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      Identification of New Mycobacterium bovis antigens and development of a multiplexed serological bead-immunoassay for the diagnosis of bovine tuberculosis in cattle

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          Abstract

          Serological assays for bovine tuberculosis diagnosis require the use of multiple Mycobacterium bovis specific antigens to ensure the detection of infected animals. In the present study, identification and selection process of antigens, based on data from published proteomic studies and involving the use of bioinformatics tools and an immuno-screening step, was firstly performed for identifying novel antigens that elicit an antibody response in M. bovis infection. Based on this approach, a panel of 10 M. bovis antigens [with known relevance (MPB70, MPB83, MPB70/83, and ESAT6/CFP10) and novel (Mb1961c, Mb1301c, Mb3871, Mb1403, Mb0592, and PE25/PPE41)] were constructed and thenused to develop a new multiplexed serological assay based on Luminex technology. The performance of the Luminex-bTB immunoassay was evaluated using sera from cattle with known tuberculosis status. Among the proteins whose ability to detect bovine tuberculosis was evaluated for the first time, PE25/PPE41 and Mb1403, but not Mb3871, showed good detection capacity. Following multiple antigen combination, the final Luminex-bTB immunoassay included seven antigens (MPB70, MPB83, MPB70/83, ESAT6/CFP10, PE25/PPE41, Mb1403, and Mb0592) and showed better global performance than the immunoassay using the four usual antigens (MPB70, MPB70/83, MPB83 and ESAT6/CFP10). The specificity and sensitivity values were, respectively, of 97.6% and 42.8% when the cut-off of two-positive antigens was used to classify samples as positive. With the use of the more-restrictive criterion of three-positive antigens, the specificity increased to 99.2% but the sensitivity decreased to 23.9%. The analysis of antigen profiles generated with the Luminex-bTB immunoassay showed that mainly serodominant proteins were recognized in samples from infected cattle. The detection of Mb1961c and Mb1301c appeared to be associated with presumed false-positive results. Moreover, sera from cattle originating from bTB-outbreaks but having inconclusive or negative skin test results were identified as positive by the Luminex-bTB immunoassay and showed an antigen pattern associated with M. bovis infection. The Luminex-bTB immunoassay including seven antigens may be useful as adjunct test for the detection of M. bovis–infected herds, and different cut-offs could be applied according to the bovine tuberculosis epidemiological context.

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          SignalP 5.0 improves signal peptide predictions using deep neural networks

          Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.
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            PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes

            Motivation: PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program. Results: We developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. It features an improved standalone program, with a new batch results delivery system complementing its web interface. We evaluated the most accurate SCL predictors using 5-fold cross validation plus we performed an independent proteomics analysis, showing that PSORTb 3.0 is the most accurate but can benefit from being complemented by Proteome Analyst predictions. Availability: http://www.psort.org/psortb (download open source software or use the web interface). Contact: psort-mail@sfu.ca Supplementary Information: Supplementary data are availableat Bioinformatics online.
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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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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: Writing – review & editing
                Role: Investigation
                Role: ConceptualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                9 October 2023
                2023
                : 18
                : 10
                : e0292590
                Affiliations
                [1 ] Department of Animal Infectious Diseases, Laboratory of Veterinary Bacteriology, National Institute for Public Health (Sciensano), Brussels, Belgium
                [2 ] Laboratory of Biochemistry and Genetics of Microorganisms, Louvain Institute of Biomolecular Science and Technology, UCLouvain, Louvain-la-Neuve, Belgium
                [3 ] Laboratory of Immuno-Biology, CER Groupe, Aye, Belgium
                [4 ] Department of Human Infectious Diseases, Laboratory of Viral Diseases, National Institute for Public Health (Sciensano), Brussels, Belgium
                [5 ] Department of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology, KU Leuven, Leuven, Belgium
                The University of Georgia, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-1948-3036
                https://orcid.org/0000-0001-6288-7936
                https://orcid.org/0009-0000-9658-6824
                Article
                PONE-D-23-23501
                10.1371/journal.pone.0292590
                10561873
                37812634
                d100c676-e6eb-4f20-9a30-5ec660a08053
                © 2023 Moens et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 25 July 2023
                : 25 September 2023
                Page count
                Figures: 4, Tables: 6, Pages: 26
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100011091, FOD Volksgezondheid, Veiligheid van de Voedselketen en Leefmilieu;
                Award ID: DIBOTUB RT 18/1
                Award Recipient :
                The research that yielded these results, was funded by the Belgian Federal Public Service of Health, Food Chain Safety and Environment through the contract RT 18/01 DIBOTUB - Obtained Funding: Charlotte Moens. https://www.health.belgium.be/en. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
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                Biology and Life Sciences
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                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Bovines
                Cattle
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