Yinfeng Zhang1, Chris Wymant2, Oliver Laeyendecker3, M Kathryn Grabowski1, Matthew Hall2, Sarah Hudelson1, Estelle Piwowar-Manning1, Marybeth McCauley4, Theresa Gamble5, Mina C Hosseinipour6,7, Nagalingeswaran Kumarasamy8, James G Hakim9, Johnstone Kumwenda10, Lisa A Mills11, Breno R Santos12, Beatriz Grinsztejn13, Jose H Pilotto14, Suwat Chariyalertsak15, Joseph Makhema16, Ying Q Chen17, Myron S Cohen18, Christophe Fraser2, Susan H Eshleman1. 1. Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 2. Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom. 3. Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 4. HIV Prevention Trials Network Leadership and Operations Center, FHI, Washington, District of Columbia, USA. 5. HIV Prevention Trials Network Leadership and Operations Center, FHI, Durham, North Carolina, USA. 6. Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. 7. University of North Carolina Project-Malawi, Institute for Global Health and Infectious Diseases, Lilongwe, Malawi. 8. Chennai Antiviral Research and Treatment Clinical Research Site, Infectious Diseases Medical Centre, Voluntary Health Services, Chennai, India. 9. Department of Medicine, University of Zimbabwe, Harare, Zimbabwe. 10. College of Medicine-Johns Hopkins Project, Blantyre, Malawi. 11. US Centers for Disease Control and Prevention, HIV Research Branch, Kisumu, Kenya. 12. Department of Infectious Diseases, Hospital Nossa Senhora da Conceição, Porto Alegre, Brazil. 13. Instituto Nacional de Infectologia Evandro Chagas -Fiocruz, Rio de Janeiro, Brazil. 14. Hospital Geral de Nova Iguacu and Laboratorio de AIDS e Imunologia Molecular-Instituto Oswaldo Cruz/Fiocruz, Rio de Janeiro, Brazil. 15. Research Institute for Health Sciences, Chiang Mai University, Chiang Mai, Thailand. 16. Botswana-Harvard AIDS Institute Partnership, Gaborone, Botswana. 17. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. 18. Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Abstract
BACKGROUND: Phylogenetic analysis can be used to assess human immunodeficiency virus (HIV) transmission in populations. We inferred the direction of HIV transmission using whole-genome HIV sequences from couples with known linked infection and known transmission direction. METHODS: Complete next-generation sequencing (NGS) data were obtained for 105 unique index-partner sample pairs from 32 couples enrolled in the HIV Prevention Trials Network (HPTN) 052 study (up to 2 samples/person). Index samples were obtained up to 5.5 years before partner infection; partner samples were obtained near the time of seroconversion. The bioinformatics method, phyloscanner, was used to infer transmission direction. Analyses were performed using samples from individual sample pairs, samples from all couples (1 sample/person; group analysis), and all available samples (multisample group analysis). Analysis was also performed using NGS data from defined regions of the HIV genome (gag, pol, env). RESULTS: Using whole-genome NGS data, transmission direction was inferred correctly (index to partner) for 98 of 105 (93.3%) of the individual sample pairs, 99 of 105 (94.3%) sample pairs using group analysis, and 31 of the 32 couples (96.9%) using multisample group analysis. There were no cases where the incorrect transmission direction (partner to index) was inferred. The accuracy of the method was higher with greater time between index and partner sample collection. Pol region sequences performed better than env or gag sequences for inferring transmission direction. CONCLUSIONS: We demonstrate the potential of a phylogenetic method to infer the direction of HIV transmission between 2 individuals using whole-genome and pol NGS data.
BACKGROUND: Phylogenetic analysis can be used to assess human immunodeficiency virus (HIV) transmission in populations. We inferred the direction of HIV transmission using whole-genome HIV sequences from couples with known linked infection and known transmission direction. METHODS: Complete next-generation sequencing (NGS) data were obtained for 105 unique index-partner sample pairs from 32 couples enrolled in the HIV Prevention Trials Network (HPTN) 052 study (up to 2 samples/person). Index samples were obtained up to 5.5 years before partner infection; partner samples were obtained near the time of seroconversion. The bioinformatics method, phyloscanner, was used to infer transmission direction. Analyses were performed using samples from individual sample pairs, samples from all couples (1 sample/person; group analysis), and all available samples (multisample group analysis). Analysis was also performed using NGS data from defined regions of the HIV genome (gag, pol, env). RESULTS: Using whole-genome NGS data, transmission direction was inferred correctly (index to partner) for 98 of 105 (93.3%) of the individual sample pairs, 99 of 105 (94.3%) sample pairs using group analysis, and 31 of the 32 couples (96.9%) using multisample group analysis. There were no cases where the incorrect transmission direction (partner to index) was inferred. The accuracy of the method was higher with greater time between index and partner sample collection. Pol region sequences performed better than env or gag sequences for inferring transmission direction. CONCLUSIONS: We demonstrate the potential of a phylogenetic method to infer the direction of HIV transmission between 2 individuals using whole-genome and pol NGS data.
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