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The Use of Large-Scale Chemically-Induced Transcriptome Data Acquired from LINCS to Study Small Molecules

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Book cover Systems Chemical Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1888))

Abstract

Identification of the modes of action of bioactive compounds is an important issue in chemical systems biology. In this chapter we review a recently developed data-driven approach using large-scale chemically induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures to elucidate the modes of action of bioactive compounds. First, we present a method for pathway enrichment analyses of regulated genes to reveal biological pathways activated by compounds. Next, we present a method using the pre-knowledge on chemical–protein interactome for predicting potential target proteins, including primary targets and off-targets, with transcriptional similarity. Finally, we present a method based on the target proteins for predicting new therapeutic indications for a variety of diseases. These approaches are expected to be useful for mode-of-action analysis, drug discovery, and drug repositioning.

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References

  1. Whitebread S, Hamon J, Bojanic D, Urban L (2005) Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development. Drug Discov Today 10:1421–1433

    Article  CAS  PubMed  Google Scholar 

  2. Blagg J (2006) Structure–activity relationships for in vitro and in vivo toxicity. Annu Rep Med Chem 41:353–368

    CAS  Google Scholar 

  3. Lin SF, Xiao KT, Huang YT, Chiu CC, Soo VW (2010) Analysis of adverse drug reactions using drug and drug target interactions and graph-based methods. Artif Intell Med 48:161–166

    Article  PubMed  Google Scholar 

  4. Ashburn TT, Thor KB (2004) Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov 3:673–683

    Article  CAS  PubMed  Google Scholar 

  5. Chong CR, Sullivan DJ (2007) New uses for old drugs. Nature 448:645–646

    Article  CAS  PubMed  Google Scholar 

  6. Novac N (2013) Challenges and opportunities of drug repositioning. Trends Pharmacol Sci 34:267–272

    Article  CAS  PubMed  Google Scholar 

  7. Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H (2015) Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res 43:D921–D927

    Article  CAS  PubMed  Google Scholar 

  8. 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 313:1929–1935

    Article  CAS  PubMed  Google Scholar 

  9. Dudley JT, Sirota M, Shenoy M, Pai RK, Roedder S, Chiang AP, Morgan AA, Sarwal MM, Pasricha PJ, Butte AJ (2011) Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci Transl Med 3:96ra76

    Google Scholar 

  10. Kosaka T, Nagamatsu G, Saito S, Oya M, Suda T, Horimoto K (2013) Identification of drug candidate against prostate cancer from the aspect of somatic cell reprogramming. Cancer Sci 104:1017–1026

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. van Noort V, Schölch S, Iskar M, Zeller G, Ostertag K, Schweitzer C, Werner K, Weitz J, Koch M, Bork P (2014) Novel drug candidates for the treatment of metastatic colorectal cancer through global inverse gene-expression profiling. Cancer Res 74:5690–5699

    Article  PubMed  Google Scholar 

  12. Iorio F, Tagliaferri R, di Bernardo D (2009) Identifying network of drug mode of action by gene expression profiling. J Comput Biol 16:241–251

    Article  CAS  PubMed  Google Scholar 

  13. Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P, Ferriero R, Murino L, Tagliaferri R, Brunetti-Pierri N, Isacchi A, di Bernardo D (2010) Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci USA 107:14621–14626

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hizukuri Y, Sawada R, Yamanishi Y (2015) Predicting target proteins for drug candidate compounds based on drug-induced gene expression data in a chemical structure-independent manner. BMC Med Genomics 8:82

    Article  PubMed  PubMed Central  Google Scholar 

  15. Iskar M, Zeller G, Blattmann P, Campillos M, Kuhn M, Kaminska KH, Runz H, Gavin AC, Pepperkok R, van Noort V, Bork P (2013) Characterization of drug-induced transcriptional modules: towards drug repositioning and functional understanding. Mol Syst Biol 9:662

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Parkkinen JA, Kaski S (2014) Probabilistic drug connectivity mapping. BMC Bioinformatics 15:113

    Article  PubMed  PubMed Central  Google Scholar 

  17. IIskar M, Campillos M, Kuhn M, Jensen LJ, van Noort V, Bork P (2010) Drug-induced regulation of target expression. PLoS Comput Biol 6:e1000,925

    Article  Google Scholar 

  18. 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:e1003,315

    Article  Google Scholar 

  19. 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 2013:5–16

    Google Scholar 

  20. Cheng J, Yang L, Kumar V, Agarwal P (2014) Systematic evaluation of connectivity map for disease indications. Genome Med 6:540

    Article  PubMed  Google Scholar 

  21. Duan Q, Flynn C, Niepel M, Hafner M, Muhlich JL, Fernandez NF, Rouillard AD, Tan CM, Chen EY, Golub TR, Sorger PK, Subramanian A, Ma’ayan A (2014) LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures. Nucleic Acids Res 42:W449–W460

