Project description:The increasing spread of drug-resistant bacterial strains presents great challenges to clinical antibacterial treatment and public health, particularly with regard to β-lactamase-producing Enterobacteriaceae. A rapid and accurate detection method that can expedite precise clinical diagnosis and rational administration of antibiotics is urgently needed.
Project description:Current clinical antibiotics are largely broad-spectrum agents that promote intestinal dysbiosis and colonisation of Enterobacteriaceae, which are often drug-resistant. Indeed, dysbiosis creates an ideal niche for adherent-invasive Escherichia coli (AIEC) in patients with inflammatory bowel disease (IBD). There is an urgent and unmet need for novel narrow-spectrum and microbiome-sparing antibiotics. Here, we screened >10,000 molecules for antibacterial activity against AIEC and discovered enterololin, an antibacterial compound with targeted activity against Enterobacteriaceae species. Molecular substructure- and deep learning-guided mechanism of action investigations revealed that enterololin perturbs lipoprotein trafficking through a mechanism involving the LolCDE complex. Moreover, enterololin can suppress an AIEC infection in mouse models, while largely preserving the overall microbiome composition. This work highlights the utility of deep learning methods for predicting molecular interactions, thereby accelerating mechanism of action elucidation of novel molecules, and identifies a promising Enterobacteriaceae-specific antibacterial candidate for further development to treat challenging infections in IBD patients.
Project description:Whole genome multilocus sequence typing of extended-spectrum beta-lactamase-producing Enterobacteriaceae
| PRJEB15226 | ENA
Project description:Genomic characterization of extended-spectrum beta-lactamase-producing and carbapenem-resistant Enterobacteriaceae isolated from urban Wastewater
