从基于人口的深度序列数据量化乌干达艾滋病毒多重感染的流行率和风险因素
Michael A Martin1, Andrea Brizzi2, Xiaoyue Xi2,3
1Department of Pathology, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America.
PLoS pathogens
|April 22, 2025
概括
艾滋病毒感染者可以感染新的,不同的艾滋病毒感染 (多重感染),增加疾病的严重程度和传播. 一个新的模型发现,4.09%的参与者患有多次艾滋病毒感染,在高流行地区的发病率更高.
科学领域:
- 病毒学 病毒学
- 流行病学 流行病学
- 遗传学 是一个遗传学.
背景情况:
- 超级感染,即获得基因上不同的艾滋病毒变体,导致多种艾滋病毒感染.
- 多次感染可能会恶化临床结果并增加艾滋病毒传播率.
- 以前用于检测多种艾滋病毒感染的方法受到敏感性和范围的限制.
研究的目的:
- 使用全基因组深度测序数据识别多种艾滋病毒感染.
- 在基于人口的队列中估计多种艾滋病毒感染的流行率和风险因素.
- 开发一种新的贝叶斯深层遗传学模型 (deep-phyloMI),用于准确检测多种感染.
主要方法:
- 分析了来自Rakai社区队列研究 (RCCS) 2,029名参与者的全基因组或近全基因组HIVRNA深度序列数据.
- 应用贝叶斯的深层遗传学多重感染模型 (deep-phyloMI) 来解释测序偏差和检测错误.
- 评估了与多次艾滋病毒感染相关的流行病学风险因素.
主要成果:
- 据估计,在2010年至2020年期间,4.09%的艾滋病毒感染者患有多次感染.
- 在维多利亚湖附近高艾滋病毒流行率社区的参与者中,多重感染的可能性增加了2.33倍.
- 证明了深度phyloMI对多种艾滋病毒感染的高通量监测的实用性.
结论:
- 多重艾滋病毒感染是一个重大问题,影响研究队列中超过4%的病毒性个体.
- 地理位置和当地艾滋病毒流行率是多种艾滋病毒感染的关键风险因素.
- 开发的深度phyloMI框架为公共卫生监测和艾滋病毒的临床管理提供了一个强大的工具.
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