Meta-Analysis of Metabolic Determinants of Exercise Response in Common Funds Data

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Abstract Exercise is associated numerous health benefits, but defining the molecular mediators of these effects remains an active focus of biomedical research. With the advent of the ‘omics sciences, studies including the ongoing Molecular Transducers of Physical Activity Consortium (MoTrPAC) and multiple smaller-scale efforts have sought to map the “complete” molecular response to acute and chronic exercise. Much research is currently focused on integration of genomics, proteomics and metabolomics data within such studies, but another important strategy is meta-analysis of distinct data sets to evaluate consistencies and differences in molecular responses observed between different modes of exercise, sex, age, species, and other factors. Of the exercise- related meta-studies performed to date, most have focused on the genome and transcriptome whereas few have included metabolomics data. Reasons for this shortcoming include differences in analytical methods, inconsistency in compound naming and data reporting, and prevalence of unknown features in untargeted metabolomics data. Unknown metabolite identification and cross-study integration is challenging and requires application of computational and experimental strategies in a coordinated manner. Yet, the potential benefits are substantial – identification of novel metabolites and detection of consistent patterns of response have led to biological insights relevant to fundamental biology and human health, including exercise. Using data from NIH Common Fund data archives, we propose to develop a multi-study, multi-organism and multi-condition database of identified and unknown exercise responsive features of the metabolome. We will integrate data across studies and, when available, across ‘omes, to prioritize and identify unknowns within this database. We will achieve these goals by carrying out two specific aims: 1) We will perform a comprehensive survey and alignment of exercise-related small molecule features in MotrPAC data and from studies in the Metabolomics Workbench. We will use computational tools we have pioneered for metabolomics data cleaning, inter-laboratory data alignment, and network- and correlation-based analysis to prioritize unknown features for follow-up. 2) We will systematically track, annotate and identify high-priority exercise-responsive unknown features in metabolomics data using software and experimental techniques we and others have devised for MS/MS data collection to identify and annotate features not tractable by routine library search. Our study represents a crucial step between the map-building aims of MoTrPAC and detailed mechanistic studies of specific pathways and that hold potential for human health benefits through targeted interventions. We will share our database and associated data with the research community through publications and uploads to public data archives. We anticipate our efforts will contribute to improved understanding of the effects of exercise at the biochemical pathway level and will offer targets for future studies to help delineate the mechanisms by which small molecules contribute to its salutary effects on health.