Identification of p-eIF4E as a new regulator of regulatory T-cell activity
Bibliographic record
Abstract
Abstract Objective Phosphorylated Eukaryotic translation initiation factor 4E (p-eIF4E) is a critical regulator of protein synthesis and is phosphorylated by MNK1/2 to promote the translation of a mRNA subset. We have shown that blocking the eIF4E phosphorylation increased the anti-tumor immune response, especially the CD8 T cell activation. However, the role of p-eIF4E in CD4 T cell subsets, and in particular regulatory T cells (Tregs) is still unknown. The aim of this study was to explore the impact that the absence of p-eIF4E could have on the Treg activity as well as in an inflammatory context. Methods To investigate the role of p-eIF4E on Tregs activity, we used genetically modified mice expressing a non-phosphorylatable form of eIF4E (KI mice). First, we analyzed Treg activity in WT and KI mice and the same mice subjected to dextran sulfate sodium (DSS)-induced colitis. We also analyzed the colonic immune cell infiltration using spectral flow cytometry and CODEX technology. Results Using our mouse models, we observed that the p-eIF4E lack led to a decrease in Treg stability as well as a decrease in their ability to control the helper T cell (Th) proliferation. In the colitis context, we observed an increase in the disease severity in KI compared to WT mice characterized by an increase in colonic immune infiltration. Moreover, our mesenteric lymph node and colon immunophenotyping revealed a significant decrease in Treg, and an increase in Th expressing IFNγ in KI compared to WT mice. Finally, we observed a decrease in KI Treg ability to migrate to the lymph nodes. Conclusion These results demonstrate for the first time the preponderant role of p-eIF4E in the control of Treg stability and Treg migration but also in their ability to regulate inflammation.
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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".