The Difference of Milk-Derived Extracellular Vesicles from Cow Colostrum and Mature Milk on miRNAs Expression and Protecting Intestinal Epithelial Cells against Lipopolysaccharide Damage.

Intestinal epithelial cells (IECs) play crucial roles in forming an essential barrier, providing host defense against pathogens and regulating nutrients absorption. Milk-derived extracellular vesicles (EVs) within its miRNAs are capable of modulating the recipient cell function. However, the differences between colostrum and mature milk EVs and their biological function in attenuating intestinal epithelial cell injury remain poorly understood. Thus, we carried out the present study to characterize the difference between colostrum and mature milk-derived miRNA of EVs and the effect of colostrum and mature milk EVs on the proliferation, apoptosis, proinflammatory cytokines and intestinal epithelial barrier related genes in IEC-6 induced by LPS. Differential expression of 329 miRNAs was identified between colostrum and mature milk EVs, with 185 miRNAs being downregulated and 144 upregulated. In addition, colostrum contains a greater number and protein concentration of EVs than mature milk. Furthermore, compared to control, EVs derived from colostrum significantly inhibited the expression of apoptosis- (Bax, p53, and caspase-3) and proinflammatory-related genes (TNFα, IL6, and IL1ß). EVs derived from mature milk did not affect expression of apoptosis-related genes (Bax, p53, bcl2, and caspase-3). The EVs derived from mature milk significantly inhibited the expression of proinflammatory-related genes (TNFα and IL6). Western blot analysis also indicated that colostrum and mature milk EVs significantly decreased the apoptosis of IEC-6 cells. The EdU assay results showed that colostrum and mature milk EVs significantly increased the proliferation of IEC-6 cells. The expression of intestinal barrier-related genes (TJP1, CLDN1, OCLN, CDX2, MUC2, and IGF1R) was significantly promoted in IEC-6 cells after colostrum and mature milk EVs addition. Importantly, colostrum and mature milk EVs significantly relieved the LPS-induced inhibition of proliferation and intestinal barrier-related genes expression and attenuated apoptosis and proinflammatory responses induced by LPS in IEC-6 cells. Flow cytometry and Western blot analysis also indicated that colostrum and mature milk EVs significantly affect the apoptosis of IEC-6 cells induced by LPS. The results also indicated that EVs derived from colostrum had better effects on inhibiting the apoptosis- and proinflammatory cytokines-related genes expression. However, the EVs derived from mature milk exhibited beneficial effects on intestinal epithelial barrier protection. The present study will provide a better understanding of the role of EVs derived from colostrum and milk in dairy cows with different responses in the regulation of intestinal cells function, and also presents new evidence for the change of EVs cargos during various stages of lactation.

Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods.

Adulteration of higher priced milks with cheaper ones to obtain extra profit can adversely affect consumer health and the market. In this study, pure buffalo milk (BM), goat milk (GM), camel milk (CM), and their mixtures with 5-50% (vol/vol) cow milk or water were used. Mid-infrared spectroscopy (MIRS) combined with modern statistical machine learning was used for the discrimination and quantification of cow milk or water adulteration in BM, GM, and CM. Compared to partial least squares (PLS), modern statistical machine learning-especially support vector machines (SVM), projection pursuit regression (PPR), and Bayesian regularized neural networks (BRNN)-exhibited superior performance for the detection of adulteration. The best prediction models for the different predictive traits are as follows: The binary classification models developed by SVM resulted in differentiation of CM-cow milk, and GM/CM-water mixtures. PLS resulted in differentiation of BM/GM-cow milk and BM-water mixtures. All of the above models have 100% classification accuracy. SVM was used to develop multi-classification models for identifying the high and low proportions of cow milk in BM, GM, and CM, as well as the high and low proportions of water adulteration in BM and GM, with correct classification rates of 94%, 100%, 100%, 99%, and 100%, respectively. In addition, a PLS-based model was developed for identifying the high and low proportions of water adulteration in CM, with correct classification rates of 100%. A regression model for quantifying cow milk in BM was developed using PCA + BRNN, with RMSEV = 5.42%, and RV2 = 0.88. A regression model for quantifying water adulteration in BM was developed using PCA + PPR, with RMSEV = 1.70%, and RV2 = 0.99. Modern statistical machine learning improved the accuracy of MIRS in predicting BM, GM, and CM adulteration more effectively than PLS.

Assessing the relationship between somatic cell count and the milk mid-infrared spectrum in Chinese Holstein cows.

