1
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Lee GQ, Khadka P, Gowanlock SN, Copertino DC, Duncan MC, Omondi FH, Kinloch NN, Kasule J, Kityamuweesi T, Buule P, Jamiru S, Tomusange S, Anok A, Chen Z, Jones RB, Galiwango RM, Reynolds SJ, Quinn TC, Brumme ZL, Redd AD, Prodger JL. HIV-1 subtype A1, D, and recombinant proviral genome landscapes during long-term suppressive therapy. Nat Commun 2024; 15:5480. [PMID: 38956017 PMCID: PMC11219899 DOI: 10.1038/s41467-024-48985-9] [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: 01/18/2023] [Accepted: 05/13/2024] [Indexed: 07/04/2024] Open
Abstract
The primary obstacle to curing HIV-1 is a reservoir of CD4+ cells that contain stably integrated provirus. Previous studies characterizing the proviral landscape, which have been predominantly conducted in males in the United States and Europe living with HIV-1 subtype B, have revealed that most proviruses that persist during antiretroviral therapy (ART) are defective. In contrast, less is known about proviral landscapes in females with non-B subtypes, which represents the largest group of individuals living with HIV-1. Here, we analyze genomic DNA from resting CD4+ T-cells from 16 female and seven male Ugandans with HIV-1 receiving suppressive ART (n = 23). We perform near-full-length proviral sequencing at limiting dilution to examine the proviral genetic landscape, yielding 607 HIV-1 subtype A1, D, and recombinant proviral sequences (mean 26/person). We observe that intact genomes are relatively rare and clonal expansion occurs in both intact and defective genomes. Our modification of the primers and probes of the Intact Proviral DNA Assay (IPDA), developed for subtype B, rescues intact provirus detection in Ugandan samples for which the original IPDA fails. This work will facilitate research on HIV-1 persistence and cure strategies in Africa, where the burden of HIV-1 is heaviest.
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Affiliation(s)
- Guinevere Q Lee
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY, USA.
| | - Pragya Khadka
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY, USA
| | - Sarah N Gowanlock
- Department of Microbiology and Immunology, Western University, London, ON, Canada
| | - Dennis C Copertino
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY, USA
| | - Maggie C Duncan
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
- British Columbia Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada
| | - F Harrison Omondi
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
- British Columbia Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada
| | - Natalie N Kinloch
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
- British Columbia Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada
| | | | | | - Paul Buule
- Rakai Health Sciences Program, Kalisizo, Uganda
| | | | | | - Aggrey Anok
- Rakai Health Sciences Program, Kalisizo, Uganda
| | - Zhengming Chen
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, NY, USA
| | - R Brad Jones
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY, 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 University School of Medicine, Baltimore, MD, USA
| | - Thomas C Quinn
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Zabrina L Brumme
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
- British Columbia Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada
| | - Andrew D Redd
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Jessica L Prodger
- Department of Microbiology and Immunology, Western University, London, ON, Canada
- Department of Epidemiology and Biostatistics, Western University, London, ON, Canada
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2
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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.
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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
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3
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Ssekagiri A, Jjingo D, Bbosa N, Bugembe DL, Kateete DP, Jordan IK, Kaleebu P, Ssemwanga D. HIVseqDB: a portable resource for NGS and sample metadata integration for HIV-1 drug resistance analysis. BIOINFORMATICS ADVANCES 2024; 4:vbae008. [PMID: 38312948 PMCID: PMC10834361 DOI: 10.1093/bioadv/vbae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 12/29/2023] [Accepted: 01/12/2024] [Indexed: 02/06/2024]
Abstract
Summary Human immunodeficiency virus (HIV) remains a public health threat, with drug resistance being a major concern in HIV treatment. Next-generation sequencing (NGS) is a powerful tool for identifying low-abundance drug resistance mutations (LA-DRMs) that conventional Sanger sequencing cannot reliably detect. To fully understand the significance of LA-DRMs, it is necessary to integrate NGS data with clinical and demographic data. However, freely available tools for NGS-based HIV-1 drug resistance analysis do not integrate these data. This poses a challenge in interpretation of the impact of LA-DRMs, mainly for resource-limited settings due to the shortage of bioinformatics expertise. To address this challenge, we present HIVseqDB, a portable, secure, and user-friendly resource for integrating NGS data with associated clinical and demographic data for analysis of HIV drug resistance. HIVseqDB currently supports uploading of NGS data and associated sample data, HIV-1 drug resistance data analysis, browsing of uploaded data, and browsing and visualizing of analysis results. Each function of HIVseqDB corresponds to an individual Django application. This ensures efficient incorporation of additional features with minimal effort. HIVseqDB can be deployed on various computing environments, such as on-premises high-performance computing facilities and cloud-based platforms. Availability and implementation HIVseqDB is available at https://github.com/AlfredUg/HIVseqDB. A deployed instance of HIVseqDB is available at https://hivseqdb.org.
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Affiliation(s)
- Alfred Ssekagiri
- Department of General Virology, Uganda Virus Research Institute, Entebbe 31405, Uganda
- Department of Immunology and Molecular Biology, Makerere University, Kampala 10206, Uganda
| | - Daudi Jjingo
- Department of Computer Science, Makerere University, Kampala 10207, Uganda
- African Centre of Excellence in Bioinformatics and Data Intensive Sciences, Makerere University, Kampala 10207, Uganda
| | - Nicholas Bbosa
- Medical Research Council (MRC)/Uganda Virus Research Institute (UVRI) and London School of Hygiene and Tropical Medicine (LSHTM) Uganda Research Unit, Entebbe 31405, Uganda
| | - Daniel L Bugembe
- Medical Research Council (MRC)/Uganda Virus Research Institute (UVRI) and London School of Hygiene and Tropical Medicine (LSHTM) Uganda Research Unit, Entebbe 31405, Uganda
| | - David P Kateete
- Department of Immunology and Molecular Biology, Makerere University, Kampala 10206, Uganda
| | - I King Jordan
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, United States
| | - Pontiano Kaleebu
- Department of General Virology, Uganda Virus Research Institute, Entebbe 31405, Uganda
- Medical Research Council (MRC)/Uganda Virus Research Institute (UVRI) and London School of Hygiene and Tropical Medicine (LSHTM) Uganda Research Unit, Entebbe 31405, Uganda
| | - Deogratius Ssemwanga
- Department of General Virology, Uganda Virus Research Institute, Entebbe 31405, Uganda
- Medical Research Council (MRC)/Uganda Virus Research Institute (UVRI) and London School of Hygiene and Tropical Medicine (LSHTM) Uganda Research Unit, Entebbe 31405, Uganda
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4
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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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5
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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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6
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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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7
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Yu X. Genofunc: genome annotation and identification of genome features for automated pipelining analysis of virus whole genome sequences. BMC Bioinformatics 2023; 24:218. [PMID: 37254048 DOI: 10.1186/s12859-023-05356-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 05/25/2023] [Indexed: 06/01/2023] Open
Abstract
BACKGROUND Viral genomics and epidemiology have been increasingly important tools for analysing the spread of key pathogens affecting daily lives of individuals worldwide. With the rapidly expanding scale of pathogen genome sequencing efforts for epidemics and outbreaks efficient workflows in extracting genomic information are becoming increasingly important for answering key research questions. RESULTS Here we present Genofunc, a toolkit offering a range of command line orientated functions for processing of raw virus genome sequences into aligned and annotated data ready for analysis. The tool contains functions such as genome annotation, feature extraction etc. for processing of large genomic datasets both manual or as part of pipeline such as Snakemake or Nextflow ready for down-stream phylogenetic analysis. Originally designed for a large-scale HIV sequencing project, Genofunc has been benchmarked against annotated sequence gene coordinates from the Los Alamos HIV database as validation with downstream phylogenetic analysis result comparable to past literature as case study. CONCLUSION Genofunc is implemented fully in Python and licensed under the MIT license. Source code and documentation is available at: https://github.com/xiaoyu518/genofunc .
