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Blenkinsop A, Pantazis N, Kostaki EG, Sofocleous L, van Sighem A, Bezemer D, van de Laar T, van der Valk M, Reiss P, de Bree G, Ratmann O. Sources of Human Immunodeficiency Virus Infections Among Men Who Have Sex With Men With a Migration Background: A Viral Phylogenetic Case Study in Amsterdam, The Netherlands. J Infect Dis 2024:jiae267. [PMID: 38976562 DOI: 10.1093/infdis/jiae267] [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: 01/11/2024] [Accepted: 05/17/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND Men and women with a migration background comprise an increasing proportion of incident human immunodeficiency virus (HIV) cases across Western Europe. METHODS To characterize sources of transmission in local transmission chains, we used partial HIV consensus sequences with linked demographic and clinical data from the opt-out AIDS Therapy Evaluation in the Netherlands (ATHENA) cohort of people with HIV in the Netherlands and identified phylogenetically and epidemiologically possible HIV transmission pairs in Amsterdam. We interpreted these in the context of estimated infection dates, and quantified population-level sources of transmission to foreign-born and Dutch-born Amsterdam men who have sex with men (MSM) within Amsterdam transmission chains. RESULTS We estimate that Dutch-born MSM were the predominant sources of infections among all Amsterdam MSM who acquired their infection locally in 2010-2021, and among almost all foreign-born Amsterdam MSM subpopulations. Stratifying by 2-year intervals indicated time trends in transmission dynamics, with a majority of infections originating from foreign-born MSM since 2016, although uncertainty ranges remained wide. CONCLUSIONS Native-born MSM have predominantly driven HIV transmissions in Amsterdam in 2010-2021. However, in the context of rapidly declining incidence in Amsterdam, the contribution from foreign-born MSM living in Amsterdam is increasing, with some evidence that most local transmissions have been from foreign-born Amsterdam MSM since 2016.
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Affiliation(s)
| | - Nikos Pantazis
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Greece
| | - Evangelia Georgia Kostaki
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Greece
| | | | | | | | | | - Marc van der Valk
- Stichting HIV Monitoring, The Netherlands
- Amsterdam Institute for Infection and Immunity, Amsterdam University Medical Center, The Netherlands
| | - Peter Reiss
- Amsterdam Institute for Global Health and Development, The Netherlands
- Department of Global Health, Amsterdam University Medical Center, University of Amsterdam, The Netherlands
| | - Godelieve de Bree
- Amsterdam Institute for Infection and Immunity, Amsterdam University Medical Center, The Netherlands
- Amsterdam Institute for Global Health and Development, The Netherlands
| | - Oliver Ratmann
- Department of Mathematics, Imperial College London, United Kingdom
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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. 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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3
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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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4
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He WT, Li D, Baele G, Zhao J, Jiang Z, Ji X, Veit M, Suchard MA, Holmes EC, Lemey P, Boni MF, Su S. Newly identified lineages of porcine hemagglutinating encephalomyelitis virus exhibit respiratory phenotype. Virus Evol 2023; 9:vead051. [PMID: 37711483 PMCID: PMC10499004 DOI: 10.1093/ve/vead051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 05/18/2023] [Accepted: 08/13/2023] [Indexed: 09/16/2023] Open
Abstract
Swine pathogens have a long history of zoonotic transmission to humans, occasionally leading to sustained outbreaks or pandemics. Through a retrospective epidemiological study of swine populations in China, we describe novel lineages of porcine hemagglutinating encephalomyelitis virus (PHEV) complex coronaviruses (CoVs) that cause exclusively respiratory symptoms with no signs of the neurological symptoms typically associated with classical PHEV infection. Through large-scale epidemiological surveillance, we show that these novel lineages have circulated in at least eight provinces in southeastern China. Phylogenetic and recombination analyses of twenty-four genomes identified two major viral lineages causing respiratory symptoms with extensive recombination within them, between them, and between classical PHEV and the novel respiratory variant PHEV (rvPHEV) lineages. Divergence times among the sampled lineages in the PHEV virus complex date back to 1886-1958 (mean estimate 1928), with the two major rvPHEV lineages separating approximately 20 years later. Many rvPHEV viruses show amino acid substitutions at the carbohydrate-binding site of hemagglutinin esterase (HE) and/or have lost the cysteine required for HE dimerization. This resembles the early adaptation of human CoVs, where HE lost its hemagglutination ability to adapt to growth in the human respiratory tract. Our study represents the first report of the evolutionary history of rvPHEV circulating in swine and highlights the importance of characterizing CoV diversity and recombination in swine to identify pathogens with outbreak potential that could threaten swine farming.
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Affiliation(s)
- Wan-Ting He
- Jiangsu Engineering Laboratory of Animal Immunology, Institute of Immunology, College of Veterinary Medicine, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing 210095, China
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, Leuven 3000, Belgium
| | - Dongyan Li
- Jiangsu Engineering Laboratory of Animal Immunology, Institute of Immunology, College of Veterinary Medicine, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing 210095, China
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, Leuven 3000, Belgium
| | - Jin Zhao
- Jiangsu Engineering Laboratory of Animal Immunology, Institute of Immunology, College of Veterinary Medicine, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing 210095, China
| | - Zhiwen Jiang
- Jiangsu Engineering Laboratory of Animal Immunology, Institute of Immunology, College of Veterinary Medicine, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing 210095, China
| | - Xiang Ji
- Department of Mathematics, School of Science & Engineering, Tulane University, New Orleans, LA 70118, USA
| | - Michael Veit
- Institute for Virology, Center for Infection Medicine, Veterinary Faculty, Free University Berlin, Berlin 14163, Germany
| | - Marc A Suchard
- Department of Biostatistics, Fielding School of Public Health, and Departments of Biomathematics and Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Edward C Holmes
- Sydney Institute for Infectious Diseases, School of Life and Environmental Sciences and School of Medical Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, Leuven 3000, Belgium
| | | | - Shuo Su
- Jiangsu Engineering Laboratory of Animal Immunology, Institute of Immunology, College of Veterinary Medicine, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing 210095, China
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA 16802, USA
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5
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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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6
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Liang J, Zhu Z, Lan R, Meng J, Vrancken B, Lu S, Jin D, Yang J, Wang J, Qin T, Pu J, Zhang L, Dong K, Xu M, Tian H, Jiang T, Xu J. Evolutionary and genomic insights into the long-term colonization of Shigella flexneri in animals. Emerg Microbes Infect 2022; 11:2069-2079. [PMID: 35930371 PMCID: PMC9448383 DOI: 10.1080/22221751.2022.2109514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
The enteroinvasive bacterium Shigella flexneri is known as a highly host-adapted human pathogen. There had been no known other reservoirs reported until recently. Here 34 isolates obtained from animals (yaks, dairy cows and beef cattle) from 2016-2017 and 268 human S. flexneri isolates from China were sequenced to determine the relationships between animal and human isolates and infer the evolutionary history of animal-associated S. flexneri. The 18 animal isolates (15 yak and 3 beef cattle isolates) in PG1 were separated into 4 lineages, and the 16 animal isolates (1 yak, 5 beef cattle and 10 dairy cow isolates) in PG3 were clustered in 8 lineages. The most recent human isolates from China belonged to PG3 whereas Chinese isolates from the 1950s-1960s belonged to PG1. PG1 S. flexneri may has been transmitted to the yaks during PG1 circulation in the human population in China and has remained in the yak population since, while PG3 S. flexneri in animals were likely recent transmissions from the human population. Increased stability of the large virulence plasmid and acquisition of abundant antimicrobial resistance determinants may have enabled PG3 to expand globally and replaced PG1 in China. Our study confirms that animals may act as a reservoir for S. flexneri. Genomic analysis revealed the evolutionary history of multiple S. flexneri lineages in animals and humans in China. However, further studies are required to determine the public health threat of S. flexneri from animals.
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Affiliation(s)
- Junrong Liang
- State Key laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhen Zhu
- College of Life Science and Food Engineering, Hebei University of Engineering, Handan, China
| | - Ruiting Lan
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia
| | - Jing Meng
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.,Suzhou Institute of Systems Medicine, Suzhou, China
| | - Bram Vrancken
- Department of Microbiology and Immunology, Rega Institute, Laboratory of Evolutionary and Computational Virology, KU Leuven, Leuven, Belgium
| | - Shan Lu
- State Key laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.,Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.,Research Units of Discovery of Unknown Bacteria and Function, Chinese Academy of Medical Sciences, Beijing, China
| | - Dong Jin
- State Key laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.,Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.,Research Units of Discovery of Unknown Bacteria and Function, Chinese Academy of Medical Sciences, Beijing, China
| | - Jing Yang
- State Key laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.,Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.,Research Units of Discovery of Unknown Bacteria and Function, Chinese Academy of Medical Sciences, Beijing, China
| | - Jianping Wang
- State Key laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Tian Qin
- State Key laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Ji Pu
- State Key laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Li Zhang
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.,Suzhou Institute of Systems Medicine, Suzhou, China
| | - Kui Dong
- Shanxi Eye Hospital, Taiyuan, China
| | - Mingchao Xu
- State Key laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Taijiao Jiang
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.,Suzhou Institute of Systems Medicine, Suzhou, China.,Guangzhou Laboratory, Guangzhou, China
| | - Jianguo Xu
- State Key laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.,Research Units of Discovery of Unknown Bacteria and Function, Chinese Academy of Medical Sciences, Beijing, China.,Research Institute of Public Heath, Nankai University, Tianjin, China
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7
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Trovão NS, Thijssen M, Vrancken B, Pineda-Peña AC, Mina T, Amini-Bavil-Olyaee S, Lemey P, Baele G, Pourkarim MR. Reconstruction of the Origin and Dispersal of the Worldwide Dominant Hepatitis B Virus Subgenotype D1. Virus Evol 2022; 8:veac028. [PMID: 35712523 PMCID: PMC9194798 DOI: 10.1093/ve/veac028] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 02/18/2022] [Accepted: 04/07/2022] [Indexed: 12/01/2022] Open
Abstract
Hepatitis B is a potentially life-threatening liver infection caused by the hepatitis B virus (HBV). HBV-D1 is the dominant subgenotype in the Mediterranean basin, Eastern Europe, and Asia. However, little is currently known about its evolutionary history and spatio-temporal dynamics. We use Bayesian phylodynamic inference to investigate the temporal history of HBV-D1, for which we calibrate the molecular clock using ancient sequences, and reconstruct the viral global spatial dynamics based, for the first time, on full-length publicly available HBV-D1 genomes from a wide range of sampling dates. We pinpoint the origin of HBV subgenotype D1 before the current era (BCE) in Turkey/Anatolia. The spatial reconstructions reveal global viral transmission with a high degree of mixing. By combining modern-day and ancient sequences, we ensure sufficient temporal signal in HBV-D1 data to enable Bayesian phylodynamic inference using a molecular clock for time calibration. Our results shed light on the worldwide HBV-D1 epidemics and suggest that this originally Middle Eastern virus significantly affects more distant countries, such as those in mainland Europe.
