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The emergence of antimicrobial resistance (AMR) is one of the biggest health threats globally. In addition, the use of antimicrobial drugs in humans and livestock is considered an important driver of antimicrobial resistance. The commensal microbiota, and especially the intestinal microbiota, has been shown to have an important role in the emergence of AMR. Mobile genetic elements (MGEs) also play a central role in facilitating the acquisition and spread of AMR genes. We isolated Escherichia coli (n = 627) from fecal samples in respectively 25 poultry, 28 swine, and 15 veal calf herds from 6 European countries to investigate the phylogeny of E. coli at country, animal host and farm levels. Furthermore, we examine the evolution of AMR in E. coli genomes including an association with virulence genes, plasmids and MGEs. We compared the abundance metrics retrieved from metagenomic sequencing and whole genome sequenced of E. coli isolates from the same fecal samples and farms. The E. coli isolates in this study indicated no clonality or clustering based on country of origin and genetic markers; AMR, and MGEs. Nonetheless, mobile genetic elements play a role in the acquisition of AMR and virulence genes. Additionally, an abundance of AMR was agreeable between metagenomic and whole genome sequencing analysis for several AMR classes in poultry fecal samples suggesting that metagenomics could be used as an indicator for surveillance of AMR in E. coli isolates and vice versa.

Objectives: Farm-level quantification of antimicrobial usage (AMU) in pig farms. Methods: In a cross-sectional study, AMU data on group treatments administered to a single batch of fattening pigs from birth to slaughter (group treatment data) and antimicrobials purchased during 1 year (purchase data) were collected at 180 pig farms in nine European countries. AMU was quantified using treatment incidence (TI) based on defined (DDDvet) and used (UDDvet) daily doses and defined (DCDvet) and used (UCDvet) course doses. Results: The majority of antimicrobial group treatments were administered to weaners (69.5% of total TIDDDvet) followed by sucklers (22.5% of total TIDDDvet). AMU varied considerably between farms with a median TIDDDvet of 9.2 and 7.1 for a standardized rearing period of 200 days based on group treatment and purchase data, respectively. In general, UDDvet and UCDvet were higher than DDDvet and DCDvet, respectively, suggesting that either the defined doses were set too low or that group treatments were often dosed too high and/or administered for too long. Extended-spectrum penicillins (31.2%) and polymyxins (24.7%) were the active substances most often used in group treatments, with the majority administered through feed or water (82%). Higher AMU at a young age was associated with higher use in older pigs. Conclusions: Collecting farm-level AMU data of good quality is challenging and results differ based on how data are collected (group treatment data versus purchase data) and reported (defined versus used daily and course doses).

Background Real-time quantitative PCR (qPCR) is an affordable method to quantify antimicrobial resistance gene (ARG) targets, allowing comparisons of ARG abundance along animal production chains. Objectives We present a comparison of ARG abundance across various animal species, production environments and humans in Europe. AMR variation sources were quantified. The correlation of ARG abundance between qPCR data and previously published metagenomic data was assessed. Methods A cross-sectional study was conducted in nine European countries, comprising 9572 samples. qPCR was used to quantify abundance of ARGs [aph(3′)-III, erm(B), sul2, tet(W)] and 16S rRNA. Variance component analysis was conducted to explore AMR variation sources. Spearman’s rank correlation of ARG abundance values was evaluated between pooled qPCR data and earlier published pooled metagenomic data. Results ARG abundance varied strongly among animal species, environments and humans. This variation was dominated by between-farm variation (pigs) or within-farm variation (broilers, veal calves and turkeys). A decrease in ARG abundance along pig and broiler production chains (‘farm to fork’) was observed. ARG abundance was higher in farmers than in slaughterhouse workers, and lowest in control subjects. ARG abundance showed a high correlation (Spearman’s ρ > 0.7) between qPCR data and metagenomic data of pooled samples. Conclusions qPCR analysis is a valuable tool to assess ARG abundance in a large collection of livestock-associated samples. The between-country and between-farm variation of ARG abundance could partially be explained by antimicrobial use and farm biosecurity levels. ARG abundance in human faeces was related to livestock antimicrobial resistance exposure.

