CD8 <sup>+</sup> CD122 <sup>+</sup> CD49d <sup>low</sup> regulatory T cells maintain T-cell homeostasis by killing activated T cells via Fas/FasL-mediated cytotoxicity
Bibliographic record
Abstract
The Fas/FasL (CD95/CD178) system is required for immune regulation; however, it is unclear in which cells, when, and where Fas/FasL molecules act in the immune system. We found that CD8(+)CD122(+) cells, which are mostly composed of memory T cells in comparison with naïve cells in the CD8(+)CD122(-) population, were previously shown to include cells with regulatory activity and could be separated into CD49d(low) cells and CD49d(high) cells. We established in vitro and in vivo experimental systems to evaluate the regulatory activity of CD122(+) cells. Regulatory activity was observed in CD8(+)CD122(+)CD49d(low) but not in CD8(+)CD122(+)CD49d(high) cells, indicating that the regulatory cells in the CD8(+)CD122(+) population could be narrowed down to CD49d(low) cells. CD8(+)CD122(-) cells taken from lymphoproliferation (lpr) mice were resistant to regulation by normal CD122(+) Tregs. CD122(+) Tregs taken from generalized lymphoproliferative disease (gld) mice did not regulate wild-type CD8(+)CD122(-) cells, indicating that the regulation by CD122(+) Tregs is Fas/FasL-dependent. CD122(+) Tregs taken from IL-10-deficient mice could regulate CD8(+)CD122(-) cells as equally as wild-type CD122(+) Tregs both in vitro and in vivo. MHC class I-missing T cells were not regulated by CD122(+) Tregs in vitro. CD122(+) Tregs also regulated CD4(+) cells in a Fas/FasL-dependent manner in vitro. These results suggest an essential role of Fas/FasL as a terminal effector of the CD122(+) Tregs that kill activated T cells to maintain immune homeostasis.
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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) | 0.001 | 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) | 0.005 | 0.002 |
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".