Model-driven discovery of long-chain fatty acid metabolic reprogramming in heterogeneous prostate cancer cells.
Igor Marín de MasEsther AguilarErika ZoddaCristina BalcellsSilvia MarinGuido DallmannTimothy M ThomsonBalázs PappMarta CascantePublished in: PLoS computational biology (2018)
Epithelial-mesenchymal-transition promotes intra-tumoral heterogeneity, by enhancing tumor cell invasiveness and promoting drug resistance. We integrated transcriptomic data for two clonal subpopulations from a prostate cancer cell line (PC-3) into a genome-scale metabolic network model to explore their metabolic differences and potential vulnerabilities. In this dual cell model, PC-3/S cells express Epithelial-mesenchymal-transition markers and display high invasiveness and low metastatic potential, while PC-3/M cells present the opposite phenotype and higher proliferative rate. Model-driven analysis and experimental validations unveiled a marked metabolic reprogramming in long-chain fatty acids metabolism. While PC-3/M cells showed an enhanced entry of long-chain fatty acids into the mitochondria, PC-3/S cells used long-chain fatty acids as precursors of eicosanoid metabolism. We suggest that this metabolic reprogramming endows PC-3/M cells with augmented energy metabolism for fast proliferation and PC-3/S cells with increased eicosanoid production impacting angiogenesis, cell adhesion and invasion. PC-3/S metabolism also promotes the accumulation of docosahexaenoic acid, a long-chain fatty acid with antiproliferative effects. The potential therapeutic significance of our model was supported by a differential sensitivity of PC-3/M cells to etomoxir, an inhibitor of long-chain fatty acid transport to the mitochondria.
Keyphrases
- fatty acid
- induced apoptosis
- cell cycle arrest
- prostate cancer
- epithelial mesenchymal transition
- endoplasmic reticulum stress
- small cell lung cancer
- squamous cell carcinoma
- single cell
- machine learning
- small molecule
- oxidative stress
- rna seq
- bone marrow
- dna methylation
- cell adhesion
- cell migration
- artificial intelligence
- electronic health record