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Affiliation(s)
- Nídia Sequeira Trovão
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Rega Institute, Laboratory for Clinical and Epidemiological Virology, Leuven, Belgium, Herestraat 49, BE-3000 Leuven, Belgium
| | - Marijn Thijssen
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Rega Institute, Laboratory for Clinical and Epidemiological Virology, Leuven, Belgium, Herestraat 49, BE-3000 Leuven, Belgium
| | - Bram Vrancken
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Rega Institute, Laboratory for Clinical and Epidemiological Virology, Leuven, Belgium, Herestraat 49, BE-3000 Leuven, Belgium
| | - Andrea-Clemencia Pineda-Peña
- Global Health and Tropical Medicine, GHTM, Instituto de Higiene e Medicina Tropical, IHMT; Universidade Nova de Lisboa, UNL, Lisbon, Portugal Rua da Junqueira No 100, 1349-008, Lisbon, Portugal
- Molecular Biology and Immunology Department, Fundación Instituto de Inmunología de Colombia (FIDIC); Faculty of Animal Science, Universidad de Ciencias Aplicadas y Ambientales (U.D.C.A), Avenida 50 No. 26-20 Bogotá, Colombia
| | - Thomas Mina
- Mina Clinical Laboratory, Gregori Afxentiou, Iocasti Court Block A, Flat 22 Mesa Yitonia, 4003 Lemesos, Cyprus
| | - Samad Amini-Bavil-Olyaee
- Biosafety Development Group, Cellular Sciences Department, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Rega Institute, Laboratory for Clinical and Epidemiological Virology, Leuven, Belgium, Herestraat 49, BE-3000 Leuven, Belgium
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Rega Institute, Laboratory for Clinical and Epidemiological Virology, Leuven, Belgium, Herestraat 49, BE-3000 Leuven, Belgium
| | - Mahmoud Reza Pourkarim
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Rega Institute, Laboratory for Clinical and Epidemiological Virology, Leuven, Belgium, Herestraat 49, BE-3000 Leuven, Belgium
- Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
- Blood Transfusion Research Centre, High Institute for Research and Education in Transfusion Medicine, Hemmat Exp. Way, 14665-1157, Tehran, Iran
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8
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Methods Combining Genomic and Epidemiological Data in the Reconstruction of Transmission Trees: A Systematic Review. Pathogens 2022; 11:pathogens11020252. [PMID: 35215195 PMCID: PMC8875843 DOI: 10.3390/pathogens11020252] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/08/2022] [Accepted: 02/11/2022] [Indexed: 11/17/2022] Open
Abstract
In order to better understand transmission dynamics and appropriately target control and preventive measures, studies have aimed to identify who-infected-whom in actual outbreaks. Numerous reconstruction methods exist, each with their own assumptions, types of data, and inference strategy. Thus, selecting a method can be difficult. Following PRISMA guidelines, we systematically reviewed the literature for methods combing epidemiological and genomic data in transmission tree reconstruction. We identified 22 methods from the 41 selected articles. We defined three families according to how genomic data was handled: a non-phylogenetic family, a sequential phylogenetic family, and a simultaneous phylogenetic family. We discussed methods according to the data needed as well as the underlying sequence mutation, within-host evolution, transmission, and case observation. In the non-phylogenetic family consisting of eight methods, pairwise genetic distances were estimated. In the phylogenetic families, transmission trees were inferred from phylogenetic trees either simultaneously (nine methods) or sequentially (five methods). While a majority of methods (17/22) modeled the transmission process, few (8/22) took into account imperfect case detection. Within-host evolution was generally (7/8) modeled as a coalescent process. These practical and theoretical considerations were highlighted in order to help select the appropriate method for an outbreak.
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9
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Novitsky V, Steingrimsson J, Howison M, Dunn C, Gillani FS, Manne A, Li Y, Spence M, Parillo Z, Fulton J, Marak T, Chan P, Bertrand T, Bandy U, Alexander-Scott N, Hogan J, Kantor R. Longitudinal typing of molecular HIV clusters in a statewide epidemic. AIDS 2021; 35:1711-1722. [PMID: 34033589 PMCID: PMC8373695 DOI: 10.1097/qad.0000000000002953] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND HIV molecular epidemiology is increasingly integrated into public health prevention. We conducted cluster typing to enhance characterization of a densely sampled statewide epidemic towards informing public health. METHODS We identified HIV clusters, categorized them into types, and evaluated their dynamics between 2004 and 2019 in Rhode Island. We grouped sequences by diagnosis year, assessed cluster changes between paired phylogenies, t0 and t1, representing adjacent years and categorized clusters as stable (cluster in t0 phylogeny = cluster in t1 phylogeny) or unstable (cluster in t0 ≠ cluster in t1). Unstable clusters were further categorized as emerging (t1 phylogeny only) or growing (larger in t1 phylogeny). We determined proportions of each cluster type, of individuals in each cluster type, and of newly diagnosed individuals in each cluster type, and assessed trends over time. RESULTS A total of 1727 individuals with available HIV-1 subtype B pol sequences were diagnosed in Rhode Island by 2019. Over time, stable clusters and individuals in them dominated the epidemic, increasing over time, with reciprocally decreasing unstable clusters and individuals in them. Conversely, proportions of newly diagnosed individuals in unstable clusters significantly increased. Within unstable clusters, proportions of emerging clusters and of individuals in them declined; whereas proportions of newly diagnosed individuals in growing clusters significantly increased over time. CONCLUSION Distinct molecular cluster types were identified in the Rhode Island epidemic. Cluster dynamics demonstrated increasing stable and decreasing unstable clusters driven by growing, rather than emerging clusters, suggesting consistent in-state transmission networks. Cluster typing could inform public health beyond conventional approaches and direct interventions.
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Affiliation(s)
| | | | - Mark Howison
- Research Improving People’s Life, Providence, RI, USA
| | | | | | | | | | | | | | | | | | - Philip Chan
- Brown University, Providence, RI, USA
- Rhode Island Department of Health, Providence, RI, USA
| | | | - Utpala Bandy
- Rhode Island Department of Health, Providence, RI, USA
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10
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de Bernardi Schneider A, Osiowy C, Hostager R, Krarup H, Børresen M, Tanaka Y, Morriseau T, Wertheim JO. Analysis of Hepatitis B Virus Genotype D in Greenland Suggests the Presence of a Novel Quasi-Subgenotype. Front Microbiol 2021; 11:602296. [PMID: 33519744 PMCID: PMC7843931 DOI: 10.3389/fmicb.2020.602296] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 12/14/2020] [Indexed: 12/12/2022] Open
Abstract
A disproportionate number of Greenland's Inuit population are chronically infected with Hepatitis B virus (HBV; 5-10%). HBV genotypes B and D are most prevalent in the circumpolar Arctic. Here, we report 39 novel HBV/D sequences from individuals residing in southwestern Greenland. We performed phylodynamic analyses with ancient HBV DNA calibrators to investigate the origin and relationship of these taxa to other HBV sequences. We inferred a substitution rate of 1.4 × 10-5 [95% HPD 8.8 × 10-6, 2.0 × 10-5] and a time to the most recent common ancestor of 629 CE [95% HPD 37-1138 CE]. The Greenland taxa form a sister clade to HBV/D2 sequences, specifically New Caledonian and Indigenous Taiwanese sequences. The Greenland sequences share amino acid signatures with subgenotypes D1 and D2 and ~97% sequence identity. Our results suggest the classification of these novel sequences does not fit within the current nomenclature. Thus, we propose these taxa be considered a novel quasi-subgenotype.
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Affiliation(s)
| | - Carla Osiowy
- National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada
| | - Reilly Hostager
- Department of Medicine, University of California San Diego, San Diego, CA, United States
| | - Henrik Krarup
- Department of Molecular Diagnostics, Aalborg University Hospital, Aalborg, Denmark
- Department of Medical Gastroenterology, Aalborg University Hospital, Aalborg, Denmark
- Clinical Institute, Aalborg University, Aalborg, Denmark
| | - Malene Børresen
- Department of Epidemiological Research, Statens Serum Institut, Copenhagen, Denmark
| | - Yasuhito Tanaka
- Department of Virology & Liver, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Taylor Morriseau
- National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada
| | - Joel O. Wertheim
- Department of Medicine, University of California San Diego, San Diego, CA, United States
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11
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Schreiber SJ, Ke R, Loverdo C, Park M, Ahsan P, Lloyd-Smith JO. Cross-scale dynamics and the evolutionary emergence of infectious diseases. Virus Evol 2021; 7:veaa105. [PMID: 35186322 PMCID: PMC8087961 DOI: 10.1093/ve/veaa105] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023] Open
Abstract
When emerging pathogens encounter new host species for which they are poorly adapted, they must evolve to escape extinction. Pathogens experience selection on traits at multiple scales, including replication rates within host individuals and transmissibility between hosts. We analyze a stochastic model linking pathogen growth and competition within individuals to transmission between individuals. Our analysis reveals a new factor, the cross-scale reproductive number of a mutant virion, that quantifies how quickly mutant strains increase in frequency when they initially appear in the infected host population. This cross-scale reproductive number combines with viral mutation rates, single-strain reproductive numbers, and transmission bottleneck width to determine the likelihood of evolutionary emergence, and whether evolution occurs swiftly or gradually within chains of transmission. We find that wider transmission bottlenecks facilitate emergence of pathogens with short-term infections, but hinder emergence of pathogens exhibiting cross-scale selective conflict and long-term infections. Our results provide a framework to advance the integration of laboratory, clinical, and field data in the context of evolutionary theory, laying the foundation for a new generation of evidence-based risk assessment of emergence threats.
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Affiliation(s)
| | - Ruian Ke
- T-6: Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Claude Loverdo
- Laboratoire Jean Perrin, Sorbonne Université, CNRS, Paris 75005, France
| | - Miran Park
- Department of Ecology & Evolution, University of California, Los Angeles, CA 90095, USA
| | - Prianna Ahsan
- Department of Ecology & Evolution, University of California, Los Angeles, CA 90095, USA
| | - James O Lloyd-Smith
- Department of Ecology & Evolution, University of California, Los Angeles, CA 90095, USA
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12
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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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13
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Boskova V, Stadler T. PIQMEE: Bayesian Phylodynamic Method for Analysis of Large Data Sets with Duplicate Sequences. Mol Biol Evol 2020; 37:3061-3075. [PMID: 32492139 PMCID: PMC7530608 DOI: 10.1093/molbev/msaa136] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Next-generation sequencing of pathogen quasispecies within a host yields data sets of tens to hundreds of unique sequences. However, the full data set often contains thousands of sequences, because many of those unique sequences have multiple identical copies. Data sets of this size represent a computational challenge for currently available Bayesian phylogenetic and phylodynamic methods. Through simulations, we explore how large data sets with duplicate sequences affect the speed and accuracy of phylogenetic and phylodynamic analysis within BEAST 2. We show that using unique sequences only leads to biases, and using a random subset of sequences yields imprecise parameter estimates. To overcome these shortcomings, we introduce PIQMEE, a BEAST 2 add-on that produces reliable parameter estimates from full data sets with increased computational efficiency as compared with the currently available methods within BEAST 2. The principle behind PIQMEE is to resolve the tree structure of the unique sequences only, while simultaneously estimating the branching times of the duplicate sequences. Distinguishing between unique and duplicate sequences allows our method to perform well even for very large data sets. Although the classic method converges poorly for data sets of 6,000 sequences when allowed to run for 7 days, our method converges in slightly more than 1 day. In fact, PIQMEE can handle data sets of around 21,000 sequences with 20 unique sequences in 14 days. Finally, we apply the method to a real, within-host HIV sequencing data set with several thousand sequences per patient.
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Affiliation(s)
- Veronika Boskova
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Swiss Institute of Bioinformatics (SIB), Switzerland
- Center for Integrative Bioinformatics Vienna, Max Perutz Labs, University of Vienna and Medical University of Vienna, Vienna, Austria
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Swiss Institute of Bioinformatics (SIB), Switzerland
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14
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Düx A, Lequime S, Patrono LV, Vrancken B, Boral S, Gogarten JF, Hilbig A, Horst D, Merkel K, Prepoint B, Santibanez S, Schlotterbeck J, Suchard MA, Ulrich M, Widulin N, Mankertz A, Leendertz FH, Harper K, Schnalke T, Lemey P, Calvignac-Spencer S. Measles virus and rinderpest virus divergence dated to the sixth century BCE. Science 2020; 368:1367-1370. [PMID: 32554594 DOI: 10.1126/science.aba9411] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 04/30/2020] [Indexed: 12/11/2022]
Abstract
Many infectious diseases are thought to have emerged in humans after the Neolithic revolution. Although it is broadly accepted that this also applies to measles, the exact date of emergence for this disease is controversial. We sequenced the genome of a 1912 measles virus and used selection-aware molecular clock modeling to determine the divergence date of measles virus and rinderpest virus. This divergence date represents the earliest possible date for the establishment of measles in human populations. Our analyses show that the measles virus potentially arose as early as the sixth century BCE, possibly coinciding with the rise of large cities.
