A low-molecular-weight α-glucan from edible fungus Agaricus blazei Murrill activates macrophage TFEB-mediated antibacterial defense to combat implant-associated infection.

Implant-associated infection (IAI) is a prevalent and potentially fatal complication of orthopaedic surgery. Boosting antibacterial immunity, particularly the macrophage-mediated response, presents a promising therapeutic approach for managing persistent infections. In this study, we successfully isolated and purified a homogeneous and neutral water-soluble polysaccharide, designated as AM-1, from the edible fungus Agaricus blazei Murrill. Structure analysis revealed that AM-1 (Mw = 3.87 kDa) was a low-molecular-weight glucan characterized by a primary chain of →4)-α-D-Glcp-(1 â†’ and side chains that were linked at the O-6 and O-3 positions. In vivo assays showed that AM-1 effectively attenuated the progression of infection and mitigated infectious bone destruction in IAI mouse models. Mechanistically, AM-1 promotes intracellular autophagy-lysosomal biogenesis by inducing the nuclear translocation of transcription factor EB, finally enhancing the bactericidal capabilities and immune-modulatory functions of macrophages. These findings demonstrate that AM-1 significantly alleviates the progression of challenging IAIs as a presurgical immunoenhancer. Our research introduces a novel therapeutic strategy that employs natural polysaccharides to combat refractory infections.

Genetic Structure and Genome-Wide Association Analysis of Growth and Reproductive Traits in Fengjing Pigs.

The Fengjing pig is one of the local pig breed resources in China and has many excellent germplasm characteristics. However, research on its genome is lacking. To explore the degree of genetic diversity of the Fengjing pig and to deeply explore its excellent traits, this study took Fengjing pigs as the research object and used the Beadchip Array Infinium iSelect-96|XT KPS_PorcineBreedingChipV2 for genotyping. We analyzed the genetic diversity, relatedness, inbreeding coefficient, and population structure within the Fengjing pig population. Our findings revealed that the proportion of polymorphic markers (PN) was 0.469, and the effective population size was 6.8. The observed and expected heterozygosity were 0.301 and 0.287, respectively. The G-matrix results indicated moderate relatedness within the population, with certain individuals exhibiting closer genetic relationships. The NJ evolutionary tree classified Fengjing boars into five family lines. The average inbreeding coefficient based on ROH was 0.318, indicating a high level of inbreeding. GWAS identified twenty SNPs significantly associated with growth traits (WW, 2W, and 4W) and reproductive traits (TNB and AWB). Notably, WNT8B, RAD21, and HAO1 emerged as candidate genes influencing 2W, 4W, and TNB, respectively. Genes such as WNT8B were verified by querying the PigBiobank database. In conclusion, this study provides a foundational reference for the conservation and utilization of Fengjing pig germplasm resources and offers insights for future molecular breeding efforts in Fengjing pigs.

Integration of ssGWAS and ROH analyses for uncovering genetic variants associated with reproduction traits in Large White pigs.

The low heritability of reproduction traits such as total number born (TNB), number born alive (NBA) and adjusted litter weight until 21 days at weaning (ALW) poses a challenge for genetic improvement. In this study, we aimed to identify genetic variants that influence these traits and evaluate the accuracy of genomic selection (GS) using these variants as genomic features. We performed single-step genome-wide association studies (ssGWAS) on 17 823 Large White (LW) pigs, of which 2770 were genotyped by 50K single nucleotide polymorphism (SNP) chips. Additionally, we analyzed runs of homozygosity (ROH) in the population and tested their effects on the traits. The genomic feature best linear unbiased prediction (GFBLUP) was then carried out in an independent population of 350 LW pigs using identified trait-related SNP subsets as genomic features. As a result, our findings identified five, one and four SNP windows that explaining more than 1% of genetic variance for ALW, TNB, and NBA, respectively and discovered 358 hotspots and nine ROH islands. The ROH SSC1:21814570-27186456 and SSC11:7220366-14276394 were found to be significantly associated with ALW and NBA, respectively. We assessed the genomic estimated breeding value accuracy through 20 replicates of five-fold cross-validation. Our findings demonstrate that GFBLUP, incorporating SNPs located in effective ROH (p-value < 0.05) as genomic features, might enhance GS accuracy for ALW compared with GBLUP. Additionally, using SNPs explaining more than 0.1% of the genetic variance in ssGWAS for NBA as genomic features might improve the GS accuracy, too. However, it is important to note that the incorporation of inappropriate genomic features can significantly reduce GS accuracy. In conclusion, our findings provide valuable insights into the genetic mechanisms of reproductive traits in pigs and suggest that the ssGWAS and ROH have the potential to enhance the accuracy of GS for reproductive traits in LW pigs.

