Abstract Background Improving animal welfare and health is a key objective in modern dairy systems. However, translating routinely available dairy herd improvement records into transparent and actionable welfare-related decisions remains challenging, particularly when using complex machine learning (ML) models. This study was designed as a proof of concept to evaluate whether explainable artificial intelligence (XAI) can improve the interpretability of ML models trained to reconstruct welfare indicators (WIs) derived from the Italian Breeders Association welfare-risk framework. Results Monthly records from 798 dairy cows were used to predict individual WIs for mastitis, subclinical acidosis, subclinical ketosis, longevity, and reproduction. Random forest regression models were trained using six routinely available test-day traits: milk acetone, fat, lactose, protein, urea, and electrical conductivity. The final dataset included 125,285 monthly cow records, split into training and test sets. In the test set, the models achieved Pearson correlations of 0.982, 0.987, 0.971, 0.979, and 0.978 for longevity, mastitis, subclinical ketosis, subclinical acidosis, and reproduction, respectively. The predicted WIs were used to reconstruct the overall welfare class, classified as good, intermediate, or risk, achieving a balanced accuracy of 0.841. SHapley Additive exPlanations were used to evaluate how the fitted models used each feature, while counterfactual explanations identified minimal feature changes required to shift risk predictions toward good welfare. Most explanation patterns were biologically plausible, although some non-intuitive outputs highlighted the need for expert oversight. Conclusions This proof-of-concept study shows how XAI can improve transparency and model auditing in welfare-related decision-support systems, while emphasizing the need for external validation before on-farm implementation.

Adipose tissue development in early life is crucial for thermoregulation, energy storage, and long-term metabolic programming. Understanding depot-specific differences provides insight into fat specialization and its adaptive significance. In fat- and semi-fat-tailed lambs, perirenal and tail fat are the main depots; however, their early postnatal development remains poorly understood. Here, we compared RNA-Seq data from perirenal and tail adipose tissues in 1-month-old Assaf lambs using differential gene expression and deconvolution analyses. A total of 2,507 genes were differentially expressed between the two fat depots. Perirenal fat exhibited transcriptional signatures associated with adipose tissue remodeling, thermogenic regulation, and the transition from brown to white adipose tissue, whereas tail fat showed increased expression of genes involved in lipid metabolism, insulin responsiveness, and mature adipocyte function. Despite the marked transcriptional divergence, deconvolution analyses did not identify significant differences in cell-type proportions between depots. Cell-type deconvolution using both a bovine single-nucleus RNA-seq reference atlas and an ovine embryonic single-cell RNA-seq atlas consistently identified adipocytes as the predominant cell populations, although they recovered distinct secondary cellular populations. The bovine reference atlas yielded higher Pearson’s correlation coefficients (0.728–0.803 vs. 0.254–0.602) and lower RMSE values (0.685–0.838 vs. 0.846–1.159) than the ovine embryonic reference, indicating a better fit to the bulk transcriptomic profiles despite being derived from a different species. These results suggest that, in 1-month-old lambs, the developmental stage represented by the reference atlas may have a greater impact on deconvolution performance than species origin. Overall, our findings provide new insights into adipose tissue specialization during early postnatal development and support the hypothesis that tail fat acquires mature lipid-storage characteristics earlier than perirenal fat in Assaf lambs.