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Affiliation(s)
- Ariane Düx
- Epidemiology of Highly Pathogenic Microorganisms Project Group, Robert Koch Institute, Berlin, Germany.,Viral Evolution Project Group, Robert Koch Institute, Berlin, Germany
| | - Sebastian Lequime
- Laboratory of Clinical and Evolutionary Virology, Department of Microbiology, Immunology and Transplantation, Rega Institute, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
| | - Livia Victoria Patrono
- Epidemiology of Highly Pathogenic Microorganisms Project Group, Robert Koch Institute, Berlin, Germany.,Viral Evolution Project Group, Robert Koch Institute, Berlin, Germany
| | - Bram Vrancken
- Laboratory of Clinical and Evolutionary Virology, Department of Microbiology, Immunology and Transplantation, Rega Institute, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
| | - Sengül Boral
- Institute for Pathology, Charité, Berlin, Germany
| | - Jan F Gogarten
- Epidemiology of Highly Pathogenic Microorganisms Project Group, Robert Koch Institute, Berlin, Germany.,Viral Evolution Project Group, Robert Koch Institute, Berlin, Germany
| | - Antonia Hilbig
- Epidemiology of Highly Pathogenic Microorganisms Project Group, Robert Koch Institute, Berlin, Germany
| | - David Horst
- Institute for Pathology, Charité, Berlin, Germany
| | - Kevin Merkel
- Epidemiology of Highly Pathogenic Microorganisms Project Group, Robert Koch Institute, Berlin, Germany.,Viral Evolution Project Group, Robert Koch Institute, Berlin, Germany
| | - Baptiste Prepoint
- Viral Evolution Project Group, Robert Koch Institute, Berlin, Germany.,Département de Biologie, Ecole Normale Supérieure, PSL Université Paris, Paris, France
| | - Sabine Santibanez
- National Reference Centre for Measles, Mumps, and Rubella, Robert Koch Institute, Berlin, Germany
| | | | - Marc A Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Markus Ulrich
- Epidemiology of Highly Pathogenic Microorganisms Project Group, Robert Koch Institute, Berlin, Germany
| | - Navena Widulin
- Berlin Museum of Medical History, Charité, Berlin, Germany
| | - Annette Mankertz
- National Reference Centre for Measles, Mumps, and Rubella, Robert Koch Institute, Berlin, Germany
| | - Fabian H Leendertz
- Epidemiology of Highly Pathogenic Microorganisms Project Group, Robert Koch Institute, Berlin, Germany
| | - Kyle Harper
- Department of Classics and Letters, University of Oklahoma, Norman, OK, USA
| | | | - Philippe Lemey
- Laboratory of Clinical and Evolutionary Virology, Department of Microbiology, Immunology and Transplantation, Rega Institute, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
| | - Sébastien Calvignac-Spencer
- Epidemiology of Highly Pathogenic Microorganisms Project Group, Robert Koch Institute, Berlin, Germany. .,Viral Evolution Project Group, Robert Koch Institute, Berlin, Germany
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15
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Abstract
MOTIVATION The combination of genomic and epidemiological data holds the potential to enable accurate pathogen transmission history inference. However, the inference of outbreak transmission histories remains challenging due to various factors such as within-host pathogen diversity and multi-strain infections. Current computational methods ignore within-host diversity and/or multi-strain infections, often failing to accurately infer the transmission history. Thus, there is a need for efficient computational methods for transmission tree inference that accommodate the complexities of real data. RESULTS We formulate the direct transmission inference (DTI) problem for inferring transmission trees that support multi-strain infections given a timed phylogeny and additional epidemiological data. We establish hardness for the decision and counting version of the DTI problem. We introduce Transmission Tree Uniform Sampler (TiTUS), a method that uses SATISFIABILITY to almost uniformly sample from the space of transmission trees. We introduce criteria that prioritize parsimonious transmission trees that we subsequently summarize using a novel consensus tree approach. We demonstrate TiTUS's ability to accurately reconstruct transmission trees on simulated data as well as a documented HIV transmission chain. AVAILABILITY AND IMPLEMENTATION https://github.com/elkebir-group/TiTUS. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Palash Sashittal
- Department of Aerospace Engineering, University of Illinois at Urbana-Champaign, Urbama, IL 61801, USA
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbama, IL 61801, USA
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16
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Moshiri N, Ragonnet-Cronin M, Wertheim JO, Mirarab S. FAVITES: simultaneous simulation of transmission networks, phylogenetic trees and sequences. Bioinformatics 2020; 35:1852-1861. [PMID: 30395173 DOI: 10.1093/bioinformatics/bty921] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 10/29/2018] [Accepted: 11/01/2018] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The ability to simulate epidemics as a function of model parameters allows insights that are unobtainable from real datasets. Further, reconstructing transmission networks for fast-evolving viruses like Human Immunodeficiency Virus (HIV) may have the potential to greatly enhance epidemic intervention, but transmission network reconstruction methods have been inadequately studied, largely because it is difficult to obtain 'truth' sets on which to test them and properly measure their performance. RESULTS We introduce FrAmework for VIral Transmission and Evolution Simulation (FAVITES), a robust framework for simulating realistic datasets for epidemics that are caused by fast-evolving pathogens like HIV. FAVITES creates a generative model to produce contact networks, transmission networks, phylogenetic trees and sequence datasets, and to add error to the data. FAVITES is designed to be extensible by dividing the generative model into modules, each of which is expressed as a fixed API that can be implemented using various models. We use FAVITES to simulate HIV datasets and study the realism of the simulated datasets. We then use the simulated data to study the impact of the increased treatment efforts on epidemiological outcomes. We also study two transmission network reconstruction methods and their effectiveness in detecting fast-growing clusters. AVAILABILITY AND IMPLEMENTATION FAVITES is available at https://github.com/niemasd/FAVITES, and a Docker image can be found on DockerHub (https://hub.docker.com/r/niemasd/favites). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Niema Moshiri
- Bioinformatics and Systems Biology Graduate Program, UC San Diego, La Jolla, USA
| | | | | | - Siavash Mirarab
- Department of Electrical and Computer Engineering, UC San Diego, La Jolla, USA
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17
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Gibson KM, Jair K, Castel AD, Bendall ML, Wilbourn B, Jordan JA, Crandall KA, Pérez-Losada M. A cross-sectional study to characterize local HIV-1 dynamics in Washington, DC using next-generation sequencing. Sci Rep 2020; 10:1989. [PMID: 32029767 PMCID: PMC7004982 DOI: 10.1038/s41598-020-58410-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 12/31/2019] [Indexed: 11/08/2022] Open
Abstract
Washington, DC continues to experience a generalized HIV-1 epidemic. We characterized the local phylodynamics of HIV-1 in DC using next-generation sequencing (NGS) data. Viral samples from 68 participants from 2016 through 2017 were sequenced and paired with epidemiological data. Phylogenetic and network inferences, drug resistant mutations (DRMs), subtypes and HIV-1 diversity estimations were completed. Haplotypes were reconstructed to infer transmission clusters. Phylodynamic inferences based on the HIV-1 polymerase (pol) and envelope genes (env) were compared. Higher HIV-1 diversity (n.s.) was seen in men who have sex with men, heterosexual, and male participants in DC. 54.0% of the participants contained at least one DRM. The 40-49 year-olds showed the highest prevalence of DRMs (22.9%). Phylogenetic analysis of pol and env sequences grouped 31.9-33.8% of the participants into clusters. HIV-TRACE grouped 2.9-12.8% of participants when using consensus sequences and 9.0-64.2% when using haplotypes. NGS allowed us to characterize the local phylodynamics of HIV-1 in DC more broadly and accurately, given a better representation of its diversity and dynamics. Reconstructed haplotypes provided novel and deeper phylodynamic insights, which led to networks linking a higher number of participants. Our understanding of the HIV-1 epidemic was expanded with the powerful coupling of HIV-1 NGS data with epidemiological data.
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Grants
- P30 AI117970 NIAID NIH HHS
- U01 AI069503 NIAID NIH HHS
- UM1 AI069503 NIAID NIH HHS
- This study was supported by the DC Cohort Study (U01 AI69503-03S2), a supplement from the Women’s Interagency Study for HIV-1 (410722_GR410708), a DC D-CFAR pilot award, and a 2015 HIV-1 Phylodynamics Supplement award from the District of Columbia for AIDS Research, an NIH funded program (AI117970), which is supported by the following NIH Co-Funding and Participating Institutes and Centers: NIAID, NCI, NICHD, NHLBI, NIDA, NIMH, NIA, FIC, NIGMS, NIDDK and OAR. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
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Affiliation(s)
- Keylie M Gibson
- Computational Biology Institute, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.
| | - Kamwing Jair
- Department of Epidemiology, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Amanda D Castel
- Department of Epidemiology, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Matthew L Bendall
- Computational Biology Institute, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Brittany Wilbourn
- Department of Epidemiology, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Jeanne A Jordan
- Department of Epidemiology, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Keith A Crandall
- Computational Biology Institute, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
- Department of Biostatistics and Bioinformatics, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
| | - Marcos Pérez-Losada
- Computational Biology Institute, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
- Department of Biostatistics and Bioinformatics, The Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA
- CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, Vairão, Portugal
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18
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Hong SL, Dellicour S, Vrancken B, Suchard MA, Pyne MT, Hillyard DR, Lemey P, Baele G. In Search of Covariates of HIV-1 Subtype B Spread in the United States-A Cautionary Tale of Large-Scale Bayesian Phylogeography. Viruses 2020; 12:v12020182. [PMID: 32033422 PMCID: PMC7077180 DOI: 10.3390/v12020182] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 01/24/2020] [Accepted: 01/28/2020] [Indexed: 12/21/2022] Open
Abstract
Infections with HIV-1 group M subtype B viruses account for the majority of the HIV epidemic in the Western world. Phylogeographic studies have placed the introduction of subtype B in the United States in New York around 1970, where it grew into a major source of spread. Currently, it is estimated that over one million people are living with HIV in the US and that most are infected with subtype B variants. Here, we aim to identify the drivers of HIV-1 subtype B dispersal in the United States by analyzing a collection of 23,588 pol sequences, collected for drug resistance testing from 45 states during 2004-2011. To this end, we introduce a workflow to reduce this large collection of data to more computationally-manageable sample sizes and apply the BEAST framework to test which covariates associate with the spread of HIV-1 across state borders. Our results show that we are able to consistently identify certain predictors of spread under reasonable run times across datasets of up to 10,000 sequences. However, the general lack of phylogenetic structure and the high uncertainty associated with HIV trees make it difficult to interpret the epidemiological relevance of the drivers of spread we are able to identify. While the workflow we present here could be applied to other virus datasets of a similar scale, the characteristic star-like shape of HIV-1 phylogenies poses a serious obstacle to reconstructing a detailed evolutionary and spatial history for HIV-1 subtype B in the US.
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Affiliation(s)
- Samuel L. Hong
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, 3000 Leuven, Belgium; (S.D.); (B.V.); (P.L.); (G.B.)
- Correspondence:
| | - Simon Dellicour
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, 3000 Leuven, Belgium; (S.D.); (B.V.); (P.L.); (G.B.)
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Bram Vrancken
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, 3000 Leuven, Belgium; (S.D.); (B.V.); (P.L.); (G.B.)
| | - Marc A. Suchard
- Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095, USA;
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095, USA
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA 90095, USA
| | - Michael T. Pyne
- ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT 84108, USA;
| | - David R. Hillyard
- Department of Pathology, University of Utah, Salt Lake City, UT 84112, USA;
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, 3000 Leuven, Belgium; (S.D.); (B.V.); (P.L.); (G.B.)
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, 3000 Leuven, Belgium; (S.D.); (B.V.); (P.L.); (G.B.)
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19
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Membrebe JV, Suchard MA, Rambaut A, Baele G, Lemey P. Bayesian Inference of Evolutionary Histories under Time-Dependent Substitution Rates. Mol Biol Evol 2020; 36:1793-1803. [PMID: 31004175 PMCID: PMC6657730 DOI: 10.1093/molbev/msz094] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Many factors complicate the estimation of time scales for phylogenetic histories, requiring increasingly complex evolutionary models and inference procedures. The widespread application of molecular clock dating has led to the insight that evolutionary rate estimates may vary with the time frame of measurement. This is particularly well established for rapidly evolving viruses that can accumulate sequence divergence over years or even months. However, this rapid evolution stands at odds with a relatively high degree of conservation of viruses or endogenous virus elements over much longer time scales. Building on recent insights into time-dependent evolutionary rates, we develop a formal and flexible Bayesian statistical inference approach that accommodates rate variation through time. We evaluate the novel molecular clock model on a foamy virus cospeciation history and a lentivirus evolutionary history and compare the performance to other molecular clock models. For both virus examples, we estimate a similarly strong time-dependent effect that implies rates varying over four orders of magnitude. The application of an analogous codon substitution model does not implicate long-term purifying selection as the cause of this effect. However, selection does appear to affect divergence time estimates for the less deep evolutionary history of the Ebolavirus genus. Finally, we explore the application of our approach on woolly mammoth ancient DNA data, which shows a much weaker, but still important, time-dependent rate effect that has a noticeable impact on node age estimates. Future developments aimed at incorporating more complex evolutionary processes will further add to the broad applicability of our approach.
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Affiliation(s)
- Jade Vincent Membrebe
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven - University of Leuven, Leuven, Belgium
| | - Marc A Suchard
- Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA.,Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA.,Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom.,Fogarty International Center, National Institutes of Health, Bethesda, MD
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven - University of Leuven, Leuven, Belgium
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven - University of Leuven, Leuven, Belgium
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20
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Mak L, Perera D, Lang R, Kossinna P, He J, Gill MJ, Long Q, van Marle G. Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort. Microorganisms 2020; 8:E196. [PMID: 32023939 PMCID: PMC7074708 DOI: 10.3390/microorganisms8020196] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 01/23/2020] [Accepted: 01/29/2020] [Indexed: 01/08/2023] Open
Abstract
Keywords: HIV; Canada; molecular phylogenetics; viral evolution; person-to-person transmission inference; transmission network; summary statistics.
