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

PANGEA Consortium and Rakai Health Sciences Program

doi:10.1038/s41467-019-09139-4

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

PANGEA Consortium and Rakai Health Sciences Program

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

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.

Original language	English (US)
Article number	1411
Journal	Nature communications
Volume	10
Issue number	1
DOIs	https://doi.org/10.1038/s41467-019-09139-4
State	Published - Dec 1 2019

ASJC Scopus subject areas

Chemistry(all)
Biochemistry, Genetics and Molecular Biology(all)
General
Physics and Astronomy(all)

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10.1038/s41467-019-09139-4

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@article{63e33c01a5cb4c0aba57135d3d2173d6,

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

abstract = "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 {\textquoteleft}source{\textquoteright} 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.",

author = "{PANGEA Consortium and Rakai Health Sciences Program} and Oliver Ratmann and Grabowski, {M. Kate} and Matthew Hall and Tanya Golubchik and Chris Wymant and Lucie Abeler-D{\"o}rner and David Bonsall and Anne Hoppe and Brown, {Andrew Leigh} and {de Oliveira}, Tulio and Astrid Gall and Paul Kellam and Deenan Pillay and Joseph Kagaayi and Godfrey Kigozi and Quinn, {Thomas C.} and Wawer, {Maria J.} and Oliver Laeyendecker and David Serwadda and Gray, {Ronald H.} and Christophe Fraser and Helen Ayles and Rory Bowden and Vincent Calvez and Myron Cohen and Ann Dennis and Max Essex and Sarah Fidler and Daniel Frampton and Richard Hayes and Herbeck, {Joshua T.} and Pontiano Kaleebu and Cissy Kityo and Jairam Lingappa and Vladimir Novitsky and Nick Paton and Andrew Rambaut and Janet Seeley and Deogratius Ssemwanga and Frank Tanser and Gertrude Nakigozi and Robert Ssekubugu and Fred Nalugoda and Tom Lutalo and Ronald Galiwango and Aaron, {Aaron A.} and Reynolds, {Steven J.} and Chang, {Larry W.} and Redd, {Andrew D.} and Kennedy, {Caitlin E.}",

note = "Funding Information: We thank the participants of the RHSP RCCS; as well as the PANGEA-HIV steering committee for their input and their comments on a previous version of this article. Computations were performed at the Imperial College Research Computing Service, https://doi.org/10.14469/hpc/2232. This study was supported by the National Institute of Mental Health (K23MH086338, R01MH107275); the National Institute of Allergy and Infectious Diseases (R01AI110324, U01AI100031, R01AI110324, R01AI102939); the National Institute of Child Health and Development (RO1HD070769, R01HD050180); the Division of Intramural Research, National Institute for Allergy and Infectious Diseases, National Institutes of Health; the Bill & Melinda Gates Foundation (22006.02, OPP1084362); the Johns Hopkins University Center for AIDS Research (P30AI094189); and the European Research Council (Advanced Grant PBDR-339251). Publisher Copyright: {\textcopyright} 2019, The Author(s).",

year = "2019",

month = dec,

day = "1",

doi = "10.1038/s41467-019-09139-4",

language = "English (US)",

volume = "10",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Publishing Group",

number = "1",

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TY - JOUR

T1 - Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis

AU - PANGEA Consortium and Rakai Health Sciences Program

AU - Ratmann, Oliver

AU - Grabowski, M. Kate

AU - Hall, Matthew

AU - Golubchik, Tanya

AU - Wymant, Chris

AU - Abeler-Dörner, Lucie

AU - Bonsall, David

AU - Hoppe, Anne

AU - Brown, Andrew Leigh

AU - de Oliveira, Tulio

AU - Gall, Astrid

AU - Kellam, Paul

AU - Pillay, Deenan

AU - Kagaayi, Joseph

AU - Kigozi, Godfrey

AU - Quinn, Thomas C.

AU - Wawer, Maria J.

AU - Laeyendecker, Oliver

AU - Serwadda, David

AU - Gray, Ronald H.

AU - Fraser, Christophe

AU - Ayles, Helen

AU - Bowden, Rory

AU - Calvez, Vincent

AU - Cohen, Myron

AU - Dennis, Ann

AU - Essex, Max

AU - Fidler, Sarah

AU - Frampton, Daniel

AU - Hayes, Richard

AU - Herbeck, Joshua T.

AU - Kaleebu, Pontiano

AU - Kityo, Cissy

AU - Lingappa, Jairam

AU - Novitsky, Vladimir

AU - Paton, Nick

AU - Rambaut, Andrew

AU - Seeley, Janet

AU - Ssemwanga, Deogratius

AU - Tanser, Frank

AU - Nakigozi, Gertrude

AU - Ssekubugu, Robert

AU - Nalugoda, Fred

AU - Lutalo, Tom

AU - Galiwango, Ronald

AU - Aaron, Aaron A.

AU - Reynolds, Steven J.

AU - Chang, Larry W.

AU - Redd, Andrew D.

AU - Kennedy, Caitlin E.

N1 - Funding Information: We thank the participants of the RHSP RCCS; as well as the PANGEA-HIV steering committee for their input and their comments on a previous version of this article. Computations were performed at the Imperial College Research Computing Service, https://doi.org/10.14469/hpc/2232. This study was supported by the National Institute of Mental Health (K23MH086338, R01MH107275); the National Institute of Allergy and Infectious Diseases (R01AI110324, U01AI100031, R01AI110324, R01AI102939); the National Institute of Child Health and Development (RO1HD070769, R01HD050180); the Division of Intramural Research, National Institute for Allergy and Infectious Diseases, National Institutes of Health; the Bill & Melinda Gates Foundation (22006.02, OPP1084362); the Johns Hopkins University Center for AIDS Research (P30AI094189); and the European Research Council (Advanced Grant PBDR-339251). Publisher Copyright: © 2019, The Author(s).

PY - 2019/12/1

Y1 - 2019/12/1

N2 - 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.

AB - 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.

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UR - http://www.scopus.com/inward/citedby.url?scp=85063743809&partnerID=8YFLogxK

U2 - 10.1038/s41467-019-09139-4

DO - 10.1038/s41467-019-09139-4

M3 - Article

C2 - 30926780

AN - SCOPUS:85063743809

SN - 2041-1723

VL - 10

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 1411

ER -

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

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