Longitudinal study of antimicrobial use patterns, vaccination and disease prevalence in British sheep flocks.

BACKGROUND: The aim of the study was to describe the longitudinal dynamics of antimicrobial use (AMU) on sheep farms and explore associations between AMU and management factors, vaccination strategies, reproductive performance and prevalence of lameness. METHODS: Antimicrobial supply data were collected for 272 British sheep farms for 3-6 consecutive years between 2015 and 2021. These data were obtained from the farms' veterinary practices. RESULTS: Annual median AMU ranged from 8.1 to 11.8 mg/kg population corrected unit. AMU was skewed in each year with a small proportion of very high users. AMU within farms varied substantially between years. High AMU farms in 1 year were not necessarily high in other years. No associations between AMU and either vaccine usage or lameness prevalence were found. LIMITATIONS: The study design requires veterinarians and farmers to volunteer their data. This unavoidably introduces the potential for a participation bias. CONCLUSIONS: AMU on sheep farms is generally low, with a small number of farms being responsible for high usage. Targeting antimicrobial stewardship effort towards the small minority of persistently high users may be more appropriate than a focus on generic, industry-wide attempts to reduce overall AMU.

The impact of glutaraldehyde based footbaths on Dichelobacter nodosus prevalence and the antimicrobial resistant community of the ovine interdigital skin.

Ovine footrot, is a highly contagious polymicrobial bacterial infection, primarily caused by Dichelobacter nodosus. Preventative bactericidal footbaths are commonly used in the sheep industry to reduce the spread of bacteria. However, their effect on the bacterial community is poorly understood. This is the first study to investigate the impact of 2% Digicur (ProGiene,UK) footbath on the bacterial community of the ovine interdigital skin following a common UK footbathing routine. Swab samples were analysed by qPCR to determine prevalence and load of D. nodosus and numerated on MacConkey agar in the presence or absence of tetracycline and ampicillin to determine phenotypic antimicrobial resistance. Metagenomics were used to determine the impact of a single footbath on the bacterial community and genotypic antimicrobial resistance. The results suggest 2% Digicur is ineffective at reducing the load of D. nodosus when applied as a one off or weekly footbath, however sheep may act as a reservoir for multi-drug resistant bacteria creating opportunities to spread antimicrobial resistance to other sheep and their environment.

Discrimination of contagious and environmental strains of Streptococcus uberis in dairy herds by means of mass spectrometry and machine-learning.

Streptococcus uberis is one of the most common pathogens of clinical mastitis in the dairy industry. Knowledge of pathogen transmission route is essential for the selection of the most suitable intervention. Here we show that spectral profiles acquired from clinical isolates using matrix-assisted laser desorption ionization/time of flight (MALDI-TOF) can be used to implement diagnostic classifiers based on machine learning for the successful discrimination of environmental and contagious S. uberis strains. Classifiers dedicated to individual farms achieved up to 97.81% accuracy at cross-validation when using a genetic algorithm, with Cohen's kappa coefficient of 0.94. This indicates the potential of the proposed methodology to successfully support screening at the herd level. A global classifier developed on merged data from 19 farms achieved 95.88% accuracy at cross-validation (kappa 0.93) and 70.67% accuracy at external validation (kappa 0.34), using data from another 10 farms left as holdout. This indicates that more work is needed to develop a screening solution successful at the population level. Significant MALDI-TOF spectral peaks were extracted from the trained classifiers. The peaks were found to correspond to bacteriocin and ribosomal proteins, suggesting that immunity, growth and competition over nutrients may be correlated to the different transmission routes.

The Applied Development of a Tiered Multilocus Sequence Typing (MLST) Scheme for Dichelobacter nodosus.

