The quantitative effect of antimicrobial usage in Danish pig farms on the abundance of antimicrobial resistance genes in slaughter pigs.

Research has long established the connection between antimicrobial use (AMU) and antimicrobial resistance (AMR) in production animals, and shown that the ceasing of AMU reduces AMR. Our previous study of Danish slaughter-pig production found a quantitative relationship between lifetime AMU and abundance of antimicrobial resistance genes (ARGs). This study aimed to generate further quantitative knowledge on how changes in AMU in farms influence the abundance of ARGs both with immediate effect and over time. The study included 83 farms that were visited from 1 to 5 times. From each visit, a pooled faecal sample was produced. The abundance of ARGs was obtained by metagenomics. We used two-level linear mixed models for estimating the effect of AMU on the abundance of ARGs against six antimicrobial classes. The lifetime AMU of each batch was calculated from usage during their three rearing periods; as piglets, weaners and slaughter pigs (rearing pathway). AMU at farm level was estimated as the mean lifetime AMU of the sampled batches from each farm. At batch level, AMU was measured as the deviation between the batch-specific lifetime AMU and the general mean lifetime AMU at the farm. For peroral tetracycline and macrolide use there was a significant quantitative linear effect on the abundance of ARGs in batches within individual farms, indicating an immediate effect of changed AMU from batch to batch within farms. These estimated effects between batches within farms were approximately 1/2-1/3 of the effect estimated between farms. For all antimicrobial classes, the effect of the mean farm-level AMU and the abundance of ARGs present in the faeces of slaughter pigs was significant. This effect was identified only for peroral use, except for lincosamides, where the effect was for parenteral use. The results also indicated that the abundance of ARGs against a specific antimicrobial class also increased by the peroral usage of one or several other antimicrobial classes, except for ARGs against beta-lactams. These effects were generally lower than the AMU effect of the specific antimicrobial class. Overall, the farm peroral mean lifetime AMU affected the abundance of ARGs at antimicrobial class level and abundance of ARGs of other classes. However, the difference of AMU of the slaughter-pig batches affected only the abundance of ARGs at the same antimicrobial class level in the same antimicrobial class. The results do not exclude that parenteral usage of antimicrobials may have an effect on the abundance of ARGs.

A global phylogenomic and metabolic reconstruction of the large intestine bacterial community of domesticated cattle.

BACKGROUND: The large intestine is a colonization site of beneficial microbes complementing the nutrition of cattle but also of zoonotic and animal pathogens. Here, we present the first global gene catalog of cattle fecal microbiomes, a proxy of the large intestine microbiomes, from 436 metagenomes from six countries. RESULTS: Phylogenomics suggested that the reconstructed genomes and their close relatives form distinct branches and produced clustering patterns that were reminiscent of the metagenomics sample origin. Bacterial taxa had distinct metabolic profiles, and complete metabolic pathways were mainly linked to carbohydrates and amino acids metabolism. Dietary changes affected the community composition, diversity, and potential virulence. However, predicted enzymes, which were part of complete metabolic pathways, remained present, albeit encoded by different microbes. CONCLUSIONS: Our findings provide a global insight into the phylogenetic relationships and the metabolic potential of a rich yet understudied bacterial community and suggest that it provides valuable services to the host. However, we tentatively infer that members of that community are not irreplaceable, because similar to previous findings, symbionts of complex bacterial communities of mammals are expendable if there are substitutes that can perform the same task. Video Abstract.

Predicting Antimicrobial Resistance Using Partial Genome Alignments.

