Inferring bacterial transmission dynamics using deep sequencing genomic surveillance data.

Area of Science:

• Microbiology
• Genomics
• Epidemiology

Background:

• Identifying infectious disease transmission chains is crucial for control.
• Within-host pathogen variation is an underutilized marker for inferring transmission events.
• Limited datasets exist capturing pathogen diversity in both donor and recipient hosts.

Purpose of the Study:

• To assess the utility of deep-sequenced genomic surveillance for identifying transmission pairs.
• To develop a model for inferring transmission likelihood based on pathogen genetic variation.
• To investigate the role of within-host variation and transmission bottlenecks.

Main Methods:

• Utilized a mouse model for controlled transmission of Citrobacter rodentium.
• Employed deep-sequenced genomic surveillance to analyze pathogen diversity.
• Examined within-host single nucleotide variants (iSNVs) and their allelic frequencies.

Main Results:

• Within-host single nucleotide variants (iSNVs) were maintained across multiple transmission steps.
• A model was developed to infer transmission pair likelihood using iSNV data.
• Allelic frequency differences of iSNVs provided more precise inference of transmission pairs than presence/absence alone.
• Transmission bottlenecks were shown to play a critical role in preserving within-host diversity.

Conclusions:

• Deep-sequenced genomic surveillance is effective for identifying pathogen transmission pairs.
• Allelic frequencies of within-host variants offer a precise method for inferring transmission links.
• Understanding pathogen diversity dynamics during transmission is key for disease control strategies.