ABSTRACT
Quality control of ribosomes is critical for cellular function since protein mistranslation leads to severe physiological consequences. We report evidence of a previously unrecognized ribosome quality control system in bacteria that operates at the level of 70S to remove defective ribosomes. YbeY, a previously unidentified endoribonuclease, and the exonuclease RNase R act together by a process mediated specifically by the 30S ribosomal subunit, to degrade defective 70S ribosomes but not properly matured 70S ribosomes or individual subunits. Furthermore, there is essentially no fully matured 16S rRNA in a ΔybeY mutant at 45°C, making YbeY the only endoribonuclease to be implicated in the critically important processing of the 16S rRNA 3' terminus. These key roles in ribosome quality control and maturation indicate why YbeY is a member of the minimal bacterial gene set and suggest that it could be a potential target for antibacterial drugs.
Subject(s)
Conserved Sequence , Escherichia coli Proteins/metabolism , Escherichia coli/enzymology , Metalloproteins/metabolism , RNA Processing, Post-Transcriptional , RNA, Ribosomal, 16S/metabolism , Ribosomes/metabolism , Arginine/metabolism , Base Sequence , Escherichia coli/drug effects , Escherichia coli Proteins/chemistry , Exoribonucleases/metabolism , Histidine/metabolism , Hot Temperature , Metalloproteins/chemistry , Metals/pharmacology , Models, Biological , Molecular Sequence Data , Mutation/genetics , Protein Biosynthesis/drug effects , RNA Processing, Post-Transcriptional/drug effects , RNA, Ribosomal, 16S/genetics , Ribosome Subunits, Small, Bacterial/metabolism , Ribosomes/drug effects , Stress, Physiological/drug effects , Substrate Specificity/drug effectsABSTRACT
Identification of novel antibiotics remains a major challenge for drug discovery. The present study explores use of phenotypic readouts beyond classical antibacterial growth inhibition adopting a combined multiparametric high content screening and genomic approach. Deployment of the semi-automated bacterial phenotypic fingerprint (BPF) profiling platform in conjunction with a machine learning-powered dataset analysis, effectively allowed us to narrow down, compare and predict compound mode of action (MoA). The method identifies weak antibacterial hits allowing full exploitation of low potency hits frequently discovered by routine antibacterial screening. We demonstrate that BPF classification tool can be successfully used to guide chemical structure activity relationship optimization, enabling antibiotic development and that this approach can be fruitfully applied across species. The BPF classification tool could be potentially applied in primary screening, effectively enabling identification of novel antibacterial compound hits and differentiating their MoA, hence widening the known antibacterial chemical space of existing pharmaceutical compound libraries. More generally, beyond the specific objective of the present work, the proposed approach could be profitably applied to a broader range of diseases amenable to phenotypic drug discovery.