Rheumatoid Arthritis (RA) Cell Models for Research

Rheumatoid arthritis (RA) is a chronic autoimmune disease affecting approximately 0.5-1% of the global population, with a higher prevalence in women and older adults (WHO, 2023). It is characterized by persistent synovial inflammation, leading to progressive joint destruction, disability, and increased mortality. The global burden of RA has risen, with age-standardized prevalence rates increasing by 7.4% from 1990 to 2019 (GBD 2019). RA is associated with significant comorbidities, including cardiovascular disease, infections, and certain cancers. Early diagnosis and treatment are crucial, but many patients do not achieve remission, highlighting the need for better therapeutic targets and biomarkers.

RA is an ideal model for studying autoimmune mechanisms, chronic inflammation, and joint destruction. Its complex pathogenesis involves genetic susceptibility, environmental triggers, and dysregulated immune responses. Public datasets, such as the Gene Expression Omnibus (GEO) and the Immunological Genome Project, provide extensive transcriptomic and epigenetic data from patient samples. Key open questions include the identification of early disease drivers, the role of specific genetic variants (e.g., HLA-DRB1, PTPN22), and the mechanisms of resistance to current therapies. Gene-edited cell models enable functional validation of these variants and pathways, accelerating the development of targeted therapies.

RA is not a cancer, but it involves dysregulated signaling pathways that drive chronic inflammation and joint destruction. Key pathways include:

• • NF-κB pathway: Activation of NF-κB leads to the production of pro-inflammatory cytokines (TNF-α, IL-6, IL-1β).
• • JAK-STAT pathway: Cytokine receptors activate JAKs, which phosphorylate STATs, promoting inflammation and immune cell activation.
• • RANKL/RANK pathway: RANKL stimulates osteoclast differentiation, leading to bone erosion.
• • MAPK pathway: Stress-activated MAPKs (p38, JNK) contribute to cytokine production and synovial hyperplasia.

Data from GWAS and ImmunoChip studies (Okada et al., 2014; Stahl et al., 2010).

Key signaling networks in RA:

• • Cytokine networks: TNF-α, IL-6, IL-1β, IL-17, and GM-CSF drive inflammation and joint damage.
• • T cell signaling: TCR signaling, co-stimulation (CD28/CTLA-4), and Th17 differentiation.
• • B cell signaling: BCR signaling, autoantibody production (RF, anti-CCP).
• • Synovial fibroblast activation: MAPK, PI3K/AKT, and Wnt pathways promote proliferation and invasion.
• • Osteoclastogenesis: RANKL/RANK/OPG axis.

Organoids derived from synovial tissue or induced pluripotent stem cells (iPSCs) can recapitulate the 3D architecture and cell-cell interactions, providing more physiologically relevant models for drug testing.

Common animal models for RA:

• • Collagen-induced arthritis (CIA): Immunization with type II collagen in susceptible mouse strains (e.g., DBA/1).
• • Adjuvant-induced arthritis (AIA): Injection of complete Freund's adjuvant in rats.
• • K/BxN serum transfer model: Transfer of serum from K/BxN mice induces arthritis.
• • TNF-α transgenic mice: Overexpress TNF-α, develop spontaneous arthritis.
• • IL-1 receptor antagonist knockout mice: Develop spontaneous arthritis.

These models are valuable for studying disease mechanisms and testing therapies, but they have limitations in recapitulating human RA genetics.

CRISPR-based gene editing enables the creation of isogenic cell lines with specific genetic modifications, such as knockouts (KO) or knock-ins (KI) of disease-associated variants. For example:

• • PTPN22 knockout: In Jurkat or primary T cells, to study the role of PTPN22 in T cell activation.
• • *HLA-DRB104:01 knock-in: In antigen-presenting cells, to study presentation of citrullinated peptides.
• • TNF-α knockout**: In synovial fibroblasts, to assess its role in inflammation.

These sequence-verified models are commercially available from various sources and provide a controlled system to validate gene function, screen drugs, and identify biomarkers. They are essential for translating genetic associations into functional mechanisms.

Gene-edited cell lines are used to validate the functional role of risk genes in RA. For example, knocking out PTPN22 in T cells can reveal its impact on TCR signaling and cytokine production. Similarly, introducing the R620W variant via knock-in can model the disease-associated phenotype. These models help prioritize candidate genes from GWAS and identify novel therapeutic targets.

Isogenic pairs (wild-type vs. knockout/knock-in) are powerful tools for drug screening. For instance, screening compounds against TNF-α knockout synovial fibroblasts can identify drugs that act independently of TNF-α. Additionally, resistance to JAK inhibitors can be modeled by introducing mutations in JAK genes, allowing the study of resistance mechanisms and the development of next-generation inhibitors.

CRISPR-based synthetic lethality screens can identify genes that are essential only in the context of specific mutations. For example, in cells with a particular HLA-DRB1 allele, knocking out other genes may reveal vulnerabilities that can be targeted therapeutically. This approach can uncover novel biomarkers and drug targets for personalized medicine.