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

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. Knowledge of the timing of HIV infection is crucial for improving our understanding of viral transmission and pathogenesis, especially in infants. In this group, viral load levels have been found to be much higher than in adults and vary based on age and mode of infection. In this study, we explore viral diversity dynamics during the early stages of pediatric HIV infection. Inspired by previous studies in adults, we develop infant-specific models that measure rates of viral diversification and use these inferred rates to estimate infection timing. Applying these models to a cohort of Kenyan infants, we successfully estimate their infection timing more accurately than existing adult-specific models. We also show that viral diversity accumulates much faster in infants compared to adults. This work provides new insights into how the HIV sequence diversifies in infants, offering valuable information for understanding differences in viral pathogenesis, transmission, and disease progression between infants and adults. These findings also highlight the importance of considering these differences when developing methodologies for future studies related to HIV infection timing across different age groups, as failing to do so may result in incorrect conclusions regarding the timing of pediatric infections.