Genome-scale modeling identifies dynamic metabolic vulnerabilities during the epithelial to mesenchymal transition.

Autor: Bhowmick, Rupa, Campit, Scott, Katkam, Shiva Krishna, Keshamouni, Venkateshwar G., Chandrasekaran, Sriram
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Zdroj: Communications Biology; 12/27/2024, Vol. 7 Issue 1, p1-16, 16p
Abstrakt: Epithelial-to-mesenchymal transition (EMT) is a conserved cellular process critical for embryogenesis, wound healing, and cancer metastasis. During EMT, cells undergo large-scale metabolic reprogramming that supports multiple functional phenotypes including migration, invasion, survival, chemo-resistance and stemness. However, the extent of metabolic network rewiring during EMT is unclear. In this work, using genome-scale metabolic modeling, we perform a meta-analysis of time-course transcriptomics, time-course proteomics, and single-cell transcriptomics EMT datasets from cell culture models stimulated with TGF-β. We uncovered temporal metabolic dependencies in glycolysis and glutamine metabolism, and experimentally validated isoform-specific dependency on Enolase3 for cell survival during EMT. We derived a prioritized list of metabolic dependencies based on model predictions, literature mining, and CRISPR-Cas9 essentiality screens. Notably, enolase and triose phosphate isomerase reaction fluxes significantly correlate with survival of lung adenocarcinoma patients. Our study illustrates how integration of heterogeneous datasets using a mechanistic computational model can uncover temporal and cell-state-specific metabolic dependencies. Metabolic modeling of single-cell and time-course omics data reveals temporal and cell-state specific metabolic dependencies during the epithelial-mesenchymal transition (EMT) and their impact on clinical outcomes of lung adenocarcinoma. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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