Growth traits are important economic traits of pigs that are controlled by several major genes and multiple minor genes. To better understand the genetic architecture of growth traits, we performed a weighted single-step genome-wide association study (wssGWAS) to identify genomic regions and candidate genes that are associated with days to 100 kg (AGE), average daily gain (ADG), backfat thickness (BF) and lean meat percentage (LMP) in a Duroc pig population. In this study, 3945 individuals with phenotypic and genealogical information, of which 2084 pigs were genotyped with a 50 K single-nucleotide polymorphism (SNP) array, were used for association analyses. We found that the most significant regions explained 2.56–3.07% of genetic variance for four traits, and the detected significant regions (>1%) explained 17.07%, 18.59%, 23.87% and 21.94% for four traits. Finally, 21 genes that have been reported to be associated with metabolism, bone growth, and fat deposition were treated as candidate genes for growth traits in pigs. Moreover, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses implied that the identified genes took part in bone formation, the immune system, and digestion. In conclusion, such full use of phenotypic, genotypic, and genealogical information will accelerate the genetic improvement of growth traits in pigs.

Cryopreservation techniques have been widely used, especially in biomedical applications and preservation of germplasm resources. Ideally, biological materials would maintain functional integrity as well as a normal structure and can be recovered when needed. However, this tool does not work all the time. Ice formation and growth are the key challenges. The other major reason is that the cryoprotective agents (CPAs) currently used do not meet these needs and are always accompanied by their cytotoxicity. A comprehensive and synergistic approach that focuses on the overall frozen biological system is crucial for the evolution of cryopreservation methods. In this review, we first summarize the fundamental damage mechanisms during cryopreservation, as well as common cryoprotectants and their limitations. Next, we discuss materials that interact with ice to improve cryopreservation outcomes. We evaluated natural and synthetic materials, including sugars and polymers, AFPs and mimics, ice nucleators, and hydrogels. In addition, biochemical regulation, which enhances the tolerance of biosamples to cryopreservation-induced stresses, was also mentioned. Nanotechnology, cell encapsulation, cryomesh, and isochoric freezing, such scalable approaches, are further discussed for cryopreservation. Finally, future research directions in this field for efficient cryopreservation are proposed. We emphasized the need for multidisciplinary progress to address these challenges. The combination of cryobiology mechanisms with technologies, such as synthetic biology, nanotechnology, microfluidics, and 3D bioprinting, is highlighted.

Body conformation is the most direct production index, which can fully reflect pig growth status and is closely related to critical economic traits. In this study, we conducted a genome-wide association study (GWAS) on body conformation traits in a population of 1518 Duroc × (Landrace × Yorkshire) commercial pigs. These traits included body length (BL), body height (BH), chest circumference (CC), abdominal circumference (AC), and waist circumference (WC). Both the mixed linear model (MLM) and fixed and random model circulating probability unification (FarmCPU) approaches were employed for the analysis. Our findings revealed 60 significant single nucleotide polymorphisms (SNPs) associated with these body conformation traits in the crossbred pig population. Specifically, sixteen SNPs were significantly associated with BL, three SNPs with BH, thirteen SNPs with CC, twelve SNPs with AC, and sixteen SNPs with WC. Moreover, we identified several promising candidate genes located within the genomic regions associated with body conformation traits. These candidate genes include INTS10, KIRREL3, SOX21, BMP2, MAP4K3, SOD3, FAM160B1, ATL2, SPRED2, SEC16B, and RASAL2. Furthermore, our analysis revealed a novel significant quantitative trait locus (QTL) on SSC7 specifically associated with waist circumference, spanning an 84 kb interval. Overall, the identification of these significant SNPs and potential candidate genes in crossbred commercial pigs enhances our understanding of the genetic basis underlying body conformation traits. Additionally, these findings provide valuable genetic resources for pig breeding programs.

N⁶-methyladenosine (m⁶A) is the most common modification in eukaryotic RNAs. Accumulating evidence shows m⁶A methylation plays vital roles in various biological processes, including muscle and fat differentiation. However, there is a lack of research on lncRNAs’ m⁶A modification in regulating pig muscle-fiber-type conversion. In this study, we identified novel and differentially expressed lncRNAs in oxidative and glycolytic skeletal muscles through RNA-seq, and further reported the m⁶A-methylation patterns of lncRNAs via MeRIP-seq. We found that most lncRNAs have one m⁶A peak, and the m⁶A peaks were preferentially enriched in the last exon of the lncRNAs. Interestingly, we found that lncRNAs’ m⁶A levels were positively correlated with their expression homeostasis and levels. Furthermore, we performed conjoint analysis of MeRIP-seq and RNA-seq data and obtained 305 differentially expressed and differentially m⁶A-modified lncRNAs (dme-lncRNAs). Through QTL enrichment analysis of dme-lncRNAs and PPI analysis for their cis-genes, we finally identified seven key m⁶A-modified lncRNAs that may play a potential role in muscle-fiber-type conversion. Notably, inhibition of one of the key lncRNAs, MSTRG.14200.1, delayed satellite cell differentiation and stimulated fast-to-slow muscle-fiber conversion. Our study comprehensively analyzed m⁶A modifications on lncRNAs in oxidative and glycolytic skeletal muscles and provided new targets for the study of pig muscle-fiber-type conversion.

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.

Feed efficiency (FE) is an important trait in the porcine industry. Therefore, understanding the molecular mechanisms of FE is vital for the improvement of this trait. In this study, 6 extreme high-FE and 6 low-FE pigs were selected from 225 Duroc × (Landrace × Yorkshire) (DLY) pigs for transcriptomic analysis. RNA-seq analysis was performed to determine differentially expressed genes (DEGs) in the liver tissues of the 12 individuals, and 507 DEGs were identified between high-FE pigs (HE- group) and low-FE pigs (LE- group). A gene ontology (GO) enrichment and pathway enrichment analysis were performed and revealed that glycolytic metabolism and lipid synthesis-related pathways were significantly enriched within DEGs; all of these DEGs were downregulated in the HE- group. Moreover, Weighted gene co-expression analysis (WGCNA) revealed that oxidative phosphorylation, thermogenesis, and energy metabolism-related pathways were negatively related to HE- group, which might result in lower energy consumption in higher efficiency pigs. These results implied that the higher FE in the HE- group may be attributed to a lower glycolytic, energy consumption and lipid synthesizing potential in the liver. Furthermore, our findings suggested that the inhibition of lipid synthesis and glucose metabolic activity in the liver may be strategies for improving the FE of DLY pigs.