Integrated Comparative Transcriptome and circRNA-lncRNA-miRNA-mRNA ceRNA Regulatory Network Analyses Identify Molecular Mechanisms Associated with Intramuscular Fat Content in Beef Cattle.

Intramuscular fat content (IMF), one of the most important carcass traits in beef cattle, is controlled by complex regulatory factors. At present, molecular mechanisms involved in regulating IMF and fat metabolism in beef cattle are not well understood. Our objective was to integrate comparative transcriptomic and competing endogenous RNA (ceRNA) network analyses to identify candidate messenger RNAs (mRNAs) and regulatory RNAs involved in molecular regulation of longissimus dorsi muscle (LDM) tissue for IMF and fat metabolism of 5 beef cattle breeds (Angus, Chinese Simmental, Luxi, Nanyang, and Shandong Black). In total, 34 circRNAs, 57 lncRNAs, 15 miRNAs, and 374 mRNAs were identified by integrating gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, 7 key subnets with 16 circRNAs, 43 lncRNAs, 7 miRNAs, and 237 mRNAs were detected through clustering analyses, whereas GO enrichment analysis of identified RNAs revealed 48, 13, and 28 significantly enriched GO terms related to IMF in biological process, molecular function, and cellular component categories, respectively. The main metabolic-signaling pathways associated with IMF and fat metabolism that were enriched included metabolic, calcium, cGMP-PKG, thyroid hormone, and oxytocin signaling pathways. Moreover, MCU, CYB5R1, and BAG3 genes were common among the 10 comparative groups defined as important candidate marker genes for fat metabolism in beef cattle. Contributions of transcriptome profiles from various beef breeds and a competing endogenous RNA (ceRNA) regulatory network underlying phenotypic differences in IMF provided novel insights into molecular mechanisms associated with meat quality.

Construction of a circRNA- lincRNA-lncRNA-miRNA-mRNA ceRNA regulatory network identifies genes and pathways linked to goat fertility.

Background: There is growing interest in the genetic improvement of fertility traits in female goats. With high-throughput genotyping, single-cell RNA sequencing (scRNA-seq) is a powerful tool for measuring gene expression profiles. The primary objective was to investigate comparative transcriptome profiling of granulosa cells (GCs) of high- and low-fertility goats, using scRNA-seq. Methods: Thirty samples from Ji'ning Gray goats (n = 15 for high fertility and n = 15 for low fertility) were retrieved from publicly available scRNA-seq data. Functional enrichment analysis and a literature mining approach were applied to explore modules and hub genes related to fertility. Then, interactions between types of RNAs identified were predicted, and the ceRNA regulatory network was constructed by integrating these interactions with other gene regulatory networks (GRNs). Results and discussion: Comparative transcriptomics-related analyses identified 150 differentially expressed genes (DEGs) between high- and low-fertility groups, based on the fold change (≥5 and ≤-5) and false discovery rate (FDR <0.05). Among these genes, 80 were upregulated and 70 were downregulated. In addition, 81 mRNAs, 58 circRNAs, 8 lincRNAs, 19 lncRNAs, and 55 miRNAs were identified by literature mining. Furthermore, we identified 18 hub genes (SMAD1, SMAD2, SMAD3, SMAD4, TIMP1, ERBB2, BMP15, TGFB1, MAPK3, CTNNB1, BMPR2, AMHR2, TGFBR2, BMP4, ESR1, BMPR1B, AR, and TGFB2) involved in goat fertility. Identified biological networks and modules were mainly associated with ovary signature pathways. In addition, KEGG enrichment analysis identified regulating pluripotency of stem cells, cytokine-cytokine receptor interactions, ovarian steroidogenesis, oocyte meiosis, progesterone-mediated oocyte maturation, parathyroid and growth hormone synthesis, cortisol synthesis and secretion, and signaling pathways for prolactin, TGF-beta, Hippo, MAPK, PI3K-Akt, and FoxO. Functional annotation of identified DEGs implicated important biological pathways. These findings provided insights into the genetic basis of fertility in female goats and are an impetus to elucidate molecular ceRNA regulatory networks and functions of DEGs underlying ovarian follicular development.

Genomic characterization and phylogenetic evolution of the canine parvoviruses in Iranian dogs, a nationwide study: CPV evolutionary analysis in Iran.

