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.

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.

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.

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.

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.

PyAGH: a python package to fast construct kinship matrices based on different levels of omic data.

BACKGROUND: Construction of kinship matrices among individuals is an important step for both association studies and prediction studies based on different levels of omic data. Methods for constructing kinship matrices are becoming diverse and different methods have their specific appropriate scenes. However, software that can comprehensively calculate kinship matrices for a variety of scenarios is still in an urgent demand. RESULTS: In this study, we developed an efficient and user-friendly python module, PyAGH, that can accomplish (1) conventional additive kinship matrces construction based on pedigree, genotypes, abundance data from transcriptome or microbiome; (2) genomic kinship matrices construction in combined population; (3) dominant and epistatic effects kinship matrices construction; (4) pedigree selection, tracing, detection and visualization; (5) visualization of cluster, heatmap and PCA analysis based on kinship matrices. The output from PyAGH can be easily integrated in other mainstream software based on users' purposes. Compared with other softwares, PyAGH integrates multiple methods for calculating the kinship matrix and has advantages in terms of speed and data size compared to other software. PyAGH is developed in python and C + + and can be easily installed by pip tool. Installation instructions and a manual document can be freely available from https://github.com/zhaow-01/PyAGH . CONCLUSION: PyAGH is a fast and user-friendly Python package for calculating kinship matrices using pedigree, genotype, microbiome and transcriptome data as well as processing, analyzing and visualizing data and results. This package makes it easier to perform predictions and association studies processes based on different levels of omic data.

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.

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.

A web tool for the global identification of pig breeds.

BACKGROUND: Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration. RESULTS: We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs ( http://alphaindex.zju.edu.cn/iDIGs_en/ ) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds. CONCLUSIONS: In this study, we proved that breed identification among a wide range of pig breeds is feasible and we developed a web tool for such pig breed identification.

The effect of high-density genotypic data and different methods on joint genomic prediction: A case study in large white pigs.

Joint genomic prediction (GP) is an attractive method to improve the accuracy of GP by combining information from multiple populations. However, many factors can negatively influence the accuracy of joint GP, such as differences in linkage disequilibrium phasing between single nucleotide polymorphisms (SNPs) and causal variants, minor allele frequencies and causal variants' effect sizes across different populations. The objective of this study was to investigate whether the imputed high-density genotype data can improve the accuracy of joint GP using genomic best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP), multi-trait GBLUP (MT-GBLUP) and GBLUP based on genomic relationship matrix considering heterogenous minor allele frequencies across different populations (wGBLUP). Three traits, including days taken to reach slaughter weight, backfat thickness and loin muscle area, were measured on 67 276 Large White pigs from two different populations, for which 3334 were genotyped by SNP array. The results showed that a combined population could substantially improve the accuracy of GP compared with a single-population GP, especially for the population with a smaller size. The imputed SNP data had no effect for single population GP but helped to yield higher accuracy than the medium-density array data for joint GP. Of the four methods, ssGLBUP performed the best, but the advantage of ssGBLUP decreased as more individuals were genotyped. In some cases, MT-GBLUP and wGBLUP performed better than GBLUP. In conclusion, our results confirmed that joint GP could be beneficial from imputed high-density genotype data, and the wGBLUP and MT-GBLUP methods are promising for joint GP in pig breeding.

The construction of a haplotype reference panel using extremely low coverage whole genome sequences and its application in genome-wide association studies and genomic prediction in Duroc pigs.

Extremely low coverage whole genome sequencing (lcWGS) is an economical technique to obtain high-density single nucleotide polymorphisms (SNPs). Here, we explored the feasibility of constructing a haplotype reference panel (lcHRP) using lcWGS and evaluated the effects of lcHRP through a genome-wide association study (GWAS) and genomic prediction in pigs. A total of 297 and 974 Duroc pigs were genotyped using lcWGS and a 50 K SNP array, respectively. We obtained 19,306,498 SNPs using lcWGS with an accuracy of 0.984. With the help of lcHRP, the accuracy of imputation from the SNP array to lcWGS was 0.922. Compared to the SNP array findings, those from the imputation-based GWAS identified more signals across four traits. With the integration of the top 1% imputation-based GWAS findings as genomic features, the accuracies of genomic prediction was improved by 6.0% to 13.2%. This study showed the great potential of lcWGS in pigs' molecular breeding.