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Affiliation(s)
- Lauren Mak
- Department of Biochemistry & Molecular Biology, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada (P.K.)
| | - Deshan Perera
- Department of Biochemistry & Molecular Biology, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada (P.K.)
| | - Raynell Lang
- Department of Medicine, Cumming School of Medicine, University of Calgary and Alberta Health Services, Calgary, AB T2N 4N1, Canada
| | - Pathum Kossinna
- Department of Biochemistry & Molecular Biology, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada (P.K.)
| | - Jingni He
- Department of Biochemistry & Molecular Biology, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada (P.K.)
| | - M. John Gill
- Department of Medicine, Cumming School of Medicine, University of Calgary and Alberta Health Services, Calgary, AB T2N 4N1, Canada
| | - Quan Long
- Department of Biochemistry & Molecular Biology, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada (P.K.)
- Department of Medical Genetics, and Mathematics & Statistics, Alberta Children’s Hospital Research Institute, O’Brien Institute for Public Health, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Mathematics & Statistics, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Guido van Marle
- Department of Microbiology, Immunology, and Infectious Diseases, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
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21
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Li WY, Huang SW, Wang SF, Liu HF, Chou CH, Wu SJ, Huang HD, Lu PL, Fann CSJ, Chen M, Chen YH, Chen YMA. Source identification of HIV-1 transmission in three lawsuits Using Ultra-Deep pyrosequencing and phylogenetic analysis. JOURNAL OF MICROBIOLOGY, IMMUNOLOGY, AND INFECTION = WEI MIAN YU GAN RAN ZA ZHI 2020; 54:596-605. [PMID: 32067946 DOI: 10.1016/j.jmii.2019.12.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 11/21/2019] [Accepted: 12/26/2019] [Indexed: 12/01/2022]
Abstract
BACKGROUND/PURPOSE Intentional transmission of HIV-1 is a crime. Identifying the source of transmission between HIV-1 infected cases using phylogenetic analysis has limitations, including delayed examinations after the initiation of infection and ambiguity of phyletic relationships. This study was the first to introduce phylogenetic tree Results as forensic evidence in a trial in Taiwan. METHODS Three lawsuit cases from different district courts in Taiwan were chosen for this study. We identified the source of transmission between individuals in each lawsuit based on the maximum likelihood and Bayesian phylogenetic tree analyses using the HIV-1 sequences from molecular cloning and ultra-deep pyrosequencing (UDPS). Two gene regions of the HIV genome, env and gag, were involved. RESULTS The results of phylogenetic analysis using sequences from molecular cloning were clear and evidential enough in lawsuits 1 and 3. Due to the delayed sampling time, the result of sequences from molecular cloning in lawsuit 2 was ambiguous. Combined with the analyzed result of sequences from UDPS and epidemiological information, the source of transmission in lawsuit 2 was further identified. CONCLUSION Hence phylogenetic analyses cannot exclude the possibility of unsampled intermediaries, the data interpretation should be more careful and conservative, and it should not be considered as the only evidence for the source identification in a trial without epidemiological or serological information. The evaluation of the introduced UDPS method in the identification of transmission source has shown that the validity and evidential effects were still limited and need further optimization.
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Affiliation(s)
- Wei-You Li
- Center for Infectious Disease and Cancer Research, Kaohsiung Medical University, Kaohsiung, Taiwan; Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Szu-Wei Huang
- Center for Infectious Disease and Cancer Research, Kaohsiung Medical University, Kaohsiung, Taiwan; Basic Research Laboratory, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - Sheng-Fan Wang
- Center for Infectious Disease and Cancer Research, Kaohsiung Medical University, Kaohsiung, Taiwan; Department of Medical Laboratory Science and Biotechnology, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Hsin-Fu Liu
- Department of Medical Research, Mackay Memorial Hospital, Taipei, Taiwan; Department of Bioscience and Biotechnology, National Taiwan Ocean University, Keelung, Taiwan
| | - Chih-Hung Chou
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan; Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
| | - Shang-Jung Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Hsien-Da Huang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan; Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
| | - Po-Liang Lu
- Center for Infectious Disease and Cancer Research, Kaohsiung Medical University, Kaohsiung, Taiwan; Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan; College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Cathy S J Fann
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Marcelo Chen
- Center for Infectious Disease and Cancer Research, Kaohsiung Medical University, Kaohsiung, Taiwan; Department of Urology, Mackay Memorial Hospital, Taipei, Taiwan; Department of Cosmetic Applications and Management, Mackay Junior College of Medicine, Nursing and Management, Taipei, Taiwan
| | - Yen-Hsu Chen
- Center for Infectious Disease and Cancer Research, Kaohsiung Medical University, Kaohsiung, Taiwan; Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan; Division of Infectious Diseases, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Yi-Ming Arthur Chen
- Center for Infectious Disease and Cancer Research, Kaohsiung Medical University, Kaohsiung, Taiwan; Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan; Clinical Pharmacogenomics and Pharmacoproteomics Program, School of Pharmacy, Taipei Medical University, Taipei, Taiwan.
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22
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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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23
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Variation in Intra-individual Lentiviral Evolution Rates: a Systematic Review of Human, Nonhuman Primate, and Felid Species. J Virol 2019; 93:JVI.00538-19. [PMID: 31167917 DOI: 10.1128/jvi.00538-19] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 06/04/2019] [Indexed: 01/18/2023] Open
Abstract
Lentiviral replication mediated by reverse transcriptase is considered to be highly error prone, leading to a high intra-individual evolution rate that promotes evasion of neutralization and persistent infection. Understanding lentiviral intra-individual evolutionary dynamics on a comparative basis can therefore inform research strategies to aid in studies of pathogenesis, vaccine design, and therapeutic intervention. We conducted a systematic review of intra-individual evolution rates for three species groups of lentiviruses-feline immunodeficiency virus (FIV), simian immunodeficiency virus (SIV), and human immunodeficiency virus (HIV). Overall, intra-individual rate estimates differed by virus but not by host, gene, or viral strain. Lentiviral infections in spillover (nonadapted) hosts approximated infections in primary (adapted) hosts. Our review consistently documents that FIV evolution rates within individuals are significantly lower than the rates recorded for HIV and SIV. FIV intra-individual evolution rates were noted to be equivalent to FIV interindividual rates. These findings document inherent differences in the evolution of FIV relative to that of primate lentiviruses, which may signal intrinsic difference of reverse transcriptase between these viral species or different host-viral interactions. Analysis of lentiviral evolutionary selection pressures at the individual versus population level is valuable for understanding transmission dynamics and the emergence of virulent and avirulent strains and provides novel insight for approaches to interrupt lentiviral infections.IMPORTANCE To the best of our knowledge, this is the first study that compares intra-individual evolution rates for FIV, SIV, and HIV following systematic review of the literature. Our findings have important implications for informing research strategies in the field of intra-individual virus dynamics for lentiviruses. We observed that FIV evolves more slowly than HIV and SIV at the intra-individual level and found that mutation rates may differ by gene sequence length but not by host, gene, strain, an experimental setting relative to a natural setting, or spillover host infection relative to primary host infection.
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24
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Bletsa M, Suchard MA, Ji X, Gryseels S, Vrancken B, Baele G, Worobey M, Lemey P. Divergence dating using mixed effects clock modelling: An application to HIV-1. Virus Evol 2019; 5:vez036. [PMID: 31720009 PMCID: PMC6830409 DOI: 10.1093/ve/vez036] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
The need to estimate divergence times in evolutionary histories in the presence of various sources of substitution rate variation has stimulated a rich development of relaxed molecular clock models. Viral evolutionary studies frequently adopt an uncorrelated clock model as a generic relaxed molecular clock process, but this may impose considerable estimation bias if discrete rate variation exists among clades or lineages. For HIV-1 group M, rate variation among subtypes has been shown to result in inconsistencies in time to the most recent common ancestor estimation. Although this calls into question the adequacy of available molecular dating methods, no solution to this problem has been offered so far. Here, we investigate the use of mixed effects molecular clock models, which combine both fixed and random effects in the evolutionary rate, to estimate divergence times. Using simulation, we demonstrate that this model outperforms existing molecular clock models in a Bayesian framework for estimating time-measured phylogenies in the presence of mixed sources of rate variation, while also maintaining good performance in simpler scenarios. By analysing a comprehensive HIV-1 group M complete genome data set we confirm considerable rate variation among subtypes that is not adequately modelled by uncorrelated relaxed clock models. The mixed effects clock model can accommodate this rate variation and results in a time to the most recent common ancestor of HIV-1 group M of 1920 (1915-25), which is only slightly earlier than the uncorrelated relaxed clock estimate for the same data set. The use of complete genome data appears to have a more profound impact than the molecular clock model because it reduces the credible intervals by 50 per cent relative to similar estimates based on short envelope gene sequences.
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Affiliation(s)
- Magda Bletsa
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, Leuven, Belgium
| | - Marc A Suchard
- Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Xiang Ji
- Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
| | - Sophie Gryseels
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, Leuven, Belgium
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Bram Vrancken
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, Leuven, Belgium
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, Leuven, Belgium
| | - Michael Worobey
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, Leuven, Belgium
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25
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Childs LM, El Moustaid F, Gajewski Z, Kadelka S, Nikin-Beers R, Smith JW, Walker M, Johnson LR. Linked within-host and between-host models and data for infectious diseases: a systematic review. PeerJ 2019; 7:e7057. [PMID: 31249734 PMCID: PMC6589080 DOI: 10.7717/peerj.7057] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 04/28/2019] [Indexed: 12/17/2022] Open
Abstract
The observed dynamics of infectious diseases are driven by processes across multiple scales. Here we focus on two: within-host, that is, how an infection progresses inside a single individual (for instance viral and immune dynamics), and between-host, that is, how the infection is transmitted between multiple individuals of a host population. The dynamics of each of these may be influenced by the other, particularly across evolutionary time. Thus understanding each of these scales, and the links between them, is necessary for a holistic understanding of the spread of infectious diseases. One approach to combining these scales is through mathematical modeling. We conducted a systematic review of the published literature on multi-scale mathematical models of disease transmission (as defined by combining within-host and between-host scales) to determine the extent to which mathematical models are being used to understand across-scale transmission, and the extent to which these models are being confronted with data. Following the PRISMA guidelines for systematic reviews, we identified 24 of 197 qualifying papers across 30 years that include both linked models at the within and between host scales and that used data to parameterize/calibrate models. We find that the approach that incorporates both modeling with data is under-utilized, if increasing. This highlights the need for better communication and collaboration between modelers and empiricists to build well-calibrated models that both improve understanding and may be used for prediction.
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Affiliation(s)
- Lauren M Childs
- Department of Mathematics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Fadoua El Moustaid
- Department of Biological Sciences, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Global Change Center, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Zachary Gajewski
- Department of Biological Sciences, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Global Change Center, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Department of Statistics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Sarah Kadelka
- Department of Mathematics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Ryan Nikin-Beers
- Department of Mathematics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Department of Mathematics, University of Florida, Gainesville, FL, USA
| | - John W Smith
- Department of Statistics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Melody Walker
- Department of Mathematics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
| | - Leah R Johnson
- Department of Biological Sciences, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Global Change Center, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Department of Statistics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.,Computational Modeling and Data Analytics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA
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26
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Abstract
PURPOSE OF REVIEW This review summarizes the use of genetic similarity clusters to understand HIV transmission and inform prevention efforts. RECENT FINDINGS Recent emphases include the development of real-time cluster identification in order to interrupt transmission chains, the use of clusters to estimate rates of transmission along the HIV care cascade, and the extension of cluster analyses to understand transmission in the generalized epidemics of sub-Saharan Africa. Importantly, this recent empirical work has been accompanied by theoretical work that elucidates the processes that underlie HIV genetic similarity clusters; multiple studies suggest that clusters are not necessarily enriched with individuals with high transmission rates, but rather can reflect variation in sampling times within a population, with individuals sampled early in infection more likely to cluster. Analyses of genetic similarity clusters have great promise to inform HIV epidemiology and prevention. Future emphases should include the collection of additional sequence data from underrepresented populations, such as those in sub-Saharan Africa, and further development and evaluation of clustering methods.
