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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 two 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 datasets were investigated using four machine-learning algorithms and a multivariate regression method. The Orthogonal Partial Least Square algorithm outperformed other ML algorithms for the FCR prediction in the scenarios using the complete milk metabolite signature (r2=0.62±0.06) and the 26 selected features (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 to the use of traditional methods, practical applications of these biomarkers might simplify and reduce costs in selecting feed-efficient ewes.

As higher feed efficiency in dairy ruminants means a higher capability to transform feed nutrients into milk and milk components, dierences in feed efficiency are expected to be partly linked to changes in the physiology of the mammary glands. Therefore, this study aimed to determine the biological functions and key regulatory genes associated with feed efficiency in dairy sheep using the milk somatic cell transcriptom

Several recent studies have demonstrated the role of long non-coding RNAs (lncRNAs) in regulating the defense mechanism against parasite infections, but no studies are available that investigated their relevance for immune response to nematode infection in sheep. Thus, the aim of the current study was to (i) detect putative lncRNAs that are expressed in the abomasal lymph node of adult sheep after an experimental infection with the gastrointestinal nematode (GIN) Teladorsagia circumcincta and (ii) to elucidate their potential functional role associated with the differential host immune response. We hypothesized that putative lncRNAs differentially expressed (DE) between samples from animals that differ in resistance to infection may play a significant regulatory role in response to nematode infection in adult sheep. To obtain further support for our hypothesis, we performed co-expression and functional gene enrichment analyses with the differentially expressed lncRNAs (DE lncRNAs). In a conservative approach, we included for this predictive analysis only those lncRNAs that are confirmed and supported by documentation of expression in gastrointestinal tissues in the current sheep gene atlas. We identified 9,105 putative lncRNA transcripts corresponding to 7,124 gene loci. Of these, 457 were differentially expressed lncRNA loci (DELs) with 683 lncRNA transcripts. Based on a gene co-expression analysis via weighted gene coexpression network analysis, 12 gene network modules (GNMs) were found significantly correlated with at least one of 10 selected target DE lncRNAs. Based on the principle of “guilt-by-association,” the DE genes from each of the three most significantly correlated GNMs were subjected to a gene enrichment analysis. The significant pathways associated with DE lncRNAs included ERK5 Signaling, SAPK/JNK Signaling, RhoGDI Signaling, EIF2 Signaling, Regulation of eIF4 and p70S6K Signaling and Oxidative Phosphorylation pathways. They belong to signaling pathwa

This work aimed to use 16S ribosomal RNA sequencing with the Illumina MiSeq platform to describe the milk microbiota from 50 healthy Assaf ewes. The global observed microbial community for clinically healthy milk samples analysed was complex and showed a vast diversity. The core microbiota of the sheep milk includes five genera: Staphylococcus, Lactobacillus, Corynebacterium, Streptococcus and Escherichia/Shigella. Although there are some differences, some of these genera are common with the microbiota core pattern of milk from other species, especially with dairy cows. The microbial composition of the studied samples, based on the definition of amplicon sequence variants, was analysed through a correlation network. A preliminary analysis by grouping the milk samples based on their somatic cell count (SCC), which is considered an indicator of subclinical mastitis (SM), showed certain differences for the core of the samples identified as SM. The differences in the microbiota diversity pattern among samples might also suggest that subclinical mastitis would be associated with the significant increase in some genera that are inhabitants of the mammary gland and a remarkable concomitant reduction in the microbial diversity. Additionally, we have also presented here a preliminary analysis to assess the impact of the sheep milk microbiome on SCC, as an indicator of subclinical mastitis. The results here reported provide a first characterization of the sheep milk microbiota and settle the basis for future studies in this field.

In dairy sheep flocks from Mediterranean countries, replacement and adult ewes are the animals most affected by gastrointestinal nematode (GIN) infections. In this study, we have exploited the information derived from an RNA-Seq experiment with the aim of identifying potential causal mutations related to GIN resistance in sheep. Considering the RNA-Seq samples from 12 ewes previously classified as six resistant and six susceptible animals to experimental infection by Teladorsagia circumcincta, we performed a variant calling analysis pipeline using two different types of software, gatk version 3.7 and Samtools version 1.4. The variants commonly identified by the two packages (high-quality variants) within two types of target regions – (i) QTL regions previously reported in sheep for parasite resistance based on SNP-chip or sequencing technology studies and (ii) functional candidate genes selected from gene expression studies related to GIN resistance in sheep – were further characterised to identify mutations with a potential functional impact. Among the genes harbouring these potential functional variants (930 and 553 respectively for the two types of regions), we identified 111 immune-related genes in the QTL regions and 132 immune-related genes from the initially selected candidate genes. For these immune-related genes harbouring potential functional variants, the enrichment analyses performed highlighted significant GO terms related to apoptosis, adhesion and inflammatory response, in relation to the QTL related variants, and significant disease-related terms such as inflammation, adhesion and necrosis, in relation to the initial candidate gene list. Overall, the study provides a valuable list of potential causal mutations that could be considered as candidate causal mutations in relation to GIN resistance in sheep. Future studies should assess the role of these suggested mutations with the aim of identifying genetic markers that could be directly implemented in

Milk fat depression (MFD) is characterized by a reduction in the content of milk fat, presumably caused by the anti-lipogenic effects of rumen biohydrogenation intermediates, such as trans-10 cis-12 conjugated linoleic acid (CLA). In this study, RNA-Seq technology was used to help elucidate the mammary responses involved in CLA-induced MFD in lactating ewes. To this end, we compared the milk somatic cell transcriptome of ewes suffering from CLA-induced MFD with control ewes (i.e., those without MFD), as well as with ewes fed a diet supplemented with fish oil (FO-MFD) that we previously reported affects the mammary transcriptome. In the differential expression analysis between CLA-MFD and controls, we identified 1,524 differentially expressed genes (DEGs), whereas 653 were detected between CLA- and FO-MFD groups. Although this article focuses on lipid metabolism, CLA affected the expression of many genes related to other biological processes, especially immunity. Among the 55 genes shared by both MFD conditions, some genes linked to fatty acid synthesis, such as ACACA, AACS, ACSS2, or ACSS3, were downregulated. In addition, this study provides a list of candidate genes that are not usually considered in the nutrigenomics of MFD but that may act as key regulators of this syndrome in dairy ewes.

La suplementación de la dieta de las ovejas con lípidos de origen marino permite mejorar el carácter saludable de la grasa de la leche. Sin embargo, produce el síndrome de baja grasa en la leche, lo que impide su implementación en condiciones prácticas.