Autonomous Multimodal Metabolomics Data Integration for Comprehensive Pathway Analysis and Systems Biology

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Abstract
Comprehensive metabolomic data can be achieved using multiple orthogonal separation and mass spectrometry (MS) analytical techniques. However, drawing biologically relevant conclusions from this data and combining it with additional layers of data collected by other omic technologies present a significant bioinformatic challenge. To address this, a data processing approach was designed to automate the comprehensive prediction of dysregulated metabolic pathways/networks from multiple data sources. The platform autonomously integrates multiple MS-based metabolomics data types without constraints due to different sample preparation/extraction, chromatographic separation, or MS detection method. This multi-modal analysis streamlines the extraction of biological information from the metabolomics data as well as the contextualization within proteomics and transcriptomics datasets. As a proof of concept, this multi-modal analysis approach was applied to a colorectal cancer (CRC) study, in which complementary liquid chromatography mass spectrometry (LC/MS) data were combined with proteomic and transcriptomic data. Our approach provided a highly resolved overview of colon cancer metabolic dysregulation, with an average 17% increase of detected dysregulated metabolites per pathway and an increase in metabolic pathway prediction confidence. Moreover, 95% of the altered metabolic pathways matched with the dysregulated genes and proteins, providing additional validation at a systems level. The analysis platform is currently available via the XCMS Online (XCMSOnline.scripps.edu).
Funding Information
  • Biological and Environmental Research (DE-AC02-05CH11231)
  • National Institute on Drug Abuse (DA028555-09)
  • National Institute of Mental Health (MH062261-17)
  • National Institute of General Medical Sciences (R01 GM114368 (G.S.), GM114368-03, PO1 A1043376-02S1 (G.S.))

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