mTORC1 inhibition promotes human Treg differentiation via privileged mRNA translation
Bibliographic record
Abstract
Abstract Maturation of regulatory T cells (Tregs) in peripheral sites is known to require TGFβ exposure and inhibition of protein kinase mTORC1. It is well known that mTOR inhibition is associated with repression of cap-dependent mRNA translation, which represents the major mechanism for protein synthesis, leaving unanswered how Tregs carry-out essential translation for development and immune suppression activity. To answer this question, we performed genome-wide transcription and translation profiling in CD4+CD127dim/ − CD25+Tregs derived from anti-CD3/CD28-activated human naïve CD4+ T cells, treated with the mTORC1 inhibitor RAD001 and/or TGFβ. We found that TGFβ activated both Treg differentiation and immune suppression genes, while mTORC1 inhibition selectively blocked translation of most T cell mRNAs except those induced by TGFβ, including FOXP3, CTLA-4, CD101 or CD103, locking in Treg lineage commitment and immune suppression function. These canonical Treg fate-determining mRNAs were resistant to mTORC1 inhibition, an effect mediated in part by their 5′-untranslated regions through an alternate form of appears to be cap-dependent, eIF4E-independent mRNA translation. In conclusion, TGFβ transcriptional reprogramming together with mTORC1-independent translational reprogramming enable a privileged translation mechanism by which activated CD4+ T cells become Tregs.
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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 | 0.000 |
| 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".