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Network-based prediction of human tissue-specific metabolism

Nature Biotechnology volume 26, pages 1003–1010 (2008)Cite this article

Abstract

Direct in vivo investigation of mammalian metabolism is complicated by the distinct metabolic functions of different tissues. We present a computational method that successfully describes the tissue specificity of human metabolism on a large scale. By integrating tissue-specific gene- and protein-expression data with an existing comprehensive reconstruction of the global human metabolic network, we predict tissue-specific metabolic activity in ten human tissues. This reveals a central role for post-transcriptional regulation in shaping tissue-specific metabolic activity profiles. The predicted tissue specificity of genes responsible for metabolic diseases and tissue-specific differences in metabolite exchange with biofluids extend markedly beyond tissue-specific differences manifest in enzyme-expression data, and are validated by large-scale mining of tissue-specificity data. Our results establish a computational basis for the genome-wide study of normal and abnormal human metabolism in a tissue-specific manner.

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Figure 1: An example of predicting flux-activity states of genes based on a metabolic network model and gene-expression measurements.
Figure 2: The fraction of all metabolic genes in the model predicted to be active and inactive in ten different tissues.
Figure 3: Predicted tissue-specific activity of disease-causing genes.
Figure 4: An example of a sub-network of glycogen metabolism.
Figure 5: Comparison of network- and pathway-based prediction.

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References

  1. Fell, D.A. Understanding the Control of Metabolism (Portland Press, London, 1996).

  2. Domach, M.M., Leung, S.K., Cahn, R.E., Cocks, G.G. & Shuler, M.L. Computer model for glucose-limited growth of a single cell of Escherichia coli B/r-A. Reprinted from Biotechnology and Bioengineering 26, 203–216 (1984). Biotechnol. Bioeng. 67, 827–840 (2000).

    Article  CAS  Google Scholar 

  3. Price, N.D., Papin, J.A., Schilling, C.H. & Palsson, B.O. Genome-scale microbial in silico models: the constraints-based approach. Trends Biotechnol. 21, 162–169 (2003).

    Article  CAS  Google Scholar 

  4. Price, N.D., Reed, J.L. & Palsson, B.O. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat. Rev. Microbiol. 2, 886–897 (2004).

    Article  CAS  Google Scholar 

  5. Lanpher, B., Brunetti-Pierri, N. & Lee, B. Inborn errors of metabolism: the flux from Mendelian to complex diseases. Nat. Rev. Genet. 7, 449–460 (2006).

    Article  CAS  Google Scholar 

  6. Muoio, D.M. & Newgard, C.B. Obesity-related derangements in metabolic regulation. Annu. Rev. Biochem. 75, 367–401 (2006).

    Article  CAS  Google Scholar 

  7. Altucci, L., Leibowitz, M.D., Ogilvie, K.M., de Lera, A.R. & Gronemeyer, H. RAR and RXR modulation in cancer and metabolic disease. Nat. Rev. Drug Discov. 6, 793–810 (2007).

    Article  CAS  Google Scholar 

  8. Shi, Y. & Burn, P. Lipid metabolic enzymes: emerging drug targets for the treatment of obesity. Nat. Rev. Drug Discov. 3, 695–710 (2004).

    Article  CAS  Google Scholar 

  9. Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).

    Article  CAS  Google Scholar 

  10. Romero, P. et al. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 6, R2 (2005).

    Article  Google Scholar 

  11. Wiback, S.J. & Palsson, B.O. Extreme pathway analysis of human red blood cell metabolism. Biophys. J. 83, 808–818 (2002).

    Article  CAS  Google Scholar 

  12. Vo, T.D., Greenberg, H.J. & Palsson, B.O. Reconstruction and functional characterization of the human mitochondrial metabolic network based on proteomic and biochemical data. J. Biol. Chem. 279, 39532–39540 (2004).

    Article  CAS  Google Scholar 

  13. Chatziioannou, A., Palaiologos, G. & Kolisis, F.N. Metabolic flux analysis as a tool for the elucidation of the metabolism of neurotransmitter glutamate. Metab. Eng. 5, 201–210 (2003).

    Article  CAS  Google Scholar 

  14. Duarte, N.C. et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl. Acad. Sci. USA 104, 1777–1782 (2007).

    Article  CAS  Google Scholar 

  15. Ma, H. et al. The Edinburgh human metabolic network reconstruction and its functional analysis. Mol. Syst. Biol. 3, 135 (2007).

