The eIF4EBP-eIF4E axis regulates CD4+ T cell differentiation through modulation of T cell activation and metabolism
Bibliographic record
Abstract
CD4 + T cells are critical for adaptive immunity, differentiating into distinct effector and regulatory subsets. Although the transcriptional programs underlying their differentiation are known, recent research has highlighted the importance of mRNA translation in determining protein abundance. We previously conducted genome-wide analysis of translation in CD4 + T cells revealing distinct translational signatures distinguishing these subsets, identifying eIF4E as a central differentially translated transcript. As eIF4E is vital for eukaryotic translation, we examined how altered eIF4E activity affected T cell function using mice lacking eIF4E-binding proteins (BP -/- ). BP -/- effector T cells showed elevated Th1 responses ex vivo and upon viral challenge with enhanced Th1 differentiation observed in vitro . This was accompanied by increased TCR activation and elevated glycolytic activity. This study highlights how regulating T cell-intrinsic eIF4E activity can influence T cell activation and differentiation, suggesting the eIF4EBP-eIF4E axis as a potential therapeutic target for controlling aberrant T cell responses.
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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".