Pairwise diversity and tMRCA as potential markers for HIV infection recency.
Intrahost human immunodeficiency virus (HIV)-1 diversity increases linearly over time. We assessed the extent to which mean pairwise distances and the time to the most recent common ancestor (tMRCA) inferred from intrahost HIV-1C env sequences were associated with the estimated time of HIV infection. Data from a primary HIV-1C infection study in Botswana were used for this analysis (N = 42). A total of 2540 HIV-1C env gp120 variable loop region 1 to conserved region 5 (V1C5) of the HIV-1 envelope gp120 viral sequences were generated by single genome amplification and sequencing, with an average of 61 viral sequences per participant and 11 sequences per time point per participant. Raw pairwise distances were calculated for each time point and participant using the ape package in R software. The tMRCA was estimated using phylogenetic inference implemented in Bayesian Evolutionary Analysis by Sampling Trees v1.8.2. Pairwise distances and tMRCA were significantly associated with the estimated time since HIV infection (both P < 0.001). Taking into account multiplicity of HIV infection strengthened these associations. HIV-1C env-based pairwise distances and tMRCA can be used as potential markers for HIV recency. However, the tMRCA estimates demonstrated no advantage over the pairwise distances estimates.
Division of Medical Virology, Stellenbosch University, Tygerberg, South Africa Botswana-Harvard AIDS Institute Partnership, Gaborone, Botswana Africa Health Research Institute, School of Nursing and Public Health, University of KwaZulu-Natal, Durban, Republic of South Africa Harvard T.H. Chan School of Public Health, Boston, MA, USA National Health Laboratory Services (NHLS), Tygerberg Coastal, South Africa Research Department of Infection, University College London, London, United Kingdom College of Health Sciences, University of KwaZulu-Natal, Durban, Republic of South Africa Division of Sleep Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
Sikhulile
S
Moyo
Eduan
E
Wilkinson
Alain
A
Vandormael
Rui
R
Wang
Jia
J
Weng
Kenanao P
KP
Kotokwe
Simani
S
Gaseitsiwe
Rosemary
R
Musonda
Joseph
J
Makhema
Max
M
Essex
Susan
S
Engelbrecht
Tulio
T
de Oliveira
Vladimir
V
Novitsky
D001499:Bayes Theorem; D003430:Cross-Sectional Studies; D015658:HIV Infections / Q000235:genetics* / Q000635:transmission* / Q000821:virology; D015497:HIV-1 / Q000235:genetics*; D006801:Humans; D013997:Time Factors