Objective: To identify rheumatoid arthritis (RA)-associated susceptibility genes and pathways through integrating genome-wide association study (GWAS) and gene expression profile data. Methods: A transcriptome-wide association study (TWAS) was conducted by the FUSION software for RA considering EBV-transformed lymphocytes (EL), transformed fibroblasts (TF), peripheral blood (NBL), and whole blood (YBL). GWAS summary data was driven from a large-scale GWAS, involving 5539 autoantibody-positive RA patients and 20,169 controls. The TWAS-identified genes were further validated using the mRNA expression profiles and made a functional exploration. Results: TWAS identified 692 genes with P_TWAS values < 0.05 for RA. CRIPAK (P^EL = 0.01293, P^TF = 0.00038, P^NBL = 0.02839, P^YBL = 0.0978), MUT (P^EL = 0.00377, P^TF = 0.00076, P^NBL = 0.00778, P^YBL = 0.00096), FOXRED1 (P^EL = 0.03834, P^TF = 0.01120, P^NBL = 0.01280, P^YBL = 0.00583), and EBPL (P^EL = 0.00806, P^TF = 0.03761, P^NBL = 0.03540, P^YBL = 0.04254) were collectively expressed in all the four tissues/cells. Eighteen genes, including ANXA5, AP4B1, ATIC (P_TWAS = 0.0113, downregulated expression), C12orf65, CMAH, PDHB, RUNX3 (P_TWAS = 0.0346, downregulated expression), SBF1, SH2B3, STK38, TMEM43, XPNPEP1, KIAA1530, NUFIP2, PPP2R3C, RAB24, STX6, and TLR5 (P_TWAS = 0.04665, upregulated expression), were validated with integrative analysis of TWAS and mRNA expression profiles. TWAS-identified genes functionally involved in endoplasmic reticulum organization, regulation of cytokine production, TNF signaling pathway, immune response-regulating signaling pathway, regulation of autophagy, etc. Conclusion: We identified multiple candidate genes and pathways, providing novel clues for the genetic mechanism of RA.

Objective: The objective of this study was to determine the matrix metalloproteinase-10 (MMP-10) expression pattern and to assess how it contributes to endochondral osteogenesis in Kashin-Beck disease (KBD). Design: The cartilages of KBD patients, Sprague-Dawley rats fed with selenium (Se)-deficient diet and/or T-2 toxin, and ATDC5 cells were used in this study. ATDC5 cells were induced into hypertrophic chondrocytes using a 1% insulin-transferrin-selenium (ITS) culture medium for 21 days. The expressions of MMP-10 in the cartilages were visualized by immunohistochemistry. The messenger RNA (mRNA) and protein expression levels were determined by real-time polymerase chain reaction (RT-PCR) and Western blotting. MMP-10 short hairpin RNA (shRNA) was transfected into hypertrophic chondrocytes to knock down the gene expression of MMP-10. Meanwhile, the cell death of MMP-10-knockdown chondrocyte was detected using flow cytometry. Results: The expression of MMP-10 was decreased in the growth plates of children with KBD. A decreased expression of MMP-10 also was observed in the growth plates of rats fed with an Se-deficient diet and/or T-2 toxin exposure. The mRNA and protein expression levels of MMP-10 increased during the chondrogenic differentiation of ATDC5 cells. MMP-10 knockdown in hypertrophic chondrocytes significantly decreased the gene and protein expression of collagen type II (Col II), Col X, Runx2, and MMP-13. Besides, the percentage of cell apoptosis was significantly increased after MMP-10 knockdown in hypertrophic chondrocytes. Conclusion: MMP-10 deficiency disrupts chondrocyte terminal differentiation and induces the chondrocyte’s death, which impairs endochondral osteogenesis in the pathogenesis of KBD.

Dietary habits have considerable impact on brain development and mental health. Despite long-standing interest in the association of dietary habits with mental health, few population-based studies of dietary habits have assessed depression and fluid intelligence. Our aim is to investigate the association of dietary habits with depression and fluid intelligence. In total, 814 independent loci were utilized to calculate the individual polygenic risk score (PRS) for 143 dietary habit-related traits. The individual genotype data were obtained from the UK Biobank cohort. Regression analyses were then conducted to evaluate the association of dietary habits with depression and fluid intelligence, respectively. PLINK 2.0 was utilized to detect the single nucleotide polymorphism (SNP) × dietary habit interaction effect on the risks of depression and fluid intelligence. We detected 22 common dietary habit-related traits shared by depression and fluid intelligence, such as red wine glasses per month, and overall alcohol intake. For interaction analysis, we detected that OLFM1 interacted with champagne/white wine in depression, while SYNPO2 interacted with coffee type in fluid intelligence. Our study results provide novel useful information for understanding how eating habits affect the fluid intelligence and depression.

