Prediction of key milk biomarkers in dairy cows through milk mid-infrared spectra and international collaborations.

At the individual cow level, suboptimum fertility, mastitis, negative energy balance, and ketosis are major issues in dairy farming. These problems are widespread on dairy farms and have an important economic impact. The objectives of this study were (1) to assess the potential of milk mid-infrared (MIR) spectra to predict key biomarkers of energy deficit (citrate, isocitrate, glucose-6 phosphate [glucose-6P], free glucose), ketosis (ß-hydroxybutyrate [BHB] and acetone), mastitis (N-acetyl-ß-d-glucosaminidase activity [NAGase] and lactate dehydrogenase), and fertility (progesterone); (2) to test alternative methodologies to partial least squares (PLS) regression to better account for the specific asymmetric distribution of the biomarkers; and (3) to create robust models by merging large datasets from 5 international or national projects. Benefiting from this international collaboration, the dataset comprised a total of 9,143 milk samples from 3,758 cows located in 589 herds across 10 countries and represented 7 breeds. The samples were analyzed by reference chemistry for biomarker contents, whereas the MIR analyses were performed on 30 instruments from different models and brands, with spectra harmonized into a common format. Four quantitative methodologies were evaluated to address the strongly skewed distribution of some biomarkers. Partial least squares regression was used as the reference basis, and compared with a random modification of distribution associated with PLS (random-downsampling-PLS), an optimized modification of distribution associated with PLS (KennardStone-downsampling-PLS), and support vector machine (SVM). When the ability of MIR to predict biomarkers was too low for quantification, different qualitative methodologies were tested to discriminate low versus high values of biomarkers. For each biomarker, 20% of the herds were randomly removed within all countries to be used as the validation dataset. The remaining 80% of herds were used as the calibration dataset. In calibration, the 3 alternative methodologies outperform the PLS performances for the majority of biomarkers. However, in the external herd validation, PLS provided the best results for isocitrate, glucose-6P, free glucose, and lactate dehydrogenase (coefficient of determination in external herd validation [R2v] = 0.48, 0.58, 0.28, and 0.24, respectively). For other molecules, PLS-random-downsampling and PLS-KennardStone-downsampling outperformed PLS in the majority of cases, but the best results were provided by SVM for citrate, BHB, acetone, NAGase, and progesterone (R2v = 0.94, 0.58, 0.76, 0.68, and 0.15, respectively). Hence, PLS and SVM based on the entire dataset provided the best results for normal and skewed distributions, respectively. Complementary to the quantitative methods, the qualitative discriminant models enabled the discrimination of high and low values for BHB, acetone, and NAGase with a global accuracy around 90%, and glucose-6P with an accuracy of 83%. In conclusion, MIR spectra of milk can enable quantitative screening of citrate as a biomarker of energy deficit and discrimination of low and high values of BHB, acetone, and NAGase, as biomarkers of ketosis and mastitis. Finally, progesterone could not be predicted with sufficient accuracy from milk MIR spectra to be further considered. Consequently, MIR spectrometry can bring valuable information regarding the occurrence of energy deficit, ketosis, and mastitis in dairy cows, which in turn have major influences on their fertility and survival.

Influence of the concentrate inclusion level in a grass silage-based diet on hepatic transcriptomic profiles in Holstein-Friesian dairy cows in early lactation.

