Cutting Edge: CD47 Controls the In Vivo Proliferation and Homeostasis of Peripheral CD4+CD25+Foxp3+ Regulatory T Cells That Express CD103
Bibliographic record
Abstract
Peripheral CD103(+)Foxp3(+) regulatory T cells (Tregs) can develop both from conventional naive T cells upon cognate Ag delivery under tolerogenic conditions and from thymic-derived, expanded/differentiated natural Tregs. We here show that CD47 expression, a marker of self on hematopoietic cells, selectively regulated CD103(+)Foxp3(+) Treg homeostasis at the steady state. First, the proportion of effector/memory-like (CD44(high)CD62L(low)) CD103(+)Foxp3(+) Tregs rapidly augmented with age in CD47-deficient mice (CD47(-/-)) as compared with age-matched control littermates. Yet, the percentage of quiescent (CD44(low)CD62L(high)) CD103(-)Foxp3(+) Tregs remained stable. Second, the increased proliferation rate (BrdU incorporation) observed within the CD47(-/-)Foxp3(+) Treg subpopulation was restricted to those Tregs expressing CD103. Third, CD47(-/-) Tregs maintained a normal suppressive function in vitro and in vivo and their increased proportion in old mice led to a decline of Ag-specific T cell responses. Thus, sustained CD47 expression throughout life is critical to avoid an excessive expansion of CD103(+) Tregs that may overwhelmingly inhibit Ag-specific T cell responses.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".