Update on pain in arthritis

Rare variant association studies: Significance, methods, and applications in chronic pain studies

Rare genetic variants, characterized by their low frequency in a population, have emerged as essential components in the study of complex disease genetics. The biology of rare variants underscores their significance, as they can exert profound effects on phenotypic variation and disease susceptibility. Recent advancements in sequencing technologies have yielded the availability of large-scale sequencing data such as the UK Biobank whole-exome sequencing (WES) cohort empowered researchers to conduct rare variant association studies (RVASs). This review paper discusses the significance of rare variants, available methodologies, and applications. We provide an overview of RVASs, emphasizing their relevance in unraveling the genetic architecture of complex diseases with special focus on chronic pain and Arthritis. Additionally, we discuss the strengths and limitations of various rare variant association testing methods, outlining a typical pipeline for conducting rare variant association. This pipeline encompasses crucial steps such as quality control of WES data, rare variant annotation, and association testing. It serves as a comprehensive guide for researchers in the field of chronic pain diseases interested in rare variant association studies in large-scale sequencing datasets like the UK Biobank WES cohort. Lastly, we discuss how the identified variants can be further investigated through detailed experimental studies in animal models to elucidate their functional impact and underlying mechanisms.

Regulation of fibroblast phenotype in osteoarthritis using CDKN1A-loaded copper sulfide nanoparticles delivered by mesenchymal stem cells

This study aimed to investigate the regulation of fibroblast phenotypes by mesenchymal stem cells (MSCs) delivering copper sulfide (CuS) nanoparticles (NPs) loaded with CDKN1A plasmids and their role in cartilage repair during osteoarthritis (OA). Single-cell RNA sequencing data from the GEO database were analyzed to identify subpopulations within the OA immune microenvironment. Quality control, filtering, principal component analysis (PCA) dimensionality reduction, and tSNE clustering were performed to obtain detailed cell subtypes. Pseudotime analysis was used to understand the developmental trajectory of fibroblasts, and GO/KEGG enrichment analyses highlighted biological processes related to fibroblast function. Transcriptomic data and WGCNA identified CDKN1A as a key regulatory gene. A biomimetic CuS@CDKN1A nanosystem was constructed and loaded into MSCs to create MSCs@CuS@CDKN1A. The characterization of this system confirmed its efficient cellular uptake by fibroblasts. In vitro experiments demonstrated that MSCs@CuS@CDKN1A significantly modulated fibroblast phenotypes and improved the structure, proliferation, reduced apoptosis, and enhanced migration of IL-1β-stimulated chondrocytes. In vivo, an OA mouse model was treated with intra-articular injections of MSCs@CuS@CDKN1A. Micro-CT scans revealed a significant reduction in osteophyte formation and improved joint space compared with control groups. Histological analysis, including H&E, Safranin O-Fast Green, and toluidine blue staining, confirmed improved cartilage integrity, whereas the International Osteoarthritis Research Society (OARSI) scoring indicated reduced disease severity. Immunofluorescence showed upregulated CDKN1A expression, decreased MMP13, and reduced α-SMA expression in fibroblast subtypes. Major organs exhibited no signs of toxicity, confirming the biocompatibility and safety of the treatment. These findings suggest that MSCs@CuS@CDKN1A can effectively regulate fibroblast activity and promote cartilage repair, providing a promising therapeutic strategy for OA treatment.NEW & NOTEWORTHY This study introduces MSCs@CuS@CDKN1A, a nanoengineered MSC platform that targets fibroblast phenotypes in osteoarthritis (OA). By modulating CDKN1A expression, this innovative approach not only enhances cartilage repair but also effectively mitigates fibroblast-driven inflammation, marking a significant advancement in OA therapeutics with demonstrated efficacy and biocompatibility.

