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Descriptive proteomic analysis shows protein variability between closely related clinical isolates of Mycobacterium tuberculosis
Authors:Carolina Mehaffy  Ann Hess  Jessica E. Prenni  Barun Mathema  Barry Kreiswirth  Karen M. Dobos
Affiliation:1. Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, USA;2. Center for bioinformatics and Department of Statistics, Colorado State University, Fort Collins, CO, USA;3. Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO, USA;4. Public Health Research Institute, Newark, NJ, USA
Abstract:The use of isobaric tags such as iTRAQ allows the relative and absolute quantification of hundreds of proteins in a single experiment for up to eight different samples. More classical techniques such as 2‐DE can offer a complimentary approach for the analysis of complex protein samples. In this study, the proteomes of secreted and cytosolic proteins of genetically closely related strains of Mycobacterium tuberculosis were analyzed. Analysis of 2‐D gels afforded 28 spots with variations in protein abundance between strains. These were identified by MS/MS. Meanwhile, a rigorous statistical analysis of iTRAQ data allowed the identification and quantification of 101 and 137 proteins in the secreted and cytosolic fractions, respectively. Interestingly, several differences in protein levels were observed between the closely related strains BE, C28 and H6. Seven proteins related to cell wall and cell processes were more abundant in BE, while enzymes related to metabolic pathways (GltA2, SucC, Gnd1, Eno) presented lower levels in the BE strain. Proteins involved in iron and sulfur acquisition (BfrB, ViuB, TB15.3 and SseC2) were more abundant in C28 and H6. In general, iTRAQ afforded rapid identification of fine differences between protein levels such as those presented between closely related strains. This provides a platform from which the relevance of these differences can be assessed further using complimentary proteomic and biological modeling methods.
Keywords:Clinical isolates  iTRAQ  Microbiology  Mycobacterium tuberculosis
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