Systematic Investigation of Metabolic Reprogramming in Different - - PowerPoint PPT Presentation
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 zhuowang@sjtu.edu.cn Department of
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zhuowang@sjtu.edu.cn Department of Bioinformatics and Biostatistics Shanghai Jiao Tong University
Shanghai Jiao Tong University
Shanghai Jiao Tong University
Yurkovich JT & Palsson BO. Proceedings of the IEEE, 2016
Genotype–phenotype relationship in biology and an analogous view in an electrical system
3
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Biomass yield Redox potential Nutrient uptake Reaction flux Reaction steps to biomass ATP yield
Schuetz et al. (2007) Molecular Systems Biology
Genome-scale Metabolic Network uOverproduce products for metabolic engineering. uIdentify targets and biomarkers for complex diseases.
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Biochemically, Genetically and Genomically (BiGG) Genome-Scale Metabolic Reconstructions
Mitoc.
RBC
Orth J D, Thiele I, Palsson B Ø. Nature biotechnology, 2010;28(3): 245-248.
Representing a metabolic network as a stoichiometric set
steady state, it is possible to represent it as a stoichiometric set of equations.
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(Collaboration with Institute for Systems Biology)
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Condition/tissue -specific model is necessary to reflect the real metabolic state
Hyduke et al. Mol Biosyst, 2013
Omics-Integrated metabolic network
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Integration of reconstructed metabolic network and regulatory network
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EGRIN
(Environment & Gene Regulatory Influence Network)
Identify Condition Specific Regulators (Bonneau et al, 2007; Danziger et al, 2014 ) PROM
(Probabilistic Regulation of Metabolism)
Predict Condition Specific Growth Rates (Chandrasekaran & Price, PNAS 2010)
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TF Target FDR Activator? YDR253C YAL012W TRUE YOL108C YAL012W 0.19 FALSE YHR124W YAL012W 0.43 FALSE YFL031W YAL012W 0.46 TRUE YFL031W YAL022C 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.
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Zhuo Wang et al. Cell Systems (In second round review)
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Composition of the integrated models PROM and IDREAM
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Correlation between measured growth and predicted growth when TFs are deleted using Yeast 6.06
Measured growth ratio from Sarah-Maria et al. 2010. Molecular Systems Biology Integrative model Correlation P-value Sum of squared error Normalized sum of squared error/ permutation P-value PROM_TF90 0.2110 0.0459 4.298 0.205/0.029 PROM _TF51 0.1019 0.4723 3.566 0.249/0.144 IDREAM-hybrid 0.4183 0.0020 2.481 0.118/0.004 IDREAM 0.4325 0.0014 2.506 0.121/0.003
Glucose minimal media
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permutation test by 500 random regulatory association and expression dataset with the same size, and found all p<0.05
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ROC curves for growth defect predictions using IDREAM and PROM on Yeast6 model
Integrative model can predict growth change better than only metabolic network
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Integrative model Pearson corrcoef p- value normalized sum of squared error Permu p-value 0.95 MCC 0.5 MCC 0.2 MCC galactose with ammonium medium PROM 0.162 0.126 0.339 0.064 0.039 0.132
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
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Matthews correlation coefficients between predicted and experimental growth changes across different media
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YAL051W YBL005W YBL021C YBL103C YBR049C YBR083W YBR182C YCL055W YCR065W YDL020C YDL056W 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
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
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Gene encoding TF Metabolic gene SingleTF_KO grRatio SingleGene_ KO grRatio Double_KO grRatio Consistent with experiment 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
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Discovery of genetic interactions between OAF1 and three components of the PDH complex
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Yizhak K, et al. Molecular systems biology, 2015;11(6): 817
Vander Heiden et al., science, 2009;324(5930):1029-1033
Most cancer cells utilize high amounts of glucose and secrete it as lactate even in the presence of oxygen
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 Lung cancer Macrophages Pneumocytes Renal cancer Cells in glomeruli Cells in tubules Urothelial cancer Urothelial cells
more reactions are required to be active in liver cancers for the cell growth.
log value of raw flux
The difference among cancer tissues investigated here is more dramatic than that among normal tissues.
log value of raw flux
flux ratio greater than 1.5 are regarded as up-regulated, and smaller than 0.67 are down-regulated
Glycine consumption and expression of glycine biosynthetic pathway strongly correlated with rates of proliferation in cancer. Amino acid metabolism involving serine and glycine, and glycerophospholipid provide components as cellular building blocks for cell growth and proliferation. Glutothione metabolism is upregulated specifically in lung cancer compared with normal lung macrophages, and such elevated level is a protection for cancercells from some anticancerdrugs. Cholesterol metabolism is down-regulated specifically in lung cancer, which has been detected in lung cancerpatients. Exchange reaction
strong consistency among all studied tissues, where normal cells consume VLDL but cancer cells produce it a lot, which has been demonstrated by the significantly higher levels of VLDL from lipid profile in breast cancer patients.
Consistent with the protein expression and metabolite changes in ccRCC
We predicted 24 genes essential for renal cancer cell growth, but not essential for normal cells. There are 4 genes among these candidates having corresponding measurements in the study of the in-vivo screening of cell number reductions for five cell lines of clear cell renal cell carcinoma (ccRCC), and AGPAT6 and GALT have been validated as essential. Matthews correlation coefficient (MCC) to evaluate the accuracy of gene essentiality prediction, and got MCC =0.277 for renal cancer model prediction (p=0.05) Gatto, et al., Sci Rep.2015
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Integration of GRN and MN can predict phenotype change for gene knockouts better than only using MN. The Integrative IDREAM model can predict metabolic phenotype better than using traditional PROM. The similarity and heterogenicity
metabolic reprogramming in different cancers are crucial for understanding the aberrant mechanisms
cancer proliferation, which is fundamental for identifying drug targets and biomarkers.
Shanghai Jiao Tong University Acknowledgement
Institute for Systems Biology Nathan Price Sam Danziger Ben Heavner Shuyi Ma Jennifer Smith Nitin Baliga John Aitchison SJTU
Yixue Li Hui Lu Chuanli Wang Lin Liu Fangzhou Shen Jian Li Ying Zhu