Canine Parvo Virus 2 (CPV-2) culminated in lots of fatalities in domestic dogs since its emergence in 1978. Mainly, it is responsible for severe hemorrhagic diarrhea, vomiting, and dehydration. CPV-2 has three main variants known as 2a, 2b, and 2c. Due to the necessity of monitoring the evolutionary parameters of the virus, and also the lack of comprehensive study of CPV2 in Iran, this study is done for the first time in this country not only to characterize Iranian CPV genomes but also to study the evolutionary parameters and phylodynamics of CPV. The phylogenetic trees were constructed using the Maximum Likelihood (ML) method. By the use of the Bayesian Monte Carlo Markov Chain (BMCMC) method, evolutionary analysis and phylodynamics of the virus were investigated. Phylogenetic results showed that all Iranian isolates were classified in the CPV-2a variant. The central part of Iran was suggested to be the origin of the virus, especially the Alborz province. Before its prevalence throughout the country, the virus circulated in the central part, in Thran, Karaj, and Qom. Mutational analysis showed a positive selection pressure of CPV-2a. Investigating the evolutionary parameters of the virus proposed 1970 to be the date of birth of the virus, with a 95% credible interval between 1953 and 1987. The effective number of infections increased dramatically from 2012 to 2015, then faced a slightly decreasing trend from 2015 to 2019. A considerable up warding pattern was witnessed from the middle of 2019, which can be taken as a concern about the risk of vaccination failure.

Genetic structure analysis in several populations of cattle using SNP genotypes.

Parameters such as effective population size (Ne), runs of homozygosity (ROH), and inbreeding based on ROH (FROH) can give new insight into the level of genetic diversity for the population under selection. This research aimed to measure the extent of linkage disequilibrium (LD), effective population size (Ne), Haplotype Block Structure, and runs of homozygosity (ROHs) in several populations of cattle using SNP genotypes. In this study, that the average r2 decreased with the increasing distance of SNP pairs. A general decrease in Ne can be seen for all four populations, indicating a loss of genetic diversity. The Iranian Holstein had the lowest level of genomic inbreeding at an ROH of 1, 5, 10 Mb, while the French Holstein had the highest. The maximum number of ROH is seen at a distance of less than 1 Mb, and the lowest number of ROH is seen at a distance of 10 Mb. The number of ROH decreases with increasing distance due to the increased recombination rate. This is a concern as an increase in inbreeding leads to a reduction in the effective population size, which was also evident in the study populations.

Omics Multi-Layers Networks Provide Novel Mechanistic and Functional Insights Into Fat Storage and Lipid Metabolism in Poultry.

Fatty acid metabolism in poultry has a major impact on production and disease resistance traits. According to the high rate of interactions between lipid metabolism and its regulating properties, a holistic approach is necessary. To study omics multilayers of adipose tissue and identification of genes and miRNAs involved in fat metabolism, storage and endocrine signaling pathways in two groups of broiler chickens with high and low abdominal fat, as well as high-throughput techniques, were used. The gene-miRNA interacting bipartite and metabolic-signaling networks were reconstructed using their interactions. In the analysis of microarray and RNA-Seq data, 1,835 genes were detected by comparing the identified genes with significant expression differences (p.adjust < 0.01, fold change ≥ 2 and ≤ -2). Then, by comparing between different data sets, 34 genes and 19 miRNAs were detected as common and main nodes. A literature mining approach was used, and seven genes were identified and added to the common gene set. Module finding revealed three important and functional modules, which were involved in the peroxisome proliferator-activated receptor (PPAR) signaling pathway, biosynthesis of unsaturated fatty acids, Alzheimer's disease metabolic pathway, adipocytokine, insulin, PI3K-Akt, mTOR, and AMPK signaling pathway. This approach revealed a new insight to better understand the biological processes associated with adipose tissue.

Genome Diversity and the Origin of the Arabian Horse.

The Arabian horse, one of the world's oldest breeds of any domesticated animal, is characterized by natural beauty, graceful movement, athletic endurance, and, as a result of its development in the arid Middle East, the ability to thrive in a hot, dry environment. Here we studied 378 Arabian horses from 12 countries using equine single nucleotide polymorphism (SNP) arrays and whole-genome re-sequencing to examine hypotheses about genomic diversity, population structure, and the relationship of the Arabian to other horse breeds. We identified a high degree of genetic variation and complex ancestry in Arabian horses from the Middle East region. Also, contrary to popular belief, we could detect no significant genomic contribution of the Arabian breed to the Thoroughbred racehorse, including Y chromosome ancestry. However, we found strong evidence for recent interbreeding of Thoroughbreds with Arabians used for flat-racing competitions. Genetic signatures suggestive of selective sweeps across the Arabian breed contain candidate genes for combating oxidative damage during exercise, and within the "Straight Egyptian" subgroup, for facial morphology. Overall, our data support an origin of the Arabian horse in the Middle East, no evidence for reduced global genetic diversity across the breed, and unique genetic adaptations for both physiology and conformation.

Genetic Diversity of Persian Arabian Horses and Their Relationship to Other Native Iranian Horse Breeds.

