Endogenous Regulatory T Cells Adhere in Inflamed Dermal Vessels via ICAM-1: Association with Regulation of Effector Leukocyte Adhesion
Bibliographic record
Abstract
Regulatory T cells (Tregs) must express appropriate skin-homing adhesion molecules to exert suppressive effects on dermal inflammation. However, the mechanisms whereby they control local inflammation remain unclear. In this study we used confocal intravital microscopy in wild-type and Foxp3-GFP mice to examine adhesion of effector T cells and Tregs in dermal venules. These experiments examined a two-challenge model of contact sensitivity (CS) in which Treg abundance in the skin progressively increases during the course of the response. Adhesion of CD4(+) T cells increased during CS, peaking 8-24 h after an initial hapten challenge, and within 4 h of a second challenge. At these time points, 40% of adherent CD4(+) T cells were Foxp3(+) Tregs. CD4(+) T cell adhesion was highly dependent on ICAM-1, and consistent with this finding, anti-ICAM-1 prevented Treg adhesion. Skin TGF-β levels were elevated in skin during both challenges, in parallel with Treg adhesion. In the two-challenge CS model, inhibition of ICAM-1 eliminated Treg adhesion, an effect associated with a significant increase in neutrophil adhesion. Similarly, total CD4(+) T cell depletion caused an increase in adhesion of CD8(+) T cells. Because Treg adhesion was restricted by both of these treatments, these experiments suggest that adherent Tregs can control adhesion of proinflammatory leukocytes in vivo. Moreover, the critical role of ICAM-1 in Treg adhesion provides a potential explanation for the exacerbation of inflammation reported in some studies of ICAM-1-deficient mice.
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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".