MiR-204/-211 double knockout exacerbates rheumatoid arthritis progression by promoting splenic inflammation.

OBJECTIVE: Collagen-induced arthritis (CIA) model was induced in C57BL/6 wild-type (wt) and C57BL/6 miR-204/-211 double-knockout (dKO) mice to investigate the role of miR-204/-211 in suppressing splenic inflammation in rheumatoid arthritis (RA). METHODS: Differences of miR-204/-211 and structure-specific recognition protein 1 (SSRP1) in the spleen of DBA/1J wt and CIA mice were detected via PCR and immunohistochemistry. CIA was induced in both C57BL/6 wt and C57BL/6 miR-204/-211 dKO mice, and the onset of CIA and disease severity were statistically analyzed. Immunohistochemistry staining of interleukin-1ß (IL-1ß), tumor necrosis factor-α (TNF-α), and SSRP1 in spleen or knee joints was performed and analyzed. In CIA miR-204/-211 dKO mice, AAV-shSSRP1 was intra-articularly injected, with both the AAV-shRNA Ctrl and AAV-shRNA Ctrl CIA groups receiving the same dose of AAV-shRNA. Spleen sections were stained with hematoxylin and eosin (H&E). RESULTS: Compared to wt mouse spleens, aberrant expression of miR-204/-211 and SSRP1 was observed in the spleens of CIA mice. Immunized dKO mice exhibited a higher incidence of CIA onset and a more exacerbated RA disease phenotype, characterized by increased spleen inflammation score and elevated levels of IL-1ß, TNF-α, and SSRP1 expression. AAV-shSSRP1 injection in CIA dKO mice significantly reduced spleen inflammation scores, IL-1ß and TNF-α expression levels, and down-regulated Ki-67 expression compared to CIA dKO mice. CONCLUSION: Knockout of miR-204/-211 exacerbated the onset of CIA in C57BL/6 mice, while miR-204/-211 played a protective role against the progression of splenic inflammatory and proliferative progression in RA by targeting SSRP1.

Exploring the Interplay between the Hologenome and Complex Traits in Bovine and Porcine Animals Using Genome-Wide Association Analysis.

Genome-wide association studies (GWAS) significantly enhance our ability to identify trait-associated genomic variants by considering the host genome. Moreover, the hologenome refers to the host organism's collective genetic material and its associated microbiome. In this study, we utilized the hologenome framework, called Hologenome-wide association studies (HWAS), to dissect the architecture of complex traits, including milk yield, methane emissions, rumen physiology in cattle, and gut microbial composition in pigs. We employed four statistical models: (1) GWAS, (2) Microbial GWAS (M-GWAS), (3) HWAS-CG (hologenome interaction estimated using COvariance between Random Effects Genome-based restricted maximum likelihood (CORE-GREML)), and (4) HWAS-H (hologenome interaction estimated using the Hadamard product method). We applied Bonferroni correction to interpret the significant associations in the complex traits. The GWAS and M-GWAS detected one and sixteen significant SNPs for milk yield traits, respectively, whereas the HWAS-CG and HWAS-H each identified eight SNPs. Moreover, HWAS-CG revealed four, and the remaining models identified three SNPs each for methane emissions traits. The GWAS and HWAS-CG detected one and three SNPs for rumen physiology traits, respectively. For the pigs' gut microbial composition traits, the GWAS, M-GWAS, HWAS-CG, and HWAS-H identified 14, 16, 13, and 12 SNPs, respectively. We further explored these associations through SNP annotation and by analyzing biological processes and functional pathways. Additionally, we integrated our GWA results with expression quantitative trait locus (eQTL) data using transcriptome-wide association studies (TWAS) and summary-based Mendelian randomization (SMR) methods for a more comprehensive understanding of SNP-trait associations. Our study revealed hologenomic variability in agriculturally important traits, enhancing our understanding of host-microbiome interactions.

Context-Enhanced Network with Spatial-Aware Graph for Smartphone Screen Defect Detection.

