85: CD28 Controls Differentiation of Regulatory T Cells From Naive CD4 T Cells
Bibliographic record
Abstract
CD28 is required for the development of regulatory T cells (Tregs, CD4+CD25+Foxp3+) in the thymus and also contributes to their survival and homeostasis in the periphery. We studied whether and how CD28 and ICOS control the differentiation of Tregs from naive T cells. By using WT, CD28-, ICOS- or CD28/ICOS-knockout mice on C57BL/6 background as T-cell sources, we found that CD28 is essential, while ICOS is dispensable, for the development and homeostasis of Tregs as well as for the generation of Tregs from naive CD4+CD25- T cells in vivo. The requirement of CD28 for Treg differentiation was mediated by IL-2, because neutralization of IL-2 with its specific mAb blocked Treg differentiation from wild-type CD4+CD25- T cells and addition of IL-2 restored Treg differentiation from CD28-/- T cells. Other common γ-chain cytokines, IL-4, IL-7 or IL-15, do not share such a role of IL-2. However, a strong CD28-signal promotes expansion of T effector cells (CD25+Foxp3-) while inhibiting differentiation of Tregs. Our study demonstrates that CD28 delicately controls the process of Treg differentiation and T effector cell expansion in the periphery, which provides the “fine-tuning” of the balance between immune activation and suppression.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".