The Deubiquitinating Enzyme Ubiquitin-Specific Peptidase 11 Potentiates TGF-β Signaling in CD4+ T Cells to Facilitate Foxp3+ Regulatory T and TH17 Cell Differentiation
Bibliographic record
Abstract
Abstract Foxp3+ regulatory T (TREG) cells are central mediators in the control of peripheral immune responses. Genome-wide transcriptional profiles show canonical signatures for Foxp3+ TREG cells, distinguishing them from Foxp3− effector T (TEFF) cells. We previously uncovered distinct mRNA translational signatures differentiating CD4+ TEFF and TREG cells through parallel measurements of cytosolic (global) and polysome-associated (translationally enhanced) mRNA levels in both subsets. We show that the mRNA encoding for the ubiquitin-specific peptidase 11 (USP11), a known modulator of TGF-β signaling, was preferentially translated in TCR-activated TREG cells compared with conventional, murine CD4+ T cells. TGF-β is a key cytokine driving the induction and maintenance of Foxp3 expression in T cells. We hypothesized that differential translation of USP11 mRNA endows TREG cells with an advantage to respond to TGF-β signals. In an in vivo mouse model promoting TREG cells plasticity, we found that USP11 protein was expressed at elevated levels in stable TREG cells, whereas ectopic USP11 expression enhanced the suppressive capacity and lineage commitment of these cells in vitro and in vivo. USP11 overexpression in TEFF cells enhanced the activation of the TGF-β pathway and promoted TREG or TH17, but not Th1, cell differentiation in vitro and in vivo, an effect abrogated by USP11 gene silencing or the inhibition of enzymatic activity. Thus, USP11 potentiates TGF-β signaling in both TREG and TEFF cells, in turn driving increased suppressive function and lineage commitment in thymic-derived TREG cells and potentiating the TGF-β–dependent differentiation of TEFF cells to peripherally induced TREG and TH17 cells.
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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".