Dataset Information

Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage.

ABSTRACT: Antimicrobial resistance (AMR) is a serious threat to global public health, but obtaining representative data on AMR for healthy human populations is difficult. Here, we use metagenomic analysis of untreated sewage to characterize the bacterial resistome from 79 sites in 60 countries. We find systematic differences in abundance and diversity of AMR genes between Europe/North-America/Oceania and Africa/Asia/South-America. Antimicrobial use data and bacterial taxonomy only explains a minor part of the AMR variation that we observe. We find no evidence for cross-selection between antimicrobial classes, or for effect of air travel between sites. However, AMR gene abundance strongly correlates with socio-economic, health and environmental factors, which we use to predict AMR gene abundances in all countries in the world. Our findings suggest that global AMR gene diversity and abundance vary by region, and that improving sanitation and health could potentially limit the global burden of AMR. We propose metagenomic analysis of sewage as an ethically acceptable and economically feasible approach for continuous global surveillance and prediction of AMR.

SUBMITTER: Hendriksen RS

PROVIDER: S-EPMC6408512 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage.

Hendriksen Rene S RS Munk Patrick P Njage Patrick P van Bunnik Bram B McNally Luke L Lukjancenko Oksana O Röder Timo T Nieuwenhuijse David D Pedersen Susanne Karlsmose SK Kjeldgaard Jette J Kaas Rolf S RS Clausen Philip Thomas Lanken Conradsen PTLC Vogt Josef Korbinian JK Leekitcharoenphon Pimlapas P van de Schans Milou G M MGM Zuidema Tina T de Roda Husman Ana Maria AM Rasmussen Simon S Petersen Bent B Amid Clara C Cochrane Guy G Sicheritz-Ponten Thomas T Schmitt Heike H Alvarez Jorge Raul Matheu JRM Aidara-Kane Awa A Pamp Sünje J SJ Lund Ole O Hald Tine T Woolhouse Mark M Koopmans Marion P MP Vigre Håkan H Petersen Thomas Nordahl TN Aarestrup Frank M FM

Nature communications 20190308 1

Antimicrobial resistance (AMR) is a serious threat to global public health, but obtaining representative data on AMR for healthy human populations is difficult. Here, we use metagenomic analysis of untreated sewage to characterize the bacterial resistome from 79 sites in 60 countries. We find systematic differences in abundance and diversity of AMR genes between Europe/North-America/Oceania and Africa/Asia/South-America. Antimicrobial use data and bacterial taxonomy only explains a minor part of ...[more]

PMID: 30850636

Similar Datasets

Project description:Metagenomics can unveil the genetic content of the total microbiota in different environments, such as food products and the guts of humans and livestock. It is therefore considered of great potential to investigate the transmission of foodborne hazards as part of source-attribution studies. Source-attribution of antimicrobial resistance (AMR) has traditionally relied on pathogen isolation, while metagenomics allows investigating the full span of AMR determinants. In this study, we hypothesized that the relative abundance of fecal resistome components can be associated with specific reservoirs, and that resistomes can be used for AMR source-attribution. We used shotgun-sequences from fecal samples of pigs, broilers, turkeys- and veal calves collected across Europe, and fecal samples from humans occupationally exposed to livestock in one country (pig slaughterhouse workers, pig and broiler farmers). We applied both hierarchical and flat forms of the supervised classification ensemble algorithm Random Forests to classify resistomes into corresponding reservoir classes. We identified country-specific and -independent AMR determinants, and assessed the impact of country-specific determinants when attributing AMR resistance in humans. Additionally, we performed a similarity percentage analysis with the full spectrum of AMR determinants to identify resistome signatures for the different reservoirs. We showed that the number of AMR determinants necessary to attribute a resistome into the correct reservoir increases with a larger reservoir heterogeneity, and that the impact of country-specific resistome signatures on prediction varies between countries. We predicted a higher occupational exposure to AMR determinants among workers exposed to pigs than among those exposed to broilers. Additionally, results suggested that AMR exposure on pig farms was higher than in pig slaughterhouses. Human resistomes were more similar to pig and veal calves' resistomes than to those of broilers and turkeys, and the majority of these resistome dissimilarities can be explained by a small set of AMR determinants. We identified resistome signatures for each individual reservoir, which include AMR determinants significantly associated with on-farm antimicrobial use. We attributed human resistomes to different livestock reservoirs using Random Forests, which allowed identifying pigs as a potential source of AMR in humans. This study thus demonstrates that it is possible to apply metagenomics in AMR source-attribution.

Dataset Information

Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage.

Publications

Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets