A Combinatorial mRNA Therapy for Treating Rheumatoid Arthritis and Osteoarthritis by Inhibiting Inflammation and Promoting Cartilage Regeneration
Wang, G.; Guo, Y.; Guo, K.; Zhang, C.; Zhou, Y.; Xia, J.; Liu, J.; Ren, J.; Mohamed, A. O.; Tang, K.; Chen, X.; Sun, S.; Yang, Y.; Shen, M.; Huang, Y.; Li, X.; Lin, A.
Show abstract
Rheumatoid arthritis (RA) and osteoarthritis (OA) are debilitating joint disorders with distinct etiologies but share common pathological features of chronic inflammation and progressive cartilage damage. Current therapeutic options are largely palliative and yet fail to achieve desired effect. In this study, we developed a combinatorial mRNA therapy using a rationally engineered lipid nanoparticle (LNP) containing a novel ionizable lipid for enhanced cartilage penetration upon intra-articular administration. This LNP co-delivers mRNAs encoding two complementary therapeutic proteins: interleukin-1 receptor antagonist (IL-1Ra) for inflammation attenuation and a C-terminal truncated derivative of angiopoietin-like 3 (ANL3) for cartilage regeneration. In pre-clinical models including collagen-induced arthritis mice, TNF-transgenic mice with spontaneous RA and destabilization of medial meniscus (DMM) surgical OA model, this "one-shot" combinatorial therapy significantly ameliorated disease severity, reduced synovitis and bone erosion, and potently promoted the regeneration of hyaline-like cartilage. Mechanistically, transcriptomic profiling of joint specimens revealed that the combinatorial therapy concurrently suppressed inflammatory pathways and extracellular matrix degradation pathways, meanwhile upregulating anabolic genes facilitating chondrogenesis. Collectively, our study establishes a versatile and synergistic mRNA therapy that offers a potent disease-modifying immunomodulatory strategy capable of addressing the long-standing unmet needs in arthritis treatment.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- MicroRNA-378 suppressed osteogenesis of mesenchymal stem cells and impaired bone formation via inactivating Wnt/β-catenin signaling 92%
- Single-Cell Transcriptomics of Multi-Site Cell Therapy in Osteoarthritis: Tissue-Specific Traits and Treatment Correlations 91%
- Improving angiogenesis ameliorates the efficacy of ASO-based exon-skipping for the treatment of Duchenne muscular dystrophy 91%
Similar papers in this journal
- Therapeutic TNF-alpha Delivery After CRISPR Receptor Modulation in the Intervertebral Disc 92%
- Langerhans Cell-targeted Protein Delivery Enhances Antigen-Specific Cellular Immune Response 91%
- Corticosteroids and cellulose purification improve respectively the in vivo translation and vaccination efficacy of self-amplifying mRNAs 91%
Similar papers in this journal
- Aberrant methylation and expression of TNXB promote chondrocyte apoptosis and extracullar matrix degradation in hemophilic arthropathy via AKT signaling 95%
- GLI1 facilitates rheumatoid arthritis by collaborative regulation of DNA methyltransferases 95%
- Down-regulated GAS6 impairs synovial macrophage efferocytosis and promotes obesity-associated osteoarthritis 94%
Similar papers in this journal
- A multi-functional small molecule alleviates fracture pain and promotes bone healing 93%
- Massively parallel screening of TIR-derived peptides reveals vast TLR-targeting immunomodulatory peptides 92%
- TSHR-targeting nucleic acid aptamer treats Graves' ophthalmopathy via novel allosteric inhibition 92%
Similar papers in this journal
- Interleukin-17A causes osteoarthritis-like transcriptional changes in human osteoarthritis-derived chondrocytes and synovial fibroblasts in vitro 92%
- The macrophage reprogramming ability of antifolates reveals soluble CD14 as a potential biomarker for methotrexate response in rheumatoid arthritis 92%
- Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.