Novel genetic parameters for genetic residual feed intake in dairy cattle using time series data from multiple parities and countries in North America and Europe.

Residual feed intake is viewed as an important trait in breeding programs that could be used to enhance genetic progress in feed efficiency. In particular, improving feed efficiency could improve both economic and environmental sustainability in the dairy cattle industry. However, data remain sparse, limiting the development of reliable genomic evaluations across lactation and parity for residual feed intake. Here, we estimated novel genetic parameters for genetic residual feed intake (gRFI) across the first, second, and third parity, using a random regression model. Research data on the measured feed intake, milk production, and body weight of 7,379 cows (271,080 records) from 6 countries in 2 continents were shared through the Horizon 2020 project Genomic Management Tools to Optimise Resilience and Efficiency, and the Resilient Dairy Genome Project. The countries included Canada (1,053 cows with 47,130 weekly records), Denmark (1,045 cows with 72,760 weekly records), France (329 cows with 16,888 weekly records), Germany (938 cows with 32,614 weekly records), the Netherlands (2,051 cows with 57,830 weekly records), and United States (1,963 cows with 43,858 weekly records). Each trait had variance components estimated from first to third parity, using a random regression model across countries. Genetic residual feed intake was found to be heritable in all 3 parities, with first parity being predominant (range: 22-34%). Genetic residual feed intake was highly correlated across parities for mid- to late lactation; however, genetic correlation across parities was lower during early lactation, especially when comparing first and third parity. We estimated a genetic correlation of 0.77 ± 0.37 between North America and Europe for dry matter intake at first parity. Published literature on genetic correlations between high input countries/continents for dry matter intake support a high genetic correlation for dry matter intake. In conclusion, our results demonstrate the feasibility of estimating variance components for gRFI across parities, and the value of sharing data on scarce phenotypes across countries. These results can potentially be implemented in genetic evaluations for gRFI in dairy cattle.

Predicting enteric methane emission in lactating Holsteins based on reference methane data collected by the GreenFeed system.

Methane emission is not included in the current breeding goals for dairy cattle mainly due to the expense and difficulty in obtaining sufficient data to generate accurate estimates of the relevant traits. While several models have been developed to predict methane emission from milk spectra using reference methane data obtained by the respiration chamber, SF6 and sniffer methods, the prediction of methane emission from milk mid-infrared (MIR) spectra using reference methane data collected by the GreenFeed system has not yet been explored. Methane emission was monitored for 151 cows using the GreenFeed system. Prediction models were developed for daily and average (for the trial period of 12 or 14 days) methane production (g/d), yield (g/kg DM intake (DMI)) and intensity (g/kg of fat- and protein-corrected milk) using partial least squares regression. The predictions were evaluated in 100 repeated validation cycles, where animals were randomly partitioned into training (80%) and testing (20%) populations for each cycle. The best performing model was observed for average methane intensity using MIR, parity and DMI with validation coefficient of determination (R2val) and RMSE of prediction of 0.66 and 4.7 g/kg of fat- and protein-corrected milk, respectively. The accuracy of the best models for average methane production and average methane yield were poor (R2val = 0.28 and 0.12, respectively). A lower accuracy of prediction was observed for methane intensity and production (R2val = 0.42 and 0.17) when daily records were used while prediction for methane yield was comparable to that for average methane yield (R2val = 0.16). Our results suggest the potential to predict methane intensity with moderate accuracy. In this case, prediction models for average methane values were generally better than for daily measures when using the GreenFeed system to obtain reference methane emission measurements.

Estimating the environmental impact of dairy cattle breeding programs through emission intensity.

