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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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Pearson T, Furstenau T, Wood C, Rigas V, Sahl J, Maltinsky S, Currie BJ, Mayo M, Hall C, Keim P, Fofanov V. Population sequencing for diversity and transmission analyses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.18.599478. [PMID: 38948873 PMCID: PMC11212992 DOI: 10.1101/2024.06.18.599478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Genomic diversity in a pathogen population is the foundation for evolution and adaptations in virulence, drug resistance, pathogenesis, and immune evasion. Characterizing, analyzing, and understanding population-level diversity is also essential for epidemiological and forensic tracking of sources and revealing detailed pathways of transmission and spread. For bacteria, culturing, isolating, and sequencing the large number of individual colonies required to adequately sample diversity can be prohibitively time-consuming and expensive. While sequencing directly from a mixed population will show variants among reads, they cannot be linked to reveal allele combinations associated with particular traits or phylogenetic inheritance patterns. Here, we describe the theory and method of how population sequencing directly from a mixed sample can be used in conjunction with sequencing a very small number of colonies to describe the phylogenetic diversity of a population without haplotype reconstruction. To demonstrate the utility of population sequencing in capturing phylogenetic diversity, we compared isogenic clones to population sequences of Burkholderia pseudomallei from the sputum of a single patient. We also analyzed population sequences of Staphylococcus aureus derived from different people and different body sites. Sequencing results confirm our ability to capture and characterize phylogenetic diversity in our samples. Our analyses of B. pseudomallei populations led to the surprising discovery that the pathogen population is highly structured in sputum, suggesting that for some pathogens, sputum sampling may preserve structuring in the lungs and thus present a non-invasive alternative to understanding colonization, movement, and pathogen/host interactions. Our analyses of S. aureus samples show how comparing phylogenetic diversity across populations can reveal directionality of transmission between hosts and across body sites, demonstrating the power and utility for characterizing the spread of disease and identification of reservoirs at the finest levels. We anticipate that population sequencing and analysis can be broadly applied to accelerate research in a broad range of fields reliant on a foundational understanding of population diversity.
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Affiliation(s)
- Talima Pearson
- Pathogen &Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Tara Furstenau
- Pathogen &Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Colin Wood
- Pathogen &Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Vanessa Rigas
- Global and Tropical Health Division, Menzies School of Health Research, Darwin, Northern Territory, Australia
| | - Jason Sahl
- Pathogen &Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Sara Maltinsky
- Pathogen &Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Bart J Currie
- Global and Tropical Health Division, Menzies School of Health Research, Darwin, Northern Territory, Australia
- Infectious Diseases Department and Northern Territory Medical Program, Royal Darwin Hospital, Darwin, Northern Territory, Australia
| | - Mark Mayo
- Global and Tropical Health Division, Menzies School of Health Research, Darwin, Northern Territory, Australia
| | - Carina Hall
- Pathogen &Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Paul Keim
- Pathogen &Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Viacheslav Fofanov
- Pathogen &Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, United States of America
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3
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Nascimento FF, Mehta SR, Little SJ, Volz EM. Assessing transmission attribution risk from simulated sequencing data in HIV molecular epidemiology. AIDS 2024; 38:865-873. [PMID: 38126363 PMCID: PMC10994139 DOI: 10.1097/qad.0000000000003820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 12/08/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND HIV molecular epidemiology (ME) is the analysis of sequence data together with individual-level clinical, demographic, and behavioral data to understand HIV epidemiology. The use of ME has raised concerns regarding identification of the putative source in direct transmission events. This could result in harm ranging from stigma to criminal prosecution in some jurisdictions. Here we assessed the risks of ME using simulated HIV genetic sequencing data. METHODS We simulated social networks of men-who-have-sex-with-men, calibrating the simulations to data from San Diego. We used these networks to simulate consensus and next-generation sequence (NGS) data to evaluate the risks of identifying direct transmissions using different HIV sequence lengths, and population sampling depths. To identify the source of transmissions, we calculated infector probability and used phyloscanner software for the analysis of consensus and NGS data, respectively. RESULTS Consensus sequence analyses showed that the risk of correctly inferring the source (direct transmission) within identified transmission pairs was very small and independent of sampling depth. Alternatively, NGS analyses showed that identification of the source of a transmission was very accurate, but only for 6.5% of inferred pairs. False positive transmissions were also observed, where one or more unobserved intermediaries were present when compared to the true network. CONCLUSION Source attribution using consensus sequences rarely infers direct transmission pairs with high confidence but is still useful for population studies. In contrast, source attribution using NGS data was much more accurate in identifying direct transmission pairs, but for only a small percentage of transmission pairs analyzed.
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Affiliation(s)
- Fabrícia F. Nascimento
- MRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Sanjay R. Mehta
- Division of Infectious Diseases, University of California San Diego, San Diego, CA, USA
| | - Susan J. Little
- Division of Infectious Diseases, University of California San Diego, San Diego, CA, USA
| | - Erik M. Volz
- MRC Centre for Global Infectious Disease Analysis and the Department of Infectious Disease Epidemiology, Imperial College London, London, UK
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4
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Kemp SA, Kamelian K, Cuadros DF, Cheng MTK, Okango E, Hanekom W, Ndung'u T, Pillay D, Bonsall D, Wong EB, Tanser F, Siedner MJ, Gupta RK. HIV transmission dynamics and population-wide drug resistance in rural South Africa. Nat Commun 2024; 15:3644. [PMID: 38684655 PMCID: PMC11059351 DOI: 10.1038/s41467-024-47254-z] [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: 11/20/2023] [Accepted: 03/20/2024] [Indexed: 05/02/2024] Open
Abstract
Despite expanded antiretroviral therapy (ART) in South Africa, HIV-1 transmission persists. Integrase strand transfer inhibitors (INSTI) and long-acting injectables offer potential for superior viral suppression, but pre-existing drug resistance could threaten their effectiveness. In a community-based study in rural KwaZulu-Natal, prior to widespread INSTI usage, we enroled 18,025 individuals to characterise HIV-1 drug resistance and transmission networks to inform public health strategies. HIV testing and reflex viral load quantification were performed, with deep sequencing (20% variant threshold) used to detect resistance mutations. Phylogenetic and geospatial analyses characterised transmission clusters. One-third of participants were HIV-positive, with 21.7% having detectable viral loads; 62.1% of those with detectable viral loads were ART-naïve. Resistance to older reverse transcriptase (RT)-targeting drugs was found, but INSTI resistance remained low (<1%). Non-nucleoside reverse transcriptase inhibitor (NNRTI) resistance, particularly to rilpivirine (RPV) even in ART-naïve individuals, was concerning. Twenty percent of sequenced individuals belonged to transmission clusters, with geographic analysis highlighting higher clustering in peripheral and rural areas. Our findings suggest promise for INSTI-based strategies in this setting but underscore the need for RPV resistance screening before implementing long-acting cabotegravir (CAB) + RPV. The significant clustering emphasises the importance of geographically targeted interventions to effectively curb HIV-1 transmission.
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Affiliation(s)
- Steven A Kemp
- Department of Medicine, University of Cambridge, Cambridge, UK
- Pandemic Science Institute, Big Data Institute, University of Oxford, Oxford, UK
| | - Kimia Kamelian
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Diego F Cuadros
- Digital Epidemiology Laboratory, Digital Futures, University of Cincinnati, Cincinnati, OH, USA
| | - Mark T K Cheng
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Elphas Okango
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Willem Hanekom
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
- University College London, London, UK
| | - Thumbi Ndung'u
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
- University College London, London, UK
| | | | - David Bonsall
- Pandemic Science Institute, Big Data Institute, University of Oxford, Oxford, UK
| | - Emily B Wong
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
| | - Frank Tanser
- University of Stellenbosch, Cape Town, South Africa
| | - Mark J Siedner
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA
- University of KwaZulu-Natal, Durban, South Africa
- Harvard University, Cambridge, MA, England
| | - Ravindra K Gupta
- Department of Medicine, University of Cambridge, Cambridge, UK.
- Africa Health Research Institute, KwaZulu-Natal, Durban, South Africa.
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5
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McHugh MP, Pettigrew KA, Taori S, Evans TJ, Leanord A, Gillespie SH, Templeton KE, Holden MTG. Consideration of within-patient diversity highlights transmission pathways and antimicrobial resistance gene variability in vancomycin-resistant Enterococcus faecium. J Antimicrob Chemother 2024; 79:656-668. [PMID: 38323373 PMCID: PMC11090465 DOI: 10.1093/jac/dkae023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 01/02/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND WGS is increasingly being applied to healthcare-associated vancomycin-resistant Enterococcus faecium (VREfm) outbreaks. Within-patient diversity could complicate transmission resolution if single colonies are sequenced from identified cases. OBJECTIVES Determine the impact of within-patient diversity on transmission resolution of VREfm. MATERIALS AND METHODS Fourteen colonies were collected from VREfm positive rectal screens, single colonies were collected from clinical samples and Illumina WGS was performed. Two isolates were selected for Oxford Nanopore sequencing and hybrid genome assembly to generate lineage-specific reference genomes. Mapping to closely related references was used to identify genetic variations and closely related genomes. A transmission network was inferred for the entire genome set using Phyloscanner. RESULTS AND DISCUSSION In total, 229 isolates from 11 patients were sequenced. Carriage of two or three sequence types was detected in 27% of patients. Presence of antimicrobial resistance genes and plasmids was variable within genomes from the same patient and sequence type. We identified two dominant sequence types (ST80 and ST1424), with two putative transmission clusters of two patients within ST80, and a single cluster of six patients within ST1424. We found transmission resolution was impaired using fewer than 14 colonies. CONCLUSIONS Patients can carry multiple sequence types of VREfm, and even within related lineages the presence of mobile genetic elements and antimicrobial resistance genes can vary. VREfm within-patient diversity could be considered in future to aid accurate resolution of transmission networks.
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Affiliation(s)
- Martin P McHugh
- School of Medicine, University of St Andrews, St Andrews, UK
- Medical Microbiology, Department of Laboratory Medicine, Royal Infirmary of Edinburgh, Edinburgh, UK
| | | | - Surabhi Taori
- Medical Microbiology, Department of Laboratory Medicine, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Thomas J Evans
- School of Infection and Immunity, University of Glasgow, Glasgow, UK
| | - Alistair Leanord
- School of Infection and Immunity, University of Glasgow, Glasgow, UK
- Scottish Microbiology Reference Laboratories, Glasgow Royal Infirmary, Glasgow, UK
| | | | - Kate E Templeton
- Medical Microbiology, Department of Laboratory Medicine, Royal Infirmary of Edinburgh, Edinburgh, UK
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Bu F, Kagaayi J, Grabowski MK, Ratmann O, Xu J. Inferring HIV transmission patterns from viral deep-sequence data via latent typed point processes. Biometrics 2024; 80:ujad015. [PMID: 38372402 PMCID: PMC10875513 DOI: 10.1093/biomtc/ujad015] [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: 02/23/2023] [Revised: 10/09/2023] [Accepted: 11/27/2023] [Indexed: 02/20/2024]
Abstract
Viral deep-sequencing data play a crucial role toward understanding disease transmission network flows, providing higher resolution compared to standard Sanger sequencing. To more fully utilize these rich data and account for the uncertainties in outcomes from phylogenetic analyses, we propose a spatial Poisson process model to uncover human immunodeficiency virus (HIV) transmission flow patterns at the population level. We represent pairings of individuals with viral sequence data as typed points, with coordinates representing covariates such as gender and age and point types representing the unobserved transmission statuses (linkage and direction). Points are associated with observed scores on the strength of evidence for each transmission status that are obtained through standard deep-sequence phylogenetic analysis. Our method is able to jointly infer the latent transmission statuses for all pairings and the transmission flow surface on the source-recipient covariate space. In contrast to existing methods, our framework does not require preclassification of the transmission statuses of data points, and instead learns them probabilistically through a fully Bayesian inference scheme. By directly modeling continuous spatial processes with smooth densities, our method enjoys significant computational advantages compared to previous methods that rely on discretization of the covariate space. We demonstrate that our framework can capture age structures in HIV transmission at high resolution, bringing valuable insights in a case study on viral deep-sequencing data from Southern Uganda.
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Affiliation(s)
- Fan Bu
- Department of Biostatistics, University of California - Los Angeles, Los Angeles, CA 90024, United States
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Joseph Kagaayi
- School of Public Health, Makerere University, Kampala, Uganda
| | - Mary Kate Grabowski
- School of Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Oliver Ratmann
- Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom
| | - Jason Xu
- Department of Statistical Science, Duke University, Durham, NC 27708, United States
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7
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Carson J, Keeling M, Wyllie D, Ribeca P, Didelot X. Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host. Mol Biol Evol 2024; 41:msad288. [PMID: 38168711 PMCID: PMC10798190 DOI: 10.1093/molbev/msad288] [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: 07/28/2023] [Revised: 12/21/2023] [Accepted: 12/29/2023] [Indexed: 01/05/2024] Open
Abstract
In recent times, pathogen genome sequencing has become increasingly used to investigate infectious disease outbreaks. When genomic data is sampled densely enough amongst infected individuals, it can help resolve who infected whom. However, transmission analysis cannot rely solely on a phylogeny of the genomes but must account for the within-host evolution of the pathogen, which blurs the relationship between phylogenetic and transmission trees. When only a single genome is sampled for each host, the uncertainty about who infected whom can be quite high. Consequently, transmission analysis based on multiple genomes of the same pathogen per host has a clear potential for delivering more precise results, even though it is more laborious to achieve. Here, we present a new methodology that can use any number of genomes sampled from a set of individuals to reconstruct their transmission network. Furthermore, we remove the need for the assumption of a complete transmission bottleneck. We use simulated data to show that our method becomes more accurate as more genomes per host are provided, and that it can infer key infectious disease parameters such as the size of the transmission bottleneck, within-host growth rate, basic reproduction number, and sampling fraction. We demonstrate the usefulness of our method in applications to real datasets from an outbreak of Pseudomonas aeruginosa amongst cystic fibrosis patients and a nosocomial outbreak of Klebsiella pneumoniae.
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Affiliation(s)
- Jake Carson
- Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry CV4 7AL, UK
| | - Matt Keeling
- Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry CV4 7AL, UK
| | | | | | - Xavier Didelot
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry CV4 7AL, UK
- Department of Statistics, University of Warwick, Coventry CV4 7AL, UK
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8
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Hall M, Golubchik T, Bonsall D, Abeler-Dörner L, Limbada M, Kosloff B, Schaap A, de Cesare M, MacIntyre-Cockett G, Otecko N, Probert W, Ratmann O, Bulas Cruz A, Piwowar-Manning E, Burns DN, Cohen MS, Donnell DJ, Eshleman SH, Simwinga M, Fidler S, Hayes R, Ayles H, Fraser C. Demographics of sources of HIV-1 transmission in Zambia: a molecular epidemiology analysis in the HPTN 071 PopART study. THE LANCET. MICROBE 2024; 5:e62-e71. [PMID: 38081203 PMCID: PMC10789608 DOI: 10.1016/s2666-5247(23)00220-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 07/07/2023] [Accepted: 07/14/2023] [Indexed: 01/19/2024]
Abstract
BACKGROUND In the last decade, universally available antiretroviral therapy (ART) has led to greatly improved health and survival of people living with HIV in sub-Saharan Africa, but new infections continue to appear. The design of effective prevention strategies requires the demographic characterisation of individuals acting as sources of infection, which is the aim of this study. METHODS Between 2014 and 2018, the HPTN 071 PopART study was conducted to quantify the public health benefits of ART. Viral samples from 7124 study participants in Zambia were deep-sequenced as part of HPTN 071-02 PopART Phylogenetics, an ancillary study. We used these sequences to identify likely transmission pairs. After demographic weighting of the recipients in these pairs to match the overall HIV-positive population, we analysed the demographic characteristics of the sources to better understand transmission in the general population. FINDINGS We identified a total of 300 likely transmission pairs. 178 (59·4%) were male to female, with 130 (95% CI 110-150; 43·3%) from males aged 25-40 years. Overall, men transmitted 2·09-fold (2·06-2·29) more infections per capita than women, a ratio peaking at 5·87 (2·78-15·8) in the 35-39 years source age group. 40 (26-57; 13·2%) transmissions linked individuals from different communities in the trial. Of 288 sources with recorded information on drug resistance mutations, 52 (38-69; 18·1%) carried viruses resistant to first-line ART. INTERPRETATION HIV-1 transmission in the HPTN 071 study communities comes from a wide range of age and sex groups, and there is no outsized contribution to new infections from importation or drug resistance mutations. Men aged 25-39 years, underserved by current treatment and prevention services, should be prioritised for HIV testing and ART. FUNDING National Institute of Allergy and Infectious Diseases, US President's Emergency Plan for AIDS Relief, International Initiative for Impact Evaluation, Bill & Melinda Gates Foundation, National Institute on Drug Abuse, and National Institute of Mental Health.
