ArticleCommunications biology2024
Genome-scale modeling identifies dynamic metabolic vulnerabilities during the epithelial to mesenchymal transition.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- Bidirectional Feedback Between Metabolic Reprogramming and Epithelial-Mesenchymal Transition: From Mechanisms to Therapeutic Interventions.Molecules (Basel, Switzerland) · 2026Review
- Hallmarks of epithelial-mesenchymal plasticity in cancer.Molecular cancer · 2026Review
- Targeting metabolism to combat anticancer and antibacterial drug resistance.Trends in pharmacological sciences · 2026Review
- Mathematical Modeling in Cancer Metabolism: Tools for Translational Applications in Metabolism-Based Therapy.Advances in experimental medicine and biology · 2026Review
- A fusion-based multiomics classification approach for enhanced gene discovery in non-small cell lung cancer.Bioinformatics advances · 2026Article
- Metabolic Objectives and Trade-Offs: Inference and Applications.Metabolites · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.