Perturbation-Specific Transcriptional Mapping for unbiased target elucidation of antibiotics

We developed an unbiased strategy for MOA prediction, called Perturbation-Specific Transcriptional Mapping (PerSpecTM), in which large-throughput expression profiling of wildtype or hypomorphic mutants, depleted for essential targets, enables a computational strategy to address this challenge. We applied PerSpecTM to perform reference-based MOA prediction based on the principle that similar perturbations, whether small molecule or genetic, will elicit similar transcriptional responses. Using this approach, we elucidated the MOAs of three new molecules with activity against Pseudomonas aeruginosa by mapping their expression profiles to those of a reference set of antimicrobial compounds with known MOAs. We also show that transcriptional responses to small molecule inhibition maps to those resulting from genetic depletion of essential targets by CRISPRi by PerSpecTM, demonstrating proof-of-concept that correlations between expression profiles of small molecule and genetic perturbations can facilitate MOA prediction when no chemical entities exist to serve as a reference. Empowered by PerSpecTM, this work lays the foundation for an unbiased, readily scalable, systematic reference-based strategy for MOA elucidation that could transform antibiotic discovery efforts. Overall design: We obtained RNAseq data from Pseudomonas aeurginosa (PA14) under various drug treatments. The majority of the data were taken at 90 minutes after drug exposure, but a subset of the data include 30, 60, 90, and 120 minute time points. There are at least three replicates per condition. RNAseq data from CRISPRi strains are also included, at 270 minutes after arabiniose induction.