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Affiliation(s)
- Xiaoyu Yu
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, EH9 3FL, Scotland, UK.
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8
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Bhebhe L, Moyo S, Gaseitsiwe S, Pretorius-Holme M, Yankinda EK, Manyake K, Kgathi C, Mmalane M, Lebelonyane R, Gaolathe T, Bachanas P, Ussery F, Letebele M, Makhema J, Wirth KE, Lockman S, Essex M, Novitsky V, Ragonnet-Cronin M. Epidemiological and viral characteristics of undiagnosed HIV infections in Botswana. BMC Infect Dis 2022; 22:710. [PMID: 36031617 PMCID: PMC9420270 DOI: 10.1186/s12879-022-07698-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 08/17/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
HIV-1 is endemic in Botswana. The country’s primary challenge is identifying people living with HIV who are unaware of their status. We evaluated factors associated with undiagnosed HIV infection using HIV-1 phylogenetic, behavioural, and demographic data.
Methods
As part of the Botswana Combination Prevention Project, 20% of households in 30 villages were tested for HIV and followed from 2013 to 2018. A total of 12,610 participants were enrolled, 3596 tested HIV-positive at enrolment, and 147 participants acquired HIV during the trial. Extensive socio-demographic and behavioural data were collected from participants and next-generation sequences were generated for HIV-positive cases. We compared three groups of participants: (1) those previously known to be HIV-positive at enrolment (n = 2995); (2) those newly diagnosed at enrolment (n = 601) and (3) those who tested HIV-negative at enrolment but tested HIV-positive during follow-up (n = 147). We searched for differences in demographic and behavioural factors between known and newly diagnosed group using logistic regression. We also compared the topology of each group in HIV-1 phylogenies and used a genetic diversity-based algorithm to classify infections as recent (< 1 year) or chronic (≥ 1 year).
Results
Being male (aOR = 2.23) and younger than 35 years old (aOR = 8.08) was associated with undiagnosed HIV infection (p < 0.001), as was inconsistent condom use (aOR = 1.76). Women were more likely to have undiagnosed infections if they were married, educated, and tested frequently. For men, being divorced increased their risk. The genetic diversity-based algorithm classified most incident infections as recent (75.0%), but almost none of known infections (2.0%). The estimated proportion of recent infections among new diagnoses was 37.0% (p < 0.001).
Conclusion
Our results indicate that those with undiagnosed infections are likely to be young men and women who do not use condoms consistently. Among women, several factors were predictive: being married, educated, and testing frequently increased risk. Men at risk were more difficult to delineate. A sizeable proportion of undiagnosed infections were recent based on a genetic diversity-based classifier. In the era of “test and treat all”, pre-exposure prophylaxis may be prioritized towards individuals who self-identify or who can be identified using these predictors in order to halt onward transmission in time.
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9
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Nascimento FF, Ragonnet-Cronin M, Golubchik T, Danaviah S, Derache A, Fraser C, Volz E. Evaluating whole HIV-1 genome sequence for estimation of incidence and migration in a rural South African community. Wellcome Open Res 2022. [DOI: 10.12688/wellcomeopenres.17891.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: South Africa has the largest number of people living with HIV (PLWHIV) in the world, with HIV prevalence and transmission patterns varying greatly between provinces. Transmission between regions is still poorly understood, but phylodynamics of HIV-1 evolution can reveal how many infections are attributable to contacts outside a given community. We analysed whole genome HIV-1 genetic sequences to estimate incidence and the proportion of transmissions between communities in Hlabisa, a rural South African community. Methods: We separately analysed HIV-1 for gag, pol, and env genes sampled from 2,503 PLWHIV. We estimated time-scaled phylogenies by maximum likelihood under a molecular clock model. Phylodynamic models were fitted to time-scaled trees to estimate transmission rates, effective number of infections, incidence through time, and the proportion of infections imported to Hlabisa. We also partitioned time-scaled phylogenies with significantly different distributions of coalescent times. Results: Phylodynamic analyses showed similar trends in epidemic growth rates between 1980 and 1990. Model-based estimates of incidence and effective number of infections were consistent across genes. Parameter estimates with gag were generally smaller than those estimated with pol and env. When estimating the proportions of new infections in Hlabisa from immigration or transmission from external sources, our posterior median estimates were 85% (95% credible interval (CI) = 78%–92%) for gag, 62% (CI = 40%–78%) for pol, and 77% (CI = 58%–90%) for env in 2015. Analysis of phylogenetic partitions by gene showed that most close global reference sequences clustered within a single partition. This suggests local evolving epidemics or potential unmeasured heterogeneity in the population. Conclusions: We estimated consistent epidemic dynamic trends for gag, pol and env genes using phylodynamic models. There was a high probability that new infections were not attributable to endogenous transmission within Hlabisa, suggesting high inter-connectedness between communities in rural South Africa.
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10
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Omooja J, Bbosa N, Lule DB, Nannyonjo M, Lunkuse S, Nassolo F, Nabirye SE, Suubi HN, Kaleebu P, Ssemwanga D. HIV-1 drug resistance genotyping success rates and correlates of Dried-blood spots and plasma specimen genotyping failure in a resource-limited setting. BMC Infect Dis 2022; 22:474. [PMID: 35581555 PMCID: PMC9112432 DOI: 10.1186/s12879-022-07453-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 05/03/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND HIV-1 drug resistance genotyping is critical to the monitoring of antiretroviral treatment. Data on HIV-1 genotyping success rates of different laboratory specimen types from multiple sources is still scarce. METHODS In this cross-sectional study, we determined the laboratory genotyping success rates (GSR) and assessed the correlates of genotyping failure of 6837 unpaired dried blood spot (DBS) and plasma specimens. Specimens from multiple studies in a resource-constrained setting were analysed in our laboratory between 2016 and 2019. RESULTS We noted an overall GSR of 65.7% and specific overall GSR for DBS and plasma of 49.8% and 85.9% respectively. The correlates of genotyping failure were viral load (VL) < 10,000 copies/mL (aOR 0.3 95% CI: 0.24-0.38; p < 0.0001), lack of viral load testing prior to genotyping (OR 0.85 95% CI: 0.77-0.94; p = 0.002), use of DBS specimens (aOR 0.10 95% CI: 0.08-0.14; p < 0.0001) and specimens from routine clinical diagnosis (aOR 1.4 95% CI: 1.10-1.75; p = 0.005). CONCLUSIONS We report rapidly decreasing HIV-1 genotyping success rates between 2016 and 2019 with increased use of DBS specimens for genotyping and note decreasing median viral loads over the years. We recommend improvement in DBS handling, pre-genotyping viral load testing to screen samples to enhance genotyping success and the development of more sensitive assays with well-designed primers to genotype specimens with low or undetectable viral load, especially in this era where virological suppression rates are rising due to increased antiretroviral therapy roll-out.
