Tocilizumab Contributes to the Inflammatory Status of Mature Dendritic Cells through Interleukin-6 Receptor Subunits Modulation
Bibliographic record
Abstract
Tocilizumab, a humanized anti-IL-6 receptor α (IL-6Rα), is widely used in the treatment of a panel of pathologies such as adult and juvenile rheumatoid arthritis and the systemic form of juvenile idiopathic arthritis in children. Its indications are expected to be largely extended to others inflammatory diseases in close future. Dendritic cells (DCs) appear to be deeply involved in the immunopathology of these diseases, yet the effects of tocilizumab on these cells were poorly studied. In the present study, we explored the effect of tocilizumab on the regulation of IL-6R subunits (gp130, sIL-6Rα and mIL-6Rα) in human monocyte-derived DCs. Human dendritic cells were derived from CD14+ monocytes purified with beads with IL-4 and GMCS-F. Ex vivo cultures of DCs were performed in the presence of tocilizumab. Using LPS maturation of DCs, we demonstrate that tocilizumab did not inhibit IL-6 secretion, enhanced mIL-6Rα expression and largely increased sIL-6Rα secretion. MAPK modulated STAT3 phosphorylation and surface expression of IL-6Rα in LPS-DCs. Tocilizumab had not impact on STAT3 phosphorylation in LPS-DCs while both LPS and IL-6 increased its activation. Tocilizumab modulated the regulation of IL-6R subunits leading to an inflammatory status of DCs and a massive secretion of IL-6Rα. Our results demonstrate that DCs acquire a pro-inflammatory profile following tocilizumab treatment, becoming a major source of IL-6 trans-signalling activation that might explain the poor clinical benefit in some RA patients.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".