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Liu C, Su J, Yang F, Wei K, Ma J, Zhou X (2015) Compound signature detection on LINCS L1000 big data. Mol Biosyst 11:714–722

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Chen B, Greenside P, Paik H, Sirota M, Hadley D, Butte AJ (2015) Relating chemical structure to cellular response: an integrative analysis of gene expression, bioactivity, and structural data across 11,000 compounds. CPT Pharmacomet Syst Pharmacol 4:576–584

    Article  CAS  Google Scholar 

  24. Iwata M, Sawada R, Iwata H, Kotera M, Yamanishi Y (2017) Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics. Sci Rep 7:40164

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Peck D, Crawford ED, Ross KN, Stegmaier K, Golub TR, Lamb J (2006) A method for high-throughput gene expression signature analysis. Genome Biol 7:R61

    Article  PubMed  PubMed Central  Google Scholar 

  26. Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Edgar R (2007) NCBI GEO: mining tens of millions of expression profiles–database and tools update. Nucleic Acids Res 35:D760–D765

    Article  CAS  PubMed  Google Scholar 

  27. Seiler KP, George GA, Happ MP, Bodycombe NE, Carrinski HA, Norton S, Brudz S, Sullivan JP, Muhlich J, Serrano M, Ferraiolo P, Tolliday NJ, Schreiber SL, Clemons PA (2008) ChemBank: a small-molecule screening and cheminformatics resource database. Nucleic Acids Res 36:D351–D359

    Article  CAS  PubMed  Google Scholar 

  28. Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP (2003) Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 31:e15

    Article  PubMed  PubMed Central  Google Scholar 

  29. Mizutani S, Pauwels E, Stoven V, Goto S, Yamanishi Y (2012) Relating drug–protein interaction network with drug side effects. Bioinformatics 28:i522–i528

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Hung JH (2013) Gene set/pathway enrichment analysis. Methods Mol Biol 939:201–213

    Article  CAS  PubMed  Google Scholar 

  31. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 57:289–300

    Google Scholar 

  32. Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100–D1107

    Article  CAS  PubMed  Google Scholar 

  33. Günther S, Kuhn M, Dunkel M, Campillos M, Senger C, Petsalaki E, Ahmed J, Urdiales EG, Gewiess A, Jensen LJ, Schneider R, Skoblo R, Russell RB, Bourne PE, Bork P, Preissner R (2008) SuperTarget and Matador: resources for exploring drug–target relationships. Nucleic Acids Res 36:D919–D922

    Article  PubMed  Google Scholar 

  34. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Wishart DS (2011) DrugBank 3.0: a comprehensive resource for ’omics’ research on drugs. Nucleic Acids Res 39:D1035–D1041

    Article  CAS  PubMed  Google Scholar 

  35. Roth BL, Lopez E, Patel S, Kroeze WK (2000) The multiplicity of serotonin receptors: uselessly diverse molecules or an embarrassment of riches? Neuroscientist 6:252–262

    Article  CAS  Google Scholar 

  36. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M (2010) KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 38:D355–D360

    Article  CAS  PubMed  Google Scholar 

  37. Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK (2007) BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Res 35:D198–D201

    Article  CAS  PubMed  Google Scholar 

  38. Zhu F, Shi Z, Qin C, Tao L, Liu X, Xu F, Zhang L, Song Y, Liu X, Zhang J, Han B, Zhang P, Chen Y (2012) Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery. Nucleic Acids Res 40:D1128–D1136

    Article  CAS  PubMed  Google Scholar 

  39. Papadakis MA, McPhee SJ, Rabow MW (2014) Current medical diagnosis and treatment 2014. McGraw Hill Medical, New York

    Google Scholar 

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Aknowledgements

This work is supported by JST PRESTO Grant Number JPMJPR15D8.

Author information

Authors and Affiliations

  1. Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan

    Michio Iwata & Yoshihiro Yamanishi

  2. PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan

    Yoshihiro Yamanishi

Authors
  1. Michio Iwata
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  2. Yoshihiro Yamanishi
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Corresponding author

Correspondence to Yoshihiro Yamanishi .

Editor information

Editors and Affiliations

  1. Department of Chemical Biology, Max Planck Institute of Molecular Physiology, Dortmund, Germany

    Slava Ziegler

  2. Department of Chemical Biology, Max Planck Institute of Molecular Physiology, Dortmund, Germany

    Herbert Waldmann

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Iwata, M., Yamanishi, Y. (2019). The Use of Large-Scale Chemically-Induced Transcriptome Data Acquired from LINCS to Study Small Molecules. In: Ziegler, S., Waldmann, H. (eds) Systems Chemical Biology. Methods in Molecular Biology, vol 1888. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8891-4_11

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  • DOI: https://doi.org/10.1007/978-1-4939-8891-4_11

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8890-7

  • Online ISBN: 978-1-4939-8891-4

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