Objective. Age‐related mitochondrial dysfunction and associated oxidative stress may contribute to the development of osteoarthritis. The aim of this study was to identify hub genes associated with mitochondrial dysfunction in osteoarthritis (OA) patients, helping predict the risk of OA, and revealing the mechanism of OA progression. Methods. OA expression data and mitochondrial dysfunction genes were downloaded from GEO (GSE55235, GSE82107, and GSE114007) and GeneCard databases. The differentially expressed mitochondrial dysfunction genes (DEMDFGs) between OA and control samples were screened. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes pathways were analyzed for DEMDFGs. The hub genes were determined by WGCNA and LASSO regression analysis. ROC curves manifested the diagnostic efficacy of each hub gene. A nomogram model was constructed and validated to predict OA risk. The expression of hub genes in OA and normal chondrocytes was verified by external datasets, qRT‐PCR and western blotting. Results. A total of 31 DEMDFGs were identified, with 15 genes upregulated and 16 genes downregulated. GO functional enrichment analysis revealed that DEMDFGs were enriched in biological processes related to energy metabolism and cellular respiration. By employing weighted gene coexpression network analysis, we identified four distinct coexpression modules, among which the blue module exhibited the strongest correlation with OA. The intersection between DEMDFGs and this module yielded eight candidate genes. After LASSO analysis of the data, four hub genes (ACADL, CYBA, SLC19A2, and UCP2) were identified as potential biomarkers for OA. The expression levels of these four genes were externally validated in the GSE114007 dataset. And the biologically differential expression of these four genes has been verified in OA and normal chondrocytes. Moreover, the four hub genes had good sensitivity and specificity by ROC curve analysis, and the risk model constructed with these four genes showed promising performance. In conclusion, our study may provide novel mitochondrial dysfunction hub genes with potential clinical applications for understanding the pathology, diagnosis, and treatment of OA.

Limited efforts have been paid to evaluate the potential relationships between structural and functional brain imaging and intelligence until now. We performed a two-stage analysis to systematically explore the relationships between 3144 brain image-derived phenotypes (IDPs) and intelligence. First, by integrating genome-wide association studies (GWAS) summaries data of brain IDPs and two GWAS summary datasets of intelligence, we systematically scanned the relationship between each of the 3144 brain IDPs and intelligence through linkage disequilibrium score regression (LDSC) analysis. Second, using the individual-level genotype and intelligence data of 160 124 subjects derived from UK Biobank datasets, polygenetic risk scoring (PRS) analysis was performed to replicate the common significant associations of the first stage. In the first stage, LDSC identified 6 and 2 significant brain IDPs significantly associated with intelligence dataset1 and dataset2, respectively. It is interesting that NET100_0624 showed genetic correlations with intelligence in the two datasets of intelligence. After adjusted for age and sex as the covariates, NET100_0624 (P = 5.26 × 10−20, Pearson correlation coefficients = −0.02) appeared to be associated with intelligence by PRS analysis of UK Biobank samples. Our findings may help to understand the genetic mechanisms of the effects of brain structure and function on the development of intelligence.

Objective: This study aims to identify novel candidate genes associated with osteonecrosis of the femoral head (ONFH).Methods: A transcriptome-wide association study (TWAS) was performed by integrating the genome-wide association study dataset of osteonecrosis (ON) in the UK Biobank with pre-computed mRNA expression reference weights of muscle skeleton (MS) and blood. The ON-associated genes identified by TWAS were further subjected to gene ontology (GO) analysis by the DAVID tool. Finally, a trans-omics comparative analysis of TWAS and genome-wide mRNA expression profiling was conducted to identify the common genes and the GO terms shared by both DNA-level TWAS and mRNA-level expression profile for ONFH.Results: TWAS totally identified 564 genes that were with P_TWAS value <0.05 for MS and blood, such as CBX1 (P_TWAS = 0.0001 for MS), SRPK2 (P_TWAS = 0.0002 for blood), and MYO5A (P_TWAS = 0.0005 for blood). After comparing the genes detected by TWAS with the differentially expressed genes identified by mRNA expression profiling, we detected 59 overlapped genes, such as STEAP4 [P_TWAS = 0.0270, FC (fold change)_mRNA = 7.03], RABEP1 (P_TWAS = 0.010, FC_mRNA = 2.22), and MORC3 (P_TWAS = 0.0053, FC_mRNA = 2.92). The GO analysis of TWAS-identified genes discovered 53 GO terms for ON. Further comparing the GO results of TWAS and mRNA expression profiling identified four overlapped GO terms, including cysteine-type endopeptidase activity (P_TWAS = 0.0006, P_mRNA = 0.0227), extracellular space (P_TWAS = 0.0342, P_mRNA = 0.0012), protein binding (P_TWAS = 0.0112, P_mRNA = 0.0106), and ATP binding (P_TWAS = 0.0464, P_mRNA = 0.0033).Conclusion: Several ONFH-associated genes and GO terms were identified by integrating TWAS and mRNA expression profiling. It provides novel clues to reveal the pathogenesis of ONFH.