Schwann cell reprogramming into repair cells increases miRNA-21 expression in exosomes promoting axonal growth
Bibliographic record
Abstract
Functional recovery after peripheral nerve damage is dependent on the reprogramming of differentiated Schwann cells (dSCs) into repair Schwann cells (rSCs), which promotes axonal regeneration and tissue homeostasis. Transition into a repair phenotype requires expression of c-Jun and Sox2, which transcriptionally mediates inhibition of the dSC program of myelination and activates a non-cell-autonomous repair program, characterized by the secretion of neuronal survival and regenerative molecules, formation of a cellular scaffold to guide regenerating axons and activation of an innate immune response. Moreover, rSCs release exosomes that are internalized by peripheral neurons, promoting axonal regeneration. Here, we demonstrate that reprogramming of Schwann cells (SCs) is accompanied by a shift in the capacity of their secreted exosomes to promote neurite growth, which is dependent on the expression of c-Jun (also known as Jun) and Sox2 by rSCs. Furthermore, increased expression of miRNA-21 is responsible for the pro-regenerative capacity of rSC exosomes, which is associated with PTEN downregulation and PI3-kinase activation in neurons. We propose that modification of exosomal cargo constitutes another important feature of the repair program of SCs, contributing to axonal regeneration and functional recovery after nerve injury.
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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".