Development and Optimization of an Unbiased, Metagenomics-Based Pathogen Detection Workflow for Infectious Disease...

Area of Science:

• Microbiology
• Genomics
• Bioinformatics

Background:

• Traditional culture methods for pathogen identification are slow.
• Molecular methods like PCR lack comprehensiveness and may miss novel strains.
• Metagenomic shotgun next-generation sequencing (NGS) offers broad, specific pathogen detection but lacks standardized workflows.

Purpose of the Study:

• To develop and assess a standardized, end-to-end workflow for rapid pathogen identification.
• To create a user-friendly bioinformatics tool for data analysis.
• To enable applications in clinical diagnostics and biosurveillance.

Main Methods:

• Developed the PanGIA (Pan-Genomics for Infectious Agents) workflow.
• Included simplified wet-lab procedures: nucleic acid extraction, host depletion, library preparation, and shotgun sequencing.
• Integrated an easy-to-use bioinformatics tool for data analysis.
• Validated with clinical whole blood and environmental swabs.

Main Results:

• The PanGIA workflow provides a sample-to-answer solution within 24 hours.
• Successfully detected bacteria and viruses from both clinical and environmental samples.
• Demonstrated specific pathogen detection for bloodstream infections and environmental surveillance.
• Workflow is compatible with Illumina MiSeq sequencing.

Conclusions:

• PanGIA offers a standardized, rapid, and comprehensive approach to pathogen detection.
• This workflow can significantly improve infectious disease diagnosis and biosurveillance.
• The system reduces reliance on targeted assay development for pathogen identification.