Evidence currently suggests that as a species Mycobacterium tuberculosis exhibits very little genomic sequence diversity. Despite limited genetic variability, members of the M. tuberculosis complex (MTBC) have been shown to exhibit vast discrepancies in phenotypic presentation in terms of virulence, elicited immune response and transmissibility. Here, we used qualitative and quantitative mass spectrometry tools to investigate the proteomes of seven clinically-relevant mycobacterial strains—four M. tuberculosis strains, M. bovis, M. bovis BCG, and M. avium—that show varying degrees of pathogenicity and virulence, in an effort to rationalize the observed phenotypic differences. Following protein preparation, liquid chromatography mass spectrometry (LC MS/MS) and data capture were carried out using an LTQ Orbitrap Velos. Data analysis was carried out using a novel bioinformatics strategy, which yielded high protein coverage and was based on high confidence peptides. Through this approach, we directly identified a total of 3788 unique M. tuberculosis proteins out of a theoretical proteome of 4023 proteins and identified an average of 3290 unique proteins for each of the MTBC organisms (representing 82% of the theoretical proteomes), as well as 4250 unique M. avium proteins (80% of the theoretical proteome). Data analysis showed that all major classes of proteins are represented in every strain, but that there are significant quantitative differences between strains. Targeted selected reaction monitoring (SRM) assays were used to quantify the observed differential expression of a subset of 23 proteins identified by comparison to gene expression data as being of particular relevance to virulence. This analysis revealed differences in relative protein abundance between strains for proteins which may promote bacterial fitness in the more virulent W. Beijing strain. These differences may contribute to this strain's capacity for surviving within the host and resisting treatment, which has contributed to its rapid spread. Through this approach, we have begun to describe the proteomic portrait of a successful mycobacterial pathogen. Data are available via ProteomeXchange with identifier PXD004165.
A growing number of proteogenomics and metaproteomics studies indicate potential limitations of the application of the "decoy" database paradigm used to separate correct peptide identifications from incorrect ones in traditional shotgun proteomics. We therefore propose a binary classifier called Nokoi that allows fast yet reliable decoy-free separation of correct from incorrect peptide-to-spectrum matches (PSMs). Nokoi was trained on a very large collection of heterogeneous data using ranks supplied by the Mascot search engine to label correct and incorrect PSMs. We show that Nokoi outperforms Mascot and achieves a performance very close to that of Percolator at substantially higher processing speeds.
The use of protein tagging to facilitate detailed characterization of target proteins has not only revolutionized cell biology, but also enabled biochemical analysis through efficient recovery of the protein complexes wherein the tagged proteins reside. The endogenous use of these tags for detailed protein characterization is widespread in lower organisms that allow for efficient homologous recombination. With the recent advances in genome engineering, tagging of endogenous proteins is now within reach for most experimental systems, including mammalian cell lines cultures. In this work, we describe the selection of peptides with ideal mass spectrometry characteristics for use in quantification of tagged proteins using targeted proteomics. We mined the proteome of the hyperthermophile Pyrococcus furiosus to obtain two peptides that are unique in the proteomes of all known model organisms (proteotypic) and allow sensitive quantification of target proteins in a complex background. By combining these ’Proteotypic peptides for Quantification by SRM’ (PQS peptides) with epitope tags, we demonstrate their use in co-immunoprecipitation experiments upon transfection of protein pairs, or after introduction of these tags in the endogenous proteins through genome engineering. Endogenous protein tagging for absolute quantification provides a powerful extra dimension to protein analysis, allowing the detailed characterization of endogenous proteins.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.