Altered Innate-like T Cell Development in Vα14-Jα18 TCRα Transgenic Mice
Bibliographic record
Abstract
Abstract CD1d-restricted invariant NKT (iNKT) cells are innate-like T cells that respond to glycolipids, a class of Ags that are invisible to conventional T cells. iNKT cells develop in the thymus where they receive strong “agonist” TCR signals. During their ontogeny, iNKT cells differentiate into discrete iNKT1, iNKT2, and iNKT17 effector subsets akin to helper CD4 T cells. In this study, we found that transgenic (Tg) expression of the canonical Vα14-Jα18 TCRα-chain at the double-positive thymocyte stage led to premature iNKT cell development and a cell-intrinsic bias toward iNKT2 cells, due to increased TCR signaling upon selection. Consistent with the strong iNKT2 bias, innate memory CD8+ T cells were found in greater numbers in Vα14 Tg mice, whereas the prevalence of mucosa-associated invariant T cells was reduced. iNKT cells from Vα14 Tg mice were hyporesponsive to stimulation by their cognate Ag α-galactosylceramide. Finally, Vα14 Tg mice displayed increased B16F10 melanoma tumor growth compared with wild-type mice. This study reveals some of the limitations of Vα14 Tg mice and warrants the cautious interpretation of past and future findings using this mouse model.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".