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: 10653922
• 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/10653922
• 关键词: Affect; Cells; Cellular biology; ChIP-seq; Chronic; Clinical; Communicable Diseases; Computer Models; Data; Detection; Development; Dimensions; Disease; Exclusion; 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; 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.

总结：通过基因组学的透镜剖析宿主与病原体的相互作用 目前，对许多疾病的机制的研究依赖于对多维信息的获取。 基因组学数据。然而，这些数据的效用被工具和模型的开发滞后所抵消， 审问他们。在传染病的背景下，这些数据包含分子信息，包括基因 转录、调节和来自感染病原体和宿主细胞的变异，提供了一个快照。 宿主和病原体相互作用（HPI）。这些HPI决定感染结果。例如，当 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抗性， 持续感染、慢性炎症、恶性转化和/或死亡率升高。最近取得的成功 用检查点抑制剂克服受感染肿瘤细胞的免疫逃避， 这可以通过识别和专门针对HPI的基本机制来实现。因此，准确地说， 识别新的HPI模式对于开发有效和个性化的干预措施至关重要。 支持疾病的HPI的分子机制可以从基因组学数据中鉴定。例如，在一个示例中， 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 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， 基因组规模数据的分析可以为大规模和无偏检测提供平台， 在宿主细胞中的HPI的三维和新颖的方面。然而，缺乏数据挖掘工具和模型， 提取这些信息。更重要的是，可用的分析工具通常关注来自 宿主或病原体，而不是两者之间发生的相互作用，排除我们调查 完整的HPI光谱。因此，通过同时模拟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图，并通过实验验证 他们作为原理证明，我们从来自大样本的测序样品中产生了一个全面的HPI图谱。 EB病毒引起的肿瘤数量。这张地图提供了前所未有的见解， 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 来提供个性化的癌症治疗。因此，我的实验室是唯一的定位， 从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几种疾病：1）表达，整合和突变如何影响 宿主和病原体的景观影响疾病的发病机制; 2)物理HPI的性质是什么， 调节基因表达的主要宿主和病原体因子（如TF和RNA）的交叉调节 结合蛋白？3)HPI如何定义分子亚型以指导个性化治疗？我们期望 识别新的HPI并提供对细胞生物学关键机制的系统级理解。