Interactive devices such as touch screens have gained widespread usage in daily life; this has directed the attention of researchers to the quality of screen glass. Consequently, defect detection in screen glass is essential for improving the quality of smartphone screens. In recent years, defect detection methods based on deep learning have played a crucial role in improving detection accuracy and robustness. However, challenges have arisen in achieving high-performance detection due to the small size, irregular shapes and low contrast of defects. To address these challenges, this paper proposes CE-SGNet, a Context-Enhanced Network with a Spatial-aware Graph, for smartphone screen defect detection. It consists of two novel components: the Adaptive Receptive Field Attention Module (ARFAM) and the Spatial-aware Graph Reasoning Module (SGRM). The ARFAM enhances defect features by adaptively extracting contextual information to capture the most relevant contextual region of defect features. The SGRM constructs a region-to-region graph and encodes region features with spatial relationships. The connections among defect regions are enhanced during the propagation process through a graph attention network. By enriching the feature representations of defect regions, the CE-SGNet can accurately identify and locate defects of various shapes and scales. Experimental results demonstrate that the CE-SGNet achieves outstanding performance on two public datasets.

Mapping and functional characterization of structural variation in 1060 pig genomes.

BACKGROUND: Structural variations (SVs) have significant impacts on complex phenotypes by rearranging large amounts of DNA sequence. RESULTS: We present a comprehensive SV catalog based on the whole-genome sequence of 1060 pigs (Sus scrofa) representing 101 breeds, covering 9.6% of the pig genome. This catalog includes 42,487 deletions, 37,913 mobile element insertions, 3308 duplications, 1664 inversions, and 45,184 break ends. Estimates of breed ancestry and hybridization using genotyped SVs align well with those from single nucleotide polymorphisms. Geographically stratified deletions are observed, along with known duplications of the KIT gene, responsible for white coat color in European pigs. Additionally, we identify a recent SINE element insertion in MYO5A transcripts of European pigs, potentially influencing alternative splicing patterns and coat color alterations. Furthermore, a Yorkshire-specific copy number gain within ABCG2 is found, impacting chromatin interactions and gene expression across multiple tissues over a stretch of genomic region of ~200 kb. Preliminary investigations into SV's impact on gene expression and traits using the Pig Genotype-Tissue Expression (PigGTEx) data reveal SV associations with regulatory variants and gene-trait pairs. For instance, a 51-bp deletion is linked to the lead eQTL of the lipid metabolism regulating gene FADS3, whose expression in embryo may affect loin muscle area, as revealed by our transcriptome-wide association studies. CONCLUSIONS: This SV catalog serves as a valuable resource for studying diversity, evolutionary history, and functional shaping of the pig genome by processes like domestication, trait-based breeding, and adaptive evolution.

Unveiling the Genetic Mechanism of Meat Color in Pigs through GWAS, Multi-Tissue, and Single-Cell Transcriptome Signatures Exploration.

Meat color traits directly influence consumer acceptability and purchasing decisions. Nevertheless, there is a paucity of comprehensive investigation into the genetic mechanisms underlying meat color traits in pigs. Utilizing genome-wide association studies (GWAS) on five meat color traits and the detection of selection signatures in pig breeds exhibiting distinct meat color characteristics, we identified a promising candidate SNP, 6_69103754, exhibiting varying allele frequencies among pigs with different meat color characteristics. This SNP has the potential to affect the redness and chroma index values of pork. Moreover, transcriptome-wide association studies (TWAS) analysis revealed the expression of candidate genes associated with meat color traits in specific tissues. Notably, the largest number of candidate genes were observed from transcripts derived from adipose, liver, lung, spleen tissues, and macrophage cell type, indicating their crucial role in meat color development. Several shared genes associated with redness, yellowness, and chroma indices traits were identified, including RINL in adipose tissue, ENSSSCG00000034844 and ITIH1 in liver tissue, TPX2 and MFAP2 in lung tissue, and ZBTB17, FAM131C, KIFC3, NTPCR, and ENGSSSCG00000045605 in spleen tissue. Furthermore, single-cell enrichment analysis revealed a significant association between the immune system and meat color. This finding underscores the significance of the immune system associated with meat color. Overall, our study provides a comprehensive analysis of the genetic mechanisms underlying meat color traits, offering valuable insights for future breeding efforts aimed at improving meat quality.

Pig pangenome graph reveals functional features of non-reference sequences.

