Dataset Information

Characterization of Holstein and Normande whole milk miRNomes highlights breed specificities.

ABSTRACT: The concept of milk as a healthy food has opened the way for studies on milk components, from nutrients to microRNAs, molecules with broad regulatory properties present in large quantities in milk. Characterization of these components has been performed in several species, such as humans and bovine, depending on the stages of lactation. Here, we have studied the variation in milk microRNA composition according to genetic background. Using high throughput sequencing, we have characterized and compared the milk miRNomes of Holstein and Normande cattle, dairy breeds with distinct milk production features, in order to highlight microRNAs that are essential for regulation of the lactation process. In Holstein and Normande milk, 2,038 and 2,030 microRNAs were identified, respectively, with 1,771 common microRNAs, of which 1,049 were annotated and 722 were predicted. The comparison of the milk miRNomes of two breeds allowed to highlight 182 microRNAs displaying significant differences in the abundance. They are involved in the regulation of lipid metabolism and mammary morphogenesis and development, which affects lactation. Our results provide new insights into the regulation of molecular mechanisms involved in milk production.

SUBMITTER: Le Guillou S

PROVIDER: S-EPMC6937266 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Characterization of Holstein and Normande whole milk miRNomes highlights breed specificities.

Le Guillou S S Leduc A A Laubier J J Barbey S S Rossignol M-N MN Lefebvre R R Marthey S S Laloë D D Le Provost F F

Scientific reports 20191230 1

The concept of milk as a healthy food has opened the way for studies on milk components, from nutrients to microRNAs, molecules with broad regulatory properties present in large quantities in milk. Characterization of these components has been performed in several species, such as humans and bovine, depending on the stages of lactation. Here, we have studied the variation in milk microRNA composition according to genetic background. Using high throughput sequencing, we have characterized and com ...[more]

PMID: 31889100

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Project description:BackgroundGenome-wide association studies (GWAS) were performed at the sequence level to identify candidate mutations that affect the expression of six major milk proteins in Montbéliarde (MON), Normande (NOR), and Holstein (HOL) dairy cattle. Whey protein (α-lactalbumin and β-lactoglobulin) and casein (αs1, αs2, β, and κ) contents were estimated by mid-infrared (MIR) spectrometry, with medium to high accuracy (0.59 ≤ R2 ≤ 0.92), for 848,068 test-day milk samples from 156,660 cows in the first three lactations. Milk composition was evaluated as average test-day measurements adjusted for environmental effects. Next, we genotyped a subset of 8080 cows (2967 MON, 2737 NOR, and 2306 HOL) with the BovineSNP50 Beadchip. For each breed, genotypes were first imputed to high-density (HD) using HD single nucleotide polymorphisms (SNPs) genotypes of 522 MON, 546 NOR, and 776 HOL bulls. The resulting HD SNP genotypes were subsequently imputed to the sequence level using 27 million high-quality sequence variants selected from Run4 of the 1000 Bull Genomes consortium (1147 bulls). Within-breed, multi-breed, and conditional GWAS were performed.ResultsThirty-four distinct genomic regions were identified. Three regions on chromosomes 6, 11, and 20 had very significant effects on milk composition and were shared across the three breeds. Other significant effects, which partially overlapped across breeds, were found on almost all the autosomes. Multi-breed analyses provided a larger number of significant genomic regions with smaller confidence intervals than within-breed analyses. Combinations of within-breed, multi-breed, and conditional analyses led to the identification of putative causative variants in several candidate genes that presented significant protein-protein interactions enrichment, including those with previously described effects on milk composition (SLC37A1, MGST1, ABCG2, CSN1S1, CSN2, CSN1S2, CSN3, PAEP, DGAT1, AGPAT6) and those with effects reported for the first time here (ALPL, ANKH, PICALM).ConclusionsGWAS applied to fine-scale phenotypes, multiple breeds, and whole-genome sequences seems to be effective to identify candidate gene variants. However, although we identified functional links between some candidate genes and milk phenotypes, the causality between candidate variants and milk protein composition remains to be demonstrated. Nevertheless, the identification of potential causative mutations that underlie milk protein composition may have immediate applications for improvements in cheese-making.

Project description:In general, Fourier-transform infrared (FTIR) predictions are developed using a single-breed population split into a training and a validation set. However, using populations formed of different breeds is an attractive way to design cross-validation scenarios aimed at increasing prediction for difficult-to-measure traits in the dairy industry. This study aimed to evaluate the potential of FTIR prediction using training set combining specialized and dual-purpose dairy breeds to predict different phenotypes divergent in terms of biological meaning, variability, and heritability, such as body condition score (BCS), serum β-hydroxybutyrate (BHB), and kappa casein (k-CN) in the major cattle breed, i.e., Holstein-Friesian. Data were obtained from specialized dairy breeds: Holstein (468 cows) and Brown Swiss (657 cows), and dual-purpose breeds: Simmental (157 cows), Alpine Grey (75 cows), and Rendena (104 cows), giving a total of 1461 cows from 41 multi-breed dairy herds. The FTIR prediction model was developed using a gradient boosting machine (GBM), and predictive ability for the target phenotype in Holstein cows was assessed using different cross-validation (CV) strategies: a within-breed scenario using 10-fold cross-validation, for which the Holstein population was randomly split into 10 folds, one for validation and the remaining nine for training (10-fold_HO); an across-breed scenario (BS_HO) where the Brown Swiss cows were used as the training set and the Holstein cows as the validation set; a specialized multi-breed scenario (BS+HO_10-fold), where the entire Brown Swiss and Holstein populations were combined then split into 10 folds, and a multi-breed scenario (Multi-breed), where the training set comprised specialized (Holstein and Brown Swiss) and dual-purpose (Simmental, Alpine Grey, and Rendena) dairy cows, combined with nine folds of the Holstein cows. Lastly a Multi-breed CV2 scenario was implemented, assuming the same number of records as the reference scenario and using the same proportions as the multi-breed. Within-Holstein, FTIR predictions had a predictive ability of 0.63 for BCS, 0.81 for BHB, and 0.80 for k-CN. Using a specific breed (Brown Swiss) as the training set for prediction in the Holstein population reduced the prediction accuracy by 10% for BCS, 7% for BHB, and 11% for k-CN. Notably, the combination of Holstein and Brown Swiss cows in the training set increased the predictive ability of the model by 6%, which was 0.66 for BCS, 0.85 for BHB, and 0.87 for k-CN. Using multiple specialized and dual-purpose animals in the training set outperforms the 10-fold_HO (standard) approach, with an increase in predictive ability of 8% for BCS, 7% for BHB, and 10% for k-CN. When the Multi-breed CV2 was implemented, no improvement was observed. Our findings suggest that FTIR prediction of different phenotypes in the Holstein breed can be improved by including different specialized and dual-purpose breeds in the training population. Our study also shows that predictive ability is enhanced when the size of the training population and the phenotypic variability are increased.

