Mycobacterium tuberculosis functional network analysis by global subcellular protein profiling

• Published in: Molecular biology of the cell 2005-01, Vol.16 (1), p.396-404
• Main Authors: Mawuenyega, Kwasi G, Forst, Christian V, Dobos, Karen M, Belisle, John T, Chen, Jin, Bradbury, E Morton, Bradbury, Andrew R M, Chen, Xian
• Format: Article
• Language: English
• Subjects: Automation; Cell Membrane - metabolism; Cell Wall - metabolism; Computational Biology; Cytosol - metabolism; Databases, Protein; Fatty Acids - metabolism; Genome; Lipid Metabolism; Models, Biological; Models, Statistical; Mycobacterium tuberculosis - physiology; Protein Array Analysis - methods; Proteins - chemistry; Proteome; Proteomics; Software; Spectrometry, Mass, Electrospray Ionization; Subcellular Fractions - metabolism
• ISSN: 1059-1524; 1939-4586
• DOI: 10.1091/mbc.E04-04-0329

Trends in increased tuberculosis infection and a fatality rate of approximately 23% have necessitated the search for alternative biomarkers using newly developed postgenomic approaches. Here we provide a systematic analysis of Mycobacterium tuberculosis (Mtb) by directly profiling its gene products. This analysis combines high-throughput proteomics and computational approaches to elucidate the globally expressed complements of the three subcellular compartments (the cell wall, membrane, and cytosol) of Mtb. We report the identifications of 1044 proteins and their corresponding localizations in these compartments. Genome-based computational and metabolic pathways analyses were performed and integrated with proteomics data to reconstruct response networks. From the reconstructed response networks for fatty acid degradation and lipid biosynthesis pathways in Mtb, we identified proteins whose involvements in these pathways were not previously suspected. Furthermore, the subcellular localizations of these expressed proteins provide interesting insights into the compartmentalization of these pathways, which appear to traverse from cell wall to cytoplasm. Results of this large-scale subcellular proteome profile of Mtb have confirmed and validated the computational network hypothesis that functionally related proteins work together in larger organizational structures.