Skin transcriptomic analysis reveals candidate genes and pathways associated with thermotolerance in hair sheep.

The skin plays an important role in thermoregulation. Identification of genes on the skin that contribute to increased heat tolerance can be used to select animals with the best performance in warm environments. Our objective was to identify candidate genes associated with the heat stress response in the skin of Santa Ines sheep. A group of 80 sheep assessed for thermotolerance was kept in a climatic chamber for 8 days at a stress level temperature of 36 °C (10 am to 04 pm) and a maintenance temperature of 28 °C (04 pm to 10 am). Two divergent groups, with seven animals each, were formed after ranking them by thermotolerance using rectal temperature. From skin biopsy samples, total RNA was extracted, quantified, and used for RNA-seq analysis. 15,989 genes were expressed in sheep skin samples, of which 4 genes were differentially expressed (DE; FDR < 0.05) and 11 DE (FDR 0.05-0.177) between the two divergent groups. These genes are involved in cellular protection against stress (HSPA1A and HSPA6), ribosome assembly (28S, 18S, and 5S ribosomal RNA), and immune response (IGHG4, GNLY, CXCL1, CAPN14, and SAA-4). The candidate genes and main pathways related to heat tolerance in Santa Ines sheep require further investigation to understand their response to heat stress in different climatic conditions and under solar radiation. It is essential to verify whether these genes and pathways are present in different breeds and to understand the relationship between heat stress and other genes identified in this study.

Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle.

Data integration using hierarchical analysis based on the central dogma or common pathway enrichment analysis may not reveal non-obvious relationships among omic data. Here, we applied factor analysis (FA) and Bayesian network (BN) modeling to integrate different omic data and complex traits by latent variables (production, carcass, and meat quality traits). A total of 14 latent variables were identified: five for phenotype, three for miRNA, four for protein, and two for mRNA data. Pearson correlation coefficients showed negative correlations between latent variables miRNA 1 (mirna1) and miRNA 2 (mirna2) (-0.47), ribeye area (REA) and protein 4 (prot4) (-0.33), REA and protein 2 (prot2) (-0.3), carcass and prot4 (-0.31), carcass and prot2 (-0.28), and backfat thickness (BFT) and miRNA 3 (mirna3) (-0.25). Positive correlations were observed among the four protein factors (0.45-0.83): between meat quality and fat content (0.71), fat content and carcass (0.74), fat content and REA (0.76), and REA and carcass (0.99). BN presented arcs from the carcass, meat quality, prot2, and prot4 latent variables to REA; from meat quality, REA, mirna2, and gene expression mRNA1 to fat content; from protein 1 (prot1) and mirna2 to protein 5 (prot5); and from prot5 and carcass to prot2. The relations of protein latent variables suggest new hypotheses about the impact of these proteins on REA. The network also showed relationships among miRNAs and nebulin proteins. REA seems to be the central node in the network, influencing carcass, prot2, prot4, mRNA1, and meat quality, suggesting that REA is a good indicator of meat quality. The connection among miRNA latent variables, BFT, and fat content relates to the influence of miRNAs on lipid metabolism. The relationship between mirna1 and prot5 composed of isoforms of nebulin needs further investigation. The FA identified latent variables, decreasing the dimensionality and complexity of the data. The BN was capable of generating interrelationships among latent variables from different types of data, allowing the integration of omics and complex traits and identifying conditional independencies. Our framework based on FA and BN is capable of generating new hypotheses for molecular research, by integrating different types of data and exploring non-obvious relationships.

Vaginal Microbiota Diversity in Response to Lipopolysaccharide in Gilts Housed Under Three Housing Systems.

The United Kingdom and European Union have banned crates for pregnant sows. However, animals are kept in a restrictive environment for up to four weeks after mating, leading to stress and different responses of the animals' immune system. Here, we used vaginal flushing of gilts to investigate whether housing systems or an experimental inflammatory challenge with lipopolysaccharide (LPS) can modify the gilt vaginal microbiome. Alpha-diversity indices showed differences in the microbiota of gilts housed under different systems (q = 0.04). Shannon alpha-diversity richness was higher in gilts group-housed in pens than in gilts housed in crates (q = 0.035), but not higher than in other groups. The relative abundance of the operational taxonomic unit (OTU) (q < 0.05) revealed specific differences in housing systems before a LPS or saline (SAL control) challenge. We found different abundances in taxa of Actinobacteria, Bacteroidetes, Cyanobacteria, Firmicutes, and Proteobacteria in gilts housed in the different systems before challenge. After the LPS challenge, significant differences were detected in the relative abundance of OTUs (q < 0.05) for the LPS-challenged group compared with SAL animals for each housing system. The phylum Staphylococcus showed higher abundance among the LPS-challenged gilts than in SAL-challenged animals. Furthermore, Enterobacter was more abundant in the LPS-challenged gilts housed in crates than in SAL-challenged gilts housed in crates. Streptococcus suis, Conchiformibius, Globicatella and Actinobacillus were more abundant in LPS-challenged gilts in indoor group housing than in SAL gilts in the same housing system. Gilts kept outdoors did not show changes in vaginal microbiota after an LPS challenge. Gilts housed in crates showed clinical signs of urogenital infection, whereas gilts housed outdoors and in indoor group housing did not. The relationship between environment, immune response, and microbiota suggested that animals in a poor environments experience difficulties responding to a challenge and their vaginal microbiota is altered as a consequence, with decreased richness of normal vaginal microbiota, and increased opportunistic bacteria. Welfare indicators measured by gilts' responses to housing systems however, do not fully explain mechanisms associated with the unique signature in vaginal microbiota encountered in the different housing systems.

