Background: T-2 toxin is thought to induce the growth plate and articular cartilage damage of Kashin-Beck disease (KBD), an endemic osteochondropathy in China. This study aims to explore the potential underlying mechanism of such toxic effects by integrating DNA methylation and gene expression profiles. Methods: In this study, C28/I2 chondrocytes were treated with T-2 toxin (5 ng/mL) for 24 h and 72 h. Global DNA methylation level of chondrocyte was tested by Enzyme-Linked Immuno Sorbent Assay. Genome-wide DNA methylation and expression profiles were detected using Illumina Infinium HumanMethylation850 BeadChip and RNA-seq technique, respectively. Differentially methylated genes (DMGs) and differentially expressed genes (DEGs) were identified mainly for two stages including 24 h group versus Control group and 72 h group versus 24 h group. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed by Metascape. DMGs and DEGs were further validated by Sequenom MassARRAY system and quantitative real-time polymerase chain reaction. Results: The global DNA methylation levels of chondrocytes exposed to T-2 toxin were significantly increased (P < 0.05). For 24 h group versus Control group (24 VS C), 189 DEGs and 590 DMGs were identified, and 4 of them were overlapping. For 72 h group versus 24 h group (72 VS 24), 1671 DEGs and 637 DMGs were identified, and 45 of them were overlapping. The enrichment analysis results of DMGs and DEGs both showed that MAPK was the one of the mainly involved signaling pathways in the regulation of chondrocytes after T-2 toxin exposure (DEGs: P_24VSc = 1.62 × 10^− 7; P_72VS24 = 1.20 × 10^− 7; DMGs: P_24VSc = 0.0056; P_72VS24 = 3.80 × 10^− 5). Conclusions: The findings depicted a landscape of genomic methylation and transcriptome changes of chondrocytes after T-2 toxin exposure and suggested that dysfunction of MAPK pathway may play important roles in the chondrocytes damage induced by T-2 toxin, which could provide new clues for understanding the potential biological mechanism of KBD cartilage damage induced by T-2 toxin.

The British Editorial Society of Bone & Joint Surgery

Background: Kashin–Beck disease (KBD) is a distinct osteoarthropathy in China with an unclear pathogenesis. This study aims to explore whether perturbations in the intestine metabolome could be linked to KBD individuals. Methods: An investigation was conducted in KBD endemic villages and fecal samples were collected. After applying inclusion and exclusion criteria, a total of 75 subjects were enrolled for this study, including 46 KBD (including 19 Grade I KBD and 27 Grade II KBD) and 29 controls. Untargeted metabolomics analysis was performed on the platform of UHPLC-MS. PLS-DA and OPLS-DA were conducted to compare the groups and identify the differential metabolites (DMs). Pathway analysis was conducted on MPaLA platform to explore the functional implication of the DMs. Results: Metabolomics analysis showed that compared with the control group, KBD individuals have a total of 584 differential metabolites with dysregulated levels such as adrenic acid (log₂FC = −1.87, VIP = 4.84, p = 7.63 × 10⁻⁷), hydrogen phosphate (log₂FC = −2.57, VIP = 1.27, p = 1.02 × 10⁻³), taurochenodeoxycholic acid (VIP = 1.16, log₂FC = −3.24, p = 0.03), prostaglandin E3 (VIP = 1.17, log₂FC = 2.67, p = 5.61 × 10⁻⁴), etc. Pathway analysis revealed several significantly perturbed pathways associated with KBD such as selenium micronutrient network (Q value = 3.11 × 10⁻³, Wikipathways), metabolism of lipids (Q value = 8.43 × 10⁻⁴, Reactome), free fatty acid receptors (Q value = 3.99 × 10⁻³, Reactome), and recycling of bile acids and salts (Q value = 2.98 × 10⁻³, Reactome). Subgroup comparisons found a total of 267 differential metabolites were shared by KBD vs. control, KBD II vs. control, and KBD I vs. control, while little difference was found between KBD II and KBD I (only one differential metabolite detected). Conclusions: KBD individuals showed distinct metabolic features characterized by perturbations in lipid metabolism and selenium-related bioprocesses. Our findings suggest that the loss of nutrients metabolism balance in intestine was involved in KBD pathogenesis. Linking the nutrients metabolism (especially selenium and lipid) to KBD cartilage damage should be a future direction of KBD study.

Intelligence quotient is a vital index to evaluate the ability of an individual to think rationally, learn from experience and deal with the environment effectively. However, limited efforts have been paid to explore the potential associations of intelligence quotient traits with the tissue proteins from the brain, CSF and plasma. The information of protein quantitative trait loci was collected from a recently released genome-wide association study conducted on quantification data of proteins from the tissues including the brain, CSF and plasma. Using the individual-level genotypic data from the UK Biobank cohort, we calculated the polygenic risk scores for each protein based on the protein quantitative trait locus data sets above. Then, Pearson correlation analysis was applied to evaluate the relationships between intelligence quotient traits (including 120 330 subjects for ‘fluid intelligence score’ and 38 949 subjects for ‘maximum digits remembered correctly’) and polygenic risk scores of each protein in the brain (17 protein polygenic risk scores), CSF (116 protein polygenic risk scores) and plasma (59 protein polygenic risk scores). The Bonferroni corrected P-value threshold was P < 1.30 × 10⁻⁴ (0.05/384). Finally, Mendelian randomization analysis was conducted to test the causal relationships between ‘fluid intelligence score’ and pre-specific proteins from correlation analysis results. Pearson correlation analysis identified significant association signals between the protein of macrophage-stimulating protein and fluid intelligence in brain and CSF tissues (P_brain = 1.21 × 10⁻⁸, P_CSF = 1.10 × 10⁻⁷), as well as between B-cell lymphoma 6 protein and fluid intelligence in CSF (P_CSF = 1.23 × 10⁻⁴). Other proteins showed close-to-significant associations with the trait of ‘fluid intelligence score’, such as plasma protease C1 inhibitor (P_CSF = 4.19 × 10⁻⁴, P_plasma = 6.97 × 10⁻⁴), and with the trait of ‘maximum digits remembered correctly’, such as tenascin (P_plasma = 3.42 × 10⁻⁴). Additionally, Mendelian randomization analysis results suggested that macrophage-stimulating protein (Mendelian randomization-Egger: β = 0.54, P = 1.64 × 10⁻⁶¹ in the brain; β = 0.09, P = 1.60 × 10⁻¹² in CSF) had causal effects on fluid intelligence score. We observed functional relevance of specific tissue proteins to intelligence quotient and identified several candidate proteins, such as macrophage-stimulating protein. This study provided a novel insight to the relationship between tissue proteins and intelligence quotient traits.