Shanghai Jiao Tong University Systematic Investigation of Metabolic Reprogramming in Different Cancers Based on Tissue-specific Metabolic Models Fangzhou Shen, Jian Li, Ying Zhu, Zhuo Wang 王卓 zhuowang@sjtu.edu.cn Department of Bioinformatics and Biostatistics Shanghai Jiao Tong University GIW 2016 Shanghai
Outline Shanghai Jiao Tong University Introduction Omics-Integrated metabolic network analysis Metabolic reprogramming in different types of cancer Conclusion
Genotype–phenotype relationship in biology and an analogous view in an electrical system Shanghai Jiao Tong University 3 Yurkovich JT & Palsson BO. Proceedings of the IEEE, 2016
Genome-scale metabolic model Shanghai Jiao Tong University Genome-scale Metabolic Network Biomass yield ATP yield Nutrient uptake u Overproduce products for metabolic Redox potential engineering. u Identify targets and biomarkers for complex diseases. Reaction flux Reaction steps to biomass Schuetz et al. (2007) Molecular Systems Biology
Biochemically, Genetically and Genomically (BiGG) Genome-Scale Metabolic Reconstructions Shanghai Jiao Tong University S. aureus M. barkeri H. sapiens S. typhimurium • 640 Reactions • 619 Reactions • 3311 Reactions RBC • 898 Reactions Mitoc. • 619 Genes • 692 Genes • 1496 Genes • 39 Rxns • 218 Rxns • 826 Genes S. aureus S. typhimurium H. influenzae H. pylori E. coli • 2035 Reactions H. pylori • 1260 Genes S. cerevisiae • 558 Reactions • 1402 Reactions • 341 Genes M. tuberculosis • 910 Genes • 939 Reactions • 661 Genes H. influenzae • 472 Reactions • 376 Genes
Flux Balance Analysis (FBA) Representing a metabolic network as a stoichiometric set of equations and implying the steady state, it is possible to represent it as a stoichiometric set of equations. Orth J D, Thiele I, Palsson B Ø. Nature biotechnology, 2010;28(3): 245-248.
Outline Shanghai Jiao Tong University Introduction Omics-Integrated metabolic network analysis (Collaboration with Institute for Systems Biology) Metabolic reprogramming in different types of cancer Conclusion
Condition/tissue -specific model is necessary to reflect the real metabolic state Shanghai Jiao Tong University Omics-Integrated metabolic network Hyduke et al. Mol Biosyst, 2013
Integration of reconstructed metabolic network and regulatory network Shanghai Jiao Tong University
Integrated Deduced REgulation And Metabolism (IDREAM) Shanghai Jiao Tong University PROM ( P robabilistic R egulation o f M etabolism) Predict Condition Specific Growth Rates (Chandrasekaran & Price, PNAS 2010) EGRIN ( E nvironment & G ene R egulatory I nfluence N etwork) Identify Condition Specific Regulators (Bonneau et al, 2007; Danziger et al, 2014 )
Bootstrap for Gene Level Predictions Shanghai Jiao Tong University 1000 x + … + = TF Target FDR Activator? YDR253C YAL012W 0 TRUE YOL108C YAL012W 0.19 FALSE YHR124W YAL012W 0.43 FALSE YFL031W YAL012W 0.46 TRUE YFL031W YAL022C 0 TRUE YJL110C YAL022C 0.005 TRUE YPL089C YAL022C 0.015 FALSE YMR042W YAL022C 0.015 TRUE YHR084W YAL022C 0.025 FALSE Starting Assumption: If FDR=0.46, then 54% probability for the target to be controlled by TF. Wrong, but maybe useful.
