Kashin-Beck disease (KBD) is an endemic chronic osteochondropathy. The etiology of KBD remains unknown. In this study, we conducted an integrative analysis of genome-wide DNA methylation and mRNA expression profiles between KBD and normal controls to identify novel candidate genes and pathways for KBD. Articular cartilage samples from 17 grade III KBD patients and 17 healthy controls were used in this study. DNA methylation profiling of knee cartilage and mRNA expression profile data were obtained from our previous studies. InCroMAP was performed to integrative analysis of genome-wide DNA methylation profiles and mRNA expression profiles. Gene ontology (GO) enrichment analysis was conducted by online DAVID 6.7. The quantitative real-time polymerase chain reaction (qPCR), Western blot, immunohistochemistry (IHC), and lentiviral vector transfection were used to validate one of the identified pathways. We identified 298 common genes (such as COL4A1, HOXA13, TNFAIP6 and TGFBI), 36 GO terms (including collagen function, skeletal system development, growth factor), and 32 KEGG pathways associated with KBD (including Selenocompound metabolism pathway, PI3K-Akt signaling pathway, and TGF-beta signaling pathway). Our results suggest the dysfunction of many genes and pathways implicated in the pathogenesis of KBD, most importantly, both the integrative analysis and in vitro study in KBD cartilage highlighted the importance of selenocompound metabolism pathway in the pathogenesis of KBD for the first time.

Chondropathies are a group of cartilage diseases, which share some common pathogenetic features. The etiology of chondropathies is still largely obscure now. A transcriptome-wide association study (TWAS) was performed using the UK Biobank genome-wide association study (GWAS) data of chondropathies (including 1314 chondropathy patients and 450,950 controls) with gene expression references of muscle skeleton (MS) and peripheral blood (YBL). The candidate genes identified by TWAS were further compared with three gene expression profiles of osteoarthritis (OA), cartilage tumor (CT), and spinal disc herniation (SDH), to confirm the functional relevance between the chondropathies and the candidate genes identified by TWAS. Functional mapping and annotation (FUMA) was used for the gene ontology enrichment analyses. Immunohistochemistry (IHC) was conducted to validate the accuracy of integrative analysis results. Integrating TWAS and mRNA expression profiles detected 84 candidate genes for knee OA, such as DDX20 (PTWAS YBL = 1.79 × 10− 3, fold change (FC) = 2.69), 10 candidate genes for CT, such as SRGN (PTWAS YBL = 1.46 × 10− 3, FC = 3.36), and 4 candidate genes for SDH, such as SUPV3L1 (PTWAS YBL = 3.59 × 10− 3, FC = 3.22). Gene set enrichment analysis detected 73 GO terms for knee OA, 3 GO terms for CT, and 1 GO term for SDH, such as mitochondrial protein complex (P = 7.31 × 10− 5) for knee OA, cytokine for CT (P = 1.13 × 10− 4), and ion binding for SDH (P = 3.55 × 10− 4). IHC confirmed that the protein expression level of DDX20 was significantly different between knee OA cartilage and healthy control cartilage (P = 0.0358). Multiple candidate genes and GO terms were detected for chondropathies. Our findings may provide a novel insight in the molecular mechanisms of chondropathies.

Objective: Nowadays, more than 90% of people over 50 years suffer from intervertebral disc degeneration (IDD), but there are exist no ideal drugs. The aim of this study is to identify a new drug for IDD.Methods: An approved small molecular drug library including 2040 small molecular compounds was used here. We found that taurocholic acid sodium hydrate (NAT) could induce chondrogenesis and osteogenesis in mesenchymal stem cells (MSCs). Then, an in vivo mouse model of IDD was established and the coccygeal discs transcriptome analysis and surface plasmon resonance analysis (SPR) integrated with liquid chromatography–tandem mass spectrometry assay (LC‐MS) were performed in this study to study the therapy effect and target proteins of NAT for IDD. Micro‐CT was used to evaluate the cancellous bone. The expression of osteogenic (OCN, RNX2), chondrogenic (COL2A1, SOX9), and the target related (ERK1/2, p‐ERK1/2) proteins were detected. The alkaline phosphatase staining was performed to estimate osteogenic differentiation. Blood routine and blood biochemistry indexes were analyzed for the safety of NAT.Results: The results showed that NAT could induce chondrogenesis and osteogenesis in MSCs. Further experiments confirmed NAT could ameliorate the secondary osteoporosis and delay the development of IDD in mice. Transcriptome analysis identified 128 common genes and eight Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for NAT. SPR‐LC–MS assay detected 57 target proteins for NAT, including MAPK3 (mitogen‐activated protein kinase 3), also known as ERK1 (extracellular regulated protein kinase 1). Further verification experiment confirmed that NAT significantly reduced the expression of ERK1/2 phosphorylation.Conclusion: NAT would induce chondrogenesis and osteogenesis of MSCs, ameliorate the secondary osteoporosis and delay the progression of IDD in mice by targeting MAPK3.Furthermore, MAPK3, especially the phosphorylation of MAPK3, would be a potential therapeutic target for IDD treatment.

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.