Melanoma cell adhesion molecule–positive<scp>CD</scp>8<scp>T</scp>lymphocytes mediate central nervous system inflammation
Bibliographic record
Abstract
OBJECTIVE: Although Tc17 lymphocytes are enriched in the central nervous system (CNS) of multiple sclerosis (MS) subjects and of experimental autoimmune encephalomyelitis (EAE) animals, limited information is available about their recruitment into the CNS and their role in neuroinflammation. Identification of adhesion molecules used by autoaggressive CD8(+) T lymphocytes to enter the CNS would allow further characterization of this pathogenic subset and could provide new therapeutic targets in MS. We propose that melanoma cell adhesion molecule (MCAM) is a surface marker and adhesion molecule used by pathogenic CD8(+) T lymphocytes to access the CNS. METHODS: Frequency, phenotype, and function of MCAM(+) CD8(+) T lymphocytes was characterized using a combination of ex vivo, in vitro, in situ, and in vivo approaches in humans and mice, including healthy controls, MS subjects, and EAE animals. RESULTS: Herein, we report that MCAM is expressed by human effector CD8(+) T lymphocytes and it is strikingly upregulated during MS relapses. We further demonstrate that MCAM(+) CD8(+) T lymphocytes express more interleukin 17, interferon γ, granulocyte-macrophage colony-stimulating factor, and tumor necrosis factor than MCAM(-) lymphocytes, and exhibit an enhanced killing capacity toward oligodendrocytes. MCAM blockade restricts the transmigration of CD8(+) T lymphocytes across human blood-brain barrier endothelial cells in vitro, and blocking or depleting MCAM in vivo reduces chronic neurological deficits in active, transfer, and spontaneous progressive EAE models. INTERPRETATION: Our data demonstrate that MCAM identifies encephalitogenic CD8(+) T lymphocytes, suggesting that MCAM could represent a biomarker of MS disease activity and a valid target for the treatment of neuroinflammatory conditions.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".