The use of wide-ranging dairy herd improvement (DHI) measurements has resulted in the investigation of somatic cell count (SCC) and the identification of many genes associated with mastitis resistance. In this study, blood samples of Xinjiang brown cattle with different SCCs were collected, and genome-wide DNA methylation was analyzed by MeDIP-seq. The results showed that peaks were mostly in intergenic regions, followed by introns, exons, and promoters. A total of 1,934 differentially expressed genes (DEGs) associated with mastitis resistance in Xinjiang brown cattle were identified. The enrichment of differentially methylated CpG islands of the TRAPPC9 and CD4 genes was analyzed by bisulfate genome sequencing. The methylation rate of differentially methylated CpGs was higher in the TRAPPC9 gene of cattle with clinical mastitis (mastitis group) compared with healthy cattle (control group), while methylation of differentially methylated CpGs was significantly lower in CD4 of the mastitis group compared with the control group. RT-qRCR analysis showed that the mastitis group had significantly reduced expression of CD4 and TRAPPC9 genes compared to the control group (p < 0.05). Furthermore, Mac-T cells treated with lipopolysaccharide and lipoteichoic acid showed significant downregulation of the TRAPPC9 gene in the mastitis group compared with the control group. The identified epigenetic biomarkers provide theoretical reference for treating cow mastitis, breeding management, and the genetic improvement of mastitis resistance in Xinjiang brown cattle.

Mastitis is one of the most predominant diseases with a negative impact on ranch products worldwide. It reduces milk production, damages milk quality, increases treatment costs, and even leads to the premature elimination of animals. In addition, failure to take effective measures in time will lead to widespread disease. The key to reducing the losses caused by mastitis lies in the early detection of the disease. The application of deep learning with powerful feature extraction capability in the medical field is receiving increasing attention. The main purpose of this study was to establish a deep learning network for buffalo quarter-level mastitis detection based on 3054 ultrasound images of udders from 271 buffaloes. Two data sets were generated with thresholds of somatic cell count (SCC) set as 2 × 105 cells/mL and 4 × 105 cells/mL, respectively. The udders with SCCs less than the threshold value were defined as healthy udders, and otherwise as mastitis-stricken udders. A total of 3054 udder ultrasound images were randomly divided into a training set (70%), a validation set (15%), and a test set (15%). We used the EfficientNet_b3 model with powerful learning capabilities in combination with the convolutional block attention module (CBAM) to train the mastitis detection model. To solve the problem of sample category imbalance, the PolyLoss module was used as the loss function. The training set and validation set were used to develop the mastitis detection model, and the test set was used to evaluate the network\'s performance. The results showed that, when the SCC threshold was 2 × 105 cells/mL, our established network exhibited an accuracy of 70.02%, a specificity of 77.93%, a sensitivity of 63.11%, and an area under the receiver operating characteristics curve (AUC) of 0.77 on the test set. The classification effect of the model was better when the SCC threshold was 4 × 105 cells/mL than when the SCC threshold was 2 × 105 cells/mL. Therefore, when SCC ≥ 4 × 105 cells/mL was defined as mastitis, our established deep neural network was determined as the most suitable model for farm on-site mastitis detection, and this network model exhibited an accuracy of 75.93%, a specificity of 80.23%, a sensitivity of 70.35%, and AUC 0.83 on the test set. This study established a 1/4 level mastitis detection model which provides a theoretical basis for mastitis detection in buffaloes mostly raised by small farmers lacking mastitis diagnostic conditions in developing countries.

Subclinical mastitis is a common disease that threatens the welfare and health of dairy cows and causes huge economic losses. Somatic cell count (SCC) is the most suitable indirect index used to evaluate the degree of mastitis. To explore the relationship between SCC, diversity in the microbiome, and subclinical mastitis, we performed next-generation sequencing of the 16S rRNA gene of cow\'s milk with different SCC ranges. The data obtained showed that the microbiota was rich and coordinated with SCC below 2 × 105. SCC above 2 × 105 showed a decrease in the diversity of microbial genera. When SCC was below 2 × 105, the phylum Actinobacteriota accounted for the most. When SCC was between 2 × 105 and 5 × 105, Firmicutes accounted for the most, and when SCC exceeded 5 × 105, Firmicutes and Proteobacteria accounted for the most. Pathogenic genera such as Streptococcus spp. were absent, while SCC above 2 × 105 showed a decrease in the diversity of microbial genera. SCC was positively correlated with the percentage of Romboutsia, Turicibacter, and Paeniclostridium and negatively correlated with the percentage of Staphylococcus, Psychrobacter, Aerococcus, and Streptococcus. Romboutsia decreased 6.19 times after the SCC exceeded 2 × 105; the SCC increased exponentially from 2 × 105 to 5 × 105 and above 1 × 106 in Psychrobacter. Analysis of the microbiota of the different SCC ranges suggests that the development of mastitis may not only be a primary infection but may also be the result of dysbiosis in the mammary gland.

