ABSTRACT
The rising prevalence of antibiotic resistance threatens human health. While more sophisticated strategies for antibiotic discovery are being developed, target elucidation of new chemical entities remains challenging. In the post-genomic era, expression profiling can play an important role in mechanism-of-action (MOA) prediction by reporting on the cellular response to perturbation. However, the broad application of transcriptomics has yet to fulfill its promise of transforming target elucidation due to challenges in identifying the most relevant, direct responses to target inhibition. 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. Significance Statement: New antibiotics are critically needed in the face of increasing antibiotic resistance. However, mechanism-of-action (MOA) elucidation remains challenging and imposes a major bottleneck in antibiotic discovery and development. Building on the principle that molecules with similar MOAs elicit similar transcriptional responses, we have developed a highly scalable strategy for MOA prediction in the important bacterial pathogen Pseudomonas aeruginosa based on correlations between the expression profiles of new molecules and known perturbations, either small molecule inhibition by known antibiotics or transcriptional repression of essential targets by CRISPRi. By rapidly assigning MOAs to three new molecules with anti-pseudomonal activity, we provide proof-of-concept for a rapid, comprehensive, systematic, reference-based approach to MOA prediction with the potential to transform antibiotic discovery efforts.
ABSTRACT
The surge of antimicrobial resistance threatens efficacy of current antibiotics, particularly against Pseudomonas aeruginosa , a highly resistant gram-negative pathogen. The asymmetric outer membrane (OM) of P. aeruginosa combined with its array of efflux pumps provide a barrier to xenobiotic accumulation, thus making antibiotic discovery challenging. We adapted PROSPECT 1 , a target-based, whole-cell screening strategy, to discover small molecule probes that kill P. aeruginosa mutants depleted for essential proteins localized at the OM. We identified BRD1401, a small molecule that has specific activity against a P. aeruginosa mutant depleted for the essential lipoprotein, OprL. Genetic and chemical biological studies identified that BRD1401 acts by targeting the OM ß-barrel protein OprH to disrupt its interaction with LPS and increase membrane fluidity. Studies with BRD1401 also revealed an interaction between OprL and OprH, directly linking the OM with peptidoglycan. Thus, a whole-cell, multiplexed screen can identify species-specific chemical probes to reveal novel pathogen biology.
