Gel-free sample preparation techniques and bioinformatic enrichment analysis to in depth characterise the cell wall proteome of mycobacteria

• Main Authors: Clemens Hermann, Alexander D. Giddey, Andrew J.M. Nel, Nelson C. Soares, Jonathan M. Blackburn
• Format: Article
• Language: English
• Published: Elsevier 2018-01-01
• Series: MethodsX
• Online Access: http://www.sciencedirect.com/science/article/pii/S2215016118300797

The comprehensive characterisation of the cell wall proteome of mycobacteria is of considerable relevance to both the discovery of new drug targets as well as to the design of new vaccines against Mycobacterium tuberculosis. However, due to its extremely hydrophobic nature, the coverage of proteomic studies of this subcellular compartment is still far from complete. Here, we report novel gel-free cell wall sample preparation procedures and quantitative LC–MS/MS measurements on a Q Exactive mass spectrometer. We combine these with a novel post-measurement bioinformatic analysis to filter out likely cytosolic contaminants. This reveals a subset of proteins that are highly enriched for cell wall proteins. The success of this approach is verified by peptide-centric measurement of the abundance of known subcellular markers, as well as analysis of the percentage of predicted membrane proteins within the purified fraction. While M. smegmatis was used during this study to establish and optimise the sample preparation procedures, these can easily be applied to other mycobacterial species, such as M. bovis BCG or M. tuberculosis. • Improved gel-free cell wall sample preparation gives higher yields of tryptic peptides for LC–MS/MS measurement. • Higher yields of tryptic peptides provide better quantitation and coverage of cell wall proteome. • Post-measurement enrichment analysis filters out high abundance cytosolic contaminants that have carried through the experimental analysis. Method name: Cell wall enrichment and bioinformatic enrichment analysis, Keywords: Cell wall proteomics, Mycobacteria, Proteomics, Bioinformatic enrichment analysis