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

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
Ayles, Helen
Bowden, Rory
Calvez, Vincent
Cohen, Myron
Dennis, Ann
Essex, Max
Fidler, Sarah
Frampton, Daniel
Hayes, Richard
Herbeck, Joshua T.
Kaleebu, Pontiano
Kityo, Cissy
Lingappa, Jairam
Novitsky, Vladimir
Paton, Nick
Rambaut, Andrew
Seeley, Janet
Ssemwanga, Deogratius
Tanser, Frank
Nakigozi, Gertrude
Ssekubugu, Robert
Nalugoda, Fred
Lutalo, Tom
Galiwango, Ronald
Makumbi, Fred
Sewankambo, Nelson K.
R. Tobian, Aaron A.
Reynolds, Steven J.
Mondo, George
Santelli, John
Kennedy, Caitlin E.
Wagman, Jennifer
Global Health
Graduate School
AII - Infectious diseases
APH - Personalized Medicine
APH - Quality of Care
Source :
Nature communications, 10(1):1411. Nature Publishing Group
Publication Year :
2019

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.

Details

Language :
English
Database :
OpenAIRE
Journal :
Nature communications, 10(1):1411. Nature Publishing Group
Accession number :
edsair.narcis........e57a3f2b49592cc9f1a691c08c6e1601