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Animal rehabilitation centres provide a unique opportunity to study the microbiome of wild animals because subjects will be handled for their treatment and can therefore be sampled longitudinally. However, rehabilitation may have unintended consequences on the animals’ microbiome because of a less varied and suboptimal diet, possible medical treatment and exposure to a different environment and human handlers. Our study describes the gut microbiome of two large seal cohorts, 50 pups (0–30 days old at arrival) and 23 weaners (more than 60 days old at arrival) of stranded harbour seals admitted for rehabilitation at the Sealcentre Pieterburen in the Netherlands, and the effect of rehabilitation on it. Faecal samples were collected from all seals at arrival, two times during rehabilitation and before release. Only seals that did not receive antimicrobial treatment were included in the study. The average time in rehabilitation was 95 days for the pups and 63 days for the weaners. We observed that during rehabilitation, there was an increase in the relative abundance of some of the Campylobacterota spp and Actinobacteriota spp. The alpha diversity of the pups’ microbiome increased significantly during their rehabilitation (p-value <0.05), while there were no significant changes in alpha diversity over time for weaners. We hypothesize that aging is the main reason for the observed changes in the pups’ microbiome. At release, the sex of a seal pup was significantly associated with the microbiome’s alpha (i.e., Shannon diversity was higher for male pups, p-value <0.001) and beta diversity (p-value 0.001). For weaners, variation in the microbiome composition (beta diversity) at release was partly explained by sex and age of the seal (p-values 0.002 and 0.003 respectively). We mainly observed variables known to change the gut microbiome composition (e.g., age and sex) and conclude that rehabilitation in itself had only minor effects on the gut microbiome of seal pups and seal

Livestock feces with antimicrobial resistant bacteria reaches the farm floor, manure pit, farm land and wider environment by run off and aerosolization. Little research has been done on the role of dust in the spread of antimicrobial resistance (AMR) in farms. Concentrations and potential determinants of antimicrobial resistance genes (ARGs) in farm dust are at present not known. Therefore in this study absolute ARG levels, representing the levels people and animals might be exposed to, and relative abundances of ARGs, representing the levels in the bacterial population, were quantified in airborne farm dust using qPCR. Four ARGs were determined in 947 freshly settled farm dust samples, captured with electrostatic dustfall collectors (EDCs), from 174 poultry (broiler) and 159 pig farms across nine European countries. By using linear mixed modeling, associations with fecal ARG levels, antimicrobial use (AMU) and farm and animal related parameters were determined. Results show similar relative abundances in farm dust as in feces and a significant positive association (ranging between 0.21 and 0.82) between the two reservoirs. AMU in pigs was positively associated with ARG abundances in dust from the same stable. Higher biosecurity standards were associated with lower relative ARG abundances in poultry and higher relative ARG abundances in pigs. Lower absolute ARG levels in dust were driven by, among others, summer season and certain bedding materials for poultry, and lower animal density and summer season for pigs. This study indicates different pathways that contribute to shaping the dust resistome in livestock farms, related to dust generation, or affecting the bacterial microbiome. Farm dust is a large reservoir of ARGs from which transmission to bacteria in other reservoirs can possibly occur. The identified determinants of ARG abundances in farm dust can guide future research and potentially farm management policy.

Objectives: The occurrence and zoonotic potential of antimicrobial resistance (AMR) in pigs and broilers has been studied intensively in past decades. Here, we describe AMR levels of European pig and broiler farms and determine the potential risk factors. Methods: We collected faeces from 181 pig farms and 181 broiler farms in nine European countries. Real-time quantitative PCR (qPCR) was used to quantify the relative abundance of four antimicrobial resistance genes (ARGs) [aph(3′)-III, erm(B), sul2 and tet(W)] in these faeces samples. Information on antimicrobial use (AMU) and other farm characteristics was collected through a questionnaire. A mixed model using country and farm as random effects was performed to evaluate the relationship of AMR with AMU and other farm characteristics. The correlation between individual qPCR data and previously published pooled metagenomic data was evaluated. Variance component analysis was conducted to assess the variance contribution of all factors. Results: The highest abundance of ARG was for tet(W) in pig faeces and erm(B) in broiler faeces. In addition to the significant positive association between corresponding ARG and AMU levels, we also found on-farm biosecurity measures were associated with relative ARG abundance in both pigs and broilers. Between-country and between-farm variation can partially be explained by AMU. Different ARG targets may have different sample size requirements to represent the overall farm level precisely. Conclusions: qPCR is an efficient tool for targeted assessment of AMR in livestock-related samples. The AMR variation between samples was mainly contributed to by between-country, between-farm and within-farm differences, and then by on-farm AMU.

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.

