Transcriptome Integration Analysis at Different Embryonic Ages Reveals Key lncRNAs and mRNAs for Chicken Skeletal Muscle

Understanding lncRNAs: key regulators of myogenesis and lipogenesis in farm animals

Long non-coding RNAs (lncRNAs) are RNA molecules exceeding 200 nucleotides in length. Recent studies have demonstrated their involvement in regulating gene expression and various biological processes. Among these, myogenesis and lipogenesis are particularly important because of their direct effects on muscle development and fat deposition in farm animals. These processes are crucial for determining meat quality, growth rates, and overall economic value in animal husbandry. Although the specific mechanisms through which lncRNAs influence these pathways are still under investigation, further research into their roles in muscle and fat development is crucial for optimizing farm animal breeding strategies. Here, we review the characteristics of lncRNAs, including their biogenesis, localization, and structures, with a particular focus on their association with myogenesis and adipogenesis. This review seeks to establish a theoretical foundation for enhancing farm animal production. In particular, focusing on lncRNAs may reveal how these molecules can enhance the economic traits of farm animals, thereby contributing to the optimization of farm animal breeding processes.

Transcriptomic analysis reveals diverse expression patterns underlying the fiber diameter of oxidative and glycolytic skeletal muscles in steers

Skeletal muscles consist of heterogeneous fibers with various contractile and metabolic properties that affect meat quality. The size of muscle fibers contributes to muscle mass and myopathy. Thus, improved understanding of the expression patterns underlying fiber size might open possibilities to change them using genetic methods. The aim of this study was to reveal transcriptomic landscapes of one oxidative (Psoas major) and three glycolytic (Longissimus lumborum, Triceps brachii, and Semimembranosus) muscles. Principal component analysis (PCA) showed significant differences in gene expression among the four muscles. Specifically, 2777 differentially expressed genes (DEGs) were detected between six pairwise comparisons of the four muscles. Weighted gene co-expression network analysis (WGCNA) identified six modules, which were significantly associated with muscle fiber diameter. We also identified 23 candidate genes, and enrichment analysis showed that biosynthesis of amino acids (bta01230), sarcomere (GO:0030017), and regulation of actin cytoskeleton (bta04810) overlapped in DEGs and WGCNA. Nineteen of these genes (e.g., EEF1A2, FARSB, and PINK1) have been reported to promote or inhibit muscle growth and development. Our findings contribute to the understanding of fiber size differences among oxidative and glycolytic muscles, which may provide a basis for breeding to improve meat yield.

Transcriptome sequencing promotes insights on the molecular mechanism of SKP-SC-EVs mitigating denervation-induced muscle atrophy

BACKGROUND

Complex pathophysiological changes accompany denervation-induced skeletal muscle atrophy, but no effective treatment strategies exist. Our previous study indicated that extracellular vesicles derived from skin-derived precursors-derived Schwann cells (SKP-SC-EVs) can effectively mitigate denervation-induced muscle atrophy. However, the specific molecular mechanism remains unclear.

METHODS AND RESULTS

In this study, we used bioinformatics methods to scrutinize the impact of SKP-SC-EVs on gene expression in denervation-induced skeletal muscle atrophy. We found that SKP-SC-EVs altered the expression of 358 genes in denervated skeletal muscles. The differentially expressed genes were predominantly participated in biological processes, including cell cycle, inflammation, immunity, and adhesion, and signaling pathways, such as FoxO and PI3K.Using the Molecular Complex Detection (MCODE) plugin, we identified the two clusters with the highest score: cluster 1 comprised 37 genes, and Cluster 2 consisted of 24 genes. Then, fifty hub genes were identified using CytoHubba. The intersection of Hub genes and genes obtained by MCODE showed that all 23 genes related to the cell cycle in Cluster 1 were hub genes, and 5 genes in Cluster 2 were hub genes and associated with inflammation.

CONCLUSIONS

Overall, the differentially expressed genes in denervated skeletal muscle following SKP-SC-EVs treatment are primarily linked to the cell cycle and inflammation. Consequently, promoting proliferation and inhibiting inflammation may be the critical process in which SKP-SC-EVs delay denervation-induced muscle atrophy. Our findings contribute to a better understanding of the molecular mechanism of SKP-SC-EVs delaying denervation-induced muscle atrophy, offering a promising new avenue for muscle atrophy treatment.

Transcriptome Analysis Reveals the Profile of Long Non-Coding RNAs during Myogenic Differentiation in Goats

The long non-coding RNAs (lncRNAs) are emerging as essential regulators of the growth and development of skeletal muscles. However, little is known about the expression profiles of lncRNAs during the proliferation and differentiation of skeletal muscle satellite cells (MuSCs) in goats. In this study, we investigate potential regulatory lncRNAs that govern muscle development by performing lncRNA expression profiling analysis during the proliferation (cultured in the growth medium, GM) and differentiation (cultured in the differentiation medium, DM1/DM5) of MuSCs. In total, 1001 lncRNAs were identified in MuSC samples, and 314 differentially expressed (DE) (FDR < 0.05, |log2FC| > 1) lncRNAs were screened by pairwise comparisons from three comparison groups (GM-vs-DM1, GM-vs-DM5, DM1-vs-DM5). Moreover, we identified the cis-, trans-, and antisense-regulatory target genes of DE lncRNAs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that these target genes were significantly enriched in muscle development-related GO terms and KEGG pathways. In addition, the network of interactions between DE lncRNAs and their target genes was identified, which included well-known myogenesis regulators such as Myogenic differentiation 1 (MyoD), Myogenin (MyoG), and Myosin heavy chain (MyHC). Meanwhile, competing endogenous RNA (ceRNA) network analysis showed that 237 DE lncRNAs could bind to 329 microRNAs (miRNAs), while miRNAs could target 564 mRNAs. Together, our results provide a genome-wide resource of lncRNAs that may contribute to myogenic differentiation in goats and lay the groundwork for future investigation into their functions during skeletal muscle development.

Characterization of lncRNA/circRNA-miRNA-mRNA network to reveal potential functional ceRNAs in the skeletal muscle of chicken

Skeletal muscle, comprising approximately 40% of body mass, is a highly complex and heterogeneous tissue serving a multitude of functions in the organism. Non-coding RNAs (ncRNAs) are known to participate in skeletal muscle development as critical regulators. However, the regulatory mechanisms of ncRNAs on chicken muscle traits are not well understood. In the present study, we collected the leg muscle from male embryos of Tibetan chicken at embryonic (E) 10 and E18 for RNA sequencing. A total of 6,583 differentially expressed mRNAs (DEMs) including 3,055 down-regulated and 3,528 up-regulated were identified in E18. We identified 695 differentially expressed lncRNAs (DELs) (187 down-regulated and 508 up-regulated) and 1,906 differentially expressed circRNAs (DECs) (1,224 down-regulated and 682 up-regulated) in E18. Among the 130 differentially expressed miRNAs (DEMIs), 59 were up-regulated and 71 were down-regulated in E18. Numerous DEMs and target genes for miRNAs/lncRNAs were significantly enriched in the muscle system process and cell cycle. We constructed a miRNA-gene-pathway network by considering target relationships between genes related to skeletal muscle development and miRNAs. A competing endogenous RNA (ceRNA) network was also constructed by integrating competing relationships between DEMs, DELs, and DECs. Several DELs and DECs were predicted to regulate the ADRA1B, ATP2A2, ATP2B1, CACNA1S, CACNB4, MYLK2, and ROCK2 genes. We discovered the crosstalk between the ncRNAs and their competing mRNAs, which provides insights into ceRNA function and mechanisms in the skeletal muscle development of chicken.