Dataset Information

Factors influencing estimates of HIV-1 infection timing using BEAST.

ABSTRACT: While large datasets of HIV-1 sequences are increasingly being generated, many studies rely on a single gene or fragment of the genome and few comparative studies across genes have been done. We performed genome-based and gene-specific Bayesian phylogenetic analyses to investigate how certain factors impact estimates of the infection dates in an acute HIV-1 infection cohort, RV217. In this cohort, HIV-1 diagnosis corresponded to the first RNA positive test and occurred a median of four days after the last negative test, allowing us to compare timing estimates using BEAST to a narrow window of infection. We analyzed HIV-1 sequences sampled one week, one month and six months after HIV-1 diagnosis in 39 individuals. We found that shared diversity and temporal signal was limited in acute infection, and insufficient to allow timing inferences in the shortest HIV-1 genes, thus dated phylogenies were primarily analyzed for env, gag, pol and near full-length genomes. There was no one best-fitting model across participants and genes, though relaxed molecular clocks (73% of best-fitting models) and the Bayesian skyline (49%) tended to be favored. For infections with single founders, the infection date was estimated to be around one week pre-diagnosis for env (IQR: 3-9 days) and gag (IQR: 5-9 days), whilst the genome placed it at a median of 10 days (IQR: 4-19). Multiply-founded infections proved problematic to date. Our ability to compare timing inferences to precise estimates of HIV-1 infection (within a week) highlights that molecular dating methods can be applied to within-host datasets from early infection. Nonetheless, our results also suggest caution when using uniform clock and population models or short genes with limited information content.

SUBMITTER: Dearlove B

PROVIDER: S-EPMC7877758 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Factors influencing estimates of HIV-1 infection timing using BEAST.

Dearlove Bethany B Tovanabutra Sodsai S Owen Christopher L CL Lewitus Eric E Li Yifan Y Sanders-Buell Eric E Bose Meera M O'Sullivan Anne-Marie AM Kijak Gustavo G Miller Shana S Poltavee Kultida K Lee Jenica J Bonar Lydia L Harbolick Elizabeth E Ahani Bahar B Pham Phuc P Kibuuka Hannah H Maganga Lucas L Nitayaphan Sorachai S Sawe Fred K FK Kim Jerome H JH Eller Leigh Anne LA Vasan Sandhya S Gramzinski Robert R Michael Nelson L NL Robb Merlin L ML Rolland Morgane M

PLoS computational biology 20210201 2

While large datasets of HIV-1 sequences are increasingly being generated, many studies rely on a single gene or fragment of the genome and few comparative studies across genes have been done. We performed genome-based and gene-specific Bayesian phylogenetic analyses to investigate how certain factors impact estimates of the infection dates in an acute HIV-1 infection cohort, RV217. In this cohort, HIV-1 diagnosis corresponded to the first RNA positive test and occurred a median of four days afte ...[more]

PMID: 33524022

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Dataset Information

Factors influencing estimates of HIV-1 infection timing using BEAST.

Publications

Factors influencing estimates of HIV-1 infection timing using BEAST.

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