1
|
Kim S, Kigozi G, Martin MA, Galiwango RM, Quinn TC, Redd AD, Ssekubugu R, Bonsall D, Ssemwanga D, Rambaut A, Herbeck JT, Reynolds SJ, Foley B, Abeler-Dörner L, Fraser C, Ratmann O, Kagaayi J, Laeyendecker O, Grabowski MK. Intra- and inter-subtype HIV diversity between 1994 and 2018 in southern Uganda: a longitudinal population-based study. Virus Evol 2024; 10:veae065. [PMID: 39399152 PMCID: PMC11468842 DOI: 10.1093/ve/veae065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 07/20/2024] [Accepted: 09/03/2024] [Indexed: 10/15/2024] Open
Abstract
There is limited data on human immunodeficiency virus (HIV) evolutionary trends in African populations. We evaluated changes in HIV viral diversity and genetic divergence in southern Uganda over a 24-year period spanning the introduction and scale-up of HIV prevention and treatment programs using HIV sequence and survey data from the Rakai Community Cohort Study, an open longitudinal population-based HIV surveillance cohort. Gag (p24) and env (gp41) HIV data were generated from people living with HIV (PLHIV) in 31 inland semi-urban trading and agrarian communities (1994-2018) and four hyperendemic Lake Victoria fishing communities (2011-2018) under continuous surveillance. HIV subtype was assigned using the Recombination Identification Program with phylogenetic confirmation. Inter-subtype diversity was evaluated using the Shannon diversity index, and intra-subtype diversity with the nucleotide diversity and pairwise TN93 genetic distance. Genetic divergence was measured using root-to-tip distance and pairwise TN93 genetic distance analyses. Demographic history of HIV was inferred using a coalescent-based Bayesian Skygrid model. Evolutionary dynamics were assessed among demographic and behavioral population subgroups, including by migration status. 9931 HIV sequences were available from 4999 PLHIV, including 3060 and 1939 persons residing in inland and fishing communities, respectively. In inland communities, subtype A1 viruses proportionately increased from 14.3% in 1995 to 25.9% in 2017 (P < .001), while those of subtype D declined from 73.2% in 1995 to 28.2% in 2017 (P < .001). The proportion of viruses classified as recombinants significantly increased by nearly four-fold from 12.2% in 1995 to 44.8% in 2017. Inter-subtype HIV diversity has generally increased. While intra-subtype p24 genetic diversity and divergence leveled off after 2014, intra-subtype gp41 diversity, effective population size, and divergence increased through 2017. Intra- and inter-subtype viral diversity increased across all demographic and behavioral population subgroups, including among individuals with no recent migration history or extra-community sexual partners. This study provides insights into population-level HIV evolutionary dynamics following the scale-up of HIV prevention and treatment programs. Continued molecular surveillance may provide a better understanding of the dynamics driving population HIV evolution and yield important insights for epidemic control and vaccine development.
Collapse
Affiliation(s)
- Seungwon Kim
- Department of Pathology, Johns Hopkins School of Medicine, 600 N. Wolfe Street, Baltimore, MD 21205, United States
| | - Godfrey Kigozi
- Research Department, Rakai Health Sciences Program, 4-6 Sanitary Lane, Old Bukoba Road, Kalisizo, Uganda
| | - Michael A Martin
- Department of Pathology, Johns Hopkins School of Medicine, 600 N. Wolfe Street, Baltimore, MD 21205, United States
| | - Ronald M Galiwango
- Research Department, Rakai Health Sciences Program, 4-6 Sanitary Lane, Old Bukoba Road, Kalisizo, Uganda
| | - Thomas C Quinn
- Research Department, Rakai Health Sciences Program, 4-6 Sanitary Lane, Old Bukoba Road, Kalisizo, Uganda
- Department of Medicine, Johns Hopkins School of Medicine, 600 N. Wolfe Street, Baltimore, MD 21205, United States
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 5601 Fishers Lane, Bethesda, MD 20892, United States
| | - Andrew D Redd
- Department of Medicine, Johns Hopkins School of Medicine, 600 N. Wolfe Street, Baltimore, MD 21205, United States
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 5601 Fishers Lane, Bethesda, MD 20892, United States
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Anzio Road, Cape Town 7925, South Africa
| | - Robert Ssekubugu
- Research Department, Rakai Health Sciences Program, 4-6 Sanitary Lane, Old Bukoba Road, Kalisizo, Uganda
| | - David Bonsall
- Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7DQ, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, United Kingdom
| | - Deogratius Ssemwanga
- Medical Research Council (MRC)/Uganda Virus Research Institute (UVRI) and London School of Hygiene and Tropical Medicine (LSHTM) Uganda Research Unit, Plot 51-59 Nakiwogo Road, Entebbe, Uganda
- Uganda Virus Research Institute, Plot 51-59 Nakiwogo Road, Entebbe, Uganda
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Charlotte Auerbach Road, Edinburgh EH9 3FL, United Kingdom
| | - Joshua T Herbeck
- Department of Global Health, University of Washington, 3980 15th Ave NE, Seattle, WA 98195, United States
| | - Steven J Reynolds
- Research Department, Rakai Health Sciences Program, 4-6 Sanitary Lane, Old Bukoba Road, Kalisizo, Uganda
- Department of Medicine, Johns Hopkins School of Medicine, 600 N. Wolfe Street, Baltimore, MD 21205, United States
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 5601 Fishers Lane, Bethesda, MD 20892, United States
| | - Brian Foley
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545, United States
| | - Lucie Abeler-Dörner
- Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7DQ, United Kingdom
| | - Christophe Fraser
- Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7DQ, United Kingdom
| | - Oliver Ratmann
- Department of Mathematics, Imperial College London, 180 Queen’s Gate, London SW7 2AZ, United Kingdom
| | - Joseph Kagaayi
- Research Department, Rakai Health Sciences Program, 4-6 Sanitary Lane, Old Bukoba Road, Kalisizo, Uganda
- Department of Epidemiology, Makerere University School of Public Health, New Mulago Hill Road, Kampala, Uganda
| | - Oliver Laeyendecker
- Department of Medicine, Johns Hopkins School of Medicine, 600 N. Wolfe Street, Baltimore, MD 21205, United States
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 5601 Fishers Lane, Bethesda, MD 20892, United States
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street Baltimore, MD 21205, United States
| | - Mary K Grabowski
- Department of Pathology, Johns Hopkins School of Medicine, 600 N. Wolfe Street, Baltimore, MD 21205, United States
- Research Department, Rakai Health Sciences Program, 4-6 Sanitary Lane, Old Bukoba Road, Kalisizo, Uganda
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street Baltimore, MD 21205, United States
| |
Collapse
|
2
|
Hu L, Zhao B, Liu M, Gao Y, Ding H, Hu Q, An M, Shang H, Han X. Optimization of genetic distance threshold for inferring the CRF01_AE molecular network based on next-generation sequencing. Front Cell Infect Microbiol 2024; 14:1388059. [PMID: 38846352 PMCID: PMC11155296 DOI: 10.3389/fcimb.2024.1388059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 03/28/2024] [Indexed: 06/09/2024] Open
Abstract
Introduction HIV molecular network based on genetic distance (GD) has been extensively utilized. However, the GD threshold for the non-B subtype differs from that of subtype B. This study aimed to optimize the GD threshold for inferring the CRF01_AE molecular network. Methods Next-generation sequencing data of partial CRF01_AE pol sequences were obtained for 59 samples from 12 transmission pairs enrolled from a high-risk cohort during 2009 and 2014. The paired GD was calculated using the Tamura-Nei 93 model to infer a GD threshold range for HIV molecular networks. Results 2,019 CRF01_AE pol sequences and information on recent HIV infection (RHI) from newly diagnosed individuals in Shenyang from 2016 to 2019 were collected to construct molecular networks to assess the ability of the inferred GD thresholds to predict recent transmission events. When HIV transmission occurs within a span of 1-4 years, the mean paired GD between the sequences of the donor and recipient within the same transmission pair were as follow: 0.008, 0.011, 0.013, and 0.023 substitutions/site. Using these four GD thresholds, it was found that 98.9%, 96.0%, 88.2%, and 40.4% of all randomly paired GD values from 12 transmission pairs were correctly identified as originating from the same transmission pairs. In the real world, as the GD threshold increased from 0.001 to 0.02 substitutions/site, the proportion of RHI within the molecular network gradually increased from 16.6% to 92.3%. Meanwhile, the proportion of links with RHI gradually decreased from 87.0% to 48.2%. The two curves intersected at a GD of 0.008 substitutions/site. Discussion A suitable range of GD thresholds, 0.008-0.013 substitutions/site, was identified to infer the CRF01_AE molecular transmission network and identify HIV transmission events that occurred within the past three years. This finding provides valuable data for selecting an appropriate GD thresholds in constructing molecular networks for non-B subtypes.
