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Data and R scripts for the paper:Comparing the transmission of carbapenemase-producing and extended-spectrum beta-lactamase-producing Escherichia coli between broiler chickens. Data includes the phyloseq RDS for the 16S rRNA v3v4 sequencing data and its metadata. Analysis scripts as used for the paper for both the modeling and the microbiome. See README for more details. Links: NCBI SRA BioProject:PRJNA948179 Paper doi: Abstract paper: Comparing the transmission of carbapenemase-producing and extended-spectrum beta-lactamase-producing Escherichia coli between broiler chickens Natcha Dankittipong, Jesse B. Alderliesten, Jan Van den Broek, M. Anita Dame-Korevaar, Michael S. M. Brouwer, Francisca C. Velkers, Alex Bossers, Clazien J. de Vos, Jaap A. Wagenaar, J. Arjan Stegeman, Egil A. J. Fischer The emergence of carbapenemase-producing Enterobacteriaceae (CPE) is a threat to public health, because of their resistance to clinically important carbapenem antibiotics. The emergence of CPE in meat-producing animals is particularly worrying because consumption of meat contaminated with resistant bacteria similar to CPE, such as extended-spectrum beta-lactamase (ESBL)-producing Enterobacteriaceae, contributed to colonization in humans worldwide. Currently, no data on the transmission of CPE in livestock is available. We performed a transmission experiment to quantify the transmission of CPE between broilers to fill this knowledge gap and to compare the transmission rates of CPE and other antibiotic-resistant E. coli. A total of 180 Ross 308 broiler chickens were distributed on the day of hatch (day 0) over 12 pens. On day 5, half of the chickens in each pen were orally inoculated with 5·102 colony-forming units of CPE, ESBL, or chloramphenicol-resistant E. coli (catA1). Amoxicillin drinking water treatment was given twice daily in 6 of the 12 pens from days 2 to 6 to evaluate the effect of antibiotic treatment on the transmission rates. Cloacal swabs of all animals were taken to determine the number of infectious broilers. We used a Bayesian hierarchical model to quantify the transmission of the E. coli strains. E. coli can survive in the environment and serve as a reservoir. Therefore, the susceptible-infectious transmission model was adapted to account for the transmission of resistant bacteria from the environment. In addition, the caecal microbiome was analyzed on day 5 and at the end of the experiment on day 14 to assess the relationship between the caecal microbiome and the transmission rates. The transmission rates of CPE were 52 – 68 per cent lower compared to ESBL and catA1, but it is not clear if these differences were caused by differences between the resistance genes or between the E. coli strains. Differences between the groups in transmission rates and microbiome diversity did not correspond to each other, indicating that differences in transmission rates were probably not caused by major differences in the community structure in the caecal microbiome. Amoxicillin treatment from day 2 to 6 increased the transmission rate more than three-fold in all inoculums. It also increased alpha-diversity compared to untreated animals on day 5, but not on day 14, suggesting only a temporary effect. Future research could incorporate more complex transmission models with different species of resistant bacteria into the Bayesian hierarchical model.

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.

Chlamydia (C.) caviae is a known pathogen in guinea pigs, causing conjunctivitis, respiratory infections and abortions. Recently, a C. caviae-induced zoonotic link was identified as the etiology of severe community-acquired pneumonia in humans. Here, 784 conjunctival and rectal swabs originating from 260 guinea pigs and 110 rabbits from 64 husbandries in Switzerland, as well as 200 composite conjunctival swabs originating from 878 guinea pigs from 37 husbandries in The Netherlands were examined by real-time PCR followed by conventional PCR and sequencing. Chlamydiaceae were detected in 2.3% (18/784) and 12.5% (25/200) of all Swiss and Dutch samples, respectively. An overall C. caviae occurrence was detected in 2.7% (7/260) and 8.9% (78/878) of all Swiss and Dutch guinea pigs, respectively. OmpA genotyping of 64 C. caviae-positive samples resulted in 33 sequences sharing 100% nucleotide identity with the strains isolated from the zoonotic transmission cases in The Netherlands. However, all ompA sequences of this study were distinct from the C. caviae GPIC reference strain. C. caviae was not detected in rabbits but C. psittaci genotype A was identified in guinea pigs and rabbits, raising concerns about the importance of these animal species as novel zoonotic sources for C. psittaci.

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.

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Application of animal manure to soils results in the introduction of manure-derived bacteria and their antimicrobial resistance genes (ARGs) into soils. ResCap is a novel targeted-metagenomic approach that allows the detection of minority components of the resistome gene pool without the cost-prohibitive coverage depths and can provide a valuable tool to study the spread of antimicrobial resistance (AMR) in the environment. We used high-throughput sequencing and qPCR for 16S rRNA gene fragments as well as ResCap to explore the dynamics of bacteria, and ARGs introduced to soils and adjacent water ditches, both at community and individual scale, over a period of three weeks. The soil bacteriome and resistome showed strong resilience to the input of manure, as manuring did not impact the overall structure of the bacteriome, and its effects on the resistome were transient. Initially, manure application resulted in a substantial increase of ARGs in soils and adjacent waters, while not affecting the overall bacterial community composition. Still, specific families increased after manure application, either through the input of manure (e.g., Dysgonomonadaceae) or through enrichment after manuring (e.g., Pseudomonadaceae). Depending on the type of ARG, manure application resulted mostly in an increase (e.g., aph(6)-Id), but occasionally also in a decrease (e.g., dfrB3) of the absolute abundance of ARG clusters (FPKM/kg or L). This study shows that the structures of the bacteriome and resistome are shaped by different factors, where the bacterial community composition could not explain the changes in ARG diversity or abundances. Also, it highlights the potential of applying targeted metagenomic techniques, such as ResCap, to study the fate of AMR in the environment.

One Health surveillance of antimicrobial resistance (AMR) depends on a harmonised method for detection of AMR. Metagenomics-based surveillance offers the possibility to compare resistomes within and between different target populations. Its potential to be embedded into policy in the future calls for a timely and integrated knowledge dissemination strategy. We developed a blended training (e-learning and a workshop) on the use of metagenomics in surveillance of pathogens and AMR. The objectives were to highlight the potential of metagenomics in the context of integrated surveillance, to demonstrate its applicability through hands-on training and to raise awareness to bias factors. The target participants included staff of competent authorities responsible for AMR monitoring and academic staff. The training was organized in modules covering the workflow, requirements, benefits and challenges of surveillance by metagenomics. The training had 41 participants. The face-to-face workshop was essential to understand the expectations of the participants about the transition to metagenomics-based surveillance. After the training, the e-learning component was revised and released as a Massive Open Online Course (MOOC), now available at https://www.coursera.org/learn/metagenomics. This course has run in more than 20 sessions, with more than 3000 learners enrolled, from more than 120 countries. Blended learning and MOOCs are useful tools to deliver knowledge globally and across disciplines. The released MOOC can be a reference knowledge source for the international public health community engaged in the application of metagenomics in surveillance.

Additional file 4. Sample ranking to their relative levels of N-terminal PrPres epitopes of antibodies P4 and 12B2. Figure in histogram form to compare the ranking of goat study sample series from high to low N-terminal epitope in ISS-WB and Triplex-WB.