Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis

• Published in: Nature communications Vol. 10; no. 1; p. 1411
• Main Authors: Ratmann, Oliver, Grabowski, M. Kate, Hall, Matthew, Golubchik, Tanya, Wymant, Chris, Abeler-Dörner, Lucie, Bonsall, David, Hoppe, Anne, Brown, Andrew Leigh, de Oliveira, Tulio, Gall, Astrid, Kellam, Paul, Pillay, Deenan, Kagaayi, Joseph, Kigozi, Godfrey, Quinn, Thomas C., Wawer, Maria J., Laeyendecker, Oliver, Serwadda, David, Gray, Ronald H., Fraser, Christophe
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29-03-2019; Nature Publishing Group; Nature Portfolio
• Subjects: 49; 49/91; 631/114/794; 631/181/757; 692/699/255/1901; 706/689/179; Adolescent; Adult; Africa - epidemiology; Base Sequence; Disease transmission; Epidemics; High-Throughput Nucleotide Sequencing; HIV; HIV Infections - epidemiology; HIV Infections - transmission; HIV-1 - physiology; Human health and pathology; Human immunodeficiency virus; Humanities and Social Sciences; Humans; Life Sciences; Middle Aged; multidisciplinary; Networks; Phylogenetics; Phylogeny; Populations; Science; Science (multidisciplinary); Viruses; Young Adult; Africa
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-019-09139-4

To prevent new infections with human immunodeficiency virus type 1 (HIV-1) in sub-Saharan Africa, UNAIDS recommends targeting interventions to populations that are at high risk of acquiring and passing on the virus. Yet it is often unclear who and where these ‘source’ populations are. Here we demonstrate how viral deep-sequencing can be used to reconstruct HIV-1 transmission networks and to infer the direction of transmission in these networks. We are able to deep-sequence virus from a large population-based sample of infected individuals in Rakai District, Uganda, reconstruct partial transmission networks, and infer the direction of transmission within them at an estimated error rate of 16.3% [8.8–28.3%]. With this error rate, deep-sequence phylogenetics cannot be used against individuals in legal contexts, but is sufficiently low for population-level inferences into the sources of epidemic spread. The technique presents new opportunities for characterizing source populations and for targeting of HIV-1 prevention interventions in Africa.
Here, Ratmann et al. show how viral deep sequencing data can be used to reconstruct HIV-1 transmission networks and to infer the direction of transmission in these networks.