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

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.

[1]  F. Watanabe,et al.  Methylmalonic acid inhibits respiration in rat liver mitochondria. , 1995, The Journal of nutrition.

[2]  Ehsan Motamedian,et al.  Prediction of proton exchange and bacterial growth on various substrates using constraint-based modeling approach , 2011 .

[3]  Monica L. Mo,et al.  Global reconstruction of the human metabolic network based on genomic and bibliomic data , 2007, Proceedings of the National Academy of Sciences.

[4]  R. Moreno-Sánchez,et al.  Bioenergetic pathways in tumor mitochondria as targets for cancer therapy and the importance of the ROS-induced apoptotic trigger. , 2010, Molecular aspects of medicine.

[5]  R. Agarwal,et al.  Ovarian cancer: strategies for overcoming resistance to chemotherapy , 2003, Nature Reviews Cancer.

[6]  Eyal Gottlieb,et al.  Metabolic transformation in cancer. , 2009, Carcinogenesis.

[7]  M. Andreeff,et al.  Mitochondrial uncoupling and the Warburg effect: molecular basis for the reprogramming of cancer cell metabolism. , 2009, Cancer research.

[8]  E. Partridge,et al.  Cyclophosphamide and cisplatin compared with paclitaxel and cisplatin in patients with stage III and stage IV ovarian cancer , 1996, New England Journal of Medicine.

[9]  D. Stewart,et al.  Mechanisms of resistance to cisplatin and carboplatin. , 2007, Critical reviews in oncology/hematology.

[10]  Michael C. Jewett,et al.  Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p , 2009, Proceedings of the National Academy of Sciences.

[11]  M. Yoshino,et al.  Effects of short and medium chain length fatty acids on pyruvate oxidation by cultured human fibroblasts and rat liver mitochondria , 1984, Journal of Inherited Metabolic Disease.

[12]  Roded Sharan,et al.  Genome-Scale Metabolic Modeling Elucidates the Role of Proliferative Adaptation in Causing the Warburg Effect , 2011, PLoS Comput. Biol..

[13]  Bernhard O. Palsson,et al.  Context-Specific Metabolic Networks Are Consistent with Experiments , 2008, PLoS Comput. Biol..

[14]  Jason A. Papin,et al.  Functional integration of a metabolic network model and expression data without arbitrary thresholding , 2011, Bioinform..

[15]  H. Agha-alinejad,et al.  The effect of exercise training on the level of tissue IL-6 and vascular endothelial growth factor in breast cancer bearing mice , 2014, Iranian journal of basic medical sciences.

[16]  Ronan M. T. Fleming,et al.  A community-driven global reconstruction of human metabolism , 2013, Nature Biotechnology.

[17]  Nathan D. Price,et al.  Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE , 2012, BMC Systems Biology.

[18]  J. Reed,et al.  RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations , 2012, Genome Biology.

[19]  Xiaobo Zhou,et al.  Predicting enzyme targets for cancer drugs by profiling human Metabolic reactions in NCI-60 cell lines , 2010, BMC Bioinformatics.

[20]  B. Palsson,et al.  A protocol for generating a high-quality genome-scale metabolic reconstruction , 2010 .

[21]  M. Gomez-Lazaro,et al.  Malonate induces cell death via mitochondrial potential collapse and delayed swelling through an ROS‐dependent pathway , 2005, British journal of pharmacology.

[22]  K. Nephew,et al.  Integrated analysis of DNA methylation and gene expression reveals specific signaling pathways associated with platinum resistance in ovarian cancer. , 2009, BMC medical genomics.

[23]  Desmond S. Lun,et al.  Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production , 2009, PLoS Comput. Biol..

[24]  Jason A. Papin,et al.  Integration of expression data in genome-scale metabolic network reconstructions , 2012, Front. Physio..

[25]  R. Deberardinis,et al.  The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. , 2008, Cell metabolism.

[26]  A. Schulze,et al.  Targeting cancer metabolism--aiming at a tumour's sweet-spot. , 2012, Drug discovery today.

[27]  Markus J. Herrgård,et al.  Network-based prediction of human tissue-specific metabolism , 2008, Nature Biotechnology.

[28]  D. Bowtell,et al.  Identification of Novel Therapeutic Targets in Microdissected Clear Cell Ovarian Cancers , 2011, PloS one.

[29]  Natapol Pornputtapong,et al.  Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT , 2012, PLoS Comput. Biol..

[30]  A. Bordbar,et al.  Using the reconstructed genome‐scale human metabolic network to study physiology and pathology , 2012, Journal of internal medicine.

[31]  Sten Orrenius,et al.  The Warburg effect and mitochondrial stability in cancer cells. , 2010, Molecular aspects of medicine.

[32]  Crispin J. Miller,et al.  Cell Culture , 2010, Cell.

[33]  Matej Oresic,et al.  Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer. , 2012, Cancer research.

[34]  M. Wajner,et al.  Methylmalonate inhibits succinate-supported oxygen consumption by interfering with mitochondrial succinate uptake , 2008, Journal of Inherited Metabolic Disease.

[35]  B. Palsson,et al.  Genome-scale models of microbial cells: evaluating the consequences of constraints , 2004, Nature Reviews Microbiology.

[36]  Ronan M. T. Fleming,et al.  Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0 , 2007, Nature Protocols.

[37]  J. Stuart,et al.  Superoxide activates mitochondrial uncoupling proteins , 2002, Nature.