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Affiliation(s)
- Matthew Hall
- Pandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tanya Golubchik
- Pandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Sydney Infectious Diseases Institute, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - David Bonsall
- Pandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Lucie Abeler-Dörner
- Pandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Barry Kosloff
- Zambart, University of Zambia, Lusaka, Zambia; Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, UK
| | - Ab Schaap
- Zambart, University of Zambia, Lusaka, Zambia
| | - Mariateresa de Cesare
- Pandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - George MacIntyre-Cockett
- Pandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Newton Otecko
- Pandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - William Probert
- Pandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Oliver Ratmann
- Department of Mathematics, Imperial College London, London, UK
| | - Ana Bulas Cruz
- Pandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - David N Burns
- Division of AIDS, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA
| | - Myron S Cohen
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Susan H Eshleman
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Sarah Fidler
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Richard Hayes
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Helen Ayles
- Zambart, University of Zambia, Lusaka, Zambia; Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, UK
| | - Christophe Fraser
- Pandemic Sciences Institute and Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
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9
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Monod M, Brizzi A, Galiwango RM, Ssekubugu R, Chen Y, Xi X, Kankaka EN, Ssempijja V, Abeler-Dörner L, Akullian A, Blenkinsop A, Bonsall D, Chang LW, Dan S, Fraser C, Golubchik T, Gray RH, Hall M, Jackson JC, Kigozi G, Laeyendecker O, Mills LA, Quinn TC, Reynolds SJ, Santelli J, Sewankambo NK, Spencer SEF, Ssekasanvu J, Thomson L, Wawer MJ, Serwadda D, Godfrey-Faussett P, Kagaayi J, Grabowski MK, Ratmann O. Longitudinal population-level HIV epidemiologic and genomic surveillance highlights growing gender disparity of HIV transmission in Uganda. Nat Microbiol 2024; 9:35-54. [PMID: 38052974 PMCID: PMC10769880 DOI: 10.1038/s41564-023-01530-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 10/16/2023] [Indexed: 12/07/2023]
Abstract
HIV incidence in eastern and southern Africa has historically been concentrated among girls and women aged 15-24 years. As new cases decline with HIV interventions, population-level infection dynamics may shift by age and gender. Here, we integrated population-based surveillance of 38,749 participants in the Rakai Community Cohort Study and longitudinal deep-sequence viral phylogenetics to assess how HIV incidence and population groups driving transmission have changed from 2003 to 2018 in Uganda. We observed 1,117 individuals in the incidence cohort and 1,978 individuals in the transmission cohort. HIV viral suppression increased more rapidly in women than men, however incidence declined more slowly in women than men. We found that age-specific transmission flows shifted: whereas HIV transmission to girls and women (aged 15-24 years) from older men declined by about one-third, transmission to women (aged 25-34 years) from men that were 0-6 years older increased by half in 2003 to 2018. Based on changes in transmission flows, we estimated that closing the gender gap in viral suppression could have reduced HIV incidence in women by half in 2018. This study suggests that HIV programmes to increase HIV suppression in men are critical to reduce incidence in women, close gender gaps in infection burden and improve men's health in Africa.
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Affiliation(s)
- Mélodie Monod
- Department of Mathematics, Imperial College London, London, UK
| | - Andrea Brizzi
- Department of Mathematics, Imperial College London, London, UK
| | | | | | - Yu Chen
- Department of Mathematics, Imperial College London, London, UK
| | - Xiaoyue Xi
- Department of Mathematics, Imperial College London, London, UK
| | - Edward Nelson Kankaka
- Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Research Department, Rakai Health Sciences Program, Rakai, Uganda
| | - Victor Ssempijja
- Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
- Statistics Department, Rakai Health Sciences Program, Rakai, Uganda
| | | | | | | | - David Bonsall
- Wellcome Centre for Human Genomics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Larry W Chang
- Rakai Health Sciences Program, Kalisizo, Uganda
- Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Shozen Dan
- Department of Mathematics, Imperial College London, London, UK
| | - Christophe Fraser
- Big Data Institute, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Tanya Golubchik
- Big Data Institute, University of Oxford, Oxford, UK
- Sydney Infectious Diseases Institute, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Ronald H Gray
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Matthew Hall
- Big Data Institute, University of Oxford, Oxford, UK
| | - Jade C Jackson
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | - Oliver Laeyendecker
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Lisa A Mills
- Division of Global HIV and TB, US Centers for Disease Control and Prevention, Kampala, Uganda
| | - Thomas C Quinn
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Steven J Reynolds
- Rakai Health Sciences Program, Kalisizo, Uganda
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - John Santelli
- Population and Family Health and Pediatrics, Columbia Mailman School of Public Health, New York, NY, USA
| | - Nelson K Sewankambo
- College of Health Sciences, School of Medicine, Makerere University, Kampala, Uganda
| | | | - Joseph Ssekasanvu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Laura Thomson
- Big Data Institute, University of Oxford, Oxford, UK
| | - Maria J Wawer
- Rakai Health Sciences Program, Kalisizo, Uganda
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - David Serwadda
- Rakai Health Sciences Program, Kalisizo, Uganda
- College of Health Sciences, School of Medicine, Makerere University, Kampala, Uganda
| | - Peter Godfrey-Faussett
- Department of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | | | - M Kate Grabowski
- Rakai Health Sciences Program, Kalisizo, Uganda.
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
| | - Oliver Ratmann
- Department of Mathematics, Imperial College London, London, UK.
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10
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Walter KS, Cohen T, Mathema B, Colijn C, Sobkowiak B, Comas I, Goig GA, Croda J, Andrews JR. Signatures of transmission in within-host M. tuberculosis variation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.28.23300451. [PMID: 38234741 PMCID: PMC10793532 DOI: 10.1101/2023.12.28.23300451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Background Because M. tuberculosis evolves slowly, transmission clusters often contain multiple individuals with identical consensus genomes, making it difficult to reconstruct transmission chains. Finding additional sources of shared M. tuberculosis variation could help overcome this problem. Previous studies have reported M. tuberculosis diversity within infected individuals; however, whether within-host variation improves transmission inferences remains unclear. Methods To evaluate the transmission information present in within-host M. tuberculosis variation, we re-analyzed publicly available sequence data from three household transmission studies, using household membership as a proxy for transmission linkage between donor-recipient pairs. Findings We found moderate levels of minority variation present in M. tuberculosis sequence data from cultured isolates that varied significantly across studies (mean: 6, 7, and 170 minority variants above a 1% minor allele frequency threshold, outside of PE/PPE genes). Isolates from household members shared more minority variants than did isolates from unlinked individuals in the three studies (mean 98 shared minority variants vs. 10; 0.8 vs. 0.2, and 0.7 vs. 0.2, respectively). Shared within-host variation was significantly associated with household membership (OR: 1.51 [1.30,1.71], for one standard deviation increase in shared minority variants). Models that included shared within-host variation improved the accuracy of predicting household membership in all three studies as compared to models without within-host variation (AUC: 0.95 versus 0.92, 0.99 versus 0.95, and 0.93 versus 0.91). Interpretation Within-host M. tuberculosis variation persists through culture and could enhance the resolution of transmission inferences. The substantial differences in minority variation recovered across studies highlights the need to optimize approaches to recover and incorporate within-host variation into automated phylogenetic and transmission inference. Funding NIAID: 5K01AI173385.
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Affiliation(s)
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, USA
| | - Barun Mathema
- Department of Epidemiology, Columbia University Mailman School of Public Health; New York, United States
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University; Burnaby, Canada
| | - Benjamin Sobkowiak
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, USA
| | - Iñaki Comas
- Institute of Biomedicine of Valencia (CSIC), Valencia, Spain
| | - Galo A Goig
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Julio Croda
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, USA
- Federal University of Mato Grosso do Sul - UFMS, Campo Grande, MS, Brazil
- Oswaldo Cruz Foundation Mato Grosso do Sul, Mato Grosso do Sul, Brazil
| | - Jason R Andrews
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, CA, USA
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11
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Monod M, Brizzi A, Galiwango RM, Ssekubugu R, Chen Y, Xi X, Kankaka EN, Ssempijja V, Dörner LA, Akullian A, Blenkinsop A, Bonsall D, Chang LW, Dan S, Fraser C, Golubchik T, Gray RH, Hall M, Jackson JC, Kigozi G, Laeyendecker O, Mills LA, Quinn TC, Reynolds SJ, Santelli J, Sewankambo NK, Spencer SE, Ssekasanvu J, Thomson L, Wawer MJ, Serwadda D, Godfrey-Faussett P, Kagaayi J, Grabowski MK, Ratmann O. Longitudinal population-level HIV epidemiologic and genomic surveillance highlights growing gender disparity of HIV transmission in Uganda. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.16.23287351. [PMID: 36993261 PMCID: PMC10055554 DOI: 10.1101/2023.03.16.23287351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
HIV incidence in eastern and southern Africa has historically been concentrated among girls and women aged 15-24 years. As new cases decline with HIV interventions, population-level infection dynamics may shift by age and gender. Here, we integrated population-based surveillance of 38,749 participants in the Rakai Community Cohort Study and longitudinal deep sequence viral phylogenetics to assess how HIV incidence and population groups driving transmission have changed from 2003 to 2018 in Uganda. We observed 1,117 individuals in the incidence cohort and 1,978 individuals in the transmission cohort. HIV viral suppression increased more rapidly in women than men, however incidence declined more slowly in women than men. We found that age-specific transmission flows shifted, while HIV transmission to girls and women (aged 15-24 years) from older men declined by about one third, transmission to women (aged 25-34 years) from men that were 0-6 years older increased by half in 2003 to 2018. Based on changes in transmission flows, we estimated that closing the gender gap in viral suppression could have reduced HIV incidence in women by half in 2018. This study suggests that HIV programs to increase HIV suppression in men are critical to reduce incidence in women, close gender gaps in infection burden and improve men's health in Africa.
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12
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Hayati M, Sobkowiak B, Stockdale JE, Colijn C. Phylogenetic identification of influenza virus candidates for seasonal vaccines. SCIENCE ADVANCES 2023; 9:eabp9185. [PMID: 37922357 PMCID: PMC10624341 DOI: 10.1126/sciadv.abp9185] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 10/05/2023] [Indexed: 11/05/2023]
Abstract
The seasonal influenza (flu) vaccine is designed to protect against those influenza viruses predicted to circulate during the upcoming flu season, but identifying which viruses are likely to circulate is challenging. We use features from phylogenetic trees reconstructed from hemagglutinin (HA) and neuraminidase (NA) sequences, together with a support vector machine, to predict future circulation. We obtain accuracies of 0.75 to 0.89 (AUC 0.83 to 0.91) over 2016-2020. We explore ways to select potential candidates for a seasonal vaccine and find that the machine learning model has a moderate ability to select strains that are close to future populations. However, consensus sequences among the most recent 3 years also do well at this task. We identify similar candidate strains to those proposed by the World Health Organization, suggesting that this approach can help inform vaccine strain selection.
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Affiliation(s)
- Maryam Hayati
- School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Benjamin Sobkowiak
- Department of Mathematics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | | | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
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13
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Senghore M, Read H, Oza P, Johnson S, Passarelli-Araujo H, Taylor BP, Ashley S, Grey A, Callendrello A, Lee R, Goddard MR, Lumley T, Hanage WP, Wiles S. Inferring bacterial transmission dynamics using deep sequencing genomic surveillance data. Nat Commun 2023; 14:6397. [PMID: 37907520 PMCID: PMC10618251 DOI: 10.1038/s41467-023-42211-8] [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: 11/13/2022] [Accepted: 09/27/2023] [Indexed: 11/02/2023] Open
Abstract
Identifying and interrupting transmission chains is important for controlling infectious diseases. One way to identify transmission pairs - two hosts in which infection was transmitted from one to the other - is using the variation of the pathogen within each single host (within-host variation). However, the role of such variation in transmission is understudied due to a lack of experimental and clinical datasets that capture pathogen diversity in both donor and recipient hosts. In this work, we assess the utility of deep-sequenced genomic surveillance (where genomic regions are sequenced hundreds to thousands of times) using a mouse transmission model involving controlled spread of the pathogenic bacterium Citrobacter rodentium from infected to naïve female animals. We observe that within-host single nucleotide variants (iSNVs) are maintained over multiple transmission steps and present a model for inferring the likelihood that a given pair of sequenced samples are linked by transmission. In this work we show that, beyond the presence and absence of within-host variants, differences arising in the relative abundance of iSNVs (allelic frequency) can infer transmission pairs more precisely. Our approach further highlights the critical role bottlenecks play in reserving the within-host diversity during transmission.
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Affiliation(s)
- Madikay Senghore
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.
| | - Hannah Read
- Bioluminescent Superbugs Lab, Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
| | - Priyali Oza
- Bioluminescent Superbugs Lab, Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
| | - Sarah Johnson
- Bioluminescent Superbugs Lab, Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
| | - Hemanoel Passarelli-Araujo
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
- Department of Biochemistry and Immunology, Federal University of Minas Gerais, Minas Gerais, Brazil
| | - Bradford P Taylor
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Stephen Ashley
- Bioluminescent Superbugs Lab, Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
| | - Alex Grey
- Bioluminescent Superbugs Lab, Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
| | - Alanna Callendrello
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Robyn Lee
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
- University of Toronto Dalla Lana School of Public Health, Toronto, ON, Canada
| | - Matthew R Goddard
- School of Biological Sciences, University of Auckland, Auckland, New Zealand
- School of Life and Environmental Sciences, University of Lincoln, Lincoln, UK
| | - Thomas Lumley
- Department of Statistics, University of Auckland, Auckland, New Zealand
| | - William P Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Siouxsie Wiles
- Bioluminescent Superbugs Lab, Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand.
- Te Pūnaha Matatini, Centre of Research Excellence in Complex Systems, Auckland, New Zealand.
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14
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Hackman J, Sheppard C, Phelan J, Jones-Warner W, Sobkowiak B, Shah S, Litt D, Fry NK, Toizumi M, Yoshida LM, Hibberd M, Miller E, Flasche S, Hué S. Phylogenetic inference of pneumococcal transmission from cross-sectional data, a pilot study. Wellcome Open Res 2023; 8:427. [PMID: 38638914 PMCID: PMC11024593 DOI: 10.12688/wellcomeopenres.19219.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/03/2023] [Indexed: 04/20/2024] Open
Abstract
Background: Inference on pneumococcal transmission has mostly relied on longitudinal studies which are costly and resource intensive. Therefore, we conducted a pilot study to test the ability to infer who infected whom from cross-sectional pneumococcal sequences using phylogenetic inference. Methods: Five suspected transmission pairs, for which there was epidemiological evidence of who infected whom, were selected from a household study. For each pair, Streptococcus pneumoniae full genomes were sequenced from nasopharyngeal swabs collected on the same day. The within-host genetic diversity of the pneumococcal population was used to infer the transmission direction and then cross-validated with the direction suggested by the epidemiological records. Results: The pneumococcal genomes clustered into the five households from which the samples were taken. The proportion of concordantly inferred transmission direction generally increased with increasing minimum genome fragment size and single nucleotide polymorphisms. We observed a larger proportion of unique polymorphic sites in the source bacterial population compared to that of the recipient in four of the five pairs, as expected in the case of a transmission bottleneck. The only pair that did not exhibit this effect was also the pair that had consistent discordant transmission direction compared to the epidemiological records suggesting potential misdirection as a result of false-negative sampling. Conclusions: This pilot provided support for further studies to test if the direction of pneumococcal transmission can be reliably inferred from cross-sectional samples if sequenced with sufficient depth and fragment length.
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Affiliation(s)
- Jada Hackman
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
| | - Carmen Sheppard
- Vaccine Preventable Bacteria Section, UK Health Security Agency, London, UK
| | - Jody Phelan
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - William Jones-Warner
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Ben Sobkowiak
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Sonal Shah
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - David Litt
- Vaccine Preventable Bacteria Section, UK Health Security Agency, London, UK
| | - Norman K. Fry
- Vaccine Preventable Bacteria Section, UK Health Security Agency, London, UK
- Immunisation & Countermeasures Division, UK Health Security Agency, London, UK
| | - Michiko Toizumi
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
- Department of Paediatric Infectious Diseases, Nagasaki University, Nagasaki, Japan
| | - Lay-Myint Yoshida
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
- Department of Paediatric Infectious Diseases, Nagasaki University, Nagasaki, Japan
| | - Martin Hibberd
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Elizabeth Miller
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Stefan Flasche
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Stéphane Hué
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
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15
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Torres Ortiz A, Kendall M, Storey N, Hatcher J, Dunn H, Roy S, Williams R, Williams C, Goldstein RA, Didelot X, Harris K, Breuer J, Grandjean L. Within-host diversity improves phylogenetic and transmission reconstruction of SARS-CoV-2 outbreaks. eLife 2023; 12:e84384. [PMID: 37732733 PMCID: PMC10602588 DOI: 10.7554/elife.84384] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 09/20/2023] [Indexed: 09/22/2023] Open
Abstract
Accurate inference of who infected whom in an infectious disease outbreak is critical for the delivery of effective infection prevention and control. The increased resolution of pathogen whole-genome sequencing has significantly improved our ability to infer transmission events. Despite this, transmission inference often remains limited by the lack of genomic variation between the source case and infected contacts. Although within-host genetic diversity is common among a wide variety of pathogens, conventional whole-genome sequencing phylogenetic approaches exclusively use consensus sequences, which consider only the most prevalent nucleotide at each position and therefore fail to capture low-frequency variation within samples. We hypothesized that including within-sample variation in a phylogenetic model would help to identify who infected whom in instances in which this was previously impossible. Using whole-genome sequences from SARS-CoV-2 multi-institutional outbreaks as an example, we show how within-sample diversity is partially maintained among repeated serial samples from the same host, it can transmitted between those cases with known epidemiological links, and how this improves phylogenetic inference and our understanding of who infected whom. Our technique is applicable to other infectious diseases and has immediate clinical utility in infection prevention and control.
