Comprehensive analysis on subchondral bone marrow lesions of human osteoarthritis by integrating bulk and single-cell transcriptomes.

OBJECTIVE: This study aims to demonstrate the cellular composition and underlying mechanisms in subchondral bone marrow lesions (BMLs) of knee osteoarthritis (OA). METHODS: BMLs were assessed by MRI Osteoarthritis Knee Score (MOAKS)≥2. Bulk RNA-sequencing (bulk-seq) and BML-specific differentially expressed genes (DEGs) analysis were performed among subchondral bone samples (including OA-BML=3, paired OA-NBML=3; non-OA=3). The hub genes of BMLs were identified by verifying in independent datasets and multiple bioinformatic analyses. To further estimate cell-type composition of subchondral bone, we utilized two newly developed deconvolution algorithms (MuSiC, MCP-counter) in transcriptomic datasets, based on signatures from open-accessed single-cell RNA sequencing (scRNA-seq). Finally, competing endogenous RNA (ceRNA) and transcription factor (TF) networks were constructed through multiple predictive databases, and validated by public non-coding RNA profiles. RESULTS: A total of 86 BML-specific DEGs (up 79, down 7) were identified. IL11 and VCAN were identified as core hub genes. The "has-miR-424-5p/lncRNA PVT1" was determined as crucial network, targeting IL11 and VCAN, respectively. More importantly, two deconvolution algorithms produced approximate estimations of cell-type composition, and the cluster of heterotopic-chondrocyte was discovered abundant in BMLs, and positively correlated with the expression of hub genes. CONCLUSION: IL11 and VCAN were identified as the core hub genes of BMLs, and their molecular networks were determined as well. We profiled the characteristics of subchondral bone at single-cell level and determined that the heterotopic-chondrocyte was abundant in BMLs and was closely linked to IL11 and VCAN. Our study may provide new insights into the microenvironment and pathological molecular mechanism of BMLs, and could lead to novel therapeutic strategies.

Enhanced osteoarthritis therapy by nanoengineered mesenchymal stem cells using biomimetic CuS nanoparticles loaded with plasmid DNA encoding TGF-ß1.

Mesenchymal stem cells (MSCs) therapy shows the potential benefits to relieve clinical symptoms of osteoarthritis (OA), but it is uncertain if it can repair articular cartilage lesions - the main pathology of OA. Here, we prepared biomimetic cupper sulfide@phosphatidylcholine (CuS@PC) nanoparticles (NPs) loaded with plasmid DNA (pDNA) encoding transforming growth factor-beta 1 (TGF-ß1) to engineer MSCs for enhanced OA therapy via cartilage regeneration. We found that the NPs not only promoted cell proliferation and migration, but also presented a higher pDNA transfection efficiency relative to commercial transfection reagent lipofectamine 3000. The resultant CuS/TGF-ß1@PC NP-engineered MSCs (termed CTP-MSCs) were better than pure MSCs in terms of chondrogenic gene expression, glycosaminoglycan deposition and type II collagen formation, favoring cartilage repair. Further, CTP-MSCs inhibited extracellular matrix degradation in interleukin-1ß-induced chondrocytes. Consequently, intraarticular administration of CTP-MSCs significantly enhanced the repair of damaged cartilage, whereas pure MSCs exhibited very limited effects on cartilage regeneration in destabilization of the medial meniscus (DMM) surgical instability mice. Hence, this work provides a new strategy to overcome the limitation of current stem cell therapy in OA treatment through developing more effective nanoengineered MSCs.

Associations of childhood-to-adulthood body size trajectories and genetic susceptibility with the risks of osteoarthritis: a population-based cohort study of UK Biobank data.

BACKGROUND: Large adulthood body size was associated with increased risk of osteoarthritis. We aimed to examine the association between body size trajectories from childhood to adulthood and potential interactions with genetic susceptibility on osteoarthritis risk. METHODS: We included participants from the UK Biobank aged 38-73 years in 2006-10. Childhood body size information was collected by questionnaire. Adulthood BMI was assessed and transformed into three categories (<25 kg/m2 for normal, 25-29·9 kg/m2 for overweight, and >30 kg/m2 for obesity). A Cox proportional hazards regression model was applied to assess the association between body size trajectories and osteoarthritis incidence. Osteoarthritis-related polygenic risk score (PRS) was constructed to evaluate its interactions with body size trajectories on osteoarthritis risk. FINDINGS: For the 466 292 participants included, we identified nine body size trajectories [thinner to normal (11·6%), overweight (17·2%), or obesity (26·9%); average to normal (11·8%), overweight (16·2%), or obesity (23·7%); and plumper to normal (12·3%), overweight (16·2%), or obesity (23·6%)]. Compared with individuals in the average-to-normal group, all other trajectory groups had higher risks of osteoarthritis, after adjustment for demographic, social-economic and lifestyle covariates (hazard ratios [HRs] 1·05-2·41; all p<0·01). Among them, thinner-to-obesity (HR 2·41; 95% CI 2·23-2·49) had the most prominent association with increased osteoarthritis risk. A high PRS was significantly associated with an increased risk of osteoarthritis (1·14; 1·11-1·16), whereas no interaction between childhood-to-adulthood body size trajectories and PRS on osteoarthritis risks was observed. The population attributable fraction suggested that body size towards normal in adulthood could eliminate osteoarthritis cases by 18·67% for thinner-to-overweight to 38·74% for plumper-to-obesity. INTERPRETATION: Average-to-normal body size seems to be the healthiest childhood-to-adulthood trajectory for osteoarthritis risk, whereas a trajectory of increased body size from thinner to obesity has the highest risk for osteoarthritis. These associations are independent of osteoarthritis genetic susceptibility. FUNDING: The National Natural Science Foundation of China (32000925) and Guangzhou Science and Technology Program (202002030481).

