Differential Roles of Cytokine Receptors in the Development of Epidermal γδ T Cells
Bibliographic record
Abstract
IL-7 and IL-15 play important roles in gammadelta T cell development. These receptors transmit proliferation and/or survival signals in gammadelta T cells. In addition, the IL-7R promotes recombination and transcription in the TCR gamma locus. To clarify the role of the cytokine receptors in the development of epidermal gammadelta T cells, we introduced a Vgamma3/Vdelta1 TCR transgene, derived from Thy-1+ dendritic epidermal T cells (DETC), into IL-7Ralpha-deficient mice, and we found that they partly rescued gammadelta T cells in the adult thymus but not in the spleen. Introduction of an additional Bcl-2 transgene had a minimal effect on gammadelta T cells in the adult thymus of these mice. In contrast to the adult thymus, the introduction of the Vgamma3/Vdelta1 TCR transgene into IL-7Ralpha-/- mice completely restored Vgamma3+ T cells in the fetal thymus and DETC in the adult skin. On the contrary, the same Vgamma3/Vdelta1 TCR transgene failed to rescue DETC in the skin of IL-2Rbeta-deficient mice, even with the additional Bcl-2 transgene. These results suggest that the IL-2/IL-15R, rather than the IL-7R, plays an essential role in proliferation and survival of DETC in the fetal thymus and the skin. In contrast, the IL-7R is probably essential in the induction of V-J recombination of TCRgamma genes. Thus, this study proves that IL-7R and IL-2/IL-15R serve differential functions in epidermal gammadelta T cell development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".