Assessment of milk metabolites as biomarkers for predicting feed efficiency in dairy sheep.

Estimating feed efficiency (FE) in dairy sheep is challenging due to the high cost of systems that measure individual feed intake. Identifying proxies that can serve as effective predictors of FE could make it possible to introduce FE into breeding programs. Here, 39 Assaf ewes in first lactation were evaluated regarding their FE by 2 metrics, residual feed intake (RFI) and feed conversion ratio (FCR). The ewes were classified into high, medium and low groups for each metric. Milk samples of the 39 ewes were subjected to untargeted metabolomics analysis. The complete milk metabolomic signature was used to discriminate the FE groups using partial least squares discriminant analysis. A total of 41 and 26 features were selected as the most relevant features for the discrimination of RFI and FCR groups, respectively. The predictive ability when utilizing the complete milk metabolomic signature and the reduced data sets were investigated using 4 machine learning (ML) algorithms and a multivariate regression method. The orthogonal partial least squares algorithm outperformed other ML algorithms for FCR prediction in the scenarios using the complete milk metabolite signature (R2 = 0.62 ± 0.06) and the 26 selected features (R2 = 0.62 ± 0.15). Regarding RFI predictions, the scenarios using the 41 selected features outperformed the scenario with the complete milk metabolite signature, where the multilayer feedforward artificial neural network (R2 = 0.18 ± 0.14) and extreme gradient boosting (R2 = 0.17 ± 0.15) outperformed other algorithms. The functionality of the selected metabolites implied that the metabolism of glucose, galactose, fructose, sphingolipids, amino acids, insulin, and thyroid hormones was at play. Compared with the use of traditional methods, practical applications of these biomarkers might simplify and reduce costs in selecting feed-efficient ewes.

Epigenetic regulation of functional candidate genes for milk production traits in dairy sheep subjected to protein restriction in the prepubertal stage.

BACKGROUND: As the prepubertal stage is a crucial point for the proper development of the mammary gland and milk production, this study aims to evaluate how protein restriction at this stage can affect methylation marks in milk somatic cells. Here, 28 Assaf ewes were subjected to 42.3% nutritional protein restriction (14 animals, NPR) or fed standard diets (14 animals, C) during the prepubertal stage. During the second lactation, the milk somatic cells of these ewes were sampled, and the extracted DNA was subjected to whole-genome bisulfite sequencing. RESULTS: A total of 1154 differentially methylated regions (DMRs) were identified between the NPR and C groups. Indeed, the results of functional enrichment analyses of the genes harboring these DMRs suggested their relevant effects on the development of the mammary gland and lipid metabolism in sheep. The additional analysis of the correlations of the mean methylation levels within these DMRs with fat, protein, and dry extract percentages in the milk and milk somatic cell counts suggested associations between several DMRs and milk production traits. However, there were no phenotypic differences in these traits between the NPR and C groups. CONCLUSION: In light of the above, the results obtained in the current study might suggest potential candidate genes for the regulation of milk production traits in the sheep mammary gland. Further studies focusing on elucidating the genetic mechanisms affected by the identified DMRs may help to better understand the biological mechanisms modified in the mammary gland of dairy sheep as a response to nutritional challenges and their potential effects on milk production.

Multi-breed host rumen epithelium transcriptome and microbiome associations and their relationship with beef cattle feed efficiency.

Understanding host-microbial interactions in the rumen and its influence on desirable production traits may lead to potential microbiota manipulation or genetic selection for improved cattle feed efficiency. This study investigated the host transcriptome and its correlation with the rumen archaea and bacteria differential abundance of two pure beef cattle breeds (Angus and Charolais) and one composite beef hybrid (Kinsella) divergent for residual feed intake (RFI; low-RFI vs. high-RFI). Using RNA-Sequencing of rumen tissue and 16S rRNA gene amplicon sequencing, differentially expressed genes (FDR ≤ 0.05, |log2(Fold-change) >|2) and differentially abundant (p-value < 0.05) archaea and bacteria amplicon sequence variants (ASV) were determined. Significant correlations between gene expression and ASVs (p-value < 0.05) were determine using Spearman correlation. Interesting associations with muscle contraction and the modulation of the immune system were observed for the genes correlated with bacterial ASVs. Potential functional candidate genes for feed efficiency status were identified for Angus (CCL17, CCR3, and CXCL10), Charolais (KCNK9, GGT1 and IL6), and Kinsella breed (ESR2). The results obtained here provide more insights regarding the applicability of target host and rumen microbial traits for the selection and breeding of more feed efficient beef cattle.

Genome-wide association study using haplotype libraries and repeated-measures model to identify candidate genomic regions for stillbirth in Holstein cattle.