Excessive negative energy balance in early lactation is linked to an increased disease risk but may be mitigated by appropriate nutrition. The liver plays central roles in both metabolism and immunity. Hepatic transcriptomic profiles were compared between 3 dietary groups in each of 40 multiparous and 18 primiparous Holstein-Friesian cows offered isonitrogenous grass silage-based diets with different proportions of concentrates: (1) low concentrate (LC, 30% concentrate + 70% grass silage); (2) medium concentrate (MC, 50% concentrate + 50% grass silage), or (3) high concentrate (HC, 70% concentrate + 30% grass silage). Liver biopsies were taken from all cows at around 14 d in milk for RNA sequencing, and blood metabolites were measured. The sequencing data were analyzed separately for primiparous and multiparous cows using CLC Genomics Workbench V21 (Qiagen Digital Insights), focusing on comparisons between HC and LC groups. More differentially expressed genes (DEG) were seen between the primiparous cows receiving HC versus LC diets than for multiparous cows (597 vs. 497), with only 73 in common, indicating differential dietary responses. Multiparous cows receiving the HC diet had significantly higher circulating glucose and insulin-like growth factor-1 and lower urea than those receiving the LC diet. In response to HC, only the multiparous cows produced more milk. In these animals, bioinformatic analysis indicated expression changes in genes regulating fatty acid metabolism and biosynthesis (e.g., ACACA, ELOVL6, FADS2), increased cholesterol biosynthesis (e.g., CYP7A1, FDPS, HMGCR), downregulation in hepatic AA synthesis (e.g., GPT, GCLC, PSPH, SHMT2), and decreased expression of acute phase proteins (e.g., HP, LBP, SAA2). The primiparous cows on the HC diet also downregulated genes controlling AA metabolism and synthesis (e.g., CTH, GCLC, GOT1, ODC1, SHMT2) but showed higher expression of genes indicative of inflammation (e.g., CCDC80, IL1B, S100A8) and fibrosis (e.g., LOX, LUM, PLOD2). This potentially adverse response to a HC diet in physically immature animals warrants further investigation.

Inflammatory cytokines, high-sensitivity C-reactive protein, and risk of one-year vascular events, death, and poor functional outcome after stroke and transient ischemic attack.

BACKGROUND: Inflammation driven by pro-inflammatory cytokines is a new therapeutic target in coronary disease. Few data exist on the association of key upstream cytokines and post-stroke recurrence. In a prospective cohort study, we investigated the association between pivotal cytokines, high-sensitivity C-reactive protein (hsCRP) and one-year outcomes. METHODS: BIO-STROKETIA is a multi-center prospective cohort study of non-severe ischemic stroke (modified Rankin score ≤ 3) and transient ischemic attack. Controls were patients with transient symptoms attending transient ischemic attack clinics with non-ischemic final diagnosis. Exclusion criteria were severe stroke, infection, and other pro-inflammatory disease; hsCRP and cytokines (interleukin (IL) 6, IL-1ß, IL-8, IL-10, IL-12, interferon-γ (IFN-γ), tumor-necrosis factor-α (TNF-α)) were measured. The primary outcome was one-year recurrent stroke/coronary events (fatal and non-fatal). RESULTS: In this study, 680 patients (439 stroke, 241 transient ischemic attack) and 68 controls were included. IL-6, IL-1ß, IL-8, IFN-γ, TNF-α, and hsCRP were higher in stroke/transient ischemic attack cases (p ≤ 0.01 for all). On multivariable Cox regression, IL-6, IL-8, and hsCRP independently predicted one-year recurrent vascular events (adjusted hazard ratios (aHR) per-quartile increase IL-6 1.31, confidence interval (CI) 1.02-1.68, p = 0.03; IL-8 1.47, CI 1.15-1.89, p = 0.002; hsCRP 1.28, CI 1.01-1.62, p = 0.04). IL-6 (aHR 1.98, CI 1.26-3.14, p = 0.003) and hsCRP (aHR 1.81, CI 1.20-2.74, p = 0.005) independently predicted one-year fatality. IL-6 and hsCRP (adjusted odds ratio per-unit increase 1.02, CI 1.01-1.04) predicted poor functional outcome, with a trend for IL-1ß (p = 0.054). CONCLUSION: Baseline inflammatory cytokines independently predicted late recurrence, supporting a rationale for randomized trials of anti-inflammatory agents for prevention after stroke and suggesting that targeted therapy to high-risk patients with high baseline inflammation may be beneficial.

Estimation of the true prevalence of inaccurate artificial inseminations in Irish milk recording dairy cows using a Bayesian latent class analysis.

