Multivariate statistical analysis of diverse strains of Yersinia pestis by comparative proteomics

Todd H. Corzett, Angela M. Eldridge, Jennifer S. Knaack, Christopher H. Corzett, Sandra L. McCutchen-Maloney, Brett A. Chromy

Research output: Contribution to journalArticle

Abstract

To address the difficulty in characterizing unusual, engineered or emergent pathogens in clinical and environmental samples, novel methods to discover proteins that differentiate pathogenic strains are needed. Differentially expressed proteins that reveal the function of an uncharacterized strain of bacteria can be considered biomarkers; panels of these can lead to improved pathogen classification and characterization. To this end, the protein expression patterns of differentially virulent isolates of the plague pathogen, Yersinia pestis, were studied using two-dimensional difference gel electrophoresis (2-D DIGE). The resulting characterization was used to identify a protein expression panel for the clustering and classification of Y. pestis strains. Two different methods were used to produce different biomarker panels based on either experimental- or pattern-based clustering. Each panel is able to successfully classify unknown samples in a blinded fashion, allowing an unbiased discovery of differentially expressed proteins, as well as the rapid classification of protein expression patterns.

Original languageEnglish (US)
Pages (from-to)202-208
Number of pages7
JournalJournal of Proteomics and Bioinformatics
Volume6
Issue number9
DOIs
StatePublished - 2013

Keywords

  • 2-D DIGE
  • Biomarkers
  • Chemometrics
  • DeCyder
  • Extended data analysis
  • Plague
  • Proteomics
  • Strain diversity
  • Yersinia pestis

ASJC Scopus subject areas

  • Biochemistry
  • Cell Biology
  • Molecular Biology
  • Computer Science Applications

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  • Cite this

    Corzett, T. H., Eldridge, A. M., Knaack, J. S., Corzett, C. H., McCutchen-Maloney, S. L., & Chromy, B. A. (2013). Multivariate statistical analysis of diverse strains of Yersinia pestis by comparative proteomics. Journal of Proteomics and Bioinformatics, 6(9), 202-208. https://doi.org/10.4172/jpb.1000282