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Affiliation(s)
- Mary Kate Grabowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Rakai Health Sciences Program, Baltimore, MD, USA
| | - Joshua T Herbeck
- International Clinical Research Center, Department of Global Health, University of Washington, Seattle, WA, USA.
| | - Art F Y Poon
- Department of Pathology and Laboratory Medicine, Western University, London, ON, Canada
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27
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Laenen L, Vergote V, Vanmechelen B, Tersago K, Baele G, Lemey P, Leirs H, Dellicour S, Vrancken B, Maes P. Identifying the patterns and drivers of Puumala hantavirus enzootic dynamics using reservoir sampling. Virus Evol 2019; 5:vez009. [PMID: 31024739 PMCID: PMC6476162 DOI: 10.1093/ve/vez009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Hantaviruses are zoonotic hemorrhagic fever viruses for which prevention of human spillover remains the first priority in disease management. Tailored intervention measures require an understanding of the drivers of enzootic dynamics, commonly inferred from distorted human incidence data. Here, we use longitudinal sampling of approximately three decades of Puumala orthohantavirus (PUUV) evolution in isolated reservoir populations to estimate PUUV evolutionary rates, and apply these to study the impact of environmental factors on viral spread. We find that PUUV accumulates genetic changes at a rate of ∼10−4 substitutions per site per year and that land cover type defines the dispersal dynamics of PUUV, with forests facilitating and croplands impeding virus spread. By providing reliable short-term PUUV evolutionary rate estimates, this work facilitates the evaluation of spatial risk heterogeneity starting from timed phylogeographic reconstructions based on virus sampling in its animal reservoir, thereby side-stepping the need for difficult-to-collect human disease incidence data.
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Affiliation(s)
- Lies Laenen
- KU Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Herestraat 49, 3000 Leuven, Belgium
| | - Valentijn Vergote
- KU Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Herestraat 49, 3000 Leuven, Belgium
| | - Bert Vanmechelen
- KU Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Herestraat 49, 3000 Leuven, Belgium
| | - Katrien Tersago
- Evolutionary Ecology Group, Department of Biology, University of Antwerp, Antwerp, Belgium.,Epidemiology of Infectious Diseases, Belgian Institute of Health, Sciensano, Brussels, Belgium
| | - Guy Baele
- KU Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Herestraat 49, 3000 Leuven, Belgium
| | - Philippe Lemey
- KU Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Herestraat 49, 3000 Leuven, Belgium
| | - Herwig Leirs
- Evolutionary Ecology Group, Department of Biology, University of Antwerp, Antwerp, Belgium
| | - Simon Dellicour
- KU Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Herestraat 49, 3000 Leuven, Belgium.,Spatial Epidemiology Lab (spELL), Université Libre de Bruxelles, Bruxelles, Belgium
| | - Bram Vrancken
- KU Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Herestraat 49, 3000 Leuven, Belgium
| | - Piet Maes
- KU Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Herestraat 49, 3000 Leuven, Belgium
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28
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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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29
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Rose R, Rodriguez C, Dollar JJ, Lamers SL, Massaccesi G, Osburn W, Ray SC, Thomas DL, Cox AL, Laeyendecker O. Inconsistent temporal patterns of genetic variation of HCV among high-risk subjects may impact inference of transmission networks. INFECTION GENETICS AND EVOLUTION 2019; 71:1-6. [PMID: 30802530 DOI: 10.1016/j.meegid.2019.02.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 02/19/2019] [Accepted: 02/21/2019] [Indexed: 01/03/2023]
Abstract
Hepatitis-C Virus (HCV) sequences are often used to establish networks of people who inject drugs (PWID). However, the degree to which within-host evolutionary dynamics affect those inferences has not been carefully studied. Here, we analyzed 702 longitudinally-sampled HCV E1 sequences from 88 HCV+ people who inject drugs (PWID) in the Baltimore Before and After Acute Study of Hepatitis (BBAASH) cohort. Individuals were tested for HCV RNA over multiple visits to the clinic, and the HCV E1 gene was sequenced for HCV+ samples. Genetic clustering was performed on the full set of sequences using a 3% genetic distance threshold to define epidemiological linkage. Maximum-likelihood (ML) phylogenies were inferred to assess evolutionary relationships. We found 22 clusters containing sequences sampled over five or more years (long-term clusters, LTC), of which 17 had >1 subject. In six of the multi-subject LTC, one subject had a sequence sampled >3 years earlier or later than the next-closest subject in the cluster (time-gap LTC). ML trees showed that, in three of the time-gap LTC, two subjects had identical sequences despite 7-10 years separating the sampling times. In four of the time-gap LTC for whom additional data were available, the subject with the later detected shared variant had both different variants and visits with no detectable HCV RNA (RNA-) prior to the appearance of the shared variant. In the subject with the earlier detection of the shared variant, different variants and RNA- visits were also detected in multiple cases subsequent to appearance of the shared variant. Complex patterns of shared viral variation among PWID reflect on-going re-infection, multiple transmission partners, and/or inconsistent detection of viral variants. Our results suggest that transmission events are currently underestimated by analysis of sequences at a single point in time.
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Affiliation(s)
- Rebecca Rose
- BioInfoExperts LLC, Thibodaux, LA, United States.
| | | | | | | | - Guido Massaccesi
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - William Osburn
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Stuart C Ray
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - David L Thomas
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Andrea L Cox
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Oliver Laeyendecker
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States; NIAID, NIH, Baltimore, MD, United States
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30
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Thompson RN, Wymant C, Spriggs RA, Raghwani J, Fraser C, Lythgoe KA. Link between the numbers of particles and variants founding new HIV-1 infections depends on the timing of transmission. Virus Evol 2019; 5:vey038. [PMID: 30723550 PMCID: PMC6354028 DOI: 10.1093/ve/vey038] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Understanding which HIV-1 variants are most likely to be transmitted is important for vaccine design and predicting virus evolution. Since most infections are founded by single variants, it has been suggested that selection at transmission has a key role in governing which variants are transmitted. We show that the composition of the viral population within the donor at the time of transmission is also important. To support this argument, we developed a probabilistic model describing HIV-1 transmission in an untreated population, and parameterised the model using both within-host next generation sequencing data and population-level epidemiological data on heterosexual transmission. The most basic HIV-1 transmission models cannot explain simultaneously the low probability of transmission and the non-negligible proportion of infections founded by multiple variants. In our model, transmission can only occur when environmental conditions are appropriate (e.g. abrasions are present in the genital tract of the potential recipient), allowing these observations to be reconciled. As well as reproducing features of transmission in real populations, our model demonstrates that, contrary to expectation, there is not a simple link between the number of viral variants and the number of viral particles founding each new infection. These quantities depend on the timing of transmission, and infections can be founded with small numbers of variants yet large numbers of particles. Including selection, or a bias towards early transmission (e.g. due to treatment), acts to enhance this conclusion. In addition, we find that infections initiated by multiple variants are most likely to have derived from donors with intermediate set-point viral loads, and not from individuals with high set-point viral loads as might be expected. We therefore emphasise the importance of considering viral diversity in donors, and the timings of transmissions, when trying to discern the complex factors governing single or multiple variant transmission.
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Affiliation(s)
- Robin N Thompson
- Department of Zoology, University of Oxford, South Parks Road, Oxford, UK.,Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Oxford, UK.,Christ Church, University of Oxford, St Aldates, Oxford, UK
| | - Chris Wymant
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Rebecca A Spriggs
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, UK
| | - Jayna Raghwani
- Department of Zoology, University of Oxford, South Parks Road, Oxford, UK.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Katrina A Lythgoe
- Department of Zoology, University of Oxford, South Parks Road, Oxford, UK.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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31
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Phylogenetic approach to recover integration dates of latent HIV sequences within-host. Proc Natl Acad Sci U S A 2018; 115:E8958-E8967. [PMID: 30185556 PMCID: PMC6156657 DOI: 10.1073/pnas.1802028115] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Given that HIV evolution and latent reservoir establishment occur continually within-host, and that latently infected cells can persist long-term, the HIV reservoir should comprise a genetically heterogeneous archive recapitulating within-host HIV evolution. However, this has yet to be conclusively demonstrated, in part due to the challenges of reconstructing within-host reservoir establishment dynamics over long timescales. We developed a phylogenetic framework to reconstruct the integration dates of individual latent HIV lineages. The framework first involves inference and rooting of a maximum-likelihood phylogeny relating plasma HIV RNA sequences serially sampled before the initiation of suppressive antiretroviral therapy, along with putative latent sequences sampled thereafter. A linear model relating root-to-tip distances of plasma HIV RNA sequences to their sampling dates is used to convert root-to-tip distances of putative latent lineages to their establishment (integration) dates. Reconstruction of the ages of putative latent sequences sampled from chronically HIV-infected individuals up to 10 y following initiation of suppressive therapy revealed a genetically heterogeneous reservoir that recapitulated HIV's within-host evolutionary history. Reservoir sequences were interspersed throughout multiple within-host lineages, with the oldest dating to >20 y before sampling; historic genetic bottleneck events were also recorded therein. Notably, plasma HIV RNA sequences isolated from a viremia blip in an individual receiving otherwise suppressive therapy were highly genetically diverse and spanned a 20-y age range, suggestive of spontaneous in vivo HIV reactivation from a large latently infected cell pool. Our framework for reservoir dating provides a potentially powerful addition to the HIV persistence research toolkit.
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32
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Raghwani J, Redd AD, Longosz AF, Wu CH, Serwadda D, Martens C, Kagaayi J, Sewankambo N, Porcella SF, Grabowski MK, Quinn TC, Eller MA, Eller LA, Wabwire-Mangen F, Robb ML, Fraser C, Lythgoe KA. Evolution of HIV-1 within untreated individuals and at the population scale in Uganda. PLoS Pathog 2018; 14:e1007167. [PMID: 30052678 PMCID: PMC6082572 DOI: 10.1371/journal.ppat.1007167] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 08/08/2018] [Accepted: 06/20/2018] [Indexed: 12/15/2022] Open
Abstract
HIV-1 undergoes multiple rounds of error-prone replication between transmission events, resulting in diverse viral populations within and among individuals. In addition, the virus experiences different selective pressures at multiple levels: during the course of infection, at transmission, and among individuals. Disentangling how these evolutionary forces shape the evolution of the virus at the population scale is important for understanding pathogenesis, how drug- and immune-escape variants are likely to spread in populations, and the development of preventive vaccines. To address this, we deep-sequenced two regions of the HIV-1 genome (p24 and gp41) from 34 longitudinally-sampled untreated individuals from Rakai District in Uganda, infected with subtypes A, D, and inter-subtype recombinants. This dataset substantially increases the availability of HIV-1 sequence data that spans multiple years of untreated infection, in particular for different geographical regions and viral subtypes. In line with previous studies, we estimated an approximately five-fold faster rate of evolution at the within-host compared to the population scale for both synonymous and nonsynonymous substitutions, and for all subtypes. We determined the extent to which this mismatch in evolutionary rates can be explained by the evolution of the virus towards population-level consensus, or the transmission of viruses similar to those that establish infection within individuals. Our findings indicate that both processes are likely to be important.
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Affiliation(s)
- Jayna Raghwani
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Department of Zoology, Peter Medawar Building, University of Oxford, Oxford, United Kingdom
| | - Andrew D. Redd
- Laboratory of Immunoregulation, Division of Intramural Research, NIAID, NIH, Baltimore MD, United States of America
- Department of Medicine, Johns Hopkins Medical Institute, Johns Hopkins University, Baltimore MD, United States of America
| | - Andrew F. Longosz
- Laboratory of Immunoregulation, Division of Intramural Research, NIAID, NIH, Baltimore MD, United States of America
| | - Chieh-Hsi Wu
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - David Serwadda
- Rakai Health Sciences Program, Kalisizo, Uganda
- School of Public Health, Makerere University, Kampala, Uganda
| | - Craig Martens
- Genomics Unit, RTS, RTB, Rocky Mountain Laboratories, Division of Intramural Research, NIAID, NIH, Hamilton MT, United States of America
| | | | - Nelson Sewankambo
- Rakai Health Sciences Program, Kalisizo, Uganda
- School of Medicine, Makerere University, Kampala, Uganda
| | - Stephen F. Porcella
- Genomics Unit, RTS, RTB, Rocky Mountain Laboratories, Division of Intramural Research, NIAID, NIH, Hamilton MT, United States of America
| | - Mary K. Grabowski
- Department of Pathology, Johns Hopkins Medical Institute, Johns Hopkins University, Baltimore, MD, United States of America
| | - Thomas C. Quinn
- Laboratory of Immunoregulation, Division of Intramural Research, NIAID, NIH, Baltimore MD, United States of America
- Department of Medicine, Johns Hopkins Medical Institute, Johns Hopkins University, Baltimore MD, United States of America
| | - Michael A. Eller
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States of America
| | - Leigh Anne Eller
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States of America
| | - Fred Wabwire-Mangen
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States of America
| | - Merlin L. Robb
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States of America
| | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Katrina A. Lythgoe
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Department of Zoology, Peter Medawar Building, University of Oxford, Oxford, United Kingdom
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Abstract
: Because HIV is a fast-evolving virus, HIV genomic sequences of several individuals can be used to investigate whether they belong to a transmission network. Since the infamous 'Florida dentist case' in the beginning of the 1990s, phylogenetic analyses has been recurrently used in court settings as a forensic tool in HIV transmission investigations, for example cases where one or more complainants allege that a defendant has unlawfully infected them with HIV. Such cases can arise both in the context of HIV-specific criminal laws - in countries where transmission of HIV infection is specifically criminalized - or in the context of general laws, for example, by applying physical or sexual assault laws to HIV-related cases. Although phylogenetic analysis as a forensic technique for HIV transmission investigations has become common in several countries, the methodologies have not yet been standardized, sometimes giving rise to unwarranted conclusions. In this literature review, we revisit HIV court case investigations published in the scientific literature, as well as the methodological aspects important for the application and standardization of phylogenetic analyses methods as a forensic tool. Phylogenetic methodologies are improving quickly, such that more recently, phylogenetic relatedness, directionality of transmission and timing of nodes in the tree are used to assess whether the phylogenetic transmission analysis is consistent with or contradicting the charges. We find that there has been a lack of consistency between methods used in court case investigations and that it is essential to define guidelines to be used by phylogenetic forensic experts in HIV transmission cases in court.
