Abstract
Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.
Highlights
Regulation of metabolism through rewiring of biochemical pathways occurs in response to physiological and pathobiological signals, and dysregulation is increasingly linked to progression of complex diseases, including cancer (1)
Since perturbations in metabolic pathways caused by physiological regulation or disease likely propagate through a wider network encompassing proteins that interact physically and functionally with these enzymes (37,38), we reasoned that high-confidence protein-protein interactions (PPI) could be used to extend the coverage of metabolic-pathway models to a greater fraction of the proteome
While recent progress continues to be made on correlative relationships between the metabolome and genome (55), we have demonstrated the utility and importance of expanded functional models for exploring connections between metabolism and the proteome
Summary
P, Emili A, Multi-Omic Metabolic Enrichment Network Analysis reveals metabolite-protein physical interaction subnetworks altered in cancer, Molecular & Cellular Proteomics (2022), doi: https://. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism.
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