Inaccurate artificial insemination (IAI) refers to an artificial insemination (AI) that is performed when a cow is not in oestrus. IAIs have economic impacts on the dairy industry through of semen wastage or iatrogenic pregnancy loss. However, few studies have quantified the prevalence of IAIs in a population. The primary objective of this prospective study was to estimate the cow-level true prevalence of IAIs in Irish milk recording dairy herds using a latent class model with a Bayesian framework. Milk samples were collected at a milk recording laboratory from 576 dairy cows in 125 herds who had received an AI on the same day they were sampled for routine milk constituent analysis. Milk progesterone (MP4) analysis was conducted on these samples using radioimmunoassay to determine the progesterone concentration. Fertility data (i.e., subsequent calving date) was retrospectively obtained from the Irish National Cattle Breeding Federation for milk sampled cows and an apparent conception (AC) to the sample AI was determined based on an estimated gestational range of 270-290 days. Both tests (MP4 and AC) were used in a latent class model to estimate the true prevalence of IAI. For the MP4 test, a concentration of ≥ 5 ng/mL in whole milk was deemed to be test positive while for the AC test, a cow that did not conceive to the sampled AI was deemed test positive. Prior information for prevalence of IAI was obtained from a literature review while MP4 sensitivity (Se) and specificity (Sp) were obtained from expert opinion. Non-informative priors were used for the Se and Sp of the AC test. Posterior inferences (median and 95 % Bayesian probability intervals; BPI) were obtained using the 'rjags' package in the R statistical software. In the final model, median cow-level true prevalence of IAI was 4.4 % (BPI; 1.7-9.0 %). Median Se and Sp estimates for MP4, were 83.0 % (BPI; 65.0-96.2 %) and were 97.4 % (BPI; 94.6-99.6 %), respectively. Median Se and Sp estimates for AC, were 64.8 % (BPI; 44.5-88.6 %) and 49.8 % (BPI; 45.3-54.1 %), respectively. The present study estimates that the overall cow-level true prevalence of IAI in Irish dairy cows is relatively low. This is the first study to report the cow-level true prevalence of IAI using a Bayesian latent class model.

Relationships between metabolic profiles and gene expression in liver and leukocytes of dairy cows in early lactation.

Homeorhetic mechanisms assist dairy cows in the transition from pregnancy to lactation. Less successful cows develop severe negative energy balance (NEB), placing them at risk of metabolic and infectious diseases and reduced fertility. We have previously placed multiparous Holstein Friesian cows from 4 herds into metabolic clusters, using as biomarkers measurements of plasma nonesterified fatty acids, ß-hydroxybutyrate, glucose and IGF-1 collected at 14 and 35 d in milk (DIM). This study characterized the global transcriptomic profiles of liver and circulating leukocytes from the same animals to determine underlying mechanisms associated with their metabolic and immune function. Liver biopsy and whole-blood samples were collected around 14 DIM for RNA sequencing. All cows with available RNA sequencing data were placed into balanced (BAL, n = 44), intermediate (n = 44), or imbalanced (IMBAL, n = 19) metabolic cluster groups. Differential gene expression was compared between the 3 groups using ANOVA, but only the comparison between BAL and IMBAL cows is reported. Pathway analysis was undertaken using DAVID Bioinformatic Resources (https://david.ncifcrf.gov/). Milk yields did not differ between BAL and IMBAL cows but dry matter intake was less in IMBAL cows and they were in greater energy deficit at 14 DIM (-4.48 v -11.70 MJ/d for BAL and IMBAL cows). Significantly differentially expressed pathways in hepatic tissue included AMPK signaling, glucagon signaling, adipocytokine signaling, and insulin resistance. Genes involved in lipid metabolism and cholesterol transport were more highly expressed in IMBAL cows but IGF1 and IGFALS were downregulated. Leukocytes from BAL cows had greater expression of histones and genes involved in nucleosomes and cell division. Leukocyte expression of heat shock proteins increased in IMBAL cows, suggesting an unfolded protein response, and several key genes involved in immune responses to pathogens were upregulated (e.g., DEFB13, HP, OAS1Z, PTX3, and TLR4). Differentially expressed genes upregulated in IMBAL cows in both tissues included CD36, CPT1, KFL11, and PDK4, all central regulators of energy metabolism. The IMBAL cows therefore had greater difficulty maintaining glucose homeostasis and had dysregulated hepatic lipid metabolism. Their energy deficit was associated with a reduced capacity for cell division and greater evidence of stress responses in the leukocyte population, likely contributing to an increased risk of infectious disease.

Genome-wide association for metabolic clusters in early-lactation Holstein dairy cows.