Antimicrobial resistance (AMR) is an important global health threat that impacts millions of people worldwide each year. Developing methods that can detect and predict AMR phenotypes can help to mitigate the spread of AMR by informing clinical decision making and appropriate mitigation strategies. Many bioinformatic methods have been developed for predicting AMR phenotypes from whole-genome sequences and AMR genes, but recent studies have indicated that predictions can be made from incomplete genome sequence data. In order to more systematically understand this, we built random forest-based machine learning classifiers for predicting susceptible and resistant phenotypes for Klebsiella pneumoniae (1,640 strains), Mycobacterium tuberculosis (2,497 strains), and Salmonella enterica (1,981 strains). We started by building models from alignments that were based on a reference chromosome for each species. We then subsampled each chromosomal alignment and built models for the resulting subalignments, finding that very small regions, representing approximately 0.1 to 0.2% of the chromosome, are predictive. In K. pneumoniae, M. tuberculosis, and S. enterica, the subalignments are able to predict multiple AMR phenotypes with at least 70% accuracy, even though most do not encode an AMR-related function. We used these models to identify regions of the chromosome with high and low predictive signals. Finally, subalignments that retain high accuracy across larger phylogenetic distances were examined in greater detail, revealing genes and intergenic regions with potential links to AMR, virulence, transport, and survival under stress conditions. IMPORTANCE Antimicrobial resistance causes thousands of deaths annually worldwide. Understanding the regions of the genome that are involved in antimicrobial resistance is important for developing mitigation strategies and preventing transmission. Machine learning models are capable of predicting antimicrobial resistance phenotypes from bacterial genome sequence data by identifying resistance genes, mutations, and other correlated features. They are also capable of implicating regions of the genome that have not been previously characterized as being involved in resistance. In this study, we generated global chromosomal alignments for Klebsiella pneumoniae, Mycobacterium tuberculosis, and Salmonella enterica and systematically searched them for small conserved regions of the genome that enable the prediction of antimicrobial resistance phenotypes. In addition to known antimicrobial resistance genes, this analysis identified genes involved in virulence and transport functions, as well as many genes with no previous implication in antimicrobial resistance.

Towards a European health research and innovation cloud (HRIC).

The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe.

Prediction of Acquired Antimicrobial Resistance for Multiple Bacterial Species Using Neural Networks.

Machine learning has proven to be a powerful method to predict antimicrobial resistance (AMR) without using prior knowledge for selected bacterial species-antimicrobial combinations. To date, only species-specific machine learning models have been developed, and to the best of our knowledge, the inclusion of information from multiple species has not been attempted. The aim of this study was to determine the feasibility of including information from multiple bacterial species to predict AMR for an individual species, since this may make it easier to train and update resistance predictions for multiple species and may lead to improved predictions. Whole-genome sequence data and susceptibility profiles from 3,528 Mycobacterium tuberculosis, 1,694 Escherichia coli, 658 Salmonella enterica, and 1,236 Staphylococcus aureus isolates were included. We developed machine learning models trained by the features of the PointFinder and ResFinder programs detected to predict binary (susceptible/resistant) AMR profiles. We tested four feature representation methods to determine the most efficient way for introducing features into the models. When training the model only on the Mycobacterium tuberculosis isolates, high prediction performances were obtained for the six AMR profiles included. By adding information on ciprofloxacin from the additional 3,588 isolates, there was no reduction in performance for the other antimicrobials but an increased performance for ciprofloxacin AMR profile prediction for Mycobacterium tuberculosis and Escherichia coli In conclusion, the species-independent models can predict multi-AMR profiles for multiple species without losing any robustness.IMPORTANCE Machine learning is a proven method to predict AMR; however, the performance of any machine learning model depends on the quality of the input data. Therefore, we evaluated different methods of representing information about mutations as well as mobilizable genes, so that the information can serve as input for a robust model. We combined data from multiple bacterial species in order to develop species-independent machine learning models that can predict resistance profiles for multiple antimicrobials and species with high performance.

Predicting effects of changed antimicrobial usage on the abundance of antimicrobial resistance genes in finisher' gut microbiomes.

