A novel approach to pinpoint predisposed recombination regions in HIV for a global profile of HIV recombinants' occurrence and evolution

一种查明 HIV 中易发生重组区域的新方法，以了解 HIV 重组体发生和进化的全球概况

• 批准号: 10762779
• 负责人: Hanwen Huang
• 金额: $ 18.88万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10762779
• 关键词: Address; Antiviral Agents; Classification; Data; Decision Making; Deposition; Detection; Development; Disease Progression; Disease Surveillance; Drug resistance; Epidemic; Epidemiology; Event; Evolution; Family; Genbank; Genetic; Genetic Recombination; Genome; Geographic Locations; HIV; HIV Genome; HIV Infections; Human; Immune; Individual; Investigation; Knowledge; Length; Methods; Mutate; Mutation; Patients; Pilot Projects; Point Mutation; Population; Population Surveillance; Positioning Attribute; Public Health; Publishing; Recombinants; Reporting; Research; Role; Sample Size; Sampling Biases; Shapes; Signal Transduction; Statistical Methods; System; Technology; Vaccines; Validation; Viral; Viral Genome; Virus; data mining; design; epidemiologic data; experience; improved; markov model; method development; novel strategies; preference; statistical and machine learning; tool; Address; Antiviral Agents; Classification; Data; Decision Making; Deposition; Detection; Development; Disease Progression; Disease Surveillance; Drug resistance; Epidemic; Epidemiology; Event; Evolution; Family; Genbank; Genetic; Genetic Recombination; Genome; Geographic Locations; HIV; HIV Genome; HIV Infections; Human; Immune; Individual; Investigation; Knowledge; Length; Methods; Mutate; Mutation; Patients; Pilot Projects; Point Mutation; Population; Population Surveillance; Positioning Attribute; Public Health; Publishing; Recombinants; Reporting; Research; Role; Sample Size; Sampling Biases; Shapes; Signal Transduction; Statistical Methods; System; Technology; Vaccines; Validation; Viral; Viral Genome; Virus; data mining; design; epidemiologic data; experience; improved; markov model; method development; novel strategies; preference; statistical and machine learning; tool

Project Summary/Abstract Occurring more frequent than point mutations, the recombination events in HIV have resulted in epidemiologically important founder strains in various geographic regions and contribute to at least 20-30% of global HIV infections. However, after 33 years of recognizing HIV recombination, two critical questions still remain elusive as to how HIV recombination events have occurred and how to predict emerging recombinant clusters to better inform public health decision making. Based on nearly 20 years of HIV experience, we believe that both questions relate to the flaws inherent in the current classification system for HIV recombinant families (CRFs), which has inadequately appreciated the rapid virus evolution within and between recombinant families. As a result, this static definition of CRFs not only creates a small-sample-size problem for most CRFs, but also makes it difficult to track viral evolution in a dynamic means; while both issues are essential for public health surveillance and for improved vaccine and antivirals design targeting HIV recombinants. In this proposed study, our objectives are to: 1) strategically fulfill the inadequate and missing CRF information to overcome the small- sample-size problem created by the CRF definition, and 2) provide a global profile along the HIV genome about HIV’s recombination occurrence and evolution via pinpointing HIV predisposed recombination regions based on the enriched CRF information. Our central hypothesis is that the inadequate and missing CRF information, which accounts for the limited sample size for most CRFs and the lack of a dynamic view of CRFs, can be strategically fulfilled by adding information from HIV fragment sequences (i.e., non-full-length sequences) that consist of over 90% of all published HIV data deposited in GenBank. Our hypothesis is based on several important lines of evidence, including results from our studies. Our rationale for this proposed study is that fulfilling the inadequate and missing CRF information will increase our capabilities in the surveillance and tracking of existing HIV recombinants and for improved prediction of emerging HIV recombinants’ clusters. Leveraged by our nearly 20 years of experience in HIV data mining, statistical method development, statistical machine learning, and statistical genetics, we will develop and validate two new methods in three Aims. By the end of this proposed project, we expect to obtain new methods and new findings to advance our understanding of the CRFs (e.g., recombination mechanisms and evolution) and for improved public health surveillance for existing and emerging CRF clusters. Finally, our methods and results will be released for free to facilitate other viruses’ research in recombination.

项目摘要/摘要 艾滋病毒中的重组事件比点突变更频繁，导致 在各个地理区域中，流行病学上重要的创始人菌株，至少有20-30％ 全球艾滋病毒感染。但是，经过33年的识别HIV重组，仍然有两个关键问题 对于如何发生HIV重组事件以及如何预测新兴的重组，仍然难以捉摸 群集更好地为公共卫生决策提供信息。根据近20年的艾滋病毒经验，我们相信 这两个问题均与HIV重组家庭当前分类系统固有的缺陷有关 （CRF），这已经不足以欣赏重组家庭内部和重组之间的快速病毒进化。 结果，这种CRF的静态定义不仅为大多数CRF创造了小样本大小的问题，而且还会产生 使以动态手段跟踪病毒进化变得困难；虽然这两个问题对于公共卫生至关重要 监视和改善疫苗和抗病毒药设计针对HIV重组剂的设计。在这项拟议的研究中， 我们的目标是：1）战略性地实现不足和缺少CRF信息，以克服小型 - CRF定义创建的样本大小问题，以及2）关于HIV基因组的全局特征 HIV的重组发生和进化，通过根据基于HIV易感重组区域 丰富的CRF信息。我们的核心假设是不充分和缺少CRF信息， 对于大多数CRF的样本量有限，并且缺乏CRF的动态视图，可以在战略上是 通过添加来自艾滋病毒片段序列（即，非满足长度序列）的信息来实现 所有已发表的HIV数据中的90％存放在GenBank中。我们的假设是基于几条重要行的 证据，包括我们研究的结果。我们对这项拟议的研究的理由是使不足 缺少CRF信息将增加我们在现有艾滋病毒的监视和跟踪中的能力 重组和改善了新兴的HIV重组簇的预测。被我们接近20的利用 艾滋病毒数据挖掘，统计方法开发，统计机器学习和 统计遗传学，我们将在三个目标中开发和验证两种新方法。到这个提议的结尾 项目，我们希望获得新的方法和新发现，以促进我们对CRF的理解（例如， 重组机制和进化），并改善了现有和新兴的公共卫生监视 CRF集群。最后，我们的方法和结果将免费发布，以促进其他病毒的研究 重组。