SRP124730 PRJNA417822 Genome-wide mutant fitness profiling predicts the mechanism of action of a Lipid II binding antibiotic ABSTRACTIdentifying molecular targets of antibacterial compounds remains a challenging step in antibiotic development. 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 well-characterized antibiotics. Upregulation signatures, identified from directional biases in transposon insertions, revealed known molecular targets and resistance mechanisms for many of them. Because upregulation of single genes does not always confer resistance, we developed a complementary machine learning approach that uses inactivation mutant fitness profiles to predict mechanism. This approach suggested the cell wall precursor Lipid II as the molecular target of the lysocins. We confirmed that these compounds bind Lipid II and conclude that docking to Lipid II in cell membranes precedes the selective bacteriolysis that is a distinguishing feature of these rapidly lytic antibiotics. pubmed 29662210 GitHub page for resources to analyze data https://github.com/SuzanneWalkerLab/TnSeqMOAPrediction