    Article  Google Scholar 

  16. Levine, D.M. et al. Pathway and gene-set activation measurement from mRNA expression data: the tissue distribution of human pathways. Genome Biol. 7, R93 (2006).

    Article  Google Scholar 

  17. Son, C.G. et al. Database of mRNA gene expression profiles of multiple human organs. Genome Res. 15, 443–450 (2005).

    Article  CAS  Google Scholar 

  18. Yanai, I. et al. Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification. Bioinformatics 21, 650–659 (2005).

    Article  CAS  Google Scholar 

  19. Fong, S.S. & Palsson, B.O. Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes. Nat. Genet. 36, 1056–1058 (2004).

    Article  CAS  Google Scholar 

  20. Daran-Lapujade, P. et al. Role of transcriptional regulation in controlling fluxes in central carbon metabolism of Saccharomyces cerevisiae. A chemostat culture study. J. Biol. Chem. 279, 9125–9138 (2004).

    Article  CAS  Google Scholar 

  21. Schuster, S., Klamt, S., Weckwerth, S., Moldenhauer, F. & Pfeiffer, T. Use of network analysis of metabolic systems in bioengineering. Bioprocess Biosyst. Eng. 24, 363–372 (2002).

    Article  CAS  Google Scholar 

  22. Bilu, Y., Shlomi, T., Barkai, N. & Ruppin, E. Conservation of expression and sequence of metabolic genes is reflected by activity across metabolic states. PLOS Comput. Biol. 2, e106 (2006).

    Article  Google Scholar 

  23. Famili, I., Forster, J., Nielsen, J. & Palsson, B.O. Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proc. Natl. Acad. Sci. USA 100, 13134–13139 (2003).

    Article  CAS  Google Scholar 

  24. Akesson, M., Forster, J. & Nielsen, J. Integration of gene expression data into genome-scale metabolic models. Metab. Eng. 6, 285–293 (2004).

    Article  CAS  Google Scholar 

  25. Covert, M.W., Knight, E.M., Reed, J.L., Herrgard, M.J. & Palsson, B.O. Integrating high-throughput and computational data elucidates bacterial networks. Nature 429, 92–96 (2004).

    Article  CAS  Google Scholar 

  26. Shlomi, T., Eisenberg, Y., Sharan, R. & Ruppin, E. A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Mol. Syst. Biol. 3, 101 (2007).

    Article  Google Scholar 

  27. Mahadevan, R. & Schilling, C.H. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab. Eng. 5, 264–276 (2003).

    Article  CAS  Google Scholar 

  28. Shmueli, O. et al. GeneNote: whole genome expression profiles in normal human tissues. C. R. Biol. 326, 1067–1072 (2003).

    Article  CAS  Google Scholar 

  29. Mishra, G.R. et al. Human protein reference database–2006 update. Nucleic Acids Res. 34, D411–D414 (2006).

    Article  CAS  Google Scholar 

  30. Wishart, D.S. et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 35, D521–D526 (2007).

    Article  CAS  Google Scholar 

  31. Schomburg, I. et al. BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Res. 32, D431–D433 (2004).

    Article  CAS  Google Scholar 

  32. Winter, E.E., Goodstadt, L. & Ponting, C.P. Elevated rates of protein secretion, evolution, and disease among tissue-specific genes. Genome Res. 14, 54–61 (2004).

    Article  CAS  Google Scholar 

  33. Hubbard, T.J. et al. Ensembl 2007. Nucleic Acids Res. 35, D610–D617 (2007).

    Article  CAS  Google Scholar 

  34. Yan, Q. & Sadee, W. Human membrane transporter database: a Web-accessible relational database for drug transport studies and pharmacogenomics. AAPS PharmSci 2, E20 (2000).

    Article  CAS  Google Scholar 

  35. Saier, M.H., Jr ., Tran, C.V. & Barabote, R.D. TCDB: the Transporter Classification Database for membrane transport protein analyses and information. Nucleic Acids Res. 34, D181–D186 (2006).

    Article  CAS  Google Scholar 

  36. Bohm, M., La Rosee, K., Schwinger, R.H. & Erdmann, E. Evidence for reduction of norepinephrine uptake sites in the failing human heart. J. Am. Coll. Cardiol. 25, 146–153 (1995).