BACKGROUND: The reliance on a solitary linear reference genome has imposed a significant constraint on our comprehensive understanding of genetic variation in animals. This constraint is particularly pronounced for non-reference sequences (NRSs), which have not been extensively studied. RESULTS: In this study, we constructed a pig pangenome graph using 21 pig assemblies and identified 23,831 NRSs with a total length of 105 Mb. Our findings revealed that NRSs were more prevalent in breeds exhibiting greater genetic divergence from the reference genome. Furthermore, we observed that NRSs were rarely found within coding sequences, while NRS insertions were enriched in immune-related Gene Ontology terms. Notably, our investigation also unveiled a close association between novel genes and the immune capacity of pigs. We observed substantial differences in terms of frequencies of NRSs between Eastern and Western pigs, and the heat-resistant pigs exhibited a substantial number of NRS insertions in an 11.6 Mb interval on chromosome X. Additionally, we discovered a 665 bp insertion in the fourth intron of the TNFRSF19 gene that may be associated with the ability of heat tolerance in Southern Chinese pigs. CONCLUSIONS: Our findings demonstrate the potential of a graph genome approach to reveal important functional features of NRSs in pig populations.

Transcriptomic analysis of the longissimus thoracis muscle in pigs has identified molecular regulatory patterns associated with meat quality.

Meat quality is a critical aspect of pig breeding. In addition to genetics, meat quality is also influenced by nutritional and environmental factors. In this study, three pig breeds, Shengxianhua, Jiaxing, and Qinglian Black (SXH, JXB and QLB), were used as experimental animals. Transcriptional analysis was performed on the longissimus thoracis (LT) muscle to investigate variations in intramuscular fat (IMF), inosine monophosphate (IMP), amino acids, and muscle fiber morphology across different breeds. Ingenuity canonical pathway analysis (IPA) identified biological processes and key driver genes related to metabolism and muscle development. Additionally, weighted gene co-expression network analysis (WGCNA) revealed gene modules associated with IMP. KEGG and GO analyses identified specific biological processes and signaling pathways related to IMP, including the Oxidative Phosphorylation pathway and rRNA Metabolic Processes. These findings provide novel insights into the molecular regulatory mechanisms underlying meat quality variations among pig breeds.

Detection of Runs of Homozygosity and Identification of Candidate Genes in the Whole Genome of Tunchang Pigs.

Tunchang pigs are an indigenous pig population in China known for their high tolerance to roughage, delicious meat, and fecundity. However, the number of Tunchang pigs has been declining due to the influence of commercial breeds and African swine fever, which could potentially lead to inbreeding. To assess the inbreeding level and the genetic basis of important traits in Tunchang pigs, our research investigated the patterns in "runs of homozygosity" (ROHs) using whole genome resequencing data from 32 Tunchang pigs. The study aimed to determine the length, number, coverage, and distribution model of ROHs in Tunchang pigs, as well as genomic regions with high ROH frequencies. The results of the study revealed that a total of 20,499,374 single-nucleotide polymorphisms (SNPs) and 1953 ROH fragments were recognized in 32 individuals. The ROH fragments in Tunchang pigs were predominantly short, ranging from 0.5 to 1 megabases (Mb) in length. Furthermore, the coverage of ROHs varied across chromosomes, with chromosome 3 having the highest coverage and chromosome 11 having the lowest coverage. The genetic diversity of Tunchang pigs was found to be relatively high based on the values of HE (expected heterozygosity), HO (observed heterozygosity), pi (nucleotide diversity), Ne (effective population size), and MAF (minor allele frequency). The average inbreeding coefficients of Tunchang pigs, as determined by three different methods (FHOM, FGRM, and FROH), were 0.019, 0.0138, and 0.0304, respectively. These values indicate that the level of inbreeding in Tunchang pigs is currently low. Additionally, the study identified a total of 13 ROH islands on all chromosomes, which in total contained 38,913 SNPs and 120 genes. These ROH islands included genes associated with economically important traits, including meat quality (GYS1, PHLPP1, SLC27A5, and CRTC1), growth and development (ANKS1A, TAF11, SPDEF, LHB, and PACSIN1), and environmental adaptation (SLC26A7). The findings of this research offer valuable perspectives on the present status of Tunchang pig resources and offer a reference for breeding conservation plans and the efficient utilization of Tunchang pigs in the future. By understanding the inbreeding level and genetic basis of important traits in Tunchang pigs, conservation efforts can be targeted towards maintaining genetic diversity and promoting the sustainable development of this indigenous pig population.

