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）：该提案旨在开发和应用创新的生物信息学分析方法，以整合非常大的公开可用数据集与来自最先进的转录组学和代谢组学技术的新数据集。在这样做的过程中，我们计划产生一个强大的系统生物学方法来表征一种无法治愈的疾病，肺动脉高压（PAH）。我们的团队已经证明，整合公开的数据集可以揭示疾病的基本和不可预见的机制，并可以为急性排斥反应和癌症等各种疾病提供新的诊断和治疗方法。将通过生成新的整合组学（IO）数据集来评估肺血管病理生物学中炎症和代谢之间的密切关系。在目标1中，我们将大数据分析技术应用于公开的PAH数据集，其中大部分是转录组，以开发一个通用的PAH模块。我们还将纳入更多的公共数据集，因为它们可以访问。在目标2中，我们将生成在肺移植时从移植的人PAH肺和未使用的供体对照肺中分离的血管细胞（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）的转录组（通过RNA-seq）和代谢组（通过质谱法）。由此生成的IO数据集将用于在PAH中找到不同血管和炎症细胞中的常见异常通路，这些通路可以在治疗上靶向。在啮齿动物中的伴随研究将侧重于确定的途径与PAH演变的关系。目标3结合了目标1和目标2的数据，并扩展了我们基于拓扑结构的影响因子途径分析方法，以考虑代谢物和基因之间以及途径之间的相互作用。基于令人信服的初步数据，我们预计，主导过程将从这些分析中出现，我们可以优先进行假设检验。将开发最接近目标1和2中涉及的中枢PAH通路的动物模型，以测试最终目标中与人类PAH的相关性。这些模型以及来自PAH患者的培养细胞将用于探索治疗方法，从抑制关键途径的治疗方法开始。由于这些研究从无偏数据分析延伸，我们预计PAH的病理生理学将有新的见解，并有机会重新利用现有药物或设计新药物来逆转疾病。