MCAM+ brain endothelial cells contribute to neuroinflammation by recruiting pathogenic CD4+ T lymphocytes
Bibliographic record
Abstract
The trafficking of autoreactive leucocytes across the blood-brain barrier endothelium is a hallmark of multiple sclerosis pathogenesis. Although the blood-brain barrier endothelium represents one of the main CNS borders to interact with the infiltrating leucocytes, its exact contribution to neuroinflammation remains understudied. Here, we show that Mcam identifies inflammatory brain endothelial cells with pro-migratory transcriptomic signature during experimental autoimmune encephalomyelitis. In addition, MCAM was preferentially upregulated on blood-brain barrier endothelial cells in multiple sclerosis lesions in situ and at experimental autoimmune encephalomyelitis disease onset by molecular MRI. In vitro and in vivo, we demonstrate that MCAM on blood-brain barrier endothelial cells contributes to experimental autoimmune encephalomyelitis development by promoting the cellular trafficking of TH1 and TH17 lymphocytes across the blood-brain barrier. Last, we showcase ST14 as an immune ligand to brain endothelial MCAM, enriched on CD4+ T lymphocytes that cross the blood-brain barrier in vitro, in vivo and in multiple sclerosis lesions as detected by flow cytometry on rapid autopsy derived brain tissue from multiple sclerosis patients. Collectively, our findings reveal that MCAM is at the centre of a pathological pathway used by brain endothelial cells to recruit pathogenic CD4+ T lymphocyte from circulation early during neuroinflammation. The therapeutic targeting of this mechanism is a promising avenue to treat multiple sclerosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".