Investigation on metabolism of cisplatin resistant ovarian cancer using a genome scale metabolic model and microarray data

Document Type: Original Article

Authors

1 Biotechnology Group, Department of Chemical Engineering, Tarbiat Modares University, Tehran, Iran. Drug Design and Bioinformatics Group, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran

2 Drug Design and Bioinformatics Group, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran. Eastern Mediterranean Health Genomics and Biotechnology Network (EMGEN), Tehran, Iran

3 Drug Design and Bioinformatics Group, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran

Abstract

Objective(s): Many cancer cells show significant resistance to drugs that kill drug sensitive cancer cells and non-tumor cells and such resistance might be a consequence of the difference in metabolism. Therefore, studying the metabolism of drug resistant cancer cells and comparison with drug sensitive and normal cell lines is the objective of this research.
Material and Methods:Metabolism of cisplatin resistant and sensitive A2780 epithelial ovarian cancer cells and normal ovarian epithelium has been studied using a generic human genome-scale metabolic model and transcription data.
Result:The results demonstrate that the most different metabolisms belong to resistant and normal models, and the different reactions are involved in various metabolic pathways. However, large portion of distinct reactions are related to extracellular transport for three cell lines. Capability of metabolic models to secrete lactate was investigated to find the origin of Warburg effect. Computational results introduced SLC25A10 gene, which encodes mitochondrial dicarboxylate transporter involved in exchanging of small metabolites across the mitochondrial membrane that may play key role in high growing capacity of sensitive and resistant cancer cells. The metabolic models were also used to find single and combinatorial targets that reduce the cancer cells growth. Effect of proposed target genes on growth and oxidative phosphorylation of normal cells were determined to estimate drug side-effects.
Conclusion: The deletion results showed that although the cisplatin did not cause resistant cancer cells death, but it shifts the cancer cells to a more vulnerable metabolism

Keywords


1.   Li M, Balch C, Montgomery J, Jeong M, Chung J, Yan P, et al. Integrated analysis of DNA methylation and gene expression reveals specific signaling pathways associated with platinum resistance in ovarian cancer. BMC Medical Genomics 2009; 2:34.

2.   McGuire WP, Hoskins WJ, Brady MF, Kucera PR, Partridge EE, Look KY, et al. Cyclophosphamide and cisplatin compared with paclitaxel and cisplatin in patients with stage iii and stage iv ovarian cancer. N Engl J Med 1996; 334:1-6.

3.   Agarwal R, Kaye SB. Ovarian cancer: strategies for overcoming resistance to chemotherapy. Nat Rev Cancer 2003; 3:502-516.

4.   Stewart DJ. Mechanisms of resistance to cisplatin and carboplatin. Crit Rev Oncol Hematol 2007; 63:12-31.

5.   Bordbar A, Palsson BO. Using the reconstructed genome-scale human metabolic network to study physiology and pathology. J Intern Med 2012; 271:131-141.

6.   Shalamzari SA, Agha-Alinejad H, Alizadeh S, Shahbazi S, Khatib ZK, Kazemi A, et al. The effect of exercise training on the level of tissue IL-6 and vascular endothelial growth factor in breast cancer bearing mice. Iran J Basic Med Sci 2014; 17:231-258.

7.   Jones NP, Schulze A. Targeting cancer metabolism–aiming at a tumour's sweet-spot. Drug Discov Today 2012; 17:232-241.

8.   Thiele I, Palsson BO. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 2010; 5:93-121.

9.   Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD, et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci 2007; 104:1777-1782.

10. Thiele I, Swainston N, Fleming RMT, Hoppe A, Sahoo S, Aurich MK, et al. A community-driven global reconstruction of human metabolism. Nat Biotechnol 2013; 31:419-425.

11. Folger O, Jerby L, Frezza C, Gottlieb E, Ruppin E, Shlomi T. Predicting selective drug targets in cancer through metabolic networks. Mol Syst Biol 2011; 7:501.

12. DeBerardinis RJ, Lum JJ, Hatzivassiliou G, Thompson CB. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab 2008; 7:11-20.

13. Tennant DA, Durán RV, Boulahbel H, Gottlieb E. Metabolic transformation in cancer. Carcinogenesis 2009; 30:1269-1280.

14. Price ND, Reed JL, Palsson BO. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2004; 2:886-897.

15. Motamedian E, Naeimpoor F. Prediction of proton exchange and bacterial growth on various substrates using constraint-based modeling approach. Biotechnol Bioproc E 2011; 16:875-84.