Collapse
Affiliation(s)
- Lijuan Hu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- National Health Commission (NHC) Key Laboratory of AIDS Prevention and Treatment, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- Laboratory Medicine Innovation Unit, Chinese Academy of Medical Sciences, Shenyang, China
- Key Laboratory of AIDS Immunology of Liaoning Province, Shenyang, China
- Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou, China
| | - Bin Zhao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- National Health Commission (NHC) Key Laboratory of AIDS Prevention and Treatment, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- Laboratory Medicine Innovation Unit, Chinese Academy of Medical Sciences, Shenyang, China
- Key Laboratory of AIDS Immunology of Liaoning Province, Shenyang, China
- Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou, China
| | - Mingchen Liu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- National Health Commission (NHC) Key Laboratory of AIDS Prevention and Treatment, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- Laboratory Medicine Innovation Unit, Chinese Academy of Medical Sciences, Shenyang, China
- Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou, China
| | - Yang Gao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- National Health Commission (NHC) Key Laboratory of AIDS Prevention and Treatment, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- Laboratory Medicine Innovation Unit, Chinese Academy of Medical Sciences, Shenyang, China
- Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou, China
| | - Haibo Ding
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- National Health Commission (NHC) Key Laboratory of AIDS Prevention and Treatment, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- Laboratory Medicine Innovation Unit, Chinese Academy of Medical Sciences, Shenyang, China
- Key Laboratory of AIDS Immunology of Liaoning Province, Shenyang, China
- Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou, China
| | - Qinghai Hu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- National Health Commission (NHC) Key Laboratory of AIDS Prevention and Treatment, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- Laboratory Medicine Innovation Unit, Chinese Academy of Medical Sciences, Shenyang, China
- Key Laboratory of AIDS Immunology of Liaoning Province, Shenyang, China
- Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou, China
| | - Minghui An
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- National Health Commission (NHC) Key Laboratory of AIDS Prevention and Treatment, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- Laboratory Medicine Innovation Unit, Chinese Academy of Medical Sciences, Shenyang, China
- Key Laboratory of AIDS Immunology of Liaoning Province, Shenyang, China
- Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou, China
| | - Hong Shang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- National Health Commission (NHC) Key Laboratory of AIDS Prevention and Treatment, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- Laboratory Medicine Innovation Unit, Chinese Academy of Medical Sciences, Shenyang, China
- Key Laboratory of AIDS Immunology of Liaoning Province, Shenyang, China
- Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou, China
| | - Xiaoxu Han
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- National Health Commission (NHC) Key Laboratory of AIDS Prevention and Treatment, National Clinical Research Center for Laboratory Medicine, The First Hospital of China Medical University, China Medical University, Shenyang, China
- Laboratory Medicine Innovation Unit, Chinese Academy of Medical Sciences, Shenyang, China
- Key Laboratory of AIDS Immunology of Liaoning Province, Shenyang, China
- Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou, China
| |
Collapse
|
3
|
Kim S, Kigozi G, Martin MA, Galiwango RM, Quinn TC, Redd AD, Ssekubugu R, Bonsall D, Ssemwanga D, Rambaut A, Herbeck JT, Reynolds SJ, Foley B, Abeler-Dörner L, Fraser C, Ratmann O, Kagaayi J, Laeyendecker O, Grabowski MK. Increasing intra- and inter-subtype HIV diversity despite declining HIV incidence in Uganda. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.14.24303990. [PMID: 38558994 PMCID: PMC10980117 DOI: 10.1101/2024.03.14.24303990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
HIV incidence has been declining in Africa with scale-up of HIV interventions. However, there is limited data on HIV evolutionary trends in African populations with waning epidemics. We evaluated changes in HIV viral diversity and genetic divergence in southern Uganda over a twenty-five-year period spanning the introduction and scale-up of HIV prevention and treatment programs using HIV sequence and survey data from the Rakai Community Cohort Study, an open longitudinal population-based HIV surveillance cohort. Gag (p24) and env (gp41) HIV data were generated from persons living with HIV (PLHIV) in 31 inland semi-urban trading and agrarian communities (1994 to 2018) and four hyperendemic Lake Victoria fishing communities (2011 to 2018) under continuous surveillance. HIV subtype was assigned using the Recombination Identification Program with phylogenetic confirmation. Inter-subtype diversity was estimated using the Shannon diversity index and intra-subtype diversity with the nucleotide diversity and pairwise TN93 genetic distance. Genetic divergence was measured using root-to-tip distance and pairwise TN93 genetic distance analyses. Evolutionary dynamics were assessed among demographic and behavioral sub-groups, including by migration status. 9,931 HIV sequences were available from 4,999 PLHIV, including 3,060 and 1,939 persons residing in inland and fishing communities, respectively. In inland communities, subtype A1 viruses proportionately increased from 14.3% in 1995 to 25.9% in 2017 (p<0.001), while those of subtype D declined from 73.2% in 1995 to 28.2% in 2017 (p<0.001). The proportion of viruses classified as recombinants significantly increased by more than four-fold. Inter-subtype HIV diversity has generally increased. While p24 intra-subtype genetic diversity and divergence leveled off after 2014, diversity and divergence of gp41 increased through 2017. Inter- and intra-subtype viral diversity increased across all population sub-groups, including among individuals with no recent migration history or extra-community sexual partners. This study provides insights into population-level HIV evolutionary dynamics in declining African HIV epidemics following the scale-up of HIV prevention and treatment programs. Continued molecular surveillance may provide a better understanding of the dynamics driving population HIV evolution and yield important insights for epidemic control and vaccine development.