Small molecule targeting Na V 1.7 via inhibition of CRMP2-Ubc9 interaction reduces pain-related outcomes in a rodent osteoarthritic model

ABSTRACT

Osteoarthritis (OA) is a highly prevalent and disabling joint disease, characterized by pathological progressive joint deformation and clinical symptoms of pain. Disease-modifying treatments remain unavailable, and pain-mitigation is often suboptimal, but recent studies suggest beneficial effects by inhibition of the voltage-gated sodium channel Na V 1.7. We previously identified compound 194 as an indirect inhibitor of Na V 1.7 by preventing SUMOylation of the Na V 1.7-trafficking protein, collapsin response mediator protein 2. Compound 194 reduces the functional activity of Na V 1.7 channels and produces effective analgesia in a variety of acute and neuropathic pain models. However, its effectiveness has not yet been evaluated in models of OA. Here, we explore the effects of 194 on pain-related outcomes in the OA-like monoiodoacetate model using behavioral assessment, biochemistry, novel in vivo fiber photometry, and patch clamp electrophysiology. We found that the monoiodoacetate model induced (1) increased pain-like behaviors and calcium responses of glutamatergic neurons in the parabrachial nucleus after evoked cold and mechanical stimuli, (2) conditioned place aversion to mechanical stimulation, (3) functional weight bearing asymmetry, (4) increased sodium currents in dorsal root ganglia neurons, and (5) increased calcitonin gene-related peptide-release in the spinal cord. Crucially, administration of 194 improved all these pain-related outcomes. Collectively, these findings support indirect inhibition of Na V 1.7 as an effective treatment of OA-related pain through the inhibition of collapsin response mediator protein 2-SUMOylation via compound 194.

A nomogram based on radiological features of MRI for predicting the risk of severe pain in patients with osteoarthritis of the knee

Methods

This study aimed to develop and validate a nomogram for predicting the risk of severe pain in patients with knee osteoarthritis. A total of 150 patients with knee osteoarthritis were enrolled from our hospital, and nomogram was established through a validation cohort (n = 150). An internal validation cohort (n = 64) was applied to validate the model.

Results

Eight important variables were identified using the Least absolute shrinkage and selection operator (LASSO) and then a nomogram was developed by Logistics regression analysis. The accuracy of the nomogram was determined based on the C-index, calibration plots, and Receiver Operating Characteristic (ROC) curves. Decision curves were plotted to assess the benefits of the nomogram in clinical decision-making. Several variables were employed to predict severe pain in knee osteoarthritis, including sex, age, height, body mass index (BMI), affected side, Kellgren-Lawrance (K-L) degree, pain during walking, pain going up and down stairs, pain sitting or lying down, pain standing, pain sleeping, cartilage score, Bone marrow lesion (BML) score, synovitis score, patellofemoral synovitis, bone wear score, patellofemoral bone wear, and bone wear scores. The LASSO regression results showed that BMI, affected side, duration of knee osteoarthritis, meniscus score, meniscus displacement, BML score, synovitis score, and bone wear score were the most significant risk factors predicting severe pain.

Conclusions

Based on the eight factors, a nomogram model was developed. The C-index of the model was 0.892 (95% CI: 0.839-0.945), and the C-index of the internal validation was 0.822 (95% CI: 0.722-0.922). Analysis of the ROC curve of the nomogram showed that the nomogram had high accuracy in predicting the occurrence of severe pain [Area Under the Curve (AUC) = 0.892] in patients with knee osteoarthritis (KOA). The calibration curves showed that the prediction model was highly consistent. Decision curve analysis (DCA) showed a higher net benefit for decision-making using the developed nomogram, especially in the >0.1 and <0.86 threshold probability intervals. These findings demonstrate that the nomogram can predict patient prognosis and guide personalized treatment.

