Full-Length and Fragmented Netrin-1 in Multiple Sclerosis Plaques Are Inhibitors of Oligodendrocyte Precursor Cell Migration
Bibliographic record
Abstract
Oligodendrocytes exhibit a limited capacity to remyelinate in multiple sclerosis. Factors present in multiple sclerosis lesions are thought to inhibit oligodendrocyte precursor cell migration, limiting their recruitment to axons requiring remyelination; however, few inhibitors have been identified. A candidate inhibitor is netrin-1, a secreted protein that repels migrating oligodendrocyte precursor cells during neural development and is expressed by myelinating oligodendrocytes in the mature rodent central nervous system. Herein, we examined the distribution of netrin-1 in adult human white matter and multiple sclerosis lesions. We detected full-length netrin-1 protein and shorter netrin-1 fragments in samples of normal white matter and of multiple sclerosis lesions from adult human brain. We demonstrate that peptides corresponding to amino terminal domains VI and V of netrin-1 repel migrating oligodendrocyte precursor cells, but lack the chemoattractant activity of full-length netrin-1. Furthermore, recombinant domains VI-V of netrin-1 disrupt the chemoattractant activity of full-length netrin-1, consistent with a competitive mechanism of action. These findings indicate that full-length and fragmented forms of netrin-1, found in multiple sclerosis lesions, have the capacity to inhibit oligodendrocyte precursor migration, identifying netrin-1 as a potential target for therapies that promote remyelination.
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 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".