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Affiliation(s)
- Arturo Torres Ortiz
- Department of Infectious Diseases, Imperial College LondonLondonUnited Kingdom
- Department of Infection, Immunity and Inflammation, University College LondonLondonUnited Kingdom
| | - Michelle Kendall
- Department of Statistics, University of WarwickCoventryUnited Kingdom
| | - Nathaniel Storey
- Department of Microbiology, Great Ormond Street HospitalLondonUnited Kingdom
| | - James Hatcher
- Department of Microbiology, Great Ormond Street HospitalLondonUnited Kingdom
| | - Helen Dunn
- Department of Microbiology, Great Ormond Street HospitalLondonUnited Kingdom
| | - Sunando Roy
- Department of Infection, Immunity and Inflammation, University College LondonLondonUnited Kingdom
| | | | | | | | - Xavier Didelot
- Department of Statistics, University of WarwickCoventryUnited Kingdom
| | - Kathryn Harris
- Department of Microbiology, Great Ormond Street HospitalLondonUnited Kingdom
- Department of Virology, East & South East London Pathology Partnership, Royal London Hospital, Barts Health NHS TrustLondonUnited Kingdom
| | - Judith Breuer
- Department of Infection, Immunity and Inflammation, University College LondonLondonUnited Kingdom
| | - Louis Grandjean
- Department of Infection, Immunity and Inflammation, University College LondonLondonUnited Kingdom
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16
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Abdullahi A, Kida IM, Maina UA, Ibrahim AH, Mshelia J, Wisso H, Adamu A, Onyemata JE, Edun M, Yusuph H, Aliyu SH, Charurat M, Abimiku A, Abeler-Dorner L, Fraser C, Bonsall D, Kemp SA, Gupta RK. Limited emergence of resistance to integrase strand transfer inhibitors (INSTIs) in ART-experienced participants failing dolutegravir-based antiretroviral therapy: a cross-sectional analysis of a Northeast Nigerian cohort. J Antimicrob Chemother 2023; 78:2000-2007. [PMID: 37367727 PMCID: PMC10393879 DOI: 10.1093/jac/dkad195] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 05/30/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Due to the high prevalence of resistance to NNRTI-based ART since 2018, consolidated recommendations from the WHO have indicated dolutegravir as the preferred drug of choice for HIV treatment globally. There is a paucity of resistance outcome data from HIV-1 non-B subtypes circulating across West Africa. AIMS We characterized the mutational profiles of persons living with HIV from a cross-sectional cohort in North-East Nigeria failing a dolutegravir-based ART regimen. METHODS WGS of plasma samples collected from 61 HIV-1-infected participants following virological failure of dolutegravir-based ART were sequenced using the Illumina platform. Sequencing was successfully completed for samples from 55 participants. Following quality control, 33 full genomes were analysed from participants with a median age of 40 years and median time on ART of 9 years. HIV-1 subtyping was performed using SNAPPy. RESULTS Most participants had mutational profiles reflective of exposure to previous first- and second-line ART regimens comprised NRTIs and NNRTIs. More than half of participants had one or more drug resistance-associated mutations (DRMs) affecting susceptibility to NRTIs (17/33; 52%) and NNRTIs (24/33; 73%). Almost a quarter of participants (8/33; 24.4%) had one or more DRMs affecting tenofovir susceptibility. Only one participant, infected with HIV-1 subtype G, had evidence of DRMs affecting dolutegravir susceptibility-this was characterized by the T66A, G118R, E138K and R263K mutations. CONCLUSIONS This study found a low prevalence of resistance to dolutegravir; the data are therefore supportive of the continual rollout of dolutegravir as the primary first-line regimen for ART-naive participants and the preferred switch to second-line ART across the region. However, population-level, longer-term data collection on dolutegravir outcomes are required to further guide implementation and policy action across the region.
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Affiliation(s)
- Adam Abdullahi
- Department of Medicine, Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Cambridge, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
- Institute of Human Virology Nigeria, Abuja, Nigeria
| | - Ibrahim Musa Kida
- Department of Infectious Disease and Clinical Immunology, University of Maiduguri, Borno, Nigeria
| | - Umar Abdullahi Maina
- Department of Veterinary Pharmacology and Toxicology, Faculty of Veterinary Medicine, University of Maiduguri, Borno, Nigeria
| | | | - James Mshelia
- Department of Infectious Disease and Clinical Immunology, University of Maiduguri, Borno, Nigeria
| | - Haruna Wisso
- Institute of Human Virology Nigeria, Abuja, Nigeria
| | - Abdullahi Adamu
- Department of Veterinary Pharmacology and Toxicology, Faculty of Veterinary Medicine, University of Maiduguri, Borno, Nigeria
| | | | - Martin Edun
- Institute of Human Virology Nigeria, Abuja, Nigeria
| | - Haruna Yusuph
- Department of Infectious Disease and Clinical Immunology, University of Maiduguri, Borno, Nigeria
| | - Sani H Aliyu
- Department of Microbiology, Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Man Charurat
- Institute of Human Virology, University of Maryland School of Medicine, Baltimore, USA
| | | | - Lucie Abeler-Dorner
- Nuffield Department of Medicine, Big Data Institute, University of Oxford, Oxford, UK
| | - Christophe Fraser
- Nuffield Department of Medicine, Big Data Institute, University of Oxford, Oxford, UK
| | - David Bonsall
- Nuffield Department of Medicine, Big Data Institute, University of Oxford, Oxford, UK
| | - Steven A Kemp
- Department of Medicine, Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Cambridge, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
- Nuffield Department of Medicine, Big Data Institute, University of Oxford, Oxford, UK
| | - Ravindra K Gupta
- Department of Medicine, Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Cambridge, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
- Africa Health Research Institute, Durban, South Africa
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17
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Tully DC, Power KA, Sarette J, Stopka TJ, Friedmann PD, Korthuis PT, Cooper H, Young AM, Seal DW, Westergaard RP, Allen TM. Validation of Dried Blood Spots for Capturing Hepatitis C Virus Diversity for Genomic Surveillance. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.06.23292160. [PMID: 37461565 PMCID: PMC10350139 DOI: 10.1101/2023.07.06.23292160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
Dried blood spots (DBS) have emerged as a promising alternative to traditional venous blood for HCV testing. However, their capacity to accurately reflect the genetic diversity of HCV remains poorly understood. We employed deep sequencing and advanced phylogenetic analyses on paired plasma and DBS samples to evaluate the suitability of DBS for genomic surveillance. Results demonstrated that DBS captured equivalent viral diversity compared to plasma with no phylogenetic discordance observed. The ability of DBS to accurately reflect the profile of viral genetic diversity suggests it may be a promising avenue for future surveillance efforts to curb HCV outbreaks.
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Affiliation(s)
- Damien C. Tully
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- Center for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK
| | - Karen A. Power
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
| | - Jacklyn Sarette
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
| | - Thomas J. Stopka
- Tufts University School of Medicine Public Health and Community Medicine, 136 Harrison Avenue, Boston, MA, 02111, USA
| | - Peter D. Friedmann
- Baystate Medical Center—University of Massachusetts, Office of Research, UMass Chan Medical School - Baystate, 3601 Main Street, 3rd Floor, Springfield, MA, 01199, USA
| | - P. Todd Korthuis
- Oregon Health & Science University, 3270 Southwest Pavilion Loop OHSU Physicians Pavilion, Suite 350, Portland, OR, 97239, USA
| | - Hannah Cooper
- Rollins School of Public Health, Emory University, Grace Crum Rollins Building 1518 Clifton Road, Atlanta, GA, 30322, USA
| | - April M. Young
- University of Kentucky, 760 Press Avenue Suite 280, Lexington, KY, 40536, USA
| | - David W. Seal
- Tulane University, 1440 Canal Street, Suite 2210, New Orleans, LA, 70112, USA
| | - Ryan P. Westergaard
- University of Wisconsin-Madison, 1685 Highland Avenue, 5th Floor, Madison, WI, 53705-2281, USA
| | - Todd M. Allen
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
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18
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Jamrozik E, Munung NS, Abeler-Dorner L, Parker M. Public health use of HIV phylogenetic data in sub-Saharan Africa: ethical issues. BMJ Glob Health 2023; 8:e011884. [PMID: 37407228 DOI: 10.1136/bmjgh-2023-011884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 06/05/2023] [Indexed: 07/07/2023] Open
Abstract
Phylogenetic analyses of HIV are an increasingly accurate method of clarifying population-level patterns of transmission and linking individuals or groups with transmission events. Viral genetic data may be used by public health agencies to guide policy interventions focused on clusters of transmission or segments of the population in which transmission is concentrated. Analyses of HIV phylogenetics in high-income countries have often found that clusters of transmission play a significant role in HIV epidemics. In sub-Saharan Africa, HIV phylogenetic analyses to date suggest that clusters of transmission play a relatively minor role in local epidemics. Such analyses could nevertheless be used to guide priority setting and HIV public health programme design in Africa for sub-populations in which transmission events are more concentrated. Phylogenetic analysis raises ethical issues, in part due to the range of potential benefits and potential harms (ie, risks). Potential benefits include (1) improving knowledge of transmission patterns, (2) informing the design of focused public health interventions for subpopulations in which transmission is concentrated, (3) identifying and responding to clusters of transmission, (4) reducing stigma (in some cases) and (5) informing estimates of the (cost-)effectiveness of HIV treatment programmes. Potential harms include (1) privacy infringements, (2) increasing stigma (in some cases), (3) reducing trust in public health programmes, and (4) increased prosecution of legal cases where HIV transmission, homosexuality or sex work is criminalised. This paper provides analysis of relevant issues with a focus on sub-Saharan Africa in order to inform consultations regarding ethical best practice for HIV phylogenetics.
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Affiliation(s)
- Euzebiusz Jamrozik
- Ethox and the Wellcome Centre for Ethics and Humanities, University of Oxford, Oxford, UK
- Royal Melbourne Hospital Department of Medicine, University of Melbourne, Parkville, Victoria, Australia
- Monash Bioethics Centre, Monash University, Melbourne, Victoria, Australia
| | | | | | - Michael Parker
- Ethox and the Wellcome Centre for Ethics and Humanities, University of Oxford, Oxford, UK
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19
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Robert A, Tsui Lok Hei J, Watson CH, Gsell PS, Hall Y, Rambaut A, Longini IM, Sakoba K, Kucharski AJ, Touré A, Danmadji Nadlaou S, Saidou Barry M, Fofana TO, Lansana Kaba I, Sylla L, Diaby ML, Soumah O, Diallo A, Niare A, Diallo A, Eggo RM, Caroll MW, Henao-Restrepo AM, Edmunds WJ, Hué S. Quantifying the value of viral genomics when inferring who infected whom in the 2014-16 Ebola virus outbreak in Guinea. Virus Evol 2023; 9:vead007. [PMID: 36926449 PMCID: PMC10013732 DOI: 10.1093/ve/vead007] [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: 06/10/2022] [Revised: 11/17/2022] [Accepted: 03/06/2023] [Indexed: 03/16/2023] Open
Abstract
Transmission trees can be established through detailed contact histories, statistical or phylogenetic inference, or a combination of methods. Each approach has its limitations, and the extent to which they succeed in revealing a 'true' transmission history remains unclear. In this study, we compared the transmission trees obtained through contact tracing investigations and various inference methods to identify the contribution and value of each approach. We studied eighty-six sequenced cases reported in Guinea between March and November 2015. Contact tracing investigations classified these cases into eight independent transmission chains. We inferred the transmission history from the genetic sequences of the cases (phylogenetic approach), their onset date (epidemiological approach), and a combination of both (combined approach). The inferred transmission trees were then compared to those from the contact tracing investigations. Inference methods using individual data sources (i.e. the phylogenetic analysis and the epidemiological approach) were insufficiently informative to accurately reconstruct the transmission trees and the direction of transmission. The combined approach was able to identify a reduced pool of infectors for each case and highlight likely connections among chains classified as independent by the contact tracing investigations. Overall, the transmissions identified by the contact tracing investigations agreed with the evolutionary history of the viral genomes, even though some cases appeared to be misclassified. Therefore, collecting genetic sequences during outbreak is key to supplement the information contained in contact tracing investigations. Although none of the methods we used could identify one unique infector per case, the combined approach highlighted the added value of mixing epidemiological and genetic information to reconstruct who infected whom.
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Affiliation(s)
- Alexis Robert
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 6HT, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 6HT, UK
| | - Joseph Tsui Lok Hei
- Department of Biology, University of Oxford, South Parks Road, Oxford OX1 3RB, UK
| | - Conall H Watson
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 6HT, UK
- Epidemic Diseases Research Group Oxford, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LG, UK
| | | | - Yper Hall
- UK Health Security Agency, Manor Farm Rd, Porton Down, Salisbury SP4 0JG, UK
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, Charlotte Auerbach Road, Edinburgh EH9 3FL, UK
| | - Ira M Longini
- Department of Biostatistics, University of Florida, 2004 Mowry Road, 5th Floor CTRB, Gainesville, FL 32611-7450, USA
| | - Keïta Sakoba
- World Health Organization Ebola Vaccination Team, Sonfonia T.7, Conakry, Guinea
| | - Adam J Kucharski
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 6HT, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 6HT, UK
| | - Alhassane Touré
- World Health Organization Ebola Vaccination Team, Sonfonia T.7, Conakry, Guinea
| | | | | | | | | | - Lansana Sylla
- World Health Organization Ebola Vaccination Team, Sonfonia T.7, Conakry, Guinea
| | | | - Ousmane Soumah
- World Health Organization Ebola Vaccination Team, Sonfonia T.7, Conakry, Guinea
| | - Abdourahime Diallo
- World Health Organization Ebola Vaccination Team, Sonfonia T.7, Conakry, Guinea
| | - Amadou Niare
- World Health Organization Ebola Vaccination Team, Sonfonia T.7, Conakry, Guinea
| | | | - Rosalind M Eggo
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 6HT, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 6HT, UK
| | - Miles W Caroll
- Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Dr, Headington, Oxford OX3 7BN, UK
| | | | - W John Edmunds
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 6HT, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 6HT, UK
| | - Stéphane Hué
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 6HT, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 6HT, UK
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20
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Hadjirin NF, van Tonder AJ, Blane B, Lees JA, Kumar N, Delappe N, Brennan W, McGrath E, Parkhill J, Cormican M, Peacock SJ, Ludden C. Dissemination of carbapenemase-producing Enterobacterales in Ireland from 2012 to 2017: a retrospective genomic surveillance study. Microb Genom 2023; 9:mgen000924. [PMID: 36916881 PMCID: PMC10132065 DOI: 10.1099/mgen.0.000924] [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: 05/03/2022] [Accepted: 11/03/2022] [Indexed: 03/16/2023] Open
Abstract
The spread of carbapenemase-producing Enterobacterales (CPE) is of major public health concern. The transmission dynamics of CPE in hospitals, particularly at the national level, are not well understood. Here, we describe a retrospective nationwide genomic surveillance study of CPE in Ireland between 2012 and 2017. We sequenced 746 national surveillance CPE samples obtained between 2012 and 2017. After clustering the sequences, we used thresholds based on pairwise SNPs, and reported within-host diversity along with epidemiological data to infer recent putative transmissions. All clusters in circulating clones, derived from high-resolution phylogenies, of a species (Klebsiella pneumoniae, Escherichia coli, Klebsiella oxytoca, Enterobacter cloacae, Enterobacter hormaechei and Citrobacter freundii) were individually examined for evidence of transmission. Antimicrobial resistance trends over time were also assessed. We identified 352 putative transmission events in six species including widespread and frequent transmissions in three species. We detected putative outbreaks in 4/6 species with three hospitals experiencing prolonged outbreaks. The bla OXA-48 gene was the main cause of carbapenem resistance in Ireland in almost all species. An expansion in the number of sequence types carrying bla OXA-48 was an additional cause of the increasing prevalence of carbapenemase-producing K. pneumoniae and E. coli.
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Affiliation(s)
- Nazreen F. Hadjirin
- Department of Medicine, University of Cambridge, Box 157, Addenbrooke’s Hospital, Hills Rd, Cambridge, CB2 0QQ, UK
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Andries J. van Tonder
- Department of Veterinary Medicine, University of Cambridge, Madingley Rd, Cambridge, CB3 0ES, UK
| | - Beth Blane
- Department of Medicine, University of Cambridge, Box 157, Addenbrooke’s Hospital, Hills Rd, Cambridge, CB2 0QQ, UK
| | - John A. Lees
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Narender Kumar
- Wellcome Trust Sanger Institute Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Niall Delappe
- National CPE Reference Laboratory, University Hospital Galway, Galway, Ireland
| | - Wendy Brennan
- National CPE Reference Laboratory, University Hospital Galway, Galway, Ireland
| | - Elaine McGrath
- National CPE Reference Laboratory, University Hospital Galway, Galway, Ireland
| | - Julian Parkhill
- Department of Veterinary Medicine, University of Cambridge, Madingley Rd, Cambridge, CB3 0ES, UK
| | - Martin Cormican
- National CPE Reference Laboratory, University Hospital Galway, Galway, Ireland
- Antimicrobial Resistance and Microbial Ecology Group, School of Medicine, University of Galway, Galway, Ireland
| | - Sharon J. Peacock
- Department of Medicine, University of Cambridge, Box 157, Addenbrooke’s Hospital, Hills Rd, Cambridge, CB2 0QQ, UK
| | - Catherine Ludden
- Department of Medicine, University of Cambridge, Box 157, Addenbrooke’s Hospital, Hills Rd, Cambridge, CB2 0QQ, UK
- Wellcome Trust Sanger Institute Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
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21
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Low Viral Diversity Limits the Effectiveness of Sequence-Based Transmission Inference for SARS-CoV-2. mSphere 2023; 8:e0054422. [PMID: 36695609 PMCID: PMC9942562 DOI: 10.1128/msphere.00544-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Tracking the spread of infection amongst individuals within and between communities has been a major challenge during viral outbreaks. With the unprecedented scale of viral sequence data collection during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the possibility of using phylogenetics to reconstruct past transmission events has been explored as a more rigorous alternative to traditional contact tracing; however, the reliability of sequence-based inference of transmission networks has yet to be directly evaluated. E. E. Bendall, G. Paz-Bailey, G. A. Santiago, C. A. Porucznik, et al. (mSphere 7:e00400-22, 2022, https://doi.org/10.1128/mSphere.00400-22) evaluate the potential of this technique by applying best practices sequence comparison methods to three geographically distinct cohorts that include known transmission pairs and demonstrate that linked pairs are often indistinguishable from unrelated samples. This study clearly establishes how low viral diversity limits the utility of genomic methods of epidemiological inference for SARS-CoV-2.
