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      Quantitative Proteomic Analyses of a Pathogenic Strain and Its Highly Passaged Attenuated Strain of Mycoplasma hyopneumoniae

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          Abstract

          Mycoplasma hyopneumoniae is the causative agent of porcine enzootic pneumonia, a chronic respiratory disease in swine resulting in enormous economic losses. To identify the components that contribute to virulence and unveil those biological processes potentially related to attenuation, we used isobaric tags for relative and absolute quantification technology (iTRAQ) to compare the protein profiles of the virulent M. hyopneumoniae strain 168 and its attenuated highly passaged strain 168L. We identified 489 proteins in total, 70 of which showing significant differences in level of expression between the two strains. Remarkably, proteins participating in inositol phosphate metabolism were significantly downregulated in the virulent strain, while some proteins involved in nucleoside metabolism were upregulated. We also mined a series of novel promising virulence-associated factors in our study compared with those in previous reports, such as some moonlighting adhesins, transporters, lipoate-protein ligase, and ribonuclease and several hypothetical proteins with conserved functional domains, deserving further research. Our survey constitutes an iTRAQ-based comparative proteomic analysis of a virulent M. hyopneumoniae strain and its attenuated strain originating from a single parent with a well-characterized genetic background and lays the groundwork for future work to mine for potential virulence factors and identify candidate vaccine proteins.

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          Prediction of lipoprotein signal peptides in Gram-negative bacteria.

          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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            Non-classical protein secretion in bacteria

            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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              Multifunctional alpha-enolase: its role in diseases.

              V Pancholi (2001)
              Enolase, a key glycolytic enzyme, belongs to a novel class of surface proteins which do not possess classical machinery for surface transport, yet through an unknown mechanism are transported on the cell surface. Enolase is a multifunctional protein, and its ability to serve as a plasminogen receptor on the surface of a variety of hematopoetic, epithelial and endothelial cells suggests that it may play an important role in the intravascular and pericellular fibrinolytic system. Its role in systemic and invasive autoimmune disorders was recognized only very recently. In addition to this property, its ability to function as a heat-shock protein and to bind cytoskeletal and chromatin structures indicate that enolase may play a crucial role in transcription and a variety of pathophysiological processes.
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                Author and article information

                Contributors
                Journal
                Biomed Res Int
                Biomed Res Int
                BMRI
                BioMed Research International
                Hindawi
                2314-6133
                2314-6141
                2019
                1 July 2019
                : 2019
                : 4165735
                Affiliations
                1State Key Laboratory of Agricultural Microbiology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China
                2The Key Laboratory of Preventive Veterinary Medicine in Hubei Province, The Cooperative Innovation Center for Sustainable Pig Production, Wuhan 430070, China
                3Key Laboratory of Prevention and Control Agents for Animal Bacteriosis (Ministry of Agriculture), Institute of Animal Husbandry and Veterinary Sciences, Hubei Academy of Agricultural Sciences, Wuhan 430070, China
                Author notes

                Academic Editor: Clara G. de los Reyes-Gavilan

                Author information
                https://orcid.org/0000-0001-9580-4578
                https://orcid.org/0000-0003-0023-9188
                Article
                10.1155/2019/4165735
                6634062
                dc0d341a-6cc3-4902-8b7e-e50d347b593e
                Copyright © 2019 Sha Li et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 December 2018
                : 14 April 2019
                : 27 May 2019
                Funding
                Funded by: Key Technology R&D Programme of China
                Award ID: 2015BAD12B02
                Funded by: National Natural Science Foundation of China
                Award ID: 31170160
                Award ID: 31502069
                Categories
                Research Article

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