Abundance and diversity of the faecal resistome in slaughter pigs and broilers in nine European countries.

Patrick Munk ,Magdalena Zając ,Alieda Van Essen ,Katharina Wadepohl ,Roosmarijn E C Luiken ,Thomas Nordahl Petersen ,H Daskalov ,Jeroen Dewulf ,Sünje Johanna Pamp ,Alex Bossers ,Maximiliane Brandt ,Julie David ,Pascal Sandérs ,Gabriel Moyano ,Jaap A Wagenaar ,Katharina D.c Stärk ,Magdalena Skarżyńska ,Dariusz Wasyl ,Andrea Caprioli ,Dick Heederik ,Claire Chauvin ,Oksana Lukjancenko ,Antonio Battisti ,Berith Elkær Knudsen ,Ole Lund ,Dik Mevius ,Thomas Blaha ,Rasmus Borup Hansen ,Haitske Graveland ,Håkan Vigre ,Alejandro Dorado García ,Heike Schmitt ,Lidwien A.m Smit ,H.w Saatkamp ,Ana Sofia Ribeiro Duarte ,Étienne Ruppé ,Bruno González‐Zorn ,Liese Van Gompel ,Tine Hald ,Frank Møller Aarestrup

doi:10.1038/s41564-018-0192-9

Abstract

Antimicrobial resistance (AMR) in bacteria and associated human morbidity and mortality is increasing. The use of antimicrobials in livestock selects for AMR that can subsequently be transferred to humans. This flow of AMR between reservoirs demands surveillance in livestock and in humans. We quantified and characterized the acquired resistance gene pools (resistomes) of 181 pig and 178 poultry farms from nine European countries, sequencing more than 5,000 Gb of DNA using shotgun metagenomics. We quantified acquired AMR using the ResFinder database and a second database constructed for this study, consisting of AMR genes identified through screening environmental DNA. The pig and poultry resistomes were very different in abundance and composition. There was a significant country effect on the resistomes, more so in pigs than in poultry. We found higher AMR loads in pigs, whereas poultry resistomes were more diverse. We detected several recently described, critical AMR genes, including mcr-1 and optrA, the abundance of which differed both between host species and between countries. We found that the total acquired AMR level was associated with the overall country-specific antimicrobial usage in livestock and that countries with comparable usage patterns had similar resistomes. However, functionally determined AMR genes were not associated with total drug use.

Highlights

Antimicrobial resistance (AMR) is considered one of the largest threats to human health.[1]
We found that the total acquired AMR level, was associated with the overall country-specific antimicrobial usage in livestock and that countries with comparable usage patterns had similar resistomes
The total AMR gene level varied significantly across samples, both depending on host animal and country of origin

Summary

Introduction

Antimicrobial resistance (AMR) is considered one of the largest threats to human health.[1]. That reduction of usage will eventually lead to reduced resistance.[3,4,5,6,7,8] A number of national surveillance programs have been implemented to monitor the occurrence of AMR in different reservoirs and follow trends over time.[1,9,10,11] There are major differences in antimicrobial consumption patterns between different countries globally and within Europe.[12] Major differences in the occurrence of AMR have been observed among indicator organisms (e.g. E_coli) isolated from different European countries.[3,13] Current monitoring efforts are mainly based on culturing indicator bacteria followed by phenotypic AMR determination.[13,14] This procedure only targets a limited number of species present in the gut microbiota and likely represents only a fraction of its resistome (the collective pool of AMR genes). Such metagenomic approaches have been used in a number of recent studies and it has been shown that metagenomic read mapping describes AMR abundance in bacterial communities more accurately than commonly used technologies on selected indicator organisms.[15,16,17] A recent study focused on sampling a diverse group of individual pigs from eleven farms in three countries showed that genetics, age, diet and country all likely influence the pig microbiota, but little information is available for poultry.[16]

Methods

Results

Conclusion