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22
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Walter KS, Kim E, Verma R, Altamirano J, Leary S, Carrington YJ, Jagannathan P, Singh U, Holubar M, Subramanian A, Khosla C, Maldonado Y, Andrews JR. Challenges in Harnessing Shared Within-Host Severe Acute Respiratory Syndrome Coronavirus 2 Variation for Transmission Inference. Open Forum Infect Dis 2023; 10:ofad001. [PMID: 36751652 PMCID: PMC9898879 DOI: 10.1093/ofid/ofad001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 01/06/2023] [Indexed: 01/09/2023] Open
Abstract
Background The limited variation observed among severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) consensus sequences makes it difficult to reconstruct transmission linkages in outbreak settings. Previous studies have recovered variation within individual SARS-CoV-2 infections but have not yet measured the informativeness of within-host variation for transmission inference. Methods We performed tiled amplicon sequencing on 307 SARS-CoV-2 samples, including 130 samples from 32 individuals in 14 households and 47 longitudinally sampled individuals, from 4 prospective studies with household membership data, a proxy for transmission linkage. Results Consensus sequences from households had limited diversity (mean pairwise distance, 3.06 single-nucleotide polymorphisms [SNPs]; range, 0-40). Most (83.1%, 255 of 307) samples harbored at least 1 intrahost single-nucleotide variant ([iSNV] median, 117; interquartile range [IQR], 17-208), above a minor allele frequency threshold of 0.2%. Pairs in the same household shared significantly more iSNVs (mean, 1.20 iSNVs; 95% confidence interval [CI], 1.02-1.39) than did pairs in different households infected with the same viral clade (mean, 0.31 iSNVs; 95% CI, .28-.34), a signal that decreases with increasingly stringent minor allele frequency thresholds. The number of shared iSNVs was significantly associated with an increased odds of household membership (adjusted odds ratio, 1.35; 95% CI, 1.23-1.49). However, the poor concordance of iSNVs detected across sequencing replicates (24.8% and 35.0% above a 0.2% and 1% threshold) confirms technical concerns that current sequencing and bioinformatic workflows do not consistently recover low-frequency within-host variants. Conclusions Shared within-host variation may augment the information in consensus sequences for predicting transmission linkages. Improving sensitivity and specificity of within-host variant identification will improve the informativeness of within-host variation.
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Affiliation(s)
- Katharine S Walter
- Correspondence: Katharine S. Walter, PhD, Division of Epidemiology, University of Utah, 295 Chipeta Way, Salt Lake City, UT 84108, USA ()
| | - Eugene Kim
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Renu Verma
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Jonathan Altamirano
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA
| | - Sean Leary
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Yuan J Carrington
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Prasanna Jagannathan
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA,Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA
| | - Upinder Singh
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA,Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA
| | - Marisa Holubar
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Aruna Subramanian
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Chaitan Khosla
- Stanford ChEM-H, Stanford University, Stanford, California, USA,Department of Chemistry and Chemical Engineering, Stanford University, Stanford, California, USA
| | - Yvonne Maldonado
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA,Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Jason R Andrews
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA
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23
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Cholette F, Lazarus L, Macharia P, Thompson LH, Githaiga S, Mathenge J, Walimbwa J, Kuria I, Okoth S, Wambua S, Albert H, Mwangi P, Adhiambo J, Kasiba R, Juma E, Battacharjee P, Kimani J, Sandstrom P, Meyers AFA, Joy JB, Thomann M, McLaren PJ, Shaw S, Mishra S, Becker ML, McKinnon L, Lorway R. Community Insights in Phylogenetic HIV Research: The CIPHR Project Protocol. Glob Public Health 2023; 18:2269435. [PMID: 37851872 DOI: 10.1080/17441692.2023.2269435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 10/04/2023] [Indexed: 10/20/2023]
Abstract
Inferring HIV transmission networks from HIV sequences is gaining popularity in the field of HIV molecular epidemiology. However, HIV sequences are often analyzed at distance from those affected by HIV epidemics, namely without the involvement of communities most affected by HIV. These remote analyses often mean that knowledge is generated in absence of lived experiences and socio-economic realities that could inform the ethical application of network-derived information in 'real world' programmes. Procedures to engage communities are noticeably absent from the HIV molecular epidemiology literature. Here we present our team's protocol for engaging community activists living in Nairobi, Kenya in a knowledge exchange process - The CIPHR Project (Community Insights in Phylogenetic HIV Research). Drawing upon a community-based participatory approach, our team will (1) explore the possibilities and limitations of HIV molecular epidemiology for key population programmes, (2) pilot a community-based HIV molecular study, and (3) co-develop policy guidelines on conducting ethically safe HIV molecular epidemiology. Critical dialogue with activist communities will offer insight into the potential uses and abuses of using such information to sharpen HIV prevention programmes. The outcome of this process holds importance to the development of policy frameworks that will guide the next generation of the global response.
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Affiliation(s)
- François Cholette
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada
- Sexually Transmitted and Blood-Borne Infections, National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, Canada
| | - Lisa Lazarus
- Institute for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada
| | - Pascal Macharia
- Health Options for Young Men on HIV/AIDS and STIs (HOYMAS), Nairobi, Kenya
| | - Laura H Thompson
- Sexually Transmitted and Blood-Borne Infections Surveillance Division, Centre for Communicable Diseases and Infection Control, Public Health Agency of Canada, Ottawa, Canada
| | - Samuel Githaiga
- Health Options for Young Men on HIV/AIDS and STIs (HOYMAS), Nairobi, Kenya
| | - John Mathenge
- Health Options for Young Men on HIV/AIDS and STIs (HOYMAS), Nairobi, Kenya
| | | | - Irene Kuria
- Key Population Consortium of Kenya, Nairobi, Kenya
| | - Silvia Okoth
- Bar Hostess Empowerment and Support Programme, Nairobi, Kenya
| | | | - Harrison Albert
- Health Options for Young Men on HIV/AIDS and STIs (HOYMAS), Nairobi, Kenya
| | - Peninah Mwangi
- Bar Hostess Empowerment and Support Programme, Nairobi, Kenya
| | - Joyce Adhiambo
- Partners for Health Development in Africa (PHDA), Nairobi, Kenya
- Sex Worker Outreach Programme (SWOP), Nairobi, Kenya
| | | | - Esther Juma
- Sex Worker Outreach Programme (SWOP), Nairobi, Kenya
| | | | - Joshua Kimani
- Sex Worker Outreach Programme (SWOP), Nairobi, Kenya
- Department of Medical Microbiology, University of Nairobi, Nairobi, Kenya
| | - Paul Sandstrom
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada
- Sexually Transmitted and Blood-Borne Infections, National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, Canada
| | - Adrienne F A Meyers
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada
- Sexually Transmitted and Blood-Borne Infections, National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, Canada
| | - Jeffrey B Joy
- British Columbia Centre for Excellence in HIV/AIDS (BCCfE), St. Paul's Hospital, Vancouver, Canada
- Division of Infectious Diseases, Department of Medicine, University of British Columbia, Vancouver, Canada
- Bioinformatics Programme, University of British Columbia, Vancouver, Canada
| | - Matthew Thomann
- Department of Anthropology, University of Maryland, College Park, MD, USA
| | - Paul J McLaren
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada
- Sexually Transmitted and Blood-Borne Infections, National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, Canada
| | - Souradet Shaw
- Institute for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada
| | - Sharmistha Mishra
- MAP Centre for Urban Health Solutions, St. Michael's Hospital, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
| | - Marissa L Becker
- Institute for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada
| | - Lyle McKinnon
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada
- Department of Medical Microbiology, University of Nairobi, Nairobi, Kenya
- Centre for the AIDS Programme of Research in South Africa, Durban, South Africa
| | - Robert Lorway
- Institute for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada
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24
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Johnson PCD, Hägglund S, Näslund K, Meyer G, Taylor G, Orton RJ, Zohari S, Haydon DT, Valarcher JF. Evaluating the potential of whole-genome sequencing for tracing transmission routes in experimental infections and natural outbreaks of bovine respiratory syncytial virus. Vet Res 2022; 53:107. [PMID: 36510312 PMCID: PMC9746130 DOI: 10.1186/s13567-022-01127-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/09/2022] [Indexed: 12/14/2022] Open
Abstract
Bovine respiratory syncytial virus (BRSV) is a major cause of respiratory disease in cattle. Genomic sequencing can resolve phylogenetic relationships between virus populations, which can be used to infer transmission routes and potentially inform the design of biosecurity measures. Sequencing of short (<2000 nt) segments of the 15 000-nt BRSV genome has revealed geographic and temporal clustering of BRSV populations, but insufficient variation to distinguish viruses collected from herds infected close together in space and time. This study investigated the potential for whole-genome sequencing to reveal sufficient genomic variation for inferring transmission routes between herds. Next-generation sequencing (NGS) data were generated from experimental infections and from natural outbreaks in Jämtland and Uppsala counties in Sweden. Sufficient depth of coverage for analysis of consensus and sub-consensus sequence diversity was obtained from 47 to 20 samples respectively. Few (range: 0-6 polymorphisms across the six experiments) consensus-level polymorphisms were observed along experimental transmissions. A much higher level of diversity (146 polymorphic sites) was found among the consensus sequences from the outbreak samples. The majority (144/146) of polymorphisms were between rather than within counties, suggesting that consensus whole-genome sequences show insufficient spatial resolution for inferring direct transmission routes, but might allow identification of outbreak sources at the regional scale. By contrast, within-sample diversity was generally higher in the experimental than the outbreak samples. Analyses to infer known (experimental) and suspected (outbreak) transmission links from within-sample diversity data were uninformative. In conclusion, analysis of the whole-genome sequence of BRSV from experimental samples discriminated between circulating isolates from distant areas, but insufficient diversity was observed between closely related isolates to aid local transmission route inference.
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Affiliation(s)
- Paul C D Johnson
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK.
| | - Sara Hägglund
- HPIG. Unit of Ruminant Medicine. Department of Clinical Sciences, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
| | - Katarina Näslund
- Department of Microbiology, National Veterinary Institute, SVA, Uppsala, Sweden
| | - Gilles Meyer
- IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France
| | | | - Richard J Orton
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Siamak Zohari
- Department of Microbiology, National Veterinary Institute, SVA, Uppsala, Sweden
| | - Daniel T Haydon
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
| | - Jean François Valarcher
- HPIG. Unit of Ruminant Medicine. Department of Clinical Sciences, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
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25
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Goldstein IH, Bayer D, Barilar I, Kizito B, Matsiri O, Modongo C, Zetola NM, Niemann S, Minin VM, Shin SS. Using genetic data to identify transmission risk factors: Statistical assessment and application to tuberculosis transmission. PLoS Comput Biol 2022; 18:e1010696. [PMID: 36469509 DOI: 10.1371/journal.pcbi.1010696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 12/15/2022] [Accepted: 10/31/2022] [Indexed: 12/12/2022] Open
Abstract
Identifying host factors that influence infectious disease transmission is an important step toward developing interventions to reduce disease incidence. Recent advances in methods for reconstructing infectious disease transmission events using pathogen genomic and epidemiological data open the door for investigation of host factors that affect onward transmission. While most transmission reconstruction methods are designed to work with densely sampled outbreaks, these methods are making their way into surveillance studies, where the fraction of sampled cases with sequenced pathogens could be relatively low. Surveillance studies that use transmission event reconstruction then use the reconstructed events as response variables (i.e., infection source status of each sampled case) and use host characteristics as predictors (e.g., presence of HIV infection) in regression models. We use simulations to study estimation of the effect of a host factor on probability of being an infection source via this multi-step inferential procedure. Using TransPhylo-a widely-used method for Bayesian estimation of infectious disease transmission events-and logistic regression, we find that low sensitivity of identifying infection sources leads to dilution of the signal, biasing logistic regression coefficients toward zero. We show that increasing the proportion of sampled cases improves sensitivity and some, but not all properties of the logistic regression inference. Application of these approaches to real world data from a population-based TB study in Botswana fails to detect an association between HIV infection and probability of being a TB infection source. We conclude that application of a pipeline, where one first uses TransPhylo and sparsely sampled surveillance data to infer transmission events and then estimates effects of host characteristics on probabilities of these events, should be accompanied by a realistic simulation study to better understand biases stemming from imprecise transmission event inference.
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Affiliation(s)
- Isaac H Goldstein
- Department of Statistics, University of California, Irvine, California, United States of America
| | - Damon Bayer
- Department of Statistics, University of California, Irvine, California, United States of America
| | - Ivan Barilar
- German Center for Infection Research, Research Center Borstel, Borstel, Germany
| | | | | | | | - Nicola M Zetola
- Augusta University, Augusta, Georgia, United States of America
| | - Stefan Niemann
- German Center for Infection Research, Research Center Borstel, Borstel, Germany
| | - Volodymyr M Minin
- Department of Statistics, University of California, Irvine, California, United States of America
| | - Sanghyuk S Shin
- Sue & Bill Gross School of Nursing, University of California, Irvine, California, United States of America
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26
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Chao E, Chato C, Vender R, Olabode AS, Ferreira RC, Poon AFY. Molecular source attribution. PLoS Comput Biol 2022; 18:e1010649. [PMID: 36395093 PMCID: PMC9671344 DOI: 10.1371/journal.pcbi.1010649] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Elisa Chao
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
| | - Connor Chato
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
| | - Reid Vender
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
- School of Medicine, Queen’s University, Kingston, Ontario, Canada
| | - Abayomi S. Olabode
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
| | - Roux-Cil Ferreira
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
| | - Art F. Y. Poon
- Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada
- * E-mail:
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27
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Skums P, Mohebbi F, Tsyvina V, Baykal PI, Nemira A, Ramachandran S, Khudyakov Y. SOPHIE: Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework. Cell Syst 2022; 13:844-856.e4. [PMID: 36265470 PMCID: PMC9590096 DOI: 10.1016/j.cels.2022.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 07/05/2022] [Accepted: 07/19/2022] [Indexed: 01/26/2023]
Abstract
Genomic epidemiology is now widely used for viral outbreak investigations. Still, this methodology faces many challenges. First, few methods account for intra-host viral diversity. Second, maximum parsimony principle continues to be employed for phylogenetic inference of transmission histories, even though maximum likelihood or Bayesian models are usually more consistent. Third, many methods utilize case-specific data, such as sampling times or infection exposure intervals. This impedes study of persistent infections in vulnerable groups, where such information has a limited use. Finally, most methods implicitly assume that transmission events are independent, although common source outbreaks violate this assumption. We propose a maximum likelihood framework, SOPHIE, based on the integration of phylogenetic and random graph models. It infers transmission networks from viral phylogenies and expected properties of inter-host social networks modeled as random graphs with given expected degree distributions. SOPHIE is scalable, accounts for intra-host diversity, and accurately infers transmissions without case-specific epidemiological data.
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Affiliation(s)
- Pavel Skums
- Department of Computer Science, Georgia State University, Atlanta, GA, USA.
| | - Fatemeh Mohebbi
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vyacheslav Tsyvina
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Pelin Icer Baykal
- Department of Biosystems Science & Engineering, ETH Zurich, Basel, Switzerland
| | - Alina Nemira
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Sumathi Ramachandran
- Division of Viral Hepatitis, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Yury Khudyakov
- Division of Viral Hepatitis, Centers for Disease Control and Prevention, Atlanta, GA, USA
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28
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A deep learning approach to real-time HIV outbreak detection using genetic data. PLoS Comput Biol 2022; 18:e1010598. [PMID: 36240224 PMCID: PMC9604978 DOI: 10.1371/journal.pcbi.1010598] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 10/26/2022] [Accepted: 09/23/2022] [Indexed: 12/15/2022] Open
Abstract
Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0 ≥ 2.5, and overall a specificity exceeding 98% and sensitivity better than 92%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification.
