The Purinergic Receptor P2X4 Promotes Th17 Activation and the Development of Arthritis
Bibliographic record
Abstract
Abstract Purinergic signaling plays a major role in T cell activation leading to IL-2 production and proliferation. However, it is unclear whether purinergic signaling contributes to the differentiation and activation of effector T cells. In this study, we found that the purinergic receptor P2X4 was associated with human Th17 cells but not with Th1 cells. Inhibition of P2X4 receptor with the specific antagonist 5-BDBD and small interfering RNA inhibited the development of Th17 cells and the production of IL-17 by effector Th17 cells stimulated via the CD3/CD28 pathway. Our results showed that P2X4 was required for the expression of retinoic acid-related orphan receptor C, which is the master regulator of Th17 cells. In contrast, inhibition of P2X4 receptor had no effect on Th1 cells and on the production of IFN-γ and it did not affect the expression of the transcription factor T-bet (T-box transcription factor). Furthermore, inhibition of P2X4 receptor reduced the production of IL-17 but not of IFN-γ by effector/memory CD4+ T cells isolated from patients with rheumatoid arthritis. In contrast to P2X4, inhibition of P2X7 and P2Y11 receptors had no effects on Th17 and Th1 cell activation. Finally, treatment with the P2X4 receptor antagonist 5-BDBD reduced the severity of collagen-induced arthritis in mice by inhibiting Th17 cell expansion and activation. Our findings provide novel insights into the role of purinergic signaling in T cell activation and identify a critical role for the purinergic receptor P2X4 in Th17 activation and in autoimmune 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) | 0.000 | 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) | 0.003 | 0.001 |
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".