BACKGROUND: Milk produced by dairy cows is a complex combination of many components, but the effect of mastitis has only been investigated for a few of these components. Milk mid-infrared (MIR) spectra can reflect the global composition of milk, and this study aimed to detect the relationships between milk MIR spectral wavenumbers and milk somatic cell count (SCC)-a sensitive biomarker for mastitis. METHODS: Pearson correlation analysis was used to calculate the correlation coefficient between somatic count score (SCS) and spectral wavenumbers. A general linear mixed model was applied to investigate the effect of three different classes of SCC (low, middle and high) on spectral wavenumbers. RESULTS: The mean correlation coefficient between the 'fingerprint region' (wavenumbers 925-1582 cm-1 ) and the SCS was higher than that for other regions of the MIR spectrum, and the specific wavenumber with the strongest correlation with the SCS was within the 'fingerprint region'. SCC class had a significant (p < 0.05) effect on 639 spectral wavenumbers. In particular, some spectral wavenumbers within the 'fingerprint region' were highly affected by the SCC class. LIMITATION: The data were collected from only one province in China, so the generalisability of the findings may be limited. CONCLUSION: SCC had close relationships with milk spectral wavenumbers related to important milk components or chemical bonds.

Differences in Milk Proteomic Profiles between Estrous and Non-Estrous Dairy Cows.

Efficient reproductive management of dairy cows depends primarily upon accurate estrus identification. However, the currently available estrus detection methods, such as visual observation, are poor. Hence, there is an urgent need to discover novel biomarkers in non-invasive bodily fluids such as milk to reliably detect estrus status. Proteomics is an emerging and promising tool to identify biomarkers. In this study, the proteomics approach was performed on milk sampled from estrus and non-estrus dairy cows to identify potential biomarkers of estrus. Dairy cows were synchronized and timed for artificial insemination, and the cows with insemination leading to conception were considered to be in estrus at the day of insemination (day 0). Milk samples of day 0 (estrus group) and day -3 (non-estrus group) from dairy cows confirming to be pregnant were collected for proteomic analysis using the tandem mass tags (TMT) proteomics approach. A total of 89 differentially expressed proteins were identified, of which 33 were upregulated and 56 were downregulated in the estrus milk compared with the non-estrus milk. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that acetyl coenzyme A carboxylase α (ACACA), apolipoprotein B (APOB), NAD(P)H steroid dehydrogenase-like (NSDHL), perilipin 2 (PLIN2), and paraoxonase 1 (PON1) participated in lipid binding, lipid storage, lipid localization, and lipid metabolic process, as well as fatty acid binding, fatty acid biosynthesis, and fatty acid metabolism, and these processes are well documented to be related to estrus regulation. These milk proteins are proposed as possible biomarkers of estrus in dairy cows. Further validation studies are required in a large population to determine their potential as estrus biomarkers.

Influence of Estrus on Dairy Cow Milk Exosomal miRNAs and Their Role in Hormone Secretion by Granulosa Cells.

Estrus is crucial for cow fertility in modern dairy farms, but almost 50% of cows do not show the behavioral signs of estrus due to silent estrus and lack of suitable and high-accuracy methods to detect estrus. MiRNA and exosomes play essential roles in reproductive function and may be developed as novel biomarkers in estrus detection. Thus, we analyzed the miRNA expression patterns in milk exosomes during estrus and the effect of milk exosomes on hormone secretion in cultured bovine granulosa cells in vitro. We found that the number of exosomes and the exosome protein concentration in estrous cow milk were significantly lower than in non-estrous cow milk. Moreover, 133 differentially expressed exosomal miRNAs were identified in estrous cow milk vs. non-estrous cow milk. Functional enrichment analyses indicated that exosomal miRNAs were involved in reproduction and hormone-synthesis-related pathways, such as cholesterol metabolism, FoxO signaling pathway, Hippo signaling pathway, mTOR signaling pathway, steroid hormone biosynthesis, Wnt signaling pathway and GnRH signaling pathway. Consistent with the enrichment signaling pathways, exosomes derived from estrous and non-estrous cow milk both could promote the secretion of estradiol and progesterone in cultured bovine granulosa cells. Furthermore, genes related to hormonal synthesis (CYP19A1, CYP11A1, HSD3B1 and RUNX2) were up-regulated after exosome treatment, while exosomes inhibited the expression of StAR. Moreover, estrous and non-estrous cow-milk-derived exosomes both could increase the expression of bcl2 and decrease the expression of p53, and did not influence the expression of caspase-3. To our knowledge, this is the first study to investigate exosomal miRNA expression patterns during dairy cow estrus and the role of exosomes in hormone secretion by bovine granulosa cells. Our findings provide a theoretical basis for further investigating milk-derived exosomes and exosomal miRNA effects on ovary function and reproduction. Moreover, bovine milk exosomes may have effects on the ovaries of human consumers of pasteurized cow milk. These differential miRNAs might provide candidate biomarkers for the diagnosis of dairy cow estrus and will assist in developing new therapeutic targets for cow infertility.

