Yan Wen

Aims: This study aims to investigate the molecular differences of cellular heterogeneity, metabolic, and cell death signatures between osteoarthritis (OA) and Kashin-Beck disease (KBD) at single-cell resolution. Methods: Single-cell RNA sequencing was performed on 139,943 chondrocytes isolated from 17 human cartilage samples, comparising three healthy controls, eight patients with OA, and six patients with KBD. Chondrocyte populations were identified using the Louvain algorithm and systematically compared across the three groups. Differentially expressed genes, functional enrichment, metabolic activity, and cell death-related signatures were analyzed. Key findings were further validated using immunohistochemistry staining and quantitative real-time polymerase chain reaction (PCR). Results: We found that pre-hypertrophic chondrocytes (preHTC), fibrocartilage chondrocytes (FC), pre-inflammatory chondrocytes (preInfC), and inflammatory chondrocytes (InfC) populations were enriched in OA, whereas proliferation chondrocytes (ProC) and homeostasis chondrocytes (HomC) predominated in KBD. In OA cartilage, OA-associated genes displayed upregulation of extracellular matrix (ECM) remodelling and immune activation pathways in preInfC and InfC populations. In contrast, KBD-associated genes exhibited marked activation of endoplasmic reticulum stress and oxidative stress responses in ProC-2, ProC-3, and HomC-2 subpopulations. OA was characterized by metabolic reprogramming with enhanced ECM remodelling and inflammation-related pathways, whereas KBD exhibited a globally suppressed metabolic profile. Additionally, ferroptosis activity was significantly higher in KBD cartilage compared with OA and control cartilage, with the strongest enrichment observed in ProC and HomC populations. Conclusion: Although OA and KBD chondrocytes were broadly conserved across 11 identified populations, they exhibited distinct disease-specific transcriptional profiles, metabolic activities, and cell death signatures. These findings enhance our understanding of chondrocyte heterogeneity and identify the InfC population in OA, as well as the ProC and HomC populations in KBD, as potential targets for therapeutic intervention. Cite this article: Bone Joint Res 2026;15(11):1327–1340.

T-2 toxin, a potent mycotoxin produced by Fusarium species, is recognized for its neurotoxic, hepatotoxic, and reproductive toxic effects. However, its impact on chondrocyte differentiation and cartilage development is not well understood. This study combines transcriptomic and metabolomic analyses to investigate the impact of T-2 toxin on ATDC5 cells during differentiation at days 3, 7, 14, and 21, following 48 h of exposure. Additionally, real-time quantitative PCR (RT-qPCR) was used to evaluate the expression levels of key chondrocyte differentiation-related genes (e.g., Fn1, Sox9, Runx2, Acan, Col1a1, Col2a1, and Col10a1) under T-2 toxin exposure alone or in combination with recombinant Mouse BMP2, BMP4, or both. Our results show that T-2 toxin disrupts several genes and signaling pathways crucial for chondrogenesis, including the BMP signaling pathway, extracellular matrix organization, and arachidonic acid metabolism. Significant temporal alterations in Bmp2, Bmp4, and other key genes were observed, disrupting chondrocyte differentiation. Notably, these changes were mitigated by BMP2 and BMP4 co-treatment, highlighting their protective effects. These findings provide insights into the molecular mechanisms underlying T-2 toxin-induced disruptions in chondrocyte differentiation and support the potential use of BMP signaling pathways as the therapeutic strategies to counteract the detrimental effect of T-2 toxin exposure.

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.

