CCR4 and CXCR3 play different roles in the migration of T cells to inflammation in skin, arthritic joints, and lymph nodes
Bibliographic record
Abstract
CCR4 and CXCR3 are expressed on several T-cell subsets in inflamed tissues, yet their role in tissue-specific recruitment is unclear. We examined the contributions of CCR4 and CXCR3 to T-cell recruitment into inflamed joints in collagen-induced arthritis, antigen-draining lymph nodes (LNs) and dermal inflammatory sites (poly I:C, LPS, concanavalin A, and delayed type hypersensitivity), using labeled activated T cells from CXCR3(-/-), CCR4(-/-), and WT mice. Both CXCR3 and CCR4 deficiency reduced the development of arthritis, but did not affect Th1-cell recruitment to the inflamed joints. Accumulation in inflamed LNs was highly CXCR3 dependent. In contrast, CCR4-deficient Th1 cells had an increased accumulation in these LNs. Migration to all four dermal inflammatory sites by activated Th1 and T cytotoxic cells and memory CD4(+) T cells was partially CXCR3-dependent, but Treg-cell migration was independent of CXCR3. The subset of cells expressing CCR4 has skin-migrating properties, but CCR4 itself is not required for the migration. Thus, migration into these inflamed tissues is CCR4-independent, and partially dependent on CXCR3, except for Treg cells, which require neither receptor. CCR4 may therefore affect retention of T cells in different tissues rather than trafficking out of the blood.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".