Fat-tail content of sheep breeds is varied and the molecular mechanisms regulating fat-tail development have not been well characterized. Aiming at better identifying the important candidate genes and their functional pathways contributing to fat deposition in the tail, a comparative transcriptome analysis was performed between fat- (Lori-Bakhtiari) and thin-tailed (Zel) Iranian sheep breeds using RNA-seq. The experiment was conducted on six male lambs (three lambs per each breed) at seven months of age. Four different combinations of aligners and statistical methods including Hisat2 + edgeR, Hisat2 + DESeq2, STAR + edgeR and STAR + DESeq2 were used to identify the differentially expressed genes (DEGs). The DEGs were selected for functional enrichment analysis and protein-protein interaction (PPI) network construction. Module analysis was also conducted to mine the functional sub-networks from the PPI network. In total, 264 genes including 80 up- and 184 down-regulated genes were identified as DEGs. The RNA-Seq results were validated by Q-RT-PCR. Functional analysis of DEGs and the module analysis of PPI network demonstrated that in addition to pathways affecting lipid metabolism, a series of enriched functional terms related to “response to interleukin”, “MAPK signaling pathways”, “Wnt signaling pathway”, “ECM-receptor interaction”, “regulation of actin cytoskeleton”, and “response to cAMP” might contribute to the deposition of fat in tails of sheep. Overall results using RNA-Seq analysis characterized important candidate genes involved in the fatty acid metabolism and regulation of fat deposition, suggesting novel insights into molecular aspects of fat-tail metabolism in sheep. Selected DEGs should be further investigated as potential markers associated with the fat-tail development in sheep breeds.

RNA editing increases the diversity of the transcriptome and proteome. Adenosine-to-inosine (A-to-I) editing is the predominant type of RNA editing in mammals and it is catalyzed by the adenosine deaminases acting on RNA (ADARs) family. Here, we used a largescale computational analysis of transcriptomic data from brain, heart, colon, lung, spleen, kidney, testes, skeletal muscle and liver, from three adult animals in order to identify RNA editing sites in bovine. We developed a computational pipeline and used a rigorous strategy to identify novel editing sites from RNA-Seq data in the absence of corresponding DNA sequence information. Our methods take into account sequencing errors, mapping bias, as well as biological replication to reduce the probability of obtaining a false-positive result. We conducted a detailed characterization of sequence and structural features related to novel candidate sites and found 1,600 novel canonical A-to-I editing sites in the nine bovine tissues analyzed. Results show that these sites 1) occur frequently in clusters and short interspersed nuclear elements (SINE) repeats, 2) have a preference for guanines depletion/enrichment in the flanking 5′/3′ nucleotide, 3) occur less often in coding sequences than other regions of the genome, and 4) have low evolutionary conservation. Further, we found that a positive correlation exists between expression of ADAR family members and tissue-specific RNA editing. Most of the genes with predicted A-to-I editing in each tissue were significantly enriched in biological terms relevant to the function of the corresponding tissue. Lastly, the results highlight the importance of the RNA editome in nervous system regulation. The present study extends the list of RNA editing sites in bovine and provides pipelines that may be used to investigate the editome in other organisms.

Background: The Osteopontin (OPN) is a highly phosphorylated glycoprotein in numbers of bovine tissues and milk. OPN has been reported to be associated with milk production in cattle. Objective: The genotype and allelic frequencies for OPN and its association with milk production will be evaluated in Iranian Holstein Bulls. Materials and Methods: Bulls DNA (100) was isolated. Oligo was used for primer design. Polymerase Chain Reaction was implemented to amplify a 826 bp fragment and the amplicon was digested by BsrI. Restricted Maximum likelihood (REML) method based on average information algorithm using ASRMEL programs (version 3.1) was employed to estimate the genetic parameters and variance of components. The association of OPN genotypes with milk production traits were analysed by the least square method as applied in the general linear model (GLM) procedure of SAS. Allele substitution effects were performed by regression analyses. Results: Allele frequencies of T and C were 0.59±0.03 and 0.41±0.03, respectively. Genotype frequencies of TT, CT and CC were 34.69, 48.62, and 16.69, respectively. The chi-square test showed the deviation from Hardy-Weinberg equilibrium. Estimated heritability for milk yield, fat yield and its percent, protein yield and its percent were 0.28±0.0061, 0.21±0.0064, 0.22±0.0086, 0.32±0.0065 and 0.34±0.0096 respectively. Allelic substitution effects and differences between genotypes were not significant for milk production traits. Conclusions: This study suggested that the C allele frequency of OPN was noticeable in Iranian proven bull Holstein population, but was not associated with milk production traits. However, before being practical for the breeding improvement of Iranian Holsteins a larger sample size is required. Iranian Journal of Biotechnology (IJB)