The aim of this study was to detect the genomic region or regions associated with metabolic clusters in early-lactation Holstein cows. This study was carried out in 2 experiments. In experiment I, which was carried out on 105 multiparous Holstein cows, animals were classified through k-means clustering on log-transformed and standardized concentrations of blood glucose, insulin-like growth factor I, free fatty acids, and ß-hydroxybutyrate at 14 and 35 d in milk (DIM), into metabolic clusters, either balanced (BAL) or other (OTR). Forty percent of the animals were categorized in the BAL group, and the remainder were categorized as OTR. The cows were genotyped for a total of 777,962 SNP. A genome-wide association study was performed, using a case-control approach through the GEMMA software, accounting for population structure. We found 8 SNP (BTA11, BTA23, and BTAX) associated with the predicted metabolic clusters. In experiment II, carried out on 4,267 second-parity Holstein cows, milk samples collected starting from the first week until 50 DIM were used to determine Fourier-transform mid-infrared (FT-MIR) spectra and subsequently to classify the animals into the same metabolic clusters (BAL vs. OTR). Twenty-eight percent of the animals were categorized in the BAL group, and the remainder were classified in the OTR category. Although daily milk yield was lower in BAL cows, we found no difference in daily fat- and protein-corrected milk yield in cows from the BAL metabolic cluster compared with those in the OTR metabolic cluster. In the next step, a single-step genomic BLUP was used to identify the genomic region(s) associated with the predicted metabolic clusters. The results revealed that prediction of metabolic clusters is a highly polygenic trait regulated by many small-sized effects. The region of 36,258 to 36,295 kb on BTA27 was the highly associated region for the predicted metabolic clusters, with the closest genes to this region (ANK1 and miR-486) being related to hematopoiesis, erythropoiesis, and mammary gland development. The heritability for metabolic clustering was 0.17 (SD 0.03), indicating that the use of FT-MIR spectra in milk to predict metabolic clusters in early-lactation across a large number of cows has satisfactory potential to be included in genetic selection programs for modern dairy cows.

Potential of milk mid-infrared spectra to predict nitrogen use efficiency of individual dairy cows in early lactation.

Improving nitrogen use efficiency (NUE) at both the individual cow and the herd level has become a key target in dairy production systems, for both environmental and economic reasons. Cost-effective and large-scale phenotyping methods are required to improve NUE through genetic selection and by feeding and management strategies. The aim of this study was to evaluate the possibility of using mid-infrared (MIR) spectra of milk to predict individual dairy cow NUE during early lactation. Data were collected from 129 Holstein cows, from calving until 50 d in milk, in 3 research herds (Denmark, Ireland, and the UK). In 2 of the herds, diets were designed to challenge cows metabolically, whereas a diet reflecting local management practices was offered in the third herd. Nitrogen intake (kg/d) and nitrogen excreted in milk (kg/d) were calculated daily. Nitrogen use efficiency was calculated as the ratio between nitrogen in milk and nitrogen intake, and expressed as a percentage. Individual daily values for NUE ranged from 9.7 to 81.7%, with an average of 36.9% and standard deviation of 10.4%. Milk MIR spectra were recorded twice weekly and were standardized into a common format to avoid bias between apparatus or sampling periods. Regression models predicting NUE using milk MIR spectra were developed on 1,034 observations using partial least squares or support vector machines regression methods. The models were then evaluated through (1) a cross-validation using 10 subsets, (2) a cow validation excluding 25% of the cows to be used as a validation set, and (3) a diet validation excluding each of the diets one by one to be used as validation sets. The best statistical performances were obtained when using the support vector machines method. Inclusion of milk yield and lactation number as predictors, in combination with the spectra, also improved the calibration. In cross-validation, the best model predicted NUE with a coefficient of determination of cross-validation of 0.74 and a relative error of 14%, which is suitable to discriminate between low- and high-NUE cows. When performing the cow validation, the relative error remained at 14%, and during the diet validation the relative error ranged from 12 to 34%. In the diet validation, the models showed a lack of robustness, demonstrating difficulties in predicting NUE for diets and for samples that were not represented in the calibration data set. Hence, a need exists to integrate more data in the models to cover a maximum of variability regarding breeds, diets, lactation stages, management practices, seasons, MIR instruments, and geographic regions. Although the model needs to be validated and improved for use in routine conditions, these preliminary results showed that it was possible to obtain information on NUE through milk MIR spectra. This could potentially allow large-scale predictions to aid both further genetic and genomic studies, and the development of farm management tools.

