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.

Follicular dynamics, luteal characteristics, and progesterone concentrations in synchronized lactating Holstein cows with high and low antral follicle counts

We performed two experiments in high milk-producing Holstein cows. First, we evaluated the repeatability of the antral follicle count (AFC) - where all follicles were ≥2 mm in diameter - during the days of the ovulation synchronization protocol for timed artificial insemination (TAI) in dairy cows (n = 20). In the second study we investigated the follicular dynamics and Doppler ultrasonography in cows with low (≤15 follicles, n = 9) and high (≥25 follicles, n = 9) AFC, and measured progesterone (P4) concentrations during CL evaluation. In addition, the diameter of the preovulatory follicle, area of blood flow of the preovulatory follicle, CL area, and CL blood flow were compared between groups with high and low AFC. Ultrasound examinations were performed during the days of the protocol and 10 days after ovulation to assess the CL. The constancy of the AFC was analyzed by calculating the repeatability. Parametric variables were analyzed using the t-test, and nonparametric variables were analyzed using the Mann-Whitney test (P ≤ 0.05). The repeatability of the AFC on different days of the protocol was 0.98. Cows with low AFC showed a greater diameter of the preovulatory follicle (16 ± 0.3 vs. 15 ± 0.4 mm), blood flow area of the preovulatory follicle (17.3 ± 2.3 vs. 8.1 ± 3.2 mm²), CL area (579.1 ± 16.9 vs. 405.8 ± 21.2 mm²), area of CL perfusion (97.1 ± 9.4 vs. 68.3 ± 5.2 mm²), as well as higher P4 levels (3.1 ± 0.2 vs. 2.2 ± 0.2 ng/mL). In high milk-producing Holstein cows, the AFC has high repeatability on different days of the TAI protocol, and cows with a low AFC show larger preovulatory follicles and CLs with greater perfusion, in addition to greater P4 concentrations in comparison to those cows with a high AFC.

Changes in metabolic and hormonal profiles during transition period in dairy cattle - the role of spexin

BACKGROUND

This study aimed to evaluate spexin as a novel blood marker and to describe the relationship of this peptide with selected biochemical metabolites measured during the transition period in dairy cows. Additionally, mRNA expression of the spexin gene as well as spexin receptors - galanin receptor type 2 and galanin receptor type 3, was investigated in several bovine tissues. Blood samples were collected at weekly intervals starting at 21 days before the estimated parturition day until 21 days in milk to determine concentrations of spexin, nonesterified fatty acids, β-hydroxybutyrate acid, total and active ghrelin, progesterone, glucose, insulin, IGF-I, triglycerides, cholesterol, leptin, corticosterone and 17-β-estradiol as well as the activity of aspartate transaminase, alkaline phosphatase and gamma-glutamyl transferase.

RESULTS

Spexin concentration decreased from 21 d before parturition to calving day and next it rose during the first 14 d of lactation. The lowest concentration of spexin was recorded on the calving day and it differed from the mean level of this peptide before parturition as well as postpartum. Moreover, differences were observed between mean spexin concentrations before and after calving. Spexin levels were moderately negatively correlated with NEFA (r = - 0.39) and total ghrelin contents (r = - 0.41), weakly correlated with BHBA (r = - 0.35) while they showed a moderate positive relationship with progesterone concentrations (r = 0.42). Moreover, we detected that mRNA expression of GALR2, GALR3 and SPX is present in various bovine tissues (kidney, bowel, rumen, spinal cord, lung, skeletal muscle, liver, heart, fat and spleen).

CONCLUSION

A negative correlation between spexin concentration and NEFA, BHBA and total ghrelin contents as well as a positive relationship with levels of progesterone, metabolites and hormones, which are key players in the dairy cow transition period, may confirm an important function of this peptide in metabolism regulation. Thus measurement of spexin concentration could provide useful supplementary information for dairy cow herd health monitoring.

