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.

从基因组学的角度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获得多维的 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 审问他们。在传染病的背景下，这种数据包含包括基因在内的分子信息 来自感染病原体和宿主细胞的转录、调控和变异，提供了快照 寄主和病原体的相互作用(HPI)。这些高致病性感染决定感染结果。例如，当一个 病原体通过多方面的HPI逃避或进化对防御性宿主免疫的抵抗力，它可以导致 持续性感染、慢性炎症、恶变和/或死亡率升高。最近的成功案例 在用检查点抑制剂克服感染的肿瘤细胞的免疫逃逸方面，体现了临床上的收获 这可以通过确定和专门针对高性能指标的基本机制来实现。因此，准确地说， 确定新的高绩效指标模式(S)对于开发有效和个性化的干预措施至关重要。 从基因组学数据中可以确定HPI支持疾病的分子机制。例如, 关于转录因子(Tf)是否调节来自寄主或病原体或两者的基因的信息可以是 通过染色质免疫沉淀(ChIP)对受感染的宿主细胞进行测序捕获。这意味着，一体化 对基因组规模数据的分析可以为大规模和公正地检测往往是多个 宿主细胞中HPI的维度和新的方面。然而，缺乏数据挖掘工具和模型来 提取这样的信息。更重要的是，可用的分析工具通常侧重于来自 宿主或病原体，而不是两者之间发生的相互作用，排除了我们对 完整的HPI频谱。因此，通过同时对寄主和病原体建模来确定HPI的新方法 数据对于了解关键的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图并进行实验验证 他们。作为原则上的证明，我们从大量的样本中测序得到了一个全面的HPI图 由爱泼斯坦-巴尔病毒引起的肿瘤数量。这张地图提供了前所未有的洞察力，识别了 病毒整合，与病毒重新激活有关的突变，并提供肿瘤的分子分类 以产生个性化的癌症治疗方法。因此，我的实验室在揭示机械洞察方面具有独特的地位 来自HPI。我们的计划寻求开发新的模型和机器学习工具来构建HPI图 几种疾病通过集中解决以下主要问题：1)如何表达、整合和突变 宿主和病原体的景观影响疾病的发病机制？2)物理上的HPI和 调节基因表达的主要寄主和病原体因子的交叉调节，如转录因子和RNA 结合蛋白？；3)HPI如何定义分子亚型以指导个性化治疗？我们希望 识别新的HPI，并提供对细胞生物学至关重要的机制的系统级理解。