Association between Days Open and Parity, Calving Season or Milk Spectral Data.

Milk spectral data on 2118 cows from nine herds located in northern China were used to access the association of days open (DO). Meanwhile, the parity and calving season of dairy cows were also studied to characterize the difference in DO between groups of these two cow-level factors. The result of the linear mixed-effects model revealed that no significant differences were observed between the parity groups. However, a significant difference in DO exists between calving season groups. The interaction between parity and calving season presented that primiparous cows always exhibit lower DO among all calving season groups, and the variation in DO among parity groups was especially clearer in winter. Survival analysis revealed that the difference in DO between calving season groups might be caused by the different P/AI at the first TAI. In addition, the summer group had a higher chance of conception in the subsequent services than other groups, implying that the micro-environment featured by season played a critical role in P/AI. A weak linkage between DO and wavenumbers ranging in the mid-infrared region was detected. In summary, our study revealed that the calving season of dairy cows can be used to optimize the reproduction management. The potential application of mid-infrared spectroscopy in dairy cows needs to be further developed.

Evaluation of Ovsynch versus modified Ovsynch program on pregnancy rate in water buffaloes: a meta-analysis.

Ovsynch is a widely accepted estrus synchronization protocol for improving the reproductive performance of water buffaloes who manifest low reproductive efficiency. Recently, some modified protocols based on Ovsynch such as 2 injections of prostaglandin 14 days apart following the Ovsynch are also introduced to enhance the reproductive potential of this species. In the present study, a meta-analytical assessment was performed with the objective to evaluate the reproductive performance of water buffaloes synchronized with Ovsynch or modified Ovsynch programs. Meta-analysis of the fixed or random effects model was determined by the heterogeneity among the studies. Reproductive outcome of interest was pregnancy per artificial insemination (P/AI) measured on day 25 (25-100). A total of 32 articles including 4003 buffaloes using either Ovsynch or modified Ovsynch protocol were reviewed. In the random effects model for buffaloes, the overall proportion of P/AI was 42.55% [95% confidence interval (CI): 37.48-47.70; n = 3,089] and 46.44% (95% CI: 39.63-53.31; n = 914) on day 25 after AI for Ovsynch and modified Ovsynch, respectively. Results for P/AI were then categorized by ovarian activity, where P/AI was available for 3575 cyclic buffaloes and 320 non-cyclic buffaloes. For cyclic buffaloes, the overall proportion of P/AI was 47.54% (95% CI: 42.72-52.38; n = 2911) and 57.97% (95% CI: 54.12-61.77; n = 664) on day 25 after AI for Ovsynch and modified Ovsynch, respectively. In the fixed effects model for non-cyclic buffaloes, the overall proportion of P/AI was 19.68% (95% CI: 13.48-26.58; n = 167) and 33.01% (95% CI: 25.50-40.94; n = 153) on day 25 after AI for Ovsynch and modified Ovsynch, respectively. In conclusion, a benefit for P/AI is detected in buffaloes with the modified Ovsynch protocol. Besides, whichever estrus synchronization protocols (Ovsynch or modified Ovsynch), cyclic buffaloes have higher P/AI compared with non-cyclic buffaloes.

Effects of parity and days in milk on milk composition in correlation with ß-hydroxybutyrate in tropic dairy cows.

The current study was conducted to evaluate the effect of parity and days in milk on milk yield and milk production traits and their correlation with ß-hydroxybutyrate (BHB) concentrations in milk of Chinese tropic Holstein dairy cows which are adapted to a humid subtropical climate in central China. About 3055 milking records of Holstein cows were obtained from three farms in the hot region in the center of China. The records were classified according to parity to 4 categories: first parity, second parity, third parity, and greater than third parity. According to days in milk, there were 4 groups, first group from (1-100 days), second group from (101-200 days), third group from (201-305 days), and fourth group (>305 days). Milk samples collected between April and November 2019 from the three farms were routinely checked for milk components including BHB using mid-infrared spectroscopy a MilkoScan FT+ (Foss, Hillerød, Denmark). Data were analyzed by multivariate analysis of variance (generalized linear model, GLM). Pearson's correlation coefficients were used to measure the correlation between SCC and BHB with milk yield and milk production traits. Results showed the significant effect of parity and days in milk on milk yield and milk production traits. There was a negative effect of parity and days in milk on milk quality, with increasing parity and days in milk being associated with higher somatic cell count (SCC) (P <0.001). Days in milk significantly affected (P=0.001) BHB. It was concluded that with increasing parity and prolonged days in milk, there was a negative effect on milk quality and udder health of the tropic dairy cows in central China. Based on the results of the current study, sampling milk for specific metabolites, somatic cell count, and quality are sufficient to asses herd health.

