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Prediction of Therapeutic microRNA based on the Human Metabolic Network

Prediction of Therapeutic microRNA based on the Human Metabolic Network. Ming Wu, Christina Chan Bioinformatics Advance Access Published January 7, 2014. MicroRNAs. MicroRNA’s (miRNA’s) are ~21 nt long noncoding RNA molecules

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Prediction of Therapeutic microRNA based on the Human Metabolic Network

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  1. Prediction of Therapeutic microRNA based on the Human Metabolic Network Ming Wu, Christina Chan Bioinformatics Advance Access Published January 7, 2014

  2. MicroRNAs • MicroRNA’s (miRNA’s) are • ~21 nt long • noncoding RNA molecules • regulate eukaryotic gene expression at the translation level

  3. MicroRNA Biogenesis and Targeting RCAF 2013

  4. Cancer

  5. Bioinformatics in Cancer Therapeutics

  6. Research Statement • Integrate • putative miRNA-target-gene interactions, • metabolic modeling and • context-specific gene expression data • Why human metabolic system? • Abnormal metabolic functions are known to be involved in supporting tumor growth and proliferation

  7. Hypothesis • miRNAs could be implicated in the metabolic regulation of cancer

  8. Approach • Current knowledge of the human metabolic network to reconstruct a context-specific metabolic system for human liver cancer (hepatocellular carcinoma, or HCC). • The model is used to predict miRNAs whose overexpression or delivery could inhibit cancer cell growth by downregulating its target metabolic genes.

  9. Network Reconstruction • Assumptions- • relative gene expression state change between phenotypes, rather than the “expression level/state” of a gene • The state-change of the genes/enzymes as indicated by the context information, determines the state-change of the reactions, by modulating the maximum reaction rate

  10. Dataset • Metabolic network and the reconstruction • Human metabolic network is obtained from Duarte et al., 2007 • Liver specific gene expression: Shlomiet al., 2008 • Biomass production: experimentally measured compositions of DNA, RNA, amino acids and lipids in cancer cells • focused on the potential of miRNAs on affecting tumor growth • assumed that the miRNAs affect the maximal rate of cancer cell growth

  11. Incorporation of miRNA regulation • The metabolic gene targets of each miRNA were obtained from TargetScan • 153 conserved miRNA families • Simulated the liver specific metabolic network by optimizing the biomass production to predict whether a miRNA could effectively inhibit cancer growth

  12. Test the Accuracy of Prediction • Literature search • 50 related papers in peer-reviewed journals, which involve experimental studies on HCC or related human liver cell lines with regards to 41 of the 153 miRNA families. • Test-set includes 41 miRNAs that have been experimentally studied in liver cancer. • 23 of these 41 miRNAs inhibit liver cancer growth/metastasis if over-expressed • 18 have no effect or have enhancing effect

  13. Comparison with existing methods • Comparison with GIMME • reconstructs tissue-specific networks and simulates by optimizing an objective function

  14. Exploring mechanisms of how miRNAs affect cancer metabolism • “essential metabolic genes” is obtained from • COLT-Cancer, a genome-wide pooled shRNA screen dataset (Kohet al., 2011). • Essential is defined by a significant (p<0.05) reduction in the survival and proliferation of the cells upon their knockdown by shRNA in more than one cancer cell lines.

  15. Perturbation of miRNA levels that regulate metabolic genes • Condition-specific metabolic system is constructed by • overexpressing each miRNA with their targets downregulated • simulated to obtain the maximum achievable biomass production rate F upon the perturbation of the miRNA. • This is compared with the rate F0 in the “wild-type” liver cancer metabolic network without up-regulation of the miRNAs. • For each miRNA, Score F0 - F is computed • miRNAs are ranked by this score.

  16. Approach

  17. Results

  18. Warburg Effect • most well-known cancer biochemical phenotype • A metabolic adaptive response in cancer cells to satisfy the high demand of the molecular building blocks of the cell, i.e. nucleotides, fatty acids, lipids and amino acids, and the energy requirements through ATP, to facilitate proliferation • Cancer cells metabolize glucose at high rates, and shift the flux downstream of glucose from the mitochondrial TCA cycle to the more rapid anerobic glycolysis, thereby producing vast amounts of lactate that is secreted from the cancer cells • Excessive lactate production • Analyzed the flux change in response to miRNA perturbations on the reaction catalyzed by lactate dehydrogenase, which converts pyruvate into lactate.

  19. Pathways Affected by Essential Enzymes

  20. Conclusion • miRNAs could regulate cancer growth by directly modulating metabolic functions • Targeting essential enzymes in metabolic reactions • The limitation of the study is that there could be indirect effects potentially responsive to signaling and transcriptional regulations that are mediated by the miRNAs. • The possible interactions between miRNA, signaling processes, transcription factors and the expression of metabolic genes complicated the study in exploring the functional roles of miRNAs in cancer

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