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By constructing mimics and inhibitor vector transfecting into 3T3-L1 cells to explore the effect of miR-193a-5p on cell proliferation and differentiation, we demonstrated that overexpression of miR-193a-5p inhibited 3T3-L1 preadipocyte proliferation, as evidenced by decreased mRNA and protein expression of CDK4 and CyclinB. CCK-8 assay showed that miR-193a-5p significantly inhibited cell proliferation. Similarly, the overexpression of miR-193a-5p inhibited 3T3-L1 preadipocyte differentiation and adipocyte-specific molecular markers' expression, leading to a decrease in PPARγ and C/EBPα and ACAA2. Inhibition of miR-193a-5p had the opposite effects. Our study lists the miRNAs associated with intramuscular lipid deposition in sheep and their potential targets, striving to improve sheep meat quality.Bayesian regression models are widely used in genomic prediction for various species. By introducing the global parameter τ, which can shrink marker effects to zero, and the local parameter λ k , which can allow markers with large effects to escape from the shrinkage, we developed two novel Bayesian models, named BayesHP and BayesHE. The BayesHP model uses Horseshoe+ prior, whereas the BayesHE model assumes local parameter λ k , after a half-t distribution with an unknown degree of freedom. The performances of BayesHP and BayesHE models were compared with three classical prediction models, including GBLUP, BayesA, and BayesB, and BayesU, which also applied global-local prior (Horseshoe prior). To assess model performances for traits with various genetic architectures, simulated data and real data in cattle (milk production, health, and type traits) and mice (type and growth traits) were analyzed. The results of simulation data analysis indicated that models based on global-local priors, including BayesU, BayesHP, and BayesHE, performed better in traits with higher heritability and fewer quantitative trait locus. The results of real data analysis showed that BayesHE was optimal or suboptimal for all traits, whereas BayesHP was not superior to other classical models. For BayesHE, its flexibility to estimate hyperparameter automatically allows the model to be more adaptable to a wider range of traits. The BayesHP model, however, tended to be suitable for traits having major/large quantitative trait locus, given its nature of the "U" type-like shrinkage pattern. Our results suggested that auto-estimate the degree of freedom (e.g., BayesHE) would be a better choice other than increasing the local parameter layers (e.g., BayesHP). In this study, we introduced the global-local prior with unknown hyperparameter to Bayesian regression models for genomic prediction, which can trigger further investigations on model development.Tibetan pigs are native mammalian species on the Tibetan Plateau that have evolved distinct physiological traits that allow them to tolerate high-altitude hypoxic environments. However, the genetic mechanism underlying this adaptation remains elusive. Here, based on multitissue transcriptional data from high-altitude Tibetan pigs and low-altitude Rongchang pigs, we performed a weighted correlation network analysis (WGCNA) and identified key modules related to these tissues. Complex network analysis and bioinformatics analysis were integrated to identify key genes and three-node network motifs. We found that among the six tissues (muscle, liver, heart, spleen, kidneys, and lungs), lung tissue may be the key organs for Tibetan pigs to adapt to hypoxic environment. In the lung tissue of Tibetan pigs, we identified KLF4, BCL6B, EGR1, EPAS1, SMAD6, SMAD7, KDR, ATOH8, and CCN1 genes as potential regulators of hypoxia adaption. We found that KLF4 and EGR1 genes might simultaneously regulate the BCL6B gene, forming a KLF4-EGR1-BCL6B complex. This complex, dominated by KLF4, may enhance the hypoxia tolerance of Tibetan pigs by mediating the TGF-β signaling pathway. Lificiguat The complex may also affect the PI3K-Akt signaling pathway, which plays an important role in angiogenesis caused by hypoxia. Therefore, we postulate that the KLF4-EGR1-BCL6B complex may be beneficial for Tibetan pigs to survive better in the hypoxia environments. Although further molecular experiments and independent large-scale studies are needed to verify our findings, these findings may provide new details of the regulatory architecture of hypoxia-adaptive genes and are valuable for understanding the genetic mechanism of hypoxic adaptation in mammals.Objectives Transcriptional changes in cartilage can impact function by causing degradation such as that which occurs during the development of osteoarthritis (OA). Epigenetic regulation may be a key factor leading to transcriptional changes in OA. In this study, we performed a combined analysis of DNA methylation and gene expression microarray datasets and identified key transcription factors (TFs) central to the regulation of gene expression in OA. Methods A DNA methylation profile dataset (GSE63106) and a gene expression profiling dataset (GSE114007) were extracted from the Gene Expression Omnibus (GEO). We used ChAMP methylation analysis and the Limma package to identify differentially methylation genes (DMGs) and differentially expressed genes (DEGs) from normal and human knee cartilage samples in OA. Function enrichment analysis of DMGs was conducted using the DAVID database. A combined analysis of DEGs and DMGs was conducted to identify key TFs in OA. We then validated the mRNA expression of selected TFA methylation on the transcriptional regulation is related to the distribution of methylated sites across the genome. Epigenetic studies on the positions of DMS in transcriptional units can inform a better understanding of the function of DNA methylation and its transcription regulation.

The functions of most glioma risk alleles are unknown. Very few studies had evaluated expression quantitative trait loci (eQTL), and insights of susceptibility genes were limited due to scarcity of available brain tissues. Moreover, no prior study had examined the effect of glioma risk alleles on alternative RNA splicing.

This study explored splicing quantitative trait loci (sQTL) as molecular QTL and improved the power of QTL mapping through meta-analyses of both

eQTL and sQTL.

We first evaluated eQTLs and sQTLs of the CommonMind Consortium (CMC) and Genotype-Tissue Expression Project (GTEx) using genotyping, or whole-genome sequencing and RNA-seq data. Alternative splicing events were characterized using an annotation-free method that detected intron excision events. Then, we conducted meta-analyses by pooling the eQTL and sQTL results of CMC and GTEx using the inverse variance-weighted model. Afterward, we integrated QTL meta-analysis results (Q < 0.05) with the Glioma International Case Control Study (GICC) GWAS meta-analysis (case12,496, control18,190), using a summary statistics-based mendelian randomization (SMR) method.