<scp>IL</scp>‐2 promotes the function of memory‐like autoregulatory <scp>CD</scp>8<sup>+</sup><scp>T</scp> cells but suppresses their development via <scp>F</scp>ox<scp>P</scp>3<sup>+</sup><scp>T</scp>reg cells
Bibliographic record
Abstract
IL-2 plays a critical role in both effector T-cell development and FoxP3(+) CD4(+) Treg-cell homeostasis. A reduction in Il2 transcription results in impaired FoxP3(+) CD4(+) Treg-cell recruitment and function, and accounts for the association between murine Il2 and type 1 diabetes (T1D). The progression of T1D elicits a disease-countering negative feedback regulatory loop that involves the differentiation of low-avidity autoreactive CD8(+) T cells into memory-like autoregulatory T cells in a CD4(+) Th-dependent manner. Since these auto-regulatory T cells express IL-2Rβ (CD122), we hypothesized that their development might also be regulated by IL-2. Here, we investigate the effects of differences in IL-2 expression on this autoregulatory subset. We show that decreased IL-2 production impairs the regulatory capacity of memory-like autoregulatory CD8(+) CD122(+) T cells. Surprisingly, we also find that a reduction in IL-2 production capacity increases memory autoregulatory CD8(+) T-cell formation indirectly, by decreasing the development and function of FoxP3(+) Treg cells in nonobese diabetic mice. These results illustrate a complex homeostatic interplay between IL-2, CD4(+) Th cells, FoxP3(+) CD4(+) Treg cells and autoregulatory CD8(+) T-cell memory whereby IL-2 controls the function of both Treg-cell subsets, but IL-2-potentiation of FoxP3(+) CD4(+) Treg-cell function results in the suppression of CD4(+) Th-cell activation and autoregulatory memory CD8(+) T-cell formation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 0.006 |
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; both teacher heads agree on what is shown here.
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".