Dissecting host-pathogen interactions through the lens of genomics

通过基因组学的视角剖析宿主与病原体的相互作用

• 批准号: 10028454
• 负责人: Majid Kazemian
• 金额: $ 38.18万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10028454
• 关键词: Affect; Cells; Cellular biology; ChIP-seq; Chronic; Clinical; Communicable Diseases; Computer Models; Data; Detection; Development; Disease; Gene Expression; Genes; Genetic Transcription; Genomics; Human Herpesvirus 4; Immune Evasion; Immune checkpoint inhibitor; Immunity; Infection; Inflammation; Investigation; Link; Machine Learning; Malignant - descriptor; Maps; Methods; Modeling; Molecular; Molecular Classification of Tumors; Mutation; Nature; Outcome; Pathogenesis; Positioning Attribute; RNA-Binding Proteins; Regulation; Resistance; Sampling; System; Therapeutic; Transcriptional Regulation; Variant; Viral; Virus Integration; computerized tools; data mining; effective intervention; genome analysis; genome-wide; genomic data; insight; lens; molecular subtypes; mortality; neoplastic cell; novel; pathogen; personalized cancer therapy; personalized intervention; personalized medicine; programs; success; tool; tool development; transcription factor; treatment strategy; tumor

Summary: Dissecting host-pathogen interactions through the lens of genomics Current investigation of mechanisms underlying many diseases relies on the acquisition of multi-dimensional genomics data. The utility of these data is, however, offset by the lag in development of tools and models to fully interrogate them. In the context of infectious diseases, such data contains molecular information including gene transcription, regulation, and variations from both the infecting pathogen and the host cell, providing a snapshot of the host and pathogen interactions (HPIs). These HPIs determine infection outcomes. For instance, when a pathogen evades, or evolves resistance to defensive host immunity via a multifaceted HPI, it can result in persisting infection, chronic inflammation, malignant transformation, and/or elevated mortality. Recent successes in overcoming immune-evasion of infected tumor cells with checkpoint inhibitors exemplifies the clinical gains that can be made by identifying and specifically targeting essential mechanisms of HPIs. Hence, precisely identifying new mode(s) of HPIs is critical for development of effective and personalized interventions. The molecular mechanisms of HPIs underpinning disease can be identified from genomics data. For example, information on whether a transcription factor (TF) regulates genes from either host or pathogen, or both, can be captured by chromatin immunoprecipitation (ChIP) sequencing of infected host cells. This means that integrative analysis of genome-scale data can provide a platform for large-scale and unbiased detection of often multi- dimensional and novel facets of HPIs in host cells. However, there is a lack of data mining tools and models to extract such information. More importantly, the available analysis tools typically focus on data from either the host or the pathogen and not on the interactions occurring between the two, excluding us from investigating the full HPI spectrum. Thus, novel methods to determine HPIs by simultaneously modeling both host and pathogen data are critical for understanding key cellular mechanisms and developing treatment strategies. My lab specializes in developing computational models to construct HPI maps and to experimentally validate them. As proof-of-principle, we produced a comprehensive HPI map from sequencing samples from large numbers of tumors caused by Epstein–Barr virus. This map delivered unprecedented insights, identifying novel viral integrations, mutations linked to viral reactivation and providing molecular classification of tumors expected to yield individualized cancer therapy. Therefore, my lab is uniquely positioned to uncover mechanistic insights from HPIs. Our program seeks to develop new models and machine learning tools to construct HPI maps in several diseases by focusing on the following major questions: 1) how do expression, integration, and mutational landscapes of host and pathogen affect pathogenesis of disease?; 2) what is the nature of physical HPIs and cross-regulation by major host and pathogen factors that modulate gene expression, such as TFs and RNA binding proteins?; 3) how do HPIs define molecular subtypes to guide personalized treatments? We expect to identify novel HPIs and provide systems-level understanding of mechanisms critical to cell biology.

摘要：通过基因组学的视角剖析宿主与病原体的相互作用 目前对许多疾病机制的研究依赖于多维信息的获取 基因组学数据。然而，这些数据的效用被工具和模型开发的滞后所抵消，以充分利用这些数据。 审问他们。在传染病的背景下，此类数据包含分子信息，包括基因 感染病原体和宿主细胞的转录、调控和变异，提供快照 宿主和病原体相互作用（HPI）。这些 HPI 决定感染结果。例如，当一个 病原体通过多方面的 HPI 逃避或进化出对防御性宿主免疫的抵抗力，它可能导致 持续感染、慢性炎症、恶变和/或死亡率升高。最近的成功 使用检查点抑制剂克服受感染肿瘤细胞的免疫逃避证明了临床收益 这可以通过识别并专门针对 HPI 的基本机制来实现。因此，准确地说 确定 HPI 的新模式对于制定有效和个性化的干预措施至关重要。 HPIs 导致疾病的分子机制可以从基因组数据中确定。例如， 关于转录因子（TF）是否调节来自宿主或病原体或两者的基因的信息可以是 通过感染宿主细胞的染色质免疫沉淀 (ChIP) 测序捕获。这意味着综合 基因组规模数据的分析可以为大规模和公正的检测提供一个平台，这些检测通常是多方面的。 宿主细胞中 HPIs 的维度和新颖方面。但目前缺乏数据挖掘工具和模型 提取此类信息。更重要的是，可用的分析工具通常关注来自以下任一方面的数据： 宿主或病原体，而不是两者之间发生的相互作用，使我们无法调查 全 HPI 谱。因此，通过同时建模宿主和病原体来确定 HPI 的新方法 数据对于理解关键细胞机制和制定治疗策略至关重要。 我的实验室专门开发计算模型来构建 HPI 地图并进行实验验证 他们。作为原理验证，我们通过对大型样本进行测序，生成了全面的 HPI 图谱。 由 Epstein-Barr 病毒引起的肿瘤数量。这张地图提供了前所未有的见解，识别出新颖的 病毒整合、与病毒再激活相关的突变并提供预期的肿瘤分子分类 产生个体化的癌症治疗。因此，我的实验室具有独特的定位来揭示机制见解 来自 HPI。我们的计划旨在开发新的模型和机器学习工具来构建 HPI 地图 通过关注以下主要问题来研究多种疾病：1）表达、整合和突变如何进行 宿主和病原体的景观影响疾病的发病机制？ 2) 物理 HPI 的本质是什么？ 调节基因表达的主要宿主和病原体因子（例如 TF 和 RNA）的交叉调节 结合蛋白？； 3）HPI如何定义分子亚型来指导个性化治疗？我们期望 识别新的 HPIs 并提供对细胞生物学至关重要的机制的系统级理解。