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Bayesian Network to Infer Drug-Induced Apoptosis Circuits from Connectivity Map Data

  • Jiyang Yu
  • Jose M. Silva
Part of the Methods in Molecular Biology book series (MIMB, volume 1783)

Abstract

The Connectivity Map (CMAP) project profiled human cancer cell lines exposed to a library of anticancer compounds with the goal of connecting cancer with underlying genes and potential treatments. As most targeted anticancer therapeutics aim to induce tumor-selective apoptosis, it is critical to understand the specific cell death pathways triggered by drugs. This can help to better understand the mechanism of how cancer cells respond to chemical stimulations and improve the treatment of human tumors. In this study, using Connectivity MAP microarray-based gene expression data, we applied a Bayesian network modeling approach and identified apoptosis as a major drug-induced cellular pathway. We focused on 13 apoptotic genes that showed significant differential expression across all drug-perturbed samples to reconstruct the apoptosis network. In our predicted subnetwork, 9 out of 15 high-confidence interactions were validated in literature, and our inferred network captured two major cell death pathways by identifying BCL2L11 and PMAIP1 as key interacting players for the intrinsic apoptosis pathway, and TAXBP1 and TNFAIP3 for the extrinsic apoptosis pathway. Our inferred apoptosis network also suggested the role of BCL2L11 and TNFAIP3 as “gateway” genes in the drug-induced intrinsic and extrinsic apoptosis pathways.