Project description:Milk oligosaccharides (MOs) can be prebiotic and antiadhesive, while fatty acids (MFAs) can be antimicrobial. Both have been associated with milk microbes or mammary gland inflammation in humans. Relationships between these milk components and milk microbes or inflammation have not been determined for cows and could help elucidate a novel approach for the dairy industry to promote desired milk microbial composition for improvement of milk quality and reduction of milk waste. We aimed to determine relationships among milk microbiota, MFAs, MOs, lactose, and somatic cell counts (SCC) from Holstein cows, using our previously published data. Raw milk samples were collected at three time points, ranging from early to late lactation. Data were analyzed using linear mixed-effects modeling and repeated-measures correlation. Unsaturated MFA and short-chain MFA had mostly negative relationships with potentially pathogenic genera, including Corynebacterium, Pseudomonas, and an unknown Enterobacteriaceae genus but numerous positive relationships with symbionts Bifidobacterium and Bacteroides. Conversely, many MOs were positively correlated with potentially pathogenic genera (e.g., Corynebacterium, Enterococcus, and Pseudomonas), and numerous MOs were negatively correlated with the symbiont Bifidobacterium. The neutral, nonfucosylated MO composed of eight hexoses had a positive relationship with SCC, while lactose had a negative relationship with SCC. One interpretation of these trends might be that in milk, MFAs disrupt primarily pathogenic bacterial cells, causing a relative increase in abundance of beneficial microbial taxa, while MOs respond to and act on pathogenic taxa primarily through antiadhesive methods. Further research is needed to confirm the potential mechanisms driving these correlations. IMPORTANCE Bovine milk can harbor microbes that cause mastitis, milk spoilage, and foodborne illness. Fatty acids found in milk can be antimicrobial and milk oligosaccharides can have antiadhesive, prebiotic, and immune-modulatory effects. Relationships among milk microbes, fatty acids, oligosaccharides, and inflammation have been reported for humans. To our knowledge, associations among the milk microbial composition, fatty acids, oligosaccharides, and lactose have not been reported for healthy lactating cows. Identifying these potential relationships in bovine milk will inform future efforts to characterize direct and indirect interactions of the milk components with the milk microbiota. Since many milk components are associated with herd management practices, determining if these milk components impact milk microbes may provide valuable information for dairy cow management and breeding practices aimed at minimizing harmful and spoilage-causing microbes in raw milk.

Project description:Milk is a highly nutritious natural product that provides not only a rich source of amino acids to the consumer but also hundreds of bioactive peptides and proteins known to elicit health-benefitting activities. We investigated the milk protein profile produced by Holstein and Jersey dairy cows maintained under the same diet, management and environmental conditions using proteomic approaches that optimize protein extraction and characterization of the low abundance proteins within the skim milk fraction of bovine milk. In total, 935 low abundance proteins were identified. Gene ontology classified all proteins identified into various cellular localization and function categories. A total of 43 low abundance proteins were differentially expressed between the two dairy breeds. Bioactive proteins involved in host-defense, including lactotransferrin (P=0.0026) and complement C2 protein (P=0.0001), were differentially expressed by the two breeds, whereas others such as osteopontin (P=0.1788) and lactoperoxidase (P=0.2973) were not. This work is the first to outline the protein profile produced by two important breeds of dairy cattle maintained under the same diet, environment and management conditions in order to observe likely true breed differences. This research now allows us to better understand and contrast further research examining the bovine proteome that includes these different breeds.Biological significanceWithin the last decade, the amount of research characterizing the bovine milk proteome has increased due to growing interest in the bioactive proteins that are present in milk. Proteomic analysis of low abundance whey proteins has mainly focused on human breast milk; however, previous research has highlighted the presence of bioactive proteins in bovine milk. Recent publications outlining the cross-reactivity of bovine bioactive proteins on human biological function highlight the need for further investigation into the bovine milk proteome. The rationale behind this study is to characterize and compare the low abundance protein profile in the skim milk fraction produced from Holstein and Jersey breeds of dairy cattle, which are two major dairy cattle breeds in the USA. A combination of fractionation strategies was used to efficiently enrich the low abundance proteins from bovine skim milk for proteomic profiling. A total of 935 low abundance proteins were identified and compared between the two bovine breeds. The results from this study provide insight into breed differences and similarities in the milk proteome profile produced by two breeds of dairy cattle.

Dataset Information

Characterization of Holstein and Normande whole milk miRNomes highlights breed specificities.

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Characterization of Holstein and Normande whole milk miRNomes highlights breed specificities.

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