Deborah A. Hogan

This study analyzes 450 Pseudomonas aeruginosa isolates from CF patients to show structured population dynamics, migration, and parallel adaptive evolution within lung infections.

This study identifies Mrr1 as a transcription factor coordinating MDR1 and CDR1 expression in Candida lusitaniae, contributing to antifungal resistance through efflux pumps and stress response genes.

This study shows that Pseudomonas aeruginosa LasR mutants are more sensitive to methylglyoxal and hydrogen peroxide due to reduced intracellular glutathione.

This paper shows that loss of LasR function in Pseudomonas aeruginosa increases PhoB activity at physiological phosphate concentrations, contributing to increased virulence.

This study shows Pseudomonas aeruginosa consumes nitrate in oxic conditions, requiring Dnr and Dnr-regulated NorCB for detoxification, despite no growth advantage from nitrate.

This paper shows tobramycin reduces inflammation in cystic fibrosis by increasing 5' tRNA-fMet halves in P. aeruginosa outer membrane vesicles, which suppress IL-8 and neutrophilic inflammation in human cells and mice.

CF-Seq provides a web tool for re-analyzing CF pathogen RNA-seq data, enabling efficient filtering, visualization, and hypothesis generation across multiple studies.

The paper analyzes transcriptional patterns of core and accessory genes in P. aeruginosa strains using a transcriptome compendium, revealing stable core gene expression patterns across strains.

This paper introduces SOPHIE, a method using generative neural networks to distinguish common from specific transcriptional responses across datasets.

The paper simulates gene expression compendia to show that technical variability masks biological signals, but correction methods help when few experiments are combined, while more experiments make detection easier without correction.

PathCORE-T identifies and visualizes globally co-occurring pathways across transcriptomic datasets by analyzing features from machine learning models.

ADAGE signature analysis identifies perturbed biological processes by learning gene signatures from public data, outperforming curated gene sets in revealing novel processes in Pseudomonas aeruginosa.

The paper introduces eADAGE, an ensemble method to extract stable expression signatures from public data, revealing a phosphate starvation response in Pseudomonas aeruginosa controlled by PhoB and requiring KinB.