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.

Emerging evidence suggests that long non-coding RNAs (lncRNAs) participate in the regulation of a diverse range of biological processes. However, most studies have been focused on a few established model organisms and little is known about lncRNAs in fat-tail development in sheep. Here, the first profile of lncRNA in sheep fat-tail along with their possible roles in fat deposition were investigated, based on a comparative transcriptome analysis between fat-tailed (Lori-Bakhtiari) and thin-tailed (Zel) Iranian sheep breeds. Among all identified lncRNAs candidates, 358 and 66 transcripts were considered novel intergenic (lincRNAs) and novel intronic (ilncRNAs) corresponding to 302 and 58 gene loci, respectively. Our results indicated that a low percentage of the novel lncRNAs were conserved. Also, synteny analysis identified 168 novel lincRNAs with the same syntenic region in human, bovine and chicken. Only seven lncRNAs were identified as differentially expressed genes between fat and thin tailed breeds. Q-RT-PCR results were consistent with the RNA-Seq data and validated the findings. Target prediction analysis revealed that the novel lncRNAs may act in cis or trans and regulate the expression of genes that are involved in the lipid metabolism. A gene regulatory network including lncRNA-mRNA interactions were constructed and three significant modules were found, with genes relevant to lipid metabolism, insulin and calcium signaling pathway. Moreover, integrated analysis with AnimalQTLdb database further suggested six lincRNAs and one ilncRNAs as candidates of sheep fat-tail development. Our results highlighted the putative contributions of lncRNAs in regulating expression of genes associated with fat-tail development in sheep.

Genetic basis of fat deposition in sheep tail have not been completely elucidated yet. Understanding the genetic mechanisms controlling fat-tail size can improve breeding strategies to modulate fat deposition. RNA sequencing has made it possible to discover genetic variants that may underlie various phenotypic differences. Hence, to identify genetic variants that are important for describing different fat-tail phenotypes in sheep, RNA sequencing was used for single nucleotide polymorphism (SNP) calling in two Iranian sheep breeds (Lori-Bakhtiari, fat-tailed; n = 4, vs Zel, thin-tailed; n = 4). Using a stringent pipeline, a total of 112,344 known SNPs were genotyped, of which 30,550 and 42,906 SNPs were shared by at least two Lori-Bakhtiari and Zel, respectively. Comparing these SNPs showed 2,774 (including 209 missense and 25 deleterious SNPs) and 10,470 (including 1,054 missense and 116 deleterious SNPs) breed-specific SNPs in Lori-Bakhtiari and Zel sheep, respectively. Potential breed-specific SNPs were detected by considering those located in QTL regions associated with fatness or reported as important candidates in previous similar studies. Of the breed-specific SNPs, 724 and 2,905 were located in the QTL regions. Functional enrichment analysis of the affected genes revealed several enriched gene ontologies and KEGG pathways related to fat metabolism. Based on the results, several affected genes were proposed to be strongly linked with fat deposition such as DGAT2, ACSL1, ACACA, ADIPOQ, ACLY, FASN, CPT2, SCD, ADCY6, PER3, CSF1R, SLC22A4, GFPT1, CDS2, BMP6, ACSS2, ELOVL6, HOXA10 and FABP4. Moreover, several SNPs were found in the candidate genes related to fatty acid oxidation introducing them as promising candidates responsible for lower fat content in tail of Zel. Our findings provided new insights into the genetic mechanisms of fat deposition in sheep, which can serve to designing appropriate breeding programs.

Transcriptome resources can facilitate to increase yield and quality of walnuts. Finding the best transcriptome assembly has not been the subject of walnuts research as yet. This research generated 240,179,782 reads from 11 walnut leaves according to cDNA libraries. The reads provided a complete de novo transcriptome assembly. Fifteen different transcriptome assemblies were constructed from five different well-known assemblers used in scientific literature with different k-mer lengths (Bridger, BinPacker, SOAPdenovo-Trans, Trinity and SPAdes) as well as two merging approaches (EvidentialGene and Transfuse). Based on the four quality metrics of assembly, the results indicated an efficiency in the process of merging the assemblies after being generated by de novo assemblers. Finally, EvidentialGene was recognized as the best assembler for the de novo assembly of the leaf transcriptome in walnut. Among a total number of 183,191 transcripts which were generated by EvidentialGene, there were 109,413 transcripts capable of protein potential (59.72%) and 104,926 were recognized as ORFs (57.27%). In addition, 79,185 transcripts were predicted to exist with at least one hit to the Pfam database. A number of 3,931 transcription factors were identified by BLAST searching against PlnTFDB. Furthermore, 6,591 of the predicted peptide sequences contained signaling peptides, while 92,704 contained transmembrane domains. Comparison of the assembled transcripts with transcripts of the walnut and published genome assembly for the ‘Chandler’ cultivar using the BLAST algorithm led to identify a total number of 27,304 and 19,178 homologue transcripts, respectively. De novo transcriptomes in walnut leaves can be developed for the future studies in functional genomics and genetic studies of walnuts.

Objective: Mastitis is defined as the inflammation of the mammary gland, which impact directly on the production performance and welfare of dairy cattle. Since, mastitis is a multifactorial complex disease and the molecular pathways underlying this disorder have not been clearly understood yet, a system biology approach was used in this study to a better understanding of the molecular mechanisms behind mastitis. Methods: Publicly available RNA-Seq data containing samples from milk of five infected and five healthy Holstein cows at five time points were retrieved. Gene Co-expression network analysis (WGCNA) approach and functional enrichment analysis were then applied with the aim to find the non-preserved module of genes that their connectivity were altered under infected condition. Hub genes were identified in the non-preserved modules and were subjected to protein-protein interactions (PPI) network construction. Results: Among the 25 modules identified, eight modules were non-preserved and were also biologically associated with inflammation, immune response and mastitis development. Interestingly most of the hub genes in the eight modules were also densely connected in the PPI network. Of the hub genes, 250 genes were hubs in both co-expression and PPI networks and most of them were reported to play important roles in immune response or inflammatory pathways. The blue module was highly enriched in inflammatory responses and STAT1 was suggested to play an important role in mastitis development by regulating the immune related genes in this module. Moreover, a set of highly connected genes were identified such as BIRC3, PSMA6, FYN, F11R, NFKBIZ, NFKBIA, GRO1, PHB, CD3E, IL16, GSN, SOCS2, HCK, VAV1 and TLR6, which have been established to be critical for mastitis pathogenesis. Conclusion: This study improved the understanding of the mechanisms underlying bovine mastitis and suggested eight non-preserved modules along with several most important genes with promising potential in etiology of mastitis.