Accurate interpretation of within-host dissemination using barcoded bacteria.

Area of Science:

• Microbiology and Immunology
• Infectious Diseases
• Computational Biology

Background:

• Bacterial dissemination across tissues is crucial for infection outcomes.
• Current methods using barcoded bacteria can confound dissemination interpretations due to identical barcodes in different tissues.
• Distinguishing true dissemination from local replication is challenging.

Purpose of the Study:

• To develop a robust method for accurately interpreting within-host bacterial dissemination using barcoded bacteria.
• To address the confounding effect of identical barcodes shared by distinct lineages.

Main Methods:

• Developed a simulation-based distance metric to quantify the significance of shared barcodes between tissues.
• Validated the metric using simulated datasets with varying barcode diversities.
• Applied the approach to reanalyze three published experimental infection datasets.

Main Results:

• The new metric accurately quantifies the significance of observed barcode sharing between tissues.
• Reanalysis revealed novel patterns of *Escherichia coli* spread during liver abscess formation.
• Clarified the role of Muc2 mucin in *Listeria monocytogenes* systemic spread and quantified *Klebsiella pneumoniae* lung replication driving dissemination.

Conclusions:

• The developed metric provides an essential tool for accurate interpretation of bacterial dissemination studies.
• This approach enhances understanding of microbial spread in various infection models.
• Ensures more reliable insights into bacterial pathogenesis and host-pathogen interactions.