Subjective well-being (SWB) is an important measure for mental health status. Previous research has shown that physical activity can affect an individual’s well-being, yet the underlying molecular mechanism remains to be clarified. In this study, we aim to evaluate the potential interactions between mitochondrial genes and physical activity (PA) as well as their combined effects on individual well-being. SWB phenotype data in UK Biobank were enrolled for this study including nine aspects such as work/job satisfaction, health satisfaction, family relationship satisfaction, friendships satisfaction, financial situation satisfaction, ever depressed for a whole week, general happiness, general happiness with own health and belief that own life is meaningful. We made analysis for each aspects separately. Firstly, mitochondria-wide association studies (MiWAS) was conducted to assess the association of mitochondrial Single Nucleotide Polymorphisms SNP with each aspect of SWB. Then an interaction analysis of mitochondrial DNA (mtDNA) mutation and PA was performed to evaluate their joint effect on SWB status. Meanwhile, these two analysis were made for female and male group separately as well as the total samples, all under the control of possible confounding factors including gender, age, Townsend Deprivation Index (TDI), education, alcohol consumption, smoking habits, and 10 principal components. MiWAS analysis identified 45 mtSNPs associated with 9 phenotypes of SWB. For example, m.15218A > G on MT-CYB in the health satisfaction phenotype of the total subjects. Gender-specific analyses found 30 mtSNPs in females and 58 in males, involving 13 mtGenes. In mtDNA-PA interaction analysis, we also identified 10 significant mtDNA-PA interaction sets for SWB. For instance, m.13020 T > C (MT-ND5) was associated with the SWB financial situation satisfaction phenotype in all subjects (P = 0.00577). In addition, MiWAS analysis identified 12 mtGene variants associated with SWB, as MT-ND1 and MT-ND2. However, in mtDNA-PA interactions we detected 7 mtDNA affecting psychiatric disorders occurring, as in the friendships satisfaction phenotype (m.3394 T > C on MT-ND1). Our study results suggest an implication of the interaction between mitochondrial function and physical activity in the risk of psychiatric disorder development.

The establishment of 3ʹaQTLs comprehensive database provides an opportunity to help explore the functional interpretation from the genome-wide association study (GWAS) data of psychiatric disorders. In this study, we aim to search novel susceptibility genes, pathways, and related chemicals of five psychiatric disorders via GWAS and 3ʹaQTLs datasets. The GWAS datasets of five psychiatric disorders were collected from the open platform of Psychiatric Genomics Consortium (PGC, https://www.med.unc.edu/pgc/) and iPSYCH (https://ipsych.dk/) (Demontis et al. in Nat Genet 51(1):63–75, 2019; Grove et al. in Nat Genet 51:431–444, 2019; Genomic Dissection of Bipolar Disorder and Schizophrenia in Cell 173: 1705-1715.e1716, 2018; Mullins et al. in Nat Genet 53: 817–829; Howard et al. in Nat Neurosci 22: 343–352, 2019). The 3′untranslated region (3′UTR) alternative polyadenylation (APA) quantitative trait loci (3′aQTLs) summary datasets of 12 brain regions were obtained from another public platform (https://wlcb.oit.uci.edu/3aQTLatlas/) (Cui et al. in Nucleic Acids Res 50: D39–D45, 2022). First, we aligned the GWAS-associated SNPs of psychiatric disorders and datasets of 3′aQTLs, and then, the GWAS-associated 3ʹaQTLs were identified from the overlap. Second, gene ontology (GO) and pathway analysis was applied to investigate the potential biological functions of matching genes based on the methods provided by MAGMA. Finally, chemical-related gene-set analysis (GSA) was also conducted by MAGMA to explore the potential interaction of GWAS-associated 3ʹaQTLs and multiple chemicals in the mechanism of psychiatric disorders. A number of susceptibility genes with 3ʹaQTLs were found to be associated with psychiatric disorders and some of them had brain-region specificity. For schizophrenia (SCZ), HLA-A showed associated with psychiatric disorders in all 12 brain regions, such as cerebellar hemisphere (P = 1.58 × 10^–36) and cortex (P = 1.58 × 10^–36). GO and pathway analysis identified several associated pathways, such as Phenylpropanoid Metabolic Process (GO:0009698, P = 6.24 × 10^–7 for SCZ). Chemical-related GSA detected several chemical-related gene sets associated with psychiatric disorders. For example, gene sets of Ferulic Acid (P = 6.24 × 10^–7), Morin (P = 4.47 × 10^–2) and Vanillic Acid (P = 6.24 × 10^–7) were found to be associated with SCZ. By integrating the functional information from 3ʹaQTLs, we identified several susceptibility genes and associated pathways especially chemical-related gene sets for five psychiatric disorders. Our results provided new insights to understand the etiology and mechanism of psychiatric disorders.

