Role of ninjurin‐1 in the migration of myeloid cells to central nervous system inflammatory lesions
Bibliographic record
Abstract
OBJECTIVE: Blood-derived myeloid antigen-presenting cells (APCs) account for a significant proportion of the leukocytes found within lesions of multiple sclerosis (MS) and experimental allergic encephalomyelitis (EAE). These APCs along with activated microglia are thought to be pivotal in the initiation of the central nervous system (CNS)-targeted immune response in MS and EAE. However, the exact molecules that direct the migration of myeloid cells from the periphery across the blood-brain barrier (BBB) remain largely unknown. METHODS: We identified Ninjurin-1 in a proteomic screen of human BBB endothelial cells (ECs). We assessed the expression of Ninjurin-1 by BBB-ECs and immune cells, and we determined the role of Ninjurin-1 in immune cell migration to the CNS in vivo in EAE mice. RESULTS: Ninjurin-1 was found to be weakly expressed in the healthy human and mouse CNS but upregulated on BBB-ECs and on infiltrating APCs during the course of EAE and in active MS lesions. In human peripheral blood, Ninjurin-1 was predominantly expressed by monocytes, whereas it was barely detectable on T and B lymphocytes. Moreover, Ninjurin-1 neutralization specifically abrogated the adhesion and migration of human monocytes across BBB-ECs, without affecting lymphocyte recruitment. Finally, Ninjurin-1 blockade reduced clinical disease activity and histopathological indices of EAE and decreased infiltration of macrophages, dendritic cells, and APCs into the CNS. INTERPRETATION: Our study uncovers an important cell-specific role for Ninjurin-1 in the transmigration of inflammatory APCs across the BBB and further emphasizes the importance of myeloid cell recruitment during the development of neuroinflammatory lesions.
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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) | 0.001 | 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 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".