Early netrin‐1 expression impairs central nervous system remyelination
Bibliographic record
Abstract
OBJECTIVE: Chronically demyelinated multiple sclerosis (MS) lesions are frequently characterized by scarce undifferentiated oligodendrocyte progenitor cells (OPCs), suggesting the exhaustion of a local OPC pool followed by failure of recruitment and differentiation. Stimulating prompt OPC recruitment following demyelination could improve myelin repair by providing sufficient numbers of remyelinating cells during the repair-permissive period. Understanding mechanisms that determine this process may have important therapeutic implications. We therefore investigated the role of the guidance molecule netrin-1 in OPC recruitment and central nervous system (CNS) remyelination. METHODS: Netrin-1 expression was analyzed immunohistochemically in different types of MS lesions and in the murine lysolecithin model of demyelination. The influence of netrin-1 on CNS remyelination was examined using gain and loss of function experiments. RESULTS: We show that in MS lesions, astrocytes upregulate netrin-1 expression early during demyelination and netrin-1 receptors are expressed by OPCs. In contrast, in the efficiently repairing lysolecithin model of demyelination (astrocyte-free), netrin-1 expression is absent during early phases and detected concomitant with completion of OPC recruitment. In vitro migration assays demonstrated that netrin-1 is a chemorepellent for migrating adult OPCs. In mouse lesions, antibody-mediated disruption of netrin-1 function at the peak phase of recruitment increased OPC numbers. Conversely, lentiviral-mediated induction of netrin-1 expression prior to OPC recruitment reduced the number of cells recruited and impaired remyelination. INTERPRETATION: Our findings support the conclusion that netrin-1 expression within demyelinating MS plaques blocks OPC recruitment, which with repeated demyelinating episodes contributes to permanent remyelination failure.
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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 | 0.000 | 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) | 0.000 | 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".