Dichelobacter nodosus (D. nodosus) is the causative pathogen of ovine footrot, a disease that has a significant welfare and financial impact on the global sheep industry. Previous studies into the phylogenetics of D. nodosus have focused on Australia and Scandinavia, meaning the current diversity in the United Kingdom (U.K.) population and its relationship globally, is poorly understood. Numerous epidemiological methods are available for bacterial typing; however, few account for whole genome diversity or provide the opportunity for future application of new computational techniques. Multilocus sequence typing (MLST) measures nucleotide variations within several loci with slow accumulation of variation to enable the designation of allele numbers to determine a sequence type. The usage of whole genome sequence data enables the application of MLST, but also core and whole genome MLST for higher levels of strain discrimination with a negligible increase in experimental cost. An MLST database was developed alongside a seven loci scheme using publically available whole genome data from the sequence read archive. Sequence type designation and strain discrimination was compared to previously published data to ensure reproducibility. Multiple D. nodosus isolates from U.K. farms were directly compared to populations from other countries. The U.K. isolates define new clades within the global population of D. nodosus and predominantly consist of serogroups A, B and H, however serogroups C, D, E, and I were also found. The scheme is publically available at https://pubmlst.org/dnodosus/.

Quantitative analysis of antimicrobial use on British dairy farms.

Antimicrobial resistance has been reported to represent a growing threat to both human and animal health, and concerns have been raised around levels of antimicrobial usage (AMU) within the livestock industry. To provide a benchmark for dairy cattle AMU and identify factors associated with high AMU, data from a convenience sample of 358 dairy farms were analysed using both mass-based and dose-based metrics following standard methodologies proposed by the European Surveillance of Veterinary Antimicrobial Consumption project. Metrics calculated were mass (mg) of antimicrobial active ingredient per population correction unit (mg/PCU), defined daily doses (DDDvet) and defined course doses (DCDvet). AMU on dairy farms ranged from 0.36 to 97.79 mg/PCU, with a median and mean of 15.97 and 20.62 mg/PCU, respectively. Dose-based analysis ranged from 0.05 to 20.29 DDDvet, with a median and mean of 4.03 and 4.60 DDDvet, respectively. Multivariable analysis highlighted that usage of antibiotics via oral and footbath routes increased the odds of a farm being in the top quartile (>27.9 mg/PCU) of antimicrobial users. While dairy cattle farm AMU appeared to be lower than UK livestock average, there were a selection of outlying farms with extremely high AMU, with the top 25 per cent of farms contributing greater than 50 per cent of AMU by mass. Identification of these high use farms may enable targeted AMU reduction strategies and facilitate a significant reduction in overall dairy cattle AMU.

Prediction of Streptococcus uberis clinical mastitis risk using Matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS) in dairy herds.

The purpose of this study was to evaluate whether the risk of Streptococcus uberis clinical mastitis at cow level could be predicted from the historical presence of specific strains of S. uberis on dairy farms. Matrix-assisted laser desorption ionization time of flight mass spectrometry was used to identify S. uberis isolates potentially capable of contagious transmission. Data were available from 10,652 cows from 52 English and Welsh dairy farms over a 14 month period, and 521 isolates of S. uberis from clinical mastitis cases were available for analysis. As well as the temporal herd history of clinical mastitis associated with particular S. uberis strains, other exposure variables included cow parity, stage of lactation, milk yield, and somatic cell count. Observations were structured longitudinally as repeated weekly measures through the study period for each cow. Data were analyzed in a Bayesian framework using multilevel logistic regression models. Similarity of mass spectral profiles between isolates of S. uberis from consecutive clinical cases of mastitis in herds was used to indicate potential for contagious phenotypic characteristics. Cross validation showed that new isolates with these characteristics could be identified with an accuracy of 90% based on bacterial protein mass spectral characteristics alone. The cow-level risk in any week of these S. uberis clinical mastitis cases increased with the presence of the same specific strains of S. uberis in other cows in the herd during the previous 2 weeks. The final statistical model indicated there would be a 2-3 fold increase in the risk of S. uberis clinical mastitis associated with particular strains if these occurred in the herd 1 and 2 weeks previously. The results suggest that specific strains of S. uberis may be involved with contagious transmission, and predictions based on their occurrence could be used as an early warning surveillance system to enhance the control of S. uberis mastitis.