Project description:The Enterobacteriaceae are a scientifically and medically important clade of bacteria, containing the gut commensal and model organism Escherichia coli, as well as several major human pathogens including Salmonella enterica and Klebsiella pneumoniae. Essential gene sets have been determined for several members of the Enterobacteriaceae, and the E. coli Keio single-gene deletion library is often regarded as a gold standard for gene essentiality studies. However, it remains unclear how much essential genes vary between strains and species. To investigate this, we have assembled a collection of thirteen sequenced high-density transposon mutant libraries from five genera of the Enterobacteriaceae. We first benchmark a number of gene essentiality prediction approaches, investigate the effects of transposon density on essentiality prediction, and identify biases in transposon insertion sequencing data. Based on these investigations we develop a new classifier for gene essentiality. Using gene essentiality defined by this new classifier, we define a core essential genome in the Enterobacteriaceae of 201 universally essential genes, and reconstruct an ancestral essential gene set of 296 genes. Despite the presence of a large cohort of variably essential genes, we find an absence of evidence for genus-specific essential genes. A clear example of this sporadic essentiality is given by the set of genes regulating the σE extracytoplasmic stress response, which appears to have independently become essential multiple times in the Enterobacteriaceae. Finally, we compare our essential gene sets to the natural experiment of gene loss in obligate insect endosymbionts closely related to the Enterobacteriaceae. This isolates a remarkably small set of genes absolutely required for survival, and uncovers several instances of essential stress responses masked by redundancy in free-living bacteria.
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.