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Daniel J Wilson
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
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