Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

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.     

A sticky situation: CCN1 promotes both proliferation and apoptosis of cancer cells

Connective tissue growth factor (CTGF/CCN2) is overexpressed in diabetes. Diabetic rats possess myocardial and cardiomyocyte hypertrophy. In a recent report, Wang and colleagues (Am J Physiol Cell Physiol. 2009 Jul 22. [Epub ahead of print]) show that CCN2 directly mediates cardiomyocyte hypertrophy as well as that induced by high glucose and fatty acid. CCN2 acted via the TrkA receptor. These data are the subject of this commentary, and emphasize that CCN2 may be an excellent target for therapy in diabetes.