Background Improving animal welfare and health is a key objective in modern dairy systems. However, translating the nowcommonly available high-frequency bovine-dedicated sensor instrument data into transparent, actionable decisions remains challenging, particularly when using complex machine learning (ML) models. In this study, we used ML to predict several welfare indicators (WIs) from routinely recorded milk-related data in dairy cows, and explainable AI (XAI) to interpret and visualise ML models for practical use. Results Monthly individual WIs for mastitis, subclinical acidosis, subclinical ketosis, longevity, and reproduction were predicted used with random forest models trained on routinely available test-day traits in dairy cows (milk acetone, fat, lactose, protein, urea, and electrical conductivity). The individual WIs were then used to derive an overall welfare score, with both individual and overall scores classified into good (G), intermediate (I), and risk (B) classes. SHapley Additive exPlanations (SHAP) values quantified feature importance and interactions, revealing relationships largely consistent with known physiology (e.g., extreme high and low values of milk components and urea associated with increased welfare risk) and highlighting class-specific and U-shaped effects that call for context-dependent interpretation. Counterfactual explanations were used to identify minimal changes in milk traits required to shift predictions from B to G class, thereby translating model outputs into candidate management adjustments. While most counterfactuals followed biologically plausible patterns, occasional non-intuitive suggestions underscored the need for expert oversight. Conclusions This study illustrates how SHAP and counterfactual explanations can be layered on top of ML models to generate interpretable, customizable decision-support tools for precision dairy cattle welfare, while emphasizing the need to field-validate their usability, economic value, and ethical implications.

Nutrigenomics investigates how nutrients modulate gene expression. Among them, fatty acids (FA) play important roles in regulating gene transcription, while long non-coding RNAs (lncRNAs) may be associated with gene regulation and metabolic diseases. This study aimed to analyze the hepatic transcriptome of pigs, a species frequently used as a model for nutrigenomic studies, to identify novel lncRNAs and their potential target genes in response to diets containing different sources of FA. Seventy-two pigs were fed four diets supplemented with 1.5% soybean oil (control), 3% canola oil, 3% fish oil, and 3% soybean oil. RNA sequencing of liver samples was performed to identify novel lncRNAs. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify modules associated with phenotypic traits related to lipid metabolism and inflammation. Functional enrichment analyses were then conducted to annotate genes within these modules using Gene Ontology (GO) terms and to assess overlap with Quantitative Trait Loci (QTL). The results revealed 106 novel lncRNAs potentially regulating genes associated with lipid metabolism and immune responses in pigs fed diets with different FA sources. These findings enhance understanding of the regulatory role of lncRNAs in pigs and reinforce their relevance as models for human metabolic diseases.

Lactation curve modeling plays a key role in dairy production, supporting on-farm management decisions and the selection of animals with superior productivity and resilience. However, accurately choosing a single model to represent the diversity of lactation patterns across individuals remains challenging. In this context, we presented in this study a flexible ensemble modeling (EM) framework, implemented in the EMOTIONS R package, to improve the prediction of daily milk yield and related traits in dairy cows and ewes. EMOTIONS integrates a wide set of lactation curve models and allows users to generate ensembles by weighting model predictions based on multiple criteria. The package provides tools for model fitting, ensemble creation, visualization, milk yield imputation, detection of milk loss events, and the estimation of residual-based resilience indicators. The advantages of the ensembles were evident in subsets of animals with poor individual model fits, underscoring the value of EMs in capturing individual variation and reducing underfitting. In both ewes' and cows' data sets analyzed as examples, the EM advantages were clear for subsets of individual lactations with overall poor model fittings (based on BIC). Overall, EMs represent a robust and adaptable approach for modeling lactation data, offering improved predictive accuracy while retaining the ability to interpret biologically meaningful model parameters.

The global demand for improved productivity, sustainability, welfare, and quality in livestock production presents significant challenges for breeders. Understanding trait correlations, often driven by pleiotropy, is essential for simultaneously improving traits of economic interest. Integrating multi-omics data and functional annotations can improve the disentangling of biological processes underlying the pleiotropic effect. Network-based machine learning (ML) models offer a robust solution for this integration. This study estimated gene-level P-values for pleiotropic effects using two phenotypic datasets: (i) Trait_GWAS, with phenotypic values of 12 traits covering milk production, composition, cheeseability, and mastitis resistance; and (ii) EBV_GWAS, with estimated breeding values for five similar traits, excluding cheeseability. Weighted gene co-expression networks (WGCNs) were constructed from milk somatic cell transcriptomics of Assaf ewes. Gene-term networks were built from gene ontology, metabolic pathways, and quantitative trait loci annotation for the genes in the WGCN. These networks were processed through a representative learning pipeline to create a latent vector representing gene importance. A hierarchical model integrated gene-level P-values and the latent vector, generating posterior probabilities of association for each gene. Significant results included 14 and 111 genes for Trait_GWAS and EBV_GWAS, respectively, with three shared genes (PHGDH, SLC1A4, and CSN3). Prioritized genes were linked to biological processes such as amino acid transport, lipid metabolism, mammary gland development, and immune regulation, often involving multiple biological functions. This reinforces the potential pleiotropic role of these genes. These findings highlight the utility of network-based ML models for prioritizing candidate genes with pleiotropic effects on milk, cheese, and health-related traits in dairy sheep.

