Iranian biomedical journal · 2025
Genome-Scale Metabolic Modeling Identifies Synergistic Metabolites that Enhance 5-Fluorouracil Efficacy in Colon Cancer.
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Rahimi-Tamandegani Hasan, Marashi Ayed-Amir, Ghavami Ghazaleh, Mehrmohamadi Mahya, Sardari Soroush
Аннотация
BACKGROUND: Colon cancer remains a leading cause of cancer-related mortality, with the efficacy of standard chemotherapy agents such as 5-FU limited by resistance and toxicity. This study aimed to identify metabolites that enhance 5-FU efficacy in CRC using GEM.
METHODS: GEM was applied using the FVSEOF algorithm to identify metabolites that enhance the therapeutic efficacy of 5-FU in CRC. Context-specific metabolic models were constructed from TCGA data using the ftINIT algorithm. By simulating TS inhibition, we identified aspartate, lysine, and valine as candidate metabolites with altered uptake under constrained biomass production. Among them, lysine and valine are essential amino acids and were chosen for experimental validation using MTT assays and flow cytometry in HT-29 CRC cells and HU02 normal fibroblasts.
RESULTS: Our approach identified aspartate, lysine, and valine as candidate synergistic metabolites. Experimental validation confirmed the strong synergy of lysine and valine with 5-FU in HT-29 cells, while showing significantly reduced effects in normal fibroblasts. Mechanistic analysis suggested that these amino acids enhance nucleotide demand and metabolic activity, amplifying 5-FU-induced stress.
CONCLUSION: This study demonstrates synergistic interventions and introduces amino acid co-supplementation as a potential strategy to improve CRC therapy with reduced toxicity. To our knowledge, this is the first study to employ GEM for the systematic prediction of metabolites that synergize with 5-FU, aiming to improve therapeutic outcomes in CRC.
METHODS: GEM was applied using the FVSEOF algorithm to identify metabolites that enhance the therapeutic efficacy of 5-FU in CRC. Context-specific metabolic models were constructed from TCGA data using the ftINIT algorithm. By simulating TS inhibition, we identified aspartate, lysine, and valine as candidate metabolites with altered uptake under constrained biomass production. Among them, lysine and valine are essential amino acids and were chosen for experimental validation using MTT assays and flow cytometry in HT-29 CRC cells and HU02 normal fibroblasts.
RESULTS: Our approach identified aspartate, lysine, and valine as candidate synergistic metabolites. Experimental validation confirmed the strong synergy of lysine and valine with 5-FU in HT-29 cells, while showing significantly reduced effects in normal fibroblasts. Mechanistic analysis suggested that these amino acids enhance nucleotide demand and metabolic activity, amplifying 5-FU-induced stress.
CONCLUSION: This study demonstrates synergistic interventions and introduces amino acid co-supplementation as a potential strategy to improve CRC therapy with reduced toxicity. To our knowledge, this is the first study to employ GEM for the systematic prediction of metabolites that synergize with 5-FU, aiming to improve therapeutic outcomes in CRC.