Mastitis, inflammation of the bovine mammary gland, is generally caused by intramammary infection with bacteria, and antimicrobials have long been a corner stone of mastitis control. As societal concern about antimicrobial use in animal agriculture grows, there is pressure to reduce antimicrobial use in dairy farming. Point-of-care tests for on-farm use are increasingly available as tools to support this. In this Research Reflection, we consider available culture-dependent and culture-independent tests in the context of ASSURED criteria for low-resource settings, including convenience criteria, scientific criteria and societal criteria that can be used to evaluate test performance. As tests become more sophisticated and sensitive, we may be generating more data than we need. Special attention is given to the relationship between test outcomes and treatment decisions, including issues of diagnostic refinement, antimicrobial susceptibility testing, and detection of viable organisms. In addition, we explore the role of technology, big data and people in improved performance and uptake of point-of-care tests, recognising that societal barriers may limit uptake of available or future tests. Finally, we propose that the 3Rs of reduction, refinement and replacement, which have been used in an animal welfare context for many years, could be applied to antimicrobial use for mastitis control on dairy farms.

Introduction: The objective of this study is to show the importance of interspecies links and temporal network dynamics of a multi-species livestock movement network. Although both cattle and sheep networks have been previously studied, cattle-sheep multi-species networks have not generally been studied in-depth. The central question of this study is how the combination of cattle and sheep movements affects the potential for disease spread on the combined network. Materials and methods: Our analysis considers static and temporal representations of networks based on recorded animal movements. We computed network-based node importance measures of two single-species networks, and compared the top-ranked premises with the ones in the multi-species network. We propose the use of a measure based on contact chains calculated in a network weighted with transmission probabilities to assess the importance of premises in an outbreak. To ground our investigation in infectious disease epidemiology, we compared this suggested measure with the results of disease simulation models with asymmetric probabilities of transmission between species. Results: Our analysis of the temporal networks shows that the premises which are likely to drive the epidemic in this multi-species network differ from the ones in both the cattle and the sheep networks. Although sheep movements are highly seasonal, the estimated size of an epidemic is significantly larger in the multi-species network than in the cattle network, independently of the period of the year. Finally, we demonstrate that a measure based on contact chains allow us to identify around 30% of the key farms in a simulated epidemic, ignoring markets, whilst static network measures identify less than 10% of these farms. Conclusion: Our results ascertain the importance of combining species networks, as well as considering layers of temporal livestock movements in detail for the study of disease spread.

Antimicrobial usage (AMU) could be reduced by differentiating the causative bacteria in cases of clinical mastitis (CM) as either Gram-positive or Gram-negative bacteria or identifying whether the case is culture-negative (no growth, NG) mastitis. Immunoassays for biomarker analysis and a Tandem Mass Tag (TMT) proteomic investigation were employed to identify differences between samples of milk from cows with CM caused by different bacteria. A total of 94 milk samples were collected from cows diagnosed with CM across seven farms in Scotland, categorized by severity as mild (score 1), moderate (score 2), or severe (score 3). Bovine haptoglobin (Hp), milk amyloid A (MAA), C-reactive protein (CRP), lactoferrin (LF), α-lactalbumin (LA) and cathelicidin (CATHL) were significantly higher in milk from cows with CM, regardless of culture results, than in milk from healthy cows (all P-values <0.001). Milk cathelicidin (CATHL) was evaluated using a novel ELISA technique that utilises an antibody to a peptide sequence of SSEANLYRLLELD (aa49-61) common to CATHL 1-7 isoforms. A classification tree was fitted on the six biomarkers to predict Gram-positive bacteria within mastitis severity scores 1 or 2, revealing that compared to the rest of the samples, Gram-positive samples were associated with CRP < 9.5 μg/ml and LF ≥ 325 μg/ml and MAA < 16 μg/ml. Sensitivity of the tree model was 64%, the specificity was 91%, and the overall misclassification rate was 18%. The area under the ROC curve for this tree model was 0.836 (95% bootstrap confidence interval: 0.742; 0.917). TMT proteomic analysis revealed little difference between the groups in protein abundance when the three groups (Gram-positive, Gram-negative and no growth) were compared, however when each group was compared against the entirety of the remaining samples, 28 differentially abundant protein were identified including β-lactoglobulin and ribonuclease. Whilst further research is required to draw together and refine a suitable biomarker panel and diagnostic algorithm for differentiating Gram- positive/negative and NG CM, these results have highlighted a potential panel and diagnostic decision tree. Host-derived milk biomarkers offer significant potential to refine and reduce AMU and circumvent the many challenges associated with microbiological culture, both within the lab and on the farm, while providing the added benefit of reducing turnaround time from 14 to 16 h of microbiological culture to just 15 min with a lateral flow device (LFD).

