Project description:In order to determine the mechanism of Cajanin Stilbene Acid inhibiting vancomycin-resistant enterococci, we compared the changes in protein expression of enterococci V583 strain before and after treated by Cajanin Stilbene Acid.
Project description:The antibiotic fosfomycin is widely recognized for treatment of lower urinary tract infections caused by Escherichia coli and lately gained importance as a therapeutic option to combat multidrug resistant bacteria. Still, resistance to fosfomycin frequently develops through mutations reducing its uptake. Whereas the inner membrane transport of fosfomycin has been extensively studied in E. coli, its outer membrane (OM) transport remains insufficiently understood. While evaluating minimal inhibitory concentrations in OM porin-deficient mutants, we observed that the E. coli ΔompCΔompF strain is five times more resistant to fosfomycin than the wild type and the respective single mutants. Continuous monitoring of cell lysis of porin-deficient strains in response to fosfomycin additionally indicated the relevance of LamB. Furthermore, the physiological relevance of OmpF, OmpC and LamB for fosfomycin uptake was confirmed by electrophysiological and transcriptional analysis. This study expands the knowledge of how fosfomycin crosses the OM of E. coli.
Project description:Understanding novel mechanism bacteria ustilize in the clinics to become resistant to antibiotics is critical. The study aims to identify genes associated with Vancomycin resistance. Clinical isolates from a single patient with increasing resistance to vancomycin were grown in the presence and absence of vancomycin.Staphylococcus aureus strain 2275 is the reference for this series.
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
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: The model predicts the probability of growth inhibition.
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/eos18ie