Advertisement

Metabolic Network Reconstructions to Predict Drug Targets and Off-Target Effects

  • Kristopher Rawls
  • Bonnie V. Dougherty
  • Jason PapinEmail author
Protocol
First Online:
Part of the Methods in Molecular Biology book series (MIMB, volume 2088)

Abstract

The drug development pipeline has stalled because of the difficulty in identifying new drug targets while minimizing off-target effects. Computational methods, such as the use of metabolic network reconstructions, may provide a cost-effective platform to test new hypotheses for drug targets and prevent off-target effects. Here, we summarize available methods to identify drug targets and off-target effects using either reaction-centric, gene-centric, or metabolite-centric approaches with genome-scale metabolic network reconstructions.

Key words

Genome-scale metabolic network reconstruction (GENRE) Drug targets Off-target effects Constraint-based modeling Flux balance analysis (FBA) 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Mougin F, Auber D, Bourqui R et al (2018) Visualizing omics and clinical data: Which challenges for dealing with their variety? Methods 132:3–18PubMedCrossRefGoogle Scholar
  2. 2.
    Schuster D, Laggner C, Langer T (2005) Why drugs fail—a study on side effects in new chemical entities. Curr Pharm Des 11:3545–3559PubMedCrossRefGoogle Scholar
  3. 3.
    Edwards IR, Aronson JK (2000) Adverse drug reactions: definitions, diagnosis, and management. Lancet 356:1255–1259PubMedCrossRefGoogle Scholar
  4. 4.
    Sawada R, Iwata M, Tabei Y et al (2018) Predicting inhibitory and activatory drug targets by chemically and genetically perturbed transcriptome signatures. Sci Rep 8:156PubMedPubMedCentralCrossRefGoogle Scholar
  5. 5.
    Suthers PF, Zomorrodi A, Maranas CD (2009) Genome-scale gene/reaction essentiality and synthetic lethality analysis. Mol Syst Biol 5:301PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Pey J, San José-Eneriz E, Ochoa MC et al (2017) In-silico gene essentiality analysis of polyamine biosynthesis reveals APRT as a potential target in cancer. Sci Rep 7:14358PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Bordbar A, Lewis NE, Schellenberger J et al (2010) Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol Syst Biol 6:422PubMedPubMedCentralCrossRefGoogle Scholar
  8. 8.
    Orth JD, Palsson BØ, Fleming RMT (2010) Reconstruction and use of microbial metabolic networks: the core Escherichia coli metabolic model as an educational guide. EcoSal Plus 4:PMID: 26443778CrossRefGoogle Scholar
  9. 9.
    Oberhardt MA, Puchalka J, Fryer KE et al (2008) Genome-scale metabolic network analysis of the opportunistic pathogen Pseudomonas aeruginosa PAO1. J Bacteriol 190:2790–2803PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Förster J, Famili I, Fu P et al (2003) Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res 13:244–253PubMedPubMedCentralCrossRefGoogle Scholar
  11. 11.
    Thiele I, Swainston N, Fleming RMT et al (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol 31:419–425PubMedCrossRefGoogle Scholar
  12. 12.
    Mardinoglu A, Agren R, Kampf C et al (2014) Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat Commun 5:3083PubMedCrossRefGoogle Scholar
  13. 13.
    Blais EM, Rawls KD, Dougherty BV et al (2017) Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions. Nat Commun 8:14250PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Yizhak K, Chaneton B, Gottlieb E et al (2015) Modeling cancer metabolism on a genome scale. Mol Syst Biol 11:817PubMedPubMedCentralCrossRefGoogle Scholar
  15. 15.
    Ghaffari P, Mardinoglu A, Asplund A et al (2015) Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling. Sci Rep 5:8183PubMedPubMedCentralCrossRefGoogle Scholar
  16. 16.
    Chang RL, Xie L, Xie L et al (2010) Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLoS Comput Biol 6:e1000938PubMedPubMedCentralCrossRefGoogle Scholar
  17. 17.
    Zielinski DC, Filipp FV, Bordbar A et al (2015) Pharmacogenomic and clinical data link non-pharmacokinetic metabolic dysregulation to drug side effect pathogenesis. Nat Commun 6:7101PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Shaked I, Oberhardt MA, Atias N et al (2016) Metabolic network prediction of drug side effects. Cell Syst 2:209–213PubMedCrossRefGoogle Scholar
  19. 19.
    Heirendt L, Arreckx S, Pfau T et al (2019) Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat Protoc 14:639–702PubMedCrossRefGoogle Scholar
  20. 20.
    Folger O, Jerby L, Frezza C et al (2011) Predicting selective drug targets in cancer through metabolic networks. Mol Syst Biol 7:501PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    Rawls KD, Dougherty BV, Blais EM et al (2019) A simplified metabolic network reconstruction to promote understanding and development of flux balance analysis tools. Comput Biol Med 105:64–71PubMedCrossRefGoogle Scholar
  22. 22.
    Ma H, Sorokin A, Mazein A et al (2007) The Edinburgh human metabolic network reconstruction and its functional analysis. Mol Syst Biol 3:135PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Swainston N, Smallbone K, Hefzi H et al (2016) Recon 2.2: from reconstruction to model of human metabolism. Metabolomics 12:109PubMedPubMedCentralCrossRefGoogle Scholar