A recently developed methodological approach for determining the greenhouse gas emissions impact of national breeding programs was applied to measure the effects of current and future breeding goals on the emission intensity (EI) of the Canadian dairy industry. Emission intensity is the ratio of greenhouse gas outputted in comparison to the product generated. Traits under investigation affected EI by either decreasing the direct emissions yield (i.e. increasing feed performance), changing herd structure (i.e. prolonging herd life) or through the dilution effect of increased production (i.e. increasing fat yield). The intensity value (IV) of each trait, defined as the change in emissions' intensity per unit change in each trait, was calculated for each of the investigated traits. The IV trend of these traits was compared for the current and prospective selection index, as well as for a system with and without quota (the supply management policy designed to prevent overproduction). The overall EI of the average genetic merit Canadian dairy herd per breeding female was 5.07 kg CO2eq/kg protein equivalent output. The annual reduction in EI due to the improvement of production traits was -0.027, -0.018 and -0.006 for fat, protein and milk other solids, respectively. The functional traits, herd life and mastitis resistance, had more modest effects (-0.008 and -0.001, respectively). These results are consistent with international studies that identified traits related to production, survival, health and fertility as having the largest impact on the environmental footprint of dairy cattle. Overall, the dairy industry is becoming more efficient by reducing its EI through selection of environmentally favorable traits, with a 1% annual reduction of EI in Canada.

Urine metabolic fingerprinting can be used to predict the risk of metritis and highlight the pathobiology of the disease in dairy cows.

INTRODUCTION: Metritis is an uterine pathology that causes economic losses for the dairy industry. It is associated with lower reproductive efficiency, increased culling rates, decreased milk production and increased veterinary costs. OBJECTIVES: To gain a more detailed view of the urine metabolome and to detect metabolite signature in cows with metritis. In addition, we aimed to identify early metabolites which can help to detect cows at risk to develop metritis in the future. METHODS: We used nuclear magnetic resonance spectroscopy starting at 8 and 4 weeks prior to the expected day of parturition, during the week of diagnosis of metritis, and at 4 and 8 weeks after diagnosis of metritis in Holstein dairy cows. RESULTS: At 8 weeks before parturition, pre-metritic cows had a total of 30 altered metabolites. Interestingly, 28 of them increased in urine when compared with control cows (P < 0.05). At 4 weeks before parturition, 34 metabolites were altered. At the week of diagnosis of metritis a total of 20 metabolites were altered (P < 0.05). The alteration continued at 4 and 8 weeks after diagnosis. CONCLUSIONS: The metabolic fingerprints in the urine of pre-metritic and metritic cows point toward excretion of multiple amino acids, tricarboxylic acid cycle metabolites and monosaccharides. Combination of galactose, leucine, lysine and panthotenate at 8 weeks before parturition might serve as predictive biomarkers for metritis.

Identification of predictive biomarkers of disease state in transition dairy cows.

In dairy cows, periparturient disease states, such as metritis, mastitis, and laminitis, are leading to increasingly significant economic losses for the dairy industry. Treatments for these pathologies are often expensive, ineffective, or not cost-efficient, leading to production losses, high veterinary bills, or early culling of the cows. Early diagnosis or detection of these conditions before they manifest themselves could lower their incidence, level of morbidity, and the associated economic losses. In an effort to identify predictive biomarkers for postpartum or periparturient disease states in dairy cows, we undertook a cross-sectional and longitudinal metabolomics study to look at plasma metabolite levels of dairy cows during the transition period, before and after becoming ill with postpartum diseases. Specifically we employed a targeted quantitative metabolomics approach that uses direct flow injection mass spectrometry to track the metabolite changes in 120 different plasma metabolites. Blood plasma samples were collected from 12 dairy cows at 4 time points during the transition period (-4 and -1 wk before and 1 and 4 wk after parturition). Out of the 12 cows studied, 6 developed multiple periparturient disorders in the postcalving period, whereas the other 6 remained healthy during the entire experimental period. Multivariate data analysis (principal component analysis and partial least squares discriminant analysis) revealed a clear separation between healthy controls and diseased cows at all 4 time points. This analysis allowed us to identify several metabolites most responsible for separating the 2 groups, especially before parturition and the start of any postpartum disease. Three metabolites, carnitine, propionyl carnitine, and lysophosphatidylcholine acyl C14:0, were significantly elevated in diseased cows as compared with healthy controls as early as 4 wk before parturition, whereas 2 metabolites, phosphatidylcholine acyl-alkyl C42:4 and phosphatidylcholine diacyl C42:6, could be used to discriminate healthy controls from diseased cows 1 wk before parturition. A 3-metabolite plasma biomarker profile was developed that could predict which cows would develop periparturient diseases, up to 4 wk before clinical symptoms appearing, with a sensitivity of 87% and a specificity of 85%. This is the first report showing that periparturient diseases can be predicted in dairy cattle before their development using a multimetabolite biomarker model. Further research is warranted to validate these potential predictive biomarkers.

