In commercial dairy farms, mastitis is associated with increased antimicrobial use and associated resistance, which may affect milk production. This study aimed to develop sensor-based prediction models for naturally occurring clinical bovine mastitis using nine machine learning algorithms with data from 447 mastitic and 2146 healthy cows obtained from five commercial farms in Northeast China. The variables were related to daily activity, rumination time, and daily milk yield of cows, as well as milk electrical conductivity. Both Z-standardized and non-standardized datasets pertaining to four specific stages of lactation were used to train and test prediction models. For all four subgroups, the Z-standardized dataset yielded better results than those of the non-standardized one, with the multilayer artificial neural net algorithm showing the best performance. Variables of importance had a similar rank in this algorithm, indicating the consistency of these variables as predictors for bovine mastitis in commercial farms with similar automatic systems. Moreover, the peak milk yield (PMY) of mastitic cows was significantly higher than that of healthy cows (p < 0.005), indicating that high-yielding cattle are more prone to mastitis. Our results show that machine learning algorithms are effective tools for predicting mastitis in dairy cows for immediate intervention and management in commercial farms.

Antimicrobial use (AMU) is the major driver of antimicrobial resistance (AMR) among bacteria in dairy herds. There have been numerous studies on AMU and AMR in dairy cows; however, studies on AMU and AMR in dairy calves are limited. A comprehensive overview of the current state of knowledge of AMU and AMR among pathogens in dairy calves is important for the development of scientifically supported and applicable measures to curb antimicrobial use and the increasing risk of AMR. Therefore, we performed a systematic review of research on AMU and AMR in dairy calves. A total of 75 publications were included, of which 19 studies reported AMU data for dairy calves and 68 described AMR profiles of the four most prevalent bacteria that are associated with calf diarrhea and calf pneumonia. Large variation in AMU was found among herds across different regions. There seems to be a positive association between exposure to antimicrobials and occurrence of resistance. Most AMU was accounted for by treatment of diseases, while a small proportion of AMU was prophylactic. AMU was more common in treating calf diarrhea than in treating pneumonia, and the resistance rates in bacteria associated with diarrhea were higher than those in pathogens related to pneumonia. Organic farms used significantly fewer antimicrobials to treat calf disease; however, the antimicrobial resistance rates of bacteria associated with calf diarrhea and pneumonia on both types of farms were comparable. Feeding waste or pasteurized milk was associated with a higher risk of AMR in pathogens. Altogether, this review summarizes AMU and AMR data for dairy calves and suggests areas for future research, providing evidence for the design of antimicrobial use stewardship programs in dairy calf farming.

Automatic milking systems (AMS) are installed on a growing number of dairy farms worldwide. Management to support good udder health might be different on farms with an AMS compared with farms milking with a conventional milking system, as risk factors for mastitis on farms using an AMS may differ. The aim of this study was to identify farm level factors associated with mastitis on Dutch dairy farms using an AMS. In 2008, risk factor data were collected using a questionnaire combined with on-farm recordings of cow, stall, and AMS hygiene on 135 farms. These risk factor data were linked to 4 udder-health-associated dependent variables: average herd somatic cell count (HeSCCav), variance of the average herd somatic cell count (SCC) on test days (HeSCCvar), the average proportion of new high SCC cases (NHiSCC), and the farmer-reported annual incidence rate of clinical mastitis (IRCM). We employed regression models using multiple imputation to deal with missing values. Due to the high dimensionality of the risk factor data, we also performed nonlinear principal component analysis (NLPCA) and regressed the dependent variables on the principal components (PC). Good hygiene of cows and of AMS were found to be related to a lower HeSCCav and less NHiSCC. Effective postmilking teat disinfection was associated with a lower NHiSCC. A higher bulk tank milk SCC threshold for farmers' action was related to more NHiSCC. Larger farm size was related to lower HeSCCvar but higher NHiSCC. Negative attitude of farmers to animal health, higher frequency of checking AMS, and more time spent on viewing computer data were all positively related to higher IRCM. An NLPCA with 3 PC explained 16.3% of the variance in the risk factor variables. Only the first 2 PC were associated with mastitis. The first PC reflected older and larger farms with poor cow hygiene and AMS hygiene, and was related to higher HeSCCav and NHiSCC, whereas the second PC reflected newly built smaller farms with poor cow hygiene and low milk production, and was associated with higher HeSCCvar and NHiSCC, but lower IRCM. Our study suggests that many of the risk factors on conventional milking system farms are applicable to AMS farms, specifically concerning hygiene of the cows and the milking machine, but on large AMS farms, udder health may need more attention than on smaller AMS farms. Multiple imputation is instrumental to deal with missing values and NLPCA is a useful technique to process high dimensional data in our study.

This study provides important genomic insights into a bovine mastitis pathogen, Lactococcus garvieae . Comprehensive genomic analyses of L. garvieae from dairy farms have not been reported.