S100A7 is an inflammation-related protein and plays an essential role in host defenses, yet there is little research about the relationship between mastitis and S100A7 expression in dairy goats. Here, according to the clinical diagnosis of udders, SCC, and bacteriological culture (BC) of milk, 84 dairy goats were grouped into healthy goats (n = 25), subclinical mastitis goats (n = 36), and clinical mastitis goats (n = 23). The S100A7 concentration in subclinical mastitis goats was significantly upregulated than in healthy dairy goats (p = 0.0056) and had a limited change with clinical mastitis dairy goats (p = 0.8222). The relationship between log10 SCC and S100A7 concentration in milk was positive and R = 0.05249; the regression equation was Y = 0.1446 × X + 12.54. According to the three groups, the log10 SCC and S100A7 were analyzed using the receiver operating characteristics (ROC) curve; in subclinical mastitis goats, the area under the ROC curve (AUC) of log10 SCC was 0.9222 and p < 0.0001, and the AUC of S100A7 concentration was 0.7317 and p = 0.0022, respectively; in clinical mastitis goats, the AUC of log10 SCC was 0.9678 and p < 0.0001, and the AUC of S100A7 concentration was 0.5487 and p = 0.5634, respectively. In healthy goats, S100A7 was expressed weakly in the alveolus of the mammary gland of healthy goats while expressed densely in the collapsed alveolus of mastitis goats. Moreover, S100A7 expression increased significantly in mastitis goats than in healthy dairy goats. In this research, results showed the effects of mastitis on the S100A7 expression in the mammary gland and S100A7 concentration in milk and the limited relationship between SCC and mastitis, which provided a new insight into S100A7\'s role in the host defenses of dairy goats.

Mastitis is a relatively common disease in rabbit does. The aim of this study was to investigate a relationship between the severity of clinical signs and pathological observations and to analyze differentially expressed genes (DEGs) in the mammary gland with mastitis versus healthy mammary gland. The result showed that rectal temperatures of the rabbits with both mild mastitis and severe mastitis were higher than that of control. Cell counting results showed that the somatic cell count (SCC) only in milk of the rabbit with severe mastitis was significantly higher than that in the control group. However, the number of heterophils in the histological sections of mammary glands with mild mastitis was significantly higher than that of control. A total of 1,096 DEGs between the control and mastitis mammary glands was identified by RNA-sequencing (RNA-seq). Gene ontology (GO) showed that most of up-regulated genes were enriched in terms such as response to stimulus, signal transduction, and cell communication. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that these genes were mostly enriched in the pathways such as Rap1 signaling pathway, proteoglycans in cancer, and PI3K-Akt signaling pathway. However, the downregulated genes were mainly enriched in metabolic processes and significantly involved in metabolic pathways. The data provides useful information to further dissect the molecular genetic mechanisms underlying rabbit mastitis, which is a prerequisite for designing effective intervention strategies.

This study aimed to (1) estimate the prevalence of cow-level high somatic cell count (SCC) in Chinese dairy herds and (2) identify potential factors associated with cow- and herd-level SCC variables. The monthly data on dairy herd improvement were collected from a total of 131 dairy herds in 11 provinces in China in 2019. Mixed models were constructed using the cow composite milk SCC and the variance of cow SCC as dependent variables separately and parity, seasons, days in milk (DIM), herd size, and farm types (family-owned vs. company-owned) as fixed effects, accounting for the nested random herd and cow effect. We used negative binomial regression using herd-level SCC-related variables, namely, monthly proportion of high SCC, monthly proportion of new high SCC, monthly proportion of chronic high SCC, and monthly proportion of new chronic high SCC as dependent variables separately against seasons, herd size, and farm types with the random herd effect. The overall average prevalence of high SCCs for each month per farm was 0.26 (2.5-97.5% quantile: 0-0.56). Company-owned farms performed better in herd SCC management. Seasons were significantly associated with all the aforementioned variables, and summer and autumn were the seasons associated with worse outcomes in herd SCCs. This study is the first to assess high SCC in a large number of Chinese dairy herds, which is useful for farms to tailor the on-farm mastitis control programs in China.

Somatic cell count (SCC) is an important indicator of the health state of bovine udders. However, the exact cut-off value used for differentiating the cows with healthy quarters from the cows with subclinical mastitis remains controversial. Here, we collected composite milk (milk from four udder quarters) and peripheral blood samples from individual cows in two different dairy farms and used 16S rRNA gene sequencing combined with RNA-seq to explore the differences in the milk microbial composition and transcriptome of cows with three different SCC levels (LSCC: <100,000 cells/mL, MSCC: 100,000−200,000 cells/mL, HSCC: >200,000 cells/mL). Results showed that the milk microbial profiles and gene expression profiles of samples derived from cows in the MSCC group were indeed relatively easily discriminated from those from cows in the LSCC group. Discriminative analysis also uncovered some differentially abundant microbiota at the genus level, such as Bifidobacterium and Lachnospiraceae_AC2044_group, which were more abundant in milk samples from cows with SCC below 100,000 cells/mL. As for the transcriptome profiling, 79 differentially expressed genes (DEGs) were found to have the same direction of regulation in two sites, and functional analyses also showed that biological processes involved in inflammatory responses were more active in MSCC and HSCC cows. Overall, these results showed a similarity between the milk microbiota and gene expression profiles of MSCC and HSCC cows, which presented further evidence that 100,000 cells/ml is a more optimal cut-off value than 200,000 cells/mL for intramammary infection detection at the cow level.