It is accepted that usage of antimicrobials (AMs) in food animals causes the emergence and spread of antimicrobial resistance (AMR) in this sector, while also contributing to the burden of AMR in humans. Curbing the increasing occurrence of AMR in food animals requires in-depth knowledge of the quantitative relationship between antimicrobial usage (AMU) and AMR to achieve desired resistance reductions from interventions targeting AMU. In the observational study, the relationships between lifetime AMU in 83 finisher batches from Danish farms and the AMR gene abundances of seven antimicrobial classes in their gut microbiomes were quantified using multi-variable linear regression models. These relationships and the national lifetime AMU in pigs were included in the predictive modelling that allowed for testing of scenarios with changed lifetime AMU for finishers produced in Denmark in 2014. A total of 50 farms from the observational study were included in validating the observational study and the predictive modelling. The results from the observational study showed that the relationship was linear, and that the parenteral usage of AMs had a high effect on specific AM-classes of resistance, whereas the peroral usage had a lower but broader effect on several classes. Three different scenarios of changed lifetime AMU were simulated in the predictive modelling. When all tetracycline usage ceased, the predicted interval reductions of aminoglycoside, lincosamide and tetracycline resistance were 4-42 %, 0-8 % and 9-18 %, respectively. When the peroral tetracycline usage of the 10 % highest users was replaced with peroral macrolide usage, the tetracycline resistance fell by 1-2 % and the macrolide and MLSb resistance increased by 5-8 %. When all extended-spectrum penicillin usage was replaced with parenteral lincosamide usage, the beta-lactam resistance fell by 2-7 %, but the lincosamide usage and resistance increased by 194 % and 10-45 %, respectively. The external validation provided results within the 95 % CI of the predictive modelling outcome at national level, while the external validation at farm level was less accurate. In conclusion, interventions targeting AMU will reduce AMR abundance, though differently depending on the targeted AM-class and provided the reduction of one AM-class usage is not replaced with usage of another AM-class. Predicting several classes of AMR gene abundance simultaneously will support stakeholders when deciding on interventions targeting AMU in the finisher production to avoid adverse and unforeseen effects on the AMR abundance. This study provides a sound predictive modelling framework for further development, including the dynamics of AMU on AMR in finishers at national level.

Validation of the register-based lifetime antimicrobial usage measurement for finisher batches based on comparison with recorded antimicrobial usage at farm level.

Assessing the relationship between antimicrobial usage (AMU) and antimicrobial resistance (AMR) requires the accurate and precise utilisation of register data. Therefore, validation of register-based data is essential for evaluating the quality and, subsequently, the internal validity of studies based on the data. In this study, different smoothing methods for Veterinary Medicine Statistic Program database (VetStat)-records were validated by comparing these with farm-records. Comparison between measurements included accuracy as; completeness and correctness, and precision as; a relative difference of the error, correlation with Fisher's z transformation and reliability coefficient. The most valid methods of those examined were then used in re-analyses of the abundance of AMR genes in 10 finisher batches from a previous study. Improved accuracy was found when detailed smoothing methods were applied. Although the precision also increased, the effect was not as pronounced, as the usage estimate of all smoothing methods deviated moderately compared with the farm-registrations. Applying the most valid methods to the 10 finisher batches increased estimates of statistical model fit for aminoglycosides, lincosamides, tetracyclines and decreased estimates of statistical model fit for macrolides. The estimates of statistical model fit for sulfonamides and broad-spectrum penicillins remained the same. Through refined data transformation, VetStat-records can be used to calculate a daily amount of AMU per pig reflecting the true usage accurately and moderately precisely, which is the foundation for calculating lifetime AMU.

Developing a framework to assess the costeffectiveness of COMPARE - a global platform for the exchange of sequence-based pathogen data.

Analysing the genomic data of pathogens with the help of next-generation sequencing (NGS) is an increasingly important part of disease outbreak investigations and helps guide responses. While this technology has already been successfully employed to elucidate and control disease outbreaks, wider implementation of NGS also depends on its cost-effectiveness. COMPARE - short for 'Collaborative Management Platform for detection and Analyses of (Re-) emerging and foodborne outbreaks' - is a major project, funded by the European Union, to develop a global platform for sharing and analysing NGS data and thereby improve the rapid identification, containment and mitigation of emerging infectious diseases and foodborne outbreaks. This article introduces the project and presents the results of a review of the literature, composed of previous relevant cost-benefit and cost-effectiveness analyses. The authors also outline the implications for a methodological framework to assess the costeffectiveness of COMPARE and similar systems.

L'analyse des données sur le génome des agents pathogènes grâce au séquençage de nouvelle génération (SNG) joue un rôle de plus en plus important dans les enquêtes sur les foyers de maladies et contribue à l'élaboration de stratégies de réponse. Si cette technologie a été utilisée avec succès pour élucider la cause des certains foyers et pour les contrôler, une application plus large du SNG dépend également de sa rentabilité. La plate-forme COMPARE (plate-forme de gestion collaborative pour la détection et l'analyse des foyers émergents et ré-émergents et des toxi-infections alimentaires) est un projet de grande envergure financé par l'Union européenne, visant à mettre en place une plate-forme mondiale d'échanges et d'analyse des données de séquençage de nouvelle génération et à améliorer ainsi l'identification précoce, le confinement et l'atténuation des maladies infectieuses émergentes et des foyers de toxiinfections alimentaires. Les auteurs présentent le projet ainsi que les résultats d'une étude bibliographique intégrant des analyses pertinentes coûts­avantages et coûts­efficacité réalisées dans le passé. Ils soulignent également les enseignements de ces analyses pour l'élaboration d'un cadre méthodologique d'évaluation de la relation coûts­efficacité applicable au système COMPARE et à d'autres systèmes similaires.

