New Genomics Tools and Strategies for Studying Antibiotics and Antibiotic-Resistance in Staphylococcus Aureus

Abstract

Staphylococcus aureus is a gram positive coccoid pathogen that causes intractable infections in hospitals and communities around the world, and tens of thousands of people die of these infections every year. In order to combat these antibiotic-resistant infections, we need to better understand the genes involved in resistance to the cell stress caused by antibiotic treatment, which will enable the discovery of new antimicrobials and the development of novel therapeutic strategies. We chose to use an approach to this problem that utilizes a new phage-based high frequency of transposition system. In this work, we adapted this system so that transposon mutant libraries can be made and sequenced using next-generation sequencing (NGS) in any strain of S. aureus. We validated our new platform by performing a temperature screen and identifying mutants that are significantly resistant or sensitive to temperature-stress. Next, we created transposon libraries in two MRSA strains to show that this system can be broadly applied to other S. aureus strains, and we used one of these libraries to identify a new interaction between two genes involved in the secretion of sortase-anchored surface proteins. To better understand antibiotic-resistance, we performed Tn-Seq on transposon libraries treated with a small panel of six different antibiotics to identify intrinsic resistance factors to these antibiotics. We identified two new intrinsic resistance factors, SAOUHSC_01025 and SAOUHSC_01050, that sensitize to many cell envelope targeting antibiotics and may be involved in hemolysin regulation. Finally, we expanded this approach to sequence transposon libraries treated with 25 different antibiotics. Based on these data, we were able to develop methods for predicting the mechanism of action of new antibiotics. These methods involve identifying genes upregulated by transposon insertion and applying machine learning algorithms to identify similarities to a curated panel of well-studied antibiotics with known mechanisms of action. This work will enable many new functional genomics studies in S. aureus, and it will allow us to gain a better understanding of antibiotic resistance in this dangerous pathogen.