Label-free quantitative proteomics of Corynebacterium pseudotuberculosis isolates reveals differences between Biovars ovis and equi strains

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Background

Corynebacterium pseudotuberculosis is a pathogen classified into two biovars: C. pseudotuberculosis biovar ovis, the etiologic agent of caseous lymphadenitis and C. pseudotuberculosis biovar equi, which causes ulcerative lymphangitis. The available whole genome sequences of different C. pseudotuberculosis strains have enabled identify difference of genes related both virulence and physiology of each biovar. To evaluate be this difference could reflect at proteomic level and to better understand the shared factors and the exclusive ones of biovar ovis and biovar equi strains, we applied the label-free quantitative proteomic to characterize the proteome of the strains: 1002_ ovis and 258_ equi, isolated from goat (Brazil) and equine (Belgium), respectively.

Results

From this analysis, we characterized a total of 1230 proteins in 1002_ ovis and 1220 in 258_ equi with high confidence. Moreover, the core-proteome between 1002_ ovis and 258_ equi obtained here is composed of 1122 proteins involved in different cellular processes, which could be necessary for the free living of C. pseudotuberculosis. In addition, 120 proteins from this core-proteome presented change in abundant with statistically significant differences. Considering the exclusive proteome, we detected strain-specific proteins to each strain. When correlated, the exclusive proteome of each strain and proteome with change in abundant, the proteomic differences, between the 1002_ ovis and 258_ equi, this related to proteins involved in cellular metabolism, information storage and processing, cellular processes and signaling.

Conclusions

This study reports the first comparative proteomic study of the biovars ovis and equi of C. pseudotuberculosis. The results generated in this study provide information about factors which can contribute to understanding both the physiology and the virulence of this pathogen.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3835-y) contains supplementary material, which is available to authorized users.

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Most cited references 51

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The thioredoxin antioxidant system.

Jun Lu, Arne Holmgren (2014)

The thioredoxin (Trx) system, which is composed of NADPH, thioredoxin reductase (TrxR), and thioredoxin, is a key antioxidant system in defense against oxidative stress through its disulfide reductase activity regulating protein dithiol/disulfide balance. The Trx system provides the electrons to thiol-dependent peroxidases (peroxiredoxins) to remove reactive oxygen and nitrogen species with a fast reaction rate. Trx antioxidant functions are also shown by involvement in DNA and protein repair by reducing ribonucleotide reductase, methionine sulfoxide reductases, and regulating the activity of many redox-sensitive transcription factors. Moreover, Trx systems play critical roles in the immune response, virus infection, and cell death via interaction with thioredoxin-interacting protein. In mammalian cells, the cytosolic and mitochondrial Trx systems, in which TrxRs are high molecular weight selenoenzymes, together with the glutathione-glutaredoxin (Grx) system (NADPH, glutathione reductase, GSH, and Grx) control the cellular redox environment. Recently mammalian thioredoxin and glutathione systems have been found to be able to provide the electrons crossly and to serve as a backup system for each other. In contrast, bacteria TrxRs are low molecular weight enzymes with a structure and reaction mechanism distinct from mammalian TrxR. Many bacterial species possess specific thiol-dependent antioxidant systems, and the significance of the Trx system in the defense against oxidative stress is different. Particularly, the absence of a GSH-Grx system in some pathogenic bacteria such as Helicobacter pylori, Mycobacterium tuberculosis, and Staphylococcus aureus makes the bacterial Trx system essential for survival under oxidative stress. This provides an opportunity to kill these bacteria by targeting the TrxR-Trx system. Copyright © 2013 Elsevier Inc. All rights reserved.

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Prediction of lipoprotein signal peptides in Gram-negative bacteria.

Agnieszka S Juncker, Hanni Willenbrock, Gunnar von Heijne … (2003)

A method to predict lipoprotein signal peptides in Gram-negative Eubacteria, LipoP, has been developed. The hidden Markov model (HMM) was able to distinguish between lipoproteins (SPaseII-cleaved proteins), SPaseI-cleaved proteins, cytoplasmic proteins, and transmembrane proteins. This predictor was able to predict 96.8% of the lipoproteins correctly with only 0.3% false positives in a set of SPaseI-cleaved, cytoplasmic, and transmembrane proteins. The results obtained were significantly better than those of previously developed methods. Even though Gram-positive lipoprotein signal peptides differ from Gram-negatives, the HMM was able to identify 92.9% of the lipoproteins included in a Gram-positive test set. A genome search was carried out for 12 Gram-negative genomes and one Gram-positive genome. The results for Escherichia coli K12 were compared with new experimental data, and the predictions by the HMM agree well with the experimentally verified lipoproteins. A neural network-based predictor was developed for comparison, and it gave very similar results. LipoP is available as a Web server at www.cbs.dtu.dk/services/LipoP/.