A model-based quantitative analysis of efficacy and associated factors of platelet rich plasma treatment for osteoarthritis.

OBJECTIVE: While platelet rich plasma (PRP) has been extensively studied in treating osteoarthritis (OA), there has been an ongoing debate regarding the efficacy of PRP and the optimal subpopulation for PRP treatment remains unknown. The authors hereby aim to establish a pharmacodynamic model-based meta-analysis to quantitatively evaluate PRP efficacy, comparing with hyaluronic acid (HA) and identify relevant factors that significantly affect the efficacy of PRP treatment for OA. METHODS: The authors searched for PubMed and the Cochrane Library Central Register of Controlled Trials of PRP randomized controlled trials (RCTs) for the treatment of symptomatic or radiographic OA from the inception dates to 15 July 2022. Participants' clinical and demographic characteristics and efficacy data, defined as Western Ontario and McMaster Universities Osteoarthritis Index and visual analog scale pain scores at each time point were extracted. RESULTS: A total of 45 RCTs (3829 participants) involving 1805 participants injected with PRP were included in the analysis. PRP reached a peak efficacy at ~ 2-3 months after injection in patients with OA. Both conventional meta-analysis and pharmacodynamic maximal effect models showed that PRP was significantly more effective than HA for joint pain and function impairment (additional decrease of 1.1, 0.5, 4.3, and 1.1 scores compared to HA treatment at 12 months for Western Ontario and McMaster Universities Osteoarthritis Index pain, stiffness, function, and visual analog scale pain scores, respectively). Higher baseline symptom scores, older age (≥60 years), higher BMI (≥30), lower Kellgren-Lawrence grade (≤2) and shorter OA duration (<6 months) were significantly associated with greater efficacy of PRP treatment. CONCLUSION: These findings sugges t that PRP is a more effective treatment for OA than the more well-known HA treatment. The authors also determined the time when the PRP injection reaches peak efficacy and optimized the targeting subpopulation of OA. Further high-quality RCTs are required to confirm the optimal population of PRP in the treatment of OA.

Osteoporosis and osteoarthritis: a bi-directional Mendelian randomization study.

OBJECTIVE: To investigate the causal relationship between low bone mineral density (BMD) and osteoarthritis (OA) using Mendelian randomization (MR) design. METHODS: Two-sample bi-directional MR analyses were performed using summary-level information on OA traits from UK Biobank and arcOGEN. Sensitivity analyses including MR-Egger, simple median, weighted median, MR pleiotropy residual sum, and outlier approaches were utilized in conjunction with inverse variance weighting (IVW). Gene ontology (GO) enrichment analyses and expression quantitative trait locus (eQTL) colocalization analyses were used to investigate the potential mechanism and shared genes between osteoporosis (OP) and OA. RESULTS: The IVW method revealed that genetically predicted low femoral neck BMD was significantly linked with hip (ß = 0.105, 95% CI: 0.023-0.188) and knee OA (ß = 0.117, 95% CI: 0.049-0.184), but not with other site-specific OA. Genetically predicted low lumber spine BMD was significantly associated with OA at any sites (ß = 0.048, 95% CI: 0.011-0.085), knee OA (ß = 0.101, 95% CI: 0.045-0.156), and hip OA (ß = 0.150, 95% CI: 0.077-0.224). Only hip OA was significantly linked with genetically predicted reduced total bone BMD (ß = 0.092, 95% CI: 0.010-0.174). In the reverse MR analyses, no evidence for a causal effect of OA on BMD was found. GO enrichment analysis and eQTL analysis illustrated that DDN and SMAD-3 were the most prominent co-located genes. CONCLUSIONS: These findings suggested that OP may be causally linked to an increased risk of OA, indicating that measures to raise BMD may be effective in preventing OA. More research is required to determine the underlying processes via which OP causes OA.

The effect of systemic iron status on osteoarthritis: A mendelian randomization study.

