HIV-1 Clade B pol Evolution following Primary Infection
Author(s) -
George K. Hightower,
Susanne May,
Josué PérezSantiago,
Mary E. Pacold,
Gabriel A. Wagner,
Susan J. Little,
Douglas D. Richman,
Sanjay R. Mehta,
Davey M. Smith,
Sergei L. Kosakovsky Pond
Publication year - 2013
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0068188
Subject(s) - clade , human immunodeficiency virus (hiv) , virology , biology , medicine , genetics , phylogenetics , gene
Objective Characterize intra-individual HIV-1 subtype B pol evolution in antiretroviral naive individuals. Design Longitudinal cohort study of individuals enrolled during primary infection. Methods Eligible individuals were antiretroviral naïve participants enrolled in the cohort from December 1997-December 2005 and having at least two blood samples available with the first one collected within a year of their estimated date of infection. Population-based pol sequences were generated from collected blood samples and analyzed for genetic divergence over time in respect to dual infection status, HLA, CD4 count and viral load. Results 93 participants were observed for a median of 1.8 years (Mean = 2.2 years, SD = 1.9 years). All participants classified as mono-infected had less than 0.7% divergence between any two of their pol sequences using the Tamura-Nei model (TN93), while individuals with dual infection had up to 7.0% divergence. The global substitution rates (substitutions/nucleotide/year) for mono and dually infected individuals were significantly different (p<0.001); however, substitution rates were not associated with HLA haplotype, CD4 or viral load. Conclusions Even after a maximum of almost 9 years of follow-up, all mono-infected participants had less than 1% divergence between baseline and longitudinal sequences, while participants with dual infection had 10 times greater divergence. These data support the use of HIV-1 pol sequence data to evaluate transmission events, networks and HIV-1 dual infection.
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