IFN-γ Decreases CTL Generation by Limiting IL-2 Production: A Feedback Loop Controlling Effector Cell Production
Bibliographic record
Abstract
IFN-gamma is produced by cytotoxic T lymphocytes (CTL) but can also decrease CTL generation. We used IFN-gamma-R1-deficient (GRKO) and IFN-gamma-deficient (GKO) mice to study the effects of IFN-gamma in MLC on the generation of CTL activity and CTL number, IL-2 production and cell proliferation. CTL activity was increased in MLC when GRKO responders or GKO stimulators and responders were used, compared to wild-type (WT) MLC. The number of cells displaying the CTL phenotype (CD3+, CD8+, CD25+) was also increased, accompanied by increased IL-2 production and proliferation. Combinations of WT or GRKO CD4+ T cells with WT or GRKO CD8+ T cells as responders showed that IFN-gamma mostly affects CD4+ T cells to limit CTL generation. Intracellular staining indicated that IL-2 production was largely by CD4+ T cells. Moreover, addition of IL-2 to WT responders mimicked GKO CTL generation and activity, whereas neutralizing IL-2 decreased CTL activity in GRKO and WT responders. Thus IFN-gamma reduces CTL generation in alloimmune responses largely by limiting proliferation of IL-2 producing CD4+ T cells. This creates a feedback loop in which effectors produce IFN-gamma that limits IL-2 production which in turn limits CTL generation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".