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IAPR International Conference on Pattern Recognition in Bioinformatics

PRIB 2012: Pattern Recognition in Bioinformatics pp 141–152Cite as

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Principal Component Analysis for Bacterial Proteomic Analysis

Principal Component Analysis for Bacterial Proteomic Analysis

  • Y. -h. Taguchi23 &
  • Akira Okamoto24 
  • Conference paper
  • 1789 Accesses

  • 10 Citations

Part of the Lecture Notes in Computer Science book series (LNBI,volume 7632)

Abstract

Proteomic analysis is a very useful procedure to understand the bacterial behavioural responses to the external environmental factors. This is because bacterial genome information is mainly devoted to code enzyme for the control of the cellular metabolic networks. In this paper, we have performed proteomic analysis of Streptococcus pyogenes, which is known to be flesh-eating bacteria and can cause several human life-threatening diseases. Its proteome during growth phase is measured for four time points under two different culture conditions; with or without shaking. Its purpose is to understand the adaptivity to oxidative stresses. Principal component analysis is applied and turns out to be useful to depict biologically important proteins for both supernatant and cell components.

Keywords

  • Streptococcus pyogenes
  • proteomic analysis
  • principal component analysis

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References

  1. Barnett, T.C., Bugrysheva, J.V., Scott, J.R.: Role of mRNA stability in growth phase regulation of gene expression in the group A streptococcus. J. Bacteriol. 189, 1866–1873 (2007)

    CrossRef  Google Scholar 

  2. Beyer-Sehlmeyer, G., Kreikemeyer, B., Hörster, A., Podbielski, A.: Analysis of the growth phase-associated transcriptome of streptococcus pyogenes. International Journal of Medical Microbiology 295(3), 161–177 (2005), http://www.sciencedirect.com/science/article/pii/S1438422105000421

    CrossRef  Google Scholar 

  3. Blackman, S.A., Smith, T.J., Foster, S.J.: The role of autolysins during vegetative growth of Bacillus subtilis 168. Microbiology (Reading, Engl.) 144 ( pt. 1), 73–82 (1998)

    Google Scholar 

  4. Ferretti, J.J., McShan, W.M., Ajdic, D., Savic, D.J., Savic, G., Lyon, K., Primeaux, C., Sezate, S., Suvorov, A.N., Kenton, S., Lai, H.S., Lin, S.P., Qian, Y., Jia, H.G., Najar, F.Z., Ren, Q., Zhu, H., Song, L., White, J., Yuan, X., Clifton, S.W., Roe, B.A., McLaughlin, R.: Complete genome sequence of an M1 strain of Streptococcus pyogenes. Proc. Natl. Acad. Sci. U.S.A. 98, 4658–4663 (2001)

    CrossRef  Google Scholar 

  5. Ishihama, Y., Oda, Y., Tabata, T., Sato, T., Nagasu, T., Rappsilber, J., Mann, M.: Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein. Mol. Cell Proteomics 4, 1265–1272 (2005)

    CrossRef  Google Scholar 

  6. Lei, B., Mackie, S., Lukomski, S., Musser, J.M.: Identification and immunogenicity of group A Streptococcus culture supernatant proteins. Infect. Immun. 68, 6807–6818 (2000)

    CrossRef  Google Scholar 

  7. Len, A.C., Cordwell, S.J., Harty, D.W., Jacques, N.A.: Cellular and extracellular proteome analysis of Streptococcus mutans grown in a chemostat. Proteomics 3, 627–646 (2003)

    CrossRef  Google Scholar 

  8. Mercier, C., Durrieu, C., Briandet, R., Domakova, E., Tremblay, J., Buist, G., Kulakauskas, S.: Positive role of peptidoglycan breaks in lactococcal biofilm formation. Mol. Microbiol. 46, 235–243 (2002)

    CrossRef  Google Scholar 

  9. Okamoto, A., Taguchi, Y.H.: Principal component analysis for bacterial proteomic analysis. IPSJ SIG Technical Report 2011-BIO-26, 1–6 (2011)

    Google Scholar 

  10. Okamoto, A., Yamada, K.: Proteome driven re-evaluation and functional annotation of the Streptococcus pyogenes SF370 genome. BMC Microbiol. 11, 249 (2011)

    CrossRef  Google Scholar 

  11. Oshida, T., Sugai, M., Komatsuzawa, H., Hong, Y.M., Suginaka, H., Tomasz, A.: A Staphylococcus aureus autolysin that has an N-acetylmuramoyl-L-alanine amidase domain and an endo-beta-N-acetylglucosaminidase domain: cloning, sequence analysis, and characterization. Proc. Natl. Acad. Sci. U.S.A. 92, 285–289 (1995)

    CrossRef  Google Scholar 

  12. Rao, P.K., Li, Q.: Principal Component Analysis of Proteome Dynamics in Iron-starved Mycobacterium Tuberculosis. J. Proteomics Bioinform. 2, 19–31 (2009)

    CrossRef  Google Scholar 

  13. Shinoda, K., Tomita, M., Ishihama, Y.: emPAI Calc–for the estimation of protein abundance from large-scale identification data by liquid chromatography-tandem mass spectrometry. Bioinformatics 26, 576–577 (2010)

    CrossRef  Google Scholar 

  14. Taguchi, Y.H., Okamoto, A.: Principal component analysis for bacterial proteomic analysis. In: 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, pp. 961–963 (2011)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Physics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan

    Y. -h. Taguchi

  2. Department of School Nursing and Health, Aichi University of Education, 1 Hirosawa, Igaya-cho, Kariya, Aichi, 448-8542, Japan

    Akira Okamoto

Authors
  1. Y. -h. Taguchi
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  2. Akira Okamoto
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Editor information

Editors and Affiliations

  1. Institute of Medical Science, University of Tokyo, 4-6-1, Shirokanedai, 108-8639, Minato-ku, Tokyo, Japan

    Tetsuo Shibuya

  2. Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, 113-8654, Bunkyo-ku, Tokyo, Japan

    Hisashi Kashima

  3. Department of Comouter Science, Tokyo Institute of Technology, 2-12-1 Ookayamama, 152-8550, Meguro-ku, Tokyo, Japan

    Jun Sese

  4. Bioinformatics Project, National Institute of Biomedical Innovation, 7-6-8 Saito-Asagi, 567-0085, Suita, Osaka, Japan

    Shandar Ahmad

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© 2012 Springer-Verlag Berlin Heidelberg

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Taguchi, Y.h., Okamoto, A. (2012). Principal Component Analysis for Bacterial Proteomic Analysis. In: Shibuya, T., Kashima, H., Sese, J., Ahmad, S. (eds) Pattern Recognition in Bioinformatics. PRIB 2012. Lecture Notes in Computer Science(), vol 7632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34123-6_13

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  • DOI: https://doi.org/10.1007/978-3-642-34123-6_13

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