Holo-omics analysis reveals the influence of gut microbiota on obesity indicators in Jinhua pigs.

BACKGROUND: The mechanisms behind obesity are complex and multi-faceted, involving the interplay of both host genomics and gut microbiome. In recent years, research has largely focused on these factors separately, but rarely from the viewpoint of holo-omics, which considers the host and microbiome as an integrated entity. To address this gap in knowledge, the present study aimed to investigate the holo-omics basis of obesity in Jinhua pigs, a Chinese indigenous breed known for its high degree of fat deposition and superior meat quality. METHODS: Six pigs with extreme obesity phenotype were selected from a larger cohort of eighteen Jinhua pigs, and the contents of the jejunum, cecum, and colon regions were collected after slaughter at 240 days of age. The data obtained was processed, denoised, and annotated using QIIME2, with expression differences being analyzed using edgeR software. RESULTS: The results showed significant differences in jejunal microbial diversity and composition between the two groups, with gut transcriptomics also indicating that differentially expressed genes in the jejunum were enriched in lipid metabolism pathways. These findings provide further evidence of the influence of the gut microbiome and host gene expression on fat deposition in Jinhua pigs. CONCLUSIONS: This study provides valuable insights into the mechanisms of fat deposition in Jinhua pigs from the viewpoint of holo-omics. The integration of host transcriptomics and microbiome data helps shed light on the complex interactions between the host and gut microbiome, and highlights the importance of considering both factors in our understanding of obesity.

Factors affecting the accuracy of genomic prediction in joint pig populations.

Genomic prediction (GP) has greatly advanced animal and plant breeding over the past two decades. GP in joint populations is a feasible method to improve the accuracy of genomic estimated breeding values in small populations. However, there is still a need to understand the factors that influence GP in joint populations. This study used simulated data and real data from Duroc pig populations to examine the impact of linkage disequilibrium (LD), causal variants effect sizes (CVESs), and minor allele frequencies (MAF) of SNPs on the accuracy of genomic prediction in joint populations. Three prediction methods were used: genomic best linear unbiased prediction (GBLUP), single-step GBLUP and multi-trait GBLUP. Results from the simulated datasets showed that the accuracies of GP in joint populations were always higher than those in a single population when only LD inconsistencies existed. However, single-step GBLUP accuracy in joint populations decreased as the correlation of MAF between populations decreased, while the accuracy of GBLUP is consistently higher in joint populations than in a single population. As the correlation of CVES between populations decreased, the accuracy of both GBLUP and single-step GBLUP in joint populations declined. Analysis of real Duroc populations showed low genetic correlation, similar to the simulated relationship between the most distant populations. In most cases in Duroc populations, GP have higher accuracies in joint populations than in individual population. In conclusion, the consistency of CVES plays a more important role in multi-population GP. The genetic relatedness of the Duroc populations is so weak that the prediction accuracy of GP in joint populations is reduced in some traits. Multi-trait GBLUP is a competitive method for the joint breeding evaluation.

Multi-Omics Reveals the Effect of Crossbreeding on Some Precursors of Flavor and Nutritional Quality of Pork.

Over the last several decades, China has continuously introduced Duroc boars and used them as breeding boars. Although this crossbreeding method has increased pork production, it has affected pork quality. Nowadays, one of the primary goals of industrial breeding and production systems is to enhance the quality of meat. This research analyzed the molecular mechanisms that control the quality of pork and may be used as a guide for future efforts to enhance meat quality. The genetic mechanisms of cross-breeding for meat quality improvement were investigated by combining transcriptome and metabolome analysis, using Chinese native Jiaxing black (JXB) pigs and crossbred Duroc × Duroc × Berkshire × JXB (DDBJ) pigs. In the longissimus Dorsi muscle, the content of inosine monophosphate, polyunsaturated fatty acid, and amino acids were considerably higher in JXB pigs in contrast with that of DDBJ pigs, whereas DDBJ pigs have remarkably greater levels of polyunsaturated fatty acids than JXB pigs. Differentially expressed genes (DEGs) and differential metabolites were identified using transcriptomic and metabolomic KEGG enrichment analyses. Differential metabolites mainly include amino acids, fatty acids, and phospholipids. In addition, several DEGs that may explain differences in meat quality between the two pig types were found, including genes associated with the metabolism of lipids (e.g., DGKA, LIPG, and LPINI), fatty acid (e.g., ELOVL5, ELOVL4, and ACAT2), and amino acid (e.g., SLC7A2, SLC7A4). Combined with the DEGS-enriched signaling pathways, the regulatory mechanisms related to amino acids, fatty acids, and phospholipids were mapped. The abundant metabolic pathways and DEGs may provide insight into the specific molecular mechanism that regulates meat quality. Optimizing the composition of fatty acids, phospholipids, amino acids, and other compounds in pork is conducive to improving meat quality. Overall, these findings will provide useful information and further groundwork for enhancing the meat quality that may be achieved via hybrid breeding.

