Multivariate Statistical Analysis of Diverse Strains of Yersinia pestis by Comparative Proteomics
- *Corresponding Author:
- Brett A Chromy
Department of Pathology and Laboratory Medicine
School of Medicine, University of California Davis
Sacramento, CA 95817, USA
Tel: (530) 752-7229
E-mail: [email protected]
Received date: July 24, 2013; Accepted date: September 27, 2013; Published date: September 29, 2013
Citation: Corzett TH, Eldridge AM, Knaack JS, Corzett CH, McCutchen-Maloney SL, et al. (2013) Multivariate Statistical Analysis of Diverse Strains of Yersinia pestis by Comparative Proteomics. J Proteomics Bioinform 6:202-208. doi:10.4172/jpb.1000282
Copyright: © 2013 Corzett TH, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.