Collapse
Affiliation(s)
- Seungwon Kim
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | - Michael A. Martin
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | - Thomas C. Quinn
- Rakai Health Sciences Program, Kalisizo, Uganda
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Andrew D. Redd
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | | | - 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, University of Oxford, Oxford, UK
| | - Deogratius Ssemwanga
- Medical Research Council (MRC)/Uganda Virus Research Institute (UVRI) and London School of Hygiene and Tropical Medicine (LSHTM) Uganda Research Unit, Entebbe, Uganda
- Uganda Virus Research Institute, Entebbe, Uganda
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Joshua T. Herbeck
- Department of Global Health, University of Washington, Seattle, WA, USA
| | - Steven J. Reynolds
- Rakai Health Sciences Program, Kalisizo, Uganda
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Brian Foley
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
| | - Lucie Abeler-Dörner
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Oliver Ratmann
- Department of Mathematics, Imperial College London, London, England, United Kingdom
| | - Joseph Kagaayi
- Rakai Health Sciences Program, Kalisizo, Uganda
- Makerere University School of Public Health, Kampala, Uganda
| | - Oliver Laeyendecker
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - M. Kate Grabowski
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Rakai Health Sciences Program, Kalisizo, Uganda
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| |
Collapse
|
4
|
Paneerselvam N, Khan A, Lawson BR. Broadly neutralizing antibodies targeting HIV: Progress and challenges. Clin Immunol 2023; 257:109809. [PMID: 37852345 PMCID: PMC10872707 DOI: 10.1016/j.clim.2023.109809] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/05/2023] [Accepted: 10/11/2023] [Indexed: 10/20/2023]
Abstract
Anti-HIV broadly neutralizing antibodies (bNAbs) offer a novel approach to treating, preventing, or curing HIV. Pre-clinical models and clinical trials involving the passive transfer of bNAbs have demonstrated that they can control viremia and potentially serve as alternatives or complement antiretroviral therapy (ART). However, antibody decay, persistent latent reservoirs, and resistance impede bNAb treatment. This review discusses recent advancements and obstacles in applying bNAbs and proposes strategies to enhance their therapeutic potential. These strategies include multi-epitope targeting, antibody half-life extension, combining with current and newer antiretrovirals, and sustained antibody secretion.
Collapse
Affiliation(s)
| | - Amber Khan
- The Scintillon Research Institute, 6868 Nancy Drive, San Diego, CA 92121, USA
| | - Brian R Lawson
- The Scintillon Research Institute, 6868 Nancy Drive, San Diego, CA 92121, USA.
| |
Collapse
|
5
|
von Delft A, Hall MD, Kwong AD, Purcell LA, Saikatendu KS, Schmitz U, Tallarico JA, Lee AA. Accelerating antiviral drug discovery: lessons from COVID-19. Nat Rev Drug Discov 2023; 22:585-603. [PMID: 37173515 PMCID: PMC10176316 DOI: 10.1038/s41573-023-00692-8] [Citation(s) in RCA: 75] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2023] [Indexed: 05/15/2023]
Abstract
During the coronavirus disease 2019 (COVID-19) pandemic, a wave of rapid and collaborative drug discovery efforts took place in academia and industry, culminating in several therapeutics being discovered, approved and deployed in a 2-year time frame. This article summarizes the collective experience of several pharmaceutical companies and academic collaborations that were active in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antiviral discovery. We outline our opinions and experiences on key stages in the small-molecule drug discovery process: target selection, medicinal chemistry, antiviral assays, animal efficacy and attempts to pre-empt resistance. We propose strategies that could accelerate future efforts and argue that a key bottleneck is the lack of quality chemical probes around understudied viral targets, which would serve as a starting point for drug discovery. Considering the small size of the viral proteome, comprehensively building an arsenal of probes for proteins in viruses of pandemic concern is a worthwhile and tractable challenge for the community.
Collapse
Affiliation(s)
- Annette von Delft
- Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Oxford Biomedical Research Centre, National Institute for Health Research, University of Oxford, Oxford, UK.
| | - Matthew D Hall
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA
| | | | | | | | | | | | - Alpha A Lee
- PostEra, Inc., Cambridge, MA, USA.
- Cavendish Laboratory, University of Cambridge, Cambridge, UK.
| |
Collapse
|
6
|
Alves E, Al-Kaabi M, Keane NM, Leary S, Almeida CAM, Deshpande P, Currenti J, Chopra A, Smith R, Castley A, Mallal S, Kalams SA, Gaudieri S, John M. Adaptation to HLA-associated immune pressure over the course of HIV infection and in circulating HIV-1 strains. PLoS Pathog 2022; 18:e1010965. [PMID: 36525463 PMCID: PMC9803285 DOI: 10.1371/journal.ppat.1010965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 12/30/2022] [Accepted: 11/01/2022] [Indexed: 12/23/2022] Open
Abstract
Adaptation to human leukocyte antigen (HLA)-associated immune pressure represents a major driver of human immunodeficiency virus (HIV) evolution at both the individual and population level. To date, there has been limited exploration of the impact of the initial cellular immune response in driving viral adaptation, the dynamics of these changes during infection and their effect on circulating transmitting viruses at the population level. Capturing detailed virological and immunological data from acute and early HIV infection is challenging as this commonly precedes the diagnosis of HIV infection, potentially by many years. In addition, rapid initiation of antiretroviral treatment following a diagnosis is the standard of care, and central to global efforts towards HIV elimination. Yet, acute untreated infection is the critical period in which the diversity of proviral reservoirs is first established within individuals, and associated with greater risk of onward transmissions in a population. Characterizing the viral adaptations evident in the earliest phases of infection, coinciding with the initial cellular immune responses is therefore relevant to understanding which changes are of greatest impact to HIV evolution at the population level. In this study, we utilized three separate cohorts to examine the initial CD8+ T cell immune response to HIV (cross-sectional acute infection cohort), track HIV evolution in response to CD8+ T cell-mediated immunity over time (longitudinal chronic infection cohort) and translate the impact of HLA-driven HIV evolution to the population level (cross-sectional HIV sequence data spanning 30 years). Using next generation viral sequencing and enzyme-linked immunospot interferon-gamma recall responses to peptides representing HLA class I-specific HIV T cell targets, we observed that CD8+ T cell responses can select viral adaptations prior to full antibody seroconversion. Using the longitudinal cohort, we uncover that viral adaptations have the propensity to be retained over time in a non-selective immune environment, which reflects the increasing proportion of pre-adapted HIV strains within the Western Australian population over an approximate 30-year period.