Background: Bovine tuberculosis (bTB) is a costly, epidemiologically complex, multi-host, endemic disease. Pathogen whole genome sequencing can improve the resolution of epidemiological tracing. We genome sequenced an exceptional data set of 619 Mycobacterium bovis isolates from badgers and cattle in a 100km ² bTB ‘hotspot’. Historical molecular subtyping data permitted the targeting of an endemic pathogen lineage, whose long-term persistence provided an opportunity to study genome epidemiology in detail. To assess whether badger population genetic structure was associated with the spatial distribution of pathogen genetic diversity, we microsatellite genotyped hair samples from 769 badgers trapped in this area. Results: Eight lineages of M. bovis were circulating in the study area, seven of which were likely non-endemic, and imported by animal movement. The endemic lineage exhibited low genetic diversity with an average inter-isolate genetic distance of 7.6 SNPs (s.d. ± 4.0), consistent with contemporary transmission. Bayesian phylogenetic methods determined an evolutionary rate of 0.30 substitutions per genome per year for this lineage, estimating its emergence 40-50 years before present, while Bayesian Skyline analysis identified significant population expansion of the endemic lineage in the 1990s and again in 2011-2012. The phylogeny revealed distinct sub-lineages, all of which contained isolates from both cattle and badger hosts, indicative of the sharing of closely related strains and inter-species transmission. However, the presence of significant badger population genetic structure was not associated with the spatial distribution of M. bovis genetic diversity. Conclusions: Our data provided unparalleled detail on the evolutionary history of an endemic M. bovis lineage. Findings are consistent with ongoing interspecies transmission in the study area but suggest that badger intra-species transmission may not be a major driver of persistence in this area. In addition, the data collected permitted the tracking of incursions of novel pathogen lineages into the study area and means to determine if they were involved in disease transmission.

The purpose of this study was to evaluate the effect of rumination times and days spent in a close-up group before calving (DCU) on early-lactation health and reproductive outcomes in dairy cows. Data were gathered for 719 cows located in a single herd. Herd management and reproductive records were analyzed for cows receiving treatment in the first 30 d of lactation (days in milk; DIM) for clinical mastitis, reproductive tract disease, ketosis, milk fever, and displaced abomasum. Rumination times for each cow were downloaded daily from the herd's automated collar system used to generate heat and health alerts for each cow beginning at 21 d precalving until 14 d postcalving. During the first 30 DIM, 121 cows (18%) developed at least 1 disease-any combination of ketosis (40 cows, 5.9% of total), mastitis (17 cows, 2.5%), metritis (75 cows, 11%), milk fever (17 cows, 2.5%), or displaced abomasum (28 cows, 4.1%); 305 cows (45%) were pregnant again at 100 DIM, and an additional 139 cows (20%) were pregnant at 150 DIM. Principal component analysis was used to determine the relationship between gestation length and DCU and their association with the odds of developing disease in early lactation. We did not find any significant association between precalving rumination time and disease within the first 30 DIM. Higher rumination time in the week before calving was shown to be strongly linked to a shorter time to subsequent pregnancy, whereas rumination times postcalving were not associated with changes in the time to pregnancy. Principal component analysis showed that a curvilinear combination of gestation length and DCU (principal component 1) was significantly associated with changes in disease incidence in the first 30 DIM. Gestation length and time spent in close up are important management factors in reducing the incidence of disease in early lactation, and rumination times around calving may help predict future reproductive outcomes.