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Affiliation(s)
- Jonah Omooja
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine, Uganda Research Unit, Entebbe, Uganda.
| | - Nicholas Bbosa
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine, Uganda Research Unit, Entebbe, Uganda
| | - Dan Bugembe Lule
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine, Uganda Research Unit, Entebbe, Uganda
| | - Maria Nannyonjo
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine, Uganda Research Unit, Entebbe, Uganda
| | - Sandra Lunkuse
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine, Uganda Research Unit, Entebbe, Uganda
| | - Faridah Nassolo
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine, Uganda Research Unit, Entebbe, Uganda
| | - Stella Esther Nabirye
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine, Uganda Research Unit, Entebbe, Uganda
| | - Hamidah Namagembe Suubi
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine, Uganda Research Unit, Entebbe, Uganda
| | - Pontiano Kaleebu
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine, Uganda Research Unit, Entebbe, Uganda
- Uganda Virus Research Institute, Entebbe, Uganda
- London School of Hygiene and Tropical Medicine, London, UK
| | - Deogratius Ssemwanga
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine, Uganda Research Unit, Entebbe, Uganda.
- Uganda Virus Research Institute, Entebbe, Uganda.
- London School of Hygiene and Tropical Medicine, London, UK.
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11
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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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12
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Nduva GM, Otieno F, Kimani J, Wahome E, McKinnon LR, Cholette F, Majiwa M, Masika M, Mutua G, Anzala O, Graham SM, Gelmon L, Price MA, Smith AD, Bailey RC, Baele G, Lemey P, Hassan AS, Sanders EJ, Esbjörnsson J. Quantifying rates of HIV-1 flow between risk groups and geographic locations in Kenya: A country-wide phylogenetic study. Virus Evol 2022; 8:veac016. [PMID: 35356640 PMCID: PMC8962731 DOI: 10.1093/ve/veac016] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 02/23/2022] [Accepted: 03/01/2022] [Indexed: 12/14/2022] Open
Abstract
In Kenya, HIV-1 key populations including men having sex with men (MSM), people who inject drugs (PWID) and female sex workers (FSW) are thought to significantly contribute to HIV-1 transmission in the wider, mostly heterosexual (HET) HIV-1 transmission network. However, clear data on HIV-1 transmission dynamics within and between these groups are limited. We aimed to empirically quantify rates of HIV-1 flow between key populations and the HET population, as well as between different geographic regions to determine HIV-1 'hotspots' and their contribution to HIV-1 transmission in Kenya. We used maximum-likelihood phylogenetic and Bayesian inference to analyse 4058 HIV-1 pol sequences (representing 0.3 per cent of the epidemic in Kenya) sampled 1986-2019 from individuals of different risk groups and regions in Kenya. We found 89 per cent within-risk group transmission and 11 per cent mixing between risk groups, cyclic HIV-1 exchange between adjoining geographic provinces and strong evidence of HIV-1 dissemination from (i) West-to-East (i.e. higher-to-lower HIV-1 prevalence regions), and (ii) heterosexual-to-key populations. Low HIV-1 prevalence regions and key populations are sinks rather than major sources of HIV-1 transmission in Kenya. Targeting key populations in Kenya needs to occur concurrently with strengthening interventions in the general epidemic.
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Affiliation(s)
- George M Nduva
- Department of Translational Medicine, Lund University, Faculty of Medicine, Lund University, Box 117 SE-221 00 Lund, Sweden
- Kenya Medical Research Institute-Wellcome Trust Research Programme, KEMRI-Center For Geographic Medicine Research, P.O. Box 230-80108, Kilifi, Kenya
| | - Frederick Otieno
- Nyanza Reproductive Health Society, United Mall, P.O. Box 1764, Kisumu, Kenya
| | - Joshua Kimani
- Department of Medical Microbiology, University of Nairobi, P.O. Box 30197-00100, Nairobi, Kenya
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Max Rady College of Medicine, Room 543-745 Bannatyne Avenue, University of Manitoba (Bannatyne campus), Winnipeg MB R3E 0J9, Canada
| | - Elizabeth Wahome
- Kenya Medical Research Institute-Wellcome Trust Research Programme, KEMRI-Center For Geographic Medicine Research, P.O. Box 230-80108, Kilifi, Kenya
| | - Lyle R McKinnon
- Department of Medical Microbiology, University of Nairobi, P.O. Box 30197-00100, Nairobi, Kenya
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Max Rady College of Medicine, Room 543-745 Bannatyne Avenue, University of Manitoba (Bannatyne campus), Winnipeg MB R3E 0J9, Canada
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), Doris Duke Medical Research Institute, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Private Bag X7, Congella 4013, South Africa
| | - Francois Cholette
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Max Rady College of Medicine, Room 543-745 Bannatyne Avenue, University of Manitoba (Bannatyne campus), Winnipeg MB R3E 0J9, Canada
- National Microbiology Laboratory at the JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, 745 Logan Avenue, Winnipeg, Canada
| | - Maxwell Majiwa
- Kenya Medical Research Institute/Center for Global Health Research, KEMRI-CGHR, P.O. Box 20778-00202, Kisumu, Kenya
| | - Moses Masika
- Faculty of Health Sciences 3RD Floor Wing B, KAVI Institute of Clinical Research, University of Nairobi, P.O. Box 19676-00202, Nairobi, Kenya
| | - Gaudensia Mutua
- Faculty of Health Sciences 3RD Floor Wing B, KAVI Institute of Clinical Research, University of Nairobi, P.O. Box 19676-00202, Nairobi, Kenya
| | - Omu Anzala
- Faculty of Health Sciences 3RD Floor Wing B, KAVI Institute of Clinical Research, University of Nairobi, P.O. Box 19676-00202, Nairobi, Kenya
| | - Susan M Graham
- Kenya Medical Research Institute-Wellcome Trust Research Programme, KEMRI-Center For Geographic Medicine Research, P.O. Box 230-80108, Kilifi, Kenya
- Department of Epidemiology, University of Washington, Office of the Chair, UW Box # 351619, Seattle, DC, USA
| | - Larry Gelmon
- Department of Medical Microbiology, University of Nairobi, P.O. Box 30197-00100, Nairobi, Kenya
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Max Rady College of Medicine, Room 543-745 Bannatyne Avenue, University of Manitoba (Bannatyne campus), Winnipeg MB R3E 0J9, Canada
| | - Matt A Price
