Joint submission for administrative supplement proposal: HIPAA aligned storage and computing solution

联合提交行政补充提案：HIPAA 一致的存储和计算解决方案

• 批准号: 10388739
• 负责人: Majid Kazemian
• 金额: $ 10万
• 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/10388739
• 关键词: Administrative Supplement; Affect; Cells; Cellular biology; ChIP-seq; Chronic; Clinical; Communicable Diseases; Computer Models; Data; Detection; Development; Disease; Gene Expression; Genes; Genetic Transcription; Genomics; Health Insurance Portability and Accountability Act; Human Herpesvirus 4; Immune Evasion; Immune checkpoint inhibitor; Immunity; Infection; Inflammation; Investigation; Joints; 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; Transcriptional Regulation; Variant; Viral; Virus Integration; 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-病原体相互作用