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Proteomic Profiling for Target Identification of Biologically Active Small Molecules Using 2D DIGE

  • Makoto Muroi
  • Hiroyuki Osada
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1888)

Abstract

Recent improvements in technologies such as omics analysis have enabled us to acquire a large amount of data regarding the biological changes in cells treated with bioactive small molecules. Using such data, a variety of profiling methods have been established for target identification of such bioactive compounds. In this chapter, we describe a proteomic profiling system, ChemProteoBase, based on proteome analysis using two-dimensional difference gel electrophoresis. This system compares the similarities in protein expression of 296 spots detected in the gel among the test compounds.

Key words

Proteomics Two-dimensional florescent electrophoresis (2D DIGE) Profiling Target identification of compounds Multivariate analysis 

Notes

Acknowledgments

We thank Ms. H. Kondo, Ms. K. Noda, Ms. Y. Nakata, Ms. Y. Hirata, and Ms. M. Tanaka for conducting proteomic analysis. This work was supported in part by JSPS KAKENHI Grant Numbers JP16H06276, JP17H06412, JP18H03945, JP17K07783, AMED under Grant Number JP18cm0106112 and the NARO Bio-oriented Technology Research Advancement Institution (Research program on development of innovative technology).

References

  1. 1.
    Futamura Y, Muroi M, Osada H (2013) Target identification of small molecules based on chemical biology approaches. Mol BioSyst 9(5):897–914.  https://doi.org/10.1039/c2mb25468aCrossRefPubMedGoogle Scholar
  2. 2.
    Shoemaker RH (2006) The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer 6(10):813–823.  https://doi.org/10.1038/nrc1951CrossRefPubMedGoogle Scholar
  3. 3.
    Nakatsu N, Nakamura T, Yamazaki K, Sadahiro S, Makuuchi H, Kanno J, Yamori T (2007) Evaluation of action mechanisms of toxic chemicals using JFCR39, a panel of human cancer cell lines. Mol Pharmacol 72(5):1171–1180.  https://doi.org/10.1124/mol.107.038836CrossRefPubMedGoogle Scholar
  4. 4.
    Muroi M, Futamura Y, Osada H (2016) Integrated profiling methods for identifying the targets of bioactive compounds: MorphoBase and ChemProteoBase. Nat Prod Rep 33(5):621–625.  https://doi.org/10.1039/c5np00106dCrossRefPubMedGoogle Scholar
  5. 5.
    Muroi M, Kazami S, Noda K, Kondo H, Takayama H, Kawatani M, Usui T, Osada H (2010) Application of proteomic profiling based on 2D-DIGE for classification of compounds according to the mechanism of action. Chem Biol 17(5):460–470.  https://doi.org/10.1016/j.chembiol.2010.03.016CrossRefPubMedGoogle Scholar
  6. 6.
    Ning F, Wu X, Wang W (2016) Exploiting the potential of 2DE in proteomics analyses. Expert Rev Proteomics:1–3.  https://doi.org/10.1080/14789450.2016.1230498
  7. 7.
    Benesova M, Hola D, Fischer L, Jedelsky PL, Hnilicka F, Wilhelmova N, Rothova O, Kocova M, Prochazkova D, Honnerova J, Fridrichova L, Hnilickova H (2012) The physiology and proteomics of drought tolerance in maize: early stomatal closure as a cause of lower tolerance to short-term dehydration? PLoS One 7(6):e38017.  https://doi.org/10.1371/journal.pone.0038017CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Kondo T, Hirohashi S (2006) Application of highly sensitive fluorescent dyes (CyDye DIGE Fluor saturation dyes) to laser microdissection and two-dimensional difference gel electrophoresis (2D-DIGE) for cancer proteomics. Nat Protoc 1(6):2940–2956.  https://doi.org/10.1038/nprot.2006.421CrossRefPubMedGoogle Scholar
  9. 9.
    Scherp P, Ku G, Coleman L, Kheterpal I (2011) Gel-based and gel-free proteomic technologies. Methods Mol Biol 702:163–190.  https://doi.org/10.1007/978-1-61737-960-4_13CrossRefPubMedGoogle Scholar
  10. 10.
    Kawatani M, Takayama H, Muroi M, Kimura S, Maekawa T, Osada H (2011) Identification of a small-molecule inhibitor of DNA topoisomerase II by proteomic profiling. Chem Biol 18(6):743–751.  https://doi.org/10.1016/j.chembiol.2011.03.012CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Futamura Y, Kawatani M, Muroi M, Aono H, Nogawa T, Osada H (2013) Identification of a molecular target of a novel fungal metabolite, pyrrolizilactone, by phenotypic profiling systems. Chembiochem 14(18):2456–2463.  https://doi.org/10.1002/cbic.201300499CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Minegishi H, Futamura Y, Fukashiro S, Muroi M, Kawatani M, Osada H, Nakamura H (2015) Methyl 3-((6-methoxy-1,4-dihydroindeno[1,2-c]pyrazol-3-yl)amino)benzoate (GN39482) as a tubulin polymerization inhibitor identified by MorphoBase and ChemProteoBase profiling methods. J Med Chem 58(10):4230–4241.  https://doi.org/10.1021/acs.jmedchem.5b00035CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Kawatani M, Muroi M, Wada A, Inoue G, Futamura Y, Aono H, Shimizu K, Shimizu T, Igarashi Y, Takahashi-Ando N, Osada H (2016) Proteomic profiling reveals that collismycin A is an iron chelator. Sci Rep 6:38385.  https://doi.org/10.1038/srep38385CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Kawamura T, Kawatani M, Muroi M, Kondoh Y, Futamura Y, Aono H, Tanaka M, Honda K, Osada H (2016) Proteomic profiling of small-molecule inhibitors reveals dispensability of MTH1 for cancer cell survival. Sci Rep 6:26521.  https://doi.org/10.1038/srep26521CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    de Hoon MJ, Imoto S, Nolan J, Miyano S (2004) Open source clustering software. Bioinformatics 20(9):1453–1454.  https://doi.org/10.1093/bioinformatics/bth078CrossRefPubMedGoogle Scholar
  16. 16.
    Saldanha AJ (2004) Java Treeview–extensible visualization of microarray data. Bioinformatics 20(17):3246–3248.  https://doi.org/10.1093/bioinformatics/bth349CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Chemical Biology Research Group, RIKEN CSRSWakoJapan

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