Regulatory T Cells Dynamically Regulate Selectin Ligand Function during Multiple Challenge Contact Hypersensitivity
Bibliographic record
Abstract
Regulatory T cells (Tregs) play critical roles in restricting T cell-mediated inflammation. In the skin, this is dependent on expression of selectin ligands required for leukocyte rolling in dermal microvessels. However, whether there are differences in the molecules used by Tregs and proinflammatory T cells to undergo rolling in the skin remains unclear. In this study, we used spinning disk confocal microscopy in Foxp3-GFP mice to visualize rolling of endogenous Tregs in dermal postcapillary venules. Tregs underwent consistent but low-frequency rolling interactions under resting and inflamed conditions. At the early stage of the response, Treg adhesion was minimal. However, at the peak of inflammation, Tregs made up 40% of the adherent CD4(+) T cell population. In a multiple challenge model of contact hypersensitivity, rolling of Tregs and conventional CD4(+) T cells was mostly dependent on overlapping contributions of P- and E-selectin. However, after a second challenge, rolling of Tregs but not conventional CD4(+) T cells became P-selectin independent, and Tregs showed reduced capacity to bind P-selectin. Moreover, inhibition of E-selectin at this time point resulted in exacerbation of inflammation. These findings demonstrate that in this multiple challenge model of inflammation, Treg selectin binding capacity and the molecular basis of Treg rolling can be regulated dynamically.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".