Genome-wide mutant profiling predicts the mechanism of a Lipid II binding antibiotic.

ABSTRACT: Identifying targets of antibacterial compounds remains a challenging step in the development of antibiotics. We have developed a two-pronged functional genomics approach to predict mechanism of action that uses mutant fitness data from antibiotic-treated transposon libraries containing both upregulation and inactivation mutants. We treated a Staphylococcus aureus transposon library containing 690,000 unique insertions with 32 antibiotics. Upregulation signatures identified from directional biases in insertions revealed known molecular targets and resistance mechanisms for the majority of these. Because single-gene upregulation does not always confer resistance, we used a complementary machine-learning approach to predict the mechanism from inactivation mutant fitness profiles. This approac

SUBMITTER: Santiago M 

PROVIDER: S-EPMC5964011 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature