Back

ADP-MoA: a platform for screening antibiotic activity and their mechanism of action in Pseudomonas aeruginosa

Valencia Morante, E. Y.; Nunes, V. A.; Chambergo, F. S.; Spira, B.

2024-11-11 microbiology
10.1101/2024.11.08.622684 bioRxiv
Show abstract

The emergence and proliferation of multidrug-resistant bacteria pose a major threat to global public health. To address an imminent crisis, it is essential to identify and characterize new antibacterial molecules. With that in mind, we developed the ADP-MoA platform, that facilitates the discovery of new antibiotics and provides preliminary insights into their mechanisms of action. The basic idea is to simultaneously visualize antibiotic activity - growth inhibition, along with one of the three classic antibiotics mechanisms of action: DNA damage/inhibition of DNA replication, protein synthesis inhibition and cell wall damage. The platform consists of three different chromosomal fusions between the promoters of recA, ampC or armZ and the luxCDABE operon. The platform was constructed and hitherto tested in the pathogenic opportunistic bacterium Pseudomonas aeruginosa. As a proof of concept we showed that the promoter fusions were each activated by the expected antibiotics with known mechanisms of action. The armZ::luxCDABE fusion responded to antibiotics that inhibit protein synthesis (macrolides, chloramphenicol, tetracyclines and aminoglycosides), ampC::luxCDABE was induced by {beta}-lactams and recA::luxCDABE was induced by quinolones. Interestingly, ciprofloxacin induced PampC and ParmZ as well, albeit at a lower level. The ADP-MoA platform offers a readily implementable, low-cost approach with significant potential for high-throughput screening of antimicrobials against P. aeruginosa and other bacterial species.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.