Circular RNAs (circRNAs) and mRNAs are distinct transcripts from the same genes, produced by different splicing mechanisms. This study investigates the behavior of the circular transcriptome relative to the linear one across biological conditions and tissues. We analyzed transcriptomic data from 36 bovine monocyte-derived macrophage (MDM) samples collected during an ex vivo Mycobacterium avium ssp. paratuberculosis (MAP) infection experiment, stratified by Johne’s disease (JD) antibody status (JD+ or JD−) and by infection condition (control or MAP infected). We extended our analysis to healthy bovine tissues, including neonatal and post-pubertal testes, and liver and muscle samples from 12 animals stratified by sex and feed efficiency. In the 36 MDM samples, we identified 3358 exonic circRNAs derived from 1895 genes. By comparing the mean expression levels of circRNAs and linear transcripts, and considering the number of expressed genes, we estimate that the circular transcriptome is approximately 100 times smaller than the linear transcriptome. Analyses of the circular and linear transcriptomes revealed that MAP infection impacted only the linear transcriptome of MDM_JD− . The other three transcriptomes—circular JD− , circular JD+ , and linear JD+ —showed no infection-specific response. In the testes, maturation was associated with profound but uncoordinated changes in the circular and linear transcriptomes. While circRNA abundance declined, the linear transcriptome underwent a complete reorganization marked by the activation of novel genes. In the liver, female samples clustered by feed efficiency only when the entire linear and top-expressed circular transcriptomes were considered, respectively. In MDMs, the circular transcriptomes of control and infected samples, as well as the JD+ linear transcriptome, were dominated by donor-specific signatures. In contrast, the JD− linear transcriptome reflected MAP infection, with infection-specific structuring overriding inter-individual variation. In both MDM and tissue samples, circular and linear transcriptomes follow distinct and largely independent regulatory logics. While both capture inter-individual variation, circRNA expression appears more variable and may carry fewer physiological signals, especially when no clear phenotypic signature has been detected in the corresponding linear transcriptome. These findings demonstrate that circular and linear RNAs arise from complementary and nonredundant layers of gene regulation, emphasizing the importance of analyzing both in parallel.

