A de novo approach to detect and interrogate neglected HIV diversity

项目摘要/摘要 目前的艾滋病毒分支系统不能很好地监测艾滋病毒传播集群中的病毒变化 随着时间的推移：分支系统是在近20年前建立的，预先选择的分支参考是 不经常更新。此外，病毒的快速进化很容易使该分支系统在跟踪方面处于次优状态 流行病中的病毒变化。更糟糕的是，尽管我们长期以来一直认为依赖这个分支系统是理所当然的 在大多数艾滋病毒研究中，这个系统在多大程度上代表了实际的光谱仍然难以捉摸。 HIV-1的多样性。这项拟议的研究旨在解决当前分支系统的关键限制，并 提高其在识别和跟踪艾滋病毒聚集物出现和演变过程中的病毒变化方面的准确性。 我们的中心假设是，一些新的病毒多样性参与了传播的出现和进化 在当代艾滋病毒流行中已经存在聚集性；然而，由于缺乏适当的调查 和检测工具，新病毒多样性的信号在流行病学中被忽视或错过 监视直到星团扩大并变得具有威胁性。我们的假设是基于我们之前的 新的G和J谱系的发现属于经典的艾滋病毒分支之外，以及越来越多的 在目前的艾滋病毒分支系统下无法获得其基因分型的全球艾滋病毒序列。其基本原理是 这项拟议的研究是新的病毒多样性信息，有望通过开发 系统动力学集群中的新工具，将有效地提高我们跟踪新出现和 不断扩大的艾滋病毒聚集区的演变。在目标1中，通过对基因分型信息和 广泛的数据挖掘，我们将确定具有新多样性的第一个实际HIV-1序列列表 超越传统A-K分支的信息。在目标2中，我们计划开发一种新的聚类算法来 最大限度地减少因不正确或缺少参考选择而产生的错误分类。该算法将是 首次被用来确定超出当前分支系统的新病毒多样性是否 通过形成集群获得了流行病的重要性。最后，在目标3中，我们将集成来自目标1和 目标2构建一个可查找、可访问和可重复使用的网络界面，以促进有效跟踪 非传统艾滋病毒在不断扩大的艾滋病毒聚集区的出现和演变中的多样性。此接口将 成为第一个具有追踪过去被忽视的非传统艾滋病毒多样性的功能的平台。 我们将受益于我们在艾滋病毒分子流行病学、计算生物学和 算法/软件开发来执行这一提议的项目。在这项研究结束时，我们将帮助填补 关于当前艾滋病毒分支系统以外的新病毒多样性的知识差距和技术差距 检测非传统的病毒分支。我们预计将揭示更大程度的艾滋病毒多样性， 在目前的艾滋病毒研究和监测中被低估了。我们提议的方法将允许艾滋病毒 序列数据将得到更好的基因分型，从而有助于改善基于基因组的艾滋病毒保健和治疗。

Project Summary/Abstract The current HIV clade system is not well tailored to monitor viral changes in HIV transmission clusters over time: The clade system was established nearly 20 years ago, and the pre-selected clade references are not updated often. Furthermore, rapid viral evolution can easily render this clade system suboptimal in tracking viral changes in epidemics. Even worse, although we have long taken for granted to rely on this clade system in most HIV research, it remains elusive as to the degree that this system represents an actual spectrum of HIV-1 diversity. This proposed study aims to address the critical limitation of the current clade system and improve its accuracy in identifying and tracking viral changes in the emergence and evolution of HIV clusters. Our central hypothesis is that some new viral diversity involved in the emergence and evolution of transmission clusters already exists in contemporary HIV epidemics; however, due to the lack of appropriate investigation and detection tools, signals for the new viral diversity have been overlooked or missed in epidemiological surveillance until the clusters expand and become threatening. Our hypothesis is based on our previous findings of new G and J lineages that fall outside of the classic HIV clades, as well as a growing number of global HIV sequences whose genotyping is unattainable under the current HIV clade system. The rationale for this proposed study is that the new viral diversity information, hopefully revealed through the development of new tools in phylodynamic clustering, will efficiently improve our capability to track the emergence and evolution of expanding HIV clusters. In Aim 1, through rigorous quality control of genotyping information and extensive data mining, we will determine the first actual list of HIV-1 sequences that bear new diversity information beyond the traditional A-K clades. In Aim 2, we plan to develop a novel clustering algorithm to minimize misclassifications derived from incorrect or absent reference selection. This algorithm will be employed to determine for the first time whether the new viral diversity beyond the current clade system has gained epidemic importance by forming clusters. Finally, in Aim 3, we will integrate the results from Aim 1 and Aim 2 to construct a findable, accessible and reusable web interface to facilitate efficient tracking of nontraditional HIV diversities in the emergence and evolution of expanding HIV clusters. This interface would be the first platform with features to track nontraditional HIV diversities that have been neglected in the past. We will benefit from our over 17 years’ experience in HIV molecular epidemiology, computational biology, and algorithm/software development to perform this proposed project. At the end of this study, we will help fill the knowledge gap about the new viral diversity beyond the current HIV clade system and a technique gap to detect nontraditional viral clades. We expect to reveal a greater degree of HIV diversity that may have been underestimated in the current HIV research and surveillance. Our proposed approach would allow HIV sequence data to be better genotyped, thus help improve genomic-based HIV healthcare and treatment.