Table 2.

Future directions in basic research on RA, based on this paper

Focus	Knowledge gaps	Proposed future directions	Potential clinical/translational relevance
Mechanisms of peripheral tolerance breakdown	- How Tregs become functionally impaired (e.g., IL-6 exposure, hypoxia, Th17 conversion) - Factors driving TPH cell expansion in pre-RA vs. established RA - Mechanisms of defective B cell tolerance and abnormal BCR signaling - LNSC dysfunction in RA	- Restore Treg function: Identify and target pathways (e.g., Foxp3 destabilization) that undermine Treg suppressive capacity - Characterize TPH cells: Determine how HLA-DR+ TPH subsets expand and initiate autoantibody production - Investigate B cell tolerance: Examine BAFF-mediated survival of autoreactive B cells and receptor editing failures - Assess LNSC dysregulation: Uncover how altered antigen presentation (PD-L1, HLA-DR) affects T cell anergy	- Targeted immunomodulators to preserve or re-establish Treg phenotype - Predictive biomarkers (e.g., TPH cell frequencies) for progression from pre-RA to classified RA - B cell–targeted therapies (e.g., BAFF inhibitors) to rescue tolerance - Restoring LNSC function to maintain peripheral tolerance and prevent synovial damage
Pathogenesis of seronegative RA	- Weaker linkage to HLA-DRB1 shared epitope - Distinct genetic risk factors (HLA-B position 9, HLA-DRB1 position 11, CLEC16A, IRF5) - Greater emphasis on innate immune pathways - Unique synovial inflammatory patterns (e.g., macrophage-driven)	- Expand genetic/epigenetic profiling: Identify seronegative-specific variants via GWAS and single-cell techniques - Focus on innate immunity: Characterize macrophage, DC, and neutrophil roles in seronegative synovitis - Comparative single-cell analyses: Contrast cellular subsets in ACPA– vs. ACPA+ RA - Optimized treatments: Develop therapies addressing innate-driven inflammation (e.g., IL-1 or CCL2 inhibitors)	- Personalized approaches: Tailor interventions for seronegative RA using innate immunity–targeted biologics - Biomarker development: Detect early innate immune activation patterns, facilitating differential diagnosis - Better disease control: Reduce reliance on autoantibody-targeting strategies (e.g., B cell depletion), which may be less effective in seronegative RA
Molecular mechanisms in refractory & recurrent RA	- PIRRA vs. NIRRA - Epigenetic and somatic mutations driving therapy resistance - Role of Treg/B cell dysfunction in flares or refractory states - Synovial fibroblast heterogeneity sustaining chronic inflammation	- Molecular stratification: Use multi-omics (RNA-seq, methylation, proteomics) to differentiate PIRRA vs. NIRRA - Novel immunoregulatory targets: Address pathways beyond TNF/IL-6 (e.g., GM-CSF, JAK-STAT, Treg reconstitution) - B cell–focused strategies: Investigate extended/alternate B cell depletion (e.g., rituximab) for treatment resistance - Fibroblast subtyping: Identify disease-promoting FLS subsets that maintain chronic synovitis	- Improved remission rates: Tailored therapies based on refractory RA subtype - Reduced trial-and-error: Molecular profiling guides selection of alternative cytokine inhibitors - Predictive biomarkers: Epigenetic or transcriptomic signatures to foresee flares or therapy failure - Fibroblast-targeting agents: New drug classes aimed at destructive and proinflammatory FLS populations
Multi-omics for personalized therapy	- Complexity of RA heterogeneity complicates one-size-fits-all treatments - Limited integration of transcriptomic, proteomic, metabolomic, and microbiomic data - Early-stage machine learning models for predicting drug response	- Machine learning integration: Merge multi-omics datasets (genomic, proteomic, microbiomic) to build high-accuracy models for therapy response (e.g., TNF inhibitor vs. IL-6 inhibitor) - Longitudinal patient tracking: Monitor molecular changes pre- and post-treatment to assess “molecular remission” - Microbiome-directed interventions: Investigate how modifying gut flora impacts RA outcomes	- Precision medicine: Personalized therapy aligned with a patient’s unique molecular profile - Reduced non-responder rates: Pre-treatment identification of likely responders - Adaptive treatment strategies: Real-time molecular data to guide medication adjustments and achieve sustained remission - New biomarkers: Metabolomic or microbiomic signatures for monitoring disease activity and optimizing dosing

RA: rheumatoid arthritis, Tregs: regulatory T cells, IL: interleukin, T_PH: peripheral helper T cells, LNSC: lymph node stromal cell, GWAS: genome-wide association studies, DC: dendritic cell, ACPA: anti-citrullinated protein antibody, PIRRA: persistent inflammatory refractory RA, NIRRA: non-inflammatory refractory RA, RNA-seq: RNA sequencing, TNF: tumor necrosis factor, GM-CSF: granulocyte-macrophage colony-stimulating factor, JAK-STAT: Janus kinase-signal transducer and activator of transcription.