  24. 24.
    Brunk E, Sahoo S, Zielinski DC et al (2018) Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat Biotechnol 36:272–281PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Robaina Estévez S, Nikoloski Z (2014) Generalized framework for context-specific metabolic model extraction methods. Front Plant Sci 5:491PubMedPubMedCentralGoogle Scholar
  26. 26.
    Blazier AS, Papin JA (2012) Integration of expression data in genome-scale metabolic network reconstructions. Front Physiol 3:299PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Uhlen M, Hallstro m BM, Lindskog C et al (2016) Transcriptomics resources of human tissues and organs. Mol Syst Biol 12:862–862PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Schultz A, Qutub AA (2016) Reconstruction of tissue-specific metabolic networks using CORDA. PLoS Comput Biol 12:e1004808PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Agren R, Bordel S, Mardinoglu A et al (2012) Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput Biol 8:e1002518PubMedPubMedCentralCrossRefGoogle Scholar
  30. 30.
    Zhang A-D, Dai S-X, Huang J-F Reconstruction and analysis of human kidney-specific metabolic network based on omics data. https://www.hindawi.com/journals/bmri/2013/187509/
  31. 31.
    Sohrabi-Jahromi S, Marashi S-A, Kalantari S (2016) A kidney-specific genome-scale metabolic network model for analyzing focal segmental glomerulosclerosis. Mamm Genome 27:158–167PubMedCrossRefGoogle Scholar
  32. 32.
    Karlstädt A, Fliegner D, Kararigas G et al (2012) CardioNet: a human metabolic network suited for the study of cardiomyocyte metabolism. BMC Syst Biol 6:114PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Zhao Y, Huang J (2011) Reconstruction and analysis of human heart-specific metabolic network based on transcriptome and proteome data. Biochem Biophys Res Commun 415:450–454PubMedCrossRefGoogle Scholar
  34. 34.
    Bordbar A, Mo ML, Nakayasu ES et al (2012) Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation. Mol Syst Biol 8:558PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Agren R, Mardinoglu A, Asplund A et al (2014) Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol Syst Biol 10:721PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Thiele I, Palsson BØ (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5:93–121PubMedPubMedCentralCrossRefGoogle Scholar
  37. 37.
    Yizhak K, Gabay O, Cohen H et al (2013) Model-based identification of drug targets that revert disrupted metabolism and its application to ageing. Nat Commun 4:2632PubMedCrossRefGoogle Scholar
  38. 38.
    Nogiec C, Burkart A, Dreyfuss JM et al (2015) Metabolic modeling of muscle metabolism identifies key reactions linked to insulin resistance phenotypes. Mol Metab 4:151–163PubMedPubMedCentralCrossRefGoogle Scholar
  39. 39.
    Yizhak K, Le Devedec SE, Rogkoti VM et al (2014) A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration. Mol Syst Biol 10:744–744PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Stempler S, Yizhak K, Ruppin E (2014) Integrating transcriptomics with metabolic modeling predicts biomarkers and drug targets for Alzheimer’s disease. PLoS One 9:e105383PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Wishart DS, Feunang YD, Guo AC, et al (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46:D1074–D1082PubMedCentralCrossRefPubMedGoogle Scholar
  42. 42.
    Kuhn M, Campillos M, Letunic I et al (2010) A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol 6:343PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Kuhn M, Letunic I, Jensen LJ et al (2016) The SIDER database of drugs and side effects. Nucleic Acids Res 44:D1075–D1079PubMedCrossRefGoogle Scholar
  44. 44.
    Rienksma RA, Suarez-Diez M, Spina L et al (2014) Systems-level modeling of mycobacterial metabolism for the identification of new (multi-)drug targets. Semin Immunol 26:610–622PubMedCrossRefGoogle Scholar
  45. 45.
    Guarente L (1993) Synthetic enhancement in gene interaction: a genetic tool come of age. Trends Genet 9:362–366PubMedCrossRefGoogle Scholar
  46. 46.
    Jerby L, Shlomi T, Ruppin E (2010) Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol Syst Biol 6:401PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    Raškevičius V, Mikalayeva V, Antanavičiūtė I et al (2018) Genome scale metabolic models as tools for drug design and personalized medicine. PLoS One 13:e0190636PubMedPubMedCentralCrossRefGoogle Scholar
  48. 48.
    Longley DB, Harkin DP, Johnston PG (2003) 5-fluorouracil: mechanisms of action and clinical strategies. Nat Rev Cancer 3:330–338PubMedCrossRefGoogle Scholar
  49. 49.
    Mehrmohamadi M, Jeong SH, Locasale JW (2017) Molecular features that predict the response to antimetabolite chemotherapies. Cancer Metab 5:8PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Kristopher Rawls
    • 1
  • Bonnie V. Dougherty
    • 1
  • Jason Papin
    • 1
    Email author
  1. 1.Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleUSA

Personalised recommendations

Cite protocol

Buy options