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.
This project will 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 `omics' technologies that assess changes in gene expression and metabolism across the genome, and generate a powerful systems biology approach to characterize a disease for which there is no cure, pulmonary arterial hypertension (PAH). We will interrogate three types of inflammatory cells and three types of vascular cells from lungs of patients with PAH removed at transplant compared with unused donor healthy lungs to identify critical common processes that are perturbed in the clinical and experimentally-induced disease. The information will allow us to repurpose available drugs or to pursue the design of new treatments to reverse PAH.
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