The metabolic responses of cows undergo substantial changes during the transition from late pregnancy to early lactation. However, the molecular mechanisms associated with these changes in physiological metabolism have not been clearly elucidated. The objective of this study was to investigate metabolic changes in transition cows from the perspective of plasma metabolites. Plasma samples collected from 24 multiparous dairy cows on approximately d 21 prepartum and immediately postpartum were analyzed using ultrahigh-performance liquid chromatography/time-of-flight mass spectrometry in positive and negative ion modes. In conjunction with multidimensional statistical methods (principal component analysis and orthogonal partial least squares discriminant analysis), differences in plasma metabolites were identified using the t-test and fold change analysis. Sixty-seven differential metabolites were identified consisting of AA, lipids, saccharides, and nucleotides. The levels of 32 plasma metabolites were significantly higher and those of 35 metabolites significantly lower after parturition than on d 21 prepartum. Pathway analysis indicated that the metabolites that increased from late pregnancy to early lactation were primarily involved in lipid metabolism and energy metabolism, whereas decreased metabolites were related to AA metabolism.

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Bovine mastitis is a common disease occurring in dairy farms and can be caused by more than 150 species of pathogenic bacteria. One of the most common causative organisms is Streptococcus agalactiae, which is also potentially harmful to humans and aquatic animals. At present, research on S. agalactiae in China is mostly concentrated in the northern region, with limited research in the southeastern and southwestern regions. In this study, a total of 313 clinical mastitis samples from large-scale dairy farms in five regions of Sichuan were collected for isolation of S. agalactiae. The epidemiological distribution of S. agalactiae was inferred by serotyping isolates with multiplex polymerase chain reaction. Susceptibility testing and drug resistance genes were detected to guide the clinical use of antibiotics. Virulence genes were also detected to deduce the pathogenicity of S. agalactiae in Sichuan Province. One hundred and five strains of S. agalactiae (33.6%) were isolated according to phenotypic features, biochemical characteristics, and 16S rRNA sequencing. Serotype multiplex polymerase chain reaction analysis showed that all isolates were of type Ia. The isolates were up to 100% sensitive to aminoglycosides (kanamycin, gentamicin, neomycin, and tobramycin), and the resistance rate to β-lactams (penicillin, amoxicillin, ceftazidime, and piperacillin) was up to 98.1%. The TEM gene (β-lactam-resistant) was detected in all isolates, which was in accordance with a drug-resistant phenotype. Analysis of virulence genes showed that all isolates harbored the cfb, cylE, fbsA, fbsB, hylB, and α-enolase genes and none harbored bac or lmb. These data could aid in the prevention and control of mastitis and improve our understanding of epidemiological trends in dairy cows infected with S. agalactiae in Sichuan Province.

Bovine mammary epithelial cells (bMECs) are the main cells of the dairy cow mammary gland. In addition to their role in milk production, they are effector cells of mammary immunity. However, there is little information about changes in metabolites of bMECs when stimulated by lipopolysaccharide (LPS). This study describes a metabolomics analysis of the LPS-stimulated bMECs to provide a basis for the identification of potential diagnostic screening biomarkers and possible treatments for bovine mammary gland inflammation. In the present study, bMECs were challenged with 500 ng/mL LPS and samples were taken at 0 h, 12 h and 24 h post stimulation. Metabolic changes were investigated using high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF MS) with univariate and multivariate statistical analyses. Clustering and metabolic pathway changes were established by MetaboAnalyst. Sixty-three differential metabolites were identified, including glycerophosphocholine, glycerol-3-phosphate, L-carnitine, L-aspartate, glutathione, prostaglandin G2, α-linolenic acid and linoleic acid. They were mainly involved in eight pathways, including D-glutamine and D-glutamic acid metabolism; linoleic acid metabolism; α-linolenic metabolism; and phospholipid metabolism. The results suggest that bMECs are able to regulate pro-inflammatory, anti-inflammatory, antioxidation and energy-producing related metabolites through lipid, antioxidation and energy metabolism in response to inflammatory stimuli.