Feed efficiency (FE) is an essential trait in livestock species because of the constant demand to increase the productivity and sustainability of livestock production systems. A better understanding of the biological mechanisms associated with FEs might help improve the estimation and selection of superior animals. In this work, differentially methylated regions (DMRs) were identified via genome-wide bisulfite sequencing (GWBS) by comparing the DNA methylation profiles of milk somatic cells from dairy ewes that were divergent in terms of residual feed intake. The DMRs were identified by comparing divergent groups for residual feed intake (RFI), the feed conversion ratio (FCR), and the consensus between both metrics (Cons). Additionally, the predictive performance of these DMRs and genetic variants mapped within these regions was evaluated via three machine learning (ML) models (xgboost, random forest (RF), and multilayer feedforward artificial neural network (deeplearning)). The average performance of each model was based on the root mean squared error (RMSE) and squared Spearman correlation (rho2). Finally, the best model for each scenario was selected on the basis of the highest ratio between rho2 and RMSE. In total, 12,257, 9,328, and 6,723 genes were annotated for DMRs detected in the RFI, FCR, and Cons groups, respectively. These genes are associated with important pathways for regulating FE in dairy sheep, such as protein digestion and absorption, hormone synthesis and secretion, control of energy availability, cellular signaling, and feed behavior pathways. With respect to the ML predictions, the smallest mean RMSE (0.17) was obtained using RF, which was used to predict RFI. The highest mean rho2 (0.20) was obtained when the RFI was predicted via the mean methylation within the DMRs identified, the consensus groups were compared, and the genetic variants mapped within these DMRs were included. The best overall models were obtained for the prediction of RFI using the DMRs obtained in the comparison of RFI groups (RMSE = 0.10, rho2 = 0.86) using xgboost and the DMRs plus the genetic variants identified via the Cons groups (RMSE = 0.07, rho2 = 0.62) using RF. The results provide new insights into the biological mechanisms associated with FE and the control of these processes through epigenetic mechanisms. Additionally, the potential use of epigenetic information as a biomarker for the prediction of FE can be suggested based on the obtained results.

BACKGROUND:Decades of intensive breeding for rapid growth rate has resulted in increased abdominal fat content in commercial broilers, which also led to significant economic loss in this industry. In the present study, we integrated RNA-Seq datasets of 44 samples, including 22 fat- and 22 lean-line, to identify the selection signatures linked to abdominal fat content in chickens. RESULTS:In total, 68 selection signature regions in the top 0.1% were screened out via Fst analysis on chromosomes 1, 2, 4, 5, 9, 11, 13, 15, and 18 were identified under differential selection between fat- and lean-lines, which harbored 1,140 SNPs and 44 genes. Functional annotation analysis highlighted key biological processes and KEGG pathways related to fat metabolism, such as "Fatty Acid Metabolic Process", "Lipid Import Into Cell", "Triglyceride Biosynthetic Process" and "Long-Chain Fatty Acid Transport". The results confirmed several previously reported candidate genes involved in fat deposition, such as ACSL3, ACSF2, MOGAT1, TBXAS1, and NDST4. Notably, the NDST4 and YIPF7 genes were of particular interest, as they are closely linked with the QTLs associated with fatness traits, which makes them well suited for future applications in poultry breeding programs. Moreover, some novel candidate genes associated with fat metabolism were identified, including GUF1, GNPDA2, SLC25A48, RBFOX3, FRMD4A and KCNE4. While the exact mechanisms by which novel candidate genes contribute to abdominal fat deposition are not fully understood yet, but they appear to play relevant roles in fat metabolism that make them promising candidates for further investigations. Furthermore, the identified candidate regions harbored two miRNAs, of which mir-205b is of particular interest and can be considered as a potential candidate involved in the genetic control of abdominal fat deposition, as mainly participate in pathways associated with lipid metabolism. These genes can pave the way for the optimization of the breeding programs associated with fatness to promote chicken broilers performance. CONCLUSIONS:Overall, these findings enrich our understanding of the genetic mechanisms underlying abdominal fat deposition in chickens. Of note, our approach for selection signature analysis can be applied to other traits as well as species with available RNA-Seq data to identify the genomic signals associated with the divergence of different phenotypes.