El análisis de datos genómicos de los patógenos con ayuda de técnicas de secuenciación de próxima generación es un componente cada vez más importante de la investigación de brotes infecciosos, que resulta de utilidad para guiar las medidas de respuesta. Aunque estas técnicas ya se han utilizado con éxito para elucidar y combatir brotes de enfermedad, su aplicación generalizada también dependerá de la relación costo-eficacia que ofrezcan. COMPARE (acrónimo inglés de «plataforma de gestión colectiva para la detección y análisis de brotes (re)emergentes y de transmisión alimentaria¼) es un vasto proyecto financiado por la Unión Europea que apunta a instituir un dispositivo mundial de intercambio y análisis de datos de secuenciación de próxima generación y lograr así más eficacia en la rápida identificación, contención y mitigación de brotes de transmisión alimentaria y de enfermedades infecciosas emergentes. Los autores exponen el proyecto y presentan los resultados de un repaso bibliográfico de anteriores análisis de las relaciones costo-beneficio y costo-eficacia de estas técnicas. Además, explican brevemente lo que puede aportar un marco metodológico para evaluar la relación costo-eficacia del sistema COMPARE y de otros sistemas similares.

The association between measurements of antimicrobial use and resistance in the faeces microbiota of finisher batches.

The objectives were to present three approaches for calculating antimicrobial (AM) use in pigs that take into account the rearing period and rearing site, and to study the association between these measurements and phenotypical resistance and abundance of resistance genes in faeces samples from 10 finisher batches. The AM use was calculated relative to the rearing period of the batches as (i) 'Finisher Unit Exposure' at unit level, (ii) 'Lifetime Exposure' at batch level and (iii) 'Herd Exposure' at herd level. A significant effect on the occurrence of tetracycline resistance measured by cultivation was identified for Lifetime Exposure for the AM class: tetracycline. Furthermore, for Lifetime Exposure for the AM classes: macrolide, broad-spectrum penicillin, sulfonamide and tetracycline use as well as Herd Unit Exposure for the AM classes: aminoglycoside, lincosamide and tetracycline use, a significant effect was observed on the occurrence of genes coding for the AM resistance classes: aminoglycoside, lincosamide, macrolide, ß-lactam, sulfonamide and tetracycline. No effect was observed for Finisher Unit Exposure. Overall, the study shows that Lifetime Exposure is an efficient measurement of AM use in finisher batches, and has a significant effect on the occurrence of resistance, measured either by cultivation or metagenomics.

Evaluating next-generation sequencing for direct clinical diagnostics in diarrhoeal disease.

The accurate microbiological diagnosis of diarrhoea involves numerous laboratory tests and, often, the pathogen is not identified in time to guide clinical management. With next-generation sequencing (NGS) becoming cheaper, it has huge potential in routine diagnostics. The aim of this study was to evaluate the potential of NGS-based diagnostics through direct sequencing of faecal samples. Fifty-eight clinical faecal samples were obtained from patients with diarrhoea as part of the routine diagnostics at Hvidovre University Hospital, Denmark. Ten samples from healthy individuals were also included. DNA was extracted from faecal samples and sequenced on the Illumina MiSeq system. Species distribution was determined with MGmapper and NGS-based diagnostic prediction was performed based on the relative abundance of pathogenic bacteria and Giardia and detection of pathogen-specific virulence genes. NGS-based diagnostic results were compared to conventional findings for 55 of the diarrhoeal samples; 38 conventionally positive for bacterial pathogens, two positive for Giardia, four positive for virus and 11 conventionally negative. The NGS-based approach enabled detection of the same bacterial pathogens as the classical approach in 34 of the 38 conventionally positive bacterial samples and predicted the responsible pathogens in five of the 11 conventionally negative samples. Overall, the NGS-based approach enabled pathogen detection comparable to conventional diagnostics and the approach has potential to be extended for the detection of all pathogens. At present, however, this approach is too expensive and time-consuming for routine diagnostics.