Strategy for three integrative models Shanghai Jiao Tong University Zhuo Wang et al. Cell Systems (In second round review)
Shanghai Jiao Tong University
Composition of the integrated models PROM and IDREAM Shanghai Jiao Tong University
Correlation between measured growth and predicted growth when TFs are deleted using Yeast 6.06 Shanghai Jiao Tong University Correlation P-value Sum of squared Normalized sum of squared error error/ permutation P-value Integrative model 0.2110 0.0459 4.298 0.205/0.029 PROM_TF90 0.1019 0.4723 3.566 0.249/0.144 PROM _TF51 0.4183 0.0020 2.481 0.118/0.004 IDREAM-hybrid 0.4325 0.0014 2.506 0.121/0.003 IDREAM Glucose minimal media Measured growth ratio from Sarah-Maria et al. 2010. Molecular Systems Biology
IDREAM has higher Matthew correlation coefficient than PROM Shanghai Jiao Tong University permutation test by 500 random regulatory association and expression dataset with the same size, and found all p<0.05
ROC curves for growth defect predictions using IDREAM and PROM on Yeast6 model Shanghai Jiao Tong University Integrative model can predict growth change better than only metabolic network
Other media Shanghai Jiao Tong University normalized Integrative model Pearson p- sum of Permu 0.95 0.5 0.2 squared error corrcoef value p-value MCC MCC MCC galactose with ammonium medium - PROM 0.162 0.126 0.339 0.064 0.039 0.132 0.158 IDREAM-hybrid 0.227 0.106 0.196 0.058 0.010 0.111 0.312 IDREAM 0.288 0.038 0.182 0.025 0.308 0.146 0.347 glucose with urea medium PROM 0.188 0.075 0.213 0.040 0.093 0.096 0.009 IDREAM-hybrid 0.294 0.034 0.158 0.027 0.104 0.369 0.077 IDREAM 0.308 0.026 0.162 0.023 0.123 0.369 0.077
Matthews correlation coefficients between predicted and experimental growth changes across different media Shanghai Jiao Tong University
YAL051W YBL005W YBL021C YBL103C YBR049C YBR083W YBR182C YCL055W YCR065W YDL020C Shanghai Jiao Tong University YDL056W Double deletion of TF and metabolic genes YDL170W YDR034C YDR123C YDR146C YDR207C YDR216W YDR253C YDR259C YDR423C YEL009C YER040W YER111C YFL021W YFL031W YFR034C YGL013C YGL035C YGL071W YGL073W YGL166W YGL209W YGL237C YGL254W YGR044C YHR084W YHR124W YHR178W YHR206W YIL036W YIL101C YIL131C YIR023W YJL056C YJL089W YJL110C YJR060W YJR094C YKL015W YKL038W YKL062W YKL109W YKL112W YKL185W YKR034W YKR064W YLR014C YLR098C YLR131C YLR176C YLR228C YLR256W YLR451W YML007W YML099C YMR021C YMR037C YMR042W YMR043W YMR070W YMR280C YNL027W YNL068C YNL103W YNL167C YNL204C YNL216W YNL314W YOL028C YOL067C YOL108C YOR028C YOR140W YOR337W YOR358W YOR363C YPL075W YPL089C YPL248C YPR065W YPR199C 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Highly synthetic lethal TF and metabolic genes Shanghai Jiao Tong University Consistent with Gene Metabolic SingleTF_KO SingleGene_ Double_KO experiment encoding TF gene grRatio KO grRatio grRatio 9 pairs of highly synthetic lethal TF and metabolic genes PIP2 TES1 1.000 1.000 0.000 N CIN5 GRX5 0.994 1.000 0.000 N CIN5 ALD2 0.994 1.000 0.000 N OAF1 PDB1 0.984 1.000 0.000 Y OAF1 PDA1 0.984 1.000 0.000 Y OAF1 PDX1 0.984 1.000 0.000 Y OAF1 LAT1 0.984 1.000 0.000 Y OAF1 LPD1 0.984 0.989 0.000 Y ECM22 CDC8 1.000 1.000 0.005 N 9 control pairs (consisting of the same TFs and randomly selected metabolic genes) PIP2 ARO9 1.000 1.000 1.000 Y CIN5 ALD6 0.994 0.994 0.994 Y CIN5 CTT1 0.994 1.000 0.994 Y OAF1 ACS1 0.984 0.984 0.984 Y OAF1 PAN6 0.984 0.999 0.983 Y OAF1 AVT1 0.984 1.000 0.984 Y OAF1 SDT1 0.984 1.000 0.984 Y OAF1 THI6 0.984 1.000 0.984 Y ECM22 RIP1 1.000 1.000 1.000 Y
Discovery of genetic interactions between OAF1 and three components of the PDH complex Shanghai Jiao Tong University
Outline Shanghai Jiao Tong University Introduction Omics-Integrated metabolic network analysis Metabolic reprogramming in different types of cancer Conclusion
Applications of Genome-scale metabolic models in cancer Yizhak K, et al. Molecular systems biology, 2015;11(6): 817
Warburg effect in cancer Most cancer cells utilize high amounts of glucose and secrete it as lactate even in the presence of oxygen Vander Heiden et al., science, 2009;324(5930):1029-1033
Cancer-specific model by integrating proteome data with human metabolic reconstruction HMR 2 8100 reactions,6000 metabolites 3668 enzyme-coding genes New opportunities in studying metabolic alteration in various kinds of cancers by tissue-specific reconstructed models. Breast, liver, lung, renal, urothelial cancer
INIT Cancer INIT Normal Breast cancer Glandular cells Liver cancer Hepatocytes Macrophages Lung cancer Pneumocytes Cells in glomeruli Renal cancer Cells in tubules Urothelial cancer Urothelial cells
Number of reactions in different cancer specific models more reactions are required to be active in liver cancers for the cell growth.
Flux distributions in normal cells log value of raw flux
Flux distributions in cancer cells log value of raw flux The difference among cancer tissues investigated here is more dramatic than that among normal tissues.
Up-&down- regulated fluxes in cancer vs. normal flux ratio greater than 1.5 are regarded as up-regulated, and smaller than 0.67 are down-regulated
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