Prototheca bovis, a highly contagious pathogen, causes bovine mastitis, resulting in premature culling of affected cows and severe economic losses. Infection with P. bovis caused oxidative stress and apoptosis in bovine mammary epithelial cells (bMECs); however, mechanisms underlying P. bovis-induced autophagy remain unclear. Therefore, the autophagy flux induced by P. bovis in bMECs was analyzed by Western blot and laser scanning confocal microscopy. Expression levels of proteins in the HIF-1α and AMPKα/ULK1 pathway, including HIF-1α, AMPKα, p-AMPKα, ULK1, p-ULK1, mTOR, and p-mTOR, plus expression of autophagy-related genes including SQSTM1/p62, Atg5, Beclin1, and LC3II/LC3I, were quantified with Western blot. Infection with P. bovis induced autophagosomes and LC3 puncta in bMECs that were detected using transmission electron microscopy and laser scanning confocal microscopy, respectively. In addition, lysosome-associated proteins Rab7 and LAMP2a, and lysosomal activity were measured with Western blot and laser scanning confocal microscopy. Infection with P. bovis induced an unobstructed autophagic flux, increased protein expression of LC3II/LC3I, and decreased SQSTM1/p62 protein expression at 6 hpi. Furthermore, P. bovis upregulated protein expression in the HIF-1α and AMPKα/ULK1 pathway and increased the ratio of LC3II/LC3I, implying autophagy was activated in bMECs. However, deletion of AMPKα or ULK1 decreased LC3II/LC3I expression levels and LC3 puncta numbers, suggesting that autophagy was inhibited in bMECs. Additionally, deficiency of HIF-1α decreased protein expression of AMPKα and ULK1 as well as LC3 puncta numbers, and autophagy induced by P. bovis was also inhibited in bMECs. At 6 hpi, lysosome-associated protein Rab7 was decreased and LAMP2a was increased, indicating normal autophagy. In contrast, at 12 hpi, expression of Rab7 and LAMP2a proteins indicated that autophagy was inhibited in bMECs at that time. Therefore, we confirmed that P. bovis infection induced autophagy in bMECs via the HIF-1α and AMPKα/ULK1 pathway, with involvement of lysosome-associated protein Rab7 and LAMP2a.

Lactococcus garvieae is an emerging zoonotic pathogen, but there are few reports regarding bovine mastitis. The recent prevalence of L. garvieae poses an increasing disease threat and global public health risk. A total of 39 L. garvieae isolates were obtained from 2899 bovine clinical mastitis milk samples in 6 provinces of China from 2017 to 2021. Five clonal complexes were determined from 32 MLST types of L. garvieae ; ST46 was the predominant sequence type and 13 novel MLST types were identified. All isolates were resistant to chloramphenicol and clindamycin, but susceptible to penicillin, ampicillin, amoxicillin-clavulanic acid, imipenem, ceftiofur, enrofloxacin, and marbofloxacin. Based on genomic analyses, L. garvieae had 6310 genes, including 1015, 3641 and 1654 core, accessory and unique genes. All isolates had virulence genes coding for collagenase, fibronectin-binding protein, Glyceraldehyde-3-phosphate dehydrogenase, superoxide dismutase and NADH oxidase. Most isolates had lsaD and mdtA AMR genes. Based on COG results, the functions of defense, transcription and replication, recombination and repair were enhanced in unique genes, whereas functions of translation, ribosomal structure and biogenesis were enhanced in core genes. The KEGG functional categories enriched in unique genes included human disease and membrane transport, whereas COG functional categories enriched in core genes included energy metabolism, nucleotide metabolism and translation. No gene was significantly associated with host specificity. In addition, core genome SNPs analysis suggested potential host adaptation of some isolates in several sequence types. Therefore, this study characterized L. garvieae isolated from mastitis and assessed host adaptation of L. garvieae to various hosts. IMPORTANCE: This study provides important insights on bovine mastitis key topic pathogen Lactococcus garvieae , which constitutes mastitis concerns. However, comprehensive genomic analyses of L. garvieae from dairy farms have not been performed. This study gives a detailed and comprehensive novel feature in L. garvieae , an important but poorly characterized bacterium, recovered in the past 5 years in 6 Chinese provinces. We documented diverse contributory genetic processes, including predominant sequence type ST46 and 13 novel MLST types. L. garvieae had 6310 genes, including 1015, 3641 and 1654 core, accessory and unique genes. All isolates had virulence genes coding for collagenase, fibronectin-binding protein, Glyceraldehyde-3-phosphate dehydrogenase, superoxide dismutase and NADH oxidase, and resistant to chloramphenicol and clindamycin. Most isolates had lsaD and mdtA antimicrobial resistance genes. No gene was significantly associated with host specificity. This is the first absolute quantification of L. garvieae isolated from mastitis and identified host adaptation of L. garvieae to various hosts.