The relative activity of CXCR3 and CCR5 ligands in T lymphocyte migration: concordant and disparate activities in vitro and in vivo
Bibliographic record
Abstract
In chronic inflammatory reactions such as rheumatoid arthritis and multiple sclerosis, T cells in the inflamed tissue express the chemokine receptors CXCR3 and CCR5, and the chemokine ligands (CCL) of these receptors are present in the inflammatory lesions. However, the contribution of these chemokines to T cell recruitment to sites of inflammation is unclear. In addition, the relative roles of the chemokines that bind CXCR3 (CXCL9, CXCL10, CXCL11) and CCR5 (CCL3, CCL4, CCL5) in this process are unknown. The in vitro chemotaxis and in vivo migration of antigen-activated T lymphoblasts and unactivated spleen T cells to chemokines were examined. T lymphoblasts migrated in vitro to CXCR3 ligands with a relative potency of CXCL10 > CXCL11 > CXCL9, but these cells demonstrated much less chemotaxis to the CCR5 ligands. In vivo, T lymphocytes were recruited in large numbers with rapid kinetics to skin sites injected with CXCL10 and CCL5 and less to CCL3, CCL4, CXCL9, and CXCL11. The combination of CCL5 with CXCL10 but not the other chemokines markedly increased recruitment. Coinjection of interferon-gamma, tumor necrosis factor alpha, and interleukin-1alpha to up-regulate endothelial cell adhesion molecule expression with CXCL10 or CCL5 induced an additive increase in lymphoblast migration. Thus, CXCR3 ligands are more chemotactic than CCR5 ligands in vitro; however, in vivo, CXCL10 and CCL5 have comparable T cell-recruiting activities to cutaneous sites and are more potent than the other CXCR3 and CCR5 chemokines. Therefore, in vitro chemotaxis induced by these chemokines is not necessarily predictive of their in vivo lymphocyte-recruiting activity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".