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Suchard MA, Lemey P, Baele G, Ayres DL, Drummond AJ, Rambaut A. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol 2018; 4:vey016. [PMID: 29942656 PMCID: PMC6007674 DOI: 10.1093/ve/vey016] [Citation(s) in RCA: 1814] [Impact Index Per Article: 302.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
The Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package has become a primary tool for Bayesian phylogenetic and phylodynamic inference from genetic sequence data. BEAST unifies molecular phylogenetic reconstruction with complex discrete and continuous trait evolution, divergence-time dating, and coalescent demographic models in an efficient statistical inference engine using Markov chain Monte Carlo integration. A convenient, cross-platform, graphical user interface allows the flexible construction of complex evolutionary analyses.
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Affiliation(s)
- Marc A Suchard
- Department of Biomathematics, David Geffen School of MedicineUniversity of California, Los Angeles, 621 Charles E. Young Dr., South, Los Angeles, CA, 90095 USA
- Department of Biostatistics, Fielding School of Public HealthUniversity of California, Los Angeles, 650 Charles E, Young Dr., South, Los Angeles, CA, 90095 USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Dr., South, Los Angeles, CA, 90095 USA
| | - Philippe Lemey
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Guy Baele
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Daniel L Ayres
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, 125 Biomolecular Science Bldg #296, College Park, MD 20742 USA
| | - Alexei J Drummond
- Department of Computer Science, University of Auckland, 303/38 Princes St., Auckland, 1010 NZ
- Centre for Computational Evolution, University of Auckland, 303/38 Princes St., Auckland, 1010 NZ
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, Edinburgh, EH9 3FL UK
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Meixenberger K, Yousef KP, Smith MR, Somogyi S, Fiedler S, Bartmeyer B, Hamouda O, Bannert N, von Kleist M, Kücherer C. Molecular evolution of HIV-1 integrase during the 20 years prior to the first approval of integrase inhibitors. Virol J 2017; 14:223. [PMID: 29137637 PMCID: PMC5686839 DOI: 10.1186/s12985-017-0887-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Accepted: 10/31/2017] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Detailed knowledge of the evolutionary potential of polymorphic sites in a viral protein is important for understanding the development of drug resistance in the presence of an inhibitor. We therefore set out to analyse the molecular evolution of the HIV-1 subtype B integrase at the inter-patient level in Germany during a 20-year period prior to the first introduction of integrase strand inhibitors (INSTIs). METHODS We determined 337 HIV-1 integrase subtype B sequences (amino acids 1-278) from stored plasma samples of antiretroviral treatment-naïve individuals newly diagnosed with HIV-1 between 1986 and 2006. Shannon entropy was calculated to determine the variability at each amino acid position. Time trends in the frequency of amino acid variants were identified by linear regression. Direct coupling analysis was applied to detect covarying sites. RESULTS Twenty-two time trends in the frequency of amino acid variants demonstrated either single amino acid exchanges or variation in the degree of polymorphy. Covariation was observed for 17 amino acid variants with a temporal trend. Some minor INSTI resistance mutations (T124A, V151I, K156 N, T206S, S230 N) and some INSTI-selected mutations (M50I, L101I, T122I, T124 N, T125A, M154I, G193E, V201I) were identified at overall frequencies >5%. Among these, the frequencies of L101I, T122I, and V201I increased over time, whereas the frequency of M154I decreased. Moreover, L101I, T122I, T124A, T125A, M154I, and V201I covaried with non-resistance-associated variants. CONCLUSIONS Time-trending, covarying polymorphisms indicate that long-term evolutionary changes of the HIV-1 integrase involve defined clusters of possibly structurally or functionally associated sites independent of selective pressure through INSTIs at the inter-patient level. Linkage between polymorphic resistance- and non-resistance-associated sites can impact the selection of INSTI resistance mutations in complex ways. Identification of these sites can help in improving genotypic resistance assays, resistance prediction algorithms, and the development of new integrase inhibitors.
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Affiliation(s)
| | - Kaveh Pouran Yousef
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
| | - Maureen Rebecca Smith
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
| | - Sybille Somogyi
- HIV and other Retroviruses, Robert Koch Institute, Berlin, Germany
| | - Stefan Fiedler
- HIV and other Retroviruses, Robert Koch Institute, Berlin, Germany
| | - Barbara Bartmeyer
- HIV/AIDS, STI and Blood-borne Infections, Robert Koch Institute, Berlin, Germany
| | - Osamah Hamouda
- HIV/AIDS, STI and Blood-borne Infections, Robert Koch Institute, Berlin, Germany
| | - Norbert Bannert
- HIV and other Retroviruses, Robert Koch Institute, Berlin, Germany
| | - Max von Kleist
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
| | - Claudia Kücherer
- HIV and other Retroviruses, Robert Koch Institute, Berlin, Germany
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Dapp MJ, Kober KM, Chen L, Westfall DH, Wong K, Zhao H, Hall BM, Deng W, Sibley T, Ghorai S, Kim K, Chen N, McHugh S, Au L, Cohen M, Anastos K, Mullins JI. Patterns and rates of viral evolution in HIV-1 subtype B infected females and males. PLoS One 2017; 12:e0182443. [PMID: 29045410 PMCID: PMC5646779 DOI: 10.1371/journal.pone.0182443] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Accepted: 07/18/2017] [Indexed: 12/18/2022] Open
Abstract
Biological sex differences affect the course of HIV infection, with untreated women having lower viral loads compared to their male counterparts but, for a given viral load, women have a higher rate of progression to AIDS. However, the vast majority of data on viral evolution, a process that is clearly impacted by host immunity and could be impacted by sex differences, has been derived from men. We conducted an intensive analysis of HIV-1 gag and env-gp120 evolution taken over the first 6–11 years of infection from 8 Women’s Interagency HIV Study (WIHS) participants who had not received combination antiretroviral therapy (ART). This was compared to similar data previously collected from men, with both groups infected with HIV-1 subtype B. Early virus populations in men and women were generally homogenous with no differences in diversity between sexes. No differences in ensuing nucleotide substitution rates were found between the female and male cohorts studied herein. As previously reported for men, time to peak diversity in env-gp120 in women was positively associated with time to CD4+ cell count below 200 (P = 0.017), and the number of predicted N-linked glycosylation sites generally increased over time, followed by a plateau or decline, with the majority of changes localized to the V1-V2 region. These findings strongly suggest that the sex differences in HIV-1 disease progression attributed to immune system composition and sensitivities are not revealed by, nor do they impact, global patterns of viral evolution, the latter of which proceeds similarly in women and men.
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Affiliation(s)
- Michael J. Dapp
- Department of Microbiology, University of Washington School of Medicine, Seattle, Washington, United States of America
| | - Kord M. Kober
- Department of Physiological Nursing, University of California at San Francisco, California, United States of America
| | - Lennie Chen
- Department of Microbiology, University of Washington School of Medicine, Seattle, Washington, United States of America
| | - Dylan H. Westfall
- Department of Microbiology, University of Washington School of Medicine, Seattle, Washington, United States of America
| | - Kim Wong
- Department of Microbiology, University of Washington School of Medicine, Seattle, Washington, United States of America
| | - Hong Zhao
- Department of Microbiology, University of Washington School of Medicine, Seattle, Washington, United States of America
| | - Breana M. Hall
- Department of Microbiology, University of Washington School of Medicine, Seattle, Washington, United States of America
| | - Wenjie Deng
- Department of Microbiology, University of Washington School of Medicine, Seattle, Washington, United States of America
| | - Thomas Sibley
- Department of Microbiology, University of Washington School of Medicine, Seattle, Washington, United States of America
| | - Suvankar Ghorai
- Department of Microbiology, University of Washington School of Medicine, Seattle, Washington, United States of America
| | - Katie Kim
- Department of Microbiology, University of Washington School of Medicine, Seattle, Washington, United States of America
| | - Natalie Chen
- Department of Microbiology, University of Washington School of Medicine, Seattle, Washington, United States of America
| | - Sarah McHugh
- Department of Microbiology, University of Washington School of Medicine, Seattle, Washington, United States of America
| | - Lily Au
- Department of Microbiology, University of Washington School of Medicine, Seattle, Washington, United States of America
| | - Mardge Cohen
- The Core Center, Bureau of Health Services of Cook County, Chicago, Illinois, United States of America
| | - Kathryn Anastos
- Department of Medicine, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - James I. Mullins
- Department of Microbiology, University of Washington School of Medicine, Seattle, Washington, United States of America
- Department of Medicine, University of Washington School of Medicine, Seattle, Washington, United States of America
- Department of Global Health, University of Washington School of Medicine, Seattle, Washington, United States of America
- Department of Laboratory Medicine, University of Washington School of Medicine, Seattle, Washington, United States of America
- * E-mail:
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37
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Vrancken B, Suchard MA, Lemey P. Accurate quantification of within- and between-host HBV evolutionary rates requires explicit transmission chain modelling. Virus Evol 2017; 3:vex028. [PMID: 29026650 PMCID: PMC5632516 DOI: 10.1093/ve/vex028] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Analyses of virus evolution in known transmission chains have the potential to elucidate the impact of transmission dynamics on the viral evolutionary rate and its difference within and between hosts. Lin et al. (2015, Journal of Virology, 89/7: 3512–22) recently investigated the evolutionary history of hepatitis B virus in a transmission chain and postulated that the ‘colonization–adaptation–transmission’ model can explain the differential impact of transmission on synonymous and non-synonymous substitution rates. Here, we revisit this dataset using a full probabilistic Bayesian phylogenetic framework that adequately accounts for the non-independence of sequence data when estimating evolutionary parameters. Examination of the transmission chain data under a flexible coalescent prior reveals a general inconsistency between the estimated timings and clustering patterns and the known transmission history, highlighting the need to incorporate host transmission information in the analysis. Using an explicit genealogical transmission chain model, we find strong support for a transmission-associated decrease of the overall evolutionary rate. However, in contrast to the initially reported larger transmission effect on non-synonymous substitution rate, we find a similar decrease in both non-synonymous and synonymous substitution rates that cannot be adequately explained by the colonization-adaptation-transmission model. An alternative explanation may involve a transmission/establishment advantage of hepatitis B virus variants that have accumulated fewer within-host substitutions, perhaps by spending more time in the covalently closed circular DNA state between each round of viral replication. More generally, this study illustrates that ignoring phylogenetic relationships can lead to misleading evolutionary estimates.