Detection of linezolid resistance due to the optrA gene in Enterococcus faecalis from poultry meat from the American continent (Colombia).

Background: Three Enterococcus isolates obtained from retail chicken collected in 2010-11 as part of the Colombian Integrated Program for Antimicrobial Resistance Surveillance (COIPARS) showed reduced susceptibility towards linezolid (MIC 8 mg/L). Objectives: This study aimed at characterizing the isolates resistant to linezolid and detecting the resistance mechanism. Methods: Strains were analysed in 2011-12 without successful detection of the resistance mechanism. All isolates were found negative for the cfr gene and no 23S rRNA mutations were detected. In 2016, with the novel resistance gene optrA being described, the WGS data were re-analysed using in silico genomic tools for confirmation of species, detection of virulence and resistance genes, MLST and SNP analyses and comparison of the genetic environment with the previously published plasmid pE349. Results: : Three Enterococcus faecalis isolates were found positive for the optrA gene encoding resistance to linezolid and phenicols. Additional screening of 37 enterococci strains from the same study did not detect any further positives. Typing showed that two of the isolates belong to ST59, while the last belongs to ST489. All isolates carry genes encoding resistance to macrolide-lincosamide-streptogramin B, tetracycline and phenicols. In addition, the ST489 isolate also carries genes conferring aminoglycoside resistance and is resistant to quinolones, but no plasmid-mediated gene was detected. The optrA gene regions of the three plasmids showed high similarity to the originally reported optrA -carrying plasmid pE349. Conclusions: To the best of our knowledge, this is the first description of the optrA gene in E. faecalis isolated from poultry meat in the Americas.

Adhesion of Escherichia coli under flow conditions reveals potential novel effects of FimH mutations.

FimH-mediated adhesion of Escherichia coli to bladder epithelium is a prerequisite for urinary tract infections. FimH is also essential for blood-borne bacterial dissemination, but the mechanisms are poorly understood. The purpose of this study was to assess the influence of different FimH mutations on bacterial adhesion using a novel adhesion assay, which models the physiological flow conditions bacteria are exposed to. We introduced 12 different point mutations in the mannose binding pocket of FimH in an E. coli strain expressing type 1 fimbriae only (MSC95-FimH). We compared the bacterial adhesion of each mutant across several commonly used adhesion assays, including agglutination of yeast, adhesion to mono- and tri-mannosylated substrates, and static adhesion to bladder epithelial and endothelial cells. We performed a comparison of these assays to a novel method that we developed to study bacterial adhesion to mammalian cells under flow conditions. We showed that E. coli MSC95-FimH adheres more efficiently to microvascular endothelium than to bladder epithelium, and that only endothelium supports adhesion at physiological shear stress. The results confirmed that mannose binding pocket mutations abrogated adhesion. We demonstrated that FimH residues E50 and T53 are crucial for adhesion under flow conditions. The coating of endothelial cells on biochips and modelling of physiological flow conditions enabled us to identify FimH residues crucial for adhesion. These results provide novel insights into screening methods to determine the effect of FimH mutants and potentially FimH antagonists.

The role of whole genome sequencing in antimicrobial susceptibility testing of bacteria: report from the EUCAST Subcommittee.

Whole genome sequencing (WGS) offers the potential to predict antimicrobial susceptibility from a single assay. The European Committee on Antimicrobial Susceptibility Testing established a subcommittee to review the current development status of WGS for bacterial antimicrobial susceptibility testing (AST). The published evidence for using WGS as a tool to infer antimicrobial susceptibility accurately is currently either poor or non-existent and the evidence / knowledge base requires significant expansion. The primary comparators for assessing genotypic-phenotypic concordance from WGS data should be changed to epidemiological cut-off values in order to improve differentiation of wild-type from non-wild-type isolates (harbouring an acquired resistance). Clinical breakpoints should be a secondary comparator. This assessment will reveal whether genetic predictions could also be used to guide clinical decision making. Internationally agreed principles and quality control (QC) metrics will facilitate early harmonization of analytical approaches and interpretive criteria for WGS-based predictive AST. Only data sets that pass agreed QC metrics should be used in AST predictions. Minimum performance standards should exist and comparative accuracies across different WGS laboratories and processes should be measured. To facilitate comparisons, a single public database of all known resistance loci should be established, regularly updated and strictly curated using minimum standards for the inclusion of resistance loci. For most bacterial species the major limitations to widespread adoption for WGS-based AST in clinical laboratories remain the current high-cost and limited speed of inferring antimicrobial susceptibility from WGS data as well as the dependency on previous culture because analysis directly on specimens remains challenging. For most bacterial species there is currently insufficient evidence to support the use of WGS-inferred AST to guide clinical decision making. WGS-AST should be a funding priority if it is to become a rival to phenotypic AST. This report will be updated as the available evidence increases.

