Back to Search Start Over

Model-driven discovery of long-chain fatty acid metabolic reprogramming in heterogeneous prostate cancer cells.

Authors :
Marín de Mas, Igor
Aguilar, Esther
Zodda, Erika
Balcells, Cristina
Marin, Silvia
Dallmann, Guido
Thomson, Timothy M.
Papp, Balázs
Cascante, Marta
Source :
PLoS Computational Biology. 1/2/2018, Vol. 14 Issue 1, p1-20. 20p.
Publication Year :
2018

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1553734X
Volume :
14
Issue :
1
Database :
Academic Search Index
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
127061924
Full Text :
https://doi.org/10.1371/journal.pcbi.1005914