Reconstructing HIV Epidemics from HIV Phylogenetics

从 HIV 系统发育学重建 HIV 流行病

• 批准号: 8082704
• 负责人: Thomas K. Leitner
• 金额: $ 62.52万
• 依托单位: LOS ALAMOS NAT SECTY-LOS ALAMOS NAT LAB
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-15 至 2014-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8082704
• 关键词: Affect; Africa South of the Sahara; African; Amino Acids; Biology; Bypass; Categories; Codon Nucleotides; Cohort Effect; Communicable Diseases; Contact Tracing; Country; Data; Data Set; Development; Disease; Disease Outbreaks; Epidemic; Epidemiology; Event; Evolution; Generations; Genes; Genetic; Genetic Recombination; Genome; Genomics; Geographic Locations; Goals; HIV; HIV vaccine; HIV-1; High Prevalence; Human; Immune; Immune system; Incidence; Infection; Intervention; Length; Life; Medicine; Methods; Modeling; Molecular; Monitor; Patients; Pattern; Persons; Phylogenetic Analysis; Phylogeny; Population; Population Sizes; Positioning Attribute; Prevalence; Prevention; Public Health; Recording of previous events; Research; Risk; Sampling; Science; Sexual Transmission; Signal Transduction; Simulate; Site; Social Network; Sorting - Cell Movement; Speed; Structure; Time; Trees; USSR; Viral; Virus; authority; base; design; epidemiological model; glycosylation; intravenous drug user; mathematical model; mortality; pathogen; public health relevance; reconstruction; theories; tool; transmission process

DESCRIPTION (provided by applicant): HIV continues to infect human populations worldwide, emphasizing the need for epidemiological tools that can accurately describe transmission patterns. Thus, the methods we will develop will have specific impact on HIV vaccines; evolution; epidemiological parameters: spread of infection in different groups; intervention; and more generally on the fundamental science of infectious diseases. The overall goal is to understand the relationship between virus evolution and its epidemiological history, and to create epidemiological tools that can make reliable contact tracings and assess changes in epidemic dynamics. We have recently shown that the epidemic rate is inversely correlated to the virus evolutionary rate on the population level. Thus, the specific hypothesis behind the proposed research is that there is a relationship between the speed at which an epidemic moves through a human population and the rate at which the virus evolves in that population. We have observed that there are discrepancies between transmission histories and viral phylogenies, and because the inferences of epidemics are based on phylogenetics, it becomes important to understand the limitations in such inferences. Based on this the specific aims of this proposal are to: 1. Create a model that accurately describes the connection between transmission history and viral phylogeny. Preliminary results suggest that there are "hidden lineages" in viral phylogenies that are involved in transmission events, potentially misleading reconstruction of transmission events. We will especially investigate the effects of the effective population size in the donor, the bottleneck at transmission, and incomplete lineage sorting during transmission and sampling. We aim to estimate meaningful confidence levels on reconstructed person-to-person transmissions enabling us to explore alternative hypotheses in a statistical framework specifically designed for epidemiological tracking. 2. Identify the mechanism that correlates epidemic rate and virus evolutionary rate. We will decipher the connection between epidemic rate and viral evolutionary rate. Currently, we have four alternative explanations that may cause the observed correlation between epidemic and evolutionary rate (host immune selection, viral generation time effects, selection during transmission, and recombination effects). We will use different gene sequence data, codon positions as well as amino acid signatures to discriminate between these hypothetical explanations. We will use large datasets to develop epidemiological models that include these four hypothetical explanations to investigate their effects on the population level, and also model social networks and epidemic and phylogeographic dynamics. PUBLIC HEALTH RELEVANCE: The mathematical methods developed in this project aim to give better inferences of the spread of pathogens, here mainly HIV. At the contact tracing level we will estimate meaningful confidence levels on reconstructed person-to-person transmissions enabling us to explore alternative hypotheses in a statistical framework specifically designed for epidemiological tracking. At the epidemic level we will develop methods that can follow and signal when important changes in spread patterns occur, including the origin of the infection.

描述(由申请人提供)：艾滋病毒继续感染世界各地的人口，强调需要能够准确描述传播模式的流行病学工具。因此，我们将开发的方法将对艾滋病毒疫苗、进化、流行病学参数：感染在不同群体中的传播、干预以及更广泛的传染病基础科学产生具体影响。总体目标是了解病毒演变及其流行病学历史之间的关系，并创建能够进行可靠接触追踪和评估流行病动态变化的流行病学工具。我们最近已经证明，在人口水平上，流行率与病毒进化率呈负相关。因此，这项拟议研究背后的具体假设是，疫情在人群中传播的速度与病毒在该人群中的进化速度之间存在关系。我们观察到，传播史和病毒系统发育之间存在差异，由于流行病的推断是基于系统发育学，因此了解此类推断的局限性变得重要。基于此，这项提议的具体目标是：1.创建一个准确描述传播史和病毒系统发育之间联系的模型。初步结果表明，在病毒系统发育中存在与传播事件有关的“隐藏谱系”，这可能会误导传播事件的重建。我们将特别调查捐献者的有效种群大小、传播的瓶颈以及在传播和采样过程中不完全的谱系分类的影响。我们的目标是评估重建的人与人之间传播的有意义的置信度，使我们能够在专门为流行病学跟踪设计的统计框架中探索替代假设。2.找出流行率和病毒进化率之间的关系机制。我们将破译流行率和病毒进化率之间的联系。目前，我们有四种可供选择的解释，它们可能导致所观察到的流行病和进化率之间的相关性(宿主免疫选择、病毒世代时间效应、传播过程中的选择和重组效应)。我们将使用不同的基因序列数据、密码子位置以及氨基酸签名来区分这些假设解释。我们将使用大型数据集开发流行病学模型，其中包括这四种假设解释，以调查它们对人口水平的影响，并对社会网络、流行病和系统地理动力学进行建模。 公共卫生相关性：该项目中开发的数学方法旨在更好地推断病原体的传播，这里主要是艾滋病毒。在接触者追踪方面，我们将评估重建的人与人之间传播的有意义的置信度，使我们能够在专门为流行病学追踪设计的统计框架中探索替代假设。在流行病层面，我们将制定方法，以便在传播模式发生重要变化时跟踪并发出信号，包括感染的来源。