Genetic parameters associated with meat quality of Nellore cattle at different anatomical points of longissimus: Brazilian standards.

The present study estimated genetic parameters and evaluated the genetic and phenotypic correlations between meat quality characteristics of Nellore cattle evaluated at different anatomical points of the longissimus. Data from 1329 Nellore young bulls were used to evaluate, in the 5th and 12th ribs, marbling score (MAR), shear force (SF), cooking weight losses (CWL) and intramuscular fat (IMF). In addition, the subcutaneous fat thickness was measured at the 12th rib (SFT12) and between the last lumbar and the first sacral vertebrae (SFTLR), in the separation of loin and round. Results yielded moderate heritability coefficients for evaluated characteristics, except CWL. High genetic correlations (0.61) were found between measurements of SFT12 and SFTLR. MAR, IMF and SF were evaluated at the 5th and 12th rib. Meat quality and subcutaneous fat thickness measured at different anatomical points of the longissimus are genetically correlated and can be used in genetic selection programs to improve meat quality characteristics in Nellore cattle.

Predicting the shear value and intramuscular fat in meat from Nellore cattle using Vis-NIR spectroscopy.

Visible and near-infrared spectroscopy (Vis-NIRS) was tested for its effectiveness in predicting intramuscular fat (IMF) and WBSF in Nellore steers. Beef samples from longissimus thoracis, aged for either 2 or 7 days, had their spectra collected for wavelengths ranging from 400 to 1395 nm. Partial least squares regression models were developed for each trait. Determination coefficients of calibration models for WBSF ranged from 0.17 to 0.53. Considering WBSF in samples aged for 2 days, Vis-NIR correctly classified 100% of tough samples (>45 N), but wrongly classified all tender samples (≤45 N) as tough. Determination coefficients of calibration models for IMF ranged from 0.12 to 0.14. Vis-NIRS is a useful tool for identifying tough beef, but it is less effective in predicting tender samples and IMF. Additional studies are necessary to generate more robust models for the prediction of intramuscular fat in intact meat samples of Nellore cattle.

Identification of selection signatures involved in performance traits in a paternal broiler line.

BACKGROUND: Natural and artificial selection leads to changes in certain regions of the genome resulting in selection signatures that can reveal genes associated with the selected traits. Selection signatures may be identified using different methodologies, of which some are based on detecting contiguous sequences of homozygous identical-by-descent haplotypes, called runs of homozygosity (ROH), or estimating fixation index (FST) of genomic windows that indicates genetic differentiation. This study aimed to identify selection signatures in a paternal broiler TT line at generations 7th and 16th of selection and to investigate the genes annotated in these regions as well as the biological pathways involved. For such purpose, ROH and FST-based analysis were performed using whole genome sequence of twenty-eight chickens from two different generations. RESULTS: ROH analysis identified homozygous regions of short and moderate size. Analysis of ROH patterns revealed regions commonly shared among animals and changes in ROH abundance and size between the two generations. Results also suggest that whole genome sequencing (WGS) outperforms SNPchip data avoiding overestimation of ROH size and underestimation of ROH number; however, sequencing costs can limited the number of animals analyzed. FST-based analysis revealed genetic differentiation in several genomic windows. Annotation of the consensus regions of ROH and FST windows revealed new and previously identified genes associated with traits of economic interest, such as APOB, IGF1, IGFBP2, POMC, PPARG, and ZNF423. Over-representation analysis of the genes resulted in biological terms of skeletal muscle, matrilin proteins, adipose tissue, hyperglycemia, diabetes, Salmonella infections and tyrosine. CONCLUSIONS: Identification of ROH and FST-based analyses revealed selection signatures in TT line and genes that have important role in traits of economic interest. Changes in the genome of the chickens were observed between the 7th and 16th generations showing that ancient and recent selection in TT line may have acted over genomic regions affecting diseases and performance traits.