Person:

Lee, Wonsik

The majority of bacterial proteins are dispensable for growth in the laboratory, but nevertheless play important physiological roles. There are no systematic approaches to identify cell-permeable small molecule inhibitors of these proteins. We demonstrate a strategy to identify such inhibitors that exploits synthetic lethal relationships both for small molecule discovery and for target identification. Applying this strategy in Staphylococcus aureus, we have identified a compound that inhibits DltB, a component of the teichoic acid D-alanylation machinery, which has been implicated in virulence. This D-alanylation inhibitor sensitizes S. aureus to aminoglycosides and cationic peptides and is lethal in combination with a wall teichoic acid inhibitor. We conclude that DltB is a druggable target in the D-alanylation pathway. More broadly, the work described demonstrates a systematic method to identify biologically active inhibitors of important bacterial processes that can be adapted to numerous organisms.

Staphylococcus aureus is a leading cause of life-threatening infections worldwide. The MIC of an antibiotic against S. aureus, as well as other microbes, is determined by the affinity of the antibiotic for its target in addition to a complex interplay of many other cellular factors. Identifying nontarget factors impacting resistance to multiple antibiotics could inform the design of new compounds and lead to more-effective antimicrobial strategies. We examined large collections of transposon insertion mutants in S. aureus using transposon sequencing (Tn-Seq) to detect transposon mutants with reduced fitness in the presence of six clinically important antibiotics-ciprofloxacin, daptomycin, gentamicin, linezolid, oxacillin, and vancomycin. This approach allowed us to assess the relative fitness of many mutants simultaneously within these libraries. We identified pathways/genes previously known to be involved in resistance to individual antibiotics, including graRS and vraFG (graRS/vraFG), mprF, and fmtA, validating the approach, and found several to be important across multiple classes of antibiotics. We also identified two new, previously uncharacterized genes, SAOUHSC_01025 and SAOUHSC_01050, encoding polytopic membrane proteins, as important in limiting the effectiveness of multiple antibiotics. Machine learning identified similarities in the fitness profiles of graXRS/vraFG, SAOUHSC_01025, and SAOUHSC_01050 mutants upon antibiotic treatment, connecting these genes of unknown function to modulation of crucial cell envelope properties. Therapeutic strategies that combine a known antibiotic with a compound that targets these or other intrinsic resistance factors may be of value for enhancing the activity of existing antibiotics for treating otherwise-resistant S. aureus strains.