- IAVI Global Headquarters, 125 Broad Street, 9th Floor, New York, NY 10004, USA
- Department of Epidemiology and Biostatistics, University of California, Mission Hall: Global Health & Clinical Sciences Building, 550 16th Street, 2nd Floor, San Francisco, CA 94158-2549, USA
| | - Adrian D Smith
- Nuffield Department of Medicine, The University of Oxford, Old Road Campus, Headington, Oxford OX3 7BN, UK
| | - Robert C Bailey
- Nyanza Reproductive Health Society, United Mall, P.O. Box 1764, Kisumu, Kenya
- Division of Epidemiology and Biostatistics, University of Illinois at Chicago, 1603 W Taylor St, Chicago, IL 60612, USA
| | - Guy Baele
- KU Leuven Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Evolutionary and Computational Virology, Rega-Herestraat 49-box 1040, Leuven 3000, Belgium
| | - Philippe Lemey
- KU Leuven Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Evolutionary and Computational Virology, Rega-Herestraat 49-box 1040, Leuven 3000, Belgium
| | - Amin S Hassan
- Department of Translational Medicine, Lund University, Faculty of Medicine, Lund University, Box 117 SE-221 00 Lund, Sweden
- Kenya Medical Research Institute-Wellcome Trust Research Programme, KEMRI-Center For Geographic Medicine Research, P.O. Box 230-80108, Kilifi, Kenya
| | - Eduard J Sanders
- Kenya Medical Research Institute-Wellcome Trust Research Programme, KEMRI-Center For Geographic Medicine Research, P.O. Box 230-80108, Kilifi, Kenya
- Nuffield Department of Medicine, The University of Oxford, Old Road Campus, Headington, Oxford OX3 7BN, UK
| | - Joakim Esbjörnsson
- Department of Translational Medicine, Lund University, Faculty of Medicine, Lund University, Box 117 SE-221 00 Lund, Sweden
- Nuffield Department of Medicine, The University of Oxford, Old Road Campus, Headington, Oxford OX3 7BN, UK
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13
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Geanta M, Tanwar AS, Lehrach H, Satyamoorthy K, Brand A. Horizon Scanning: Rise of Planetary Health Genomics and Digital Twins for Pandemic Preparedness. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:93-100. [PMID: 34851750 DOI: 10.1089/omi.2021.0062] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The Covid-19 pandemic accelerated research and development not only in infectious diseases but also in digital technologies to improve monitoring, forecasting, and intervening on planetary and ecological risks. In the European Commission, the Destination Earth (DestinE) is a current major initiative to develop a digital model of the Earth (a "digital twin") with high precision. Moreover, omics systems science is undergoing digital transformation impacting nearly all dimensions of the field, including real-time phenotype capture to data analytics using machine learning and artificial intelligence, to name but a few emerging frontiers. We discuss the ways in which the current ongoing digital transformation in omics offers synergies with digital twins/DestinE. Importantly, we note here the rise of a new field of scholarship, planetary health genomics. We conclude that digital transformation in public and private sectors, digital twins/DestinE, and their convergence with omics systems science are poised to build robust capacities for pandemic preparedness and resilient societies in the 21st century.
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Affiliation(s)
- Marius Geanta
- Centre for Innovation in Medicine, Bucharest, Romania
- KOL Medical Media, Bucharest, Romania
- United Nations University-Maastricht Economic and Social Research Institute on Innovation and Technology, Maastricht, The Netherlands
| | - Ankit Singh Tanwar
- United Nations University-Maastricht Economic and Social Research Institute on Innovation and Technology, Maastricht, The Netherlands
- Department of Public Health Genomics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Hans Lehrach
- Max Planck Institute for Molecular Genetics (MPIMG), Berlin, Germany
- Alacris Theranostics GmbH, Berlin, Germany
| | - Kapaettu Satyamoorthy
- Department of Cell and Molecular Biology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Angela Brand
- United Nations University-Maastricht Economic and Social Research Institute on Innovation and Technology, Maastricht, The Netherlands
- Department of Public Health Genomics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
- Dr. TMA Pai Endowment Chair in Public Health Genomics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India
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14
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Limnaios S, Kostaki EG, Adamis G, Astriti M, Chini M, Mangafas N, Lazanas M, Patrinos S, Metallidis S, Tsachouridou O, Papastamopoulos V, Kakalou E, Chatzidimitriou D, Antoniadou A, Papadopoulos A, Psichogiou M, Basoulis D, Gova M, Pilalas D, Paraskeva D, Chrysos G, Paparizos V, Kourkounti S, Sambatakou H, Bolanos V, Sipsas NV, Lada M, Barbounakis E, Kantzilaki E, Panagopoulos P, Maltezos E, Drimis S, Sypsa V, Lagiou P, Magiorkinis G, Hatzakis A, Skoura L, Paraskevis D. Dating the Origin and Estimating the Transmission Rates of the Major HIV-1 Clusters in Greece: Evidence about the Earliest Subtype A1 Epidemic in Europe. Viruses 2022; 14:v14010101. [PMID: 35062305 PMCID: PMC8782043 DOI: 10.3390/v14010101] [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: 10/28/2021] [Revised: 12/20/2021] [Accepted: 12/30/2021] [Indexed: 12/16/2022] Open
Abstract
Our aim was to estimate the date of the origin and the transmission rates of the major local clusters of subtypes A1 and B in Greece. Phylodynamic analyses were conducted in 14 subtype A1 and 31 subtype B clusters. The earliest dates of origin for subtypes A1 and B were in 1982.6 and in 1985.5, respectively. The transmission rate for the subtype A1 clusters ranged between 7.54 and 39.61 infections/100 person years (IQR: 9.39, 15.88), and for subtype B clusters between 4.42 and 36.44 infections/100 person years (IQR: 7.38, 15.04). Statistical analysis revealed that the average difference in the transmission rate between the PWID and the MSM clusters was 6.73 (95% CI: 0.86 to 12.60; p = 0.026). Our study provides evidence that the date of introduction of subtype A1 in Greece was the earliest in Europe. Transmission rates were significantly higher for PWID than MSM clusters due to the conditions that gave rise to an extensive PWID HIV-1 outbreak ten years ago in Athens, Greece. Transmission rate can be considered as a valuable measure for public health since it provides a proxy of the rate of epidemic growth within a cluster and, therefore, it can be useful for targeted HIV prevention programs.
