HIV Incidence Assay via Deep Sequencing and Statistical Tests

描述（由申请人提供）：我们最近的研究建立了急性序列进化模型，该模型已应用于解释来自102名急性HIV B亚型感染患者，69名急性HIV C亚型感染患者和许多siv感染猕猴的序列克隆。我们的模型能够评估源自单一传播病毒株的HIV感染的序列多样性和感染期的估计，这对HIV研究界做出了重大而有益的贡献。我们建议扩大我们对宿主内HIV多样化建模的研究活动，以开发一种识别新的、最近的HIV感染的新方法。在艾滋病毒/艾滋病预防领域，至关重要的是评估某一地区最近有多少人受到感染，以便评估预防和干预试验的可行性。目前可以从血液样本中诊断艾滋病毒感染，但还没有开发出预测个体感染时间的可靠分析方法。假设抗体滴度和亲和度随时间增加，已经使用了各种失谐抗体测定或基于亲和度的评估。然而，血清学分析存在问题，因为(i)抗体反应的成熟速度在不同的个体之间有所不同，（ii）一些CD4计数低或病毒载量低的受试者可能被不准确地计算为最近感染，以及（iii）如果感染病毒进化分支与检测中使用的抗原进化分支不同，血清学分析可能会受到负面影响（这是混合进化分支流行病中的一个特殊问题）。本提案旨在为发明一种区分新的、偶发感染和慢性、无症状感染的新型检测方法提供经验和理论基础。该提案的主要创新包括(i)全面整合下一代超深焦磷酸测序数据和生物数学建模；（ii）估算创始病毒数量和感染持续时间的新颖统计设计。我们提出的研究将侧重于克服基于测序的分析开发的两个主要障碍。首先，该试验可能会将多种不同的方正菌株的早期感染错误地分类为慢性感染。其次，该检测方法是否能够区分偶然样本和感染晚期获得的慢性样本是值得怀疑的。我们拟从不同流行地区和不同感染阶段的不同人群中收集大规模的HIV序列数据。超深度测序数据与新颖的统计设计相结合，将引导我们发明一种能够识别突发感染的新型检测方法。拟议的工作将通过提供基于宿主内HIV多样化特征的可靠检测来推进HIV预防研究。

DESCRIPTION (provided by applicant): Our recent studies developed acute sequence evolution model which has been applied to interpret sequence clones derived from 102 subjects with acute HIV subtype B infection, from 69 subjects with acute HIV subtype C infection, and from numerous SIV-infected macaques. Our model enabled an assessment of the sequence diversity in HIV infections originating from a single transmitted viral strain and the estimation on the period of infection, which has been a significant and beneficial contribution to HIV research community. We propose to expand our research activity of modeling intrahost HIV diversification toward the development of a novel assay identifying new, recent HIV infections. In the HIV/AIDS prevention field, it is critical to assess how many people have been recently infected in a given area in order to evaluate the feasibility of prevention and intervention trials. The diagnosis of HIV infection is currently possible from blood samples but reliable assays predicting how long an individual has been infected have not been developed. Various detuned antibody assays, or avidity-based assessments, have been used, assuming that antibody titer and avidity increases with time. However, serologic assays are problematic because (i) the rate of maturation of the antibody response varies between different individuals, (ii) some subjects with low CD4 counts or low virus loads may be inaccurately counted as having recent infection, and (iii) serologic assays can be negatively affected if the infecting virus clade differs from the clade of the antigen used in the assay (this is a particular problem in mixed-clade epidemics). This proposal aims to provide empirical and theoretical foundations for inventing a novel assay that distinguishes new, incident infections from chronic, asymptomatic infections. Major innovations of the proposal include (i) its comprehensive integration of next-generation ultradeep pyrosequencing data and biomathematical modeling and (ii) a novel statistical design estimating the number of founder viruses and the duration of infection. Our proposed study will focus on overcoming two primary barriers to the development of a sequencing based assay. First, the assay potentially mis-classifies early infections with multiple distinct founder strains as chronic infections. Second, it is questionable whether the assay can distinguish incident samples from chronic ones obtained at late stages of infections. We propose to collect a large scale of HIV sequence data from diverse population in different epidemic regions and different stages of infections. Ultradeep sequencing data in conjunction with novel statistical designs will lead us to invent a novel assay that is capable of identifying incident infections. The proposed work will advance HIV prevention research by providing a reliable assay based on the characteristics of intrahost HIV diversification.