alexa Principal Component Analysis of Proteome Dynamics in Ir
ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

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Research Article

Principal Component Analysis of Proteome Dynamics in Ironstarved Mycobacterium Tuberculosis

Prahlad K. Rao1 and Qingbo Li1,2*

1Center for Pharmaceutical Biotechnology, College of Pharmacy

2Department of Microbiology and Immunology, College of Medicine, University of Illinois at Chicago, Chicago, Illinois 60607, USA

*Corresponding Author:
Dr. Qingbo Li
Department of Microbiology and Immunology
College of Medicine, University of Illinois at Chicago
Chicago, Illinois 60607, USA
Tel : 312-413-9301
Fax: 312-413-9303
E-mail : [email protected]

Received Date: November 29, 2008; Accepted Date: January 12, 2009; Published Date: January 15, 2009

Citation: Prahlad KR, Qingbo L (2009) Principal Component Analysis of Proteome Dynamics in Iron-Starved Mycobacterium Tuberculosis. J Proteomics Bioinform 2:019-031. doi: 10.4172/jpb.1000058

Copyright: © 2009 Prahlad KR, 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.

 

Abstract

The goal of this study is to use principal component analysis (PCA) for multivariate analysis of proteome dynamics based on both protein abundance and turnover information generated by high-resolution mass spectrometry. We previously reported assessing protein dynamics in iron-starved Mycobacterium tuberculosis, revealing interesting interconnection among the cellular processes involving protein synthesis, degradation, and secretion (Anal. Chem. 80, 6860-9). In this study, we use target-decoy database search approach to select peptides for quantitation at a false discovery rate of 4.2%. We further use PCA to reduce the data dimensions for simpler interpretation. The PCA results indicate that the protein turnover and relative abundance properties are approximately orthogonal in the data space defined by the first three principal components. We show the potential of the Hotelling’s T2 (T2) value as a quantifiable index for comparing changes between protein functional categories. The T2 value represents the gross change of a protein in both abundance and turnover. Close examination of the antigen 85 complex demonstrates that T2 correctly predicts the coordinated changes of the antigen 85 complex proteins. The multi-dimensional protein dynamics data further reveal the secretion of the antigen 85 complex. Overall, this study demonstrates PCA as an effective means to facilitate interpretation of the multivariate proteome dynamics dataset which otherwise would remain a significant challenge using traditional methods.

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