Betacellulin regulates schwann cell proliferation and myelin formation in the injured mouse peripheral nerve
Bibliographic record
Abstract
When a nerve fiber is cut or crushed, the axon segment that is separated from the soma degenerates distal from the injury in a process termed Wallerian degeneration (WD). C57BL/6OlaHsd‐WldS (WldS) mutant mice exhibit significant delays in WD. This results in considerably delayed Schwann cell and macrophage responses and thus in impaired nerve regenerations. In our previous work, thousands of genes were screened by DNA microarrays and over 700 transcripts were found to be differentially expressed in the injured sciatic nerve of WldS compared with wild‐type (WT) mice. One of these transcripts, betacellulin (Btc), was selected for further analysis since it has yet to be characterized in the nervous system, despite being known as a ligand of the ErbB receptor family. We show that Btc mRNA is strongly upregulated in immature and dedifferentiated Sox2+ Schwann cells located in the sciatic nerve distal stump of WT mice, but not WldS mutants. Transgenic mice ubiquitously overexpressing Btc (Tg‐Btc) have increased numbers of Schmidt‐Lantermann incisures compared with WT mice, as revealed by Coherent anti‐Stokes Raman scattering (CARS). Tg‐Btc mice also have faster nerve conduction velocity. Finally, we found that deficiency in Btc reduces the proliferation of myelinating Schwann cells after sciatic nerve injury, while Btc overexpression induces Schwann cell proliferation and improves recovery of locomotor function. Taken together, these results suggest a novel regulatory role of Btc in axon‐Schwann cell interactions involved in myelin formation and nerve repair. GLIA 2017 GLIA 2017;65:657–669
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 | 0.001 | 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".