Collapse
Affiliation(s)
- Eric Alves
- School of Human Sciences, The University of Western Australia, Perth, Western Australia, Australia
| | - Marwah Al-Kaabi
- School of Human Sciences, The University of Western Australia, Perth, Western Australia, Australia
| | - Niamh M. Keane
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
| | - Shay Leary
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
| | - Coral-Ann M. Almeida
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
| | - Pooja Deshpande
- School of Human Sciences, The University of Western Australia, Perth, Western Australia, Australia
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
| | - Jennifer Currenti
- School of Human Sciences, The University of Western Australia, Perth, Western Australia, Australia
| | - Abha Chopra
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
| | - Rita Smith
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Alison Castley
- Department of Clinical Immunology, Royal Perth Hospital, Perth, Western Australia, Australia
| | - Simon Mallal
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Spyros A. Kalams
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Silvana Gaudieri
- School of Human Sciences, The University of Western Australia, Perth, Western Australia, Australia
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Mina John
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, Western Australia, Australia
- Department of Clinical Immunology, Royal Perth Hospital, Perth, Western Australia, Australia
| |
Collapse
|
7
|
Boswell MT, Nazziwa J, Kuroki K, Palm A, Karlson S, Månsson F, Biague A, da Silva ZJ, Onyango CO, de Silva TI, Jaye A, Norrgren H, Medstrand P, Jansson M, Maenaka K, Rowland-Jones SL, Esbjörnsson J. Intrahost evolution of the HIV-2 capsid correlates with progression to AIDS. Virus Evol 2022; 8:veac075. [PMID: 36533148 PMCID: PMC9753047 DOI: 10.1093/ve/veac075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/24/2022] [Accepted: 08/23/2022] [Indexed: 11/26/2023] Open
Abstract
HIV-2 infection will progress to AIDS in most patients without treatment, albeit at approximately half the rate of HIV-1 infection. HIV-2 capsid (p26) amino acid polymorphisms are associated with lower viral loads and enhanced processing of T cell epitopes, which may lead to protective Gag-specific T cell responses common in slower progressors. Lower virus evolutionary rates, and positive selection on conserved residues in HIV-2 env have been associated with slower progression to AIDS. In this study we analysed 369 heterochronous HIV-2 p26 sequences from 12 participants with a median age of 30 years at enrolment. CD4% change over time was used to stratify participants into relative faster and slower progressor groups. We analysed p26 sequence diversity evolution, measured site-specific selection pressures and evolutionary rates, and determined if these evolutionary parameters were associated with progression status. Faster progressors had lower CD4% and faster CD4% decline rates. Median pairwise sequence diversity was higher in faster progressors (5.7x10-3 versus 1.4x10-3 base substitutions per site, P<0.001). p26 evolved under negative selection in both groups (dN/dS=0.12). Median virus evolutionary rates were higher in faster than slower progressors - synonymous rates: 4.6x10-3 vs. 2.3x10-3; and nonsynonymous rates: 6.9x10-4 vs. 2.7x10-4 substitutions/site/year, respectively. Virus evolutionary rates correlated negatively with CD4% change rates (ρ = -0.8, P=0.02), but not CD4% level. The signature amino acid at p26 positions 6, 12 and 119 differed between faster (6A, 12I, 119A) and slower (6G, 12V, 119P) progressors. These amino acid positions clustered near to the TRIM5α/p26 hexamer interface surface. p26 evolutionary rates were associated with progression to AIDS and were mostly driven by synonymous substitutions. Nonsynonymous evolutionary rates were an order of magnitude lower than synonymous rates, with limited amino acid sequence evolution over time within hosts. These results indicate HIV-2 p26 may be an attractive therapeutic target.
Collapse
Affiliation(s)
- M T Boswell
- Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, OX3 7FZ, Oxford, UK
| | - J Nazziwa
- Department of Translational Medicine, Lund University, Sölvegatan 17, 223 62, Lund, Sweden
| | - K Kuroki
- Faculty of Pharmaceutical Sciences and Global Station for Biosurfaces and Drug Discovery, Hokkaido University, Kita-12, Nishi-6, Kita-ku, Sapporo 060-0812, Japan
| | - A Palm
- Department of Translational Medicine, Lund University, Sölvegatan 17, 223 62, Lund, Sweden
| | - S Karlson
- Department of Translational Medicine, Lund University, Sölvegatan 17, 223 62, Lund, Sweden
| | - F Månsson
- Department of Translational Medicine, Lund University, Sölvegatan 17, 223 62, Lund, Sweden
| | - A Biague
- National Public Health Laboratory, V94M+HM4, Bissau, Guinea-Bissau
| | - Z J da Silva
- National Public Health Laboratory, V94M+HM4, Bissau, Guinea-Bissau
| | - C O Onyango
- US Centres for Disease Control, KEMRI Complex, Mbagathi Road off Mbagathi Way PO Box 606-00621, Kenya
| | - T I de Silva
- Department of Infection, Immunity and Cardiovascular Disease, The Medical School, University of Sheffield, Beech Hill Rd, S10 2RX, Sheffield, UK
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Boulevard, Fajara P. O. Box 273, Banjul, The Gambia
| | - A Jaye
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Boulevard, Fajara P. O. Box 273, Banjul, The Gambia
| | - H Norrgren
- Department of Clinical Sciences Lund, Lund University, Sölvegatan 19, 221 84 Lund, Sweden
| | - P Medstrand
- Department of Translational Medicine, Lund University, Sölvegatan 17, 223 62, Lund, Sweden
| | - M Jansson
- Department of Laboratory Medicine, Lund University, Sölvegatan 19, Sweden
| | - K Maenaka
- Faculty of Pharmaceutical Sciences and Global Station for Biosurfaces and Drug Discovery, Hokkaido University, Kita-12, Nishi-6, Kita-ku, Sapporo 060-0812, Japan
| | - S L Rowland-Jones
- Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, OX3 7FZ, Oxford, UK
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Boulevard, Fajara P. O. Box 273, Banjul, The Gambia
| | - J Esbjörnsson
- Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, OX3 7FZ, Oxford, UK
- Department of Translational Medicine, Lund University, Sölvegatan 17, 223 62, Lund, Sweden
| |
Collapse
|
8
|
Global Variability of V3 Loop Tetrapeptide Motif: a Concern for HIV-1 Neutralizing Antibodies-based Vaccine Design and Antiretroviral Therapy. JOURNAL OF MEDICAL MICROBIOLOGY AND INFECTIOUS DISEASES 2021. [DOI: 10.52547/jommid.9.3.108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
|
9
|
Large Evolutionary Rate Heterogeneity among and within HIV-1 Subtypes and CRFs. Viruses 2021; 13:v13091689. [PMID: 34578270 PMCID: PMC8473000 DOI: 10.3390/v13091689] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 08/19/2021] [Accepted: 08/23/2021] [Indexed: 01/06/2023] Open
Abstract
HIV-1 is a fast-evolving, genetically diverse virus presently classified into several groups and subtypes. The virus evolves rapidly because of an error-prone polymerase, high rates of recombination, and selection in response to the host immune system and clinical management of the infection. The rate of evolution is also influenced by the rate of virus spread in a population and nature of the outbreak, among other factors. HIV-1 evolution is thus driven by a range of complex genetic, social, and epidemiological factors that complicates disease management and prevention. Here, we quantify the evolutionary (substitution) rate heterogeneity among major HIV-1 subtypes and recombinants by analyzing the largest collection of HIV-1 genetic data spanning the widest possible geographical (100 countries) and temporal (1981–2019) spread. We show that HIV-1 substitution rates vary substantially, sometimes by several folds, both across the virus genome and between major subtypes and recombinants, but also within a subtype. Across subtypes, rates ranged 3.5-fold from 1.34 × 10−3 to 4.72 × 10−3 in env and 2.3-fold from 0.95 × 10−3 to 2.18 × 10−3 substitutions site−1 year−1 in pol. Within the subtype, 3-fold rate variation was observed in env in different human populations. It is possible that HIV-1 lineages in different parts of the world are operating under different selection pressures leading to substantial rate heterogeneity within and between subtypes. We further highlight how such rate heterogeneity can complicate HIV-1 phylodynamic studies, specifically, inferences on epidemiological linkage of transmission clusters based on genetic distance or phylogenetic data, and can mislead estimates about the timing of HIV-1 lineages.
