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Dynamic Network Modeling of Stem Cell Metabolism

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Computational Stem Cell Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1975))

Abstract

Stem cell metabolism is intrinsically tied to stem cell pluripotency and function. Yet, understanding metabolic rewiring in stem cells has been challenging due to the complex and highly interconnected nature of the metabolic network. Genome-scale metabolic network models are increasingly used to holistically model the metabolic behavior of various cells and tissues using transcriptomics data. However, these powerful approaches that model steady-state behavior have limited utility for studying dynamic stem cell state transitions. To address this complexity, we recently developed the dynamic flux activity (DFA) approach; DFA is a genome-scale modeling approach that uses time-course metabolic data to predict metabolic flux rewiring. This protocol outlines the steps for modeling steady-state and dynamic metabolic behavior using transcriptomics and time-course metabolomics data, respectively. Using data from naive and primed pluripotent stem cells, we demonstrate how we can use genome-scale modeling and DFA to comprehensively characterize the metabolic differences between these states.

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References

  1. Brunk E, Sahoo S, Zielinski DC et al (2018) Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat Biotechnol 36:272

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. O’Brien EJ, Monk JM, Palsson BO (2015) Using genome-scale models to predict biological capabilities. Cell 161:971–987. https://doi.org/10.1016/j.cell.2015.05.019

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Feist AM, Herrgard MJ, Thiele I et al (2009) Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol 7:129–143. https://doi.org/10.1038/nrmicro1949

    Article  CAS  PubMed  Google Scholar 

  4. Bordbar A, Monk JM, King ZA, Palsson BO (2014) Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 15:107–120. https://doi.org/10.1038/nrg3643

    Article  CAS  PubMed  Google Scholar 

  5. Lewis NE, Nagarajan H, Palsson BO (2012) Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 10:291–305. https://doi.org/10.1038/nrmicro2737

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Folger O, Jerby L, Frezza C et al (2011) Predicting selective drug targets in cancer through metabolic networks. Mol Syst Biol 7:501. https://doi.org/10.1038/msb.2011.35

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Frezza C, Zheng L, Folger O et al (2011) Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase. Nature 477:225–U132. https://doi.org/10.1038/nature10363

    Article  CAS  PubMed  Google Scholar 

  8. Uhlen M, Fagerberg L, Hallstrom BM et al (2015) Tissue-based map of the human proteome. Science 80:347. https://doi.org/10.1126/science.1260419

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  10. Chandrasekaran S, Zhang J, Sun Z et al (2017) Comprehensive mapping of pluripotent stem cell metabolism using dynamic genome-scale network modeling. Cell Rep 21:2965–2977. https://doi.org/10.1016/J.CELREP.2017.07.048

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Becker SA, Feist AM, Mo ML et al (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2:727–738. https://doi.org/10.1038/nprot.2007.99

    Article  CAS  PubMed  Google Scholar 

  12. Schellenberger J, Que R, Fleming RMT et al (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6:1290–1307. https://doi.org/10.1038/nprot.2011.308

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ebrahim A, Lerman JA, Palsson BO, Hyduke DR (2013) COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Biol 7:74. https://doi.org/10.1186/1752-0509-7-74

    Article  PubMed  PubMed Central  Google Scholar 

  14. Duarte NC, Becker SA, Jamshidi N et al (2007) Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci U S A 104:1777–1782. https://doi.org/10.1073/pnas.0610772104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. King ZA, Lu J, Drager A et al (2016) BiGG models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res 44:D515–D522. https://doi.org/10.1093/nar/gkv1049

    Article  CAS  PubMed  Google Scholar 

  16. Zhang J, Ratanasirintrawoot S, Chandrasekaran S et al (2016) LIN28 regulates stem cell metabolism and conversion to primed pluripotency. Cell Stem Cell 19:66–80. https://doi.org/10.1016/j.stem.2016.05.009

    Article  CAS  PubMed  Google Scholar 

  17. Gunawardena J (2014) Time-scale separation—Michaelis and Menten’s old idea, still bearing fruit. FEBS J 281:473–488. https://doi.org/10.1111/febs.12532

    Article  CAS  PubMed  Google Scholar 

  18. Kauffman KJ, Prakash P, Edwards JS (2003) Advances in flux balance analysis. Curr Opin Biotechnol 14:491–496. https://doi.org/10.1016/j.copbio.2003.08.001

