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

Oliver Ratmann; M Kate Grabowski; Matthew Hall; Tanya Golubchik; Chris Wymant; Lucie Abeler-Dörner; David Bonsall; Anne Hoppe; Andrew Leigh Brown; Tulio de Oliveira; Astrid Gall; Paul Kellam; Deenan Pillay; Joseph Kagaayi; Godfrey Kigozi; Thomas C Quinn; Maria J Wawer; Oliver Laeyendecker; David Serwadda; Ronald H Gray; Christophe Fraser

doi:10.1038/s41467-019-09139-4

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

Oliver Ratmann, M Kate Grabowski, Matthew Hall, Tanya Golubchik, Chris Wymant, Lucie Abeler-Dörner, David Bonsall, Anne Hoppe, Andrew Leigh Brown, Tulio de Oliveira, Astrid Gall, Paul Kellam, Deenan Pillay, Joseph Kagaayi, Godfrey Kigozi, Thomas C Quinn, Maria J Wawer, Oliver Laeyendecker, David Serwadda, Ronald H GrayChristophe Fraser

School of Biological Sciences

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

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
Article number	1411
Journal	Nature Communications
Volume	10
Early online date	1 Mar 2019
DOIs	https://doi.org/10.1038/s41467-019-09139-4
Publication status	Published - 29 Mar 2019

Keywords / Materials (for Non-textual outputs)

ethics
HIV infections
Phylogenetics
software

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10.1038/s41467-019-09139-4Licence: Creative Commons: Attribution (CC-BY)

RatmannEtalNC2019InferringHIV-1TransmissionFinal published version, 1.22 MBLicence: Creative Commons: Attribution (CC-BY)

Phylogenetics Networks to Address Tranmission of HIV
Leigh-Brown, A. (Principal Investigator)
Other
18/10/13 → 30/09/17
Project: Research

Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis
Ratmann, O. (Creator), Grabowski, M. K. (Creator), Hall, M. D. (Creator), Golubchik, T. (Creator), Wymant, C. (Creator), Abeler-Dörner, L. (Creator), Bonsall, D. (Creator), Hoppe, A. (Creator), Leigh-Brown, A. (Creator), de Oliveira, T. (Creator), Gall, A. (Creator), Kellam, P. (Creator), Pillay, D. (Creator), Kagaayi, J. (Creator), Kigozi, G. (Creator), Quinn, T. C. (Creator), Wawer, M. J. (Creator), Laeyendecker, O. (Creator), Serwadda, D. (Creator), Gray, R. H. (Creator) & Fraser, C. (Creator), Dryad, 2 Apr 2019
DOI: 10.5061/dryad.7h46hg2
Dataset

Cite this

Ratmann, O., Grabowski, M. K., Hall, M., Golubchik, T., Wymant, C., Abeler-Dörner, L., Bonsall, D., Hoppe, A., Brown, A. L., de Oliveira, T., Gall, A., Kellam, P., Pillay, D., Kagaayi, J., Kigozi, G., Quinn, T. C., Wawer, M. J., Laeyendecker, O., Serwadda, D., ... Fraser, C. (2019). Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis. Nature Communications, 10, Article 1411. https://doi.org/10.1038/s41467-019-09139-4

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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. ",

keywords = "ethics, HIV infections, Phylogenetics, software",

author = "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",

year = "2019",

month = mar,

day = "29",

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

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Ratmann, O, Grabowski, MK, Hall, M, Golubchik, T, Wymant, C, Abeler-Dörner, L, Bonsall, D, Hoppe, A, Brown, AL, de Oliveira, T, Gall, A, Kellam, P, Pillay, D, Kagaayi, J, Kigozi, G, Quinn, TC, Wawer, MJ, Laeyendecker, O, Serwadda, D, Gray, RH & Fraser, C 2019, 'Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis', Nature Communications, vol. 10, 1411. https://doi.org/10.1038/s41467-019-09139-4

TY - JOUR

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

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

PY - 2019/3/29

Y1 - 2019/3/29

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.

KW - ethics

KW - HIV infections

KW - Phylogenetics

KW - software

U2 - 10.1038/s41467-019-09139-4

DO - 10.1038/s41467-019-09139-4

M3 - Article

C2 - 30926780

SN - 2041-1723

VL - 10

JO - Nature Communications

JF - Nature Communications

M1 - 1411

ER -

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

Abstract

Keywords / Materials (for Non-textual outputs)

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Projects

Phylogenetics Networks to Address Tranmission of HIV

Datasets

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

Cite this