Collapse
|
10
|
Lythgoe KA, Hall M, Ferretti L, de Cesare M, MacIntyre-Cockett G, Trebes A, Andersson M, Otecko N, Wise EL, Moore N, Lynch J, Kidd S, Cortes N, Mori M, Williams R, Vernet G, Justice A, Green A, Nicholls SM, Ansari MA, Abeler-Dörner L, Moore CE, Peto TEA, Eyre DW, Shaw R, Simmonds P, Buck D, Todd JA, Connor TR, Ashraf S, da Silva Filipe A, Shepherd J, Thomson EC, Bonsall D, Fraser C, Golubchik T. SARS-CoV-2 within-host diversity and transmission. Science 2021; 372:eabg0821. [PMID: 33688063 PMCID: PMC8128293 DOI: 10.1126/science.abg0821] [Citation(s) in RCA: 263] [Impact Index Per Article: 65.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/03/2021] [Indexed: 12/14/2022]
Abstract
Extensive global sampling and sequencing of the pandemic virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have enabled researchers to monitor its spread and to identify concerning new variants. Two important determinants of variant spread are how frequently they arise within individuals and how likely they are to be transmitted. To characterize within-host diversity and transmission, we deep-sequenced 1313 clinical samples from the United Kingdom. SARS-CoV-2 infections are characterized by low levels of within-host diversity when viral loads are high and by a narrow bottleneck at transmission. Most variants are either lost or occasionally fixed at the point of transmission, with minimal persistence of shared diversity, patterns that are readily observable on the phylogenetic tree. Our results suggest that transmission-enhancing and/or immune-escape SARS-CoV-2 variants are likely to arise infrequently but could spread rapidly if successfully transmitted.
Collapse
Affiliation(s)
- Katrina A Lythgoe
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK.
- Department of Zoology, University of Oxford, Oxford OX1 3SZ, UK
| | - Matthew Hall
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK.
| | - Luca Ferretti
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Mariateresa de Cesare
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - George MacIntyre-Cockett
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Amy Trebes
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Monique Andersson
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
- Division of Medical Virology, Stellenbosch University, Stellenbosch, South Africa
| | - Newton Otecko
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Emma L Wise
- Hampshire Hospitals NHS Foundation Trust, Basingstoke and North Hampshire Hospital, Basingstoke RG24 9NA, UK
- School of Biosciences and Medicine, University of Surrey, Guildford GU2 7XH, UK
| | - Nathan Moore
- Hampshire Hospitals NHS Foundation Trust, Basingstoke and North Hampshire Hospital, Basingstoke RG24 9NA, UK
| | - Jessica Lynch
- Hampshire Hospitals NHS Foundation Trust, Basingstoke and North Hampshire Hospital, Basingstoke RG24 9NA, UK
| | - Stephen Kidd
- Hampshire Hospitals NHS Foundation Trust, Basingstoke and North Hampshire Hospital, Basingstoke RG24 9NA, UK
| | - Nicholas Cortes
- Hampshire Hospitals NHS Foundation Trust, Basingstoke and North Hampshire Hospital, Basingstoke RG24 9NA, UK
- Gibraltar Health Authority, Gibraltar, UK
| | - Matilde Mori
- School of Medicine, University of Southampton, Southampton SO17 1BJ, UK
| | - Rebecca Williams
- Hampshire Hospitals NHS Foundation Trust, Basingstoke and North Hampshire Hospital, Basingstoke RG24 9NA, UK
| | - Gabrielle Vernet
- Hampshire Hospitals NHS Foundation Trust, Basingstoke and North Hampshire Hospital, Basingstoke RG24 9NA, UK
| | - Anita Justice
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
| | - Angie Green
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Samuel M Nicholls
- Institute of Microbiology and Infection, University of Birmingham, Birmingham B15 2TT, UK
| | - M Azim Ansari
- Peter Medawar Building for Pathogen Research, University of Oxford, Oxford OX1 3SY, UK
| | - Lucie Abeler-Dörner
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Catrin E Moore
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Timothy E A Peto
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
| | - David W Eyre
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
- Big Data Institute, Nuffield Department of Public Health, University of Oxford, Old Road Campus, Oxford OX3 7FL, UK
| | - Robert Shaw
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
| | - Peter Simmonds
- Peter Medawar Building for Pathogen Research, University of Oxford, Oxford OX1 3SY, UK
| | - David Buck
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - John A Todd
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Thomas R Connor
- Pathogen Genomics Unit, Public Health Wales Microbiology, Cardiff CF10 4BZ, UK
- Cardiff University School of Biosciences, Cardiff University, Cardiff CF10 3AX, UK
| | - Shirin Ashraf
- MRC-University of Glasgow Centre for Virus Research, Glasgow G61 1QH, UK
| | | | - James Shepherd
- MRC-University of Glasgow Centre for Virus Research, Glasgow G61 1QH, UK
| | - Emma C Thomson
- MRC-University of Glasgow Centre for Virus Research, Glasgow G61 1QH, UK
| | - David Bonsall
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
| | - Christophe Fraser
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
- Wellcome Sanger Institute, Cambridge CB10 1SA, UK
| | - Tanya Golubchik
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK.
- Department of Zoology, University of Oxford, Oxford OX1 3SZ, UK
| |
Collapse
|
11
|
Kamau E, Otieno JR, Lewa CS, Mwema A, Murunga N, Nokes DJ, Agoti CN. Evolution of respiratory syncytial virus genotype BA in Kilifi, Kenya, 15 years on. Sci Rep 2020; 10:21176. [PMID: 33273687 PMCID: PMC7712891 DOI: 10.1038/s41598-020-78234-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 11/20/2020] [Indexed: 01/12/2023] Open
Abstract
Respiratory syncytial virus (RSV) is recognised as a leading cause of severe acute respiratory disease and deaths among infants and vulnerable adults. Clinical RSV isolates can be divided into several known genotypes. RSV genotype BA, characterised by a 60-nucleotide duplication in the G glycoprotein gene, emerged in 1999 and quickly disseminated globally replacing other RSV group B genotypes. Continual molecular epidemiology is critical to understand the evolutionary processes maintaining the success of the BA viruses. We analysed 735 G gene sequences from samples collected from paediatric patients in Kilifi, Kenya, between 2003 and 2017. The virus population comprised of several genetically distinct variants (n = 56) co-circulating within and between epidemics. In addition, there was consistent seasonal fluctuations in relative genetic diversity. Amino acid changes increasingly accumulated over the surveillance period including two residues (N178S and Q180R) that mapped to monoclonal antibody 2D10 epitopes, as well as addition of putative N-glycosylation sequons. Further, switching and toggling of amino acids within and between epidemics was observed. On a global phylogeny, the BA viruses from different countries form geographically isolated clusters suggesting substantial localized variants. This study offers insights into longitudinal population dynamics of a globally endemic RSV genotype within a discrete location.
Collapse
Affiliation(s)
- Everlyn Kamau
- Epidemiology and Demography Department, Kenya Medical Research Institute (KEMRI) - Wellcome Trust Research Programme, Kilifi, Kenya.
