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

Fingerprint

Yersinia pestis
Proteomics
Statistical methods
Multivariate Analysis
Proteins
Pathogens
Biomarkers
Cluster Analysis
Two-Dimensional Difference Gel Electrophoresis
Plague
Electrophoresis
Bacteria
Gels

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

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

Multivariate statistical analysis of diverse strains of Yersinia pestis by comparative proteomics. / Corzett, Todd H.; Eldridge, Angela M.; Knaack, Jennifer S.; Corzett, Christopher H.; McCutchen-Maloney, Sandra L.; Chromy, Brett A.

In: Journal of Proteomics and Bioinformatics, Vol. 6, No. 9, 2013, p. 202-208.

Research output: Contribution to journalArticle

Corzett, TH, Eldridge, AM, Knaack, JS, Corzett, CH, McCutchen-Maloney, SL & Chromy, BA 2013, 'Multivariate statistical analysis of diverse strains of Yersinia pestis by comparative proteomics', Journal of Proteomics and Bioinformatics, vol. 6, no. 9, pp. 202-208. https://doi.org/10.4172/jpb.1000282
Corzett, Todd H. ; Eldridge, Angela M. ; Knaack, Jennifer S. ; Corzett, Christopher H. ; McCutchen-Maloney, Sandra L. ; Chromy, Brett A. / Multivariate statistical analysis of diverse strains of Yersinia pestis by comparative proteomics. In: Journal of Proteomics and Bioinformatics. 2013 ; Vol. 6, No. 9. pp. 202-208.
@article{37fe77d5c56046818a619911c819c723,
title = "Multivariate statistical analysis of diverse strains of Yersinia pestis by comparative proteomics",
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.",
keywords = "2-D DIGE, Biomarkers, Chemometrics, DeCyder, Extended data analysis, Plague, Proteomics, Strain diversity, Yersinia pestis",
author = "Corzett, {Todd H.} and Eldridge, {Angela M.} and Knaack, {Jennifer S.} and Corzett, {Christopher H.} and McCutchen-Maloney, {Sandra L.} and Chromy, {Brett A.}",
year = "2013",
doi = "10.4172/jpb.1000282",
language = "English (US)",
volume = "6",
pages = "202--208",
journal = "Journal of Proteomics and Bioinformatics",
issn = "0974-276X",
publisher = "Omics Publishing Group",
number = "9",

}

TY - JOUR

T1 - Multivariate statistical analysis of diverse strains of Yersinia pestis by comparative proteomics

AU - Corzett, Todd H.

AU - Eldridge, Angela M.

AU - Knaack, Jennifer S.

AU - Corzett, Christopher H.

AU - McCutchen-Maloney, Sandra L.

AU - Chromy, Brett A.

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - 2-D DIGE

KW - Biomarkers

KW - Chemometrics

KW - DeCyder

KW - Extended data analysis

KW - Plague

KW - Proteomics

KW - Strain diversity

KW - Yersinia pestis

UR - http://www.scopus.com/inward/record.url?scp=84887340661&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84887340661&partnerID=8YFLogxK

U2 - 10.4172/jpb.1000282

DO - 10.4172/jpb.1000282

M3 - Article

VL - 6

SP - 202

EP - 208

JO - Journal of Proteomics and Bioinformatics

JF - Journal of Proteomics and Bioinformatics

SN - 0974-276X

IS - 9

ER -