Steroid Hormone Exposure as a Potential Hazard in Milk Consumers: A Significant Health Challenge in Iran

The presence of steroid hormones in milk is inevitable, and they can be considered as potential carcinogenic agents for consumers. The purpose of this study was to evaluate the level of 17β-estradiol, progesterone, and hydroxyprogesterone in sixty-nine bovine milk samples, collected from April to September 2020, in Hamadan, Iran. The milk samples were analyzed using enzyme-linked immunosorbent assay (ELISA). In this study, the mean contents of 17β-estradiol, progesterone, and hydroxyprogesterone in the milk samples were determined to be 330.5 ± 190.2 pg/ml, 3.57 ± 2.47 ng/ml, and 1.54 ± 0.41 ng/ml, respectively. However, the content of these steroid hormones in milk samples could be considered safe in children and adults, if the milk consumption is assumed to be in the reported range (175–240 ml/daily). Due to the effects of steroid hormones, especially 17β-estradiol, in the etiology of various cancers, regular monitoring of these hormones is recommended in milk and its dairy products in Iran.

A lateral flow-based portable platform for determination of reproductive status of cattle

Our objective was to develop and validate a tool integrating a disposable fluorescence-based lateral flow immunoassay (LFIA) coupled with a portable imaging device for estimating circulating plasma concentrations of progesterone (P4). First, we developed and optimized a competitive LFIA test strip to measure P4 in bovine plasma. The LFIA design included a sample pad, a conjugate pad that stores R-phycoerythrin-anti-P4 conjugates, a glass-fiber spacer pad, a nitrocellulose membrane with printed test and control lines, and a cellulose-fiber absorbent pad. To perform a test, 20 µL of plasma and 50 µL of running buffer were added on the sample pad. After 3 min, 45 µL of running buffer was added to initiate sample flow. After allowing 15 min to stabilize the colorimetric signal, strips were introduced in an LFIA portable reader wirelessly linked to a laptop to determine P4 concentration based on test-to-control-line signal (T/C ratio). In a series of experiments (n = 6), the ability of the LFIA to differentiate plasma samples with ≥1 or <1 ng/mL of P4 was evaluated. For each experiment, a calibration curve was constructed using plasma with known concentrations of P4 (0.1 to 3.7 ng/mL; n = 5). The resulting linear equation was then used to determine a T/C ratio cutoff to differentiate samples with ≥1 or <1 ng/mL of P4. In addition, to evaluate the ability of the platform to assign samples to P4 concentration groups without a calibration curve for individual batches, we performed a receiver operating characteristic analysis to identify a single cutoff value for T/C ratio that could potentially be used for all batches. Overall, calibration curves showed a linear relationship between T/C ratio and P4 levels (mean coefficient of determination = 0.74; range 0.42 to 0.99). Next, plasma samples from lactating dairy cows (n = 58) were tested in triplicate to determine the ability of the LFIA system to differentiate plasma samples with ≥1 or <1 ng/mL of P4 using a RIA for P4 as reference test. Overall, the LFIA assay correctly classified 90% of the samples, with 97% sensitivity, 83% specificity, 85% positive predictive value, and 96% negative predictive value. Agreement between the tests was substantial (kappa = 0.79; 95% confidence interval 0.64 to 0.95). When using a single cutoff value for T/C ratio selected by receiver operating characteristic analysis, sensitivity and specificity to determine CL presence were 97 (95% confidence interval 82 to 99) and 79% (95% confidence interval 60 to 92), respectively. These data suggest that the developed portable LFIA system can accurately differentiate plasma samples with ≥1 or <1 ng/mL of P4.

Genetic parameters of endocrine fertility traits based on in-line milk progesterone profiles in Swedish Red and Holstein dairy cows