Objective: Kashin-Beck disease (KBD) is a kind of endemic and chronic osteochondropathy in China. This study aims to explore the functional relevance and potential mechanism of Wnt-inducible signaling pathway protein 1 (WISP1) in the pathogenesis of KBD. Design: KBD and control cartilage specimens were collected for tissue section observation and primary chondrocyte culture. Firstly, the morphological and histopathological observations were made under a light and electron microscope. Then, the expression levels of WISP1 as well as molecular markers related to the autophagy pathway and extracellular matrix (ECM) synthesis were detected in KBD and control chondrocytes by qRT-PCR, Western blot, and immunohistochemistry. Furthermore, the lentiviral transfection technique was applied to make a WISP1 knockdown cell model based on KBD chondrocytes. In vitro intervention experiments were conducted on the C28/I2 human chondrocyte cell line using human recombinant WISP1 (rWISP1). Results: The results showed that the autolysosome appeared in the KBD chondrocytes. The expression of WISP1 was significantly higher in KBD chondrocytes. Additionally, T-2 toxin, a risk factor for KBD onset, could up-regulate the expression of WISP1 in C28/I2. The autophagy markers ATG4C and LC3II were upregulated after the low-concentration treatment of T-2 toxin and downregulated after the high-concentration treatment. After knocking down WISP1 expression in KBD chondrocytes, MAP1LC3B decreased while ATG4C and COL2A1 increased. Moreover, the rWISP1 protein treatment in C28/I2 chondrocytes could upregulate the expression of ATG4C and LC3II at the beginning and downregulate them then. Conclusions: Our study suggested that WISP1 might play a role in the pathogenesis of KBD through autophagy.

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.

Aims Addiction is currently seen as a neuropsychiatric disorder with genetic component involved. Multiple chronic bowel disorders could exert influence on mental status including addition. This study aims to investigate the genetic correlation of addiction-related traits and chronic bowel disorders. Methods We extracted addiction-related traits information from UK Biobank database. We then calculated the polygenic risk score (PRS) of chronic bowel disorders (ulcerative colitis (UC), Crohn’s disease (CD), UC + CD and irritable bowel syndrome (IBS) respectively) for each individual. A regression analysis was conducted to measure the correlation of addiction-related traits and chronic bowel disorder PRS. We further performed a linkage disequilibrium score regression to evaluated the genetic correlation of chronic bowel disorders (UC, CD) and addiction traits (alcohol dependence) in another public GWAS datasets. Lastly, a genome-wide genetic interaction study (GWGIS) was conducted to measure the interactive effects of chronic bowel disorders (UC, UC + CD) and genetic variants in addiction-related traits. Results Regression analysis identified positive correlation at CD PRS and “ever addicted to any substance or behavior” (P = 4.80×10^− 2, beta = 0.141), CD PRS and “ever addicted to alcohol” (P = 1.90×10^− 2, beta = 1.533), and UC + CD PRS and “ever addicted to alcohol” (P = 2.70×10^− 2, beta = 0.882). LDSC analysis detected a significant genetic correlation at CD and alcohol dependence (P = 8.60×10^− 3). GWGIS results revealed that a group of significant genetic variations, such as rs12063422 (P = 4.15×10^− 6), showed interactive effect with CD PRS in alcohol addiction. Conclusions Our results revealed a genetic correlation between CD and alcohol addiction, which might be partly attributed to accumulated effect of a number of associated SNPs.

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.