CD6-ALCAM signaling regulates multiple effector/memory T cell functions
Bibliographic record
Abstract
Abstract CD6 is a T-cell costimulatory receptor that has been implicated in the pathogenesis of multiple autoimmune and inflammatory diseases. Primarily expressed on CD4 T cells, CD6 promotes immune synapse formation, T-cell activation and T-cell migration via interaction with its ligand activated leukocyte cell adhesion molecule (ALCAM). While the contribution of CD6 to T cell activation has been well described, less is known regarding the role of CD6 on effector and memory T cells (Teff). Thus, to better characterize this, we examined phosphorylation signaling patterns during CD6 co-stimulation together with the impact of CD6 on differentiated Teff functions. Profiling of ~100 phosphorylation targets associated with T-cell receptor signaling revealed that CD6 co-stimulation on T cells activates factors in pathways involved in actin polymerization, motility, integrin activation and T-cell activation. Comparison of Tnaive cells vs. Teff demonstrated differing levels of phosphorylation in response to CD6 stimulation. Furthermore, CD6 signaling on Teff sustained phosphorylation of these pathways at later timepoints compared to CD28 stimulation. Blockade of the CD6 pathway, using the clinically tested anti-CD6 mAb itolizumab during re-stimulation of CD4 Teff cells in the presence of ALCAM, inhibited multiple effector functions including proliferation and changes in blast size. This effect was observed exclusively in the presence of ALCAM, indicating that the effect was specific to blockade of the CD6-ALCAM pathway. These findings demonstrate that the CD6-ALCAM pathway is a key regulator of effector T-cell functions and further support targeting this pathway to directly inhibit both naïve and effector T cell populations.
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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".