Reproductive efficiency and survival of Holstein-Friesian cows of divergent Economic Breeding Index, evaluated under seasonal calving pasture-based management.

The objective of the current study was to examine phenotypic fertility performance and survival, and to gain insight into underlying factors that may contribute to greater fertility performance in 2 divergent genetic groups (GG) of Holstein-Friesian, selected using the Irish Economic Breeding Index (EBI). The GG were evaluated across 3 spring calving pasture-based feeding treatments (FT) over 4 yr. The 2 divergent GG were (1) high EBI; representative of the top 5% nationally (elite), and (2) EBI representative of the national average (NA). In each year, 90 elite and 45 NA cows were randomly allocated to 1 of 3 FT: control, lower grass allowance, and high concentrate. No interaction between GG and FT was observed for any of the measures of fertility investigated. The elite cows achieved significantly greater pregnancy rate to first service (+14.9 percentage points), and significantly greater pregnancy rates after 21, 42, and 84 d of breeding (+17.3, +15.2, and +9.6 percentage points, respectively) compared with NA. The number of services per cow was fewer for elite (1.57) compared with NA (1.80). The interval from mating start date to pregnancy was significantly shorter for elite cows compared with NA. The elite cows maintained greater mean body condition score than NA throughout the study (2.91 vs. 2.72), and had greater body condition score at calving, artificial insemination, and drying off compared with NA. The elite cows had greater mean circulating concentrations of insulin-like growth factor-1 compared with NA. No significant effect was observed of GG on commencement of luteal activity, or progesterone profile variables. Greater survival to the start of fifth lactation was observed for elite cows. The elite cows were 43% less likely to be culled than NA by the beginning of the fifth lactation. The results highlight the success of the Economic Breeding Index to deliver reproductive performance and longevity consistent with industry targets across a range of seasonal pasture-based FT. The results also clearly demonstrate the potential of appropriate genetic selection to reverse negative fertility trends incurred during previous decades of selection for milk production alone.

Between- and within-herd variation in blood and milk biomarkers in Holstein cows in early lactation.

Both blood- and milk-based biomarkers have been analysed for decades in research settings, although often only in one herd, and without focus on the variation in the biomarkers that are specifically related to herd or diet. Biomarkers can be used to detect physiological imbalance and disease risk and may have a role in precision livestock farming (PLF). For use in PLF, it is important to quantify normal variation in specific biomarkers and the source of this variation. The objective of this study was to estimate the between- and within-herd variation in a number of blood metabolites (ß-hydroxybutyrate (BHB), non-esterified fatty acids, glucose and serum IGF-1), milk metabolites (free glucose, glucose-6-phosphate, urea, isocitrate, BHB and uric acid), milk enzymes (lactate dehydrogenase and N-acetyl-ß-D-glucosaminidase (NAGase)) and composite indicators for metabolic imbalances (Physiological Imbalance-index and energy balance), to help facilitate their adoption within PLF. Blood and milk were sampled from 234 Holstein dairy cows from 6 experimental herds, each in a different European country, and offered a total of 10 different diets. Blood was sampled on 2 occasions at approximately 14 days-in-milk (DIM) and 35 DIM. Milk samples were collected twice weekly (in total 2750 samples) from DIM 1 to 50. Multilevel random regression models were used to estimate the variance components and to calculate the intraclass correlations (ICCs). The ICCs for the milk metabolites, when adjusted for parity and DIM at sampling, demonstrated that between 12% (glucose-6-phosphate) and 46% (urea) of the variation in the metabolites' levels could be associated with the herd-diet combination. Intraclass Correlations related to the herd-diet combination were generally higher for blood metabolites, from 17% (cholesterol) to approximately 46% (BHB and urea). The high ICCs for urea suggest that this biomarker can be used for monitoring on herd level. The low variance within cow for NAGase indicates that few samples would be needed to describe the status and potentially a general reference value could be used. The low ICC for most of the biomarkers and larger within cow variation emphasises that multiple samples would be needed - most likely on the individual cows - for making the biomarkers useful for monitoring. The majority of biomarkers were influenced by parity and DIM which indicate that these should be accounted for if the biomarker should be used for monitoring.

