Melanoma cell adhesion molecule identifies encephalitogenic T lymphocytes and promotes their recruitment to the central nervous system
Bibliographic record
Abstract
In multiple sclerosis, encephalitogenic CD4(+) lymphocytes require adhesion molecules to accumulate into central nervous system inflammatory lesions. Using proteomic techniques, we identified expression of melanoma cell adhesion molecule (MCAM) on a subset of human effector memory CD4(+) lymphocytes and on human blood-brain barrier endothelium. Herein, we demonstrate that MCAM is a stable surface marker that refines the identification of interleukin 17(+), interleukin 22(+), RAR-related orphan receptor γ and interleukin 23 receptor(+) cells within the CD161(+)CCR6(+) subset of memory CD4(+) lymphocytes. We also show that MCAM(+) lymphocytes express significantly more granulocyte/macrophage colony stimulating factor and granzyme B than MCAM(-) lymphocytes. Furthermore, the proportion of MCAM(+) CD4(+) lymphocytes is significantly increased in the blood and in the central nervous system of patients with multiple sclerosis and experimental autoimmune encephalomyelitis animals compared with healthy controls or other neurological diseases, and MCAM expression is upregulated at the blood-brain barrier within inflammatory lesions. Moreover, blockade of MCAM or depletion of MCAM(+) CD4(+) T lymphocytes both restrict the migration of T(H)17 lymphocytes across blood-brain barrier endothelial cells and decrease the severity of experimental autoimmune encephalomyelitis. Our findings indicate that MCAM could serve as a potential biomarker for multiple sclerosis and represents a valuable target for the treatment of neuroinflammatory conditions.
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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 | 0.000 |
| 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 | 0.000 |
| 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".