Predicting clinical resistance prevalence using sewage metagenomic data
Artikel i vetenskaplig tidskrift, 2020

Antibiotic resistance surveillance through regional and up-to-date testing of clinical isolates is a foundation for implementing effective empirical treatment. Surveillance data also provides an overview of geographical and temporal changes that are invaluable for guiding interventions. Still, due to limited infrastructure and resources, clinical surveillance data is lacking in many parts of the world. Given that sewage is largely made up of human fecal bacteria from many people, sewage epidemiology could provide a cost-efficient strategy to partly fill the current gap in clinical surveillance of antibiotic resistance. Here we explored the potential of sewage metagenomic data to assess clinical antibiotic resistance prevalence using environmental and clinical surveillance data from across the world. The sewage resistome correlated to clinical surveillance data of invasive Escherichia coli isolates, but none of several tested approaches provided a sufficient resolution for clear discrimination between resistance towards different classes of antibiotics. However, in combination with socioeconomic data, the overall clinical resistance situation could be predicted with good precision. We conclude that analyses of bacterial genes in sewage could contribute to informing management of antibiotic resistance.


Antti Karkman

Helsingin Yliopisto

Fanny Berglund

Göteborgs universitet

Carl-Fredrik Flach

Göteborgs universitet

Erik Kristiansson

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Göteborgs universitet

D. G. Joakim Larsson

Göteborgs universitet

Communications Biology

23993642 (eISSN)

Vol. 3 1 711




Mikrobiologi inom det medicinska området





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