BACKGROUND:Livestock populations are under constant selective pressure for higher productivity levels for different selective purposes. This pressure results in the selection of animals with unique adaptive and production traits. The study of genomic regions associated with these unique characteristics has the potential to improve biological knowledge regarding the adaptive process and how it is connected to production levels and resilience, which is the ability of an animal to adapt to stress or an imbalance in homeostasis. Sheep is a species that has been subjected to several natural and artificial selective pressures during its history, resulting in a highly specialized species for production and adaptation to challenging environments. Here, the data from multiple studies that aim at mapping selective sweeps across the sheep genome associated with production and adaptation traits were integrated to identify confirmed selective sweeps (CSS).RESULTS:In total, 37 studies were used to identify 518 CSS across the sheep genome, which were classified as production (147 prodCSS) and adaptation (219 adapCSS) CSS based on the frequency of each type of associated study. The genes within the CSS were associated with relevant biological processes for adaptation and production. For example, for adapCSS, the associated genes were related to the control of seasonality, circadian rhythm, and thermoregulation. On the other hand, genes associated with prodCSS were related to the control of feeding behaviour, reproduction, and cellular differentiation. In addition, genes harbouring both prodCSS and adapCSS showed an interesting association with lipid metabolism, suggesting a potential role of this process in the regulation of pleiotropic effects between these classes of traits.CONCLUSIONS:The findings of this study contribute to a deeper understanding of the genetic link between productivity and adaptability in sheep breeds. This information may provide insights into the genetic mechanisms that underlie undesirable genetic correlations between these two groups of traits and pave the way for a better understanding of resilience as a positive ability to respond to environmental stressors, where the negative effects on production level are minimized.

Long non-coding RNAs (lncRNAs) are being studied in farm animals due to their association with traits of economic interest, such as fat deposition. Based on the analysis of perirenal fat transcriptomes, this research explored the relevance of these regulatory elements to fat deposition in suckling lambs. To that end, meta-analysis techniques have been implemented to efficiently characterize and detect differentially expressed transcripts from two different RNA-seq datasets, one including samples of two sheep breeds that differ in fat deposition features, Churra and Assaf (n = 14), and one generated from Assaf suckling lambs with different fat deposition levels (n = 8). The joint analysis of the 22 perirenal fat RNA-seq samples with the FEELnc software allowed the detection of 3953 novel lncRNAs. After the meta-analysis, 251 differentially expressed genes were identified, 21 of which were novel lncRNAs. Additionally, a co-expression analysis revealed that, in suckling lambs, lncRNAs may play a role in controlling angiogenesis and thermogenesis, processes highlighted in relation to high and low fat deposition levels, respectively. Overall, while providing information that could be applied for the improvement of suckling lamb carcass traits, this study offers insights into the biology of perirenal fat deposition regulation in mammals.

IntroductionSuckling lamb meat is highly appreciated in European Mediterranean countries because of its mild flavor and soft texture. In suckling lamb carcasses, perirenal and pelvic fat depots account for a large fraction of carcass fat accumulation, and their proportions are used as an indicator of carcass quality.Material and MethodsThis study aimed to characterize the genetic mechanisms that regulate fat deposition in suckling lambs by evaluating the transcriptomic differences between Spanish Assaf lambs with significantly different proportions of kidney knob and channel fat (KKCF) depots in their carcasses (4 High-KKCF lambs vs. 4 Low-KKCF lambs).ResultsThe analyzed fat tissue showed overall dominant expression of white adipose tissue gene markers, although due to the young age of the animals (17–36 days), the expression of some brown adipose tissue gene markers (e.g., UCP1, CIDEA) was still identified. The transcriptomic comparison between the High-KKCF and Low-KKCF groups revealed a total of 80 differentially expressed genes (DEGs). The enrichment analysis of the 49 DEGs with increased expression levels in the Low-KKCF lambs identified significant terms linked to the biosynthesis of lipids and thermogenesis, which may be related to the higher expression of the UCP1 gene in this group. In contrast, the enrichment analysis of the 31 DEGs with increased expression in the High-KKCF lambs highlighted angiogenesis as a key biological process supported by the higher expression of some genes, such as VEGF-A and THBS1, which encode a major angiogenic factor and a large adhesive extracellular matrix glycoprotein, respectively.DiscussionThe increased expression of sestrins, which are negative regulators of the mTOR complex, suggests that the preadipocyte differentiation stage is being inhibited in the High-KKCF group in favor of adipose tissue expansion, in which vasculogenesis is an essential process. All of these results suggest that the fat depots of the High-KKCF animals are in a later stage of development than those of the Low-KKCF lambs. Further genomic studies based on larger sample sizes and complementary analyses, such as the identification of polymorphisms in the DEGs, should be designed to confirm these results and achieve a deeper understanding of the genetic mechanisms underlying fat deposition in suckling lambs.