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.
| | - James R Otieno
- Epidemiology and Demography Department, Kenya Medical Research Institute (KEMRI) - Wellcome Trust Research Programme, Kilifi, Kenya
- Fogarty International Center, NIH, Bethesda, MD, USA
| | - Clement S Lewa
- Epidemiology and Demography Department, Kenya Medical Research Institute (KEMRI) - Wellcome Trust Research Programme, Kilifi, Kenya
| | - Anthony Mwema
- Epidemiology and Demography Department, Kenya Medical Research Institute (KEMRI) - Wellcome Trust Research Programme, Kilifi, Kenya
| | - Nickson Murunga
- Epidemiology and Demography Department, Kenya Medical Research Institute (KEMRI) - Wellcome Trust Research Programme, Kilifi, Kenya
| | - D James Nokes
- Epidemiology and Demography Department, Kenya Medical Research Institute (KEMRI) - Wellcome Trust Research Programme, Kilifi, Kenya
- School of Life Sciences and Zeeman Institute (SBIDER), University of Warwick, Coventry, UK
| | - Charles N Agoti
- Epidemiology and Demography Department, Kenya Medical Research Institute (KEMRI) - Wellcome Trust Research Programme, Kilifi, Kenya
- School of Health and Human Sciences, Pwani University, Kilifi, Kenya
| |
Collapse
|
12
|
Abstract
OBJECTIVE We investigated the duration of HIV transmission clusters. DESIGN Fifty-four individuals newly infected at enrollment in the ALIVE cohort were included, all of whom had sequences at an intake visit (T1) and from a second (T2) and/or a third (T3) follow-up visit, median 2.9 and 5.4 years later, respectively. METHODS Sequences were generated using the 454 DNA sequencing platform for portions of HIV pol and env (HXB2 positions 2717-3230; 7941-8264). Genetic distances were calculated using tn93 and sequences were clustered over a range of thresholds (1--5%) using HIV-TRACE. Analyses were performed separately for individuals with pol sequences for T1 + T2 (n = 40, 'Set 1') and T1 + T3 (n = 25; 'Set 2'), and env sequences for T1 + T2 (n = 47, 'Set 1'), and T1 + T3 (n = 30; 'Set 2'). RESULTS For pol, with one exception, a single cluster contained more than 75% of samples at all thresholds, and cluster composition was at least 90% concordant between time points/thresholds. For env, two major clusters (A and B) were observed at T1 and T2/T3, although cluster composition concordance between time points/thresholds was low (<60%) at lower thresholds for both sets 1 and 2. In addition, several individuals were included in clusters at T2/T3, although not at T1. CONCLUSION Caution should be used in applying a single threshold in population studies where seroconversion dates are unknown. However, the retention of some clusters even after 5 + years is evidence for the robustness of the clustering approach in general.
Collapse
|
13
|
Kist NC, Lambert B, Campbell S, Katzourakis A, Lunn D, Lemey P, Iversen AKN. HIV-1 p24Gag adaptation to modern and archaic HLA-allele frequency differences in ethnic groups contributes to viral subtype diversification. Virus Evol 2020; 6:veaa085. [PMID: 33343925 PMCID: PMC7733611 DOI: 10.1093/ve/veaa085] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Pathogen-driven selection and past interbreeding with archaic human lineages have resulted in differences in human leukocyte antigen (HLA)-allele frequencies between modern human populations. Whether or not this variation affects pathogen subtype diversification is unknown. Here we show a strong positive correlation between ethnic diversity in African countries and both human immunodeficiency virus (HIV)-1 p24gag and subtype diversity. We demonstrate that ethnic HLA-allele differences between populations have influenced HIV-1 subtype diversification as the virus adapted to escape common antiviral immune responses. The evolution of HIV Subtype B (HIV-B), which does not appear to be indigenous to Africa, is strongly affected by immune responses associated with Eurasian HLA variants acquired through adaptive introgression from Neanderthals and Denisovans. Furthermore, we show that the increasing and disproportionate number of HIV-infections among African Americans in the USA drive HIV-B evolution towards an Africa-centric HIV-1 state. Similar adaptation of other pathogens to HLA variants common in affected populations is likely.
Collapse
Affiliation(s)
- Nicolaas C Kist
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
- Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK
| | - Ben Lambert
- Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, Medical School Building St Mary’s Campus, Norfolk Place, London W2 1PG, UK
| | - Samuel Campbell
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
- Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK
| | - Aris Katzourakis
- Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK
| | - Daniel Lunn
- Department of Statistics, University of Oxford, St Giles’, Oxford OX1 3LB, UK
| | - Philippe Lemey
- Department of Microbiology and Immunology, Rega Institute for Medical Research, KU Leuven - University of Leuven, Leuven B-3000, Belgium
| | - Astrid K N Iversen
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| |
Collapse
|
14
|
A de novo approach to inferring within-host fitness effects during untreated HIV-1 infection. PLoS Pathog 2020; 16:e1008171. [PMID: 32492061 PMCID: PMC7295245 DOI: 10.1371/journal.ppat.1008171] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 06/15/2020] [Accepted: 05/11/2020] [Indexed: 12/15/2022] Open
Abstract
In the absence of effective antiviral therapy, HIV-1 evolves in response to the within-host environment, of which the immune system is an important aspect. During the earliest stages of infection, this process of evolution is very rapid, driven by a small number of CTL escape mutations. As the infection progresses, immune escape variants evolve under reduced magnitudes of selection, while competition between an increasing number of polymorphic alleles (i.e., clonal interference) makes it difficult to quantify the magnitude of selection acting upon specific variant alleles. To tackle this complex problem, we developed a novel multi-locus inference method to evaluate the role of selection during the chronic stage of within-host infection. We applied this method to targeted sequence data from the p24 and gp41 regions of HIV-1 collected from 34 patients with long-term untreated HIV-1 infection. We identify a broad distribution of beneficial fitness effects during infection, with a small number of variants evolving under strong selection and very many variants evolving under weaker selection. The uniquely large number of infections analysed granted a previously unparalleled statistical power to identify loci at which selection could be inferred to act with statistical confidence. Our model makes no prior assumptions about the nature of alleles under selection, such that any synonymous or non-synonymous variant may be inferred to evolve under selection. However, the majority of variants inferred with confidence to be under selection were non-synonymous in nature, and in most cases were have previously been associated with either CTL escape in p24 or neutralising antibody escape in gp41. We also identified a putative new CTL escape site (residue 286 in gag), and a region of gp41 (including residues 644, 648, 655 in env) likely to be associated with immune escape. Sites inferred to be under selection in multiple hosts have high within-host and between-host diversity although not all sites with high between-host diversity were inferred to be under selection at the within-host level. Our identification of selection at sites associated with resistance to broadly neutralising antibodies (bNAbs) highlights the need to fully understand the role of selection in untreated individuals when designing bNAb based therapies.
Collapse
|
15
|
Johnson S, Parker M. Ethical challenges in pathogen sequencing: a systematic scoping review. Wellcome Open Res 2020; 5:119. [PMID: 32864469 PMCID: PMC7445679 DOI: 10.12688/wellcomeopenres.15806.1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/01/2020] [Indexed: 11/29/2022] Open
Abstract
Background: Going forward, the routine implementation of genomic surveillance activities and outbreak investigation is to be expected. We sought to systematically identify the emerging ethical challenges; and to systematically assess the gaps in ethical frameworks or thinking and identify where further work is needed to solve practical challenges. Methods: We systematically searched indexed academic literature from PubMed, Google Scholar, and Web of Science from 2000 to April 2019 for peer-reviewed articles that substantively engaged in discussion of ethical issues in the use of pathogen genome sequencing technologies for diagnostic, surveillance and outbreak investigation. Results: 28 articles were identified; nine United States, five United Kingdom, five The Netherlands, three Canada, two Switzerland, one Australia, two South Africa, and one Italy. Eight articles were specifically about the use of sequencing in HIV. Eleven were not specific to a particular disease. Results were organized into four themes: tensions between public and private interests; difficulties with translation from research to clinical and public health practice; the importance of community trust and support; equity and global partnerships; and the importance of context. Conclusion: While pathogen sequencing has the potential to be transformative for public health, there are a number of key ethical issues that must be addressed, particularly around the conditions of use for pathogen sequence data. Ethical standards should be informed by public values, and further empirical work investigating stakeholders' views are required. Development in the field should also be under-pinned by a strong commitment to values of justice, in particular global health equity.