Influence of Estrus on the Milk Characteristics and Mid-Infrared Spectra of Dairy Cows.

Milk produced by dairy cows is a complex combination of many components. However, at present, changes in only a few milk components (e.g., fat, protein, and lactose) during the estrus cycle in dairy cows have been documented. Mid-infrared (MIR) spectroscopy is a worldwide method routinely used for milk analysis, as MIR spectra reflect the global composition of milk. Therefore, this study aimed to investigate the changes in milk MIR spectra and milk production traits (fat, protein, lactose, urea, total solids (TS), and solid not fat (SnF)) due to estrus. Cows that were successfully inseminated, leading to conception, were included. Cows confirmed to be pregnant were considered to be in estrus at the day of insemination (day 0). A general linear mixed model, which included the random effect of cows, the fixed classification effects of parity number, days in relation to estrus, as well as the interaction between parity number and days in relation to estrus, was applied to investigate the changes in milk production traits and 1060 milk infrared wavenumbers, ranging from 925 to 5011 cm-1, of 371 records from 162 Holstein cows on the days before (day -3, day -2, and day -1) and on the day of estrus (day 0). The days in relation to estrus had a significant effect on fat, protein, urea, TS, and SnF, whose contents increased from day -3 to day 0. Lactose did not seem to be significantly influenced by the occurrence of estrus. The days in relation to estrus had significant effects on the majority of the wavenumbers. Besides, we found that some of the wavenumbers in the water absorption regions were significantly changed on the days before and on the day of estrus. This suggests that these wavenumbers may contain useful information. In conclusion, the changes in the milk composition due to estrus can be observed through the analysis of the milk MIR spectrum. Further analyses are warranted to more deeply explore the potential use of milk MIR spectra in the detection of estrus.

Genetic Analysis of Milk Production Traits and Mid-Infrared Spectra in Chinese Holstein Population.

Milk composition always serves as an indicator for the cow's health status and body condition. Some non-genetic factors such as parity, days in milk (DIM), and calving season, which obviously affect milk performance, therefore, need to be considered in dairy farm management. However, only a few milk compositions are used in the current animal selection programs. The mid-infrared (MIR) spectroscopy can reflect the global composition of milk, but this information is currently underused. The objectives of this study were to detect the effect of some non-genetic factors on milk production traits as well as 1060 individual spectral points covering from 925.92 cm-1 to 5011.54 cm-1, estimate heritabilities of milk production traits and MIR spectral wavenumbers, and explore the genetic correlations between milk production traits and 1060 individual spectral points in a Chinese Holstein population. The mixed models procedure of SAS software was used to test the non-genetic factors. Single-trait animal models were used to estimate heritabilities and bivariate animal models were used to estimate genetic correlations using the package of ASReml in R software. The results showed that herd, parity, calving season, and lactation stage had significant effects on the percentages of protein and lactose, whereas herd and lactation stage had significant effects on fat percentage. Moreover, the herd showed a significant effect on all of the 1060 individual wavenumbers, whereas lactation stage, parity, and calving season had significant effect on most of the wavenumbers of the lactose-region (925 cm-1 to 1200 cm-1), protein-region (1240 cm-1 to 1600 cm-1), and fat-regions (1680 cm-1 to 1770 cm-1 and 2800 cm-1 to 3015 cm-1). The estimated heritabilities for protein percentage (PP), fat percentage (FP), and lactose percentage (LP) were 0.08, 0.05, and 0.09, respectively. Further, the milk spectrum was heritable but low for most individual points. Heritabilities of 1060 individual spectral points were 0.04 on average, ranging from 0 to 0.11. In particular, heritabilities for wavenumbers of spectral regions related to water absorption were very low and even null, and heritabilities for wavenumbers of specific MIR regions associated with fat-I, fat-II, protein, and lactose were 0.04, 0.06, 0.05, and 0.06 on average, respectively. The genetic correlations between PP and FP, PP and LP, FP, and LP were 0.78, -0.29, and -0.14, respectively. In addition, PP, FP, and LP shared the similar patterns of genetic correlations with the spectral wavenumbers. The genetic correlations between milk production traits and spectral regions related to important milk components varied from weak to very strong (0.01 to 0.94, and -0.01 to -0.96). The current study could be used as a management tool for dairy farms and also provides a further understanding of the genetic background of milk MIR spectra.