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Open Access Research

A semi-quantitative GeLC-MS analysis of temporal proteome expression in the emerging nosocomial pathogen Ochrobactrum anthropi

Robert Leslie James Graham1*, Mohit K Sharma1, Nigel G Ternan1, D Brent Weatherly2, Rick L Tarleton2 and Geoff McMullan1

Author Affiliations

1 School of Biomedical Sciences, University of Ulster, Coleraine, County Londonderry BT52 1SA, UK

2 The Center for Tropical and Emerging Global Diseases, University of Georgia, Athens, GA 30605, USA

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Genome Biology 2007, 8:R110  doi:10.1186/gb-2007-8-6-r110

Published: 13 June 2007

Abstract

Background

The α-Proteobacteria are capable of interaction with eukaryotic cells, with some members, such as Ochrobactrum anthropi, capable of acting as human pathogens. O. anthropi has been the cause of a growing number of hospital-acquired infections; however, little is known about its growth, physiology and metabolism. We used proteomics to investigate how protein expression of this organism changes with time during growth.

Results

This first gel-based liquid chromatography-mass spectrometry (GeLC-MS) temporal proteomic analysis of O. anthropi led to the positive identification of 131 proteins. These were functionally classified and physiochemically characterized. Utilizing the emPAI protocol to estimate protein abundance, we assigned molar concentrations to all proteins, and thus were able to identify 19 with significant changes in their expression. Pathway reconstruction led to the identification of a variety of central metabolic pathways, including nucleotide biosynthesis, fatty acid anabolism, glycolysis, TCA cycle and amino acid metabolism. In late phase growth we identified a number of gene products under the control of the oxyR regulon, which is induced in response to oxidative stress and whose protein products have been linked with pathogen survival in response to host immunity reactions.

Conclusion

This study identified distinct proteomic profiles associated with specific growth points for O. anthropi, while the use of emPAI allowed semi-quantitative analyses of protein expression. It was possible to reconstruct central metabolic pathways and infer unique functional and adaptive processes associated with specific growth phases, thereby resulting in a deeper understanding of the physiology and metabolism of this emerging pathogenic bacterium.