1. General introduction: In the agricultural sector, antimicrobial drugs are widely used as curative or metaphylactic treatment in food producing animals. One side effect of these usages concerns the development of resistance among the commensal bacteria of animals, mainly within guts, and their potential spread between animals. Then, the resistant bacteria could spread in all the food chain, from the animal towards the food and finally towards the consumers intestines. The risk could be worrying when looking at the plasmid-mediated resistance, like Extended-spectrum β-lactamases (ESBL) for beta-lactamin/cephalosporin or mcr for colistin, due to the possibility that a resistant bacteria originating from animals could transmit its plasmid to human commensal bacteria or pathogen bacteria that are present within guts (Madec, et al., 2017; Nordmann, Poirel, 2016). The link between antimicrobial drug (AMD) use and the development of antimicrobial resistance (AMR) in bacterial strains has been mainly studied in vitro experiments and sometimes modelled thanks to pharmacokinetics-pharmacodynamics (PKPD) approaches. Many within‐host models of antibiotic resistance are based on this pharmacokinetics/pharmacodynamics (PK/PD) approach as opposite to the between-host models, which are mainly epidemiological models. Within‐host models are useful for understanding bacteria dynamics and response to an AMD treatment, within a single individual (Tetteh, et al., 2020). PKPD models are a good basis to explore mechanistically the relationship between drug concentrations and the development/selection of resistant bacteria and may be extrapolated to in vivo situations (Nielsen, Friberg, 2013). However, there are only few PKPD models that describe the impact of AMD within the gut for several reasons: (i) these mechanisms occur in a complex environment (intestinal microbiota) with various interactions and external factors (ii) we face a lack of observed data to inform the PKPD model (need of longitudinal data); (iii) this could be time and computer-intensive. Hence, the goal of this work was to refine and develop a “within-host PKPD model” (hereafter simplified as PKPD model) to assess and predict the impact of an AMD treatment (as an input of the model) on the emergence/selection of resistant bacteria within guts and excretion towards faeces (output of the model) for pig, at the individual level and population level (taking into account the inter-individual variability).

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Objectives To determine associations between farm- and flock-level antimicrobial usage (AMU), farm biosecurity status and the abundance of faecal antimicrobial resistance genes (ARGs) on broiler farms. Methods In the cross-sectional pan-European EFFORT study, conventional broiler farms were visited and faeces, AMU information and biosecurity records were collected. The resistomes of pooled faecal samples were determined by metagenomic analysis for 176 farms. A meta-analysis approach was used to relate total and class-specific ARGs (expressed as fragments per kb reference per million bacterial fragments, FPKM) to AMU (treatment incidence per DDD, TIDDDvet) per country and subsequently across all countries. In a similar way, the association between biosecurity status (Biocheck.UGent) and the resistome was explored. Results Sixty-six (38%) flocks did not report group treatments but showed a similar resistome composition and roughly similar ARG levels to antimicrobial-treated flocks. Nevertheless, we found significant positive associations between β-lactam, tetracycline, macrolide and lincosamide, trimethoprim and aminoglycoside antimicrobial flock treatments and ARG clusters conferring resistance to the same class. Similar associations were found with purchased products. In gene-level analysis for β-lactams and macrolides, lincosamides and streptogramins, a significant positive association was found with the most abundant gene clusters blaTEM and erm(B). Little evidence was found for associations with biosecurity. Conclusions The faecal microbiome in European broilers contains a high diversity of ARGs, even in the absence of current antimicrobial selection pressure. Despite this, the relative abundance of genes and the composition of the resistome is positively related to AMU in European broiler farms for several antimicrobial classes.

International audience

International audience; International conference of the European EFFORT project

In the version of this Article originally published, the surname of author Oksana Lukjancenko was spelt incorrectly as ‘Lukjacenko’. This has now been corrected.