Objective: To assess the causal effect of systemic iron status by using four biomarkers (serum iron; transferrin saturation; ferritin; total iron-binding capacity) on knee osteoarthritis (OA), hip OA, total knee replacement, and total hip replacement using 2-sample Mendelian randomization (MR) design. Methods: Three instrument sets were used to construct the genetic instruments for the iron status: Liberal instruments (variants associated with one of the iron biomarkers), sensitivity instruments (liberal instruments exclude variants associated with potential confounders), and conservative instruments (variants associated with all four iron biomarkers). Summary-level data for four OA phenotypes, including knee OA, hip OA, total knee replacement, and total hip replacement were obtained from the largest genome-wide meta-analysis with 826,690 individuals. Inverse-variance weighted based on the random-effect model as the main approach was conducted. Weighted median, MR-Egger, and Mendelian randomization pleiotropy residual sum and outlier methods were used as sensitivity MR approaches. Results: Based on liberal instruments, genetically predicted serum iron and transferrin saturation were significantly associated with hip OA and total hip replacement, but not with knee OA and total knee replacement. Statistical evidence of heterogeneity across the MR estimates indicated that mutation rs1800562 was the SNP significantly associated with hip OA in serum iron (odds ratio, OR = 1.48), transferrin saturation (OR = 1.57), ferritin (OR = 2.24), and total-iron binding capacity (OR = 0.79), and hip replacement in serum iron (OR = 1.45), transferrin saturation (OR = 1.25), ferritin (OR = 1.37), and total-iron binding capacity (OR = 0.80). Conclusion: Our study suggests that high iron status might be a causal factor of hip OA and total hip replacement where rs1800562 is the main contributor.

Association between Metformin Use and Risk of Total Knee Arthroplasty and Degree of Knee Pain in Knee Osteoarthritis Patients with Diabetes and/or Obesity: A Retrospective Study.

Objectives: We aimed to examine whether metformin (MET) use is associated with a reduced risk of total knee arthroplasty (TKA) and low severity of knee pain in patients with knee osteoarthritis (OA) and diabetes and/or obesity. Methods: Participants diagnosed with knee OA and diabetes and/or obesity from June 2000 to July 2019 were selected from the information system of a local hospital. Regular MET users were defined as those with recorded prescriptions of MET or self-reported regular MET use for at least 6 months. TKA information was extracted from patients' surgical records. Knee pain was assessed using the numeric rating scale. Log-binomial regression, linear regression, and propensity score weighting (PSW) were performed for statistical analyses. Results: A total of 862 participants were included in the analyses. After excluding missing data, there were 346 MET non-users and 362 MET users. MET use was significantly associated with a reduced risk of TKA (prevalence ratio: 0.26, 95% CI: 0.15 to 0.45, p < 0.001), after adjustment for age, gender, body mass index, various analgesics, and insurance status. MET use was significantly associated with a reduced degree of knee pain after being adjusted for the above covariates (ß: −0.48, 95% CI: −0.91 to −0.05, p = 0.029). There was a significantly accumulative effect of MET use on the reduced risk of TKA. Conclusion: MET can be a potential therapeutic option for OA. Further clinical trials are needed to determine if MET can reduce the risk of TKA and the severity of knee pain in metabolic-associated OA patients.

Osteophytes mediate the associations between cartilage morphology and changes in knee symptoms in patients with knee osteoarthritis.

AIMS: To investigate whether the associations between cartilage defects and cartilage volumes with changes in knee symptoms were mediated by osteophytes. METHODS: Data from the Vitamin D Effects on Osteoarthritis (VIDEO) study were analyzed as a cohort. The Western Ontario and McMaster Universities Osteoarthritis Index was used to assess knee symptoms at baseline and follow-up. Osteophytes, cartilage defects, and cartilage volumes were measured using magnetic resonance imaging at baseline. Associations between cartilage morphology and changes in knee symptoms were assessed using linear regression models, and mediation analysis was used to test whether these associations were mediated by osteophytes. RESULTS: A total of 334 participants (aged 50 to 79 years) with symptomatic knee osteoarthritis were included in the analysis. Cartilage defects were significantly associated with change in total knee pain, change in weight-bearing pain, and change in non-weight-bearing pain after adjustment for age, sex, body mass index, and intervention. Cartilage volume was significantly associated with change in weight-bearing pain and change in physical dysfunction after adjustment. Lateral tibiofemoral and patellar osteophyte mediated the associations of cartilage defects with change in total knee pain (49-55%) and change in weight-bearing pain (61-62%) and the association of cartilage volume with change in weight-bearing pain (27-30%) and dysfunction (24-25%). Both cartilage defects and cartilage volume had no direct effects on change in knee symptoms. CONCLUSIONS: The significant associations between cartilage morphology and changes in knee symptoms were indirect and were partly mediated by osteophytes.

Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis.

BACKGROUND: Periodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets. METHODS: Two periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis. RESULTS: We produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three "master" immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways. CONCLUSION: This study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis.