Arkadia-SKI/SnoN signaling differentially regulates TGF-β–induced iTreg and Th17 cell differentiation
Bibliographic record
Abstract
TGF-β signaling is fundamental for both Th17 and regulatory T (Treg) cell differentiation. However, these cells differ in requirements for downstream signaling components, such as SMAD effectors. To further characterize mechanisms that distinguish TGF-β signaling requirements for Th17 and Treg cell differentiation, we investigated the role of Arkadia (RNF111), an E3 ubiquitin ligase that mediates TGF-β signaling during development. Inactivation of Arkadia in CD4+ T cells resulted in impaired Treg cell differentiation in vitro and loss of RORγt+FOXP3+ iTreg cells in the intestinal lamina propria, which increased susceptibility to microbiota-induced mucosal inflammation. In contrast, Arkadia was dispensable for Th17 cell responses. Furthermore, genetic ablation of two Arkadia substrates, the transcriptional corepressors SKI and SnoN, rescued Arkadia-deficient iTreg cell differentiation both in vitro and in vivo. These results reveal distinct TGF-β signaling modules governing Th17 and iTreg cell differentiation programs that could be targeted to selectively modulate T cell functions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".