Differing Requirements for CCR4, E-Selectin, and α4β1 for the Migration of Memory CD4 and Activated T Cells to Dermal Inflammation
Bibliographic record
Abstract
CCR4 on T cells is suggested to mediate skin homing in mice. Our objective was to determine the interaction of CCR4, E-selectin ligand (ESL), and α(4)β(1) on memory and activated T cells in recruitment to dermal inflammation. mAbs to rat CCR4 were developed. CCR4 was on 5-21% of memory CD4 cells, and 20% were also ESL(+). Anti-TCR-activated CD4 and CD8 cells were 40-55% CCR4(+), and ∼75% of both CCR4(+) and CCR4(-) cells were ESL(+). CCR4(+) memory CD4 cells migrated 4- to 7-fold more to dermal inflammation induced by IFN-γ, TNF, TLR agonists, and delayed-type hypersensitivity than CCR4(-) cells. CCR4(+) activated CD4 cells migrated only 5-50% more than CCR4(-) cells to these sites. E-selectin blockade inhibited ∼60% of CCR4(+) activated CD4 cell migration but was less effective on memory cells where α(4)β(1) was more important. Anti-α(4)β(1) also inhibited CCR4(-) activated CD4 cells more than CCR4(+) cells. Anti-E-selectin reduced activated CD8 more than CD4 cell migration. These findings modify our understanding of CCR4, ESL, α(4)β(1), and dermal tropism. There is no strict relationship between CCR4 and ESL for skin homing of CD4 cells, because the activation state and inflammatory stimulus are critical determinants. Dermal homing memory CD4 cells express CCR4 and depend more on α(4)β(1) than ESL. Activated CD4 cells do not require CCR4, but CCR4(+) cells are more dependent on ESL than on α(4)β(1), and CCR4(-) cells preferentially use α(4)β(1). The differentiation from activated to memory CD4 cells increases the dependence on CCR4 for skin homing and decreases the requirement for ESL.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".