Spatio-temporal analysis of Salmonella surveillance data in Thailand.

This study evaluates the usefulness of spatio-temporal statistical tools to detect outbreaks using routine surveillance data where limited epidemiological information is available. A dataset from 2002 to 2007 containing information regarding date, origin, source and serotype of 29,586 Salmonella isolates from Thailand was analysed. Data was grouped into human and non-human categories and the analysis was performed for the top five occurring serovars for each year of the study period. A total 91 human and 39 non-human significant spatio-temporal clusters were observed, accounting for 11% and 16% of the isolates, respectively. Serovar-specific associations between human and non-human clusters were also evaluated. Results show that these statistical tools can provide information for use in outbreak prevention and detection, in countries where only limited data is available. Moreover, it is suggested that monitoring non-human reservoirs can be relevant in predicting future Salmonella human cases.

Effect of subinhibitory concentrations of four commonly used biocides on the conjugative transfer of Tn916 in Bacillus subtilis.

OBJECTIVES: Large amounts of biocides are used to reduce and control bacterial growth in the healthcare sector, food production and agriculture. This work explores the effect of subinhibitory concentrations of four commonly used biocides (ethanol, hydrogen peroxide, chlorhexidine digluconate and sodium hypochlorite) on the conjugative transposition of the mobile genetic element Tn916. METHODS: Conjugation assays were carried out between Bacillus subtilis strains. The donor containing Tn916 was pre-exposed to subinhibitory concentrations of each biocide for a defined length of time, which was determined by an analysis of the transcriptional response of the promoter upstream of tet(M) using ß-glucuronidase reporter assays. RESULTS: Ethanol significantly (P = 0.01) increased the transfer of Tn916 by 5-fold, whereas hydrogen peroxide, chlorhexidine digluconate and sodium hypochlorite did not significantly affect the transfer frequency. CONCLUSIONS: These results suggest that exposure to subinhibitory concentrations of ethanol may induce the transfer of Tn916-like elements and any resistance genes they contain.

The first attempt of an active integrated laboratory-based Salmonella surveillance programme in the north-eastern region of Nigeria.

AIM: To identify the sources of Salmonella contamination, distribution, prevalence and antimicrobial susceptibility patterns, which have significant impact on public and animal health, and international trade. METHODS AND RESULTS: A total of 1888 samples were collected by stratified random sampling from 2009 to 2011 from cattle, camels, poultry, fish, vegetables and humans. All identified Salmonella isolates were serotyped and tested for antimicrobial susceptibility by MIC determinations. A total of 149 Salmonella isolates comprising 17 different serovars were obtained (7·9% prevalence). Salmonella Hadar (37%), S. Eko (17%), S. Enteritidis (10%), S. Kentucky (7%) and S. Uganda (7%) were isolated from different sources. The occurrence of antimicrobial resistance was generally low, but S. Enteritidis and S. Eko showed variable antimicrobial resistance patterns, while all S. Kentucky isolates were resistant to seven of 17 tested antimicrobials, including ciprofloxacin and nalidixic acid. Three S. Hadar isolates revealed reduced susceptibility to ciprofloxacin and susceptibility to nalidixic acid and harboured the plasmid-mediated quinolone resistance gene qnrS1. CONCLUSIONS: Salmonella serovars Hadar, Enteritidis and the previously very rarely reported Eko were the major serovars associated with human infections, animal and environmental contamination in the north-eastern region of Nigeria. SIGNIFICANCE AND IMPACT OF THE STUDY: These serovars constitute a health risk to poultry, environment and human population in the region.

External quality assurance system (EQAS) for identification of mastitis pathogens in Denmark from 2006 to 2011.

