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.

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.

Gelsolin expression in sheep milk somatic cells during lactation.

The identification of genes involved in phenotypes related to milk quality is important for both economic and health aspects in livestock production. The aim of this study was to assess the level of gelsolin gene expression in two breeds of dairy sheep - Sarda and Gentile - with pronounced differences in quantitative and qualitative milk traits. Gelsolin, a type of actin-modulating proteins is involved in the processes of actin remodeling during cell growth and apoptosis; therefore a role of this protein in mammary changes during lactation was here hypothesized. Individual milk samples were collected three times during lactation from 26 ewes of the two breeds. The differential gene expression of gelsolin in the two breeds and the three lactation times was estimated by quantitative PCR on RNA extracted from milk somatic cells. Correlations of gelsolin gene expression with milk yield and quality and days of lactation were also estimated. The results showed that gelsolin gene expression was significantly higher in the Sarda compared to the Gentile at each lactation stage, in agreement with the longer lactation duration and the higher daily milk yield of the first breed. Significant correlations of gelsolin gene expression were found with milk fat content in Sarda breed (-0.46, P<0.05). Gelsolin expression analysis confirmed the link between gelsolin gene function and milk fat content of sheep.

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.

Genetic parameters and genome-wide associations of twinning rate in a local breed, the Maremmana cattle.

This study seeks to verify the feasibility of increasing twinning in a herd of the Italian autochtonous Maremmana breed. The data set included 1260 individuals born from 1963 to 2014, 527 males and 733 females, 402 of them calving at least once from 1983 through 2015. Breeding values for twinning were estimated by a single-trait linear animal model. However, since twinning is a dichotomous trait and the frequency of twins is far smaller than the frequency of single births, breeding values were also estimated by a single-trait animal threshold model. Heritability of twinning was 0.014±0.018 and 0.062±0.093 for the linear and the threshold models, respectively. Repeatability was 0.071±0.004 and 0.286± 0.012, respectively, for the two models. Genotyping with the Illumina BovineSNP54 BeadChip was performed for cows living on farm in 2012 (119 cows) and a genome-wide association analysis was performed on the corrected phenotype of all calving during the lifespan of each cow, using the GenABEL package in R and a three step GRAMMAR-GC approach. Genomic heritability, calculated from the genomic kinship matrix estimated through genomic marker data, was 0.29±0.021. The most significant detected single nucleotide polymorphisms (Hapmap22923-BTA-129564) was located in proximity of two genes, ARHGAP8 and TMEM200C, which might be potential functional candidates for twinning rate in cattle.

Sequence analysis and genetic variability of stearoyl CoA desaturase (SCD) gene in the Italian Mediterranean river buffalo.

Stearoyl-CoA desaturase (SCD) plays a key metabolic role by changing the saturated FA content of ruminant milk and meat. In this study we characterized for the first time the stearoyl-CoA desaturase (SCD) gene in river buffalo (Bubalus bubalis) and investigated its genetic variability. On a total of 78 buffaloes, 15 SNPs were detected and 6 of them were preliminarily genotyped. In particular, the g.133A>C SNP was found to create a new consensus site for the SP1 binding site, thus generating a new tandem repeat in the promoter region. A preliminary association study with the milk fatty acid content showed that the C allele significantly affects the total desaturation index (P<0.01). Linkage disequilibrium analysis allowed identification of 7 haplotypes and 4 tag SNPs. Such polymorphisms could represent useful genetic markers for association studies with fatty acid composition, but further studies are needed to evaluate their potential use to improve the nutritional quality of the dairy products.

Exploring polymorphisms and effects of candidate genes on milk fat quality in dairy sheep.

The aim of the present study was to investigate the genetic control of the fatty acid (FA) composition in milk from 3 breeds of sheep: Altamurana, Gentile di Puglia, and Sarda. Single nucleotide polymorphisms within genes, encoding enzymes putatively involved in the synthesis and metabolism of milk fat, were selected for analysis, and the allele substitution effects were determined for 16 genes, which were polymorphic in the 3 sheep breeds, upon the milk fat composition. Four genes (alpha-1-antichymotrypsin-2; diacylglycerol O-acyltransferase homolog-2; propionyl Coenzyme A carboxylase, beta polypeptide; and insulin-like growth factor-I) play a role in the desaturation of stearic FA into polyunsaturated fatty acids. Furthermore, 2 genes (growth hormone receptor and zona pellucida glycoprotein-2) affect the variability of the total fat content in addition to the butyric and stearic FA profile, and the fatty acid synthetase gene has an influence on the medium-chain FA. Milk FA profiles play an important role in dairy sheep farming because they have a large effect on cheese characteristics and also because sheep milk may be marketed as a source of nutraceuticals because it contains higher levels of conjugated linoleic acid than milk from other ruminants. The current study evaluated the global effects of a large number of single nucleotide polymorphisms and haplotypes on traits that are not commonly investigated in sheep but that are potentially very useful for improving milk quality.

Discovery, characterization and validation of single nucleotide polymorphisms within 206 bovine genes that may be considered as candidate genes for beef production and quality.

A large number of putative single nucleotide polymorphisms (SNPs) have been identified from the bovine genome-sequencing project. However, few of these have been validated and many will turn out to be sequencing artefacts or have low minor allele frequencies. In addition, there is little information available on SNPs within coding regions, which are likely to be responsible for phenotypic variation. Therefore, additional SNP discovery is necessary to identify and validate polymorphisms both in specific genes and genome-wide. Sequence-tagged sites within 286 genes were resequenced from a panel of animals representing a wide range of European cattle breeds. For 80 genes, no polymorphisms were identified, and 672 putative SNPs were identified within 206 genes. Fifteen European cattle breeds (436 individuals plus available parents) were genotyped with these putative SNPs, and 389 SNPs were confirmed to have minor allele frequencies above 10%. The genes containing SNPs were localized on chromosomes by radiation hybrid mapping and on the bovine genome sequence by Blast. Flanking microsatellite loci were identified, to facilitate the alignment of the genes containing the SNPs in relation to mapped quantitative trait loci. Of the 672 putative SNPs discovered in this work, only 11 were found among the validated SNPs and 100 were found among the approximately 2.3 million putative SNPs currently in dbSNP. The genes studied in this work could be considered as candidates for traits associated with beef production and the SNPs reported will help to assess the role of the genes in the genetic control of muscle development and meat quality. The allele frequency data presented allows the general utility of the SNPs to be assessed.

Sequence analysis of myostatin promoter in cattle.

Myostatin (GDF8) acts as a negative regulator of muscle growth. Mutations in the gene are responsible for the double muscling phenotype in several European cattle breeds. Here we describe the sequence of the upstream 5' region of the myostatin gene. The sequence analysis was carried out on three animals of nine European cattle breeds, with the aim to search for polymorphisms. A T/A polymorphism at -371 and a G/C polymorphism at -805 (relative to ATG) were found. PCR- RFLP was used to further screen 353 animals of the nine breeds studied and to assess the frequencies of the SNPs. The promoter region of the gene contains several binding sites for transcription factors found also in other myogenic genes. This may play an important role in the regulation of the protein and consequently on muscular development.