In sheep, nutrition during the prepubertal stage is essential for growth performance and mammary gland development. However, the potential effects of nutrient restriction in a prepuberal stage over the progeny still need to be better understood. Here, the intergenerational effect of maternal protein restriction at prepubertal age (2 months of age) on methylation patterns was evaluated in the perirenal fat of Assaf suckling lambs. In total, 17 lambs from ewes subjected to dietary protein restriction (NPR group, 44% less protein) and 17 lambs from control ewes (C group) were analyzed. These lambs were ranked based on their carcass proportion of perirenal and cavitary fat and classified into HighPCF and LowPCF groups. The perirenal tissue from 4 NPR-LowPCF, 4 NPR-HighPCF, 4 C-LowPCF, and 4 C-HighPCF lambs was subjected to whole-genome bisulfite sequencing and differentially methylated regions (DMRs) were identified. Among other relevant processes, these DMRs were mapped in genes responsible for regulating the transition of brown to white adipose tissue and nonshivering thermoregulation, which might be associated with better adaptation/survival of lambs in the perinatal stage. The current study provides important biological insights about the intergenerational effect on the methylation pattern of an NPR in replacement ewes.

IntroductionAs higher feed efficiency in dairy ruminants means a higher capability to transform feed nutrients into milk and milk components, differences 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 transcriptome.Material and methodsRNA-Seq data from high (H-FE, n = 8) and low (L-FE, n = 8) feed efficiency ewes were compared through differential expression analysis (DEA) and sparse Partial Least Square-Discriminant analysis (sPLS-DA).ResultsIn the DEA, 79 genes were identified as differentially expressed between both conditions, while the sPLS-DA identified 261 predictive genes [variable importance in projection (VIP) > 2] that discriminated H-FE and L-FE sheep.DiscussionThe DEA between sheep with divergent feed efficiency allowed the identification of genes associated with the immune system and stress in L-FE animals. In addition, the sPLS-DA approach revealed the importance of genes involved in cell division (e.g., KIF4A and PRC1) and cellular lipid metabolic process (e.g., LPL, SCD, GPAM, and ACOX3) for the H-FE sheep in the lactating mammary gland transcriptome. A set of discriminant genes, commonly identified by the two statistical approaches, was also detected, including some involved in cell proliferation (e.g., SESN2, KIF20A, or TOP2A) or encoding heat-shock proteins (HSPB1). These results provide novel insights into the biological basis of feed efficiency in dairy sheep, highlighting the informative potential of the mammary gland transcriptome as a target tissue and revealing the usefulness of combining univariate and multivariate analysis approaches to elucidate the molecular mechanisms controlling complex traits.

Suckling lamb meat is a relevant product in Mediterranean European dairy sheep farms, and the dairy breed Spanish Assaf is widely extended through the Iberian Peninsula. Knowledge of the influence of birth body weight (bBW) and growth rate on suckling lamb carcass and meat quality is scarce, but useful for breeding optimisation and product homogeneity. In turn, these growth-related traits of lambs might be affected by dietary restrictions of their dams. In this study, 34 male Assaf suckling lambs born from two groups of ewes that had been fed diets with different protein levels when they were prepubertal female lambs (17 lambs per group) were analysed. After birth, the suckling lambs were fed ad libitum on milk replacer until their sacrifice (10-12.5 kg live body weight). The quality traits evaluated in carcasses and meat were carcass compactness, fatness and jointing, meat composition, colour, texture and oxidative stability, and fatty acid profile. The dam group did not show sig-nificant effects on lamb growth characteristics or carcass and meat quality traits. The bBW factor showed a negative effect on leg subcutaneous fatness and a positive effect on the forequarter and shoulder percentages of the carcass. The bBW also resulted in increased moisture, lipid oxidation stability, and n-3 FA content (lowering the n-6/n-3 ratio) in the meat. Suckling lambs showing very low average daily gain (ADG) tended to present low carcass quality, i.e., higher bone percentage in the loin and low percentages of muscle or fat compared to those showing high ADG. Further studies are needed to confirm and explain the mechanisms of the significant effects reported here for bBW and ADG on the affected quality traits.