MicroRNA-regulated molecular mechanism underlying bovine subclinical endometritis.

An impaired uterine environment triggered by the incidence of subclinical endometritis often compromises fertility in the bovine. The uterus is a dynamic organ with tight regulation of specific genes at the transcriptional and translational levels. Herein, we hypothesised that subclinical endometritis alters the expression of uterine microRNAs (miRNAs), which may result in the dysregulation of corresponding target genes and biological pathways. To test this hypothesis, we used a genome-wide RT(2) (Exiqon, Vedbaek, Denmark) miRNA PCR array consisting of 354 miRNA primers and analysed miRNA expression in uterine cytobrush samples taken from cows with and without subclinical endometritis. The results revealed aberrant expression of 23 miRNAs in cows with subclinical endometritis compared with healthy cows. Furthermore, we designed an in vitro endometrial cell culture model challenged by lipopolysaccharide (LPS) to validate the differential regulation of miRNAs in cytobrush samples. Interestingly, we observed similar expression miRNA patterns in cytobrush samples taken from cows with or without subclinical endometritis and in vitro cultured endometrial cells challenged by LPS. To trace signalling pathways and biological functions potentially controlled by the aberrantly expressed miRNAs, we filtered high-ranking target genes from miRBase and analysed them using ingenuity pathway analysis. The gene networks, canonical pathways and biological functions strikingly converged to signalling pathways that mediate inflammatory responses, cellular proliferation, cell movement, the cell cycle and apoptosis in the bovine endometrium. In addition, expression analysis of key genes from the gene networks confirmed their presence and the potential regulation of these genes by uterine miRNAs. Furthermore, luciferase assay data substantiated the primary information from bioinformatic prediction that generated potential target genes for the dysregulated miRNAs in subclinical endometritis. Together, these data suggest the potential regulatory role of uterine miRNAs in the development and progression of bovine subclinical endometritis.

A longitudinal comparative analysis of economic and family caregiver burden due to bipolar disorder.

OBJECTIVE: to explain comparatively how economic and family caregiver burden in families with bipolar disorder patients change overtime. METHOD: one year follow-up of economic and family caregiver burden was carried out on family caregivers of 190 bipolar, 55 diabetes, hypertension and asthma patients and 659 sick controls in the community. Population average generalized estimating equation was used to make longitudinal comparative analysis. RESULTS: bipolar patient family caregivers were found to be more burdened, for about 8 to 10 months of the year of study, than family caregivers of diabetes, hypertension and asthma and sick controls in the community. The average difference in family caregiver burden score between bipolar and diabetes, hypertension and asthma patient family caregivers was 4.36 (z = -8.75, P>|z|= 0.001); while the difference due to time between the two groups was 3.42 (z= -4.27, P>|z|= 0.001). Similarly, the average difference in family caregiver burden score between family caregivers of bipolar patient and sick controls in the community was 3.7 (z= -4.88, P>|z| 0.001). In terms of longitudinal caregiver burden difference, bipolar patients family caregivers were found to be more burdened than family caregivers of sick controls in the community with a burden score difference of 2.97 (z= -5.17, P>|z|= 0.001). CONCLUSION: more should be done to lessen the economic and family caregiver burden due to bipolar disorder.