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Affiliation(s)
- Stefanos Limnaios
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (S.L.); (E.G.K.); (M.G.); (V.S.); (P.L.); (G.M.); (A.H.)
| | - Evangelia Georgia Kostaki
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (S.L.); (E.G.K.); (M.G.); (V.S.); (P.L.); (G.M.); (A.H.)
| | - Georgios Adamis
- 1st Department of Internal Medicine, G. Gennimatas General Hospital, 11527 Athens, Greece; (G.A.); (M.A.)
| | - Myrto Astriti
- 1st Department of Internal Medicine, G. Gennimatas General Hospital, 11527 Athens, Greece; (G.A.); (M.A.)
| | - Maria Chini
- 3rd Department of Internal Medicine-Infectious Diseases Unit, “Korgialeneio-Benakeio” Red Cross General Hospital, 11526 Athens, Greece; (M.C.); (N.M.); (M.L.)
| | - Nikos Mangafas
- 3rd Department of Internal Medicine-Infectious Diseases Unit, “Korgialeneio-Benakeio” Red Cross General Hospital, 11526 Athens, Greece; (M.C.); (N.M.); (M.L.)
| | - Marios Lazanas
- 3rd Department of Internal Medicine-Infectious Diseases Unit, “Korgialeneio-Benakeio” Red Cross General Hospital, 11526 Athens, Greece; (M.C.); (N.M.); (M.L.)
| | | | - Simeon Metallidis
- 1st Department of Internal Medicine, AHEPA University Hospital, Medical School, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece; (S.M.); (O.T.)
| | - Olga Tsachouridou
- 1st Department of Internal Medicine, AHEPA University Hospital, Medical School, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece; (S.M.); (O.T.)
| | - Vasileios Papastamopoulos
- 5th Department of Internal Medicine and Infectious Diseases, Evaggelismos General Hospital, 10676 Athens, Greece; (V.P.); (E.K.)
| | - Eleni Kakalou
- 5th Department of Internal Medicine and Infectious Diseases, Evaggelismos General Hospital, 10676 Athens, Greece; (V.P.); (E.K.)
| | - Dimitrios Chatzidimitriou
- National AIDS Reference Centre of Northern Greece, Department of Microbiology, Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (D.C.); (L.S.)
| | - Anastasia Antoniadou
- 4th Department of Medicine, Attikon University General Hospital, Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece; (A.A.); (A.P.)
| | - Antonios Papadopoulos
- 4th Department of Medicine, Attikon University General Hospital, Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece; (A.A.); (A.P.)
| | - Mina Psichogiou
- 1st Department of Medicine, Laikon General Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (M.P.); (D.B.)
| | - Dimitrios Basoulis
- 1st Department of Medicine, Laikon General Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (M.P.); (D.B.)
| | - Maria Gova
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (S.L.); (E.G.K.); (M.G.); (V.S.); (P.L.); (G.M.); (A.H.)
| | - Dimitrios Pilalas
- Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;
| | - Dimitra Paraskeva
- Department of Internal Medicine, Tzaneio General Hospital, 18536 Piraeus, Greece; (D.P.); (G.C.); (S.D.)
| | - Georgios Chrysos
- Department of Internal Medicine, Tzaneio General Hospital, 18536 Piraeus, Greece; (D.P.); (G.C.); (S.D.)
| | - Vasileios Paparizos
- HIV/AIDS Unit, A. Syngros Hospital of Dermatology and Venereology, 16121 Athens, Greece; (V.P.); (S.K.)
| | - Sofia Kourkounti
- HIV/AIDS Unit, A. Syngros Hospital of Dermatology and Venereology, 16121 Athens, Greece; (V.P.); (S.K.)
| | - Helen Sambatakou
- HIV Unit, 2nd Department of Internal Medicine, Hippokration General Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (H.S.); (V.B.)
| | - Vasileios Bolanos
- HIV Unit, 2nd Department of Internal Medicine, Hippokration General Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (H.S.); (V.B.)
| | - Nikolaos V. Sipsas
- Department of Pathophysiology, Laikon General Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece;
| | - Malvina Lada
- 2nd Department of Internal Medicine, Sismanogleion General Hospital, 15126 Marousi, Greece;
| | - Emmanouil Barbounakis
- Department of Internal Medicine, University Hospital of Heraklion “PAGNI”, Medical School, University of Crete, 71110 Heraklion, Greece; (E.B.); (E.K.)
| | - Evrikleia Kantzilaki
- Department of Internal Medicine, University Hospital of Heraklion “PAGNI”, Medical School, University of Crete, 71110 Heraklion, Greece; (E.B.); (E.K.)
| | - Periklis Panagopoulos
- Department of Internal Medicine, University General Hospital, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (P.P.); (E.M.)
| | - Efstratios Maltezos
- Department of Internal Medicine, University General Hospital, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (P.P.); (E.M.)
| | - Stelios Drimis
- Department of Internal Medicine, Tzaneio General Hospital, 18536 Piraeus, Greece; (D.P.); (G.C.); (S.D.)
| | - Vana Sypsa
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (S.L.); (E.G.K.); (M.G.); (V.S.); (P.L.); (G.M.); (A.H.)
| | - Pagona Lagiou
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (S.L.); (E.G.K.); (M.G.); (V.S.); (P.L.); (G.M.); (A.H.)
| | - Gkikas Magiorkinis
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (S.L.); (E.G.K.); (M.G.); (V.S.); (P.L.); (G.M.); (A.H.)
| | - Angelos Hatzakis
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (S.L.); (E.G.K.); (M.G.); (V.S.); (P.L.); (G.M.); (A.H.)
| | - Lemonia Skoura
- National AIDS Reference Centre of Northern Greece, Department of Microbiology, Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (D.C.); (L.S.)
| | - Dimitrios Paraskevis
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (S.L.); (E.G.K.); (M.G.); (V.S.); (P.L.); (G.M.); (A.H.)
- Correspondence:
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15
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An M, Zheng C, Li H, Chen L, Yang Z, Gan Y, Han X, Zhao J, Shang H. Independent epidemic patterns of HIV-1 CRF01_AE lineages driven by mobile population in Shenzhen, an immigrant city of China. Virus Evol 2021; 7:veab094. [PMID: 35299786 PMCID: PMC8923236 DOI: 10.1093/ve/veab094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 10/19/2021] [Accepted: 11/08/2021] [Indexed: 12/14/2022] Open
Abstract
Abstract
Shenzhen, a city with >12 million migrant population, may play a key role in the spread of human immunodeficiency virus (HIV)-1 in China. The transmission dynamics of CRF01_AE, a predominant subtype in Shenzhen, is a good model to characterize the impact of human mobility on HIV-1 epidemic locally and nationally. We used phylodynamic and phylogeographic methods to estimate the viral transmission dynamics and migration trajectory of variable lineages based on 1,423 CRF01_AE sequences in Shenzhen sampled between 2006 and 2015. Eleven lineages of CRF01_AE were detected in Shenzhen. Of those, four main lineages originated during the 1990s. Their basic viral reproduction number (R0) ranged 1.96–3.92. The effective viral reproduction number (Re) of two lineages prevalent among heterosexuals/people who inject drugs had reduced <1 at the end of sampling, and the main sources were the intra-provincial immigrants (72 per cent) for one and local residents of Shenzhen (91 per cent) for another. Within two lineages among men who have sex with men (MSM), Re had been above or close to 1 at the end of sampling, and the immigrants from Jiangxi/Shaanxi and Hubei as sources accounted for 93 per cent and 68 per cent of all viral migration events, respectively. Moreover, no obvious recipients were found throughout the viral migration history for any lineage. Our findings demonstrate that HIV epidemic is declining in Shenzhen, which coincided with the initiation of the interventions during the 2000s. However, the obvious differences of the epidemic patterns between lineages emphasize the importance of further targeting interventions and continued molecular tracing, focusing on high-risk transmission sources among MSM.