This study evaluated the influence of a temporary nutritional protein restriction (NPR) performed, under commercial conditions, in prepubertal female lambs on first lactation milk production traits and the inflammatory response triggered by an inflammatory challenge of the. From 40 Assaf female lambs, we defined a control group (C n = 20), which received a standard diet for replacement lambs and the NPR group (n = 20), which received the same diet but without soybean meal between 3 and 5 months of age. About 150 days after lambing, 24 of these ewes (13 NPR, 11C) were subjected to an intramammary infusion of E. coli lipopolysaccharide (LPS). Our dynamic study identified indicator traits of local (SCC) and systemic (rectal T-a, IL-6, CXCL8, IL-10, IL-36RA, VEGF-A) response to the LPS challenge. The NPR did not show significant effects on milk production traits and did not affect the SCC and rectal T-a after the LPS challenge. However, the NPR had a significant influence on 8 of the 14 plasma biomarkers analysed, in all the cases with higher relative values in the C group. The effects observed on VEGF-A (involved in vasculogenesis during mammary gland development and vascular permeability) and IL-10 (a regulatory cytokine classically known by its anti-inflammatory action) are the most remarkable to explain the differences found between groups. Whereas further studies should be undertaken to confirm these results, our findings are of interest considering the current concern about the future world's demand for protein and the need for animal production systems to evolve toward sustainability.

DESIGN AND USE OF A CARD GAME FOR LEARNING ABOUT QUANTITATIVE GENETICS AND VARIANCE COMPONENTS APPLIED TO ANIMAL BREEDING: A PILOT STUDY

Dadas las dificultades identificadas por parte de los profesores del perfil de Genética del Dpto. de Producción Animal de la Universidad de León, integrantes a su vez del Grupo de Innovación Docente "VetGeneULE" (GI052), en relación al aprendizaje de los conceptos de Genética Cuantitativa en diferentes asignaturas relacionadas con la Mejora Genética Animal, se ha diseñado un juego de cartas didácticas para reforzar la comprensión de dichos conceptos. En base a un estudio piloto realizado previamente en un número reducido de estudiantes del Grado de Ingeniería Agrícola, se presenta aquí el proceso de desarrollo de la versión definitiva del juego de cartas, denominado "Familias Falconer" y la evaluación de una primera experiencia de gamificación desarrollada con un grupo de estudiantes de mayor tamaño de la asignatura de Cría y Mejora Animal del Grado en Veterinaria de la Universidad de León. Bayón, Yolanda; Suárez-Vega, Aroa; Fonseca, Pablo A.S.; Pelayo, Rocío; de la Fuente, Fernando; Arranz, Juan-José; Gutiérrez-Gil, Beatriz

The objective of this work was to evaluate the potential influence of a nutritional challenge performed in ewe lambs on the inflammatory response triggered against an experimental intramammary lipopolysaccharide challenge carried out at the end of the first lactation. Sixty Assaf ewes at 2 months of age were divided into a control and a nutritional challenge group, the second of which was subjected to a severe protein restriction during the prepubertal growth stage. The ewes were inseminated, and after lambing, 24 of them were subjected to an experimental inflammatory challenge of the mammary gland to induce a local inflammation. Measurements of somatic cell counts, rectal temperature and plasma concentrations of 14 cytokines were collected at different time points from the lipopolysaccharide infusion. The results showed that the effect of the nutritional challenge did not show a significant influence on the local indicator somatic cell count (SCC) traits, whereas it had a significant influence on systemic indicator traits. This research opens the way to further studies related to the potential long-time effects of the nutritional plane at early life on immune response.