Bovine mastitis is the most common and costly dairy cattle disease. Mastitis is most frequently caused by bacterial species, and to ensure optimal treatment and control strategies, proper quality assured diagnosis and identification of the causative agent is important. With the aim to assess the capacity to isolate and identify mastitis pathogens at veterinary clinics, an external quality assurance system (EQAS) was annually (from 2006 to 2011) provided for the identification of mastitis pathogens. This study presents the setup of the proficiency test and the obtained results that enabled the organizers to pinpoint areas for improvement and thereby to assist veterinary practices at strengthening their mastitis diagnostics. The proficiency test consisted of 15 milk samples spiked with a pure culture of a mastitis pathogen and distributed to veterinary practices for identification. Applying an internal quality control strain, i.e. including the same strain of Streptococcus agalactiae in all iterations of the proficiency test, served to gauge the bias caused by the year-to-year variation in the selection of test strains. A total of 73% of all uploaded results over the years were correct, with the internal quality control strain exhibiting a statistically significant ascending trend from 54% correct identifications in 2006 to 91% in 2011 (p-value=0.0082; n=13). Even if specifics were not recorded as regards the laboratory methods employed at the veterinary clinics for identification of mastitis pathogens, the results from this study indicate that the practices' application of basic biochemical analyses in this context could be optimized. In addition, dissemination of information on new methods and updated nomenclature appeared to be an area which future efforts with advantage could aim at.

Indications and patterns of therapeutic use of antimicrobial agents in the Danish pig production from 2002 to 2008.

This study describes trends in the use and indications for prescriptions of antimicrobial agents in the Danish pig production in the period between 2002 and 2008 and is the first description of a complete prescription pattern for one animal species in an entire country. Data on all prescription for pigs in Denmark were retrieved from the VetStat database. Antimicrobial use was measured in defined animal daily doses (ADD) for the specific age-group and in ADD(kg) as a measure of amounts used. According to the results of the ADD(kg) data, 26% of all antimicrobials were prescribed for sows, 38% for weaner pigs, and 33% for finisher pigs. In weaner and finisher pigs, gastrointestinal infections accounted for 74-83% and 56-65% of the use, while respiratory infections accounted for 9-17% and 18-24%, respectively. From 2002 to 2008, prescription for respiratory disease increased by 145% for sows/piglets, by 141% for weaning pigs, and by 81% for finisher pig. The most commonly used class of antibiotics was tetracycline for all age-groups, replacing the previously used macrolide/lincosamide group. The use of pleuromutilin increased in 2008 to the level of macrolides. In sow/piglets, the second most used class was penicillins. The switch in choice of antimicrobial classes prescribed seems to be related primarily to changes in the price of the drugs.

Cloning and occurrence of czrC, a gene conferring cadmium and zinc resistance in methicillin-resistant Staphylococcus aureus CC398 isolates.

We recently reported a phenotypic association between reduced susceptibility to zinc and methicillin resistance in Staphylococcus aureus CC398 isolates from Danish swine (F. M. Aarestrup, L. M. Cavaco, and H. Hasman, Vet. Microbiol. 142:455-457, 2009). The aim of this study was to identify the genetic determinant causing zinc resistance in CC398 and examine its prevalence in isolates of animal and human origin. Based on the sequence of the staphylococcal cassette chromosome mec (SCCmec) element from methicillin-resistant S. aureus (MRSA) CC398 strain SO385, a putative metal resistance gene was identified in strain 171 and cloned in S. aureus RN4220. Furthermore, 81 MRSA and 48 methicillin-susceptible S. aureus (MSSA) strains, isolated from pigs (31 and 28) and from humans (50 and 20) in Denmark, were tested for susceptibility to zinc chloride and for the presence of a putative resistance determinant, czrC, by PCR. The cloning of czrC confirmed that the zinc chloride and cadmium acetate MICs for isogenic constructs carrying this gene were increased compared to those for S. aureus RN4220. No difference in susceptibility to sodium arsenate, copper sulfate, or silver nitrate was observed. Seventy-four percent (n = 23) of the animal isolates and 48% (n = 24) of the human MRSA isolates of CC398 were resistant to zinc chloride and positive for czrC. All 48 MSSA strains from both human and pig origins were found to be susceptible to zinc chloride and negative for czrC. Our findings showed that czrC is encoding zinc and cadmium resistance in CC398 MRSA isolates, and that it is widespread both in humans and animals. Thus, resistance to heavy metals such as zinc and cadmium may play a role in the coselection of methicillin resistance in S. aureus.