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Affiliation(s)
- Minghui An
- NHC Key Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, No 155, Nanjing North Street, Shenyang, Liaoning 110001, China
- Key Laboratory of AIDS Immunology, Chinese Academy of Medical Sciences, No 155, Nanjing North Street, Shenyang, Liaoning 110001, China
| | - Chenli Zheng
- Shenzhen Center for Disease Control and Prevention, No 8, Longyuan Road, Shenzhen, Guangdong 518055, China
| | - Hao Li
- Shenzhen Center for Disease Control and Prevention, No 8, Longyuan Road, Shenzhen, Guangdong 518055, China
| | - Lin Chen
- Shenzhen Center for Disease Control and Prevention, No 8, Longyuan Road, Shenzhen, Guangdong 518055, China
| | - Zhengrong Yang
- Shenzhen Center for Disease Control and Prevention, No 8, Longyuan Road, Shenzhen, Guangdong 518055, China
| | - Yongxia Gan
- Shenzhen Center for Disease Control and Prevention, No 8, Longyuan Road, Shenzhen, Guangdong 518055, China
| | - Xiaoxu Han
- NHC Key Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, No 155, Nanjing North Street, Shenyang, Liaoning 110001, China
- Key Laboratory of AIDS Immunology, Chinese Academy of Medical Sciences, No 155, Nanjing North Street, Shenyang, Liaoning 110001, China
| | - Jin Zhao
- Shenzhen Center for Disease Control and Prevention, No 8, Longyuan Road, Shenzhen, Guangdong 518055, China
| | - Hong Shang
- NHC Key Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, No 155, Nanjing North Street, Shenyang, Liaoning 110001, China
- Key Laboratory of AIDS Immunology, Chinese Academy of Medical Sciences, No 155, Nanjing North Street, Shenyang, Liaoning 110001, China
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16
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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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17
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Nduva GM, Nazziwa J, Hassan AS, Sanders EJ, Esbjörnsson J. The Role of Phylogenetics in Discerning HIV-1 Mixing among Vulnerable Populations and Geographic Regions in Sub-Saharan Africa: A Systematic Review. Viruses 2021; 13:1174. [PMID: 34205246 PMCID: PMC8235305 DOI: 10.3390/v13061174] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/09/2021] [Accepted: 06/10/2021] [Indexed: 12/19/2022] Open
Abstract
To reduce global HIV-1 incidence, there is a need to understand and disentangle HIV-1 transmission dynamics and to determine the geographic areas and populations that act as hubs or drivers of HIV-1 spread. In Sub-Saharan Africa (sSA), the region with the highest HIV-1 burden, information about such transmission dynamics is sparse. Phylogenetic inference is a powerful method for the study of HIV-1 transmission networks and source attribution. In this review, we assessed available phylogenetic data on mixing between HIV-1 hotspots (geographic areas and populations with high HIV-1 incidence and prevalence) and areas or populations with lower HIV-1 burden in sSA. We searched PubMed and identified and reviewed 64 studies on HIV-1 transmission dynamics within and between risk groups and geographic locations in sSA (published 1995-2021). We describe HIV-1 transmission from both a geographic and a risk group perspective in sSA. Finally, we discuss the challenges facing phylogenetic inference in mixed epidemics in sSA and offer our perspectives and potential solutions to the identified challenges.
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Affiliation(s)
- George M. Nduva
- Department of Translational Medicine, Lund University, 205 02 Malmö, Sweden; (G.M.N.); (J.N.); (A.S.H.)
- Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Kilifi 80108, Kenya;
| | - Jamirah Nazziwa
- Department of Translational Medicine, Lund University, 205 02 Malmö, Sweden; (G.M.N.); (J.N.); (A.S.H.)
| | - Amin S. Hassan
- Department of Translational Medicine, Lund University, 205 02 Malmö, Sweden; (G.M.N.); (J.N.); (A.S.H.)
- Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Kilifi 80108, Kenya;
| | - Eduard J. Sanders
- Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Kilifi 80108, Kenya;
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, The University of Oxford, Oxford OX1 2JD, UK
| | - Joakim Esbjörnsson
- Department of Translational Medicine, Lund University, 205 02 Malmö, Sweden; (G.M.N.); (J.N.); (A.S.H.)
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, The University of Oxford, Oxford OX1 2JD, UK
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18
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Ragonnet-Cronin M, Golubchik T, Moyo S, Fraser C, Essex M, Novitsky V, Volz E. HIV genetic diversity informs stage of HIV-1 infection among patients receiving antiretroviral therapy in Botswana. J Infect Dis 2021; 225:1330-1338. [PMID: 34077517 PMCID: PMC9016439 DOI: 10.1093/infdis/jiab293] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 06/01/2021] [Indexed: 12/20/2022] Open
Abstract
Background Human immunodeficiency virus (HIV)-1 genetic diversity increases during infection and can help infer the time elapsed since infection. However, the effect of antiretroviral treatment (ART) on the inference remains unknown. Methods Participants with estimated duration of HIV-1 infection based on repeated testing were sourced from cohorts in Botswana (n = 1944). Full-length HIV genome sequencing was performed from proviral deoxyribonucleic acid. We optimized a machine learning model to classify infections as < or >1 year based on viral genetic diversity, demographic, and clinical data. Results The best predictive model included variables for genetic diversity of HIV-1 gag, pol, and env, viral load, age, sex, and ART status. Most participants were on ART. Balanced accuracy was 90.6% (95% confidence interval, 86.7%–94.1%). We tested the algorithm among newly diagnosed participants with or without documented negative HIV tests. Among those without records, those who self-reported a negative HIV test within <1 year were more frequently classified as recent than those who reported a test >1 year previously. There was no difference in classification between those self-reporting a negative HIV test <1 year, whether or not they had a record. Conclusions These results indicate that recency of HIV-1 infection can be inferred from viral sequence diversity even among patients on suppressive ART.
