Ephrinb1 and Ephrinb2 Are Associated with Interleukin-7 Receptor α and Retard Its Internalization from the Cell Surface
Bibliographic record
Abstract
IL-7 plays vital roles in thymocyte development, T cell homeostasis, and the survival of these cells. IL-7 receptor α (IL-7Rα) on thymocytes and T cells is rapidly internalized upon IL-7 ligation. Ephrins (Efns) are cell surface molecules and ligands of the largest receptor kinase family, Eph kinases. We discovered that T cell-specific double gene knock-out (dKO) of Efnb1 and Efnb2 in mice led to reduced IL-7Rα expression in thymocytes and T cells, and that IL-7Rα down-regulation was accelerated in dKO CD4 cells upon IL-7 treatment. On the other hand, Efnb1 and Efnb2 overexpression on T cell lymphoma EL4 cells retarded IL-7Rα down-regulation. dKO T cells manifested compromised STAT5 activation and homeostatic proliferation, an IL-7-dependent process. Fluorescence resonance energy transfer and immunoprecipitation demonstrated that Efnb1 and Efnb2 interacted physically with IL-7Rα. Such interaction likely retarded IL-7Rα internalization, as Efnb1 and Efnb2 were not internalized. Therefore, we revealed a novel function of Efnb1 and Efnb2 in stabilizing IL-7Rα expression at the post-translational level, and a previously unknown modus operandi of Efnbs in the regulation of expression of other vital cell surface receptors.
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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".