    Article  CAS  PubMed  Google Scholar 

  19. Orth JD, Thiele I, Palsson BO (2010) What is flux balance analysis? Nat Biotechnol 28:245–248. https://doi.org/10.1038/nbt.1614

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ibarra RU, Edwards JS, Palsson BO (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420:186–189. https://doi.org/10.1038/nature01149

    Article  CAS  PubMed  Google Scholar 

  21. Feist AM, Palsson BO (2016) What do cells actually want? Genome Biol 17:110. https://doi.org/10.1186/s13059-016-0983-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Reed JL (2012) Shrinking the metabolic solution space using experimental datasets. PLoS Comput Biol 8:e1002662. https://doi.org/10.1371/journal.pcbi.1002662

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zur H, Ruppin E, Shlomi T (2010) iMAT: an integrative metabolic analysis tool. Bioinformatics 26:3140–3142. https://doi.org/10.1093/bioinformatics/btq602

    Article  CAS  PubMed  Google Scholar 

  24. Chandrasekaran S, Price ND (2010) Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc Natl Acad Sci U S A 107:17845–17850. https://doi.org/10.1073/pnas.1005139107

    Article  PubMed  PubMed Central  Google Scholar 

  25. Xia JG, Sinelnikov IV, Han B, Wishart DS (2015) MetaboAnalyst 3.0-making metabolomics more meaningful. Nucleic Acids Res 43:W251–W257. https://doi.org/10.1093/nar/gkv380

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wishart DS, Feunang YD, Marcu A et al (2018) HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res 46:D608–D617. https://doi.org/10.1093/nar/gkx1089

    Article  CAS  PubMed  Google Scholar 

  27. Kanehisa M, Furumichi M, Tanabe M et al (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45:D353–D361. https://doi.org/10.1093/nar/gkw1092

    Article  CAS  PubMed  Google Scholar 

  28. Kim S, Thiessen PA, Bolton EE et al (2016) PubChem substance and compound databases. Nucleic Acids Res 44:D1202–D1213. https://doi.org/10.1093/nar/gkv951

    Article  CAS  PubMed  Google Scholar 

  29. Hastings J, Owen G, Dekker A et al (2016) ChEBI in 2016: improved services and an expanding collection of metabolites. Nucleic Acids Res 44:D1214–D1219. https://doi.org/10.1093/nar/gkv1031

    Article  CAS  PubMed  Google Scholar 

  30. Smith CA, O’Maille G, Want EJ et al (2005) METLIN—a metabolite mass spectral database. Ther Drug Monit 27:747–751. https://doi.org/10.1097/01.ftd.0000179845.53213.39

    Article  CAS  PubMed  Google Scholar 

  31. Hucka M, Finney A, Sauro HM et al (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19:524–531. https://doi.org/10.1093/bioinformatics/btg015

    Article  CAS  PubMed  Google Scholar 

  32. Raghevendran V, Gombert AK, Christensen B et al (2004) Phenotypic characterization of glucose repression mutants ofSaccharomyces cerevisiae using experiments with13C-labelled glucose. Yeast 21:769–779. https://doi.org/10.1002/yea.1136

    Article  CAS  PubMed  Google Scholar 

  33. Sabra W, Bommareddy RR, Maheshwari G et al (2017) Substrates and oxygen dependent citric acid production by Yarrowia lipolytica: insights through transcriptome and fluxome analyses. Microb Cell Factories 16:78. https://doi.org/10.1186/s12934-017-0690-0

    Article  CAS  Google Scholar 

  34. Lewis NE, Hixson KK, Conrad TM et al (2010) Omic data from evolved E-coli are consistent with computed optimal growth from genome-scale models. Mol Syst Biol 6:390. https://doi.org/10.1038/msb.2010.47

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA

    Fangzhou Shen, Camden Cheek & Sriram Chandrasekaran

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  1. Fangzhou Shen
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  2. Camden Cheek
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Correspondence to Sriram Chandrasekaran .

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Editors and Affiliations

  1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Patrick Cahan

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Shen, F., Cheek, C., Chandrasekaran, S. (2019). Dynamic Network Modeling of Stem Cell Metabolism. In: Cahan, P. (eds) Computational Stem Cell Biology. Methods in Molecular Biology, vol 1975. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9224-9_14

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  • DOI: https://doi.org/10.1007/978-1-4939-9224-9_14

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9223-2

  • Online ISBN: 978-1-4939-9224-9

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