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Validated predictive modelling of the environmental resistome.

Gregory C A Amos1, Emma Gozzard2, Charlotte E Carter1

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Summary

Wastewater treatment plants significantly drive antibiotic resistance in the River Thames environment. Predictive models integrating plant data, land cover, and rainfall can assess and mitigate environmental antibiotic resistance risks.

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Area of Science:

  • Environmental microbiology
  • Public health
  • Ecotoxicology

Background:

  • Multi-drug-resistant bacteria present a growing global health threat.
  • The environmental contribution to antibiotic resistance and human risk remains largely uncharacterized.

Purpose of the Study:

  • To identify drivers of antibiotic resistance in the River Thames catchment.
  • To develop predictive models for assessing environmental antibiotic resistance risk.

Main Methods:

  • Analysis of sediment samples from 13 River Thames sites across four time points (2011-2012).
  • Quantification of class 1 integron prevalence and third-generation cephalosporin-resistant bacteria.
  • Statistical modeling incorporating biotic, spatial, chemical, and water quality variables.

Main Results:

  • Class 1 integron prevalence served as a validated marker for antibiotic resistance.
  • Wastewater treatment plant characteristics (number, proximity, size, type) were primary drivers of resistance levels (49.5% variance).
  • A comprehensive model (Model 2) incorporating multiple factors explained 82.9% of resistance variations; predictive model (Model 3) showed >78% accuracy.

Conclusions:

  • Wastewater treatment plants are key environmental sources of antibiotic resistance.
  • Developed models offer valuable tools for prioritizing mitigation strategies to reduce the environmental resistome.
  • Understanding environmental resistome dynamics is crucial for public health protection.