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Affiliation(s)
- Bram Vrancken
- Department of Microbiology and Immunology, Rega Institute for Medical Research, KU Leuven - University of Leuven, B-3000 Leuven, Belgium
| | - Marc A Suchard
- Department of Biomathematics, University of California, Los Angeles, CA 90095, USA.,Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095, USA.,Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA 90095, USA
| | - Philippe Lemey
- Department of Microbiology and Immunology, Rega Institute for Medical Research, KU Leuven - University of Leuven, B-3000 Leuven, Belgium
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Abstract
Within-host genetic diversity and large transmission bottlenecks confound phylodynamic inference of epidemiological dynamics. Conventional phylodynamic approaches assume that nodes in a time-scaled pathogen phylogeny correspond closely to the time of transmission between hosts that are ancestral to the sample. However, when hosts harbor diverse pathogen populations, node times can substantially pre-date infection times. Imperfect bottlenecks can cause lineages sampled in different individuals to coalesce in unexpected patterns. To address realistic violations of standard phylodynamic assumptions we developed a new inference approach based on a multi-scale coalescent model, accounting for nonlinear epidemiological dynamics, heterogeneous sampling through time, non-negligible genetic diversity of pathogens within hosts, and imperfect transmission bottlenecks. We apply this method to HIV-1 and Ebola virus (EBOV) outbreak sequence data, illustrating how and when conventional phylodynamic inference may give misleading results. Within-host diversity of HIV-1 causes substantial upwards bias in the number of infected hosts using conventional coalescent models, but estimates using the multi-scale model have greater consistency with reported number of diagnoses through time. In contrast, we find that within-host diversity of EBOV has little influence on estimated numbers of infected hosts or reproduction numbers, and estimates are highly consistent with the reported number of diagnoses through time. The multi-scale coalescent also enables estimation of within-host effective population size using single sequences from a random sample of patients. We find within-host population genetic diversity of HIV-1 p17 to be 2Nμ=0.012 (95% CI 0.0066-0.023), which is lower than estimates based on HIV envelope serial sequencing of individual patients.
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Affiliation(s)
- Erik M Volz
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Ethan Romero-Severson
- Theoretical Biology and Biophysics, Group T-6, Los Alamos National Laboratory, Los Alamos
| | - Thomas Leitner
- Theoretical Biology and Biophysics, Group T-6, Los Alamos National Laboratory, Los Alamos
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Zhang Y, Vrancken B, Feng Y, Dellicour S, Yang Q, Yang W, Zhang Y, Dong L, Pybus OG, Zhang H, Tian H. Cross-border spread, lineage displacement and evolutionary rate estimation of rabies virus in Yunnan Province, China. Virol J 2017; 14:102. [PMID: 28578663 PMCID: PMC5457581 DOI: 10.1186/s12985-017-0769-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Accepted: 05/25/2017] [Indexed: 12/12/2022] Open
Abstract
Background Rabies is an important but underestimated threat to public health, with most cases reported in Asia. Since 2000, a new epidemic wave of rabies has emerged in Yunnan Province, southwestern China, which borders three countries in Southeast Asia. Method We estimated gene-specific evolutionary rates for rabies virus using available data in GenBank, then used this information to calibrate the timescale of rabies virus (RABV) spread in Asia. We used 452 publicly available geo-referenced complete nucleoprotein (N) gene sequences, including 52 RABV sequences that were recently generated from samples collected in Yunnan between 2008 and 2012. Results The RABV N gene evolutionary rate was estimated to be 1.88 × 10−4 (1.37–2.41 × 10−4, 95% Bayesian credible interval, BCI) substitutions per site per year. Phylogenetic reconstructions show that the currently circulating RABV lineages in Yunnan result from at least seven independent introductions (95% BCI: 6–9 introductions) and represent each of the three main Asian RABV lineages, SEA-1, -2 and -3. We find that Yunnan is a sink location for the domestic spread of RABV and connects RABV epidemics in North China, South China, and Southeast Asia. Cross-border spread from southeast Asia (SEA) into South China, and intermixing of the North and South China epidemics is also well supported. The influx of RABV into Yunnan from SEA was not well-supported, likely due to the poor sampling of SEA RABV diversity. We found evidence for a lineage displacement of the Yunnan SEA-2 and -3 lineages by Yunnan SEA-1 strains, and considered whether this could be attributed to fitness differences. Conclusion Overall, our study contributes to a better understanding of the spread of RABV that could facilitate future rabies virus control and prevention efforts. Electronic supplementary material The online version of this article (doi:10.1186/s12985-017-0769-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yuzhen Zhang
- Yunnan Institute of Endemic Diseases Control and Prevention, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Dali, China
| | - Bram Vrancken
- Department of Microbiology and Immunology, Division of Clinical and Epidemiological Virology, Rega Institute, KU Leuven - University of Leuven, Leuven, Belgium
| | - Yun Feng
- Yunnan Institute of Endemic Diseases Control and Prevention, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Dali, China
| | - Simon Dellicour
- Department of Microbiology and Immunology, Division of Clinical and Epidemiological Virology, Rega Institute, KU Leuven - University of Leuven, Leuven, Belgium
| | - Qiqi Yang
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Weihong Yang
- Yunnan Institute of Endemic Diseases Control and Prevention, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Dali, China
| | - Yunzhi Zhang
- Yunnan Institute of Endemic Diseases Control and Prevention, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Dali, China
| | - Lu Dong
- Ministry of Education Key Laboratory for Biodiversity and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, China
| | | | - Hailin Zhang
- Yunnan Institute of Endemic Diseases Control and Prevention, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Dali, China.
| | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.
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Lythgoe KA, Gardner A, Pybus OG, Grove J. Short-Sighted Virus Evolution and a Germline Hypothesis for Chronic Viral Infections. Trends Microbiol 2017; 25:336-348. [PMID: 28377208 PMCID: PMC5405858 DOI: 10.1016/j.tim.2017.03.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 03/03/2017] [Accepted: 03/03/2017] [Indexed: 12/24/2022]
Abstract
With extremely short generation times and high mutability, many viruses can rapidly evolve and adapt to changing environments. This ability is generally beneficial to viruses as it allows them to evade host immune responses, evolve new behaviours, and exploit ecological niches. However, natural selection typically generates adaptation in response to the immediate selection pressures that a virus experiences in its current host. Consequently, we argue that some viruses, particularly those characterised by long durations of infection and ongoing replication, may be susceptible to short-sighted evolution, whereby a virus' adaptation to its current host will be detrimental to its onward transmission within the host population. Here we outline the concept of short-sighted viral evolution and provide examples of how it may negatively impact viral transmission among hosts. We also propose that viruses that are vulnerable to short-sighted evolution may exhibit strategies that minimise its effects. We speculate on the various mechanisms by which this may be achieved, including viral life history strategies that result in low rates of within-host evolution, or the establishment of a 'germline' lineage of viruses that avoids short-sighted evolution. These concepts provide a new perspective on the way in which some viruses have been able to establish and maintain global pandemics.
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Affiliation(s)
| | - Andy Gardner
- School of Biology, University of St Andrews, St Andrews, KY16 9TH, UK
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, OX1 3PS, UK
| | - Joe Grove
- Division of Infection and Immunity, Institute of Immunity and Transplantation, University College London, London, WC1E 6BT, UK
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41
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Abstract
The human immunodeficiency virus (HIV) evolves rapidly owing to the combined activity of error-prone reverse transcriptase, recombination, and short generation times, leading to extensive viral diversity both within and between hosts. This diversity is a major contributing factor in the failure of the immune system to eradicate the virus and has important implications for the development of suitable drugs and vaccines to combat infection. This review will discuss the recent technological advances that have shed light on HIV evolution and will summarise emerging concepts in this field.
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Affiliation(s)
- Sophie M Andrews
- Nuffield Department of Clinical Medicine, University of Oxford, NDMRB, Oxford, UK
| | - Sarah Rowland-Jones
- Nuffield Department of Clinical Medicine, University of Oxford, NDMRB, Oxford, UK
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The multi-faceted dynamics of HIV-1 transmission in Northern Alberta: A combined analysis of virus genetic and public health data. INFECTION GENETICS AND EVOLUTION 2017; 52:100-105. [PMID: 28427935 DOI: 10.1016/j.meegid.2017.04.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 03/30/2017] [Accepted: 04/05/2017] [Indexed: 01/05/2023]
Abstract
Molecular epidemiology has become a key tool for tracking infectious disease epidemics. Here, the spread of the most prevalent HIV-1 subtypes in Northern Alberta, Canada, was characterized with a Bayesian phylogenetic approach using 1146 HIV-1 pol sequences collected between 2007 and 2013 for routine clinical management purposes. Available patient metadata were qualitatively interpreted and correlated with onwards transmission using Fisher exact tests and logistic regression. Most infections were from subtypes A (n=36), B (n=815) and C (n=211). Africa is the dominant origin location for subtypes A and C while the subtype B epidemic was seeded from the USA and Middle America and, from the early 1990s onwards, mostly by interprovincial spread. Subtypes A (77.8%) and C (74.0%) were usually heterosexually transmitted and circulate predominantly among Blacks (61.1% and 85% respectively). Subtype B was mostly found among Caucasians (48.6%) and First Nations (36.8%), and its modes of transmission were stratified by ethnic origin. Compared to subtypes A (5.6%) and C (3.8-10.0%), a larger portion of subtype B patients were found within putative provincial transmission networks (20.3-29.5%), and this almost doubled when focusing on nationwide transmission clusters (37.9-57.5%). No clear association between cluster membership and particular patient characteristics was found. This study reveals complex and multi-faceted transmission dynamics of the HIV-1 epidemic in this otherwise low HIV prevalence population in Northern Alberta, Canada. These findings can aid public health planning.
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Implications of hepatitis C virus subtype 1a migration patterns for virus genetic sequencing policies in Italy. BMC Evol Biol 2017; 17:70. [PMID: 28270091 PMCID: PMC5341469 DOI: 10.1186/s12862-017-0913-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2016] [Accepted: 02/14/2017] [Indexed: 02/06/2023] Open
Abstract
Background In-depth phylogeographic analysis can reveal migration patterns relevant for public health planning. Here, as a model, we focused on the provenance, in the current Italian HCV subtype 1a epidemic, of the NS3 resistance-associated variant (RAV) Q80K, known to interfere with the action of NS3/4A protease inhibitor simeprevir. HCV1a migration patterns were analysed using Bayesian phylodynamic tools, capitalising on newly generated and publicly available time and geo-referenced NS3 encoding virus genetic sequence data. Results Our results showed that both immigration and local circulation fuel the current Italian HCV1a epidemic. The United States and European continental lineages dominate import into Italy, with the latter taking the lead from the 1970s onwards. Since similar migration patterns were found for Q80K and other lineages, no clear differentiation of the risk for failing simeprevir can be made between patients based on their migration and travel history. Importantly, since HCV only occasionally recombines, these results are readily transferable to the genetic sequencing policy concerning NS5A RAVs. Conclusions The patient migration and travel history cannot be used to target only part of the HCV1a infected population for drug resistance testing before start of antiviral therapy. Consequently, it may be cost-effective to expand genotyping efforts to all HCV1a infected patients eligible for simeprevir-based therapies. Electronic supplementary material The online version of this article (doi:10.1186/s12862-017-0913-3) contains supplementary material, which is available to authorized users.
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Baele G, Suchard MA, Rambaut A, Lemey P. Emerging Concepts of Data Integration in Pathogen Phylodynamics. Syst Biol 2017; 66:e47-e65. [PMID: 28173504 PMCID: PMC5837209 DOI: 10.1093/sysbio/syw054] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2015] [Accepted: 06/02/2016] [Indexed: 12/24/2022] Open
Abstract
Phylodynamics has become an increasingly popular statistical framework to extract evolutionary and epidemiological information from pathogen genomes. By harnessing such information, epidemiologists aim to shed light on the spatio-temporal patterns of spread and to test hypotheses about the underlying interaction of evolutionary and ecological dynamics in pathogen populations. Although the field has witnessed a rich development of statistical inference tools with increasing levels of sophistication, these tools initially focused on sequences as their sole primary data source. Integrating various sources of information, however, promises to deliver more precise insights in infectious diseases and to increase opportunities for statistical hypothesis testing. Here, we review how the emerging concept of data integration is stimulating new advances in Bayesian evolutionary inference methodology which formalize a marriage of statistical thinking and evolutionary biology. These approaches include connecting sequence to trait evolution, such as for host, phenotypic and geographic sampling information, but also the incorporation of covariates of evolutionary and epidemic processes in the reconstruction procedures. We highlight how a full Bayesian approach to covariate modeling and testing can generate further insights into sequence evolution, trait evolution, and population dynamics in pathogen populations. Specific examples demonstrate how such approaches can be used to test the impact of host on rabies and HIV evolutionary rates, to identify the drivers of influenza dispersal as well as the determinants of rabies cross-species transmissions, and to quantify the evolutionary dynamics of influenza antigenicity. Finally, we briefly discuss how data integration is now also permeating through the inference of transmission dynamics, leading to novel insights into tree-generative processes and detailed reconstructions of transmission trees. [Bayesian inference; birth–death models; coalescent models; continuous trait evolution; covariates; data integration; discrete trait evolution; pathogen phylodynamics.
