Enterococcus saigonensis
Ontology highlight
ABSTRACT:
REPOSITORIES: ENA
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Project description:The enterococci comprise a genus of 49 low-GC content Gram-positive commensal species within the Firmicutes phylum that are known to occupy diverse habitats, notably the gastrointestinal core microbiota of nearly every phylum, including human. Of particular clinical relevance are two rogue species of enterococci, Enterococcus faecalis and the distantly related Enterococcus faecium, standing among the nefarious multi-drug resistant and hospital-acquired pathogens. Despite increasing evidence for RNA-based regulation in the enterococci, including regulation of virulence factors, their transcriptome structure and arsenal of regulatory small sRNAs (sRNAs) are not thoroughly understood. Using dRNA-seq, we have mapped at single-nucleotide resolution the primary transcriptomes of E. faecalis V583 and E. faecium AUS0004. We identified 2517 and 2771 transcription start sites (TSS) in E. faecalis and E. faecium, respectively. Based on the identified TSS, we created a global map of s70 promoter motifs. We also revealed features of 5’ and 3’UTRs across the genomes. The transcriptome maps also predicted 150 and 128 sRNA candidates in E. faecalis and E. faecium, respectively, some of which have been identified in previous studies and many of which are new. Finally, we validated several of the predicted sRNAs by Northern Blot in biologically relevant conditions. Comprehensive TSS mapping of two representative strains will provide a valuable resource for the continued development of RNA biology in the Enterococci.
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Project description:The authors use a large dataset (>30k) to train an explainable graph-based model to identify potential antibiotics with low cytotoxicity. The model uses a substructure-based approach to explore the chemical space. Using this method, they were able to screen 283 compounds and identify a candidate active against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci.
Model Type: Predictive machine learning model.
Model Relevance: Prediction of Human cytotoxicity endpoints.
Model Encoded by: Sarima Chiorlu (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos42ez
2024-05-13 | MODEL2405130002 | BioModels