Combining genome-wide association study based on low-coverage whole genome sequencing and transcriptome analysis to reveal the key candidate genes affecting meat color in pigs.

Meat color is an attractive trait that influences consumers' purchase decisions at the point of sale. To decipher the genetic basis of meat color traits, we performed a genome-wide association study based on low-coverage whole-genome sequencing. In total, 669 (Pietrain × Duroc) × (Landrace × Yorkshire) pigs were genotyped using low-coverage whole-genome sequencing. Single nucleotide polymorphism (SNP) calling and genotype imputation were performed using the BaseVar + STITCH channel. Six individuals with an average depth of 12.05× whole-genome resequencing were randomly selected to assess the accuracy of imputation. Heritability evaluation and genome-wide association study for meat color traits were conducted. Functional enrichment analysis of the candidate genes from genome-wide association study and integration analysis with our previous transcriptome data were conducted. The imputation accuracy parameters, allele frequency R2 , concordance rate, and dosage R2 were 0.959, 0.952, and 0.933, respectively. The heritability values of a*45 min , b*45 min , L*45 min , C*, and H0 were 0.19, 0.11, 0.06, 0.16, and 0.26, respectively. In total, 3884 significant SNPs and 15 QTL, corresponding to 382 genes, were associated with meat color traits. Functional enrichment analysis revealed that 10 genes were the potential candidates for regulating meat color. Moreover, integration analysis revealed that DMRT2, EFNA5, FGF10, and COL11A2 were the most promising candidates affecting meat color. In summary, this study provides new insights into the molecular basis of meat color traits, and provides a new theoretical basis for the molecular breeding of meat color traits in pigs.

Integration of transcriptome and machine learning to identify the potential key genes and regulatory networks affecting drip loss in pork.

Low level of drip loss (DL) is an important quality characteristic of meat with high economic value. However, the key genes and regulatory networks contributing to DL in pork remain largely unknown. To accurately identify the key genes affecting DL in muscles postmortem, 12 Duroc × (Landrace × Yorkshire) pigs with extremely high (n = 6, H group) and low (n = 6, L group) DL at both 24 and 48 h postmortem were selected for transcriptome sequencing. The analysis of differentially expressed genes and weighted gene co-expression network analysis (WGCNA) were performed to find the overlapping genes using the transcriptome data, and functional enrichment and protein-protein interaction (PPI) network analysis were conducted using the overlapping genes. Moreover, we used machine learning to identify the key genes and regulatory networks related to DL based on the interactive genes of the PPI network. Finally, nine potential key genes (IRS1, ESR1, HSPA6, INSR, SPOP, MSTN, LGALS4, MYLK2, and FRMD4B) mainly associated with the MAPK signaling pathway, the insulin signaling pathway, and the calcium signaling pathway were identified, and a single-gene set enrichment analysis (GSEA) was performed to further annotate the functions of these potential key genes. The GSEA results showed that these genes are mainly related to ubiquitin-mediated proteolysis and oxidative reactions. Taken together, our results indicate that the potential key genes influencing DL are mainly related to insulin signaling mediated differences in glycolysis and ubiquitin-mediated changes in muscle structure and improve the understanding of gene expression and regulation related to DL and contribute to future molecular breeding for improving pork quality.

A low level of drip loss (DL) is critical for the economic value of pork. However, the genetic basis underlying DL remains unclear. In this study, pigs with extremely high and low DL at both 24 and 48 h postmortem were selected, and total RNA from longissimus dorsi (LD) muscles was extracted for transcriptome sequencing. Subsequently, a variety of analytical methods, were integrated to identify the potential key genes and pathways affecting DL. As a result, nine potential key genes (IRS1, ESR1, HSPA6, INSR, SPOP, MSTN, LGALS4, MYLK2, and FRMD4B) mainly associated with the MAPK signaling pathway, insulin signaling pathway, and calcium signaling pathway, were identified, and these genes are primarily related to ubiquitin-mediated proteolysis and oxidation reactions. This study contributes new evidence for elucidating the molecular mechanism of DL and provides potential target genes for precise genetic improvement of DL.

Differential Regulation of Male-Hormones-Related Enhancers Revealed by Chromatin Accessibility and Transcriptional Profiles in Pig Liver.

Surgical castration can effectively avoid boar taint and improve pork quality by removing the synthesis of androstenone in the testis, thereby reducing its deposition in adipose tissue. The expression of genes involved in testis-derived hormone metabolism was altered following surgical castration, but the upstream regulatory factors and underlying mechanism remain unclear. In this study, we systematically profiled chromatin accessibility and transcriptional dynamics in liver tissue of castrated and intact full-sibling Yorkshire pigs. First, we identified 897 differentially expressed genes and 6864 differential accessible regions (DARs) using RNA- and ATAC-seq. By integrating the RNA- and ATAC-seq results, 227 genes were identified, and a significant positive correlation was revealed between differential gene expression and the ATAC-seq signal. We constructed a transcription factor regulatory network after motif analysis of DARs and identified a candidate transcription factor (TF) SP1 that targeted the HSD3B1 gene, which was responsible for the metabolism of androstenone. Subsequently, we annotated DARs by incorporating H3K27ac ChIP-seq data, marking 2234 typical enhancers and 245 super enhancers involved in the regulation of all testis-derived hormones. Among these, four typical enhancers associated with HSD3B1 were identified. Furthermore, an in-depth investigation was conducted on the androstenone-related enhancers, and an androstenone-related mutation was identified in a newfound candidatetypical enhancer (andEN) with dual-luciferase assays. These findings provide further insights into how enhancers function as links between phenotypic and non-coding area variations. The discovery of upstream TF and enhancers of HSD3B1 contributes to understanding the regulatory networks of androstenone metabolism and provides an important foundation for improving pork quality.

A Combined Differential Proteome and Transcriptome Profiling of Fast- and Slow-Twitch Skeletal Muscle in Pigs.

Skeletal muscle fiber types can contribute in part to affecting pork quality parameters. Biceps femoris (Bf) (fast muscle or white muscle) and Soleus (Sol) (slow muscle or red muscle) are two typical skeletal muscles characterized by obvious muscle fiber type differences in pigs. However, the critical proteins and potential regulatory mechanisms regulating porcine skeletal muscle fibers have yet to be clearly defined. In this study, the isobaric Tag for Relative and Absolute Quantification (iTRAQ)-based proteome was used to identify the key proteins affecting the skeletal muscle fiber types with Bf and Sol, by integrating the previous transcriptome data, while function enrichment analysis and a protein-protein interaction (PPI) network were utilized to explore the potential regulatory mechanisms of skeletal muscle fibers. A total of 126 differentially abundant proteins (DAPs) between the Bf and Sol were identified, and 12 genes were found to be overlapping between differentially expressed genes (DEGs) and DAPs, which are the critical proteins regulating the formation of skeletal muscle fibers. Functional enrichment and PPI analysis showed that the DAPs were mainly involved in the skeletal-muscle-associated structural proteins, mitochondria and energy metabolism, tricarboxylic acid cycle, fatty acid metabolism, and kinase activity, suggesting that PPI networks including DAPs are the main regulatory network affecting muscle fiber formation. Overall, these data provide valuable information for understanding the molecular mechanism underlying the formation and conversion of muscle fiber types, and provide potential markers for the evaluation of meat quality.