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Affiliation(s)
- Manon Ragonnet-Cronin
- MRC Centre for Global Infectious Diseases Analysis, Imperial College London, London W2 1PG, UK
| | - Tanya Golubchik
- Big Data Institute, University of Oxford, Oxford OX3 7LF, UK
| | | | | | - Max Essex
- Botswana Harvard AIDS Initiative, Gaborone, Botswana.,Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA FXB 402, USA
| | - Vlad Novitsky
- Botswana Harvard AIDS Initiative, Gaborone, Botswana.,Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA FXB 402, USA.,Brown University, Providence RI 02912, USA
| | - Erik Volz
- MRC Centre for Global Infectious Diseases Analysis, Imperial College London, London W2 1PG, UK
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19
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Valdano E, Okano JT, Colizza V, Mitonga HK, Blower S. Using mobile phone data to reveal risk flow networks underlying the HIV epidemic in Namibia. Nat Commun 2021; 12:2837. [PMID: 33990578 PMCID: PMC8121904 DOI: 10.1038/s41467-021-23051-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 04/08/2021] [Indexed: 12/22/2022] Open
Abstract
Twenty-six million people are living with HIV in sub-Saharan Africa; epidemics are widely dispersed, due to high levels of mobility. However, global elimination strategies do not consider mobility. We use Call Detail Records from 9 billion calls/texts to model mobility in Namibia; we quantify the epidemic-level impact by using a mathematical framework based on spatial networks. We find complex networks of risk flows dispersed risk countrywide: increasing the risk of acquiring HIV in some areas, decreasing it in others. Overall, 40% of risk was mobility-driven. Networks contained multiple risk hubs. All constituencies (administrative units) imported and exported risk, to varying degrees. A few exported very high levels of risk: their residents infected many residents of other constituencies. Notably, prevalence in the constituency exporting the most risk was below average. Large-scale networks of mobility-driven risk flows underlie generalized HIV epidemics in sub-Saharan Africa. In order to eliminate HIV, it is likely to become increasingly important to implement innovative control strategies that focus on disrupting risk flows.
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Affiliation(s)
- Eugenio Valdano
- Center for Biomedical Modeling, The Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Justin T Okano
- Center for Biomedical Modeling, The Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique, IPLESP, Paris, France
| | - Honore K Mitonga
- Department of Epidemiology and Biostatistics, School of Public Health, University of Namibia, Windhoek, Namibia
| | - Sally Blower
- Center for Biomedical Modeling, The Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
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20
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Genomic-informed pathogen surveillance in Africa: opportunities and challenges. THE LANCET. INFECTIOUS DISEASES 2021; 21:e281-e289. [PMID: 33587898 PMCID: PMC7906676 DOI: 10.1016/s1473-3099(20)30939-7] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/18/2020] [Accepted: 11/25/2020] [Indexed: 12/14/2022]
Abstract
The ongoing COVID-19 pandemic has highlighted the need to incorporate pathogen genomics for enhanced disease surveillance and outbreak management in Africa. The genomics of SARS-CoV-2 has been instrumental to the timely development of diagnostics and vaccines and in elucidating transmission dynamics. Global disease control programmes, including those for tuberculosis, malaria, HIV, foodborne pathogens, and antimicrobial resistance, also recommend genomics-based surveillance as an integral strategy towards control and elimination of these diseases. Despite the potential benefits, capacity remains low for many public health programmes in Africa. The COVID-19 pandemic presents an opportunity to reassess and strengthen surveillance systems and potentially integrate emerging technologies for preparedness of future epidemics and control of endemic diseases. We discuss opportunities and challenges for integrating pathogen genomics into public health surveillance systems in Africa. Improving accessibility through the creation of functional continent-wide networks, building multipathogen sequencing cores, training a critical mass of local experts, development of standards and policies to facilitate best practices for data sharing, and establishing a community of practice of genomics experts are all needed to use genomics for improved disease surveillance in Africa. Coordination and leadership are also crucial, which the Africa Centres for Disease Control and Prevention seeks to provide through its institute for pathogen genomics.
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21
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Knyazev S, Hughes L, Skums P, Zelikovsky A. Epidemiological data analysis of viral quasispecies in the next-generation sequencing era. Brief Bioinform 2021; 22:96-108. [PMID: 32568371 PMCID: PMC8485218 DOI: 10.1093/bib/bbaa101] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 04/24/2020] [Accepted: 05/04/2020] [Indexed: 01/04/2023] Open
Abstract
The unprecedented coverage offered by next-generation sequencing (NGS) technology has facilitated the assessment of the population complexity of intra-host RNA viral populations at an unprecedented level of detail. Consequently, analysis of NGS datasets could be used to extract and infer crucial epidemiological and biomedical information on the levels of both infected individuals and susceptible populations, thus enabling the development of more effective prevention strategies and antiviral therapeutics. Such information includes drug resistance, infection stage, transmission clusters and structures of transmission networks. However, NGS data require sophisticated analysis dealing with millions of error-prone short reads per patient. Prior to the NGS era, epidemiological and phylogenetic analyses were geared toward Sanger sequencing technology; now, they must be redesigned to handle the large-scale NGS datasets and properly model the evolution of heterogeneous rapidly mutating viral populations. Additionally, dedicated epidemiological surveillance systems require big data analytics to handle millions of reads obtained from thousands of patients for rapid outbreak investigation and management. We survey bioinformatics tools analyzing NGS data for (i) characterization of intra-host viral population complexity including single nucleotide variant and haplotype calling; (ii) downstream epidemiological analysis and inference of drug-resistant mutations, age of infection and linkage between patients; and (iii) data collection and analytics in surveillance systems for fast response and control of outbreaks.
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22
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Novitsky V, Zahralban-Steele M, Moyo S, Nkhisang T, Maruapula D, McLane MF, Leidner J, Bennett K, Wirth KE, Gaolathe T, Kadima E, Chakalisa U, Pretorius Holme M, Lockman S, Mmalane M, Makhema J, Gaseitsiwe S, DeGruttola V, Essex M. Mapping of HIV-1C Transmission Networks Reveals Extensive Spread of Viral Lineages Across Villages in Botswana Treatment-as-Prevention Trial. J Infect Dis 2020; 222:1670-1680. [PMID: 32492145 PMCID: PMC7936922 DOI: 10.1093/infdis/jiaa276] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 05/26/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Phylogenetic mapping of HIV-1 lineages circulating across defined geographical locations is promising for better understanding HIV transmission networks to design optimal prevention interventions. METHODS We obtained near full-length HIV-1 genome sequences from people living with HIV (PLWH), including participants on antiretroviral treatment in the Botswana Combination Prevention Project, conducted in 30 Botswana communities in 2013-2018. Phylogenetic relationships among viral sequences were estimated by maximum likelihood. RESULTS We obtained 6078 near full-length HIV-1C genome sequences from 6075 PLWH. We identified 984 phylogenetically distinct HIV-1 lineages (molecular HIV clusters) circulating in Botswana by mid-2018, with 2-27 members per cluster. Of these, dyads accounted for 62%, approximately 32% (n = 316) were found in single communities, and 68% (n = 668) were spread across multiple communities. Men in clusters were approximately 3 years older than women (median age 42 years, vs 39 years; P < .0001). In 65% of clusters, men were older than women, while in 35% of clusters women were older than men. The majority of identified viral lineages were spread across multiple communities. CONCLUSIONS A large number of circulating phylogenetically distinct HIV-1C lineages (molecular HIV clusters) suggests highly diversified HIV transmission networks across Botswana communities by 2018.
