Integrative analysis reveals CD38 as a therapeutic target for plasma cell-rich rheumatoid arthritis, pre-rheumatoid arthritis and systemic lupus erythematosus
Bibliographic record
Abstract
Abstract Autoantibodies play a significant role in the progression and pathogenesis of many autoimmune diseases such as rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). No studies have analyzed the expression of plasma cell/plasmablast genes during RA disease progression and the potential of an anti-CD38 antibody in depleting plasma cells and plasmablasts for efficacy in autoimmunity. Therefore, we interrogated the rationale of daratumumab, an anti-CD38 monoclonal antibody, as a potential therapeutic in RA and SLE. RNA-Seq analysis of synovial biopsies from various stages of RA disease development shows that plasma cell/plasmablast-related genes CD38, XBP1, IRF4, PRDM1, IGJ and TNFSF13B are significantly up-regulated in synovial biopsies from arthralgia, undifferentiated arthritis, early RA and established RA compared to healthy and osteoarthritis controls. In addition, flow cytometry analysis reveals highest CD38 expression on plasma cells and plasmablasts compared to natural killer cells, classical dendritic cell (DC), plasmacytoid DC, and T cells, in peripheral blood of healthy control, SLE and RA donors. We show that IGJ expression mRNA strongly correlates with the number of plasma cell/plasmablast in SLE patient PBMCs. Most importantly, IGJ mRNA down-regulation correlates with daratumumab-mediated depletion of these cells ex vivo. The data indicates a potential use of IGJ mRNA as a surrogate pharmacodynamic biomarker in clinical testing of daratumumab. Taken together, our data provides rationale for daratumumab in the treatment of RA and SLE.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".