Factors influencing estimates of HIV-1 infection timing using BEAST
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
PLOS Computational Biology
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 withinhost
datasets from early infection. Nonetheless, our results also suggest caution when
using uniform clock and population models or short genes with limited information content.
Description
Keywords
HIV-1 infection, Timing, BEAST
Citation
Dearlove B, Tovanabutra S, Owen CL, Lewitus E, Li Y, Sanders-Buell E, et al. (2021) Factors influencing estimates of HIV-1 infection timing using BEAST. PLoS Comput Biol 17(2): e1008537. https://doi.org/10.1371/journal. pcbi.1008537