16. Shlomi T, Benyamini T, Gottlieb E, Sharan R, Ruppin E. Genome-scale metabolic modeling elucidates the role of proliferative adaptation in causing the Warburg effect. PLoS Comput Biol 2011; 7:e1002018.

17. Blazier AS, Papin JA. Integration of expression data in genome-scale metabolic network reconstructions. Front Physiol 2012; 3:299.

18. Becker SA, Palsson BO. Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol 2008; 4:e1000082.

19. Colijn C, Brandes A, Zucker J, Lun DS, Weiner B, Farhat MR, et al. Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput Biol 2009; 5:e1000489.

20. Shlomi T, Cabili MN, Herrgard MJ, Palsson BO, Ruppin E. Network-based prediction of human tissue-specific metabolism. Nature Biotech 2008; 26:1003-1010.

21. Moxley JF, Jewett MC, Antoniewicz MR, Villas-Boas SG, Alper H, Wheeler RT, et al. Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p. Proc Natl Acad Sci USA 2009; 106:6477-6482.

22. Jensen PA, Papin JA. Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics 2011; 27:541-547.

23. Kim J, Reed JL. RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations. Genome Biol 2012; 13:R78.

24. Agren R, Bordel S, Mardinoglu A, Pornputtapong N, Nookaew I, Nielsen J. Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput Biol 2012; 8:e1002518.

25. Wang Y, Eddy JA, Price ND. Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Syst Biol 2012; 6:153.

26. Li L, Zhou X, Ching WK, Wang P. Predicting enzyme targets for cancer drugs by profiling human metabolic reactions in NCI-60 cell lines. BMC Bioinformatics 2010; 11:501.

27. Jerby L, Wolf L, Denkert C, Stein GY, Hilvo M, Oresic M, et al. Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer. Cancer Res 2012; 72:5712-5720.

28. Becker SA, Palsson BO. Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol 2008; 4:e1000082.

29. Pepper S, Saunders E, Edwards L, Wilson C, Miller C. The utility of MAS5 expression summary and detection call algorithms. BMC Bioinformatics 2007; 8:273.

30. Stany MP, Vathipadiekal V, Ozbun L, Stone RL, Mok SC, Xue H, et al. Identification of novel therapeutic targets in microdissected clear cell ovarian cancers. PLoS One 2011; 6:e21121.

31. Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 2011; 6:1290-1307.

32. Gogvadze V, Zhivotovsky B, Orrenius S. The Warburg effect and mitochondrial stability in cancer cells. Mol Aspects Med 2010; 31:60-74.

33. Ralph SJ, Rodríguez-Enríquez S, Neuzil J, Moreno-Sánchez R. Bioenergetic pathways in tumor mitochondria as targets for cancer therapy and the importance of the ROS-induced apoptotic trigger. Mol Aspects Med 2010; 31:29-59.

34. Echtay KS, Roussel D, St-Pierre J, Jekabsons MB, Cadenas S, Stuart JA, et al. Superoxide activates mitochondrial uncoupling proteins. Nature 2002; 415:96-99.

35. Toyoshima S, Watanabe F, Saido H, Miyatake K, Nakano Y. Methylmalonic Acid Inhibits Respiration in Rat Liver Mitochondria. J Nutr 1995; 125:2846-2850.

36. Fernandez-Gomez FJ, Galindo MF, Gómez-Lázaro M, Yuste VJ, Comella JX, Aguirre N, et al. Malonate induces cell death via mitochondrial potential collapse and delayed swelling through an ROS-dependent pathway. Br J Pharmacol 2005; 144:528-537.

37. Mirandola SR, Melo DR, Schuck PF, Ferreira GC, Wajner M, Castilho RF. Methylmalonate inhibits succinate-supported oxygen consumption by interfering with mitochondrial succinate uptake. J Inherit Metab Dis 2008; 31:44-54.

38. Koga Y, Yoshino M, Yamashita F. Effects of short and medium chain length fatty acids on pyruvate oxidation by cultured human fibroblasts and rat liver mitochondria. J Inherit Metab Dis 1984; 7:141-142.

39. Samudio I, Fiegl M, Andreeff M. Mitochondrial uncoupling and the Warburg effect: molecular basis for the reprogramming of cancer cell metabolism. Cancer Res 2009; 69:2163-2166