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.

Genetic diversity and selection signatures of four indigenous pig breeds from eastern China.

Chinese indigenous pig breeds have been undergoing selection for thousands of years, and have become invaluable genetic sources over the world. To investigate the population structure and genetic diversity of Jinhua (JH), Longyou Black (LYW), Shengxian Spotted (SXH), and Lanxi Spotted (LXH) breeds, a total of 200 pigs belonging to 10 diverse population were genotyped using SNP chips. The results showed that LYW pigs exhibited higher level of heterozygosity than the other indigenous pigs. In addition, gene introgression from intensively reared commercial pig breeds to LYW pigs was detected. Moreover, selection signature analysis revealed the possibility of differences between Chinese indigenous and intensively reared commercial pig breeds were mainly present for meat and carcass traits. Furthermore, we found that ANXA13, DISP1, and SRSF6 were the nearest genes located around the common selection signatures detected between each indigenous pig breed and Chinese wild boars. Our findings provide new insights into the selection signatures of Chinese indigenous pigs, and may contribute to future pig breeding.

Identifying the unique characteristics of the Chinese indigenous pig breeds in the Yangtze River Delta region for precise conservation.

BACKGROUND: China is the country with the most abundant swine genetic resources in the world. Through thousands of years of domestication and natural selection, most of pigs in China have developed unique genetic characteristics. Finding the unique genetic characteristics and modules of each breed is an essential part of their precise conservation. RESULTS: In this study, we used the partial least squares method to identify the significant specific SNPs of 19 local Chinese pig breeds and 5 Western pig breeds. A total of 37,514 significant specific SNPs (p < 0.01) were obtained from these breeds, and the Chinese local pig breed with the most significant SNPs was Hongdenglong (HD), followed by Jiaxing black (JX), Huaibei (HB), Bihu (BH), small Meishan (SMS), Shengxian Hua (SH), Jiangquhai (JQ), Mi (MI), Chunan (CA), Chalu (CL), Jinhualiangtouwu (JHL), Fengjing (FJ), middle Meishan (MMS), Shanzhu (SZ), Pudong white (PD), Dongchuan (DC), Erhualian (EH), Shawutou (SW) and Lanxi Hua (LX) pig. Furthermore, we identified the breeds with the most significant genes, GO terms, pathways, and networks using KOBAS and IPA and then ranked them separately. The results showed that the breeds with the highest number of interaction networks were Hongdenglong (12) and Huaibei (12) pigs. In contrast, the breeds with the lowest interaction networks were Shawutou (4) and Lanxi Hua pigs (3), indicating that Hongdenglong and Huaibei pigs might have the most significant genetic modules in their genome, whereas Shawutou and Lanxi Hua pigs may have the least unique characteristics. To some degree, the identified specific pathways and networks are related to the number of genes and SNPs linked to the specific breeds, but they do not appear to be the same. Most importantly, more significant modules were found to be related to the development and function of the digestive system, regulation of diseases, and metabolism of amino acids in the local Chinese pig breeds, whereas more significant modules were found to be related to the growth rate in the Western pig breeds. CONCLUSION: Our results show that each breed has some relatively unique structural modules and functional characteristics. These modules allow us to better understand the genetic differences among local Chinese and Western pig breeds and therefore implement precise conservation methods. This study could provide a basis for formulating more effective strategies for managing and protecting these genetic resources in the future.

Genome-wide selection signatures detection in Shanghai Holstein cattle population identified genes related to adaption, health and reproduction traits.