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29
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Holt KE, Aanensen DM, Achtman M. Genomic population structures of microbial pathogens. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210230. [PMID: 35989608 PMCID: PMC9393556 DOI: 10.1098/rstb.2021.0230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 06/28/2022] [Indexed: 11/12/2022] Open
Affiliation(s)
- Kathryn E. Holt
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | | | - Mark Achtman
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
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30
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Alamil M, Thébaud G, Berthier K, Soubeyrand S. Characterizing viral within-host diversity in fast and non-equilibrium demo-genetic dynamics. Front Microbiol 2022; 13:983938. [PMID: 36274731 PMCID: PMC9581327 DOI: 10.3389/fmicb.2022.983938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 09/08/2022] [Indexed: 11/13/2022] Open
Abstract
High-throughput sequencing has opened the route for a deep assessment of within-host genetic diversity that can be used, e.g., to characterize microbial communities and to infer transmission links in infectious disease outbreaks. The performance of such characterizations and inferences cannot be analytically assessed in general and are often grounded on computer-intensive evaluations. Then, being able to simulate within-host genetic diversity across time under various demo-genetic assumptions is paramount to assess the performance of the approaches of interest. In this context, we built an original model that can be simulated to investigate the temporal evolution of genotypes and their frequencies under various demo-genetic assumptions. The model describes the growth and the mutation of genotypes at the nucleotide resolution conditional on an overall within-host viral kinetics, and can be tuned to generate fast non-equilibrium demo-genetic dynamics. We ran simulations of this model and computed classic diversity indices to characterize the temporal variation of within-host genetic diversity (from high-throughput amplicon sequences) of virus populations under three demographic kinetic models of viral infection. Our results highlight how demographic (viral load) and genetic (mutation, selection, or drift) factors drive variations in within-host diversity during the course of an infection. In particular, we observed a non-monotonic relationship between pathogen population size and genetic diversity, and a reduction of the impact of mutation on diversity when a non-specific host immune response is activated. The large variation in the diversity patterns generated in our simulations suggests that the underlying model provides a flexible basis to produce very diverse demo-genetic scenarios and test, for instance, methods for the inference of transmission links during outbreaks.
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Affiliation(s)
- Maryam Alamil
- INRAE, BioSP, Avignon, France
- Department of Mathematics and Computer Science, Alfaisal University, Riyadh, Saudi Arabia
- *Correspondence: Maryam Alamil ;
| | - Gaël Thébaud
- PHIM Plant Health Institute, INRAE, Univ Montpellier, CIRAD, Institut Agro, IRD, Montpellier, France
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31
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Chaudron SE, Leemann C, Kusejko K, Nguyen H, Tschumi N, Marzel A, Huber M, Böni J, Perreau M, Klimkait T, Yerly S, Ramette A, Hirsch HH, Rauch A, Calmy A, Vernazza P, Bernasconi E, Cavassini M, Metzner KJ, Kouyos RD, Günthard HF. A Systematic Molecular Epidemiology Screen Reveals Numerous Human Immunodeficiency Virus (HIV) Type 1 Superinfections in the Swiss HIV Cohort Study. J Infect Dis 2022; 226:1256-1266. [PMID: 35485458 DOI: 10.1093/infdis/jiac166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 04/27/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Studying human immunodeficiency virus type 1 (HIV-1) superinfection is important to understand virus transmission, disease progression, and vaccine design. But detection remains challenging, with low sampling frequencies and insufficient longitudinal samples. METHODS Using the Swiss HIV Cohort Study (SHCS), we developed a molecular epidemiology screening for superinfections. A phylogeny built from 22 243 HIV-1 partial polymerase sequences was used to identify potential superinfections among 4575 SHCS participants with longitudinal sequences. A subset of potential superinfections was tested by near-full-length viral genome sequencing (NFVGS) of biobanked plasma samples. RESULTS Based on phylogenetic and distance criteria, 325 potential HIV-1 superinfections were identified and categorized by their likelihood of being detected as superinfections due to sample misidentification. NFVGS was performed for 128 potential superinfections; of these, 52 were confirmed by NFVGS, 15 were not confirmed, and for 61 sampling did not allow confirming or rejecting superinfection because the sequenced samples did not include the relevant time points causing the superinfection signal in the original screen. Thus, NFVGS could support 52 of 67 adequately sampled potential superinfections. CONCLUSIONS This cohort-based molecular approach identified, to our knowledge, the largest population of confirmed superinfections, showing that, while rare with a prevalence of 1%-7%, superinfections are not negligible events.
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Affiliation(s)
- Sandra E Chaudron
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Christine Leemann
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Katharina Kusejko
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Huyen Nguyen
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Nadine Tschumi
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Swiss Tropical and Public Health Institute, Basel, Switzerland
| | - Alex Marzel
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Schulthess Klinik, Zurich, Switzerland
| | - Michael Huber
- Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Jürg Böni
- Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Matthieu Perreau
- Service of Immunology and Allergy, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Thomas Klimkait
- Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Sabine Yerly
- Laboratory of Virology, Geneva University Hospitals, Geneva, Switzerland
| | - Alban Ramette
- Institute for Infectious Diseases, University of Bern, Bern, Switzerland
| | - Hans H Hirsch
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, Basel, Switzerland.,Clinical Virology, Laboratory Medicine, University Hospital Basel, Basel, Switzerland
| | - Andri Rauch
- Department of Infectious Diseases, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Alexandra Calmy
- Laboratory of Virology, Geneva University Hospitals, Geneva, Switzerland.,Division of Infectious Diseases and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Pietro Vernazza
- Clinic for Infectiology and Hospital Hygiene, Cantonal Hospital St Gallen, St Gallen, Switzerland
| | - Enos Bernasconi
- Division of Infectious Diseases, Regional Hospital Lugano, Lugano, Switzerland
| | - Matthias Cavassini
- Service for Infectious Diseases, Lausanne University Hospital, Lausanne, Switzerland
| | - Karin J Metzner
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Roger D Kouyos
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Huldrych F Günthard
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
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32
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Using phylogenetics to infer HIV-1 transmission direction between known transmission pairs. Proc Natl Acad Sci U S A 2022; 119:e2210604119. [PMID: 36103580 PMCID: PMC9499565 DOI: 10.1073/pnas.2210604119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Identifying the transmission direction between individuals provides unparalleled power to understand infectious disease epidemiology. With epidemiological and clinical information typically unavailable to infer transmission direction, phylogenetic analysis of pathogen sequence data offers an alternative approach. While the success of this phylogenetic analysis varies, the reasons remain unknown. We analyze sequence data from over 100 transmission pairs for which both the transmission direction of HIV is known and detailed additional information is available. We find that easily quantifiable phylogenetic and sampling characteristics discriminate whether a phylogenetically inferred transmission direction is correct. Our analysis highlights that while phylogenetic approaches to infer transmission direction are unsuitable for individual-level analysis, such as forensic investigations, confidence in source attribution can be incorporated in population-level analyses. Inferring the transmission direction between linked individuals living with HIV provides unparalleled power to understand the epidemiology that determines transmission. Phylogenetic ancestral-state reconstruction approaches infer the transmission direction by identifying the individual in whom the most recent common ancestor of the virus populations originated. While these methods vary in accuracy, it is unclear why. To evaluate the performance of phylogenetic ancestral-state reconstruction to determine the transmission direction of HIV-1 infection, we inferred the transmission direction for 112 transmission pairs where transmission direction and detailed additional information were available. We then fit a statistical model to evaluate the extent to which epidemiological, sampling, genetic, and phylogenetic factors influenced the outcome of the inference. Finally, we repeated the analysis under real-life conditions with only routinely available data. We found that whether ancestral-state reconstruction correctly infers the transmission direction depends principally on the phylogeny's topology. For example, under real-life conditions, the probability of identifying the correct transmission direction increases from 32%—when a monophyletic–monophyletic or paraphyletic–polyphyletic tree topology is observed and when the tip closest to the root does not agree with the state at the root—to 93% when a paraphyletic–monophyletic topology is observed and when the tip closest to the root agrees with the root state. Our results suggest that documenting larger differences in relative intrahost diversity increases our confidence in the transmission direction inference of linked pairs for population-level studies of HIV. These findings provide a practical starting point to determine our confidence in transmission direction inference from ancestral-state reconstruction.
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33
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Gilbertson MLJ, Fountain-Jones NM, Malmberg JL, Gagne RB, Lee JS, Kraberger S, Kechejian S, Petch R, Chiu ES, Onorato D, Cunningham MW, Crooks KR, Funk WC, Carver S, VandeWoude S, VanderWaal K, Craft ME. Apathogenic proxies for transmission dynamics of a fatal virus. Front Vet Sci 2022; 9:940007. [PMID: 36157183 PMCID: PMC9493079 DOI: 10.3389/fvets.2022.940007] [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: 05/09/2022] [Accepted: 08/18/2022] [Indexed: 11/13/2022] Open
Abstract
Identifying drivers of transmission-especially of emerging pathogens-is a formidable challenge for proactive disease management efforts. While close social interactions can be associated with microbial sharing between individuals, and thereby imply dynamics important for transmission, such associations can be obscured by the influences of factors such as shared diets or environments. Directly-transmitted viral agents, specifically those that are rapidly evolving such as many RNA viruses, can allow for high-resolution inference of transmission, and therefore hold promise for elucidating not only which individuals transmit to each other, but also drivers of those transmission events. Here, we tested a novel approach in the Florida panther, which is affected by several directly-transmitted feline retroviruses. We first inferred the transmission network for an apathogenic, directly-transmitted retrovirus, feline immunodeficiency virus (FIV), and then used exponential random graph models to determine drivers structuring this network. We then evaluated the utility of these drivers in predicting transmission of the analogously transmitted, pathogenic agent, feline leukemia virus (FeLV), and compared FIV-based predictions of outbreak dynamics against empirical FeLV outbreak data. FIV transmission was primarily driven by panther age class and distances between panther home range centroids. FIV-based modeling predicted FeLV dynamics similarly to common modeling approaches, but with evidence that FIV-based predictions captured the spatial structuring of the observed FeLV outbreak. While FIV-based predictions of FeLV transmission performed only marginally better than standard approaches, our results highlight the value of proactively identifying drivers of transmission-even based on analogously-transmitted, apathogenic agents-in order to predict transmission of emerging infectious agents. The identification of underlying drivers of transmission, such as through our workflow here, therefore holds promise for improving predictions of pathogen transmission in novel host populations, and could provide new strategies for proactive pathogen management in human and animal systems.
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Affiliation(s)
- Marie L. J. Gilbertson
- Department of Veterinary Population Medicine, University of Minnesota, Saint Paul, MN, United States
| | | | - Jennifer L. Malmberg
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO, United States
- Department of Veterinary Sciences, University of Wyoming, Laramie, WY, United States
| | - Roderick B. Gagne
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO, United States
- Wildlife Futures Program, Department of Pathobiology, University of Pennsylvania School of Veterinary Medicine, Kennett Square, PA, United States
| | - Justin S. Lee
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO, United States
| | - Simona Kraberger
- The Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, United States
| | - Sarah Kechejian
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO, United States
| | - Raegan Petch
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO, United States
| | - Elliott S. Chiu
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO, United States
| | - Dave Onorato
- Fish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission, Naples, FL, United States
| | - Mark W. Cunningham
- Fish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission, Gainesville, FL, United States
| | - Kevin R. Crooks
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO, United States
| | - W. Chris Funk
- Department of Biology, Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, United States
| | - Scott Carver
- School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia
| | - Sue VandeWoude
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO, United States
| | - Kimberly VanderWaal
- Department of Veterinary Population Medicine, University of Minnesota, Saint Paul, MN, United States
| | - Meggan E. Craft
- Department of Veterinary Population Medicine, University of Minnesota, Saint Paul, MN, United States
- Department of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, MN, United States
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34
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Lundgren E, Romero-Severson E, Albert J, Leitner T. Combining biomarker and virus phylogenetic models improves HIV-1 epidemiological source identification. PLoS Comput Biol 2022; 18:e1009741. [PMID: 36026480 PMCID: PMC9455879 DOI: 10.1371/journal.pcbi.1009741] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 09/08/2022] [Accepted: 08/02/2022] [Indexed: 01/07/2023] Open
Abstract
To identify and stop active HIV transmission chains new epidemiological techniques are needed. Here, we describe the development of a multi-biomarker augmentation to phylogenetic inference of the underlying transmission history in a local population. HIV biomarkers are measurable biological quantities that have some relationship to the amount of time someone has been infected with HIV. To train our model, we used five biomarkers based on real data from serological assays, HIV sequence data, and target cell counts in longitudinally followed, untreated patients with known infection times. The biomarkers were modeled with a mixed effects framework to allow for patient specific variation and general trends, and fit to patient data using Markov Chain Monte Carlo (MCMC) methods. Subsequently, the density of the unobserved infection time conditional on observed biomarkers were obtained by integrating out the random effects from the model fit. This probabilistic information about infection times was incorporated into the likelihood function for the transmission history and phylogenetic tree reconstruction, informed by the HIV sequence data. To critically test our methodology, we developed a coalescent-based simulation framework that generates phylogenies and biomarkers given a specific or general transmission history. Testing on many epidemiological scenarios showed that biomarker augmented phylogenetics can reach 90% accuracy under idealized situations. Under realistic within-host HIV-1 evolution, involving substantial within-host diversification and frequent transmission of multiple lineages, the average accuracy was at about 50% in transmission clusters involving 5-50 hosts. Realistic biomarker data added on average 16 percentage points over using the phylogeny alone. Using more biomarkers improved the performance. Shorter temporal spacing between transmission events and increased transmission heterogeneity reduced reconstruction accuracy, but larger clusters were not harder to get right. More sequence data per infected host also improved accuracy. We show that the method is robust to incomplete sampling and that adding biomarkers improves reconstructions of real HIV-1 transmission histories. The technology presented here could allow for better prevention programs by providing data for locally informed and tailored strategies.
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Affiliation(s)
- Erik Lundgren
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Ethan Romero-Severson
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Jan Albert
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden
| | - Thomas Leitner
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- * E-mail:
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35
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Blenkinsop A, Monod M, Sighem AV, Pantazis N, Bezemer D, Op de Coul E, van de Laar T, Fraser C, Prins M, Reiss P, de Bree GJ, Ratmann O. Estimating the potential to prevent locally acquired HIV infections in a UNAIDS Fast-Track City, Amsterdam. eLife 2022; 11:76487. [PMID: 35920649 PMCID: PMC9545569 DOI: 10.7554/elife.76487] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background: More than 300 cities including the city of Amsterdam in the Netherlands have joined the UNAIDS Fast-Track Cities initiative, committing to accelerate their HIV response and end the AIDS epidemic in cities by 2030. To support this commitment, we aimed to estimate the number and proportion of Amsterdam HIV infections that originated within the city, from Amsterdam residents. We also aimed to estimate the proportion of recent HIV infections during the 5-year period 2014–2018 in Amsterdam that remained undiagnosed. Methods: We located diagnosed HIV infections in Amsterdam using postcode data (PC4) at time of registration in the ATHENA observational HIV cohort, and used HIV sequence data to reconstruct phylogeographically distinct, partially observed Amsterdam transmission chains. Individual-level infection times were estimated from biomarker data, and used to date the phylogenetically observed transmission chains as well as to estimate undiagnosed proportions among recent infections. A Bayesian Negative Binomial branching process model was used to estimate the number, size, and growth of the unobserved Amsterdam transmission chains from the partially observed phylogenetic data. Results: Between 1 January 2014 and 1 May 2019, there were 846 HIV diagnoses in Amsterdam residents, of whom 516 (61%) were estimated to have been infected in 2014–2018. The rate of new Amsterdam diagnoses since 2014 (104 per 100,000) remained higher than the national rates excluding Amsterdam (24 per 100,000), and in this sense Amsterdam remained a HIV hotspot in the Netherlands. An estimated 14% [12–16%] of infections in Amsterdan MSM in 2014–2018 remained undiagnosed by 1 May 2019, and 41% [35–48%] in Amsterdam heterosexuals, with variation by region of birth. An estimated 67% [60–74%] of Amsterdam MSM infections in 2014–2018 had an Amsterdam resident as source, and 56% [41–70%] in Amsterdam heterosexuals, with heterogeneity by region of birth. Of the locally acquired infections, an estimated 43% [37–49%] were in foreign-born MSM, 41% [35–47%] in Dutch-born MSM, 10% [6–18%] in foreign-born heterosexuals, and 5% [2–9%] in Dutch-born heterosexuals. We estimate the majority of Amsterdam MSM infections in 2014–2018 originated in transmission chains that pre-existed by 2014. Conclusions: This combined phylogenetic, epidemiologic, and modelling analysis in the UNAIDS Fast-Track City Amsterdam indicates that there remains considerable potential to prevent HIV infections among Amsterdam residents through city-level interventions. The burden of locally acquired infection remains concentrated in MSM, and both Dutch-born and foreign-born MSM would likely benefit most from intensified city-level interventions. Funding: This study received funding as part of the H-TEAM initiative from Aidsfonds (project number P29701). The H-TEAM initiative is being supported by Aidsfonds (grant number: 2013169, P29701, P60803), Stichting Amsterdam Dinner Foundation, Bristol-Myers Squibb International Corp. (study number: AI424-541), Gilead Sciences Europe Ltd (grant number: PA-HIV-PREP-16-0024), Gilead Sciences (protocol numbers: CO-NL-276-4222, CO-US-276-1712, CO-NL-985-6195), and M.A.C AIDS Fund.
