Genetic improvement of meat production traits has always been the primary goal of pig breeding. Geographical isolation, natural and artificial selection led to significant differences in the phenotypes of meat production traits between Chinese local pigs and Western commercial pigs. Comparative genomics and transcriptomics analysis provided powerful tools to identify genetic variants and genes associated with skeletal muscle growth. However, the number of available genetic variants and genes are still limited. In this study, a comprehensive comparison of transcriptomes showed that ribosomal protein S27-like (RPS27L) gene was highly expressed in skeletal muscle and up-regulated in Chinese local pigs when compared with Western commercial pigs. Functional analysis revealed that overexpression of RPS27L promoted myoblast proliferation and repressed differentiation in pig skeletal muscle cells. Conversely, the knockdown of RPS27L led to the inhibition of myoblast proliferation and the promotion of differentiation. Notably, a 13-bp insertion-deletion (InDel) mutation was identified within the RPS27L promoter, inserted in Chinese local breeds and predominantly deleted in Western commercial breeds. Luciferase reporter assay suggested this InDel modulated RPS27L expression by influencing transcription factor 3 (TCF3) and myogenic differentiation antigen (MYOD) binding to the promoter. Furthermore, a positive correlation was observed between RPS27L expression and backfat thickness. Association studies demonstrated this InDel was significantly associated with the body weight of pigs at the age of 240 d. Together, our results suggested that RPS27L was a regulator of skeletal muscle development and growth, and was a candidate marker for improving meat production traits in pigs. This study not only provided a biomarker for animal breeding, but also was helpful for understanding skeletal muscle development and muscular disease in humans.

Deep learning (DL) methods like multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) have been applied to predict the complex traits in animal and plant breeding. However, improving the genomic prediction accuracy still presents significant challenges. In this study, we applied CNNs to predict swine traits using previously published data. Specifically, we extensively evaluated the CNN model's performance by employing various sets of single nucleotide polymorphisms (SNPs) and concluded that the CNN model achieved optimal performance when utilizing SNP sets comprising 1,000 SNPs. Furthermore, we adopted a novel approach using the one-hot encoding method that transforms the 16 different genotypes into sets of eight binary variables. This innovative encoding method significantly enhanced the CNN's prediction accuracy for swine traits, outperforming the traditional one-hot encoding techniques. Our findings suggest that the expanded one-hot encoding method can improve the accuracy of DL methods in the genomic prediction of swine agricultural economic traits. This discovery has significant implications for swine breeding programs, where genomic prediction is pivotal in improving breeding strategies. Furthermore, future research endeavors can explore additional enhancements to DL methods by incorporating advanced data pre-processing techniques.

The microRNAs (miRNAs) play an important role in regulating myogenesis by targeting mRNA. However, the understanding of miRNAs in skeletal muscle development and diseases is unclear. In this study, we firstly performed the transcriptome profiling in differentiating C2C12 myoblast cells. Totally, we identified 187 miRNAs and 4260 mRNAs significantly differentially expressed that were involved in myoblast differentiation. We carried out validation of microarray data based on 5 mRNAs and 5 miRNAs differentially expressed and got a consistent result. Then we constructed and validated the significantly up- and down-regulated mRNA-miRNA interaction networks. Four interaction pairs (miR-145a-5p-Fscn1, miR-200c-5p-Tmigd1, miR-27a-5p-Sln and miR-743a-5p-Mob1b) with targeted relationships in differentiated myoblast cells were demonstrated. They are all closely related to myoblast development. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated cell cycle signals important for exploring skeletal muscle development and disease. Functionally, we discovered that miR-743a targeting gene Mps One Binder Kinase Activator-Like 1B (Mob1b) gene in differentiated C2C12. The up-regulated miR-743a can promote the differentiation of C2C12 myoblast. While the down-regulated Mob1b plays a negative role in differentiation. In addition, the expression profile of miR-743a and Mob1b are consistent with skeletal muscle recovery after Cardiotoxin (CTX) injury. Our study revealed that miR-743a-5p regulates myoblast differentiation by targeting Mob1b involved in skeletal muscle development and regeneration. Our findings made a further exploration for mechanisms in myogenesis and might provide potential possible miRNA-based target therapies for skeletal muscle regeneration and disease in the near future.