Schwann cells direct peripheral nerve regeneration through the Netrin-1 receptors, DCC and Unc5H2
Bibliographic record
Abstract
In the peripheral nervous system, Schwann cells (SCs) promote nerve regeneration by the secretion of trophic support molecules and the establishment of a supportive growth matrix. Elucidating factors that promote SC outgrowth following nerve injury is an important strategy for improving nerve regeneration. We identified the Netrin-1 receptors, Deleted in Colorectal Cancer (DCC) and Uncoordinated (Unc)5H2 as SC receptors that influence nerve regeneration by respectively promoting or inhibiting SC outgrowth. Significantly, we show both DCC and Unc5H2 receptors are distributed within SCs. In adult nerves, DCC is localized to the paranodes and Schmidt-Lantermann incisures of myelinating SCs, as well as along unmyelinated axons. After axotomy, DCC is prominently expressed in activated SCs at the regenerating nerve front. In contrast, Unc5H2 receptor is robustly distributed in myelinating SCs of the intact nerve and it is found at low levels in the SCs of the injury site. Local in vivo DCC siRNA mRNA knockdown at the growing tip of an injured nerve impaired SC activation and, in turn, significantly decreased axon regeneration. This forced DCC inhibition was associated with a dramatic reciprocal upregulation of Unc5H2 in the remaining SCs. Local Unc5H2 knockdown at the injury site, however, facilitated axon regrowth, indicating it has a role as an intrinsic brake to peripheral nerve regeneration. Our findings demonstrate that in adult peripheral nerves, SCs respond to DCC and Unc5H2 signaling, thereby promoting or hindering axon outgrowth and providing a novel mechanism for SC regulation during nerve regeneration.
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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".