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Porcine latissimus dorsi muscle (LDM) is a crucial source of pork products. Meat quality indicators, such as the proportion of muscle fibers and intramuscular fat (IMF) deposition, vary during the growth and development of pigs. Numerous studies have highlighted the heterogeneous nature of skeletal muscle, with phenotypic differences reflecting variations in cellular composition and transcriptional profiles. This study investigates the cellular-level transcriptional characteristics of LDM in large white pigs at two growth stages (170 days vs. 245 days) using single-nucleus RNA sequencing (snRNA-seq). We identified 56,072 cells across 12 clusters, including myofibers, fibro/adipogenic progenitor (FAP) cells, muscle satellite cells (MUSCs), and other resident cell types. The same cell types were present in the LDM at both growth stages, but their proportions and states differed. A higher proportion of FAPs was observed in the skeletal muscle of 245-day-old pigs. Additionally, these cells exhibited more active communication with other cell types compared to 170-day-old pigs. For instance, more interactions were found between FAPs and pericytes or endothelial cells in 245-day-old pigs, including collagen and integrin family signaling. Three subclasses of FAPs was identified, comprising FAPs_COL3A1+, FAPs_PDE4D+, and FAPs_EBF1+, while adipocytes were categorized into Ad_PDE4D+ and Ad_DGAT2+ subclasses. The proportions of these subclasses differed between the two age groups. We also constructed differentiation trajectories for FAPs and adipocytes, revealing that FAPs in 245-day-old pigs differentiated more toward fibrosis, a characteristic reminiscent of the high prevalence of skeletal muscle fibrosis in aging humans. Furthermore, the Ad_PDE4D+ adipocyte subclass, predominant in 245-day-old pigs, originated from FAPs_PDE4D+ expressing the same gene, while the Ad_DGAT2+ subclass stemmed from FAPs_EBF1+. In conclusion, our study elucidates transcriptional differences in skeletal muscle between two growth stages of pigs and provides insights into mechanisms relevant to pork meat quality and skeletal muscle diseases. [ABSTRACT FROM AUTHOR]

Motivation Enzymatic reaction play a pivotal role in regulating cellular processes with a high degree of specificity to biological functions. When enzymatic reactions are disrupted by gene, protein, or metabolite dysfunctions in diseases, it becomes crucial to visualize the resulting perturbed enzymatic reaction-induced multi-omics network. Multi-omics network visualization aids in gaining a comprehensive understanding of the functionality and regulatory mechanisms within biological systems. Results In this study, we designed PhenoMultiOmics, an enzymatic reaction-based multi-omics web server designed to explore the scope of the multi-omics network across various cancer types. We first curated the PhenoMultiOmics database, which enables the retrieval of cancer-gene–protein-metabolite relationships based on the enzymatic reactions. We then developed the MultiOmics network visualization module to depict the interplay between genes, proteins, and metabolites in response to specific cancer-related enzymatic reactions. The biomarker discovery module facilitates functional analysis through differential omic feature expression and pathway enrichment analysis. PhenoMultiOmics has been applied to analyze the transcriptomics data of gastric cancer and the metabolomics data of lung cancer, providing mechanistic insights into interrupted enzymatic reactions and the associated multi-omics network. Availability and implementation PhenoMultiOmics is freely accessed at https://phenomultiomics.shinyapps.io/cancer/ with a user-friendly and interactive web interface. [ABSTRACT FROM AUTHOR]

The placenta is a crucial determinant of fetal survival, growth, and development. Deficiency in placental development directly causes intrauterine growth retardation (IUGR). IUGR can lead to fetal growth restriction and an increase in the mortality rate. The genetic mechanisms underlying IUGR development, however, remain unclear. In the present study, we integrated whole-genome DNA methylation and transcriptomic analyses to determine distinct gene expression patterns in various placental tissues to identify pivotal genes that are implicated with IUGR development. By performing RNA-sequencing analysis, 1487 differentially expressed genes (DEGs), with 737 upregulated and 750 downregulated genes, were identified in IUGR pigs (H_IUGR) compared with that in normal birth weight pigs (N_IUGR) (p < 0.05); furthermore, 77 miRNAs, 1331 lncRNAs, and 61 circRNAs were differentially expressed. The protein–protein interaction network analysis revealed that among these DEGs, the genes GNGT1, ANXA1, and CDC20 related to cellular developmental processes and blood vessel development were the key genes associated with the development of IUGR. A total of 495,870 differentially methylated regions were identified between the N_IUGR and H_IUGR groups, which included 25,053 differentially methylated genes (DMEs); moreover, the overall methylation level was higher in the H_IUGR group than in the N_IUGR group. Combined analysis showed an inverse correlation between methylation levels and gene expression. A total of 1375 genes involved in developmental processes, tissue development, and immune system regulation exhibited methylation differences in gene expression levels in the promoter regions and gene ontology regions. Five genes, namely, ANXA1, ADM, NRP2, SHH, and SMAD1, with high methylation levels were identified as potential contributors to IUGR development. These findings provide valuable insights that DNA methylation plays a crucial role in the epigenetic regulation of gene expression and mammalian development and that DNA-hypermethylated genes contribute to IUGR development in Rongchang pigs. [ABSTRACT FROM AUTHOR]