Reduced fertility is one of the main causes of economic losses on dairy farms, resulting in economic losses estimated at $938 per stillbirth case in Holstein herds. The identification of genomic regions associated with stillbirth could help to develop better management and breeding strategies aimed to reduce the frequency of undesirable gestation outcomes. Here, 10,570 cows and 50,541 birth records were used to perform a haplotype-based GWAS. A total of 41 significantly associated pseudo-SNPs (haplotypes within haplotype blocks converted to a binary classification) were identified after Bonferroni adjustment for multiple tests. A total of 117 positional candidate genes were annotated within or close (in a 200-kb interval) to significant pseudo-SNPs (haplotype blocks). The guilt-by-association functional prioritization identified 31 potential functional candidate genes for reproductive performance out of the 117 positional candidate genes annotated. These genes play crucial roles in biological processes associated with pregnancy persistence, fetus development, immune response, among others. These results helped us to better understand the genetic basis of stillbirth in dairy cattle and may be useful for the prediction of stillbirth in Holstein cattle, helping to reduce the related economic losses caused by this phenotype.

Identification of functional candidate variants and genes for feed efficiency in Holstein and Jersey cattle breeds using RNA-sequencing.

The identification of functional genetic variants and associated candidate genes linked to feed efficiency may help improve selection for feed efficiency in dairy cattle, providing economic and environmental benefits for the dairy industry. This study used RNA-sequencing data obtained from liver tissue from 9 Holstein cows [n = 5 low residual feed intake (RFI), n = 4 high RFI] and 10 Jersey cows (n = 5 low RFI, n = 5 high RFI), which were selected from a single population of 200 animals. Using RNA-sequencing, 3 analyses were performed to identify: (1) variants within low or high RFI Holstein cattle; (2) variants within low or high RFI Jersey cattle; and (3) variants within low or high RFI groups, which are common across both Holstein and Jersey cattle breeds. From each analysis, all variants were filtered for moderate, modifier, or high functional effect, and co-localized quantitative trait loci (QTL) classes, enriched biological processes, and co-localized genes related to these variants, were identified. The overlapping of the resulting genes co-localized with functional SNP from each analysis in both breeds for low or high RFI groups were compared. For the first two analyses, the total number of candidate genes associated with moderate, modifier, or high functional effect variants fixed within low or high RFI groups were 2,810 and 3,390 for Holstein and Jersey breeds, respectively. The major QTL classes co-localized with these variants included milk and reproduction QTL for the Holstein breed, and milk, production, and reproduction QTL for the Jersey breed. For the third analysis, the common variants across both Holstein and Jersey breeds, uniquely fixed within low or high RFI groups were identified, revealing a total of 86,209 and 111,126 functional variants in low and high RFI groups, respectively. Across all 3 analyses for low and high RFI cattle, 12 and 31 co-localized genes were overlapping, respectively. Among the overlapping genes across breeds, 9 were commonly detected in both the low and high RFI groups (INSRR, CSK, DYNC1H1, GAB1, KAT2B, RXRA, SHC1, TRRAP, PIK3CB), which are known to play a key role in the regulation of biological processes that have high metabolic demand and are related to cell growth and regeneration, metabolism, and immune function. The genes identified and their associated functional variants may serve as candidate genetic markers and can be implemented into breeding programs to help improve the selection for feed efficiency in dairy cattle.

Genome-wide association study to identify genomic regions and positional candidate genes associated with male fertility in beef cattle.

Fertility plays a key role in the success of calf production, but there is evidence that reproductive efficiency in beef cattle has decreased during the past half-century worldwide. Therefore, identifying animals with superior fertility could significantly impact cow-calf production efficiency. The objective of this research was to identify candidate regions affecting bull fertility in beef cattle and positional candidate genes annotated within these regions. A GWAS using a weighted single-step genomic BLUP approach was performed on 265 crossbred beef bulls to identify markers associated with scrotal circumference (SC) and sperm motility (SM). Eight windows containing 32 positional candidate genes and five windows containing 28 positional candidate genes explained more than 1% of the genetic variance for SC and SM, respectively. These windows were selected to perform gene annotation, QTL enrichment, and functional analyses. Functional candidate gene prioritization analysis revealed 14 prioritized candidate genes for SC of which MAP3K1 and VIP were previously found to play roles in male fertility. A different set of 14 prioritized genes were identified for SM and five were previously identified as regulators of male fertility (SOD2, TCP1, PACRG, SPEF2, PRLR). Significant enrichment results were identified for fertility and body conformation QTLs within the candidate windows. Gene ontology enrichment analysis including biological processes, molecular functions, and cellular components revealed significant GO terms associated with male fertility. The identification of these regions contributes to a better understanding of fertility associated traits and facilitates the discovery of positional candidate genes for future investigation of causal mutations and their implications.

Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle.

BACKGROUND: Optimization of an RNA-Sequencing (RNA-Seq) pipeline is critical to maximize power and accuracy to identify genetic variants, including SNPs, which may serve as genetic markers to select for feed efficiency, leading to economic benefits for beef production. This study used RNA-Seq data (GEO Accession ID: PRJEB7696 and PRJEB15314) from muscle and liver tissue, respectively, from 12 Nellore beef steers selected from 585 steers with residual feed intake measures (RFI; n = 6 low-RFI, n = 6 high-RFI). Three RNA-Seq pipelines were compared including multi-sample calling from i) non-merged samples; ii) merged samples by RFI group, iii) merged samples by RFI and tissue group. The RNA-Seq reads were aligned against the UMD3.1 bovine reference genome (release 94) assembly using STAR aligner. Variants were called using BCFtools and variant effect prediction (VeP) and functional annotation (ToppGene) analyses were performed. RESULTS: On average, total reads detected for Approach i) non-merged samples for liver and muscle, were 18,362,086.3 and 35,645,898.7, respectively. For Approach ii), merging samples by RFI group, total reads detected for each merged group was 162,030,705, and for Approach iii), merging samples by RFI group and tissues, was 324,061,410, revealing the highest read depth for Approach iii). Additionally, Approach iii) merging samples by RFI group and tissues, revealed the highest read depth per variant coverage (572.59 ± 3993.11) and encompassed the majority of localized positional genes detected by each approach. This suggests Approach iii) had optimized detection power, read depth, and accuracy of SNP calling, therefore increasing confidence of variant detection and reducing false positive detection. Approach iii) was then used to detect unique SNPs fixed within low- (12,145) and high-RFI (14,663) groups. Functional annotation of SNPs revealed positional candidate genes, for each RFI group (2886 for low-RFI, 3075 for high-RFI), which were significantly (P < 0.05) associated with immune and metabolic pathways. CONCLUSION: The most optimized RNA-Seq pipeline allowed for more accurate identification of SNPs, associated positional candidate genes, and significantly associated metabolic pathways in muscle and liver tissues, providing insight on the underlying genetic architecture of feed efficiency in beef cattle.

Genetic mechanisms regulating the host response during mastitis.

Mastitis is a very costly and common disease in the dairy industry. The study of the transcriptome from healthy and mastitic milk somatic cell samples using RNA-Sequencing technology can provide measurements of transcript levels associated with the immune response to the infection. The objective of this study was to characterize the Holstein milk somatic cell transcriptome from 6 cows to determine host response to intramammary infections. RNA-Sequencing was performed on 2 samples from each cow from 2 separate quarters, one classified as healthy (n = 6) and one as mastitic (n = 6). In total, 449 genes were differentially expressed between the healthy and mastitic quarters (false discovery rate <0.05, fold change >±2). Among the differentially expressed genes, the most expressed genes based on reads per kilobase per million mapped reads (RPKM) in the healthy group were associated with milk components (CSN2 and CSN3), and in the mastitic group they were associated with immunity (B2M and CD74). In silico functional analysis was performed using the list of 449 differentially expressed genes, which identified 36 significantly enriched metabolic pathways (false discovery rate <0.01), some of which were associated with the immune system, such as cytokine-cytokine interaction and cell adhesion molecules. Seven functional candidate genes were selected, based on the criteria of being highly differentially expressed between healthy and mastitic groups and significantly enriched in metabolic pathways that are relevant to the inflammatory process (GLYCAM1, B2M, CD74, BoLA-DRA, FCER1G, SDS, and NFKBIA). Last, we identified the differentially expressed genes that are located in quantitative trait locus regions previously known to be associated with mastitis, specifically clinical mastitis, somatic cell count, and somatic cell score. It was concluded that multiple genes within quantitative trait locus regions could potentially affect host response to mastitis-causing agents, making some cows more susceptible to intramammary infections. The identification of potential candidate genes with functional, statistical, biological, and positional relevance associated with host defense to infection will contribute to a better understanding of the underlying genetic architecture associated with mastitis. This in turn will improve the sustainability of agricultural practices by facilitating the selection of cows with improved host defense leading to increased resistance to mastitis.

Genome-wide association for milk production traits and somatic cell score in different lactation stages of Ayrshire, Holstein, and Jersey dairy cattle.

