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Updated: May 8, 2026

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Published on: July 18, 2013

E.PathDash, pathway activation analysis of publicly available pathogen gene expression data.

Lily Taub¹, Thomas H Hampton¹, Sharanya Sarkar¹

¹Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA.

|October 18, 2024

Summary

This summary is machine-generated.

E.PathDash is a new tool that analyzes pathogen gene expression data for chronic respiratory diseases. It significantly speeds up data access and analysis, aiding research into pathogen responses and potential interventions.

Keywords:

bioinformatics gene expression pathway analysis respiratory pathogens

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Area of Science:

Bioinformatics
Genomics
Microbiology

Background:

Chronic respiratory diseases increase susceptibility to severe bacterial infections.
Public gene expression data offers valuable insights but is time-consuming to analyze.
Pathogen infections, like those caused by *Pseudomonas aeruginosa* and *Staphylococcus aureus*, contribute significantly to morbidity and mortality.

Purpose of the Study:

To develop and validate E.PathDash, an application for rapid re-analysis of pathogen gene expression data.
To enable researchers to quickly assess pathogen responses to various experimental conditions.
To facilitate the generation of mechanistic hypotheses for pathogen behavior in disease-relevant environments.

Main Methods:

E.PathDash integrates data from 48 studies, encompassing 548 samples and 404 treatment comparisons.
The application allows analysis at KEGG pathway or gene ontology levels, as well as individual gene analysis.
Users can download visualizations (volcano plots, boxplots), differential gene expression results, and raw count data.

Main Results:

E.PathDash reduces data analysis time from hours to seconds.
Pathway analysis recapitulated existing findings and revealed new insights into pathogen environmental responses.
The tool proved useful for cystic fibrosis researchers, validating its utility.

Conclusions:

E.PathDash significantly accelerates the accessibility and analysis of pathogen gene expression data.
The application supports reproducible research and hypothesis generation for chronic respiratory diseases.
Freely accessible software and data promote wider adoption and research advancement.