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Affiliation(s)
| | - Mélodie Monod
- Department of Mathematics, Imperial College London, London, United Kingdom
| | | | - Nikos Pantazis
- Department of Hygiene, Epidemiology and Medical Statistics, University of Athens, Athens, Greece
| | | | - Eline Op de Coul
- Center for Infectious Diseases Prevention and Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands
| | - Thijs van de Laar
- Department of Donor Medicine Research, Sanquin, Amsterdam, Netherlands
| | - Christophe Fraser
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Maria Prins
- Academic Medical Center, Amsterdam, Netherlands
| | - Peter Reiss
- Department of Global Health, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Godelieve J de Bree
- Department of Global Health, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Oliver Ratmann
- Department of Mathematics, Imperial College London, London, United Kingdom
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36
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Carson J, Ledda A, Ferretti L, Keeling M, Didelot X. The bounded coalescent model: Conditioning a genealogy on a minimum root date. J Theor Biol 2022; 548:111186. [PMID: 35697144 DOI: 10.1016/j.jtbi.2022.111186] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 05/05/2022] [Accepted: 06/02/2022] [Indexed: 01/27/2023]
Abstract
The coalescent model represents how individuals sampled from a population may have originated from a last common ancestor. The bounded coalescent model is obtained by conditioning the coalescent model such that the last common ancestor must have existed after a certain date. This conditioned model arises in a variety of applications, such as speciation, horizontal gene transfer or transmission analysis, and yet the bounded coalescent model has not been previously analysed in detail. Here we describe a new algorithm to simulate from this model directly, without resorting to rejection sampling. We show that this direct simulation algorithm is more computationally efficient than the rejection sampling approach. We also show how to calculate the probability of the last common ancestor occurring after a given date, which is required to compute the probability density of realisations under the bounded coalescent model. Our results are applicable in both the isochronous (when all samples have the same date) and heterochronous (where samples can have different dates) settings. We explore the effect of setting a bound on the date of the last common ancestor, and show that it affects a number of properties of the resulting phylogenies. All our methods are implemented in a new R package called BoundedCoalescent which is freely available online.
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Affiliation(s)
- Jake Carson
- Mathematics Institute, University of Warwick, United Kingdom
| | - Alice Ledda
- HCAI, Fungal, AMR, AMU & Sepsis Division, UK Health Security Agency, United Kingdom
| | - Luca Ferretti
- Big Data Institute, University of Oxford, United Kingdom
| | - Matt Keeling
- Mathematics Institute, University of Warwick, United Kingdom
| | - Xavier Didelot
- Department of Statistics and School of Life Sciences, University of Warwick, United Kingdom
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37
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Ortiz AT, Kendall M, Storey N, Hatcher J, Dunn H, Roy S, Williams R, Williams C, Goldstein RA, Didelot X, Harris K, Breuer J, Grandjean L. Within-host diversity improves phylogenetic and transmission reconstruction of SARS-CoV-2 outbreaks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022:2022.06.07.495142. [PMID: 35702156 PMCID: PMC9196117 DOI: 10.1101/2022.06.07.495142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Accurate inference of who infected whom in an infectious disease outbreak is critical for the delivery of effective infection prevention and control. The increased resolution of pathogen whole-genome sequencing has significantly improved our ability to infer transmission events. Despite this, transmission inference often remains limited by the lack of genomic variation between the source case and infected contacts. Although within-host genetic diversity is common among a wide variety of pathogens, conventional whole-genome sequencing phylogenetic approaches to reconstruct outbreaks exclusively use consensus sequences, which consider only the most prevalent nucleotide at each position and therefore fail to capture low frequency variation within samples. We hypothesized that including within-sample variation in a phylogenetic model would help to identify who infected whom in instances in which this was previously impossible. Using whole-genome sequences from SARS-CoV-2 multi-institutional outbreaks as an example, we show how within-sample diversity is stable among repeated serial samples from the same host, is transmitted between those cases with known epidemiological links, and how this improves phylogenetic inference and our understanding of who infected whom. Our technique is applicable to other infectious diseases and has immediate clinical utility in infection prevention and control.
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Affiliation(s)
| | - Michelle Kendall
- Department of Statistics, University of Warwick, Coventry, CV4 7AL
| | - Nathaniel Storey
- Department of Microbiology, Great Ormond Street Hospital, London WC1N 3JH
| | - James Hatcher
- Department of Microbiology, Great Ormond Street Hospital, London WC1N 3JH
| | - Helen Dunn
- Department of Microbiology, Great Ormond Street Hospital, London WC1N 3JH
| | - Sunando Roy
- Department of Infection, Immunity and Inflammation, Institute of Child Health, UCL, London WC1N 1EH
| | - Rachel Williams
- UCL Genomics, Institute of Child Health, UCL, London WC1N 1EH
| | | | | | - Xavier Didelot
- Department of Statistics, University of Warwick, Coventry, CV4 7AL
| | - Kathryn Harris
- Department of Microbiology, Great Ormond Street Hospital, London WC1N 3JH
- Department of Virology, East South East London Pathology Partnership, Royal London Hospital, Barts Health NHS Trust, London E12ES
| | - Judith Breuer
- Department of Infection, Immunity and Inflammation, Institute of Child Health, UCL, London WC1N 1EH
| | - Louis Grandjean
- Department of Infection, Immunity and Inflammation, Institute of Child Health, UCL, London WC1N 1EH
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Griffiths ME, Broos A, Bergner LM, Meza DK, Suarez NM, da Silva Filipe A, Tello C, Becker DJ, Streicker DG. Longitudinal deep sequencing informs vector selection and future deployment strategies for transmissible vaccines. PLoS Biol 2022; 20:e3001580. [PMID: 35439242 PMCID: PMC9017877 DOI: 10.1371/journal.pbio.3001580] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 02/21/2022] [Indexed: 12/04/2022] Open
Abstract
Vaccination is a powerful tool in combating infectious diseases of humans and companion animals. In most wildlife, including reservoirs of emerging human diseases, achieving sufficient vaccine coverage to mitigate disease burdens remains logistically unattainable. Virally vectored "transmissible" vaccines that deliberately spread among hosts are a potentially transformative, but still theoretical, solution to the challenge of immunising inaccessible wildlife. Progress towards real-world application is frustrated by the absence of frameworks to guide vector selection and vaccine deployment prior to major in vitro and in vivo investments in vaccine engineering and testing. Here, we performed deep sequencing on field-collected samples of Desmodus rotundus betaherpesvirus (DrBHV), a candidate vector for a transmissible vaccine targeting vampire bat-transmitted rabies. We discovered 11 strains of DrBHV that varied in prevalence and geographic distribution across Peru. The phylogeographic structure of DrBHV strains was predictable from both host genetics and landscape topology, informing long-term DrBHV-vectored vaccine deployment strategies and identifying geographic areas for field trials where vaccine spread would be naturally contained. Multistrain infections were observed in 79% of infected bats. Resampling of marked individuals over 4 years showed within-host persistence kinetics characteristic of latency and reactivation, properties that might boost individual immunity and lead to sporadic vaccine transmission over the lifetime of the host. Further, strain acquisitions by already infected individuals implied that preexisting immunity and strain competition are unlikely to inhibit vaccine spread. Our results support the development of a transmissible vaccine targeting a major source of human and animal rabies in Latin America and show how genomics can enlighten vector selection and deployment strategies for transmissible vaccines.
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Affiliation(s)
- Megan E. Griffiths
- MRC–University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Alice Broos
- MRC–University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Laura M. Bergner
- MRC–University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Diana K. Meza
- MRC–University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Nicolas M. Suarez
- MRC–University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
| | - Ana da Silva Filipe
- MRC–University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
| | - Carlos Tello
- Association for the Conservation and Development of Natural Resources, Lima, Peru
- Yunkawasi, Lima, Peru
| | - Daniel J. Becker
- Department of Biology, University of Oklahoma, Norman, Oklahoma, United States of America
| | - Daniel G. Streicker
- MRC–University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, United Kingdom
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Xi X, Spencer SEF, Hall M, Grabowski MK, Kagaayi J, Ratmann O. Inferring the sources of HIV infection in Africa from deep‐sequence data with semi‐parametric Bayesian Poisson flow models. J R Stat Soc Ser C Appl Stat 2022. [DOI: 10.1111/rssc.12544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Xiaoyue Xi
- Department of MathematicsImperial College London LondonUK
| | | | - Matthew Hall
- Big Data Institute, Nuffield Department of MedicineUniversity of Oxford OxfordUK
| | - M. Kate Grabowski
- Department of PathologyJohns Hopkins University BaltimoreMDUSA
- Rakai Health Sciences Program KalisizoUganda
| | - Joseph Kagaayi
- Rakai Health Sciences Program KalisizoUganda
- Makerere University School of Public Health KampalaUganda
| | - Oliver Ratmann
- Department of MathematicsImperial College London LondonUK
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Magosi LE, Zhang Y, Golubchik T, DeGruttola V, Tchetgen Tchetgen E, Novitsky V, Moore J, Bachanas P, Segolodi T, Lebelonyane R, Pretorius Holme M, Moyo S, Makhema J, Lockman S, Fraser C, Essex MM, Lipsitch M. Deep-sequence phylogenetics to quantify patterns of HIV transmission in the context of a universal testing and treatment trial - BCPP/ Ya Tsie trial. eLife 2022; 11:72657. [PMID: 35229714 PMCID: PMC8912920 DOI: 10.7554/elife.72657] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 02/08/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Mathematical models predict that community-wide access to HIV testing-and-treatment can rapidly and substantially reduce new HIV infections. Yet several large universal test-and-treat HIV prevention trials in high-prevalence epidemics demonstrated variable reduction in population-level incidence. Methods: To elucidate patterns of HIV spread in universal test-and-treat trials we quantified the contribution of geographic-location, gender, age and randomized-HIV-intervention to HIV transmissions in the 30-community Ya Tsie trial in Botswana. We sequenced HIV viral whole genomes from 5,114 trial participants among the 30 trial communities. Results: Deep-sequence phylogenetic analysis revealed that most inferred HIV transmissions within the trial occurred within the same or between neighboring communities, and between similarly-aged partners. Transmissions into intervention communities from control communities were more common than the reverse post-baseline (30% [12.2 - 56.7] versus 3% [0.1 - 27.3]) than at baseline (7% [1.5 - 25.3] versus 5% [0.9 - 22.9]) compatible with a benefit from treatment-as-prevention. Conclusion: Our findings suggest that population mobility patterns are fundamental to HIV transmission dynamics and to the impact of HIV control strategies. Funding: This study was supported by the National Institute of General Medical Sciences (U54GM088558); the Fogarty International Center (FIC) of the U.S. National Institutes of Health (D43 TW009610); and the President's Emergency Plan for AIDS Relief through the Centers for Disease Control and Prevention (CDC) (Cooperative agreements U01 GH000447 and U2G GH001911).
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Affiliation(s)
- Lerato E Magosi
- Department of Epidemiology, Harvard University, Boston, United States
| | - Yinfeng Zhang
- Division of Molecular and Genomic Pathology, University of Pittsburgh Medical Center, Pittsburgh, United States
| | - Tanya Golubchik
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Victor DeGruttola
- Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, United States
| | | | - Vladimir Novitsky
- Department of Immunology and Infectious Disease, Harvard T H Chan School of Public Health, Boston, United States
| | - Janet Moore
- Division of Global HIV/AIDS and TB, Centers for Disease Control and Prevention, Atlanta, United States
| | - Pam Bachanas
- Division of Global HIV/AIDS and TB, Centers for Disease Control and Prevention, Atlanta, United States
| | - Tebogo Segolodi
- HIV Prevention Research Unit, Centers for Disease Control and Prevention, Gaborone, Botswana
| | | | - Molly Pretorius Holme
- epartment of Immunology and Infectious Disease, Harvard T H Chan School of Public Health, Boston, United States
| | - Sikhulile Moyo
- Botswana Harvard AIDS Institute Partnership, Gaborone, Botswana
| | - Joseph Makhema
- Botswana Harvard AIDS Institute Partnership, Gaborone, Botswana
| | - Shahin Lockman
- Division of Infectious Diseases, Brigham and Women's Hospital, Boston, United States
| | - Christophe Fraser
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Myron Max Essex
- Department of Immunology and Infectious Disease, Harvard T H Chan School of Public Health, Boston, United States
| | - Marc Lipsitch
- Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, United States
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41
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Guang A, Howison M, Ledingham L, D’Antuono M, Chan PA, Lawrence C, Dunn CW, Kantor R. Incorporating Within-Host Diversity in Phylogenetic Analyses for Detecting Clusters of New HIV Diagnoses. Front Microbiol 2022; 12:803190. [PMID: 35250908 PMCID: PMC8891961 DOI: 10.3389/fmicb.2021.803190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 12/22/2021] [Indexed: 11/29/2022] Open
Abstract
Background Phylogenetic analyses of HIV sequences are used to detect clusters and inform public health interventions. Conventional approaches summarize within-host HIV diversity with a single consensus sequence per host of the pol gene, obtained from Sanger or next-generation sequencing (NGS). There is growing recognition that this approach discards potentially important information about within-host sequence variation, which can impact phylogenetic inference. However, whether alternative summary methods that incorporate intra-host variation impact phylogenetic inference of transmission network features is unknown. Methods We introduce profile sampling, a method to incorporate within-host NGS sequence diversity into phylogenetic HIV cluster inference. We compare this approach to Sanger- and NGS-derived pol and near-whole-genome consensus sequences and evaluate its potential benefits in identifying molecular clusters among all newly-HIV-diagnosed individuals over six months at the largest HIV center in Rhode Island. Results Profile sampling cluster inference demonstrated that within-host viral diversity impacts phylogenetic inference across individuals, and that consensus sequence approaches can obscure both magnitude and effect of these impacts. Clustering differed between Sanger- and NGS-derived consensus and profile sampling sequences, and across gene regions. Discussion Profile sampling can incorporate within-host HIV diversity captured by NGS into phylogenetic analyses. This additional information can improve robustness of cluster detection.
