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.

At later phases of folliculogenesis, the mammalian ovarian follicle contains layers of granulosa cells surrounding an antral cavity. To better understand the molecular basis of follicular growth and granulosa cell maturation, we study transcriptome profiling of granulosa cells from small (10 mm) bovine follicles using simultaneous method of Affymetrix microarrays (24,128 probe sets) and RNA-Seq data sets. This study proposes a computational method to discover the functional miRNA–mRNA regulatory modules, that is, groups of miRNAs and their target mRNAs that are believed to take part cooperatively in post-transcriptional gene regulation under specific conditions. The reconstructed network was named Integrated miRNA–mRNA Bipartite Network. 277 genes and 6 key modules were disclosed through clustering for mRNA master list. The 66 genes are among the genes that belong to at least two modules. All these genes, being involved in at least one of the phenomena, namely cell survival, proliferation, metastasis and apoptosis, have an overexpression pattern (P < 0.01). For miRNA master list, a total of 172 sequences were differentially expressed (P < 0.01) between dominant (large) and each of subordinate (small) follicles. Within the follicle, these miRNAs were predominantly expressed in mural granulosa cells. Finally, predicted and validated targets of these miRNAs enriched in dominant (large) follicles were identified, which are mapped to signaling pathways involved in follicular cell proliferation, steroidogenesis, PI3K/AKT/mTOR and Ras/Raf/MEK/ERK. The identification of miRNAs and their target mRNAs and the construction of their regulatory networks may give new insights into biological procedures.

TEK signaling plays a very important role in folliculogenesis. It activates Ras/ERK/MYC, PI3K/AKT/mTORC1 and ovarian steroidogenesis activation pathways. These are the main pathways for cell growth, differentiation, migration, adhesion, proliferation, survival and protein synthesis. TEK signaling on each of the two important pathways where levels of pERK, pMYC, pAkt, pMCL1 and pEIF4EBP1 are increased in dominant follicles and pMYC is decreased in dominant follicles. Over activation of ERK and MYC which are the main cell growth and proliferation and over activation of Akt, MCl1, mTORC1 and EIF4EBP1 which are the main cell survival and protein synthesis factors act as promoting factors for folliculogenesis. In case of over expression of hsa-miR-30d-3p and hsa-miR-451a, MYC activity level is considerably increased in subordinate follicles. Our simulation results show that in the presence of has-miR-548v and bta-miR-22-3p, downstream factors of pathways are inhibited. Our work offers insight into the design of natural biological procedures and makes predictions that can guide further experimental studies on folliculogenesis pathways. Moreover, it defines a simple signal processing unit that may be useful for engineering synthetic biology and genes circuits to carry out cell-based computation.

This manuscript summarizes information on the diverse range of RNA molecules and their role as competing endogenous RNAs (ceRNAs). Moreover, it provides an overview of ceRNA regulatory networks and their applications in ruminant biology. Knowledge of co-expression networks has increased with microarrays, RNA-seq, and scRNA-seq characterizing molecular mediators across various biological scales, using sequences from numerous blood and tissue samples. By synthesizing existing knowledge, this study summarizes interactions between coding and non-coding RNAs through microRNA response elements (MREs), elucidating large-scale regulatory networks throughout the transcriptome that influence the expression and activities of various ceRNAs. Identification of non-coding RNAs with important regulatory functions will revolutionize understanding of RNA biology, shifting from an mRNA-centric model to a complex network of RNA crosstalk. The ceRNA networks offer a more comprehensive and arguably more realistic perspective compared to protein–protein interaction (PPI) networks and weighted gene co-expression networks (WGCN). These ceRNA regulatory networks can describe potential molecular regulatory mechanisms related to functional and economically important traits in ruminants, plus contribute to disease and pathology research, by elucidating pathogenesis and potential drug effects in disease and cancer models. Furthermore, they can provide insights into farm animal biology, e.g., reproductive traits in goats and sheep, regulation of fat metabolism in beef cattle, heat stress responses, and lactation regulation in dairy cattle, fertility and muscle characteristics in buffalo, and resistance to high-salt and water-deprivation conditions in camels. In conclusion, ceRNA and associated regulatory networks should promote a new understanding of molecular mechanisms and identify candidate genes and metabolic-signaling pathways in ruminants.

Crossbreeding is a widely adopted practice in the livestock industry, leveraging the advantages of heterosis and breed complementarity. The prediction of Crossbred Performance (CP) often relies on Purebred Performance (PB) due to limited crossbred data availability. However, the effective selection of purebred parents for enhancing CP depends on non-additive genetic effects and environmental factors. These factors are encapsulated in the genetic correlation between crossbred and purebred populations (rpc). In this study, a two-way crossbreeding simulation was employed to investigate various strategies for integrating data from purebred and crossbred populations. The goal was to identify optimal models that maximize CP across different levels of rpc. Different scenarios involving the selection of genotyped individuals from purebred and crossbred populations were explored using ssGBLUP (single-step Genomic Best Linear Unbiased Prediction) and ssGBLUP-MF (ssGBLUP with metafounders) models. The findings revealed an increase in prediction accuracy across all scenarios as rpc values increased. Notably, in the scenario incorporating genotypes from both purebred parent breeds and their crossbreds, both ssGBLUP and ssGBLUP-MF models exhibited nearly identical predictive accuracy. This scenario achieved maximum accuracy when rpc was less than 0.5. However, at rpc = 0.8, ssGBLUP, which exclusively included sire breed genotypes in the training set, achieved the highest overall prediction accuracy at 73.2%. In comparison, the BLUP-UPG (BLUP with unknown parent group) model demonstrated lower accuracy than ssGBLUP and ssGBLUP-MF across all rpc levels. Although ssGBLUP and ssGBLUP-MF did not demonstrate a definitive trend in their respective scenarios, the prediction ability for CP increased when incorporating both crossbred and purebred population genotypes at lower levels of rpc. Furthermore, when rpc was high, utilizing paternal genotype for CP predictions emerged as the most effective strategy. Predicted dispersion remained relatively similar in all scenarios, indicating a slight underestimation of breeding values. Overall, the rpc value emerged as a critical factor in predicting CP based on purebred data. However, the optimal model to maximize CP depends on the factors influencing rpc. Consequently, ongoing research aims to develop models that optimize purebred selection, further enhancing CP.