Cross-talk between IL-7Ra signaling and p53 pathway during thymopoiesis and lymphomagenesis (86.8)
Bibliographic record
Abstract
Abstract Thymopoiesis in IL-7Rα-/- mice is blocked at the CD4-CD8- double negative stage with failures in T cell receptor (TCR) γand β rearrangement. Early studies suggest that unsuccessful TCRβ rearrangement in mice defective in DNA damage repairs, incurring DNA double-strand breaks, results in p53 activation. However, it remains unknown whether p53 plays a role in the thymopoietic defect in IL-7Rα-/- mice. In this study, we demonstrated that p53 is highly activated in the thymocytes of IL-7Rα-/- mice. Considering the importance of p53 activity during TCR rearrangement and the observation of incomplete rescue of thymopoiesis in IL-7Rα-/- mice by modulating activities of Bcl-2 family members, we hypothesize that p53 activation may also contribute to the impaired thymopoiesis in IL-7Rα-/- mice and that temporal suppression of p53 activity by the IL-7Rα signaling facilitates thymopoiesis. With the generated IL-7Rα-/-p53-/- (DKO) mice, we found that p53 inactivation in IL-7Rα-/- background resulted in a 30-fold increase in thymic cellularity with increased TCRβ? cells. Furthermore, DKO mice developed thymic lymphoma at a higher incidence and died significantly earlier than p53-/- mice. Our study is the first to illustrate a functional interplay between IL-7Rα signaling and p53 pathways in facilitating TCRβ+ T cell development during thymopoiesis and lymphomagenesis.
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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".