Project description:Children with acute measles were admitted to the University Teaching Hospital in Lusaka, Zambia. Peripheral blood was collected at hospital entry, discharge and 1-month follow-up. Control samples were also collected from uninfected children. All children were HIV negative. Keywords: Clinical timecourse
Project description:Neisseria gonorrhoeae is the causative agent of gonorrhea, a leading sexually transmitted disease with severe complications on reproductive health. The U.S. Centers for Disease Control and Prevention has categorized the public health threat induced by N. gonorrhoeae as “urgent”, due to the ease of transmission and the fast emergence of multi-drug resistant strains. The need for development of vaccines and understanding the underlying factors leading to antibiotic resistance is of utmost importance. The proteomic profiles of the 14 WHO N. gonorrhoeae reference strains have been compared to the WHO F reference strain using a mass spectrometry with tandem mass tags (TMT) labeling to analyze the cell envelope and the cytoplasmic fractions extracted from each strain. Identifying novel vaccine candidates and proteomic signatures for antimicrobial resistance will further our understanding of N. gonorrhoeae proteotypes, in relationship to their respective genotypes and phenotypes, and provide deep insights that will impact the development of preventive and therapeutic tools to combat gonorrhea.
Project description:Microarray comparative genome hybridization (mCGH) data was collected from one Neisseria cinerea, two Neisseria lactamica, two Neisseria gonorrhoeae, and 48 Neisseria meningitidis isolates. For N. meningitidis, these isolates are from diverse clonal complexes, invasive and carriage strains, and all major serogroups. The microarray platform represented N. meningitidis strains MC58, Z2491, and FAM18 and N. gonorrhoeae FA1090.
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:Hfq is an RNA chaperone, which functions as a pleiotropic regulator for RNA metabolism in bacteria. To characterize the role of Hfq in pathogenicity of Neisseria gonorrhoeae we generated a N. gonorrhoeae hfq mutant, MS11hfq.Transcriptional analysis using a custom-made N. gonorrhoeae microarray revealed that 369 open reading frames were differentially regulated in MS11hfq compared to the wild-type (wt) strain (202 were upregulated, 167 were downregulated).
Project description:Comparison of transcriptional profiling between the 3 Neisseria meningitidis strains [serogroup A (Z2491), Serogroup B (MC58), and Serogroup C (FAM18)] and the 2 Neisseria gonorrhoeae strain (FA1090 and MS11).