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Affiliation(s)
- Vlad Novitsky
- Botswana Harvard AIDS Institute, Gaborone, Botswana
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Melissa Zahralban-Steele
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Sikhulile Moyo
- Botswana Harvard AIDS Institute, Gaborone, Botswana
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Tapiwa Nkhisang
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | | | - Mary Fran McLane
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Jean Leidner
- Goodtables Data Consulting LLC, Norman, Oklahoma, USA
| | - Kara Bennett
- Bennett Statistical Consulting Inc, Ballston Lake, New York, USA
| | - Kathleen E Wirth
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | | | | | | | - Molly Pretorius Holme
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Shahin Lockman
- Botswana Harvard AIDS Institute, Gaborone, Botswana
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Medicine, Division of Infectious Diseases, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | | | - Joseph Makhema
- Botswana Harvard AIDS Institute, Gaborone, Botswana
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Simani Gaseitsiwe
- Botswana Harvard AIDS Institute, Gaborone, Botswana
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Victor DeGruttola
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - M Essex
- Botswana Harvard AIDS Institute, Gaborone, Botswana
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
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A Nationwide Study about the Dispersal Patterns of the Predominant HIV-1 Subtypes A1 and B in Greece: Inference of the Molecular Transmission Clusters. Viruses 2020; 12:v12101183. [PMID: 33086773 PMCID: PMC7589601 DOI: 10.3390/v12101183] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 01/22/2023] Open
Abstract
Our aim was to investigate the dispersal patterns and parameters associated with local molecular transmission clusters (MTCs) of subtypes A1 and B in Greece (predominant HIV-1 subtypes). The analysis focused on 1751 (28.4%) and 2575 (41.8%) sequences of subtype A1 and B, respectively. Identification of MTCs was based on phylogenetic analysis. The analyses identified 38 MTCs including 2–1518 subtype A1 sequences and 168 MTCs in the range of 2–218 subtype B sequences. The proportion of sequences within MTCs was 93.8% (1642/1751) and 77.0% (1982/2575) for subtype A1 and B, respectively. Transmissions within MTCs for subtype A1 were associated with risk group (Men having Sex with Men vs. heterosexuals, OR = 5.34, p < 0.001) and Greek origin (Greek vs. non-Greek origin, OR = 6.05, p < 0.001) and for subtype B, they were associated with Greek origin (Greek vs. non-Greek origin, OR = 1.57, p = 0.019), younger age (OR = 0.96, p < 0.001), and more recent sampling (time period: 2011–2015 vs. 1999–2005, OR = 3.83, p < 0.001). Our findings about the patterns of across and within country dispersal as well as the parameters associated with transmission within MTCs provide a framework for the application of the study of molecular clusters for HIV prevention.
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Phylogenetic and Demographic Characterization of Directed HIV-1 Transmission Using Deep Sequences from High-Risk and General Population Cohorts/Groups in Uganda. Viruses 2020; 12:v12030331. [PMID: 32197553 PMCID: PMC7150763 DOI: 10.3390/v12030331] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 03/13/2020] [Accepted: 03/16/2020] [Indexed: 12/12/2022] Open
Abstract
Across sub-Saharan Africa, key populations with elevated HIV-1 incidence and/or prevalence have been identified, but their contribution to disease spread remains unclear. We performed viral deep-sequence phylogenetic analyses to quantify transmission dynamics between the general population (GP), fisherfolk communities (FF), and women at high risk of infection and their clients (WHR) in central and southwestern Uganda. Between August 2014 and August 2017, 6185 HIV-1 positive individuals were enrolled in 3 GP and 10 FF communities, 3 WHR enrollment sites. A total of 2531 antiretroviral therapy (ART) naïve participants with plasma viral load >1000 copies/mL were deep-sequenced. One hundred and twenty-three transmission networks were reconstructed, including 105 phylogenetically highly supported source–recipient pairs. Only one pair involved a WHR and male participant, suggesting that improved population sampling is needed to assess empirically the role of WHR to the transmission dynamics. More transmissions were observed from the GP communities to FF communities than vice versa, with an estimated flow ratio of 1.56 (95% CrI 0.68–3.72), indicating that fishing communities on Lake Victoria are not a net source of transmission flow to neighboring communities further inland. Men contributed disproportionally to HIV-1 transmission flow regardless of age, suggesting that prevention efforts need to better aid men to engage with and stay in care.
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25
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Okano JT, Sharp K, Valdano E, Palk L, Blower S. HIV transmission and source-sink dynamics in sub-Saharan Africa. Lancet HIV 2020; 7:e209-e214. [PMID: 32066532 DOI: 10.1016/s2352-3018(19)30407-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Revised: 10/17/2019] [Accepted: 10/31/2019] [Indexed: 12/01/2022]
Abstract
Multiple phylogenetic studies of HIV in sub-Saharan Africa have shown that mobility-driven transmission frequently occurs: many communities export and import strains. Mobility-driven transmission can result in source-sink dynamics: one community can sustain a micro-epidemic in another community in which transmission is too low to be self-sustaining. In epidemiology, the basic reproduction number (R0) is used to specify the sustainability threshold. R0 represents the average number of secondary infections generated by one infected individual in a community in which everyone is susceptible. If R0 is greater than 1, transmission is high enough to sustain an epidemic; if R0 is less than 1, it is not. Here, we discuss the conditions that are needed (in terms of R0) for source-sink transmission dynamics to occur in generalised HIV epidemics in sub-Saharan Africa, present an example of where these conditions could occur (ie, Namibia), and discuss the necessity of considering mobility-driven transmission when designing control strategies. Additionally, we discuss the need for a new generation of HIV transmission models that are more realistic than the current models. The new models should reflect not only geographical variation in epidemiology and demography, but also the spatial-temporal complexity of population-level movement patterns.
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Affiliation(s)
- Justin T Okano
- Center for Biomedical Modeling, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Katie Sharp
- Center for Biomedical Modeling, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Eugenio Valdano
- Center for Biomedical Modeling, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Laurence Palk
- Center for Biomedical Modeling, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Sally Blower
- Center for Biomedical Modeling, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
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26
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Blower S, Okano JT. Precision public health and HIV in Africa. THE LANCET. INFECTIOUS DISEASES 2019; 19:1050-1052. [PMID: 31559952 PMCID: PMC7269130 DOI: 10.1016/s1473-3099(19)30474-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 07/30/2019] [Indexed: 01/16/2023]
Affiliation(s)
- Sally Blower
- Center for Biomedical Modeling, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA.
| | - Justin T Okano
- Center for Biomedical Modeling, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
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