The blood gas profile is a routine method in the rapid disease diagnosis of farm animals, yet its potential in evaluating mammary health status of dairy cows remains to be investigated. This study was conducted to learn the potential of the blood gas parameter regarding the mammary gland health status in lactating dairy cows. Twenty animals were divided into two groups, the H-SCC group (milk SCC > 122 k/mL) and L-SCC group (milk SCC < 73.8 k/mL), to compare blood gas profiles from different blood vessels and to identify the key parameters associated with milk somatic cell count. H-SCC cows are higher in malondialdehyde content, but lower in SOD and T-AOC activities in the milk, compared to the L-SCC group. In terms of blood gas parameters, most differ across the three vessels, including K+, CO2 pressure, O2 pressure, HCO3−, base excess in the extracellular fluid compartment, and saturation of O2. The Pearson correlation analysis showed that oxygen-related variables in the mammary vein, including oxygen concentrations, O2 pressure, and saturation of O2, are negatively correlated with levels of malondialdehyde, lactate dehydrogenase, and plasmin in the milk. Our study revealed that oxygen-related variables in the mammary vein can be a marker in suggesting mammary-gland health status in high-yielding cows.

The current study was conducted to analyze the functions of blood neutrophils in transition cows and their association with postpartum mastitis risk as indicated by somatic cell counts (SCCs) in milk. Seventy-six healthy Holstein dairy cows were monitored from Week 4 prepartum to Week 4 postpartum. Five dairy cows with low SCCs (38 ± 6.0 × 103/mL) and five with high SCCs (3,753 ± 570.0 × 103/mL) were selected based on milk SCCs during the first three weeks of lactation. At Week 1 pre- and postpartum, serum samples were obtained from each cow to measure neutrophil extracellular trap (NET)-related variables, and blood neutrophils were collected for transcriptome analysis by RNA sequencing. The serum concentration of NETs was significantly higher (P < 0.05) in cows with high SCCs than in cows with low SCCs (36.5 ± 2.92 vs. 18.4 ± 1.73 ng/mL). The transcriptomic analysis revealed that the transcriptome differences in neutrophils between high- and low-SCC cows were mainly in cell cycle-related pathways (42.6%), including the cell cycle, DNA damage, and chromosomal conformation, at Week 1 prepartum. The hub genes of these pathways were mainly involved in both the cell cycle and NETosis. These results indicated that the formation of NETs in the blood of transition dairy cows was different between cows with low and high SCCs, which may be used as a potential indicator for the prognosis of postpartum mastitis risk and management strategies of perinatal dairy cows.

As one of the pioneer bacterial sources of intestinal microbiota, the information of bacterial composition in colostrum might provide a reference for developing specific probiotics for newborn calves, especially calves fed with pasteurized milk. The present study aimed to detect the core bacteria at different taxonomic levels and the common beneficial ones in colostrum by analyzing the bacterial composition in 34 colostrum samples of healthy cows selected from two dairy farms. The results of the further analysis showed that the bacterial composition in the colostrum of the two dairy farms was different, but their four most dominant phyla were the same including Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. The microbiome of all colostrum samples shared ten core operational taxonomic units (OTUs), 21 core genera, and 34 core families, and most of them had no difference in relative abundance between the two farms. The ten core OTUs did not belong to the identified commensal bacteria and have not been detected by previous study. However, several core genera found in our study were also identified as core genus in a previous study. Some well-known beneficial and pathogenic bacteria including Lactobacillus plantarum, Bacillus subtilis, Acinetobacter lwoffii, and Streptococcus pneumoniae were present in the colostrum of healthy cows. However, none had a correlation with the number of somatic cell count (SCC), but the core genera Nubella and Brevundinimas and the core families Methylobacteriaceae and Caulobacteraceae positively correlated with the number of SCC. The genus Staphylococcus, Pseudomonas, and Chryseobacterium in colostrum had a positive correlation with each other, while the probiotics unidentified-Bacteroidales-S24-7-group had a negative correlation with Pseudomonas and Chryseobacterium. In addition, more than 50% bacterial OTUs in colostrum were detected in the rectal content including some strictly anaerobic bacteria that are generally present in the intestine and rumen. However, of the top 30 commonly shared bacterial genera in the colostrum and rectal feces, no genus in colostrum was positively correlated with that same genus in rectal feces. In conclusion, the bacterial composition of colostrum microbiota is greatly influenced by external factors and individuals. There were several core OTUs, and some core genus and families in the colostrum samples. Colostrum from healthy cows contained both beneficial and pathogenic bacteria and shared many common bacteria with rectal content including some gastrointestinal anaerobes.