Evidence map›Paper›PMID 20217870›Full record

ArticleProteomics2010

Descriptive proteomic analysis shows protein variability between closely related clinical isolates of Mycobacterium tuberculosis.

Carolina Mehaffy, Ann Hess, Jessica E Prenni, Barun Mathema, Barry Kreiswirth, Karen M Dobos

Abstract read
In one paragraph

Article in Proteomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
3.1field-weighted citation impact, top 9% 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

22 citing papers in PubMed, 44 citations in OpenAlex.

  1. Article
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  3. Methods for Proteomic Analyses of Mycobacteria.Methods in molecular biology (Clifton, N.J.) · 2021
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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

6 authors at 2 institutions in 1 country.

Carolina MehaffyDepartment of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO 80523-1682, USA.
Ann Hess
Jessica E Prenni
Barun Mathema
Barry Kreiswirth
Karen M Dobos
Colorado State University · USInternational Center for Public Health · US

Funding

NIAID NIH HHS HHSN266200400091CNIAID NIH HHS N01 AI040091PHS HHS N01-A1-40091
6 · The paper itself

Abstract

The use of isobaric tags such as iTRAQ allows the relative and absolute quantification of hundreds of proteins in a single experiment for up to eight different samples. More classical techniques such as 2-DE can offer a complimentary approach for the analysis of complex protein samples. In this study, the proteomes of secreted and cytosolic proteins of genetically closely related strains of Mycobacterium tuberculosis were analyzed. Analysis of 2-D gels afforded 28 spots with variations in protein abundance between strains. These were identified by MS/MS. Meanwhile, a rigorous statistical analysis of iTRAQ data allowed the identification and quantification of 101 and 137 proteins in the secreted and cytosolic fractions, respectively. Interestingly, several differences in protein levels were observed between the closely related strains BE, C28 and H6. Seven proteins related to cell wall and cell processes were more abundant in BE, while enzymes related to metabolic pathways (GltA2, SucC, Gnd1, Eno) presented lower levels in the BE strain. Proteins involved in iron and sulfur acquisition (BfrB, ViuB, TB15.3 and SseC2) were more abundant in C28 and H6. In general, iTRAQ afforded rapid identification of fine differences between protein levels such as those presented between closely related strains. This provides a platform from which the relevance of these differences can be assessed further using complimentary proteomic and biological modeling methods.

Indexed as

Bacterial ProteinsMycobacterium tuberculosisProteomeProteomicsBacterial ProteinsProteome

Identifiers

PMID20217870
PMCPMC3517044
OpenAlexW2000666716

What OpenQuestion holds

Textmetadata
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Read underepoch 390

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.