Prediction of metabolic clusters in early-lactation dairy cows using models based on milk biomarkers.

The aim of this study was to describe metabolism of early-lactation dairy cows by clustering cows based on glucose, insulin-like growth factor I (IGF-I), free fatty acid, and ß-hydroxybutyrate (BHB) using the k-means method. Predictive models for metabolic clusters were created and validated using 3 sets of milk biomarkers (milk metabolites and enzymes, glycans on the immunogamma globulin fraction of milk, and Fourier-transform mid-infrared spectra of milk). Metabolic clusters are used to identify dairy cows with a balanced or imbalanced metabolic profile. Around 14 and 35 d in milk, serum or plasma concentrations of BHB, free fatty acids, glucose, and IGF-I were determined. Cows with a favorable metabolic profile were grouped together in what was referred to as the "balanced" group (n = 43) and were compared with cows in what was referred to as the "other balanced" group (n = 64). Cows with an unfavorable metabolic profile were grouped in what was referred to as the "imbalanced" group (n = 19) and compared with cows in what was referred to as the "other imbalanced" group (n = 88). Glucose and IGF-I were higher in balanced compared with other balanced cows. Free fatty acids and BHB were lower in balanced compared with other balanced cows. Glucose and IGF-I were lower in imbalanced compared with other imbalanced cows. Free fatty acids and BHB were higher in imbalanced cows. Metabolic clusters were related to production parameters. There was a trend for a higher daily increase in fat- and protein-corrected milk yield in balanced cows, whereas that of imbalanced cows was higher. Dry matter intake and the daily increase in dry matter intake were higher in balanced cows and lower in imbalanced cows. Energy balance was continuously higher in balanced cows and lower in imbalanced cows. Weekly or twice-weekly milk samples were taken and milk metabolites and enzymes (milk glucose, glucose-6-phosphate, BHB, lactate dehydrogenase, N-acetyl-ß-d-glucosaminidase, isocitrate), immunogamma globulin glycans (19 peaks), and Fourier-transform mid-infrared spectra (1,060 wavelengths reduced to 15 principal components) were determined. Milk biomarkers with or without additional cow information (days in milk, parity, milk yield features) were used to create predictive models for the metabolic clusters. Accuracy for prediction of balanced (80%) and imbalanced (88%) cows was highest using milk metabolites and enzymes combined with days in milk and parity. The results and models of the present study are part of the GplusE project and identify novel milk-based phenotypes that may be used as predictors for metabolic and performance traits in early-lactation dairy cows.

Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach.

Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and ß-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R 2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R 2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.

A bovine-specific FSH enzyme immunoassay and its application to study the role of FSH in ovarian follicle development during the postnatal period.

The primary aim of this study was to develop a FSH enzyme immunoassay (EIA) for the bovine species. The newly developed EIA was validated for FSH determination in bovine plasma by comparison with an existing bovine FSH radioimmunoassay. The EIA detected bovine FSH with a high sensitivity (0.1 ng/ml). Cross-reactivity of the EIA was 0.01% with bovine LH, 51% with ovine FSH, <0.1% with porcine FSH and <0.01% with equine FSH. Using this EIA on different time series of plasma in cows, we have confirmed the presence of a FSH pre-ovulatory peak at estrus, of periodic FSH fluctuations accompanying the waves of terminal follicular development, and of FSH pulses, mainly asynchronous with LH ones, in the peri-ovulatory phase of the cycle. In a second objective, the EIA was used to assess the role of FSH in regulating the development of ovarian follicles up to the small antral stage in young calves. To answer this question, six calves were submitted to weekly blood sampling during their first 3 months of life, and FSH changes were studied concomitantly to those of anti-Müllerian hormone (AMH), a well-established endocrine marker of the ovarian population of small antral follicles in cows. In the ovaries of 3-month calves, the population of 3 to 5 mm follicles contained the highest intra-follicular AMH amounts, and the number of 3 to 5 mm follicles on ovaries was closely correlated with AMH concentrations in the plasma of calves at this age (rs = 0.94). Before 3 months of age, only two out of six calves showed a clear postnatal FSH peak in plasma, and no correlation was found between plasma FSH and AMH concentrations. These results indicate that female calves undergo different patterns of FSH secretion and that postnatal activation of follicular growth up to the small antral stage appears independent and not directly related to circulating FSH levels.

