Deborah A. Hogan

Within-host population structure, migration, and parallel adaptive evolution of Pseudomonas aeruginosa in cystic fibrosis lung disease

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

Pseudomonas aeruginosa lasR Mutants Resist Phagocytosis and Alter Inflammatory Cytokine Production by Cystic Fibrosis Macrophages

Coordinated regulation of Mdr1- and Cdr1-mediated protection from antifungals by the Mrr1 transcription factor in emerging Candida spp.

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.

Pseudomonas aeruginosa LasR-deficient mutants have increased methylglyoxal and hydrogen peroxide sensitivity due to low intracellular glutathione

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

Role of Pseudomonas aeruginosa Dnr-regulated denitrification in oxic conditions

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

CF-Seq, An Accessible Web Application for Rapid Re-Analysis of Cystic Fibrosis Pathogen RNA Sequencing Studies

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

Compendium-wide analysis of P. aeruginosa core and accessory genes reveal more nuanced transcriptional patterns

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.

Generative neural networks separate common and specific transcriptional responses

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

Correcting for experiment-specific variability in expression compendia can remove underlying signals

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: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia

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

ADAGE signature analysis: differential expression analysis with data-defined gene sets

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.

Unsupervised extraction of stable expression signatures from public compendia with eADAGE

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.