Evaluating fertility traits based on endocrine progesterone profiles is becoming a promising option to improve dairy cow fertility. Several studies have been conducted on endocrine fertility traits, mainly in the Holstein breed. In this study, focusing also on the Swedish Red (SR) breed, genetic parameters were estimated for classical and endocrine fertility traits, the latter based on in-line milk progesterone records obtained for 14 Swedish herds using DeLaval Herd Navigator (DeLaval International, Tumba, Sweden). A total of 210,403 observations from 3,437 lactations of 1,107 SR and 1,538 Holstein cows were used. Mixed linear animal models were used for estimation of genetic parameters. Least squares means analysis showed that Holstein cows had a 2.5-d-shorter interval from calving to commencement of luteal activity (C-LA) and longer length of first inter-ovulatory interval (IOI) than SR cows. The highest mean interval for C-LA, IOI, and first luteal phase length (LPL) was observed in the fourth parity. The incidence of short (<18 d), normal, (18-24 d), and long (>24 d) IOI was 29.3, 40.7, and 30%, respectively. Genetic analysis indicated moderate heritability (h²) for C-LA (h² = 0.24), luteal activity during the first 60 d in milk (LA60, h² = 0.15), proportion of samples with luteal activity (PLA, h² = 0.13), and calving to first heat (CFH, h² = 0.18), and low heritability estimates for LPL (h² = 0.08) and IOI (h² = 0.03) in the combined data set for both breeds. Similar heritability estimates were obtained for each breed separately except for IOI and LPL in SR cows, for which heritability was estimated to be zero. Swedish Red cows had 0.01 to 0.06 higher heritability estimates for C-LA, LA60, and PLA than did Holstein cows. Calving interval had moderate heritability among the classical traits for Holstein and the combined data set, but h² was zero for SR. Commencement of luteal activity had a strong genetic correlation with LA60 (mean ± SE; -0.88 ± 0.06), PLA (-0.72 ± 0.11), and CFH (0.90 ± 0.04). Similarly, CFH had a strong genetic correlation with IOI (0.98 ± 0.20). Number of inseminations per series showed a weak genetic correlation with all endocrine traits except IOI. Overall, endocrine traits had higher heritability estimates than classical traits in both breeds, and may have a better potential to explain the actual reproductive status of dairy cows than classical traits. This might favor inclusion of some endocrine fertility traits-especially those related to commencement of luteal activity-as selection criteria and breeding goal traits if recording becomes more common in herds. Further studies on genetic and genomic evaluations for endocrine fertility traits may help to provide firm conclusions. A prerequisite is that the data from automatic devices be made available to recording and breeding organizations in the future and included in a central database.

Validation of a novel milk progesterone-based tool to monitor luteolysis in dairy cows: Timing of the alerts and robustness against missing values

Automated monitoring of fertility in dairy cows using milk progesterone is based on the accurate and timely identification of luteolysis. In this way, well-adapted insemination advice can be provided to the farmer to further optimize fertility management. To properly evaluate and compare the performance of new and existing data-processing algorithms, a test data set of progesterone time-series that fully covers the desired variability in progesterone profiles is needed. Further, the data should be measured with a high frequency to allow rapid onset events, such as luteolysis, to be precisely determined. Collecting this type of data would require a lot of time, effort, and budget. In the absence of such data, an alternative was developed using simulated progesterone profiles for multiple cows and lactations, in which the different fertility statuses were represented. To these, relevant variability in terms of cycle characteristics and measurement error was added, resulting in a large cost-efficient data set of well-controlled but highly variable and farm-representative profiles. Besides the progesterone profiles, information on (the timing of) luteolysis was extracted from the modeling approach and used as a reference for the evaluation and comparison of the algorithms. In this study, 2 progesterone monitoring tools were compared: a multiprocess Kalman filter combined with a fixed threshold on the smoothed progesterone values to detect luteolysis, and a progesterone monitoring algorithm using synergistic control, PMASC, which uses a mathematical model based on the luteal dynamics and a statistical control chart to detect luteolysis. The timing of the alerts and the robustness against missing values of both algorithms were investigated using 2 different sampling schemes: one sample per cow every 8 h versus 1 sample per day. The alerts for luteolysis of the PMASC algorithm were on average 20 h earlier compared with the ones of the multiprocess Kalman filter, and their timing was less sensitive to missing values. This was shown by the fact that, when 1 sample per day was used, the Kalman filter gave its alerts on average 24 h later, and the variability in timing of the alerts compared with simulated luteolysis increased with 22%. Accordingly, we postulate that implementation of the PMASC system could improve the consistency of luteolysis detection on farm and lower the analysis costs compared with the current state of the art.

Short communication: Validation of a novel milk progesterone-based tool to monitor luteolysis in dairy cows using cost-effective, on-farm measured data

The progesterone (P4) monitoring algorithm using synergistic control (PMASC) uses luteal dynamics to identify fertility events in dairy cows. This algorithm employs a combination of mathematical functions describing the increasing and decreasing P4 concentrations during the development and regression of the corpus luteum and a statistical control chart that allows identification of luteolysis. The mathematical model combines sigmoidal functions from which the cycle characteristics can be calculated. Both the moment at which luteolysis is detected and confirmed by PMASC, as well as the model features themselves, can be used to inform the farmer on the fertility status of the cows.