Collapse
Affiliation(s)
- Stephanie Johnson
- Wellcome Centre for Ethics and Humanities and Ethox Centre, University of Oxford, Oxford, OX3 7LF, UK
| | - Michael Parker
- Wellcome Centre for Ethics and Humanities and Ethox Centre, University of Oxford, Oxford, OX3 7LF, UK
| |
Collapse
|
16
|
Lamers SL, Rose R, Cross S, Rodriguez CW, Redd AD, Quinn TC, Serwadda D, Kagaayi J, Kigozi G, Galiwango R, Gray RH, Grabowski MK, Laeyendecker O. HIV-1 Subtype Distribution and Diversity Over 18 Years in Rakai, Uganda. AIDS Res Hum Retroviruses 2020; 36:522-526. [PMID: 32281387 DOI: 10.1089/aid.2020.0062] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The Rakai Community Cohort Study in south central Uganda has surveyed people aged 15-49 since 1994. Antiretroviral therapy (ART) was introduced in 2004. HIV p24 and gp41 subtype distribution and viral diversity were studied from blood samples collected at three surveys in 1994-1995, 2002-2003, and 2008-2009, which were compared with a new survey round from 2011 to 2012. These included 1364 HIV+ individuals. For both p24 and gp41 domains, the genetic diversity within subtypes A and D was significantly increasing in the pre-ART era and decreased between the last two survey rounds in the ART era (p < .01). This study suggests that despite ongoing mixing of viral subtypes, an association with the introduction of ART to a reduction of intra-subtype viral genomic diversity may be occurring, which can be explored in ongoing studies.
Collapse
Affiliation(s)
| | | | - Sissy Cross
- BioInfoExperts LLC, Thibodaux, Louisiana, USA
| | | | - Andrew D. Redd
- Laboratory of Immunoregulation, Division of Intramural Research, NIAID, NIH, Baltimore, Maryland, USA
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Thomas C. Quinn
- Laboratory of Immunoregulation, Division of Intramural Research, NIAID, NIH, Baltimore, Maryland, USA
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - David Serwadda
- Makerere University, School of Medicine, Kampala, Uganda
- Rakai Health Sciences Program, Kalisizo, Uganda
| | | | | | | | - Ronald H. Gray
- Rakai Health Sciences Program, Kalisizo, Uganda
- Department of Epidemiology, JHSPH, Baltimore, Maryland, USA
| | - M. Kate Grabowski
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Rakai Health Sciences Program, Kalisizo, Uganda
| | - Oliver Laeyendecker
- Laboratory of Immunoregulation, Division of Intramural Research, NIAID, NIH, Baltimore, Maryland, USA
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| |
Collapse
|
17
|
Abstract
Influenza viruses rapidly diversify within individual human infections. Several recent studies have deep-sequenced clinical influenza infections to identify viral variation within hosts, but it remains unclear how within-host mutations fare at the between-host scale. Here, we compare the genetic variation of H3N2 influenza within and between hosts to link viral evolutionary dynamics across scales. Synonymous sites evolve at similar rates at both scales, indicating that global evolution at these putatively neutral sites results from the accumulation of within-host variation. However, nonsynonymous mutations are depleted between hosts compared to within hosts, suggesting that selection purges many of the protein-altering changes that arise within hosts. The exception is at antigenic sites, where selection detectably favors nonsynonymous mutations at the global scale, but not within hosts. These results suggest that selection against deleterious mutations and selection for antigenic change are the main forces that act on within-host variants of influenza virus as they transmit and circulate between hosts.
Collapse
Affiliation(s)
- Katherine S Xue
- Department of Genome Sciences, University of Washington, Foege Building S-250, Box 3550653720 15th Ave NE, Seattle WA 98195-5065, USA.,Basic Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109-1024, USA.,Department of Biology, Stanford University, Stanford, CA, USA
| | - Jesse D Bloom
- Department of Genome Sciences, University of Washington, Foege Building S-250, Box 3550653720 15th Ave NE, Seattle WA 98195-5065, USA.,Basic Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109-1024, USA.,Howard Hughes Medical Institute, 1100 Fairview Ave N, Seattle, WA 98109-1024, USA
| |
Collapse
|
18
|
Currenti J, Chopra A, John M, Leary S, McKinnon E, Alves E, Pilkinton M, Smith R, Barnett L, McDonnell WJ, Lucas M, Noel F, Mallal S, Conrad JA, Kalams SA, Gaudieri S. Deep sequence analysis of HIV adaptation following vertical transmission reveals the impact of immune pressure on the evolution of HIV. PLoS Pathog 2019; 15:e1008177. [PMID: 31821379 PMCID: PMC6924686 DOI: 10.1371/journal.ppat.1008177] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 12/20/2019] [Accepted: 10/31/2019] [Indexed: 12/25/2022] Open
Abstract
Human immunodeficiency virus (HIV) can adapt to an individual’s T cell immune response via genomic mutations that affect antigen recognition and impact disease outcome. These viral adaptations are specific to the host’s human leucocyte antigen (HLA) alleles, as these molecules determine which peptides are presented to T cells. As HLA molecules are highly polymorphic at the population level, horizontal transmission events are most commonly between HLA-mismatched donor/recipient pairs, representing new immune selection environments for the transmitted virus. In this study, we utilised a deep sequencing approach to determine the HIV quasispecies in 26 mother-to-child transmission pairs where the potential for founder viruses to be pre-adapted is high due to the pairs being haplo-identical at HLA loci. This scenario allowed the assessment of specific HIV adaptations following transmission in either a non-selective immune environment, due to recipient HLA mismatched to original selecting HLA, or a selective immune environment, mediated by matched donor/recipient HLA. We show that the pattern of reversion or fixation of HIV adaptations following transmission provides insight into the replicative cost, and likely compensatory networks, associated with specific adaptations in vivo. Furthermore, although transmitted viruses were commonly heavily pre-adapted to the child’s HLA genotype, we found evidence of de novo post-transmission adaptation, representing new epitopes targeted by the child’s T cell response. High-resolution analysis of HIV adaptation is relevant when considering vaccine and cure strategies for individuals exposed to adapted viruses via transmission or reactivated from reservoirs. Highly mutable pathogens utilise genetic variations within T cell epitopes as a mechanism of immune escape (viral adaptation). The diversity of the human leucocyte antigen (HLA) molecules that present viral targets to T cells in human populations partially protects against rapid population-level accumulation of human immunodeficiency virus (HIV) adaptations through horizontal transmissions. In contrast, vertical transmissions occur between haplo-identical mother/child pairs, and potentially include adaptive changes through father-mother-child transmission, representing a pathway to complete pre-adaptation to HLA alleles in child hosts over only two transmission events. We utilised next-generation sequencing to examine HIV evolution in the unique setting of vertical HIV transmission. We predict the in vivo replicative cost and immune benefit of specific HIV adaptations that could be used to inform vaccine design and cure strategies to combat viral immune adaptation.