Key words

Microarray Gene expression Drug discovery Bayesian network CMAP Apoptosis Cancer 

References

  1. 1.
    Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, Reich M, Hieronymus H, Wei G, Armstrong SA, Haggarty SJ, Clemons PA, Wei R, Carr SA, Lander ES, Golub TR (2006) The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313:1929–1935CrossRefPubMedGoogle Scholar
  2. 2.
    Lamb J (2007) The Connectivity Map: a new tool for biomedical research. Nat Rev Cancer 7:54–60CrossRefPubMedGoogle Scholar
  3. 3.
    Lander ES (1999) Array of hope. Nat Genet 21:3–4CrossRefPubMedGoogle Scholar
  4. 4.
    Sellers WR, Fisher DE (1999) Apoptosis and cancer drug targeting. J Clin Invest 104:1655–1661CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Montero-Melendez T, Dalli J, Perretti M (2013) Gene expression signature-based approach identifies a pro-resolving mechanism of action for histone deacetylase inhibitors. Cell Death Differ 20:567–575CrossRefPubMedGoogle Scholar
  6. 6.
    Cheng J, Xie Q, Kumar V, Hurle M, Freudenberg JM, Yang L, Agarwal P (2013) Evaluation of analytical methods for connectivity map data. Pac Symp Biocomput:5–16Google Scholar
  7. 7.
    Qu XA, Rajpal DK (2012) Applications of Connectivity Map in drug discovery and development. Drug Discov Today 17:1289–1298CrossRefPubMedGoogle Scholar
  8. 8.
    Zimmer M, Lamb J, Ebert BL, Lynch M, Neil C, Schmidt E, Golub TR, Iliopoulos O (2010) The connectivity map links iron regulatory protein-1-mediated inhibition of hypoxia-inducible factor-2a translation to the anti-inflammatory 15-deoxy-delta12,14-prostaglandin J2. Cancer Res 70:3071–3079CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Wang K, Sun J, Zhou S, Wan C, Qin S, Li C, He L, Yang L (2013) Prediction of drug-target interactions for drug repositioning only based on genomic expression similarity. PLoS Comput Biol 9:e1003315CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Sandmann T, Kummerfeld SK, Gentleman R, Bourgon R (2014) gCMAP: user-friendly connectivity mapping with R. Bioinformatics 30:127–128CrossRefPubMedGoogle Scholar
  11. 11.
    Adams JM (2003) Ways of dying: multiple pathways to apoptosis. Genes Dev 17:2481–2495CrossRefPubMedGoogle Scholar
  12. 12.
    Adams JM, Cory S (2007) The Bcl-2 apoptotic switch in cancer development and therapy. Oncogene 26:1324–1337CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Green D (2011) Means to an end: apoptosis and other cell death mechanisms. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NYGoogle Scholar
  14. 14.
    Wu ZJ, Irizarry RA, Gentleman R, Martinez-Murillo F, Spencer F (2004) A model-based background adjustment for oligonucleotide expression arrays. J Am Stat Assoc 99:909–917CrossRefGoogle Scholar
  15. 15.
    Smyth GK (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:3CrossRefGoogle Scholar
  16. 16.
    Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25:25–29CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Friedman N, Linial M, Nachman I, Pe’er D (2000) Using Bayesian networks to analyze expression data. J Comput Biol 7:601–620CrossRefPubMedGoogle Scholar
  18. 18.
    Pe’er D, Regev A, Elidan G, Friedman N (2001) Inferring subnetworks from perturbed expression profiles. Bioinformatics 17(Suppl 1):S215–S224CrossRefPubMedGoogle Scholar
  19. 19.
    Friedman N (2004) Inferring cellular networks using probabilistic graphical models. Science 303:799–805CrossRefPubMedGoogle Scholar
  20. 20.
    Pe’er D (2005) Bayesian network analysis of signaling networks: a primer. Sci STKE 2005:pl4PubMedGoogle Scholar
  21. 21.
    Ellis B, Wong WH (2008) Learning causal Bayesian network structures from experimental data. J Am Stat Assoc 103:778–789CrossRefGoogle Scholar
  22. 22.
    Bøttcher S (2001) Learning Bayesian networks with mixed variables. In: Proceedings of the eighth international workshop in artificial intelligence and statisticsGoogle Scholar
  23. 23.
    Geiger D, Heckerman D (1994) Learning Gaussian networks. Technical Report MSRTR-94–10, Microsoft ResearchCrossRefGoogle Scholar
  24. 24.
    Heckerman D, Geiger D, Chickering D (1995) Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn 20:197Google Scholar
  25. 25.
    Chickering D (1996) Learning Bayesian networks is NP-complete. In: Fisher DF, Lenz H-J (eds) Learning from data: artificial intelligence and statistics, V. Springer, New YorkGoogle Scholar
  26. 26.
    Bøttcher S, Dethlefsen C (2003) Deal: a package for learning Bayesian networks. J Stat Softw 8(20):1–40Google Scholar
  27. 27.
    Bøttcher S, Dethlefsen C (2003) Learning Bayesian networks with R. In: Hornik K, Leisch F, Zeileis A (eds) Proceedings of the 3rd international workshop on distributed statistical computing. ISSN 1609-395XGoogle Scholar
  28. 28.
    Efron B, Tibshirani R (1993) An introduction to the bootstrap. Chapman & Hall, LondonCrossRefGoogle Scholar
  29. 29.
    Friedman N, Goldszmidt M, Wyner A (1999) Data analysis with Bayesian networks: a bootstrap approach. In: Proceedings of the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, San Francisco, CA, pp 206–215Google Scholar
  30. 30.
    Wang CY, Mayo MW, Baldwin AS Jr (1996) TNF- and cancer therapy-induced apoptosis: potentiation by inhibition of NF-kappaB. Science 274:784–787CrossRefPubMedGoogle Scholar
  31. 31.
    http://www.ncbi.nlm.nih.gov/gene/8887Google Scholar
  32. 32.
    Beyaert R, De Valck D, Jin DY, Heyninck K, Van de Craen M, Contreras R, Fiers W, Jeang KT (1999) The zinc finger protein A20 interacts with a novel anti-apoptotic protein which is cleaved by specific caspases. Oncogene 18:4182–4190CrossRefPubMedGoogle Scholar
  33. 33.
    Beyaert R, Klinkenberg M, Van Huffel S, Heyninck K (2001) Functional redundancy of the zinc fingers of A20 for inhibition of NF-kappa B activation and protein-protein interactions. FEBS Lett 498:93–97CrossRefPubMedGoogle Scholar
  34. 34.
    Shembade N, Ma A, Harhaj EW (2010) Inhibition of NF-kappa B signaling by A20 through disruption of ubiquitin enzyme complexes. Science 327:1135–1139CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    http://www.uniprot.org/uniprot/Q13794Google Scholar
  36. 36.
    http://thebiogrid.org/111379/summary/homo-sapiens/pmaip1.htmlGoogle Scholar
  37. 37.
    http://thebiogrid.org/115335/summary/homo-sapiens/bcl2l11.htmlGoogle Scholar
  38. 38.
    Villunger A, Michalak EM, Coultas L, Mullauer F, Bock G, Ausserlechner MJ, Adams JM, Strasser A (2003) p53- and drug-induced apoptotic responses mediated by BH3-only proteins Puma and Noxa. Science 302:1036–1038CrossRefPubMedGoogle Scholar
  39. 39.
    Villunger A, Erlacher M, Michalak EM, Kelly PN, Labi V, Niederegger H, Coultas L, Adams JM, Strasser A (2005) BH3-only proteins Puma and Bim are rate-limiting for gamma-radiation- and glucocorticoid-induced apoptosis of lymphoid cells in vivo. Blood 106:4131–4138CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computational BiologySt. Jude Children’s Research HospitalMemphisUSA
  2. 2.Department of Pathology, Icahn School of Medicine at Mount SinaiThe Mount Sinai HospitalNew YorkUSA

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