Identification of Quantitative Proteomic Differences between Mycobacterium tuberculosis Lineages with Altered Virulence.

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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.", "claims": [{"id": "claim-method", "statement": "An ANOVA test was used to determine if there was a significant difference in the expression of each of the peptides representing each protein.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:27303394", "locator": "sections[17] \"Protein expression profiling\" paragraph 7", "quote": "An ANOVA test was used to determine if there was a significant difference in the expression of each of the peptides representing each protein."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data are available via ProteomeXchange with identifier PXD004165.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:27303394", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Data are available via ProteomeXchange with identifier PXD004165."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Data analysis showed that all major classes of proteins are represented in every strain, but that there are significant quantitative differences between strains.", "kind": "quantitative", "target": {"value": 5.0, "unit": "per cent", "tolerance": null, "source": "Decoy databases were used for FDR analysis and a cut-off was set at 5% for protein identifications."}, "evidence": [{"asset": "source:pmid:27303394", "locator": "sections[14] \"Results\" paragraph 4", "quote": "At the core of our pipeline is the semi-supervised learning algorithm implemented in Percolator (Brosch et al., 2009) that has been shown to obtain high identification sensitivity."}, {"asset": "source:pmid:27303394", "locator": "sections[13] \"Protein inference\" paragraph 2", "quote": "Decoy databases were used for FDR analysis and a cut-off was set at 5% for protein identifications."}, {"asset": "source:pmid:27303394", "locator": "sections[15] \"Data alignment for downstream comparison\" paragraph 2", "quote": "To achieve this, the total non-redundant IDs from each strain obtained by searching against its individual Ensembl fasta file were then mapped back to the Ensembl H37Rv protein IDs. These were all in turn mapped to UniProt accession numbers and Tuberculist “Rv” loci numbers to facilitate downstream analysis with tools such as GO analysis and pathway mapping."}, {"asset": "source:pmid:27303394", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Data analysis showed that all major classes of proteins are represented in every strain, but that there are significant quantitative differences between strains."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Data analysis showed that all major classes of proteins are represented in every strain, but that there are significant quantitative differences between strains.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:27303394"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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