The Ligands of CXC Chemokine Receptor 3, I-TAC, Mig, and IP10, Are Natural Antagonists for CCR3
Bibliographic record
Abstract
Th1 and Th2 lymphocytes express a different repertoire of chemokine receptors (CCRs). CXCR3, the receptor for I-TAC (interferon-inducible T cell alpha-chemoattractant), Mig (monokine induced by gamma-interferon), and IP10 (interferon-inducible protein 10), is expressed preferentially on Th1 cells, whereas CCR3, the receptor for eotaxin and several other CC chemokines, is characteristic of Th2 cells. While studying responses that are mediated by these two receptors, we found that the agonists for CXCR3 act as antagonists for CCR3. I-TAC, Mig, and IP10 compete for the binding of eotaxin to CCR3-bearing cells and inhibit migration and Ca(2+) changes induced in such cells by stimulation with eotaxin, eotaxin-2, MCP-2 (monocyte chemottractant protein-2), MCP-3, MCP-4, and RANTES (regulated on activation normal T cell expressed and secreted). A hybrid chemokine generated by substituting the first eight NH(2)-terminal residues of eotaxin with those of I-TAC bound CCR3 with higher affinity than eotaxin or I-TAC (3- and 10-fold, respectively). The hybrid was 5-fold more potent than I-TAC as an inhibitor of eotaxin activity and was effective at concentrations as low as 5 nm. None of the antagonists described induced the internalization of CCR3, indicating that they lack agonistic effects and thus qualify as pure antagonists. These results suggest that chemokines that attract Th1 cells via CXCR3 can concomitantly block the migration of Th2 cells in response to CCR3 ligands, thus enhancing the polarization of T cell recruitment.
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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 | 0.000 | 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 | 0.000 | 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".