Dexamethasone Liposomes Alleviate Osteoarthritis in miR-204/-211-Deficient Mice by Repolarizing Synovial Macrophages to M2 Phenotypes.

We undertook this study to investigate the effects and mechanisms of dexamethasone liposome (Dex-Lips) on alleviating destabilization of the medial meniscus (DMM)-induced osteoarthritis (OA) in miR-204/-211-deficient mice. Dex-Lips was prepared by the thin-film hydration method. The characterization of Dex-Lips was identified by the mean size, zeta potential, drug loading, and encapsulation efficiencies. Experimental OA was established by DMM surgery in miR-204/-211-deficient mice, and then Dex-Lips was treated once a week for 3 months. Von Frey filaments was used to perform the pain test. The inflammation level was evaluated with quantitative real-time polymerase chain reaction and enzyme-linked immunosorbent assay. Polarization of macrophages was evaluated by immunofluorescent staining. X-ray, micro-CT scanning, and histological observations were conducted in vivo on DMM mice to describe the OA phenotype. We found that miR-204/-211-deficient mice displayed more severe OA symptoms than WT mice after DMM surgery. Dex-Lips ameliorated DMM-induced OA phenotype and suppressed pain and inflammatory cytokine expressions. Dex-Lips could attenuate pain by regulating PGE2. Dex-Lips treatments reduced the expression of TNF-α, IL-1ß, and IL-6 in DRG. Moreover, Dex-Lips could reduce inflammation in the cartilage and serum. Additionally, Dex-Lips repolarize synovial macrophages to M2 phenotypes in miR-204/-211-deficient mice. In conclusion, Dex-Lips inhibited the inflammatory response and alleviated the pain symptoms of OA by affecting the polarization of macrophages.

The Role of SOCS3 in Regulating Meat Quality in Jinhua Pigs.

Meat quality is an important economic trait that influences the development of the pig industry. Skeletal muscle development and glycolytic potential (GP) are two crucial aspects that significantly impact meat quality. It has been reported that abnormal skeletal muscle development and high glycogen content results in low meat quality. However, the genetic mechanisms underlying these factors are still unclear. Compared with intensive pig breeds, Chinese indigenous pig breeds, such as the Jinhua pig, express superior meat quality characteristics. The differences in the meat quality traits between Jinhua and intensive pig breeds make them suitable for uncovering the genetic mechanisms that regulate meat quality traits. In this study, the Jinhua pig breed and five intensive pig breeds, including Duroc, Landrace, Yorkshire, Berkshire, and Pietrain pig breeds, were selected as experimental materials. First, the FST and XP-EHH methods were used to screen the selective signatures on the genome in the Jinhua population. Then, combined with RNA-Seq data, the study further confirmed that SOCS3 could be a key candidate gene that influences meat quality by mediating myoblast proliferation and glycometabolism because of the down-regulated expression of SOCS3 in Jinhua pigs compared with Landrace pigs. Finally, through SOCS3 knockout (KO) and overexpression (OE) experiments in mouse C2C12 cells, the results showed that SOCS3 regulated the cell proliferation of myoblasts. Moreover, SOCS3 is involved in regulating glucose uptake by the IRS1/PI3K/AKT signaling pathway. Overall, these findings provide a basis for the genetic improvement of meat quality traits in the pig industry.

Evaluation of the Genetic Diversity, Population Structure and Selection Signatures of Three Native Chinese Pig Populations.

