Anti–Peptidylarginine Deiminase 4 Autoantibodies Derived From Patients With Rheumatoid Arthritis Exert Pathogenic Effects by Activating Monocytes and Exacerbating Inflammatory Arthritis
Bibliographic record
Abstract
OBJECTIVE: Autoantibodies targeting peptidylarginine deiminase 4 (PAD4), an enzyme involved in protein citrullination, are found in a subset of patients with rheumatoid arthritis (RA) with severe joint disease. However, the mechanisms by which anti-PAD4 antibodies participate in disease pathogenesis are incompletely defined. METHODS: We investigated the role of anti-PAD4 monoclonal antibodies derived from patients with RA using a collagen-induced arthritis (CIA) mouse model and human monocyte in vitro cultures. The cellular targets of anti-PAD4 antibodies were identified using mouse knee joint cells and human peripheral blood mononuclear cells. In addition, PAD4 gene and protein expression was assessed using human fibroblast-like synoviocyte in vitro cultures and a single-cell RNA sequencing data set obtained from patients with RA. RESULTS: We show that anti-PAD4 antibody treatment augmented disease severity in the CIA mouse model, with increased joint damage, myeloid cell infiltration, and synovial fibroblast activation. Arthritic mice administered with anti-PAD4 antibodies had an increased proportion of interleukin-17A (IL-17A), tumor necrosis factor α (TNFα), and interferon-γ (IFNγ)-producing T cells. Anti-PAD4 antibodies preferentially bound monocytes in both humans and mice, eliciting proinflammatory chemokine production by human monocytes in vitro. T cell cytokines enhanced by anti-PAD4 antibodies in the CIA model (ie, IL-17A, TNFα, and IFNγ) synergized to induce a proinflammatory phenotype in human fibroblast-like synoviocytes. CONCLUSION: Our findings suggest a model in which anti-PAD4 antibody binding to monocytes triggers an inflammatory cascade that promotes immune cell recruitment to the joint and T cell activation, culminating in synovial fibroblast activation and the development of more severe arthritis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".