Evidence map›Paper›PMID 27303394›Full record

ArticleFrontiers in microbiology2016

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

Julian S Peters, Bridget Calder, Giulia Gonnelli, Sven Degroeve, Elinambinina Rajaonarifara, Nicola Mulder, Nelson C Soares, Lennart Martens, Jonathan M Blackburn

Open access · goldAbstract read
In one paragraph

Article in Frontiers in microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
5.4field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 38 citations in OpenAlex.

  1. A Porcine-IsolatedBiology · 2026
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  10. PPE37 Is Essential forInfection and immunity · 2019
    Article
  11. Article
  12. Comparative Study of the Molecular Basis of Pathogenicity ofInternational journal of molecular sciences · 2018
    Article
  13. Review
  14. Review
  15. Article
  16. Comparative Proteomic Analysis ofFrontiers in microbiology · 2017
    Article
  17. Article
  18. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 2 countries.

Julian S PetersCentre of Excellence for Biomedical TB Research, Witwatersrand University Johannesburg, South Africa.
Bridget CalderDepartment of Integrative Biomedical Sciences, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town Cape Town, South Africa.
Giulia GonnelliVIB, Ghent University Ghent, Belgium.
Sven DegroeveVIB, Ghent University Ghent, Belgium.
Elinambinina RajaonarifaraDepartment of Integrative Biomedical Sciences, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town Cape Town, South Africa.
Nicola MulderDepartment of Integrative Biomedical Sciences, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town Cape Town, South Africa.
Nelson C SoaresDepartment of Integrative Biomedical Sciences, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town Cape Town, South Africa.
Lennart MartensVIB, Ghent University Ghent, Belgium.
Jonathan M BlackburnDepartment of Integrative Biomedical Sciences, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town Cape Town, South Africa.
University of Cape Town · ZAGhent University · BEUniversity of the Witwatersrand · ZA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

fitnessMycobacterium tuberculosisproteomicsSRMstress responsevirulence

Identifiers

PMID27303394
PMCPMC4885829
OpenAlexW2414143497

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.