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 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 | 0.001 |
| 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 | 0.000 |
| 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".