Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
Enterococcus faecalis, a Gram-positive bacterium, and Candida albicans, a polymorphic fungus, are common constituents of the microbiome as well as increasingly problematic causes of infections. Interestingly, we previously showed that these two species antagonize each other's virulence and that E. faecalis inhibition of C. albicans was specifically mediated by EntV. EntV is a bacteriocin encoded by the entV (ef1097) locus that reduces C. albicans virulence and biofilm formation by inhibiting hyphal morphogenesis. In this report, we studied the posttranslational modifications necessary for EntV antifungal activity. First, we show that the E. faecalis secreted enzyme gelatinase (GelE) is responsible for cleaving EntV into its 68-amino-acid, active form and that this process does not require the serine protease SprE. Furthermore, we demonstrate that a disulfide bond that forms within EntV is necessary for antifungal activity. Abrogating this bond by chemical treatment or genetic modification rendered EntV inactive against C. albicans Moreover, we identified the likely catalyst of this disulfide bond, a previously uncharacterized thioredoxin within the E. faecalis genome called DsbA. Loss of DsbA, or disruption of its redox-active cysteines, resulted in loss of EntV antifungal activity. Finally, we show that disulfide bond formation is not a prerequisite for cleavage; EntV cleavage proceeded normally in the absence of DsbA. In conclusion, we present a model in which following secretion, EntV undergoes disulfide bond formation by DsbA and cleavage by GelE in order to generate a peptide capable of inhibiting C. albicansIMPORTANCEEnterococcus faecalis and Candida albicans are among the most important and problematic pathobionts, organisms that normally are harmless commensals but can cause dangerous infections in immunocompromised hosts. In fact, both organisms are listed by the Centers for Disease Control and Prevention as serious global public health threats stemming from the increased prevalence of antimicrobial resistance. The rise in antifungal resistance is of particular concern considering the small arsenal of currently available therapeutics. EntV is a peptide with antifungal properties, and it, or a similar compound, could be developed into a therapeutic alternative, either alone or in combination with existing agents. However, to do so requires understanding what properties of EntV are necessary for its antifungal activity. In this work, we studied the posttranslational processing of EntV and what modifications are necessary for inhibition of C. albicans in order to fill this gap in knowledge.
Pubmed ID: 31266876
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Statistical analysis software that combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization. Designed for biological research applications in pharmacology, physiology, and other biological fields for data analysis, hypothesis testing, and modeling.
View all literature mentionsCommercial vendor and service provider of laboratory reagents and antibodies. Supplier of scientific instrumentation, reagents and consumables, and software services.
View all literature mentionsPercolator post-processes the results of a shotgun proteomics database search program, re-ranking peptide-spectrum matches so that the top of the list is enriched for correct matches. Shotgun proteomics uses liquid chromatography-tandem mass spectrometry to identify proteins in complex biological samples. We describe an algorithm, called Percolator, for improving the rate of peptide identifications from a collection of tandem mass spectra. Percolator uses semi-supervised machine learning to discriminate between correct and decoy spectrum identifications, correctly assigning peptides to 17% more spectra from a tryptic dataset and up to 77% more spectra from non-tryptic digests, relative to a fully supervised approach. The yeast-01 data is available in tab delimetered format. The SEQUEST parameter file and target database for the yeast and worm data are also available.
View all literature mentions