Indigenous pig populations in Hainan Province live in tropical climate conditions and a relatively closed geographical environment, which has contributed to the formation of some excellent characteristics, such as heat tolerance, strong disease resistance and excellent meat quality. Over the past few decades, the number of these pig populations has decreased sharply, largely due to a decrease in growth rate and poor lean meat percentage. For effective conservation of these genetic resources (such as heat tolerance, meat quality and disease resistance), the whole-genome sequencing data of 78 individuals from 3 native Chinese pig populations, including Wuzhishan (WZS), Tunchang (TC) and Dingan (DA), were obtained using a 150 bp paired-end platform, and 25 individuals from two foreign breeds, including Landrace (LR) and Large White (LW), were downloaded from a public database. A total of 28,384,282 SNPs were identified, of which 27,134,233 SNPs were identified in native Chinese pig populations. Both genetic diversity statistics and linkage disequilibrium (LD) analysis indicated that indigenous pig populations displayed high genetic diversity. The result of population structure implied the uniqueness of each native Chinese pig population. The selection signatures were detected between indigenous pig populations and foreign breeds by using the population differentiation index (FST) method. A total of 359 candidate genes were identified, and some genes may affect characteristics such as immunity (IL-2, IL-21 and ZFYVE16), adaptability (APBA1), reproduction (FGF2, RNF17, ADAD1 and HIPK4), meat quality (ABCA1, ADIG, TLE4 and IRX5), and heat tolerance (VPS13A, HSPA4). Overall, the findings of this study will provide some valuable insights for the future breeding, conservation and utilization of these three Chinese indigenous pig populations.

Integrated microbiome-metabolome-genome axis data of Laiwu and Lulai pigs.

Excessive fat deposition can trigger metabolic diseases, and it is crucial to identify factors that can break the link between fat deposition and metabolic diseases. Healthy obese Laiwu pigs (LW) are high in fat content but resistant to metabolic diseases. In this study, we compared the fecal microbiome, fecal and blood metabolome, and genome of LW and Lulai pigs (LU) to identify factors that can block the link between fat deposition and metabolic diseases. Our results show significant differences in Spirochetes and Treponema, which are involved in carbohydrate metabolism, between LW and LU. The fecal and blood metabolome composition was similar, and some anti-metabolic disease components of blood metabolites were different between the two breeds of pigs. The predicted differential RNA is mainly enriched in lipid metabolism and glucose metabolism, which is consistent with the functions of differential microbiota and metabolites. The down-regulated gene RGP1 is strongly negatively correlated with Treponema. Our omics data would provide valuable resources for further scientific research on healthy obesity in both human and porcine.

Integration of Selection Signatures and Protein Interactions Reveals NR6A1, PAPPA2, and PIK3C2B as the Promising Candidate Genes Underlying the Characteristics of Licha Black Pig.

Licha black (LI) pig has the specific characteristics of larger body length and appropriate fat deposition among Chinese indigenous pigs. Body length is one of the external traits that affect production performance, and fat deposition influences meat quality. However, the genetic characteristics of LI pigs have not yet been systematically uncovered. Here, the genomic information from 891 individuals of LI pigs, commercial pigs, and other Chinese indigenous pigs was used to analyze the breed characteristics of the LI pig with runs of homozygosity, haplotype, and FST selection signatures. The results showed the growth traits-related genes (i.e., NR6A1 and PAPPA2) and the fatness traits-related gene (i.e., PIK3C2B) were the promising candidate genes that closely related to the characteristics of LI pigs. In addition, the protein-protein interaction network revealed the potential interactions between the promising candidate genes and the FASN gene. The RNA expression data from FarmGTEx indicated that the RNA expression levels of NR6A1, PAPPA2, PIK3C2B, and FASN were highly correlated in the ileum. This study provides valuable molecular insights into the mechanisms that affect pig body length and fat deposition, which can be used in the further breeding process to improve meat quality and commercial profitability.

Molecular Design-Based Breeding: A Kinship Index-Based Selection Method for Complex Traits in Small Livestock Populations.

Genomic selection (GS) techniques have improved animal breeding by enhancing the prediction accuracy of breeding values, particularly for traits that are difficult to measure and have low heritability, as well as reducing generation intervals. However, the requirement to establish genetic reference populations can limit the application of GS in pig breeds with small populations, especially when small populations make up most of the pig breeds worldwide. We aimed to propose a kinship index based selection (KIS) method, which defines an ideal individual with information on the beneficial genotypes for the target trait. Herein, the metric for assessing selection decisions is a beneficial genotypic similarity between the candidate and the ideal individual; thus, the KIS method can overcome the need for establishing genetic reference groups and continuous phenotype determination. We also performed a robustness test to make the method more aligned with reality. Simulation results revealed that compared to conventional genomic selection methods, the KIS method is feasible, particularly, when the population size is relatively small.