Proliferation inhibition of cisplatin-resistant ovarian cancer cells using drugs screened by integrating a metabolic model and transcriptomic data.

OBJECTIVES: If screening to find effective drugs is possible, the inhibition of proliferation using existing drugs can be a practical strategy to control the drug resistance of cancer. Development of a system-oriented strategy to find effective drugs was the main aim of this research. MATERIALS AND METHODS: An algorithm (transcriptional regulated flux balance analysis [TRFBA]) integrating a generic human metabolic model with transcriptomic data was used to identify genes affecting the growth of drug-resistant cancer cells. Drugs that inhibit activation of the target genes were found and their effect on the proliferation was experimentally evaluated. RESULTS: Experimental assessments demonstrated that TRFBA improves the prediction of cancer cell growth in comparison with previous algorithms. The algorithm was then used to propose the system-oriented strategy to search drugs effective in limiting the growth rate of the cisplatin-resistant A2780 epithelial ovarian cancer cell. Experimental evaluations resulted in the selection of azathioprine, terbinafine, hydralazine and sodium valproate that appropriately inhibit the proliferation of resistant cancer cells while minimally affecting normal cells. Furthermore, experimental data indicate that the selected drugs are synergistic and can be used in combination therapies. CONCLUSIONS: The proposed strategy was successful to identify drugs effective on the viability of resistant cancer cells. This strategy can enhance the potency of treatments for drug-resistant cancer cells and provides the possibility of using existing drugs.

Reconstruction of a charge balanced genome-scale metabolic model to study the energy-uncoupled growth of Zymomonas mobilis ZM1.

Zymomonas mobilis is an ethanologenic bacterium and is known to be an example microorganism with energy-uncoupled growth. A genome-scale metabolic model could be applicable for understanding the characteristics of Z. mobilis with rapid catabolism and inefficient energy conversion. In this study, a charge balanced genome-scale metabolic model (iEM439) of Z. mobilis ATCC 10988 (ZM1) including 439 genes, 692 metabolic reactions and 658 metabolites was reconstructed based on genome annotation and previously published information. The model presents a much better prediction for biomass and ethanol concentrations in a batch culture by using dynamic flux balance analysis compared with the two previous genome-scale metabolic models. Furthermore, intracellular flux distribution obtained from the model was consistent with the fluxes for glucose fermentation determined by (13)C NMR. The model predicts that there is no difference in growth rates of Z. mobilis under aerobic and anaerobic conditions whereas ethanol production is decreased and production of other metabolites including acetate and acetoin is increased under aerobic conditions. Experimental data confirm the predicted differences between the aerobic and anaerobic growth of Z. mobilis. Finally, the model was used to study the energy-uncoupled growth of Z. mobilis and to predict its effect on flux distribution in the central metabolism. Flux distribution obtained from the model indicates that coupling growth and energy reduces ethanol secretion and changes the flux distribution to produce more biomass. This coupling is also associated with a significant increase in the proton uptake rate based on the prediction of the charge balanced model. Hence, resistance to intracellular pH reduction could be the main reason for uncoupled growth and Z. mobilis uses ATPase to pump out the proton. Experimental observations are in accordance with the predicted relationship between growth, ATP dissipation and proton exchange.