Role of early life nutrition on regulating the hypothalamic­anterior pituitary­testicular axis of the bull

The objective of this study was to examine the effect of nutrition during the first 18 weeks of life on the physiological and transcriptional functionality of the hypothalamic (arcuate nucleus region), anterior pituitary and testes in Holstein­Friesian bull calves. Holstein­Friesian bull calves with a mean (±S.D.) age and bodyweight of 19 (±8.2) days and 47.5 (±5.3) kg, respectively, were assigned to either a HIGH (n = 10) or LOW (n = 10) plane of nutrition, to achieve an overall target growth rate of 1.2 or 0.5 kg/day, respectively. At 126 ± 1.1 days of age, all calves were euthanised. Animal performance (weekly) and systemic concentrations of metabolic (monthly) and reproductive hormones (fortnightly) were assessed. Testicular histology, targeted gene and protein expression of the arcuate nucleus region, anterior pituitary and testes were also assessed using qPCR and immunohistochemistry, respectively. The expression of candidate genes in testicular tissue from post pubertal 19-month-old Holstein­Friesian bulls (n = 10) was compared to that of the 18-week-old calves. Metabolite and metabolic hormone profiles generally reflected the improved metabolic status of the calves on the HIGH (P< 0.001). Calves offered a HIGH plane of nutrition were heavier at slaughter (P < 0.001), had larger testes (P < 0.001), larger seminiferous tubule diameter (P < 0.001), more mature spermatogenic cells (P < 0.001) and more Sertoli cells (P < 0.05) in accordance with both morphological and transcriptional data. Overall, testicular gene expression profiles suggested a more mature stage of development in HIGH compared with LOW and were more closely aligned to that of mature bulls. Ghrelin receptor was the only differentially expressed gene between LOW and HIGH calves in either the anterior pituitary (P < 0.05) or arcuate nucleus region of the hypothalamus (P < 0.10) and was upregulated in LOW for both tissues. This study indicates that an enhanced plane of nutrition during early calfhood favourably alters the biochemical regulation of the hypothalamus­anterior pituitary­testicular axis, advancing testicular development and hastening spermatogenesis.

Differences in the bovine milk whey proteome between early pregnancy and the estrous cycle.

Current bovine pregnancy detection methods are not reliable until at least day 28 post artificial insemination (AI). The bovine estrous cycle is approximately 21 days; consequently, producers miss an opportunity to rebreed at the next estrous event. Therefore, commercial interest exists for the discovery of novel biomarkers of pregnancy which could reliably detect pregnancy status at or before day 21 of pregnancy. The objective of the present study was to use liquid chromatography tandem mass spectrometry (LC-MS/MS) to perform a global, label-free, proteomics study on (i) milk whey and (ii) extracellular vesicle (EV) enriched milk whey samples, from day 21 of pregnancy, compared with day 21 of the estrous cycle, in order to identify potential protein biomarkers of early pregnancy. The estrous cycles of 10 dairy cows were synchronized, they went through one (control) estrous cycle and these cows were artificially inseminated during the following estrus. These cows were confirmed pregnant by ultrasound scanning. Milk whey samples were collected on day 21 of the estrous cycle and on day 21 post AI. Milk whey samples and EV enriched milk whey samples were analyzed by LC-MS/MS and subsequent analyzes of the label-free quantitative data was performed in MaxQuant and Perseus. Four proteins (APOB, SPADH1, PLIN2 and LPO) were differentially expressed between the proteomes of milk whey from day 21 of pregnancy and day 21 of the estrous cycle (P < 0.05). Ten proteins (PIGR, PGD, QSOX1, MUC1, SRPRA, MD2, GAPDH, FOLR1, GPRC5B and HHIPL2) were differentially expressed between the proteomes of EV enriched milk whey from day 21 of pregnancy and day 21 of the estrous cycle (P < 0.05). These proteins are potential milk whey biomarkers of early pregnancy.