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Is Open Access

Non-classical protein secretion in bacteria

Jannick Bendtsen, Lars Kiemer, Anders Fausbøll … (2005)

Background We present an overview of bacterial non-classical secretion and a prediction method for identification of proteins following signal peptide independent secretion pathways. We have compiled a list of proteins found extracellularly despite the absence of a signal peptide. Some of these proteins also have known roles in the cytoplasm, which means they could be so-called "moon-lightning" proteins having more than one function. Results A thorough literature search was conducted to compile a list of currently known bacterial non-classically secreted proteins. Pattern finding methods were applied to the sequences in order to identify putative signal sequences or motifs responsible for their secretion. We have found no signal or motif characteristic to any majority of the proteins in the compiled list of non-classically secreted proteins, and conclude that these proteins, indeed, seem to be secreted in a novel fashion. However, we also show that the apparently non-classically secreted proteins are still distinguished from cellular proteins by properties such as amino acid composition, secondary structure and disordered regions. Specifically, prediction of disorder reveals that bacterial secretory proteins are more structurally disordered than their cytoplasmic counterparts. Finally, artificial neural networks were used to construct protein feature based methods for identification of non-classically secreted proteins in both Gram-positive and Gram-negative bacteria. Conclusion We present a publicly available prediction method capable of discriminating between this group of proteins and other proteins, thus allowing for the identification of novel non-classically secreted proteins. We suggest candidates for non-classically secreted proteins in Escherichia coli and Bacillus subtilis. The prediction method is available online.

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Author and article information

Contributors

Wanderson M. Silva: silvamarques@yahoo.com.br

Edson L. Folador: edson.folador@gmail.com

Siomar C. Soares: siomars@gmail.com

Gustavo H. M. F. Souza: Gustavosouza@waters.com

Agenor V. Santos: valadaresantos@gmail.com

Cassiana S. Sousa: cassissousa@gmail.com

Henrique Figueiredo: figueiredoh@yahoo.com

Anderson Miyoshi: miyoshi@icb.ufmg.br

Yves Le Loir: yves.leloir@rennes.inra.fr

Artur Silva: asilva@ufpa.br

Vasco Azevedo: +55 31 3409 2610 , vasco@icb.ufmg.br

Journal

Journal ID (nlm-ta): BMC Genomics

Journal ID (iso-abbrev): BMC Genomics

Title: BMC Genomics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2164

Publication date (Electronic): 8 June 2017

Publication date PMC-release: 8 June 2017

Publication date Collection: 2017

Volume: 18

Electronic Location Identifier: 451

Affiliations

[1 ]ISNI 0000 0001 2181 4888, GRID grid.8430.f, Departamento de Biologia Geral, Instituto de Ciências Biológicas, , Universidade Federal de Minas Gerais, ; Belo Horizonte, Minas Gerais Brasil

[2 ]ISNI 0000 0001 2171 5249, GRID grid.271300.7, Instituto de Ciências Biológicas, , Universidade Federal do Pará, ; Belém, Pará Brasil

[3 ]ISNI 0000 0004 0397 5145, GRID grid.411216.1, Centro de Biotecnologia, , Universidade Federal da Paraíba, ; João Pessoa, Paraíba Brasil

[4 ]Waters Corporation, Waters Technologies Brazil, MS Applications Laboratory, Alphaville, São Paulo, Brasil

[5 ]GRID grid.460202.2, , INRA, UMR1253 STLO, ; 35042 Rennes, France

[6 ]ISNI 0000 0001 2187 6317, GRID grid.424765.6, , Agrocampus Ouest, UMR1253 STLO, ; 35042 Rennes, France

[7 ]ISNI 0000 0001 2181 4888, GRID grid.8430.f, Escola de Veterinária, Aquavet, , Universidade Federal de Minas Gerais, ; Belo Horizonte, Minas Gerais Brasil

[8 ]ISNI 0000 0004 0643 8003, GRID grid.411281.f, Departmento de Microbiologia, Imunologia e Parasitologia, Instituto de Ciências Biológicas e Naturais, , Universidade Federal do Triângulo Mineiro, ; Uberaba, Minas Gerais Brasil

Article

Publisher ID: 3835

DOI: 10.1186/s12864-017-3835-y

PMC ID: 5463331

SO-VID: 321e9e6c-867f-4894-a90a-46549e629e06

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 26 July 2016

Date accepted : 31 May 2017

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ScienceOpen disciplines: Genetics

Keywords: corynebacterium pseudotuberculosis,caseous lymphadenitis,ulcerative lymphangitis,proteomic bacterial,label-free proteomics, proteomic

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ScienceOpen disciplines: Genetics

Keywords: corynebacterium pseudotuberculosis, caseous lymphadenitis, ulcerative lymphangitis, proteomic bacterial, label-free proteomics, proteomic

Label-free quantitative proteomics of Corynebacterium pseudotuberculosis isolates reveals differences between Biovars ovis and equi strains

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Genomic Prediction

Most cited references 51

The thioredoxin antioxidant system.

Prediction of lipoprotein signal peptides in Gram-negative bacteria.

Non-classical protein secretion in bacteria

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