The aim of this study was to investigate the effects of omega-3 fatty acids on blood biochemical parameters, histological changes in pulmonary artery, cardiomyocytes, and liver, as well as the expression of ACACA, PFK1, and ET-1 genes in broiler chickens under environmental stress (high stoking density). A total of 420 one-day-old male Ross broilers were used in a 2 × 2 factorial arrangements, with 2 levels of environmental stress (without and with stress; 9 and 17 birds/m², respectively) and 2 levels of omega-3 fatty acids (low and high; 0.057 and 0.5% of the diet, respectively) in a completely randomized design comprising 4 treatments and 5 replicates per each. The body weight decreased at d 40 because of environmental stress (P ≤ 0.05). The ascites heart index (AHI) in broilers fed high omega-3 fatty acids diets was lower (P = 0.062) than broiler fed low omega-3 fatty acids diet (0.279 vs. 0.316). Stressed birds showed a higher neutrophil: lymphocyte ratio compared to non-stressed birds (P ≤ 0.05). Broiler chickens receiving high omega-3 fatty acids diets exhibited elevated levels of hematocrit (HCT), hemoglobin (HGB), and lymphocytes (P ≤ 0.05). The neutrophil: lymphocyte ratio, and serum concentration of alanine aminotransferase (ALT), and aspartate aminotransferase (AST) decreased in broilers fed high omega-3 fatty acids diets (P ≤ 0.05). In stressed broilers on a high omega-3 diet, pulmonary artery wall thickness decreased (P ≤ 0.05). Additionally, under stress, myocardial cell diameter, hepatocyte and cell nucleus diameter significantly increased (P ≤ 0.05). Stressed broilers showed an increased relative fold change in PFK1 enzyme activity but reduced ET-1 mRNA expression in the liver compared to stressed birds on a high omega-3 diet (P ≤0.05). In conclusion, the results indicate that dietary omega-3 fatty acids have the potential to alleviate the adverse histological changes in the pulmonary artery, cardiomyocytes, and liver, while also modulating the expression of genes ACACA, PFK1, and ET-1 that are influenced by environmental stress in broiler chickens.

Whole genome evaluation of quantitative traits using suitable statistical methods enables researchers to predict genomic breeding values (GEBVs) more accurately. Recent studies suggested that the ability of methods in terms of predictive performance may depend on the genetic architecture of traits. Therefore, when choosing a statistical method, it is essential to consider the genetic architecture of the target traits. Herein, the performance of parametric methods i.e. GBLUP and BayesB and non-parametric methods i.e. Bagging GBLUP and Random Forest (RF) were compared for traits with different genetic architecture. Three scenarios of genetic architecture, including purely Additive (Add), purely Epistasis (Epis) and Additive-Dominance-Epistasis (ADE) were considered. To this end, an animal genome composed of five chromosomes, each chromosome harboring 1000 SNPs and four QTL was simulated. Predictive accuracies in the first generation of testing set under Additive genetic architectures for GBLUP, BayesB, Baging GBLUP and RF were 0.639, 0.731, 0.633 and 0.548, respectively, and were 0.278, 0.330, 0.275 and 0.444 under purely Epistatic genetic architectures. Corresponding values for the Additive-Dominance-Epistatic structure also were 0.375, 0.448, 0.369 and 0.458, respectively. The results showed that genetic architecture has a great impact on prediction accuracy of genomic evaluation methods. When genetic architecture was purely Additive, parametric methods and Bagging GBLUP were better than RF, whereas under Epistatic and Additive-Dominance-Epistatic genetic architectures, RF delivered better predictive performance than the other statistical methods.