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Conroy, S.

Publications and source records attributed to Conroy, S..

2 recordsLinked to original sources

Coenzyme A depletion causes antibiotic tolerance in Pseudomonas aeruginosa

The widespread use of antibiotics promotes both resistance and tolerance. While resistance enables bacterial growth in the presence of drugs, tolerance allows survival during treatment, generating persisters that seed relapse and promote resistance. Despite its clinical relevance, the molecular basis of tolerance remains poorly understood. Using proteomic and metabolomic profiling combined with machine learning, we identified thiol oxidation as a robust predictor of tolerance in the human pathogen Pseudomonas aeruginosa. Single-cell analyses established a direct link between thiol oxidation and drug survival, indicating that redox imbalance drives persistence. Whereas depletion of coenzyme A (CoA), a central thiol-containing metabolite, scaled with tolerance, restoring CoA using engineered catalysts from Staphylococcus aureus abolished tolerance, establishing a causal relation between CoA availability and drug susceptibility. Thiol-based predictors also accurately capture tolerance of clinical P. aeruginosa isolates. These findings establish CoA-centered redox control as a key determinant of tolerance, opening opportunities for diagnostics and therapeutic interventions to prevent infection relapses.

microbiology↗

Uncertainty-aware quantitative analysis of high-throughput live cell migration data

Accurate quantification of cell migration velocity is essential for understanding biological processes such as development, immune function, and cancer metastasis. High-throughput migration assays generate complex, hierarchically structured datasets with technical noise, batch effects, and biological variability, introducing uncertainty into velocity estimates that current methods often fail to quantify. To address this, we present cellmig, a computational tool using Bayesian hierarchical modeling to separate biological signals from technical variation while explicitly quantifying uncertainty in migration velocity. cellmig provides a robust framework for analyzing migration assays, including dose-response studies and large-scale screens with biological and technical replicates. By modeling biological variability and technical confounders within a unified Bayesian framework, it estimates condition-specific effects (e.g., drug effects) on cell velocity with probabilistic uncertainty intervals, avoiding common pitfalls of null-hypothesis testing. Its generative models simulate migration under various assumptions, aiding experimental planning. Overall, cellmig improves reproducibility and comparability across studies, offering deeper biological insight.

bioinformatics↗