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

Area of Science:

• Cell Biology
• Metabolic Engineering
• Cancer Research

Background:

• Epithelial-to-mesenchymal transition (EMT) is a fundamental cellular process involved in development and disease.
• EMT involves significant metabolic reprogramming to support altered cellular functions like migration and survival.
• The precise extent of metabolic network rewiring during EMT remains incompletely understood.

Purpose of the Study:

• To comprehensively analyze metabolic network alterations during EMT.
• To identify temporal and cell-state-specific metabolic dependencies.
• To uncover potential therapeutic targets for EMT-related conditions, particularly cancer metastasis.

Main Methods:

• Genome-scale metabolic modeling was employed for meta-analysis.
• Integration of time-course transcriptomics, proteomics, and single-cell transcriptomics datasets from TGF-β stimulated EMT models.
• Experimental validation, literature mining, and CRISPR-Cas9 essentiality screens were utilized.

Main Results:

• Temporal dependencies in glycolysis and glutamine metabolism during EMT were uncovered.
• An isoform-specific dependency on Enolase3 for cell survival during EMT was experimentally validated.
• Metabolic dependencies, including enolase and triose phosphate isomerase fluxes, were found to correlate with lung adenocarcinoma patient survival.

Conclusions:

• Integration of heterogeneous datasets via mechanistic modeling elucidates EMT-driven metabolic changes.
• The study highlights specific metabolic vulnerabilities during EMT with implications for cancer therapy.
• Enolase3 emerges as a potential therapeutic target for enhancing survival in EMT-associated cancers.