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Affiliation(s)
- August Guang
- Center for Computational Biology of Human Disease, Brown University, Providence, RI, United States
- Center for Computation and Visualization, Brown University, Providence, RI, United States
- *Correspondence: August Guang,
| | - Mark Howison
- Research Improving People’s Lives, Providence, RI, United States
| | - Lauren Ledingham
- Division of Infectious Diseases, The Alpert Medical School, Brown University, Providence, RI, United States
| | - Matthew D’Antuono
- Division of Infectious Diseases, The Alpert Medical School, Brown University, Providence, RI, United States
| | - Philip A. Chan
- Division of Infectious Diseases, The Alpert Medical School, Brown University, Providence, RI, United States
| | - Charles Lawrence
- Division of Applied Mathematics, Brown University, Providence, RI, United States
| | - Casey W. Dunn
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, United States
| | - Rami Kantor
- Division of Infectious Diseases, The Alpert Medical School, Brown University, Providence, RI, United States
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Wymant C, Bezemer D, Blanquart F, Ferretti L, Gall A, Hall M, Golubchik T, Bakker M, Ong SH, Zhao L, Bonsall D, de Cesare M, MacIntyre-Cockett G, Abeler-Dörner L, Albert J, Bannert N, Fellay J, Grabowski MK, Gunsenheimer-Bartmeyer B, Günthard HF, Kivelä P, Kouyos RD, Laeyendecker O, Meyer L, Porter K, Ristola M, van Sighem A, Berkhout B, Kellam P, Cornelissen M, Reiss P, Fraser C, Aubert V, Battegay M, Bernasconi E, Böni J, Braun DL, Bucher HC, Burton-Jeangros C, Calmy A, Cavassini M, Dollenmaier G, Egger M, Elzi L, Fehr J, Fellay J, Furrer H, Fux CA, Gorgievski M, Günthard H, Haerry D, Hasse B, Hirsch HH, Hoffmann M, Hösli I, Kahlert C, Kaiser L, Keiser O, Klimkait T, Kouyos R, Kovari H, Ledergerber B, Martinetti G, de Tejada BM, Marzolini C, Metzner K, Müller N, Nadal D, Nicca D, Pantaleo G, Rauch A, Regenass S, Rudin C, Schöni-Affolter F, Schmid P, Speck R, Stöckle M, Tarr P, Trkola A, Vernazza P, Weber R, Yerly S, van der Valk M, Geerlings SE, Goorhuis A, Hovius JW, Lempkes B, Nellen FJB, van der Poll T, Prins JM, Reiss P, van Vugt M, Wiersinga WJ, Wit FWMN, van Duinen M, van Eden J, Hazenberg A, van Hes AMH, Rajamanoharan S, Robinson T, Taylor B, Brewer C, Mayr C, Schmidt W, Speidel A, Strohbach F, Arastéh K, Cordes C, Pijnappel FJJ, Stündel M, Claus J, Baumgarten A, Carganico A, Ingiliz P, Dupke S, Freiwald M, Rausch M, Moll A, Schleehauf D, Smalhout SY, Hintsche B, Klausen G, Jessen H, Jessen A, Köppe S, Kreckel P, Schranz D, Fischer K, Schulbin H, Speer M, Weijsenfeld AM, Glaunsinger T, Wicke T, Bieniek B, Hillenbrand H, Schlote F, Lauenroth-Mai E, Schuler C, Schürmann D, Wesselmann H, Brockmeyer N, Jurriaans S, Gehring P, Schmalöer D, Hower M, Spornraft-Ragaller P, Häussinger D, Reuter S, Esser S, Markus R, Kreft B, Berzow D, Back NKT, Christl A, Meyer A, Plettenberg A, Stoehr A, Graefe K, Lorenzen T, Adam A, Schewe K, Weitner L, Fenske S, Zaaijer HL, Hansen S, Stellbrink HJ, Wiemer D, Hertling S, Schmidt R, Arbter P, Claus B, Galle P, Jäger H, Jä Gel-Guedes E, Berkhout B, Postel N, Fröschl M, Spinner C, Bogner J, Salzberger B, Schölmerich J, Audebert F, Marquardt T, Schaffert A, Schnaitmann E, Cornelissen MTE, Trein A, Frietsch B, Müller M, Ulmer A, Detering-Hübner B, Kern P, Schubert F, Dehn G, Schreiber M, Güler C, Schinkel CJ, Gunsenheimer-Bartmeyer B, Schmidt D, Meixenberger K, Bannert N, Wolthers KC, Peters EJG, van Agtmael MA, Autar RS, Bomers M, Sigaloff KCE, Heitmuller M, Laan LM, Ang CW, van Houdt R, Jonges M, Kuijpers TW, Pajkrt D, Scherpbier HJ, de Boer C, van der Plas A, van den Berge M, Stegeman A, Baas S, Hage de Looff L, Buiting A, Reuwer A, Veenemans J, Wintermans B, Pronk MJH, Ammerlaan HSM, van den Bersselaar DNJ, de Munnik ES, Deiman B, Jansz AR, Scharnhorst V, Tjhie J, Wegdam MCA, van Eeden A, Nellen J, Brokking W, Elsenburg LJM, Nobel H, van Kasteren MEE, Berrevoets MAH, Brouwer AE, Adams A, van Erve R, de Kruijf-van de Wiel BAFM, Keelan-Phaf S, van de Ven B, van der Ven B, Buiting AGM, Murck JL, de Vries-Sluijs TEMS, Bax HI, van Gorp ECM, de Jong-Peltenburg NC, de Mendonç A Melo M, van Nood E, Nouwen JL, Rijnders BJA, Rokx C, Schurink CAM, Slobbe L, Verbon A, Bassant N, van Beek JEA, Vriesde M, van Zonneveld LM, de Groot J, Boucher CAB, Koopmans MPG, van Kampen JJA, Fraaij PLA, van Rossum AMC, Vermont CL, van der Knaap LC, Visser E, Branger J, Douma RA, Cents-Bosma AS, Duijf-van de Ven CJHM, Schippers EF, van Nieuwkoop C, van Ijperen JM, Geilings J, van der Hut G, van Burgel ND, Leyten EMS, Gelinck LBS, Mollema F, Davids-Veldhuis S, Tearno C, Wildenbeest GS, Heikens E, Groeneveld PHP, Bouwhuis JW, Lammers AJJ, Kraan S, van Hulzen AGW, Kruiper MSM, van der Bliek GL, Bor PCJ, Debast SB, Wagenvoort GHJ, Kroon FP, de Boer MGJ, Jolink H, Lambregts MMC, Roukens AHE, Scheper H, Dorama W, van Holten N, Claas ECJ, Wessels E, den Hollander JG, El Moussaoui R, Pogany K, Brouwer CJ, Smit JV, Struik-Kalkman D, van Niekerk T, Pontesilli O, Lowe SH, Oude Lashof AML, Posthouwer D, van Wolfswinkel ME, Ackens RP, Burgers K, Schippers J, Weijenberg-Maes B, van Loo IHM, Havenith TRA, van Vonderen MGA, Kampschreur LM, Faber S, Steeman-Bouma R, Al Moujahid A, Kootstra GJ, Delsing CE, van der Burg-van de Plas M, Scheiberlich L, Kortmann W, van Twillert G, Renckens R, Ruiter-Pronk D, van Truijen-Oud FA, Cohen Stuart JWT, Jansen ER, Hoogewerf M, Rozemeijer W, van der Reijden WA, Sinnige JC, Brinkman K, van den Berk GEL, Blok WL, Lettinga KD, de Regt M, Schouten WEM, Stalenhoef JE, Veenstra J, Vrouenraets SME, Blaauw H, Geerders GF, Kleene MJ, Kok M, Knapen M, van der Meché IB, Mulder-Seeleman E, Toonen AJM, Wijnands S, Wttewaal E, Kwa D, van Crevel R, van Aerde K, Dofferhoff ASM, Henriet SSV, Ter Hofstede HJM, Hoogerwerf J, Keuter M, Richel O, Albers M, Grintjes-Huisman KJT, de Haan M, Marneef M, Strik-Albers R, Rahamat-Langendoen J, Stelma FF, Burger D, Gisolf EH, Hassing RJ, Claassen M, Ter Beest G, van Bentum PHM, Langebeek N, Tiemessen R, Swanink CMA, van Lelyveld SFL, Soetekouw R, van der Prijt LMM, van der Swaluw J, Bermon N, van der Reijden WA, Jansen R, Herpers BL, Veenendaal D, Verhagen DWM, Lauw FN, van Broekhuizen MC, van Wijk M, Bierman WFW, Bakker M, Kleinnijenhuis J, Kloeze E, Middel A, Postma DF, Schölvinck EH, Stienstra Y, Verhage AR, Wouthuyzen-Bakker M, Boonstra A, de Groot-de Jonge H, van der Meulen PA, de Weerd DA, Niesters HGM, van Leer-Buter CC, Knoester M, Hoepelman AIM, Arends JE, Barth RE, Bruns AHW, Ellerbroek PM, Mudrikova T, Oosterheert JJ, Schadd EM, van Welzen BJ, Aarsman K, Griffioen-van Santen BMG, de Kroon I, van Berkel M, van Rooijen CSAM, Schuurman R, Verduyn-Lunel F, Wensing AMJ, Bont LJ, Geelen SPM, Loeffen YGT, Wolfs TFW, Nauta N, Rooijakkers EOW, Holtsema H, Voigt R, van de Wetering D, Alberto A, van der Meer I, Rosingh A, Halaby T, Zaheri S, Boyd AC, Bezemer DO, van Sighem AI, Smit C, Hillebregt M, de Jong A, Woudstra T, Bergsma D, Meijering R, van de Sande L, Rutkens T, van der Vliet S, de Groot L, van den Akker M, Bakker Y, El Berkaoui A, Bezemer M, Brétin N, Djoechro E, Groters M, Kruijne E, Lelivelt KJ, Lodewijk C, Lucas E, Munjishvili L, Paling F, Peeck B, Ree C, Regtop R, Ruijs Y, Schoorl M, Schnörr P, Scheigrond A, Tuijn E, Veenenberg L, Visser KM, Witte EC, Ruijs Y, Van Frankenhuijsen M, Allegre T, Makhloufi D, Livrozet JM, Chiarello P, Godinot M, Brunel-Dalmas F, Gibert S, Trepo C, Peyramond D, Miailhes P, Koffi J, Thoirain V, Brochier C, Baudry T, Pailhes S, Lafeuillade A, Philip G, Hittinger G, Assi A, Lambry V, Rosenthal E, Naqvi A, Dunais B, Cua E, Pradier C, Durant J, Joulie A, Quinsat D, Tempesta S, Ravaux I, Martin IP, Faucher O, Cloarec N, Champagne H, Pichancourt G, Morlat P, Pistone T, Bonnet F, Mercie P, Faure I, Hessamfar M, Malvy D, Lacoste D, Pertusa MC, Vandenhende MA, Bernard N, Paccalin F, Martell C, Roger-Schmelz J, Receveur MC, Duffau P, Dondia D, Ribeiro E, Caltado S, Neau D, Dupont M, Dutronc H, Dauchy F, Cazanave C, Vareil MO, Wirth G, Le Puil S, Pellegrin JL, Raymond I, Viallard JF, Chaigne de Lalande S, Garipuy D, Delobel P, Obadia M, Cuzin L, Alvarez M, Biezunski N, Porte L, Massip P, Debard A, Balsarin F, Lagarrigue M, Prevoteau du Clary F, Aquilina C, Reynes J, Baillat V, Merle C, Lemoing V, Atoui N, Makinson A, Jacquet JM, Psomas C, Tramoni C, Aumaitre H, Saada M, Medus M, Malet M, Eden A, Neuville S, Ferreyra M, Sotto A, Barbuat C, Rouanet I, Leureillard D, Mauboussin JM, Lechiche C, Donsesco R, Cabie A, Abel S, Pierre-Francois S, Batala AS, Cerland C, Rangom C, Theresine N, Hoen B, Lamaury I, Fabre I, Schepers K, Curlier E, Ouissa R, Gaud C, Ricaud C, Rodet R, Wartel G, Sautron C, Beck-Wirth G, Michel C, Beck C, Halna JM, Kowalczyk J, Benomar M, Drobacheff-Thiebaut C, Chirouze C, Faucher JF, Parcelier F, Foltzer A, Haffner-Mauvais C, Hustache Mathieu M, Proust A, Piroth L, Chavanet P, Duong M, Buisson M, Waldner A, Mahy S, Gohier S, Croisier D, May T, Delestan M, Andre M, Zadeh MM, Martinot M, Rosolen B, Pachart A, Martha B, Jeunet N, Rey D, Cheneau C, Partisani M, Priester M, Bernard-Henry C, Batard ML, Fischer P, Berger JL, Kmiec I, Robineau O, Huleux T, Ajana F, Alcaraz I, Allienne C, Baclet V, Meybeck A, Valette M, Viget N, Aissi E, Biekre R, Cornavin P, Merrien D, Seghezzi JC, Machado M, Diab G, Raffi F, Bonnet B, Allavena C, Grossi O, Reliquet V, Billaud E, Brunet C, Bouchez S, Morineau-Le Houssine P, Sauser F, Boutoille D, Besnier M, Hue H, Hall N, Brosseau D, Souala F, Michelet C, Tattevin P, Arvieux C, Revest M, Leroy H, Chapplain JM, Dupont M, Fily F, Patra-Delo S, Lefeuvre C, Bernard L, Bastides F, Nau P, Verdon R, de la Blanchardiere A, Martin A, Feret P, Geffray L, Daniel C, Rohan J, Fialaire P, Chennebault JM, Rabier V, Abgueguen P, Rehaiem S, Luycx O, Niault M, Moreau P, Poinsignon Y, Goussef M, Mouton-Rioux V, Houlbert D, Alvarez-Huve S, Barbe F, Haret S, Perre P, Leantez-Nainville S, Esnault JL, Guimard T, Suaud I, Girard JJ, Simonet V, Debab Y, Schmit JL, Jacomet C, Weinberck P, Genet C, Pinet P, Ducroix S, Durox H, Denes É, Abraham B, Gourdon F, Antoniotti O, Molina JM, Ferret S, Lascoux-Combe C, Lafaurie M, Colin de Verdiere N, Ponscarme D, De Castro N, Aslan A, Rozenbaum W, Pintado C, Clavel F, Taulera O, Gatey C, Munier AL, Gazaigne S, Penot P, Conort G, Lerolle N, Leplatois A, Balausine S, Delgado J, Timsit J, Tabet M, Gerard L, Girard PM, Picard O, Tredup J, Bollens D, Valin N, Campa P, Bottero J, Lefebvre B, Tourneur M, Fonquernie L, Wemmert C, Lagneau JL, Yazdanpanah Y, Phung B, Pinto A, Vallois D, Cabras O, Louni F, Pialoux G, Lyavanc T, Berrebi V, Chas J, Lenagat S, Rami A, Diemer M, Parrinello M, Depond A, Salmon D, Guillevin L, Tahi T, Belarbi L, Loulergue P, Zak Dit Zbar O, Launay O, Silbermann B, Leport C, Alagna L, Pietri MP, Simon A, Bonmarchand M, Amirat N, Pichon F, Kirstetter M, Katlama C, Valantin MA, Tubiana R, Caby F, Schneider L, Ktorza N, Calin R, Merlet A, Ben Abdallah S, Weiss L, Buisson M, Batisse D, Karmochine M, Pavie J, Minozzi C, Jayle D, Castel P, Derouineau J, Kousignan P, Eliazevitch M, Pierre I, Collias L, Viard JP, Gilquin J, Sobel A, Slama L, Ghosn J, Hadacek B, Thu-Huyn N, Nait-Ighil L, Cros A, Maignan A, Duvivier C, Consigny PH, Lanternier F, Shoai-Tehrani M, Touam F, Jerbi S, Bodard L, Jung C, Goujard C, Quertainmont Y, Duracinsky M, Segeral O, Blanc A, Peretti D, Cheret A, Chantalat C, Dulucq MJ, Levy Y, Lelievre JD, Lascaux AS, Dumont C, Boue F, Chambrin V, Abgrall S, Kansau I, Raho-Moussa M, De Truchis P, Dinh A, Davido B, Marigot D, Berthe H, Devidas A, Chevojon P, Chabrol A, Agher N, Lemercier Y, Chaix F, Turpault I, Bouchaud O, Honore P, Rouveix E, Reimann E, Belan AG, Godin Collet C, Souak S, Mortier E, Bloch M, Simonpoli AM, Manceron V, Cahitte I, Hiraux E, Lafon E, Cordonnier F, Zeng AF, Zucman D, Majerholc C, Bornarel D, Uludag A, Gellen-Dautremer J, Lefort A, Bazin C, Daneluzzi V, Gerbe J, Jeantils V, Coupard M, Patey O, Bantsimba J, Delllion S, Paz PC, Cazenave B, Richier L, Garrait V, Delacroix I, Elharrar B, Vittecoq D, Bolliot C, Lepretre A, Genet P, Masse V, Perrone V, Boussard JL, Chardon P, Froguel E, Simon P, Tassi S, Avettand Fenoel V, Barin F, Bourgeois C, Cardon F, Chaix ML, Delfraissy JF, Essat A, Fischer H, Lecuroux C, Meyer L, Petrov-Sanchez V, Rouzioux C, Saez-Cirion A, Seng R, Kuldanek K, Mullaney S, Young C, Zucchetti A, Bevan MA, McKernan S, Wandolo E, Richardson C, Youssef E, Green P, Faulkner S, Faville R, Herman S, Care C, Blackman H, Bellenger K, Fairbrother K, Phillips A, Babiker A, Delpech V, Fidler S, Clarke M, Fox J, Gilson R, Goldberg D, Hawkins D, Johnson A, Johnson M, McLean K, Nastouli E, Post F, Kennedy N, Pritchard J, Andrady U, Rajda N, Donnelly C, McKernan S, Drake S, Gilleran G, White D, Ross J, Harding J, Faville R, Sweeney J, Flegg P, Toomer S, Wilding H, Woodward R, Dean G, Richardson C, Perry N, Gompels M, Jennings L, Bansaal D, Browing M, Connolly L, Stanley B, Estreich S, Magdy A, O'Mahony C, Fraser P, Jebakumar SPR, David L, Mette R, Summerfield H, Evans M, White C, Robertson R, Lean C, Morris S, Winter A, Faulkner S, Goorney B, Howard L, Fairley I, Stemp C, Short L, Gomez M, Young F, Roberts M, Green S, Sivakumar K, Minton J, Siminoni A, Calderwood J, Greenhough D, DeSouza C, Muthern L, Orkin C, Murphy S, Truvedi M, McLean K, Hawkins D, Higgs C, Moyes A, Antonucci S, McCormack S, Lynn W, Bevan M, Fox J, Teague A, Anderson J, Mguni S, Post F, Campbell L, Mazhude C, Russell H, Gilson R, Carrick G, Ainsworth J, Waters A, Byrne P, Johnson M, Fidler S, Kuldanek K, Mullaney S, Lawlor V, Melville R, Sukthankar A, Thorpe S, Murphy C, Wilkins E, Ahmad S, Green P, Tayal S, Ong E, Meaden J, Riddell L, Loay D, Peacock K, Blackman H, Harindra V, Saeed AM, Allen S, Natarajan U, Williams O, Lacey H, Care C, Bowman C, Herman S, Devendra SV, Wither J, Bridgwood A, Singh G, Bushby S, Kellock D, Young S, Rooney G, Snart B, Currie J, Fitzgerald M, Arumainayyagam J, Chandramani S. A highly virulent variant of HIV-1 circulating in the Netherlands. Science 2022; 375:540-545. [PMID: 35113714 DOI: 10.1126/science.abk1688] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We discovered a highly virulent variant of subtype-B HIV-1 in the Netherlands. One hundred nine individuals with this variant had a 0.54 to 0.74 log10 increase (i.e., a ~3.5-fold to 5.5-fold increase) in viral load compared with, and exhibited CD4 cell decline twice as fast as, 6604 individuals with other subtype-B strains. Without treatment, advanced HIV-CD4 cell counts below 350 cells per cubic millimeter, with long-term clinical consequences-is expected to be reached, on average, 9 months after diagnosis for individuals in their thirties with this variant. Age, sex, suspected mode of transmission, and place of birth for the aforementioned 109 individuals were typical for HIV-positive people in the Netherlands, which suggests that the increased virulence is attributable to the viral strain. Genetic sequence analysis suggests that this variant arose in the 1990s from de novo mutation, not recombination, with increased transmissibility and an unfamiliar molecular mechanism of virulence.
