Computational Biology Program, Fred Hutch Cancer Center, Seattle, Washington, United States of America.
Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, United States of America.
PLoS Pathog. 2023 Dec 20;19(12):e1011861. doi: 10.1371/journal.ppat.1011861. eCollection 2023 Dec.
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.
获得 HIV 的年龄可能会影响婴儿的病毒发病机制,但感染时间(即感染日期)并不总是为人所知。成人研究已经使用 HIV RNA 多样化的速率来估计感染时间,但由于病毒动力学可能存在差异,尚不清楚成人训练的模型是否可以为婴儿提供准确的预测。虽然成人的病毒多样化速率已经得到很好的定义,但用于婴儿的这些动力学数据有限。在这里,我们使用 22 名肯尼亚婴儿具有良好特征的感染时间的纵向血浆样本进行了 gag 和 pol 的 Illumina 测序。我们使用这些数据通过设计一个基于贝叶斯层次回归的婴儿训练模型来预测使用病毒多样性的感染后时间,从而描述病毒多样性随时间的变化。我们表明,对于大多数婴儿而言,多样性随时间积累(pol 内中位数速率为 0.00079 多样性/月),并且多样性的积累速度比成人快得多(比较以前报道的成人 pol 内速率为 0.00024 多样性/月 [1])。我们发现,婴儿病毒多样化的速率因个体、基因区域和感染的相对时间而有所不同,但与设定点病毒载量或 CD4+T 细胞下降率无关。我们比较了这种婴儿训练的贝叶斯层次回归模型与使用相同婴儿数据训练的简单线性回归模型以及现有的成人训练模型 [1]的预测性能。使用来自另外 15 名具有频繁 HIV 检测以确定感染时间的婴儿的独立数据集,我们证明了婴儿训练的模型比现有的成人训练模型更能准确估计感染后的时间。这项工作对于确定具有未知感染时间的婴儿的 HIV 获得时间以及深化我们对病毒多样性如何在婴儿中积累的理解将非常有用,这两者都可能对未来婴儿特异性治疗和预防干预措施的发展产生广泛影响。