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Skeletal muscle is not only the largest organ in the body that is responsible for locomotion and exercise but also crucial for maintaining the body’s energy metabolism and endocrine secretion. The trimethylation of histone H3 lysine 27 (H3K27me3) is one of the most important histone modifications that participates in muscle development regulation by repressing the transcription of genes. Previous studies indicate that the RASGRP1 gene is regulated by H3K27me3 in embryonic muscle development in pigs, but its function and regulatory role in myogenesis are still unclear. In this study, we verify the crucial role of H3K27me3 in RASGRP1 regulation. The gain/loss function of RASGRP1 in myogenesis regulation is performed using mouse myoblast C2C12 cells and primarily isolated porcine skeletal muscle satellite cells (PSCs). The results of qPCR, western blot analysis, EdU staining, CCK-8 assay and immunofluorescence staining show that overexpression of RASGRP1 promotes cell proliferation and differentiation in both skeletal muscle cell models, while knockdown of RASGRP1 leads to the opposite results. These findings indicate that RASGRP1 plays an important regulatory role in myogenesis in both mice and pigs.

Birth weight is a complex multifactorial trait relevant to health states and disease risks in later life. The placenta is essential for proper fetal growth and facilitates gas, nutrient, and waste exchange between the mother and developing fetus. How changes in placental DNA methylation affect fetal birth weight remains to be fully elucidated. In this study, we used whole-genome bisulfite sequencing and RNA sequencing to reveal a global map of DNA methylation and gene expression changes between the placentas of highest birth weight and lowest birth weight piglets in the same litters. The transcriptome analysis identified 1682 differential expressed genes and revealed key transcriptional properties in distinct placentas. We also identified key transcription factors that may drive the differences in DNA methylome patterns between placentas. The decrease in DNA methylation level in the promoter was associated with the transcriptional activation of genes associated with angiogenesis, extracellular matrix remodeling, and transmembrane transport. Our results revealed the regulatory role of DNA methylation in gene transcription activity leading to the differences in placental morphological structures and birth weights of piglets. These results could provide novel clues to clarify the underlying regulatory mechanisms of placental development and fetal growth. [ABSTRACT FROM AUTHOR]

Simple Summary: The volume of a pig, a new phenotype feature, can be used to estimate its weight due to its high correlation with body weight. The proportions of different body parts, such as the head and legs, can be determined through point cloud segmentation, providing new phenotype information for breeding pigs with smaller heads and stouter legs. However, due to the irregular shape and potential missing parts of the pig point cloud, it is challenging to form a closed surface for volume calculation. The study addresses this challenge by using an improved Poisson reconstruction algorithm, which provides smoother, more continuous, and complete reconstruction results, and its accuracy and reliability have been confirmed. The study also found that the correlation coefficient between pig body volume and weight was 0.95, indicating a strong relationship. This research could be valuable for improving the efficiency and accuracy of livestock weight estimation and breeding. Pig point cloud data can be used to digitally reconstruct surface features, calculate pig body volume and estimate pig body weight. Volume, as a pig novel phenotype feature, has the following functions: (a) It can be used to estimate livestock weight based on its high correlation with body weight. (b) The volume proportion of various body parts (such as head, legs, etc.) can be obtained through point cloud segmentation, and the new phenotype information can be utilized for breeding pigs with smaller head volumes and stouter legs. However, as the pig point cloud has an irregular shape and may be partially missing, it is difficult to form a closed loop surface for volume calculation. Considering the better water tightness of Poisson reconstruction, this article adopts an improved Poisson reconstruction algorithm to reconstruct pig body point clouds, making the reconstruction results smoother, more continuous, and more complete. In the present study, standard shape point clouds, a known-volume Stanford rabbit standard model, a measured volume piglet model, and 479 sets of pig point cloud data with known body weight were adopted to confirm the accuracy and reliability of the improved Poisson reconstruction and volume calculation algorithm. Among them, the relative error was 4% in the piglet model volume result. The average absolute error was 2.664 kg in the weight estimation obtained from pig volume by collecting pig point clouds, and the average relative error was 2.478%. Concurrently, it was determined that the correlation coefficient between pig body volume and pig body weight was 0.95. [ABSTRACT FROM AUTHOR]

This study aimed at identifying genes associated with loin muscle area (LMA), loin muscle depth (LMD) and backfat thickness (BFT). We performed single‐trait and multi‐trait genome‐wide association studies (GWASs) after genotyping 685 Duroc × (Landrace × Yorkshire) (DLY) pigs using the Geneseek Porcine 50K SNP chip. In the single‐trait GWASs, we identified two, eight and two significant SNPs associated with LMA, LMD and BFT, respectively, and searched genes within the 1 Mb region near the significant SNPs with relevant functions as candidate genes. Consequently, we identified one (DOCK5), three (PID1, PITX2, ELOVL6) and three (CCR1, PARP14, CASR) promising candidate genes for LMA, LMD and BFT, respectively. Moreover, the multi‐trait GWAS identified four significant SNPs associated with the three traits. In conclusion, the GWAS analysis of LMA, LMD and BFT in a DLY pig population identified several associated SNPs and candidate genes, further deepening our understanding of the genetic basis of these traits, and they may be useful for marker‐assisted selection to improve the three traits in DLY pigs. [ABSTRACT FROM AUTHOR]