BACKGROUND: Over several decades, a wide range of natural and artificial selection events in response to subtropical environments, intensive pasture and intensive feedlot systems have greatly changed the customary behaviour, appearance, and important economic traits of Shanghai Holstein cattle. In particular, the longevity of the Shanghai Holstein cattle population is generally short, approximately the 2nd to 3rd lactation. In this study, two complementary approaches, integrated haplotype score (iHS) and runs of homozygosity (ROH), were applied for the detection of selection signatures within the genome using genotyping by genome-reduced sequence data from 1092 cows. RESULTS: In total, 101 significant iHS genomic regions containing selection signatures encompassing a total of 256 candidate genes were detected. There were 27 significant |iHS| genomic regions with a mean |iHS| score > 2. The average number of ROH per individual was 42.15 ± 25.47, with an average size of 2.95 Mb. The length of 78 % of the detected ROH was within the range of 1-2 MB and 2-4 MB, and 99 % were shorter than 8 Mb. A total of 168 genes were detected in 18 ROH islands (top 1 %) across 16 autosomes, in which each SNP showed a percentage of occurrence > 30 %. There were 160 and 167 genes associated with the 52 candidate regions within health-related QTL intervals and 59 candidate regions within reproduction-related QTL intervals, respectively. Annotation of the regions harbouring clustered |iHS| signals and candidate regions for ROH revealed a panel of interesting candidate genes associated with adaptation and economic traits, such as IL22RA1, CALHM3, ITGA9, NDUFB3, RGS3, SOD2, SNRPA1, ST3GAL4, ALAD, EXOSC10, and MASP2. In a further step, a total of 1472 SNPs in 256 genes were matched with 352 cis-eQTLs in 21 tissues and 27 trans-eQTLs in 6 tissues. For SNPs located in candidate regions for ROH, a total of 108 cis-eQTLs in 13 tissues and 4 trans-eQTLs were found for 1092 SNPs. Eighty-one eGenes were significantly expressed in at least one tissue relevant to a trait (P value < 0.05) and matched the 256 genes detected by iHS. For the 168 significant genes detected by ROH, 47 gene-tissue pairs were significantly associated with at least one of the 37 traits. CONCLUSIONS: We provide a comprehensive overview of selection signatures in Shanghai Holstein cattle genomes by combining iHS and ROH. Our study provides a list of genes associated with immunity, reproduction and adaptation. For functional annotation, the cGTEx resource was used to interpret SNP-trait associations. The results may facilitate the identification of genes relevant to important economic traits and can help us better understand the biological processes and mechanisms affected by strong ongoing natural or artificial selection in livestock populations.

Long-term impact of conventional and optimal contribution conservation methods on genetic diversity and genetic gain in local pig breeds.

There are rich and vast genetic resources of indigenous pig breeds in the world. Currently, great attention is paid to either crossbreeding or conservation of these indigenous pig breeds, and insufficient attention is paid to the combination of conservation and breeding along with their long-term effects on genetic diversity. Therefore, the objective of this study is to compare the long-term effects of using conventional conservation and optimal contribution selection methods on genetic diversity and genetic gain. A total of 11 different methods including conventional conservation and optimal contribution selection methods were investigated using stochastic simulations. The long-term effects of using these methods were evaluated in terms of genetic diversity metrices such as expected heterozygosity (He) and the rate of genetic gain. The results indicated that the rates of true inbreeding in these conventional conservation methods were maintained at around 0.01. The optimal contribution selection methods based either on the pedigree (POCS) or genome (GOCS) information showed more genetic gain than conventional methods, and POCS achieved the largest genetic gain. Furthermore, the effect of using GOCS methods on most of the genetic diversity metrics was slightly better than the conventional conservation methods when the rate of true inbreeding was the same, but this also required more sires used in OCS methods. According to the rate of true inbreeding, there was no significant difference among these conventional methods. In conclusion, there is no significant difference in different ways of selecting sows on inbreeding when we use different conventional conservation methods. Compared with conventional methods, POCS method could achieve the most genetic gain. However, GOCS methods can not only achieve higher genetic gain, but also maintain a relatively high level of genetic diversity. Therefore, GOCS is a better choice if we want to combine conservation and breeding in actual production in the conservation farms.

