Fibulin-2 is an extracellular matrix inhibitor of oligodendrocytes relevant to multiple sclerosis
Bibliographic record
Abstract
Impairment of oligodendrocytes and myelin contributes to neurological disorders including multiple sclerosis (MS), stroke, and Alzheimer's disease. Regeneration of myelin (remyelination) decreases the vulnerability of demyelinated axons, but this repair process commonly fails with disease progression. A contributor to inefficient remyelination is the altered extracellular matrix (ECM) in lesions, which remains to be better defined. We have identified fibulin-2 (FBLN2) as a highly upregulated ECM component in lesions of MS and stroke and in proteome databases of Alzheimer's disease and traumatic brain injury. Focusing on MS, the inhibitory role of FBLN2 was suggested in the experimental autoimmune encephalomyelitis (EAE) model, in which genetic FBLN2 deficiency improved behavioral recovery by promoting the maturation of oligodendrocytes and enhancing remyelination. Mechanistically, when oligodendrocyte progenitors were cultured in differentiation medium, FBLN2 impeded their maturation into oligodendrocytes by engaging the Notch pathway, leading to cell death. Adeno-associated virus deletion of FBLN2 in astrocytes improved oligodendrocyte numbers and functional recovery in EAE and generated new myelin profiles after lysolecithin-induced demyelination. Collectively, our findings implicate FBLN2 as a hitherto unrecognized injury-elevated ECM, and a therapeutic target, that impairs oligodendrocyte maturation and myelin repair.
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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) | 0.001 | 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".