Background: By studying the entire human faecal resistome and associated microbiome, the diversity and abundance of faecal antimicrobial resistance genes (ARGs) can be comprehensively characterized. Prior culture-based studies have shown associations between occupational exposure to livestock and carriage of specific antimicrobial resistant bacteria. Using shotgun metagenomics, the present study investigated 194 faecal resistomes and bacteriomes from humans occupationally exposed to ARGs in livestock (i.e. pig and poultry farmers, employees and family members and pig slaughterhouse workers) and a control population (Lifelines cohort) in the Netherlands. In addition, we sought to identify determinants for the human resistome and bacteriome composition by applying a combination of multivariate (NMDS, PERMANOVA, SIMPER and DESeq2 analysis) and multivariable regression analysis techniques. Results: Pig slaughterhouse workers and pig farmers carried higher total ARG abundances in their stools compared to broiler farmers and control subjects. Tetracycline, β-lactam and macrolide resistance gene clusters dominated the resistome of all studied groups. No significant resistome alpha diversity differences were found among the four populations. However, the resistome beta diversity showed a separation of the mean resistome composition of pig and pork exposed workers from broiler farmers and controls, independent of their antimicrobial use. We demonstrated differences in resistome composition between slaughter line positions, pig versus poultry exposed workers, as well as differences between farmers and employees versus family members. In addition, we found a significant correlation between the bacteriome and resistome, and significant differences in the bacteriome composition between and within the studied subpopulations. Finally, an in-depth analysis of pig and poultry farms – of which also farm livestock resistomes were analysed – showed positive associations between the

Luiken REC, Van Gompel L, Bossers A, Munk P, Joosten P, Hansen RB, Knudsen BE, Garcia-Cobos S, Dewulf J, Aarestrup FM, Wagenaar JA, Smit LAM, Mevius DJ, Heederik DJJ, Schmitt H; EFFORT-group. : : : : : [ Highlights ] -- An analysis of farm dust from 79 pig and poultry farms using metagenomics. -- Farm dust resistomes are compared to animal fecal and farmers stool resistomes. -- Farm dust bacterial microbiomes have an abundant and rich resistome. -- Animal fecal resistomes are an important, but not the only, source of farm dust ARGs. -- Antimicrobial usage in the animals is positively associated with dust resistome abundance. :: [ Objectives ] We describe the resistome and bacterial microbiome of pig and poultry farm dust and their relation with animal feces resistomes and bacterial microbiomes, and on-farm antimicrobial usage (AMU). In addition, the relation between dust and farmers stool resistomes was explored. [ Conclusion ] Poultry and pig farm dust resistomes are rich and abundant and associated with the fecal resistome of the animals and the dust bacterial microbiome., Luiken REC, Van Gompel L, Bossers A, Munk P, Joosten P, Hansen RB, Knudsen BE, Garcia-Cobos S, Dewulf J, Aarestrup FM, Wagenaar JA, Smit LAM, Mevius DJ, Heederik DJJ, Schmitt H; EFFORT-group. : : : : : [ Highlights ] -- An analysis of farm dust from 79 pig and poultry farms using metagenomics. -- Farm dust resistomes are compared to animal fecal and farmers stool resistomes. -- Farm dust bacterial microbiomes have an abundant and rich resistome. -- Animal fecal resistomes are an important, but not the only, source of farm dust ARGs. -- Antimicrobial usage in the animals is positively associated with dust resistome abundance. :: [ Objectives ] We describe the resistome and bacterial microbiome of pig and poultry farm dust and their relation with animal feces resistomes and bacterial microbiomes, and on-farm antimicrobial usage (AMU). In addition, the relation between dust and farmers stool resistomes was explored. [ Conclusion ] Poultry and pig farm dust resistomes are rich and abundant and associated with the fecal resistome of the animals and the dust bacterial microbiome.

Objectives: Slaughterhouse staff is occupationally exposed to antimicrobial resistant bacteria. Studies reported high antimicrobial resistance gene (ARG) abundances in slaughter pigs. This cross-sectional study investigated occupational exposure to tetracycline (tetW) and macrolide (ermB) resistance genes and assessed determinants for faecal tetW and ermB carriage among pig slaughterhouse workers. Methods: During 2015-2016, 483 faecal samples and personal questionnaires were collected from workers in a Dutch pig abattoir, together with 60 pig faecal samples. Human dermal and respiratory exposure was assessed by examining 198 carcass, 326 gloves, and 33 air samples along the line, next to 198 packed pork chops to indicate potential consumer exposure. Samples were analyzed by qPCR (tetW, ermB). A job exposure matrix was created by calculating the percentage of tetW and ermB positive carcasses or gloves for each job position. Multiple linear regression models were used to link exposure to tetW and ermB carriage. Results: Workers are exposed to tetracycline and macrolide resistance genes along the slaughter line. Tetw and ermB gradients were found for carcasses, gloves, and air filters. One packed pork chop contained tetW, ermB was non-detectable. Human faecal tetW and ermB concentrations were lower than in pig faeces. Associations were found between occupational tetW exposure and human faecal tetW carriage, yet, not after model adjustments. Sampling round, nationality, and smoking were determinants for ARG carriage. Conclusion: We demonstrated clear environmental tetracycline and macrolide resistance gene exposure gradients along the slaughter line. No robust link was found between ARG exposure and human faecal ARG carriage.

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.

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.

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