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Isolation and Identification of Waterborne Antibiotic-Resistant Bacteria and Molecular Characterization of their Antibiotic Resistance Genes
Published on: March 3, 2023
Metagenomic strain tracking reveals patterns of bacterial spread and the impact of water chlorination
Daehyun D Kim1, Colin J Worby2, Hannah Wharton1
1Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA, USA.
Abstract:
Bacterial infections are a major cause of morbidity and mortality among children under five in low- and middle-income countries (LMICs). Children in LMICs are exposed to and colonized by a range of pathogenic bacteria, yet patterns of bacterial exchange between humans are not well known, in part because culturing and sequencing single bacterial isolates is labor-intensive. Here, we apply a machine learning strain tracking approach to metagenomic data from 511 stool samples from children and mothers across urban and rural Kenyan communities to characterize bacterial dissemination and assess if community-wide water chlorination disrupts transmission. We identified distinct strain-sharing dynamics across species; potentially pathogenic taxa (e.g., Escherichia, Enterococcus, Campylobacter) exhibited distance-dependent dissemination driven by young children, while commensal taxa (e.g., Bifidobacterium, Bacteroides) showed patterns consistent with dietary exposure. Drinking water chlorination reduced community-level strain-sharing in rural communities. Our study provides the first strain-level insights into multi-species bacterial transmission dynamics in LMIC communities, identifying distinct dissemination pathways for facultative versus mostly anaerobic bacteria. Moreover, our findings highlight the utility of metagenomic strain tracking to uncover how community spread can be disrupted.
Insights
Machine learning tracked bacterial strains in Kenyan children, revealing young children drive pathogen spread. Community water chlorination reduced bacterial sharing, highlighting a key public health intervention for reducing childhood infections.
Area of Science:
- Microbiology
- Genomics
- Public Health
Background:
- Bacterial infections are a significant cause of illness and death in children under five in low- and middle-income countries (LMICs).
- Understanding bacterial transmission patterns is crucial for developing effective interventions, but traditional methods are labor-intensive.
- Metagenomic data offers a powerful tool to study microbial communities and their interactions.
Purpose of the Study:
- To characterize bacterial dissemination patterns in LMIC communities using a machine learning strain tracking approach.
- To assess the impact of community-wide water chlorination on bacterial transmission dynamics.
- To differentiate transmission pathways for various bacterial taxa, including potentially pathogenic and commensal species.
Main Methods:
- Applied a machine learning strain tracking method to metagenomic data from 511 stool samples of children and mothers in Kenyan communities.
- Analyzed bacterial strain-sharing dynamics across different species and assessed the influence of factors like age and water treatment.
- Compared bacterial dissemination in communities with and without water chlorination.
Main Results:
- Identified distinct strain-sharing patterns for different bacterial species, with potentially pathogenic bacteria disseminated by young children.
- Commensal bacteria showed transmission patterns linked to dietary exposures.
- Community-level bacterial strain-sharing was reduced in rural communities with drinking water chlorination.
Conclusions:
- This study provides the first strain-level insights into multi-species bacterial transmission in LMIC communities, revealing distinct dissemination pathways.
- Metagenomic strain tracking is a valuable tool for understanding bacterial spread and identifying effective disruption strategies.
- Water chlorination can be an effective intervention to reduce community-level bacterial transmission.
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