The CD6-ALCAM pathway selectively modulates pathogenic T cell migration
Bibliographic record
Abstract
Abstract CD6 is a costimulatory receptor on T cells that, through binding to its ligand ALCAM, modulates both the activity and trafficking of effector T cells (Teff). ALCAM has been seen to be overexpressed on various tissues during inflammation and is associated with increased infiltration of pathogenic CD6high CD4+ T cells in patients with autoimmune and inflammatory diseases. However, the role of the CD6-ALCAM pathway in the chemotaxis of pathogenic T cells into inflamed tissues is not yet clear. Here, we investigate the contribution of CD6 in mediating chemokine-induced migration of Teff cells through the endothelial barrier by using itolizumab, a humanized anti-CD6 monoclonal antibody that inhibits the CD6-ALCAM interaction and reduces CD6 levels on T cells in the presence of monocytes. The migration of Teff cells from PBMCs or in vitro polarized TH17 cells expressing high levels of CD6, was tested under static conditions using a Boyden chamber assay incorporating human umbilical vein endothelial cells (HUVEC) which express high levels of ALCAM. Analysis of migrating CD4+ T cells from total PBMCs showed that cells expressing higher levels of CD6 preferentially migrated through the HUVEC layer in response to CXCL12. Blocking the CD6-ALCAM pathway reduces the migration of CD4+ CCR7− CD45RA− TEM cells by ~60%. Furthermore, itolizumab treatment reduced the migration of pathogenic TH17 when co-cultured with monocytes. This data demonstrates that the CD6-ALCAM pathway contributes to the chemokine-driven migration of Teff cells into inflamed organs through the endothelial layers. Consequently, blockade of this pathway will not only inhibit the activity of Teff but may also selectively affect their infiltration into inflamed organs.
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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 | 0.000 |
| 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 | 0.001 |
| 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".