Project description:Neisseria gonorrhoeae is a Gram-negative, sexually transmitted pathogen that poses a major public health threat due to rapidly increasing resistance to all recommended antibiotics. Addressing this crisis requires more efficient approaches to antibiotic discovery and the replenishment of the dwindling drug development pipeline. Here, we demonstrate that deep learning models can augment high-throughput screening to identify readily available molecules with narrow-spectrum activity against multidrug-resistant N. gonorrhoeae. We phenotypically tested 38,650 small molecules for growth inhibition and used these data to train a predictive graph neural network (GNN). Benchmarking against alternative architectures, including large language models, revealed that GNNs most effectively identified active, drug-like molecules that were structurally distinct from both the training set and known antibiotics. Applying the model to ~6 million compounds in silico, we prioritized 213 for experimental testing and found that 83 (38%) inhibited N. gonorrhoeae growth. Two compounds were structurally novel, potent against all tested multidrug-resistant strains, displayed favorable selectivity indices, and were rapidly bactericidal with low frequencies of resistance. Multi-omics analyses revealed that these compounds circumvent resistance by targeting previously unexploited pathways in N. gonorrhoeae. Our findings establish a paradigm for deep learning–enabled discovery of selective antibacterial agents and provide a promising path toward addressing the urgent threat of antimicrobial resistance in N. gonorrhoeae.
Project description:Neisseria gonorrhoeae, the etiologic agent of gonorrhea, is frequently asymptomatic in women, often leading to chronic infections. One factor contributing to this may be biofilm formation. N. gonorrhoeae can form biofilms over glass and plastic surfaces. There is also evidence that biofilm formation may occur during natural cervical infection. To further study the mechanism of this biofilm formation, transcriptional profiles of N. gonorrhoeae biofilm were compared to planktonic profiles. Biofilm RNA was extracted from N. gonorrhoeae 1291 grown for 48 hours in continuous flow chambers over glass. Planktonic RNA was extracted from the biofilm runoff. When biofilm was compared to planktonic growth, 3.8 % of the genome was differentially regulated. Genes highly up-regulated in biofilm included aniA, norB, and ccp, which play critical roles in anaerobic metabolism and oxidative stress tolerance. Down-regulated genes included the nuo gene cluster (NADH dehydrogenase) and the cytochrome bcI complex, which are involved in aerobic respiration and are thought to contribute to endogenous oxidative stress. Furthermore, we determined that aniA, ccp, and norB insertional mutants are attenuated for biofilm formation over glass and transformed human cervical epithelial cells (THCEC). This data suggests that biofilm formation could minimize oxidative stress during cervical infection and allow N. gonorrhoeae to maintain a nitric oxide steady state that may be anti-inflammatory.