The principal aims of this study were to explore genetic diversity and genome-wide selection signatures in Persian Arabian horses and to determine genetic relationship of Persian Arabians with other Iranian horse breeds. We evaluated 71 horses from 8 matrilineal strains tracing to 47 mares from the mid to late 19th century, using the equine 670k single nucleotide polymorphism (SNP) BeadChip. Mean observed and expected heterozygosity were (0.43) and (0.45), respectively, average inbreeding measures (inbreeding estimates based on runs of homozygosity and pedigree information) were low, indicating high genetic diversity in Persian Arabian horses. Analysis of population genetic structure using STRUCTURE and principal component analysis suggested that Persian Arabian horses can be divided into 3 groups, however the groups do not match traditional matrilineal strains. In total, 15 genomic regions were identified by at least 2 of the 3 implemented methods, Tajima's D, H, and H12, as potentially under selection in Persian Arabian horses. Most of these peaks were found on chromosome 9, overlapping with QTLs previously associated with horse temperament. Biological function analysis of identified candidate genes highlighted enrichment of GO term "response to lipopolysaccharide" and KEGG pathway "chemokine-mediated signaling pathway," which are associated with immune responses and may have been targets of selection in Persian Arabian horses. Independent analyses of SNP data from 30 horses of 4 other Iranian breeds suggested distinct population structure between Persian Arabian, and Turkemen and Caspian horse breeds. Overall, the results of this study suggest a rich genetic diversity in the Persian Arabian horses and a clear genetic differentiation with Turkemen and Caspian breeds.

Bio-economic evaluation of the use of sexed semen at different conception rates and herd sizes in Holstein populations.

A stochastic bio-economic model was used to determine the effects of applying artificial insemination (AI) with conventional or sexed semen at different conception rates and herd sizes on the genetic progress of productive trait and net profit over a 15-year period. The genetic means of the cattle population was predicted to increase over the years and the rate of genetic progress in the population program using sexed semen was significantly greater than the rate of progress in the population using conventional semen. The theoretical genetic gain was greater than the realized genetic gain for milk yield for all the AI scenarios. In addition, the genetic means for milk yield in the expanding herds were greater than in the fixed sized herds. The trend in net profit generated by using AI strategies with conventional semen over the time horizon was greater when herds were expanding(P < 0.01). The trend in net profit for fixed sized herds was generally negative, however,expanding herds experienced positive net profits over the years. Also, the net profit of using AI strategies with sexed semen was greater when dairy herds were expanding. On the other hand, the net profit of using AI with sexed semen was greater than that of AI with conventional semen at the end of the time horizon. The results of sensitivity analyses indicated that a 20% increase or decrease in milk sales income and feed costs have significantly modified the net profit of AI strategies using either sexed or conventional semen.

Heterogeneity of variance for milk traits at climitical regions in Holstein dairy cattle in Iran and the best method(s) for data transformation.

In this research, heterogeneity of variance components in cattle populations at climatical regions in the first three lactations was studied. The data set included the following: 161328 records of first, 123369 records of second and 81013 records of third lactations, which was collected by Animal Breeding Center of Iran from 1983 to 2004. Records of three lactations were divided in the base of Domarten method. Bartlett test for heterogeneity of variance components was significant among all subgroups. In order to decrease the heterogeneity of variance components, we used several data transformation methods including Logarithmic, Square root and Arc sin transformations. Logarithmic transformation decreased the heterogeneity of variance components in the three lactations and other methods had not effect for removing the heterogeneity in any group. Genetic parameters and heritability were estimated for three lactations by MATVEC program, using animal model. Results showed that the heritability estimates of milk yield were decreased from the first lactation to the third; also the heritability estimates of transformed data were slightly higher than the original data. Comparison between estimated parameters in single trait and two traits analysis, before and after data transformation, showed that there were not significant differences between derived results.

Effect of the threshold nature of traits on heritability estimates obtained by linear model.

In this research we used stochastic method to simulate litter size data under assumption that the phenotype of the trait of interest is categorically distributed with a liability of normally distributed background effect. For the phenotypic categorical measurements, normal distribution of liability was assumed. Analysis was conducted by DFREML for phenotypic categorical data set. In the simulation process, we applied different true background heritabilities in combination with different threshold points. The points with the lowest heritability had the lowest variation in estimated heritability and those with the highest heritability had the highest variation. Results clearly show that by moving threshold points from the population mean towards the two tails of the distribution and also by increasing the true heritability, the differences between estimated heritability and true heritability increases. The linear model has always underestimated true heritabilities. It is possible to use our estimates to obtain a confidence range for true heritability when we have information about approximate location of threshold and estimates of heritability obtained by linear model.