Integrative Omics as a Discovery Tool for Pulmonary Hypertension

综合组学作为肺动脉高压的发现工具

• 批准号: 9113600
• 负责人: Mark Robert Nicolls
• 金额: $ 124.66万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9113600
• 关键词: Accounting; Acute; Address; Animal Model; B-Lymphocytes; Big Data; Bioinformatics; Biological Models; Blood Vessels; Cells; Cellular biology; Clinical; Clinical Trials; Collection; Companions; Complex; Critical Pathways; Cultured Cells; Data; Data Analyses; Data Set; Development; Disease; Disease regression; Endothelial Cells; Evolution; Experimental Models; Factor Analysis; Fibroblasts; Funding Opportunities; Gene Expression; Generations; Genes; Genome; Grant; Health; Human; Inflammation; Inflammatory; Injury; Investigation; Joints; Knowledge; Laboratories; Lead; Lung; Lung Transplantation; Lung diseases; Malignant Neoplasms; Mass Spectrum Analysis; Mediating; Metabolic; Metabolism; Methodology; Methods; Modeling; Non-Insulin-Dependent Diabetes Mellitus; Pathogenesis; Pathway Analysis; Pathway interactions; Patients; Pharmaceutical Preparations; Positioning Attribute; Process; Pulmonary Hypertension; Research Personnel; Rodent; Role; Smooth Muscle Myocytes; Systems Biology; T-Lymphocyte; Techniques; Technology; Testing; Therapeutic; Time; Transplantation; Universities; base; design; human disease; improved; innovation; insight; macrophage; metabolome; metabolomics; novel; novel diagnostics; novel therapeutics; pre-clinical; pulmonary arterial hypertension; tool; transcriptome; transcriptome sequencing; transcriptomics

 DESCRIPTION (provided by applicant): This proposal seeks to develop and apply innovative bioinformatics methods of analysis to integrate very large publicly available data sets with novel data sets derived from state-of-the-art transcriptomic and metabolomic technologies. In so doing, we plan to generate a powerful systems biology approach to characterize a disease for which there is no cure, pulmonary arterial hypertension (PAH). Our group has already proven that integrating publicly available datasets can uncover fundamental and unanticipated mechanisms of disease and can enable new diagnostics and treatments for conditions as varied as acute rejection and cancer. The strong relationship between inflammation and metabolism in pulmonary vascular pathobiology will be evaluated through the generation of new integrative omics (IO) datasets. In Aim 1 we will apply big-data analysis techniques to publicly available PAH data sets, most of which are transcriptomic, to develop a common PAH module. We will also incorporate additional public datasets as they become accessible. In Aim 2, we will generate the transcriptomes (by RNA-seq) and the metabolomes (by mass spectrometry) of vascular cells (endothelial cells, smooth muscle cells and fibroblasts) and inflammatory cells (T cells, B cells and macrophages) isolated from explanted human PAH lungs at the time of lung transplantation and from unused donor control lungs. The IO data sets thus generated will be used to find common aberrant pathways in different vascular and inflammatory cells that could be targeted therapeutically in PAH. Companion studies in rodents will focus on the relationship of the pathways identified to the evolution of PAH. Aim 3 combines data from Aims 1 and 2 and extends our topology--*based impact factor pathway analysis method to account for interactions between metabolites and genes, as well as between pathways. We anticipate, based upon compelling preliminary data, that dominant processes will emerge from these analyses that we can prioritize for hypothesis testing. Animal models that best approximate central PAH pathways implicated in Aims 1 and 2 will be developed to test relevance to human PAH in the final aim. These models, as well as cultured cells from patients with PAH, will be used to explore therapies, beginning with those that repress critical pathways. As these studies extend from unbiased data analyses, we anticipate fresh pathophysiologic insights into PAH, and opportunities to repurpose existing drugs or to design new ones to reverse the disease.

 描述(由申请人提供)：本提案寻求开发和应用创新的生物信息学分析方法，以将非常大的公开可用的数据集与来自最先进的转录和代谢技术的新数据集整合在一起。在这样做的过程中，我们计划产生一种强大的系统生物学方法来表征一种无法治愈的疾病，即肺动脉高压(PAH)。我们的团队已经证明，整合公开可用的数据集可以揭示疾病的基本和意想不到的机制，并可以为急性排斥反应和癌症等各种情况提供新的诊断和治疗方法。将通过新的整合组学(IO)数据集的产生来评估肺血管病理生物学中炎症和新陈代谢之间的密切关系。在目标1中，我们将对公开可用的PAH数据集应用大数据分析技术，其中大多数是转录后的，以开发一个通用的PAH模块。我们还将在其他公共数据集变得可访问时纳入这些数据集。在目标2中，我们将从肺移植时移植的人PAH肺和未使用的供体对照肺中分离出血管细胞(内皮细胞、平滑肌细胞和成纤维细胞)和炎性细胞(T细胞、B细胞和巨噬细胞)的转录本(通过RNA-seq)和代谢物(通过质谱仪)。这样产生的IO数据集将被用来寻找不同血管和炎性细胞中常见的异常通路，这些通路可以作为治疗PAH的靶点。啮齿动物的配套研究将集中在已确定的途径与多环芳烃进化的关系上。AIM 3结合了来自AIMS 1和2的数据，并扩展了我们基于拓扑学的影响因子途径分析方法，以解释代谢物和基因之间以及途径之间的相互作用。我们预计，基于令人信服的初步数据，主导过程将从这些分析中浮现出来，我们可以优先进行假设检验。将开发最接近AIMS 1和AIMS 2中所涉及的中央多环芳烃途径的动物模型，以测试最终目标与人类多环芳烃的相关性。这些模型以及来自PAH患者的培养细胞将被用于探索治疗方法，首先是那些抑制关键通路的治疗方法。随着这些研究从无偏见的数据分析延伸，我们期待对PAH有新的病理生理学见解，并有机会重新调整现有药物的用途或设计新的药物来逆转疾病。