Bioscience Methods 2025, Vol.16, No.2, 83-99 http://bioscipublisher.com/index.php/bm 91 Epigenetics also indirectly regulates muscle development by affecting key genes in signaling pathways. For example, the PI3K/Akt pathway is one of the main signaling pathways that promote muscle synthesis. A joint transcriptome and methylome analysis found that multiple components of the pathway showed a trend of low methylation and high expression in high-growth performance goats, such as AKT2 and ADCY5 genes, which had decreased methylation and increased expression in growth-advantaged individuals (Ren et al., 2023). The products of these genes enhance the promoting effect of insulin/IGF signals on muscle growth. On the contrary, some negative regulatory factors such as PTEN and PRKG1 have increased promoter methylation in high-growth individuals, resulting in reduced expression and relieving their inhibition of the PI3K/Akt pathway. It can be seen that the epigenetic modification state of key signaling genes is closely related to the rate of muscle growth. By artificially intervening in epigenetic states (such as using DNA methylation inhibitors or HDAC inhibitors), these effects may be simulated to a certain extent, thereby adjusting the balance between muscle generation and degradation. However, before being applied to production practice, its safety and long-term effects need to be carefully evaluated. In addition to DNA and histone modifications, RNA epigenetics (i.e., epitranscriptome) has become a hot topic in recent years. Among them, N6-methyladenine (m6A) modification is one of the most common mRNA modifications, which also affects muscle development. Research by Wang Feng's team at Nanjing Agricultural University revealed that the m6A demethylase FTO can promote goat myoblast differentiation by reducing the m6A level of GADD45B mRNA: when FTO demethylates the GADD45B transcript, the GADD45B gene is efficiently expressed, thereby activating the p38 MAPK signaling pathway and promoting myogenic differentiation. Knocking down FTO blocks this process and inhibits myoblast differentiation (Su et al., 2025). This finding belongs to epigenetic regulation at the RNA level, indicating that mRNA modification is also involved in the fine regulation of muscle development. For livestock such as goats, the regulatory role of m6A modification may be quite extensive, because many development-related mRNAs have m6A marks that regulate their splicing, stability, and translation efficiency. Finally, the plasticity of epigenetic regulation should be pointed out. Unlike gene sequences, epigenetic marks can be changed by environmental and physiological conditions. For example, maternal malnutrition, as mentioned above, affects fetal muscle gene methylation. Exercise, stress, and disease may also change the epigenetic state of muscle cells through hormones and inflammatory mediators. This plasticity means that through nutrition, exercise, and other means, it may be possible to optimize the epigenetic state of livestock muscle development to a certain extent. For example, studies have found that moderate exercise stimulation can change the histone acetylation of some metabolic gene promoters in skeletal muscle, increase their expression, and enhance the metabolic capacity of muscle (Zheng et al, 2025). 5 Application of Multi-Omics Integration in the Discovery of Regulatory Mechanisms 5.1 Integration of transcriptome and epigenomic data Data from a single omics can often only reflect one aspect of a biological process, while multi-omics integration can link information at different levels to build a more complete regulatory picture. In the study of skeletal muscle development, it is often necessary to combine transcriptome data with epigenetic data to determine which epigenetic changes lead to changes in gene expression. A typical example is the aforementioned study of Hainan black goats: by integrating WGBS methylation data with RNA-seq expression data, researchers screened out 11 key genes from thousands of differentially expressed genes. These genes showed both differential methylation and differential expression, and were therefore identified as important regulators affecting muscle growth. This association analysis greatly narrowed the candidate range and improved the efficiency of discovering key factors. Similarly, if we have multi-omics data at the single-cell level (such as ATAC-seq and RNA-seq of the same cell), we can more directly establish a one-to-one connection between genes and regulatory elements. For example, Ranzoni et al. (2021) integrated and analyzed single-cell RNA and ATAC data of human hematopoiesis and constructed a network map of genes and potential regulatory elements during development. In this network, they identified the correlation between the opening of binding sites of certain transcription factors and the expression of downstream genes, thereby discovering new regulatory axes. Similarly, for goat muscle, if ATAC and RNA
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