Plane of nutrition before and after 6 months of age in Holstein-Friesian bulls: II. Effects on metabolic and reproductive endocrinology and identification of physiological markers of puberty and sexual maturation.

The aim of this study was (1) to examine the effect of plane of nutrition during the first and second 6 mo of life on systemic concentrations of reproductive hormones and metabolites in Holstein-Friesian dairy bulls, and (2) to establish relationships with age at puberty and postpubertal semen production potential. Holstein-Friesian bull calves (n = 83) with a mean (standard deviation) age and body weight of 17 (4.4) d and 52 (6.2) kg, respectively, were assigned to a high or low plane of nutrition for the first 6 mo of life. At 24 wk of age, bulls were reassigned, within treatment, either to remain on the same diet or to switch to the opposite diet until puberty, resulting in 4 treatment groups: high-high, high-low, low-low, and low-high. Monthly blood samples were analyzed for metabolites (albumin, urea, total protein, ß-hydroxybutyrate, glucose, nonesterified fatty acid, triglycerides and creatinine), insulin, insulin-like growth factor-1, leptin, adiponectin, FSH, and testosterone. A GnRH challenge was carried out at 16 and 32 wk of age (n = 9 bulls per treatment). Blood was collected at 15-min intervals for 165 min, with GnRH administered (0.05 mg/kg of body weight, i.v.) immediately after the third blood sample. Blood samples were subsequently analyzed for LH, FSH, and testosterone. Stepwise regression was used to detect growth and blood measurements to identify putative predictors of age at puberty and subsequent semen quality traits. Metabolic hormones and metabolites, in general, reflected metabolic status of bulls. Although FSH was unaffected by diet, it decreased with age both in monthly samples and following GnRH administration. Testosterone was greater in bulls on the high diet before and after 6 mo of age. Testosterone concentrations increased dramatically after 6 mo of age. Luteinizing hormone was unaffected by diet following GnRH administration but basal serum LH was greater in bulls on a high diet before 6 mo of age. In conclusion, the plane of nutrition offered before 6 mo of age influenced metabolic profiles, which are important for promoting GnRH pulsatility, in young bulls.

Staying well with bipolar disorder: A qualitative analysis of five-year follow-up interviews with young people.

WHAT IS ALREADY KNOWN ABOUT THE TOPIC?: Bipolar disorder is a long-term condition which causes ongoing disruptions to the individual's life. Current evidence suggests that a combination of medication in combination with psychotherapy is more effective than medication alone. WHAT THE PAPER ADDS TO EXISTING KNOWLEDGE?: There are few published reports of the effects of interventions (pharmacological or psychotherapeutic) for treatment in bipolar disorder. While both psychotherapies provided a framework for understanding bipolar disorder each had specific strategies that participants identified as effective. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: Because bipolar disorder is a long-term condition, its treatment needs to incorporate psychotherapeutic approaches that address the unique nature of its impact on each individual and provide individualized strategies for managing the disorder. Both Interpersonal and Social Rhythm Therapy and Specialist Supportive Care provide strategies that promote personal recovery. ABSTRACT: Introduction The primary outcomes from this study of psychotherapy for young people with bipolar disorder identified that most participants had continued to remain well. Given that up to 80% of people relapse within 2 years, it was important to establish how these participants described the process of staying well. Aim To examine how participants in a psychotherapy for young people with bipolar disorder study at 5-year follow-up described their experiences of the intervention and its impact on living with the disorder. Methods This qualitative study was conducted 5 years after participants had completed a psychotherapy intervention in a randomized controlled trial for young people with bipolar disorder. Thirty people were recruited into this qualitative study and interviewed regarding their experiences. The data were analysed using an inductive thematic analysis. Findings Three themes were identified from the data: self-awareness in the context of bipolar disorder; understanding my bipolar disorder; and learning to stay well with bipolar disorder. Conclusions Mental health nurses can promote the factors that participants found helpful in learning to stay well self-awareness, understanding the unique characteristics of their disorder, learning to take care of the self and stabilization of social rhythms.