    Article  CAS  Google Scholar 

  37. McKusick, V.A. Mendelian Inheritance in Man and its online version, OMIM. Am. J. Hum. Genet. 80, 588–604 (2007).

    Article  CAS  Google Scholar 

  38. Greene, H.L., Brown, B.I., McClenathan, D.T., Agostini, R.M., Jr & Taylor, S.R. A new variant of type IV glycogenosis: deficiency of branching enzyme activity without apparent progressive liver disease. Hepatology 8, 302–306 (1988).

    Article  CAS  Google Scholar 

  39. Tay, S.K.H. et al. Fatal infantile neuromuscular presentation of glycogen storage disease type IV. Neuromuscul. Disord. 14, 253–260 (2004).

    Article  Google Scholar 

  40. Brosnan, J.T. & Brosnan, M.E. Branched-chain amino acids: enzyme and substrate regulation. J. Nutr. 136, 207S–211S (2006).

    Article  CAS  Google Scholar 

  41. Rossell, S. et al. Unraveling the complexity of flux regulation: a new method demonstrated for nutrient starvation in Saccharomyces cerevisiae . Proc. Natl. Acad. Sci. USA 103, 2166–2171 (2006).

    Article  CAS  Google Scholar 

  42. Daran-Lapujade, P. et al. The fluxes through glycolytic enzymes in Saccharomyces cerevisiae are predominantly regulated at posttranscriptional levels. Proc. Natl. Acad. Sci. USA 104, 15753–15758 (2007).

    Article  CAS  Google Scholar 

  43. Lee, H.K., Braynen, W., Keshav, K. & Pavlidis, P. ErmineJ: tool for functional analysis of gene expression data sets. BMC Bioinformatics 6, 269 (2005).

    Article  Google Scholar 

  44. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  Google Scholar 

  45. Cline, M.S. et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2, 2366–2382 (2007).

    Article  CAS  Google Scholar 

  46. Kell, D.B. Metabolomic biomarkers: search, discovery and validation. Expert Rev. Mol. Diagn. 7, 329–333 (2007).

    Article  CAS  Google Scholar 

  47. Su, A.I. et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl. Acad. Sci. USA 101, 6062–6067 (2004).

    Article  CAS  Google Scholar 

  48. Shyamsundar, R. et al. A DNA microarray survey of gene expression in normal human tissues. Genome Biol. 6, R22 (2005).

    Article  Google Scholar 

  49. Schuetz, R., Kuepfer, L. & Sauer, U. Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli . Mol. Syst. Biol. 3, 119 (2007).

    Article  Google Scholar 

  50. Zhao, Y. & Srivastava, D. A developmental view of microRNA function. Trends Biochem. Sci. 32, 189–197 (2007).

    Article  CAS  Google Scholar 

  51. Andersen, J.S. et al. Nucleolar proteome dynamics. Nature 433, 77–83 (2005).

    Article  CAS  Google Scholar 

  52. Burgard, A.P., Pharkya, P. & Maranas, C.D. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 84, 647–657 (2003).

    Article  CAS  Google Scholar 

  53. Shlomi, T., Berkman, O. & Ruppin, E. Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc. Natl. Acad. Sci. USA 102, 7695–7700 (2005).

    Article  CAS  Google Scholar 

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Acknowledgements

We are grateful to Shiri Freilich and Ben Sandbank for helpful comments and suggestions. We wish to thank the reviewers for their constructive remarks that helped improve this manuscript considerably. T.S. is supported by an Eshkol Fellowship from the Israeli Ministry of Science. M.C. is a fellow of the Edmond J. Safra Program in Tel-Aviv University. M.J.H. was supported by National Institutes of Health grant no. GM071808. This research was supported by grants from the Israeli Science Foundation, the German-Israeli Foundation and the Tauber fund to E.R.

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Author notes

  1. Tomer Shlomi and Moran N Cabili: These authors contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science, Tel-Aviv University, Tel-Aviv, 69978, Israel

    Tomer Shlomi, Moran N Cabili & Eytan Ruppin

  2. Department of Bioengineering, University of California, San Diego, La Jolla, 92093-0412, California, USA

    Markus J Herrgård & Bernhard Ø Palsson

  3. School of Medicine, Tel-Aviv University, Tel-Aviv, 69978, Israel

    Eytan Ruppin

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  1. Tomer Shlomi
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Correspondence to Tomer Shlomi or Eytan Ruppin.

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Supplementary Data Set 2

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Supplementary Data Set 3

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Shlomi, T., Cabili, M., Herrgård, M. et al. Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 26, 1003–1010 (2008). https://doi.org/10.1038/nbt.1487

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