Identifying targets for potentiators in S. aureus using chemical genetic approaches

Abstract

Staphylococcus aureus is a highly feared Gram-positive pathogen. The rise in antibiotic resistance has made S. aureus infections intractable. To find new ways to treat S. aureus infections, it is important to understand how this organism protects itself from antibiotics. We probed S. aureus transposon libraries with different classes of antibiotics and used Tn-Seq to identify intrinsic resistance factors that are important in withstanding antibiotics. We identified and validated the importance of a number of previously known intrinsic resistance factors such as mprF, fmtA and the graRS/vraFG multi-component sensing system, as well as a number of novel factors whose involvement in antibiotic resistance has not been previously appreciated. In the course of this work, we realized that Tn-Seq data could be mined to predict antibiotic mechanism of action. We used a machine learning approach to predict that a class of anti-MRSA antibiotics that were thought to cause membrane damage actually bind to lipid II and inhibit cell wall synthesis. This predicted mechanism was validated. Finally, we report the identification of a class of disubstituted urea compounds against which the inactivation of mprF is protective. As the inactivation of mprF usually sensitizes to most antibiotics, these compounds might belong to a new class of inhibitors that could be used as tool compounds to further probe S. aureus cell biology. We have developed a strategy that could be useful in identifying the targets of such compounds. The results described here have opened several interesting avenues for a more in-depth understanding of S. aureus biology and antibiotic resistance.