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 wild-type, CD28-, ICOS-, or CD28/ICOS-double knockout mice on C57BL/6 background as T cell sources, we found that CD28 is essential, whereas ICOS is dispensable, for the development and homeostasis of Tregs. Furthermore, the differentiation of Tregs from naive CD4(+)CD25(-) T cells in vivo also depends on CD28. 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 gamma-chain cytokines, IL-4, IL-7, or IL-15, do not share such a role with IL-2. Although CD28 is required for the differentiation of Tregs from naive T cells, already generated Tregs do not depend on CD28 to exert their suppressive function. Our study reveals a new aspect of CD28 function in regulating T cell response.
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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) | 0.001 | 0.000 |
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".