Simple Summary: In China, soup is widely perceived as a nutritional tonic, and pig bones are a primary ingredient in its preparation. Consequently, the market price of pig bones, even when of equal weight, surpasses that of pork, particularly in southern China. Despite this economic significance, the trait of pig bone weight has not been a focus in pig breeding programs due to the inherent challenges associated with in vivo measurement. The genetic selection method stands out as an effective approach for targeting the trait of pig bone weight in pig breeding. Recognizing its significance, our research endeavors have delved into molecular genetics to gain insights into the genetic aspects of pig bone weight. In order to unravel the genetic underpinnings of specific traits, we employed bioinformatics methodologies to conduct an analysis on three-way hybrid commercial pigs. Our primary objective was to identify genetic markers and genes associated with four distinct bone weight traits. Utilizing genome-wide association studies (GWAS), we scrutinized a population comprising 571 three-way hybrid commercial pigs to pinpoint single nucleotide polymorphisms (SNPs) and genes linked to these traits. The results identified twelve genes, namely OPRM1, SLC44A5, WASHC4, NOPCHAP1, RHOT1, GLP1R, TGFB3, PLCB1, TLR4, KCNJ2, ABCA6, and ABCA9, that were related to four bone weight traits. This comprehensive genetic insight provides valuable information on the mechanisms governing bone growth and weight in the context of pig breeding. The pig bone weight trait holds significant economic importance in southern China. To expedite the selection of the pig bone weight trait in pig breeding, we conducted molecular genetic research on these specific traits. These traits encompass the bone weight of the scapula (SW), front leg bone weight (including humerus and ulna) (FLBW), hind leg bone weight (including femur and tibia) (HLBW), and spine bone weight (SBW). Up until now, the genetic structure related to these traits has not been thoroughly explored, primarily due to challenges associated with obtaining the phenotype data. In this study, we utilized genome-wide association studies (GWAS) to discern single nucleotide polymorphisms (SNPs) and genes associated with four bone weight traits within a population comprising 571 Duroc × (Landrace × Yorkshire) hybrid pigs (DLY). In the analyses, we employed a mixed linear model, and for the correction of multiple tests, both the false discovery rate and Bonferroni methods were utilized. Following functional annotation, candidate genes were identified based on their proximity to the candidate sites and their association with the bone weight traits. This study represents the inaugural application of GWAS for the identification of SNPs associated with individual bone weight in DLY pigs. Our analysis unveiled 26 SNPs and identified 12 promising candidate genes (OPRM1, SLC44A5, WASHC4, NOPCHAP1, RHOT1, GLP1R, TGFB3, PLCB1, TLR4, KCNJ2, ABCA6, and ABCA9) associated with the four bone weight traits. Furthermore, our findings on the genetic mechanisms influencing pig bone weight offer valuable insights as a reference for the genetic enhancement of pig bone traits. [ABSTRACT FROM AUTHOR]

Simple Summary: By integrating prior biological information into genomic selection methods using appropriate models, it is possible to improve prediction accuracy for complex traits. In this context, we conducted a comparative assessment of two genomic prediction models, namely, genomic best linear unbiased prediction and genomic feature best linear unbiased prediction. The accuracy of these models in predicting the growth traits of backfat thickness and loin muscle area was evaluated. Our results revealed that the genomic feature best linear unbiased prediction model can effectively integrate prior information into the model, which is superior to the genomic best linear unbiased prediction model in some cases. These findings provide valuable ideas for enhancing the genomic prediction accuracy of growth traits in pigs. Enhancing the accuracy of genomic prediction is a key goal in genomic selection (GS) research. Integrating prior biological information into GS methods using appropriate models can improve prediction accuracy for complex traits. Genome-wide association study (GWAS) is widely utilized to identify potential candidate loci associated with complex traits in livestock and poultry, offering essential genomic insights. In this study, a GWAS was conducted on 685 Duroc × Landrace × Yorkshire (DLY) pigs to extract significant single-nucleotide polymorphisms (SNPs) as genomic features. We compared two GS models, genomic best linear unbiased prediction (GBLUP) and genomic feature BLUP (GFBLUP), by using imputed whole-genome sequencing (WGS) data on 651 Yorkshire pigs. The results revealed that the GBLUP model achieved prediction accuracies of 0.499 for backfat thickness (BFT) and 0.423 for loin muscle area (LMA). By applying the GFBLUP model with GWAS-based SNP preselection, the average prediction accuracies for BFT and LMA traits reached 0.491 and 0.440, respectively. Specifically, the GFBLUP model displayed a 4.8% enhancement in predicting LMA compared to the GBLUP model. These findings suggest that, in certain scenarios, the GFBLUP model may offer superior genomic prediction accuracy when compared to the GBLUP model, underscoring the potential value of incorporating genomic features to refine GS models. [ABSTRACT FROM AUTHOR]