TYK2 inhibition enhances Treg differentiation and function while preventing Th1 and Th17 differentiation
Bibliographic record
Abstract
ABSTRACT Janus kinase (JAK) inhibitors are widely use to inhibit inflammatory cytokine signalling in autoimmune and inflammatory diseases but their effect on regulatory T cells (Tregs) is poorly characterized. We investigated the effect of JAK inhibition on human Treg differentiation, phenotype, and function using a JAK inhibitor, upadacitinib, in comparison to BMS-986202, a selective Tyrosine kinase 2 (TYK2) inhibitor. Both upadacitinib and BMS-986202 blocked the differentiation of naïve CD4 + T cells into Th1 and Th17 cells, but only BMS-986202 spared IL-2 signalling and Treg differentiation. BMS-986202 also increased Treg suppressive function and stability under Th1- and Th17-polarizing conditions, whereas upadacitinib significantly impaired the phenotype and viability of ex vivo Tregs. Analysis of lamina propria mononuclear cells from patients with inflammatory bowel disease revealed that, under Th17 polarizing conditions, BMS-986202 redirected CD4 + T cells towards a Treg phenotype. The Treg-sparing and enhancing properties of TYK2 inhibition suggest that TYK2 inhibitors are a promising pharmacological approach for tolerance induction. eTOC SUMMARY Tuomela et al. report that TYK2 inhibition does not affect human Treg induction from naïve CD4 + T cells, promotes Treg differentiation in lamina propria-derived T cells, and increases blood-derived Treg stability/function. In contrast, JAK inhibition strongly impairs Treg function.
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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".