PI: Noa Rappaport
The Systems Biology Core will provide support for data analysis across Longevity Consortium (LC) projects, such as genetic variants generated by the Centenarian and Disease Context Projects, metabolomics data from the Metabolomics Project, proteomics data from Proteomics Project and transcriptomics data from the Mice/Cells Project in order to integrate them into several different models, which will be used to generate testable hypotheses centered on mechanistic network context. Our approaches will provide focused, tissue-specific analyses, thereby increasing the statistical power and insights of the LC. We will leverage a variety of network resources we have in hand and will update and expand all of these throughout the course of the project to provide access to the latest advancements in each area to the LC. The resources include: (a) genome-scale reconstructions of the human metabolic network that will allow us to contextualize integrated analysis of metabolomics and genetic variants of the enzymes; (b) capability to contextualize this global network to any tissue, cell type, or condition for which we have sufficient expression data as we have already done for 126 different tissues and cell types in the human body using our mCADRE algorithm; (c) functional annotation of genetic variants from our atlas of inferred transcriptional regulatory networks (TRNs) and their binding sites along the genome using data from ENCODE DNase hypersensitivity footprinting information and large-scale transcriptomics repositories such as from GTEx (manuscript submitted). Another major capability provided by the Systems Biology Core will be to serve as an integrative analysis center that will perform systems biology analyses by integrating data that come from across LC projects, including analysis tools we have developed for longitudinal multi-omic analysis (mostly from blood) where we are currently analyzing multi-omic data (proteomes, metabolomes, clinical labs, microbiomes, wearable device data) from thousands of individuals.
Aim 1 will deepen our understanding of aging phenotypes by performing integrative analyses, leveraging data across projects, and capabilities developed and deployed within the Systems Biology Core.
Aim 2 will expand, maintain, and distribute network reconstruction and analysis capabilities to the LC and broader scientific community.