Inflammation suppression by dexamethasone via inhibition of CD147-mediated NF-κB pathway in collagen-induced arthritis rats.

Dexamethasone (Dex) exhibits broad-spectrum anti-inflammatory effects in chronic destructive rheumatoid arthritis. We present in vivo and in vitro evidence supporting the preventive effects and underlying mechanisms of Dex on collagen-induced arthritis (CIA)-induced synovial injuries. After successful induction of CIA, Wistar rats were administered Dex intraperitoneally (1 mg/kg) three times a week for more than 2 weeks. In vivo, paw swelling, arthritis scores, and histological evaluations were analyzed to determine the therapeutic effects of Dex on the progression of arthritis. In vitro, CIA fibroblast-like synoviocytes (FLSs) were treated for 48 h with vehicle (control group), 10 ng/mL IL-1α (IL-1α group), 10 ng/mL IL-1α + 10 µM Dex (Dex group), or 10 ng/mL IL-1α + 10 µg/mL anti-CD147 antibody (anti-CD147 group). Evaluations of FLSs proliferation, cell cycle, migration, gene expression of Cyclin D1, p27, p57, interleukin (IL)-1ß, IL-6, IL-17, tumor necrosis factor (TNF)-α, CD147, CypB, matrix metalloproteinase-3 (MMP-3), MMP-9, and MMP-13, ROS generation, protein expression of NF-κB and CD147, and translocation of NF-κB p65 were all conducted. The in vivo results showed that arthritis intensity was attenuated in the Dex-treated group. The in vitro findings demonstrated that treatment with Dex induced G0/G1 arrest and suppressed proliferation, migration, gene expression of IL-1ß, IL-6, IL-17, TNF-α, CD147, CypB, MMP-3, MMP-9, and MMP-13, ROS generation and protein expression of NF-κB and CD147. Translocation of NF-κB p65 was inhibited by both Dex and anti-CD147 monoclonal antibody treatment. We offer molecular evidence of the anti-rheumatism efficacy Dex through hindrance to CD147, splendidly stabilization of the oxidative stress by downregulating the NF-κB signaling pathway.

CypB-CD147 Signaling Is Involved in Crosstalk between Cartilage and FLS in Collagen-Induced Arthritis.

To investigate the crosstalk between cartilage and fibroblast-like synoviocytes (FLS) in rheumatoid arthritis (RA), we adopted an in vitro coculture system model of collagen-induced arthritis (CIA) cartilage and CIA FLS monolayer. CIA rat samples of the synovium and femur head were collected for isolation of FLS and coculture system. Cartilages were treated with vehicle (Ctrl group), 10 ng/mL interleukin- (IL-) 1α (IL-1α group), and 10 ng/mL IL-1α plus 10 µM dexamethasone (Dex group) for 3 days before coculture with FLS for further 2 days. After the coculture, FLS were collected to determine the influences of articular cartilage on synoviocytes. Whether the CypB-CD147 signaling pathway is involved in the interactions between cartilage and FLS is assayed. Results showed that IL-1α-stimulated CIA cartilage promoted the proliferation and reduced the apoptosis of FLS. Increased inflammatory cytokines and decreased p57 expression were found in cocultured FLS stimulated by IL-1α-challenged CIA cartilage. Upregulation of NF-κB and I-κB kinase ß (IKK-ß) and downregulation of the inhibitor of NF-κBα (I-κBα) protein were observed in cocultured FLS. After coculture, significant increases in the expression of cyclophilin B (CypB) and CD147 were observed in CIA cartilage and FLS, respectively. Furthermore, results of immunofluorescence staining showed that the anti-CD147 antibody significantly suppressed p65 nuclear translocation in cocultured FLS stimulated by IL-1α-challenged CIA cartilage. In conclusion, inflammatory effects in the cartilage-FLS coculture system are associated with the CypB-CD147 mediating NF-κB pathway which may further enhance the inflammation in RA.

