Dataset Information

Intracellular localization of the mycobacterial stressosome complex.

ABSTRACT: Microorganisms survive stresses by alternating the expression of genes suitable for surviving the immediate and present danger and eventually adapt to new conditions. Many bacteria have evolved a multiprotein "molecular machinery" designated the "Stressosome" that integrates different stress signals and activates alternative sigma factors for appropriate downstream responses. We and others have identified orthologs of some of the Bacillus subtilis stressosome components, RsbR, RsbS, RsbT and RsbUVW in several mycobacteria and we have previously reported mutual interactions among the stressosome components RsbR, RsbS, RsbT and RsbUVW from Mycobacterium marinum. Here we provide evidence that "STAS" domains of both RsbR and RsbS are important for establishing the interaction and thus critical for stressosome assembly. Fluorescence microscopy further suggested co-localization of RsbR and RsbS in multiprotein complexes visible as co-localized fluorescent foci distributed at scattered locations in the M. marinum cytoplasm; the number, intensity and distribution of such foci changed in cells under stressed conditions. Finally, we provide bioinformatics data that 17 (of 244) mycobacteria, which lack the RsbRST genes, carry homologs of Bacillus cereus genes rsbK and rsbM indicating the existence of alternative σ^F activation pathways among mycobacteria.

SUBMITTER: Ramesh M

PROVIDER: S-EPMC8115616 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Intracellular localization of the mycobacterial stressosome complex.

Ramesh Malavika M Nitharwal Ram Gopal RG Behra Phani Rama Krishna PRK Fredrik Pettersson B M BM Dasgupta Santanu S Kirsebom Leif A LA

Scientific reports 20210512 1

Microorganisms survive stresses by alternating the expression of genes suitable for surviving the immediate and present danger and eventually adapt to new conditions. Many bacteria have evolved a multiprotein "molecular machinery" designated the "Stressosome" that integrates different stress signals and activates alternative sigma factors for appropriate downstream responses. We and others have identified orthologs of some of the Bacillus subtilis stressosome components, RsbR, RsbS, RsbT and Rsb ...[more]

PMID: 33980893

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Project description:BackgroundThe computational prediction of mycobacterial proteins' subcellular localization is of key importance for proteome annotation and for the identification of new drug targets and vaccine candidates. Several subcellular localization classifiers have been developed over the past few years, which have comprised both general localization and feature-based classifiers. Here, we have validated the ability of different bioinformatics approaches, through the use of SignalP 2.0, TatP 1.0, LipoP 1.0, Phobius, PA-SUB 2.5, PSORTb v.2.0.4 and Gpos-PLoc, to predict secreted bacterial proteins. These computational tools were compared in terms of sensitivity, specificity and Matthew's correlation coefficient (MCC) using a set of mycobacterial proteins having less than 40% identity, none of which are included in the training data sets of the validated tools and whose subcellular localization have been experimentally confirmed. These proteins belong to the TBpred training data set, a computational tool specifically designed to predict mycobacterial proteins.ResultsA final validation set of 272 mycobacterial proteins was obtained from the initial set of 852 mycobacterial proteins. According to the results of the validation metrics, all tools presented specificity above 0.90, while dispersion sensitivity and MCC values were above 0.22. PA-SUB 2.5 presented the highest values; however, these results might be biased due to the methodology used by this tool. PSORTb v.2.0.4 left 56 proteins out of the classification, while Gpos-PLoc left just one protein out.ConclusionBoth subcellular localization approaches had high predictive specificity and high recognition of true negatives for the tested data set. Among those tools whose predictions are not based on homology searches against SWISS-PROT, Gpos-PLoc was the general localization tool with the best predictive performance, while SignalP 2.0 was the best tool among the ones using a feature-based approach. Even though PA-SUB 2.5 presented the highest metrics, it should be taken into account that this tool was trained using all proteins reported in SWISS-PROT, which includes the protein set tested in this study, either as a BLAST search or as a training model.

Dataset Information

Intracellular localization of the mycobacterial stressosome complex.

Publications

Intracellular localization of the mycobacterial stressosome complex.

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