We performed genome-wide association analyses for milk, fat, and protein yields and somatic cell score based on lactation stages in the first 3 parities of Canadian Ayrshire, Holstein, and Jersey cattle. The genome-wide association analyses were performed considering 3 different lactation stages for each trait and parity: from 5 to 95, from 96 to 215, and from 216 to 305 d in milk. Effects of single nucleotide polymorphisms (SNP) for each lactation stage, trait, parity, and breed were estimated by back-solving the direct breeding values estimated using the genomic best linear unbiased predictor and single-trait random regression test-day models containing only the fixed population average curve and the random genomic curves. To identify important genomic regions related to the analyzed lactation stages, traits, parities and breeds, moving windows (SNP-by-SNP) of 20 adjacent SNP explaining more than 0.30% of total genetic variance were selected for further analyses of candidate genes. A lower number of genomic windows with a relatively higher proportion of the explained genetic variance was found in the Holstein breed compared with the Ayrshire and Jersey breeds. Genomic regions associated with the analyzed traits were located on 12, 8, and 15 chromosomes for the Ayrshire, Holstein, and Jersey breeds, respectively. Especially for the Holstein breed, many of the identified candidate genes supported previous reports in the literature. However, well-known genes with major effects on milk production traits (e.g., diacylglycerol O-acyltransferase 1) showed contrasting results among lactation stages, traits, and parities of different breeds. Therefore, our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the analyzed traits across breeds, parities, and lactation stages. Further functional studies are needed to validate our findings in independent populations.

Implementation of Bayesian methods to identify SNP and haplotype regions with transmission ratio distortion across the whole genome: TRDscan v.1.0.

Realized deviations from the expected Mendelian inheritance of alleles from heterozygous parents have been previously reported in a broad range of organisms (i.e., transmission ratio distortion; TRD). Various biological mechanisms affecting gametes, embryos, fetuses, or even postnatal offspring can produce patterns of TRD. However, knowledge about its prevalence and potential causes in livestock species is still scarce. Specific Bayesian models have been recently developed for the analyses of TRD for biallelic loci, which accommodated a wide range of population structures, enabling TRD investigation in livestock populations. The parameterization of these models is flexible and allows the study of overall (parent-unspecific) TRD and sire- and dam-specific TRD. This research aimed at deriving Bayesian models for fitting TRD on the basis of haplotypes, testing the models for both haplotype- and SNP-based methods in simulated data and actual Holstein genotypes, and developing a specific software for TRD analyses. Results obtained on simulated data sets showed that the statistical power of the analysis increased with sample size of trios (n), proportion of heterozygous parents, and the magnitude of the TRD. On the other hand, the statistical power to detect TRD decreased with the number of alleles at each loci. Bayesian analyses showed a strong Pearson correlation coefficient (≥0.97) between simulated and estimated TRD that reached the significance level of Bayes factor ≥10 for both single-marker and haplotype analyses when n ≥ 25. Moreover, the accuracy in terms of the mean absolute error decreased with the increase of the sample size and increased with the number of alleles at each loci. Using real data (55,732 genotypes of Holstein trios), SNP- and haplotype-based distortions were detected with overall TRD, sire-TRD, or dam-TRD, showing different magnitudes of TRD and statistical relevance. Additionally, the haplotype-based method showed more ability to capture TRD compared with individual SNP. To discard possible random TRD in real data, an approximate empirical null distribution of TRD was developed. The program TRDscan v.1.0 was written in Fortran 2008 language and provides a powerful statistical tool to scan for TRD regions across the whole genome. This developed program is freely available at http://www.casellas.info/files/TRDscan.zip.

A new tetra-primer ARMS-PCR for genotyping bovine kappa-casein polymorphisms.

Kappa-casein (κ-casein) is one of the most abundant milk proteins. Its main function is to avoid the aggregation of casein micelles, keeping them, and therefore calcium phosphate, in pockets in solution. In bovines, a κ-casein functional polymorphism has been associated with fat, calcium, and protein milk contents and faster curd contraction in cheese production. Quicker curd contraction reduces the loss of milk solids, enhancing cheese yield. This polymorphism induces a double amino acid substitution (Thr136Ile and Ala148Asp). The polymorphism is normally detected by PCR-RFLP, which is a laborious method. An interesting methodological alternative is the tetra-primer amplification refractory mutation system PCR (tetra-primer ARMS-PCR). A tetra-primer ARMS-PCR for the detection of this κ-casein polymorphism has been described. However, specificity was not achieved, probably due to problems with primer design. We developed a new tetra-primer ARMS-PCR for the detection of the κ-casein polymorphism. This new method was validated in a double-blind test, by comparison with the results obtained for 50 Guzerá bulls formerly genotyped by PCR-RFLP. This new method achieved 100% sensitivity and specificity. We conclude that this method is a useful, cost-efficient alternative for the detection of functional κ-casein polymorphisms.