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Affiliation(s)
- Guy Baele
- Department of Microbiology and Immunology, Rega Institute, KU Leuven - University of Leuven, Leuven, Belgium
| | - Marc A. Suchard
- Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Department of Biostatistics, School of Public Health, University of California, Los Angeles, CA 90095, USA
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Kings Buildings, Edinburgh EH9 3FL, UK
- Centre for Immunity, Infection and Evolution, University of Edinburgh, Kings Buildings, Edinburgh EH9 3FL, UK
| | - Philippe Lemey
- Department of Microbiology and Immunology, Rega Institute, KU Leuven - University of Leuven, Leuven, Belgium
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Doekes HM, Fraser C, Lythgoe KA. Effect of the Latent Reservoir on the Evolution of HIV at the Within- and Between-Host Levels. PLoS Comput Biol 2017; 13:e1005228. [PMID: 28103248 PMCID: PMC5245781 DOI: 10.1371/journal.pcbi.1005228] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2016] [Accepted: 10/31/2016] [Indexed: 02/06/2023] Open
Abstract
The existence of long-lived reservoirs of latently infected CD4+ T cells is the major barrier to curing HIV, and has been extensively studied in this light. However, the effect of these reservoirs on the evolutionary dynamics of the virus has received little attention. Here, we present a within-host quasispecies model that incorporates a long-lived reservoir, which we then nest into an epidemiological model of HIV dynamics. For biologically plausible parameter values, we find that the presence of a latent reservoir can severely delay evolutionary dynamics within a single host, with longer delays associated with larger relative reservoir sizes and/or homeostatic proliferation of cells within the reservoir. These delays can fundamentally change the dynamics of the virus at the epidemiological scale. In particular, the delay in within-host evolutionary dynamics can be sufficient for the virus to evolve intermediate viral loads consistent with maximising transmission, as is observed, and not the very high viral loads that previous models have predicted, an effect that can be further enhanced if viruses similar to those that initiate infection are preferentially transmitted. These results depend strongly on within-host characteristics such as the relative reservoir size, with the evolution of intermediate viral loads observed only when the within-host dynamics are sufficiently delayed. In conclusion, we argue that the latent reservoir has important, and hitherto under-appreciated, roles in both within- and between-host viral evolution.
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Affiliation(s)
- Hilje M. Doekes
- Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
- Theoretical Biology, Utrecht University, Utrecht, The Netherlands
| | - Christophe Fraser
- Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Katrina A. Lythgoe
- Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
- Department of Zoology, University of Oxford, Oxford, United Kingdom
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Parratt SR, Numminen E, Laine AL. Infectious Disease Dynamics in Heterogeneous Landscapes. ANNUAL REVIEW OF ECOLOGY EVOLUTION AND SYSTEMATICS 2016. [DOI: 10.1146/annurev-ecolsys-121415-032321] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Infectious diseases dynamics are affected by both spatial and temporal heterogeneity in their environments. Our ability to quantify and predict how this heterogeneity impacts risks of infection and disease emergence is the key to successful disease prevention efforts. Here, we review the literature on infectious diseases from human, agricultural, and wildlife ecosystems to describe the rapid ecological and evolutionary responses in pathogens to environmental heterogeneity, with expected impacts on their epidemiology. To date, the underlying network structures through which disease transmission proceeds have been notoriously difficult to quantify because of this variation. We show that with recent advances in statistical methods and genomic approaches, it is now more feasible than ever to trace disease transmission networks, the molecular underpinning of infection, and the environmental variation relevant to disease dynamics. We end by identifying major new opportunities and challenges in understanding disease dynamics in an ever-changing world.
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Affiliation(s)
- Steven R. Parratt
- Metapopulation Research Centre, Department of Biosciences, University of Helsinki, FI-00014 Helsinki, Finland;, ,
| | - Elina Numminen
- Metapopulation Research Centre, Department of Biosciences, University of Helsinki, FI-00014 Helsinki, Finland;, ,
| | - Anna-Liisa Laine
- Metapopulation Research Centre, Department of Biosciences, University of Helsinki, FI-00014 Helsinki, Finland;, ,
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Abstract
For infectious diseases, a genetic cluster is a group of closely related infections that is usually interpreted as representing a recent outbreak of transmission. Genetic clustering methods are becoming increasingly popular for molecular epidemiology, especially in the context of HIV where there is now considerable interest in applying these methods to prioritize groups for public health resources such as pre-exposure prophylaxis. To date, genetic clustering has generally been performed with ad hoc algorithms, only some of which have since been encoded and distributed as free software. These algorithms have seldom been validated on simulated data where clusters are known, and their interpretation and similarities are not transparent to users outside of the field. Here, I provide a brief overview on the development and inter-relationships of genetic clustering methods, and an evaluation of six methods on data simulated under an epidemic model in a risk-structured population. The simulation analysis demonstrates that the majority of clustering methods are systematically biased to detect variation in sampling rates among subpopulations, not variation in transmission rates. I discuss these results in the context of previous work and the implications for public health applications of genetic clustering.
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Affiliation(s)
- Art F Y Poon
- Department of Pathology and Laboratory Medicine, Western University, London, Canada
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Ratmann O, van Sighem A, Bezemer D, Gavryushkina A, Jurriaans S, Wensing A, de Wolf F, Reiss P, Fraser C. Sources of HIV infection among men having sex with men and implications for prevention. Sci Transl Med 2016; 8:320ra2. [PMID: 26738795 DOI: 10.1126/scitranslmed.aad1863] [Citation(s) in RCA: 90] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
New HIV diagnoses among men having sex with men (MSM) have not decreased appreciably in most countries, even though care and prevention services have been scaled up substantially in the past 20 years. To maximize the impact of prevention strategies, it is crucial to quantify the sources of transmission at the population level. We used viral sequence and clinical patient data from one of Europe's nationwide cohort studies to estimate probable sources of transmission for 617 recently infected MSM. Seventy-one percent of transmissions were from undiagnosed men, 6% from men who had initiated antiretroviral therapy (ART), 1% from men with no contact to care for at least 18 months, and 43% from those in their first year of infection. The lack of substantial reductions in incidence among Dutch MSM is not a result of ineffective ART provision or inadequate retention in care. In counterfactual modeling scenarios, 19% of these past cases could have been averted with current annual testing coverage and immediate ART to those testing positive. Sixty-six percent of these cases could have been averted with available antiretrovirals (immediate ART provided to all MSM testing positive, and preexposure antiretroviral prophylaxis taken by half of all who test negative for HIV), but only if half of all men at risk of transmission had tested annually. With increasing sequence coverage, molecular epidemiological analyses can be a key tool to direct HIV prevention strategies to the predominant sources of infection, and help send HIV epidemics among MSM into a decisive decline.
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Affiliation(s)
- Oliver Ratmann
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W21PG, UK.
| | - Ard van Sighem
- Stichting HIV Monitoring, 1105 BD Amsterdam, the Netherlands
| | - Daniela Bezemer
- Stichting HIV Monitoring, 1105 BD Amsterdam, the Netherlands
| | | | - Suzanne Jurriaans
- Department of Medical Microbiology, Academic Medical Center, 1105 AZ Amsterdam, the Netherlands
| | - Annemarie Wensing
- Department of Medical Microbiology, University Medical Center Utrecht, 3584 CX Utrecht, the Netherlands
| | - Frank de Wolf
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W21PG, UK
| | - Peter Reiss
- Stichting HIV Monitoring, 1105 BD Amsterdam, the Netherlands. Department of Global Health, Academic Medical Center, 1105 BM Amsterdam, the Netherlands
| | - Christophe Fraser
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W21PG, UK
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Raghwani J, Rose R, Sheridan I, Lemey P, Suchard MA, Santantonio T, Farci P, Klenerman P, Pybus OG. Exceptional Heterogeneity in Viral Evolutionary Dynamics Characterises Chronic Hepatitis C Virus Infection. PLoS Pathog 2016; 12:e1005894. [PMID: 27631086 PMCID: PMC5025083 DOI: 10.1371/journal.ppat.1005894] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Accepted: 08/24/2016] [Indexed: 12/14/2022] Open
Abstract
The treatment of HCV infection has seen significant progress, particularly since the approval of new direct-acting antiviral drugs. However these clinical achievements have been made despite an incomplete understanding of HCV replication and within-host evolution, especially compared with HIV-1. Here, we undertake a comprehensive analysis of HCV within-host evolution during chronic infection by investigating over 4000 viral sequences sampled longitudinally from 15 HCV-infected patients. We compare our HCV results to those from a well-studied HIV-1 cohort, revealing key differences in the evolutionary behaviour of these two chronic-infecting pathogens. Notably, we find an exceptional level of heterogeneity in the molecular evolution of HCV, both within and among infected individuals. Furthermore, these patterns are associated with the long-term maintenance of viral lineages within patients, which fluctuate in relative frequency in peripheral blood. Together, our findings demonstrate that HCV replication behavior is complex and likely comprises multiple viral subpopulations with distinct evolutionary dynamics. The presence of a structured viral population can explain apparent paradoxes in chronic HCV infection, such as rapid fluctuations in viral diversity and the reappearance of viral strains years after their initial detection. Our knowledge of HCV within-host evolution is substantially limited, which is surprising given that highly successful therapies against the virus have been developed. Key aspects of HCV infection, such as rapid fluctuations in viral diversity and the reappearance of viral strains years after their initial detection, remain unexplained. To better understand this problem, we analyse viral sequences from HCV-infected patients sampled over several years. Our findings suggest that the replication dynamics during chronic HCV infection are distinct from those of HIV-1, and dominated by the co-circulation of multiple viral strains. Although a major difference between the two chronic-infecting viruses is the level of recombination, our results indicate that HCV within-host evolution is most likely to be shaped by a structured viral population. Crucially, our study shows that HCV sampled from blood does not fully represent the within-host viral population at that time. This may have important implications for HCV treatment, especially in patients that have seemingly cleared the virus, as well as for molecular epidemiology studies investigating HCV transmission.
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Affiliation(s)
- Jayna Raghwani
- Department of Zoology, University of Oxford, Oxford, United Kingdom
- * E-mail: (JR); (OGP)
| | - Rebecca Rose
- BioInfoExperts, Thibodaux, Los Angeles, California, United States of America
| | - Isabelle Sheridan
- Peter Medawar Building for Pathogen Research, University of Oxford, Oxford, United Kingdom
| | - Philippe Lemey
- Department of Microbiology and Immunology, Rega Institute, KU Leuven–University of Leuven, Leuven, Belgium
| | - Marc A. Suchard
- Departments of Biomathematics, Biostatistics, Human Genetics, University of California, Los Angeles, California, United States of America
| | | | - Patrizia Farci
- Hepatic Pathogenesis Section, Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Paul Klenerman
- Peter Medawar Building for Pathogen Research, University of Oxford, Oxford, United Kingdom
| | - Oliver G. Pybus
- Department of Zoology, University of Oxford, Oxford, United Kingdom
- * E-mail: (JR); (OGP)
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Bielejec F, Baele G, Rodrigo AG, Suchard MA, Lemey P. Identifying predictors of time-inhomogeneous viral evolutionary processes. Virus Evol 2016; 2:vew023. [PMID: 27774306 PMCID: PMC5072463 DOI: 10.1093/ve/vew023] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Various factors determine the rate at which mutations are generated and fixed in viral genomes. Viral evolutionary rates may vary over the course of a single persistent infection and can reflect changes in replication rates and selective dynamics. Dedicated statistical inference approaches are required to understand how the complex interplay of these processes shapes the genetic diversity and divergence in viral populations. Although evolutionary models accommodating a high degree of complexity can now be formalized, adequately informing these models by potentially sparse data, and assessing the association of the resulting estimates with external predictors, remains a major challenge. In this article, we present a novel Bayesian evolutionary inference method, which integrates multiple potential predictors and tests their association with variation in the absolute rates of synonymous and non-synonymous substitutions along the evolutionary history. We consider clinical and virological measures as predictors, but also changes in population size trajectories that are simultaneously inferred using coalescent modelling. We demonstrate the potential of our method in an application to within-host HIV-1 sequence data sampled throughout the infection of multiple patients. While analyses of individual patient populations lack statistical power, we detect significant evidence for an abrupt drop in non-synonymous rates in late stage infection and a more gradual increase in synonymous rates over the course of infection in a joint analysis across all patients. The former is predicted by the immune relaxation hypothesis while the latter may be in line with increasing replicative fitness during the asymptomatic stage.
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Affiliation(s)
- Filip Bielejec
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium
| | - Guy Baele
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium
| | - Allen G Rodrigo
- Research School of Biology, Australian National University, Canberra, ACT, Australia
| | - Marc A Suchard
- Department of Biomathematics and Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095, USA; Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA 90095, USA
| | - Philippe Lemey
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium
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