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Affiliation(s)
- Chris Wymant
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - François Blanquart
- Centre for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, PSL Research University, Paris, France.,IAME, UMR 1137, INSERM, Université de Paris, Paris, France
| | - Luca Ferretti
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Astrid Gall
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Matthew Hall
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tanya Golubchik
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Margreet Bakker
- Laboratory of Experimental Virology, Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Swee Hoe Ong
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Lele Zhao
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David Bonsall
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.,Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Mariateresa de Cesare
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - George MacIntyre-Cockett
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.,Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Lucie Abeler-Dörner
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jan Albert
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.,Department of Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden
| | - Norbert Bannert
- Division for HIV and Other Retroviruses, Department of Infectious Diseases, Robert Koch Institute, Berlin, Germany
| | - Jacques Fellay
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - M Kate Grabowski
- Department of Pathology, John Hopkins University, Baltimore, MD, USA
| | | | - Huldrych F Günthard
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Pia Kivelä
- Department of Infectious Diseases, Helsinki University Hospital, Helsinki, Finland
| | - Roger D Kouyos
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | | | - Laurence Meyer
- INSERM CESP U1018, Université Paris Saclay, APHP, Service de Santé Publique, Hôpital de Bicêtre, Le Kremlin-Bicêtre, France
| | - Kholoud Porter
- Institute for Global Health, University College London, London, UK
| | - Matti Ristola
- Department of Infectious Diseases, Helsinki University Hospital, Helsinki, Finland
| | | | - Ben Berkhout
- Laboratory of Experimental Virology, Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Paul Kellam
- Kymab Ltd., Cambridge, UK.,Department of Infectious Diseases, Faculty of Medicine, Imperial College London, London, UK
| | - Marion Cornelissen
- Laboratory of Experimental Virology, Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands.,Molecular Diagnostic Unit, Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Peter Reiss
- Stichting HIV Monitoring, Amsterdam, Netherlands.,Department of Global Health, Amsterdam University Medical Centers, University of Amsterdam and Amsterdam Institute for Global Health and Development, Amsterdam, Netherlands
| | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.,Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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Gagne RB, Crooks KR, Craft ME, Chiu ES, Fountain-Jones NM, Malmberg JL, Carver S, Funk WC, VandeWoude S. Parasites as conservation tools. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2022; 36:e13719. [PMID: 33586245 DOI: 10.1111/cobi.13719] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 02/08/2021] [Indexed: 06/12/2023]
Abstract
Parasite success typically depends on a close relationship with one or more hosts; therefore, attributes of parasitic infection have the potential to provide indirect details of host natural history and are biologically relevant to animal conservation. Characterization of parasite infections has been useful in delineating host populations and has served as a proxy for assessment of environmental quality. In other cases, the utility of parasites is just being explored, for example, as indicators of host connectivity. Innovative studies of parasite biology can provide information to manage major conservation threats by using parasite assemblage, prevalence, or genetic data to provide insights into the host. Overexploitation, habitat loss and fragmentation, invasive species, and climate change are major threats to animal conservation, and all of these can be informed by parasites.
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Affiliation(s)
- Roderick B Gagne
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, USA
| | - Kevin R Crooks
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA
| | - Meggan E Craft
- Department of Veterinary Population Medicine, University of Minnesota, St Paul, Minnesota, USA
| | - Elliott S Chiu
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, USA
| | | | - Jennifer L Malmberg
- Department of Veterinary Sciences, Wyoming State Veterinary Laboratory, University of Wyoming, Laramie, Wyoming, USA
| | - Scott Carver
- School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia
| | - W Chris Funk
- Graduate Degree Program in Ecology, Department of Biology, Colorado State University, Fort Collins, Colorado, USA
| | - Sue VandeWoude
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, USA
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44
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Bezemer D, Blenkinsop A, Hall M, van Sighem A, Cornelissen M, Wessels E, van Kampen J, van de Laar T, Reiss P, Fraser C, Ratmann O. Many but small HIV-1 non-B transmission chains in the Netherlands. AIDS 2022; 36:83-94. [PMID: 34618753 PMCID: PMC8655833 DOI: 10.1097/qad.0000000000003074] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 08/26/2021] [Accepted: 09/13/2021] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The aim of this study was to investigate introductions and spread of different HIV-1 subtypes in the Netherlands. DESIGN We identified distinct HIV-1 transmission chains in the Netherlands within the global epidemic context through viral phylogenetic analysis of partial HIV-1 polymerase sequences from individuals enrolled in the ATHENA national HIV cohort of all persons in care since 1996, and publicly available international background sequences. METHODS Viral lineages circulating in the Netherlands were identified through maximum parsimony phylogeographic analysis. The proportion of HIV-1 infections acquired in-country among heterosexuals and MSM was estimated from phylogenetically observed, national transmission chains using a branching process model that accounts for incomplete sampling. RESULTS As of 1 January 2019, 2589 (24%) of 10 971 (41%) HIV-1 sequenced individuals in ATHENA had non-B subtypes (A1, C, D, F, G) or circulating recombinant forms (CRF01AE, CRF02AG, CRF06-cpx). The 1588 heterosexuals were in 1224, and 536 MSM in 270 phylogenetically observed transmission chains. After adjustments for incomplete sampling, most heterosexual (75%) and MSM (76%) transmission chains were estimated to include only the individual introducing the virus (size = 1). Onward transmission occurred mostly in chains size 2-5 amongst heterosexuals (62%) and in chains size at least 10 amongst MSM (64%). Considering some chains originated in-country from other risk-groups, 40% (95% confidence interval: 36-44) of non-B-infected heterosexuals and 62% (95% confidence interval: 49-73) of MSM-acquired infection in-country. CONCLUSION Although most HIV-1 non-B introductions showed no or very little onward transmission, a considerable proportion of non-B infections amongst both heterosexuals and MSM in the Netherlands have been acquired in-country.
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Affiliation(s)
| | - Alexandra Blenkinsop
- Department of Mathematics, Imperial College London, London
- Department of Global Health, Amsterdam University Medical Centers, University of Amsterdam and Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands
| | - Matthew Hall
- Oxford Big Data Institute, University of Oxford, Oxford, UK
| | | | - Marion Cornelissen
- Laboratory of Clinical Virology, Department of Medical Microbiology, Academic Medical Center of the University of Amsterdam, Amsterdam
| | - Els Wessels
- Department of Medical Microbiology, Leiden University Medical Center, Leiden
| | | | - Thijs van de Laar
- Department of Donor Medicine Research, laboratory of Blood-borne Infections, Sanquin Research
- Department of Medical Microbiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands
| | - Peter Reiss
- Stichting HIV Monitoring, Amsterdam, The Netherlands
- Department of Global Health, Amsterdam University Medical Centers, University of Amsterdam and Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands
| | | | - Oliver Ratmann
- Department of Mathematics, Imperial College London, London
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45
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Foster-Nyarko E, Pallen MJ. OUP accepted manuscript. FEMS Microbiol Rev 2022; 46:6522174. [PMID: 35134909 PMCID: PMC9075585 DOI: 10.1093/femsre/fuac008] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 01/31/2022] [Accepted: 02/01/2022] [Indexed: 11/13/2022] Open
Abstract
Escherichia coli has a rich history as biology's ‘rock star’, driving advances across many fields. In the wild, E. coli resides innocuously in the gut of humans and animals but is also a versatile pathogen commonly associated with intestinal and extraintestinal infections and antimicrobial resistance—including large foodborne outbreaks such as the one that swept across Europe in 2011, killing 54 individuals and causing approximately 4000 infections and 900 cases of haemolytic uraemic syndrome. Given that most E. coli are harmless gut colonizers, an important ecological question plaguing microbiologists is what makes E. coli an occasionally devastating pathogen? To address this question requires an enhanced understanding of the ecology of the organism as a commensal. Here, we review how our knowledge of the ecology and within-host diversity of this organism in the vertebrate gut has progressed in the 137 years since E. coli was first described. We also review current approaches to the study of within-host bacterial diversity. In closing, we discuss some of the outstanding questions yet to be addressed and prospects for future research.
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Affiliation(s)
- Ebenezer Foster-Nyarko
- Corresponding author: Department of Infection Biology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom. E-mail:
| | - Mark J Pallen
- Quadram Institute Bioscience, Norwich Research Park, Norwich, NR4 7UQ, United Kingdom
- School of Veterinary Medicine, University of Surrey, Guildford, Surrey, GU2 7AL, United Kingdom
- School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TU, United Kingdom
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46
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Dhar S, Zhang C, Măndoiu II, Bansal MS. TNet: Transmission Network Inference Using Within-Host Strain Diversity and its Application to Geographical Tracking of COVID-19 Spread. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:230-242. [PMID: 34255632 PMCID: PMC8956368 DOI: 10.1109/tcbb.2021.3096455] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 07/03/2021] [Accepted: 07/08/2021] [Indexed: 06/13/2023]
Abstract
The inference of disease transmission networks is an important problem in epidemiology. One popular approach for building transmission networks is to reconstruct a phylogenetic tree using sequences from disease strains sampled from infected hosts and infer transmissions based on this tree. However, most existing phylogenetic approaches for transmission network inference are highly computationally intensive and cannot take within-host strain diversity into account. Here, we introduce a new phylogenetic approach for inferring transmission networks, TNet, that addresses these limitations. TNet uses multiple strain sequences from each sampled host to infer transmissions and is simpler and more accurate than existing approaches. Furthermore, TNet is highly scalable and able to distinguish between ambiguous and unambiguous transmission inferences. We evaluated TNet on a large collection of 560 simulated transmission networks of various sizes and diverse host, sequence, and transmission characteristics, as well as on 10 real transmission datasets with known transmission histories. Our results show that TNet outperforms two other recently developed methods, phyloscanner and SharpTNI, that also consider within-host strain diversity. We also applied TNet to a large collection of SARS-CoV-2 genomes sampled from infected individuals in many countries around the world, demonstrating how our inference framework can be adapted to accurately infer geographical transmission networks. TNet is freely available from https://compbio.engr.uconn.edu/software/TNet/.
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Affiliation(s)
- Saurav Dhar
- Department of Computer Science & EngineeringUniversity of ConnecticutStorrsCT06269USA
| | - Chengchen Zhang
- Department of Computer Science & EngineeringUniversity of ConnecticutStorrsCT06269USA
| | - Ion I. Măndoiu
- Department of Computer Science & EngineeringUniversity of ConnecticutStorrsCT06269USA
| | - Mukul S. Bansal
- Department of Computer Science & EngineeringUniversity of ConnecticutStorrsCT06269USA
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Leigh DM, Peranić K, Prospero S, Cornejo C, Ćurković-Perica M, Kupper Q, Nuskern L, Rigling D, Ježić M. Long-read sequencing reveals the evolutionary drivers of intra-host diversity across natural RNA mycovirus infections. Virus Evol 2021; 7:veab101. [PMID: 35299787 PMCID: PMC8923234 DOI: 10.1093/ve/veab101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/23/2021] [Accepted: 12/01/2021] [Indexed: 01/05/2023] Open
Abstract
Intra-host dynamics are a core component of virus evolution but most intra-host data come from a narrow range of hosts or experimental infections. Gaining broader information on the intra-host diversity and dynamics of naturally occurring virus infections is essential to our understanding of evolution across the virosphere. Here we used PacBio long-read HiFi sequencing to characterize the intra-host populations of natural infections of the RNA mycovirus Cryphonectria hypovirus 1 (CHV1). CHV1 is a biocontrol agent for the chestnut blight fungus (Cryphonectria parasitica), which co-invaded Europe alongside the fungus. We characterized the mutational and haplotypic intra-host virus diversity of thirty-eight natural CHV1 infections spread across four locations in Croatia and Switzerland. Intra-host CHV1 diversity values were shaped by purifying selection and accumulation of mutations over time as well as epistatic interactions within the host genome at defense loci. Geographical landscape features impacted CHV1 inter-host relationships through restricting dispersal and causing founder effects. Interestingly, a small number of intra-host viral haplotypes showed high sequence similarity across large geographical distances unlikely to be linked by dispersal.
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Affiliation(s)
- Deborah M Leigh
- Phytopathology, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf CH-8903, Switzerland
| | - Karla Peranić
- Faculty of Science, University of Zagreb, Zagreb, Grad Zagreb 10000, Croatia
| | - Simone Prospero
- Phytopathology, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf CH-8903, Switzerland
| | - Carolina Cornejo
- Phytopathology, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf CH-8903, Switzerland
| | | | | | - Lucija Nuskern
- Faculty of Science, University of Zagreb, Zagreb, Grad Zagreb 10000, Croatia
| | - Daniel Rigling
- Phytopathology, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf CH-8903, Switzerland
| | - Marin Ježić
- Faculty of Science, University of Zagreb, Zagreb, Grad Zagreb 10000, Croatia
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Blassel L, Zhukova A, Villabona-Arenas CJ, Atkins KE, Hué S, Gascuel O. Drug resistance mutations in HIV: new bioinformatics approaches and challenges. Curr Opin Virol 2021; 51:56-64. [PMID: 34597873 DOI: 10.1016/j.coviro.2021.09.009] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 08/31/2021] [Accepted: 09/13/2021] [Indexed: 12/11/2022]
Abstract
Drug resistance mutations appear in HIV under treatment pressure. Resistant variants can be transmitted to treatment-naive individuals, which can lead to rapid virological failure and can limit treatment options. Consequently, quantifying the prevalence, emergence and transmission of drug resistance is critical to effectively treating patients and to shape health policies. We review recent bioinformatics developments and in particular describe: (1) the machine learning approaches intended to predict and explain the level of resistance of HIV variants from their sequence data; (2) the phylogenetic methods used to survey the emergence and dynamics of resistant HIV transmission clusters; (3) the impact of deep sequencing in studying within-host and between-host genetic diversity of HIV variants, notably regarding minority resistant variants.
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Affiliation(s)
- Luc Blassel
- Unité Bioinformatique Evolutive, Institut Pasteur, Paris, France; Sorbonne Université, Collège Doctoral, Paris, France
| | - Anna Zhukova
- Unité Bioinformatique Evolutive, Institut Pasteur, Paris, France; Hub de Bioinformatique et Biostatistique, Institut Pasteur, Paris, France
| | - Christian J Villabona-Arenas
- Centre for the Mathematical Modelling of Infectious Diseases (CMMID), London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Katherine E Atkins
- Centre for the Mathematical Modelling of Infectious Diseases (CMMID), London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Stéphane Hué
- Centre for the Mathematical Modelling of Infectious Diseases (CMMID), London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Olivier Gascuel
- Institut de Systématique, Evolution, Biodiversité (ISYEB, UMR 7205 - CNRS, Muséum National d'Histoire Naturelle, EPHE, SU, UA), Paris, France.
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49
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Danesh G, Virlogeux V, Ramière C, Charre C, Cotte L, Alizon S. Quantifying transmission dynamics of acute hepatitis C virus infections in a heterogeneous population using sequence data. PLoS Pathog 2021; 17:e1009916. [PMID: 34520487 PMCID: PMC8462723 DOI: 10.1371/journal.ppat.1009916] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 09/24/2021] [Accepted: 08/25/2021] [Indexed: 12/27/2022] Open
Abstract
Opioid substitution and syringes exchange programs have drastically reduced hepatitis C virus (HCV) spread in France but HCV sexual transmission in men having sex with men (MSM) has recently arisen as a significant public health concern. The fact that the virus is transmitting in a heterogeneous population, with different transmission routes, makes prevalence and incidence rates poorly informative. However, additional insights can be gained by analyzing virus phylogenies inferred from dated genetic sequence data. By combining a phylodynamics approach based on Approximate Bayesian Computation (ABC) and an original transmission model, we estimate key epidemiological parameters of an ongoing HCV epidemic among MSMs in Lyon (France). We show that this new epidemic is largely independent of the previously observed non-MSM HCV epidemics and that its doubling time is ten times lower (0.44 years versus 4.37 years). These results have practical implications for HCV control and illustrate the additional information provided by virus genomics in public health.
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Affiliation(s)
- Gonché Danesh
- MIVEGEC, CNRS, IRD, Université de Montpellier – Montpellier, France
| | - Victor Virlogeux
- Clinical Research Center, Croix-Rousse Hospital, Hospices Civils de Lyon – Lyon, France
| | - Christophe Ramière
- Virology Laboratory, Croix-Rousse Hospital, Hospices Civils de Lyon – Lyon, France
| | - Caroline Charre
- Virology Laboratory, Croix-Rousse Hospital, Hospices Civils de Lyon – Lyon, France
| | - Laurent Cotte
- Infectious Diseases Department, Croix-Rousse Hospital, Hospices Civils de Lyon – Lyon, France
| | - Samuel Alizon
- MIVEGEC, CNRS, IRD, Université de Montpellier – Montpellier, France
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50
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Tully DC, Hahn JA, Bean DJ, Evans JL, Morris MD, Page K, Allen TM. Identification of Genetically Related HCV Infections Among Self-Described Injecting Partnerships. Clin Infect Dis 2021; 74:993-1003. [PMID: 34448809 DOI: 10.1093/cid/ciab596] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND The current opioid epidemic across the United States has fueled a surge in the rate of new hepatitis C virus (HCV) infections among young persons who inject drugs (PWIDs). Paramount to interrupting transmission is targeting these high-risk populations and understanding the underlying network structures facilitating transmission within these communities. METHODS Deep sequencing data were obtained for 52 participants from 32 injecting partnerships enrolled in the U-Find-Out (UFO) Partner Study, which is a prospective study of self-described injecting dyad partnerships from a large community-based study of HCV infection in young adult PWIDs from San Francisco. Phylogenetically linked transmission events were identified using traditional genetic-distance measures and viral deep sequence phylogenies reconstructed to determine the statistical support of inferences and the direction of transmission within partnerships. RESULTS Using deep sequencing data, we found that 12 of 32 partnerships were genetically similar and clustered. Three additional phylogenetic clusters were found describing novel putative transmission links outside of the injecting relationship. Transmission direction was inferred correctly for 5 partnerships with the incorrect transmission direction inferred in more than 50% of cases. Notably, we observed that phylogenetic linkage was most often associated with a lower number of network partners and involvement in a sexual relationship. CONCLUSIONS Deep sequencing of HCV among self-described injecting partnerships demonstrates that the majority of transmission events originate from outside of the injecting partnership. Furthermore, these findings caution that phylogenetic methods may be unable to routinely infer the direction of transmission among PWIDs especially when transmission events occur in rapid succession within high-risk networks.
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Affiliation(s)
- Damien C Tully
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom.,Center for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Judith A Hahn
- Department of Medicine, University of California, San Francisco, California, USA
| | - David J Bean
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
| | - Jennifer L Evans
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
| | - Meghan D Morris
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
| | - Kimberly Page
- Department of Internal Medicine, University of New Mexico Health Center, Albuquerque, New Mexico, USA
| | - Todd M Allen
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
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