{"doi":"10.1038/s42003-024-07408-7","title":"Genome-scale modeling identifies dynamic metabolic vulnerabilities during the epithelial to mesenchymal transition","abstract":"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.","journal":"Communications Biology","year":2024,"id":428806,"datarank":0.40112521845024623,"base_score":2.5649493574615367,"endowment":2.5649493574615367,"self_citation_contribution":0.38474240361923057,"citation_network_contribution":0.01638281483101567,"self_endowment_contribution":0.38474240361923057,"citer_contribution":0.01638281483101567,"corpus_percentile":null,"corpus_rank":null,"citation_count":12,"citer_count":9,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9441,"is_data_producer":true,"deposit_databanks":{"GEO":["GSE17708","GSE17518","GSE147405"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":329004,"name":"Scott Campit","orcid":"0000-0003-3454-8042","position":1,"is_corresponding":false},{"id":1230920,"name":"Shiva Krishna Katkam","orcid":null,"position":2,"is_corresponding":false},{"id":636398,"name":"Venkateshwar G. Keshamouni","orcid":"0000-0003-1947-791X","position":3,"is_corresponding":false},{"id":238048,"name":"Sriram Chandrasekaran","orcid":"0000-0002-8405-5708","position":4,"is_corresponding":false},{"id":1168587,"name":"Rupa Bhowmick","orcid":"0000-0001-5812-0513","position":0,"is_corresponding":true}],"reference_count":72,"raw_metadata":null,"created_at":"2026-07-19T01:59:02.165535Z","pmid":"39730911","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}