A neutrophil cell membrane-biomimetic nanoplatform based on L-arginine nanoparticles for early osteoarthritis diagnosis and nitric oxide therapy

Osteoarthritis (OA) is a common debilitating disease affecting articular joints for which no effective disease-modifying early diagnosis or medical therapy tools are currently available. The inefficient delivery of drugs into inflamed chondrocytes has restricted the development of anti-OA medication. Evidence has shown that inflammatory neutrophils possess the property of targeting inflammation via inflammatory tissue recruiting. Herein, we report neutrophil-cell-membrane-based biomimetic nanoparticles (NM-LANPs@Ru) as an OA theranostic nanoplatform; they act as a NO delivery system, coating neutrophil cell membrane onto the surface of self-assembled PEGylated L-arginine nanoparticles (LANPs) to act as a NO donor and loading a Ru complex to act as a ROS inducer. NM-LANPs@Ru demonstrated the specific targeting of inflamed OA with low toxicity, good NO release, and excellent fluorescence/photoacoustic (FL/PA) imaging properties. We showed that NM-LANPs@Ru exhibited enhanced cellular association in inflamed chondrocyte cells (C28/I2), much higher than NO release from ROS oxidized LA, and it improved the inhibition of the apoptosis of inflamed C28/I2 cells compared with control treatments. In vivo studies demonstrated that NM-LANPs@Ru effectively targeted inflamed OA, based on real-time dual-modal FL/PA imaging, eventually exhibiting its excellent anti-inflammatory activity. Our study may provide a new approach for the early diagnosis and treatment of osteoarthritis using a neutrophil-cell-membrane-based biomimetic nanoplatform for NO or drug delivery.

Rodent models of knee osteoarthritis for pain research

Osteoarthritis (OA) is a chronic degenerative joint disease and a leading cause of disability worldwide. Pain is the main symptom, yet no current treatment can halt disease progression or effectively provide symptomatic relief. Numerous animal models have been described for studying OA and some for the associated OA pain. This review aims to update on current models used for studying OA pain, focusing on mice and rats. These models include surgical, chemical, mechanical, and spontaneous OA models. The impact of sex and age will also be addressed in the context of OA modelling. Although no single animal model has been shown ideal for studying OA pain, increased efforts to phenotype OA will likely impact the choice of models for pre-clinical and basic research studies.

Multimodal prediction of pain and functional outcomes 6 months following total knee replacement: a prospective cohort study

BACKGROUND

Knee osteoarthritis (OA) is among the most common and disabling persistent pain conditions, with increasing prevalence and impact around the globe. In the U.S., the rising prevalence of knee OA has been paralleled by an increase in annual rates of total knee arthroplasty (TKA), a surgical treatment option for late-stage knee OA. While TKA outcomes are generally good, post-operative trajectories of pain and functional status vary substantially; a significant minority of patients report ongoing pain and impaired function following TKA. A number of studies have identified sets of biopsychosocial risk factors for poor post-TKA outcomes (e.g., comorbidities, negative affect, sensory sensitivity), but few prospective studies have systematically evaluated the unique and combined influence of a broad array of factors.

METHODS

This multi-site longitudinal cohort study investigated predictors of 6-month pain and functional outcomes following TKA. A wide spectrum of relevant biopsychosocial predictors was assessed preoperatively by medical history, patient-reported questionnaire, functional testing, and quantitative sensory testing in 248 patients undergoing TKA, and subsequently examined for their predictive capacity.

RESULTS

The majority of patients had mild or no pain at 6 months, and minimal pain-related impairment, but approximately 30% reported pain intensity ratings of 3/10 or higher. Reporting greater pain severity and dysfunction at 6 months post-TKA was predicted by higher preoperative levels of negative affect, prior pain history, opioid use, and disrupted sleep. Interestingly, lower levels of resilience-related "positive" psychosocial characteristics (i.e., lower agreeableness, lower social support) were among the strongest, most consistent predictors of poor outcomes in multivariable linear regression models. Maladaptive profiles of pain modulation (e.g., elevated temporal summation of pain), while not robust unique predictors, interacted with psychosocial risk factors such that the TKA patients with the most pain and dysfunction exhibited lower resilience and enhanced temporal summation of pain.

CONCLUSIONS

This study underscores the importance of considering psychosocial (particularly positively-oriented resilience variables) and sensory profiles, as well as their interaction, in understanding post-surgical pain trajectories.