Selection signature reveals genes associated with susceptibility loci affecting respiratory disease due to pleiotropic and hitchhiking effect in Chinese indigenous pigs.

BACKGROUND: Porcine respiratory disease is one of the most important health problems which causes significant economic losses. OBJECTIVE: To understand the genetic basis for susceptibility to swine enzootic pneumonia (EP) in pigs, we detected 102,809 SNPs in a total of 249 individuals based on genome-wide sequencing data. METHODS: Genome comparison of three susceptibility to swine EP pig breeds (Jinhua, Erhualian and Meishan) with two western lines that are considered more resistant (Duroc and Landrace) using XP-EHH and FST statistical approaches identified 691 positively selected genes. Based on QTLs, GO terms and literature search, we selected 14 candidate genes that have convincible biological functions associated with swine EP or human asthma. RESULTS: Most of these genes were tested by several methods including transcription analysis and candidated genes association study. Among these genes: CYP1A1 and CTNNB1 are involved in fertility; TGFBR3 plays a role in meat quality traits; WNT2, CTNNB1 and TCF7 take part in adipogenesis and fat deposition simultaneously; PLAUR (completely linked to AXL, r2=1) plays an essential role in the successful ovulation of matured oocytes in pigs; CLPSL2 (strongly linked to SPDEF, r2=0.848) is involved in male fertility. CONCLUSION: These adverse genes susceptible to swine EP may be selected while selecting for economic traits (especially reproduction traits) due to pleiotropic and hitchhiking effect of linked genes. Our study provided a completely new point of view to understand the genetic basis for susceptibility or resistance to swine EP in pigs thereby, provide insight for designing sustainable breed selection programs. Finally, the candidate genes are crucial due to their potential roles in respiratory diseases in a large number of species, including human.

Dihydromyricetin Inhibits Inflammation of Fibroblast-Like Synoviocytes through Regulation of Nuclear Factor-κB Signaling in Rats with Collagen-Induced Arthritis.

Dihydromyricetin (DMY), the main flavonoid of Ampelopsis grossedentata, has potent anti-inflammatory activity. However, the effect of DMY on chronic autoimmune arthritis remains undefined. In this study, we investigated the therapeutic effects of DMY on collagen-induced arthritis (CIA). Wistar rats were immunized with bovine type II collagen to establish CIA and were then administered DMY intraperitoneally (5, 25, and 50 mg/kg) every other day for 5 weeks. Paw swelling, clinical scoring, and histologic analysis were assessed to determine the therapeutic effects of DMY on the development of arthritis in CIA rats. The results showed that treatment with DMY significantly reduced erythema and swelling in the paws of CIA rats. Pathologic analysis of the knee joints and peripheral blood cytokine assay results confirmed the antiarthritic effects of DMY on synovitis and inflammation. Fibroblast-like synoviocytes (FLSs) were isolated from the synovium of CIA rats and treated with 10 ng/ml interleukin (IL)-1ß DMY significantly inhibited the proliferation, migration, and inflammation of IL-1ß-induced FLSs, whereas it significantly increased IL-1ß-induced FLS apoptosis in a dose-dependent manner (6.25-25 µM). Moreover, DMY suppressed phosphorylation of IκB kinase (IKK) and inhibitor of NF-κB α and subsequently reduced the IL-1ß-induced nucleus translocation of NF-κB in FLSs. Through a molecular docking assay, we demonstrated that DMY could directly bind to the Thr9 and Asp88 residues in IKKα and the Asp95, Asn142, and Gln167 residues in IKKß These findings demonstrate that DMY could alleviate inflammation in CIA rats and attenuate IL-1ß-induced activities in FLSs through suppression of NF-κB signaling.