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Using viral sequence diversity to estimate time of HIV infection in infants.

Authors :
Magdalena L Russell
Carolyn S Fish
Sara Drescher
Noah A J Cassidy
Pritha Chanana
Sarah Benki-Nugent
Jennifer Slyker
Dorothy Mbori-Ngacha
Rose Bosire
Barbra Richardson
Dalton Wamalwa
Elizabeth Maleche-Obimbo
Julie Overbaugh
Grace John-Stewart
Frederick A Matsen
Dara A Lehman
Source :
PLoS Pathogens, Vol 19, Iss 12, p e1011861 (2023)
Publication Year :
2023
Publisher :
Public Library of Science (PLoS), 2023.

Abstract

Age at HIV acquisition may influence viral pathogenesis in infants, and yet infection timing (i.e. date of infection) is not always known. Adult studies have estimated infection timing using rates of HIV RNA diversification, however, it is unknown whether adult-trained models can provide accurate predictions when used for infants due to possible differences in viral dynamics. While rates of viral diversification have been well defined for adults, there are limited data characterizing these dynamics for infants. Here, we performed Illumina sequencing of gag and pol using longitudinal plasma samples from 22 Kenyan infants with well-characterized infection timing. We used these data to characterize viral diversity changes over time by designing an infant-trained Bayesian hierarchical regression model that predicts time since infection using viral diversity. We show that diversity accumulates with time for most infants (median rate within pol = 0.00079 diversity/month), and diversity accumulates much faster than in adults (compare previously-reported adult rate within pol = 0.00024 diversity/month [1]). We find that the infant rate of viral diversification varies by individual, gene region, and relative timing of infection, but not by set-point viral load or rate of CD4+ T cell decline. We compare the predictive performance of this infant-trained Bayesian hierarchical regression model with simple linear regression models trained using the same infant data, as well as existing adult-trained models [1]. Using an independent dataset from an additional 15 infants with frequent HIV testing to define infection timing, we demonstrate that infant-trained models more accurately estimate time since infection than existing adult-trained models. This work will be useful for timing HIV acquisition for infants with unknown infection timing and for refining our understanding of how viral diversity accumulates in infants, both of which may have broad implications for the future development of infant-specific therapeutic and preventive interventions.

Details

Language :
English
ISSN :
15537366 and 15537374
Volume :
19
Issue :
12
Database :
Directory of Open Access Journals
Journal :
PLoS Pathogens
Publication Type :
Academic Journal
Accession number :
edsdoj.261cde92f0e40db9e5f271fcf0655db
Document Type :
article
Full Text :
https://doi.org/10.1371/journal.ppat.1011861