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      The European livestock resistome

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          ABSTRACT

          Metagenomic sequencing has proven to be a powerful tool in the monitoring of antimicrobial resistance (AMR). Here, we provide a comparative analysis of the resistome from pigs, poultry, veal calves, turkey, and rainbow trout, for a total of 538 herds across nine European countries. We calculated the effects of per-farm management practices and antimicrobial usage (AMU) on the resistome in pigs, broilers, and veal calves. We also provide an in-depth study of the associations between bacterial diversity, resistome diversity, and AMR abundances as well as co-occurrence analysis of bacterial taxa and antimicrobial resistance genes (ARGs) and the universality of the latter. The resistomes of veal calves and pigs clustered together, as did those of avian origin, while the rainbow trout resistome was different. Moreover, we identified clear core resistomes for each specific food-producing animal species. We identified positive associations between bacterial alpha diversity and both resistome alpha diversity and abundance. Network analyses revealed very few taxa–ARG associations in pigs but a large number for the avian species. Using updated reference databases and optimized bioinformatics, previously reported significant associations between AMU, biosecurity, and AMR in pig and poultry farms were validated. AMU is an important driver for AMR; however, our integrated analyses suggest that factors contributing to increased bacterial diversity might also be associated with higher AMR load. We also found that dispersal limitations of ARGs are shaping livestock resistomes, and future efforts to fight AMR should continue to emphasize biosecurity measures.

          IMPORTANCE

          Understanding the occurrence, diversity, and drivers for antimicrobial resistance (AMR) is important to focus future control efforts. So far, almost all attempts to limit AMR in livestock have addressed antimicrobial consumption. We here performed an integrated analysis of the resistomes of five important farmed animal populations across Europe finding that the resistome and AMR levels are also shaped by factors related to bacterial diversity, as well as dispersal limitations. Thus, future studies and interventions aimed at reducing AMR should not only address antimicrobial usage but also consider other epidemiological and ecological factors.

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          Conducting Meta-Analyses inRwith themetaforPackage

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            ResFinder 4.0 for predictions of phenotypes from genotypes

            Abstract Objectives WGS-based antimicrobial susceptibility testing (AST) is as reliable as phenotypic AST for several antimicrobial/bacterial species combinations. However, routine use of WGS-based AST is hindered by the need for bioinformatics skills and knowledge of antimicrobial resistance (AMR) determinants to operate the vast majority of tools developed to date. By leveraging on ResFinder and PointFinder, two freely accessible tools that can also assist users without bioinformatics skills, we aimed at increasing their speed and providing an easily interpretable antibiogram as output. Methods The ResFinder code was re-written to process raw reads and use Kmer-based alignment. The existing ResFinder and PointFinder databases were revised and expanded. Additional databases were developed including a genotype-to-phenotype key associating each AMR determinant with a phenotype at the antimicrobial compound level, and species-specific panels for in silico antibiograms. ResFinder 4.0 was validated using Escherichia coli (n = 584), Salmonella spp. (n = 1081), Campylobacter jejuni (n = 239), Enterococcus faecium (n = 106), Enterococcus faecalis (n = 50) and Staphylococcus aureus (n = 163) exhibiting different AST profiles, and from different human and animal sources and geographical origins. Results Genotype–phenotype concordance was ≥95% for 46/51 and 25/32 of the antimicrobial/species combinations evaluated for Gram-negative and Gram-positive bacteria, respectively. When genotype–phenotype concordance was <95%, discrepancies were mainly linked to criteria for interpretation of phenotypic tests and suboptimal sequence quality, and not to ResFinder 4.0 performance. Conclusions WGS-based AST using ResFinder 4.0 provides in silico antibiograms as reliable as those obtained by phenotypic AST at least for the bacterial species/antimicrobial agents of major public health relevance considered.
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              Microbiome Datasets Are Compositional: And This Is Not Optional

              Datasets collected by high-throughput sequencing (HTS) of 16S rRNA gene amplimers, metagenomes or metatranscriptomes are commonplace and being used to study human disease states, ecological differences between sites, and the built environment. There is increasing awareness that microbiome datasets generated by HTS are compositional because they have an arbitrary total imposed by the instrument. However, many investigators are either unaware of this or assume specific properties of the compositional data. The purpose of this review is to alert investigators to the dangers inherent in ignoring the compositional nature of the data, and point out that HTS datasets derived from microbiome studies can and should be treated as compositions at all stages of analysis. We briefly introduce compositional data, illustrate the pathologies that occur when compositional data are analyzed inappropriately, and finally give guidance and point to resources and examples for the analysis of microbiome datasets using compositional data analysis.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: InvestigationRole: VisualizationRole: Writing – review and editing
                Role: Formal analysisRole: Writing – review and editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: Visualization
                Role: Formal analysisRole: Visualization
                Role: Data curationRole: Resources
                Role: Data curationRole: Formal analysis
                Role: Data curationRole: MethodologyRole: Writing – review and editing
                Role: Formal analysisRole: ResourcesRole: Writing – review and editing
                Role: ConceptualizationRole: Funding acquisitionRole: Writing – review and editing
                Role: Data curationRole: MethodologyRole: Writing – review and editing
                Role: ConceptualizationRole: Writing – review and editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Resources
                Role: Formal analysisRole: InvestigationRole: Writing – review and editing
                Role: Formal analysisRole: InvestigationRole: VisualizationRole: Writing – review and editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draft
                Role: Editor
                Journal
                mSystems
                mSystems
                msystems
                mSystems
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2379-5077
                April 2024
                19 March 2024
                19 March 2024
                : 9
                : 4
                : e01328-23
                Affiliations
                [1 ]National Food Institute, Technical University of Denmark; , Lyngby, Denmark
                [2 ]Institute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University; , The Netherlands, Utrecht
                [3 ]School of Biological Sciences, University of Edinburgh, Max Born Crescent; , Edinburgh, United Kingdom
                [4 ]Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University; , The Netherlands, Utrecht
                [5 ]Wageningen Bioveterinary Research, Wageningen University & Research; , Lelystad, The Netherlands
                Kobenhavns Universitet; , Copenhagen, Denmark
                Author notes
                Address correspondence to Frank M. Aarestrup, fmaa@ 123456food.dtu.dk

                Deceased: Dik Mevius passed away during preparation of the manuscript.

                Dedication: We dedicate this paper our great colleague and longtime friend Dik J. Mevius, who was an important part of, and contributor to, the study but unfortunately passed away prior to publication.

                The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0001-8813-4019
                https://orcid.org/0000-0002-8197-7520
                https://orcid.org/0000-0002-5074-7183
                https://orcid.org/0000-0002-6586-717X
                https://orcid.org/0000-0002-7116-2723
                Article
                01328-23 msystems.01328-23
                10.1128/msystems.01328-23
                11019871
                38501800
                857e7708-f87a-4477-bc1d-4be6747ef4ae
                Copyright © 2024 Munk et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 15 December 2023
                : 21 February 2024
                Page count
                supplementary-material: 2, authors: 18, Figures: 6, Equations: 3, References: 66, Pages: 20, Words: 10321
                Funding
                Funded by: European Community 7th Framework Programme;
                Award ID: 613754
                Award Recipient :
                Funded by: Novo Nordisk Foundation;
                Award ID: NNF16OC0021856
                Award Recipient :
                Categories
                Research Article
                microbial-ecology, Microbial Ecology
                Custom metadata
                April 2024

                resistome,livestock,metagenomics,antimicrobial resistance,diversity

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