Collapse
Affiliation(s)
- Jennifer Currenti
- School of Human Sciences, University of Western Australia, Crawley, Western Australia, Australia
| | - Abha Chopra
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia
| | - Mina John
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia
- Department of Clinical Immunology, Royal Perth Hospital, Perth, Western Australia, Australia
| | - Shay Leary
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia
| | - Elizabeth McKinnon
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia
| | - Eric Alves
- School of Human Sciences, University of Western Australia, Crawley, Western Australia, Australia
| | - Mark Pilkinton
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Rita Smith
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Louise Barnett
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Wyatt J. McDonnell
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Michaela Lucas
- School of Medicine, University of Western Australia, Crawley, Western Australia, Australia
| | | | - Simon Mallal
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Joseph A. Conrad
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Spyros A. Kalams
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Silvana Gaudieri
- School of Human Sciences, University of Western Australia, Crawley, Western Australia, Australia
- Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- * E-mail:
| |
Collapse
|
19
|
Distinct rates and patterns of spread of the major HIV-1 subtypes in Central and East Africa. PLoS Pathog 2019; 15:e1007976. [PMID: 31809523 PMCID: PMC6897401 DOI: 10.1371/journal.ppat.1007976] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Accepted: 07/11/2019] [Indexed: 12/21/2022] Open
Abstract
Since the ignition of the HIV-1 group M pandemic in the beginning of the 20th century, group M lineages have spread heterogeneously throughout the world. Subtype C spread rapidly through sub-Saharan Africa and is currently the dominant HIV lineage worldwide. Yet the epidemiological and evolutionary circumstances that contributed to its epidemiological expansion remain poorly understood. Here, we analyse 346 novel pol sequences from the DRC to compare the evolutionary dynamics of the main HIV-1 lineages, subtypes A1, C and D. Our results place the origins of subtype C in the 1950s in Mbuji-Mayi, the mining city of southern DRC, while subtypes A1 and D emerged in the capital city of Kinshasa, and subtypes H and J in the less accessible port city of Matadi. Following a 15-year period of local transmission in southern DRC, we find that subtype C spread at least three-fold faster than other subtypes circulating in Central and East Africa. In conclusion, our results shed light on the origins of HIV-1 main lineages and suggest that socio-historical rather than evolutionary factors may have determined the epidemiological fate of subtype C in sub-Saharan Africa.
Collapse
|
20
|
Rose R, Rodriguez C, Dollar JJ, Lamers SL, Massaccesi G, Osburn W, Ray SC, Thomas DL, Cox AL, Laeyendecker O. Inconsistent temporal patterns of genetic variation of HCV among high-risk subjects may impact inference of transmission networks. INFECTION GENETICS AND EVOLUTION 2019; 71:1-6. [PMID: 30802530 DOI: 10.1016/j.meegid.2019.02.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 02/19/2019] [Accepted: 02/21/2019] [Indexed: 01/03/2023]
Abstract
Hepatitis-C Virus (HCV) sequences are often used to establish networks of people who inject drugs (PWID). However, the degree to which within-host evolutionary dynamics affect those inferences has not been carefully studied. Here, we analyzed 702 longitudinally-sampled HCV E1 sequences from 88 HCV+ people who inject drugs (PWID) in the Baltimore Before and After Acute Study of Hepatitis (BBAASH) cohort. Individuals were tested for HCV RNA over multiple visits to the clinic, and the HCV E1 gene was sequenced for HCV+ samples. Genetic clustering was performed on the full set of sequences using a 3% genetic distance threshold to define epidemiological linkage. Maximum-likelihood (ML) phylogenies were inferred to assess evolutionary relationships. We found 22 clusters containing sequences sampled over five or more years (long-term clusters, LTC), of which 17 had >1 subject. In six of the multi-subject LTC, one subject had a sequence sampled >3 years earlier or later than the next-closest subject in the cluster (time-gap LTC). ML trees showed that, in three of the time-gap LTC, two subjects had identical sequences despite 7-10 years separating the sampling times. In four of the time-gap LTC for whom additional data were available, the subject with the later detected shared variant had both different variants and visits with no detectable HCV RNA (RNA-) prior to the appearance of the shared variant. In the subject with the earlier detection of the shared variant, different variants and RNA- visits were also detected in multiple cases subsequent to appearance of the shared variant. Complex patterns of shared viral variation among PWID reflect on-going re-infection, multiple transmission partners, and/or inconsistent detection of viral variants. Our results suggest that transmission events are currently underestimated by analysis of sequences at a single point in time.
Collapse
Affiliation(s)
- Rebecca Rose
- BioInfoExperts LLC, Thibodaux, LA, United States.
| | | | | | | | - Guido Massaccesi
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - William Osburn
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Stuart C Ray
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - David L Thomas
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Andrea L Cox
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Oliver Laeyendecker
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States; NIAID, NIH, Baltimore, MD, United States
| |
Collapse
|
21
|
Thompson RN, Wymant C, Spriggs RA, Raghwani J, Fraser C, Lythgoe KA. Link between the numbers of particles and variants founding new HIV-1 infections depends on the timing of transmission. Virus Evol 2019; 5:vey038. [PMID: 30723550 PMCID: PMC6354028 DOI: 10.1093/ve/vey038] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Understanding which HIV-1 variants are most likely to be transmitted is important for vaccine design and predicting virus evolution. Since most infections are founded by single variants, it has been suggested that selection at transmission has a key role in governing which variants are transmitted. We show that the composition of the viral population within the donor at the time of transmission is also important. To support this argument, we developed a probabilistic model describing HIV-1 transmission in an untreated population, and parameterised the model using both within-host next generation sequencing data and population-level epidemiological data on heterosexual transmission. The most basic HIV-1 transmission models cannot explain simultaneously the low probability of transmission and the non-negligible proportion of infections founded by multiple variants. In our model, transmission can only occur when environmental conditions are appropriate (e.g. abrasions are present in the genital tract of the potential recipient), allowing these observations to be reconciled. As well as reproducing features of transmission in real populations, our model demonstrates that, contrary to expectation, there is not a simple link between the number of viral variants and the number of viral particles founding each new infection. These quantities depend on the timing of transmission, and infections can be founded with small numbers of variants yet large numbers of particles. Including selection, or a bias towards early transmission (e.g. due to treatment), acts to enhance this conclusion. In addition, we find that infections initiated by multiple variants are most likely to have derived from donors with intermediate set-point viral loads, and not from individuals with high set-point viral loads as might be expected. We therefore emphasise the importance of considering viral diversity in donors, and the timings of transmissions, when trying to discern the complex factors governing single or multiple variant transmission.
Collapse
Affiliation(s)
- Robin N Thompson
- Department of Zoology, University of Oxford, South Parks Road, Oxford, UK.,Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Oxford, UK.,Christ Church, University of Oxford, St Aldates, Oxford, UK
| | - Chris Wymant
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Rebecca A Spriggs
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, UK
| | - Jayna Raghwani
- Department of Zoology, University of Oxford, South Parks Road, Oxford, UK.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Katrina A Lythgoe
- Department of Zoology, University of Oxford, South Parks Road, Oxford, UK.,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| |
Collapse
|