Erbin regulates NRG1 signaling and myelination
Bibliographic record
Abstract
Neuregulin 1 (NRG1) plays a critical role in myelination. However, little is known about regulatory mechanisms of NRG1 signaling. We show here that Erbin, a protein that contains leucine-rich repeats (LRR) and a PSD95-Dlg-Zol (PDZ) domain and that interacts specifically with ErbB2, is necessary for NRG1 signaling and myelination of peripheral nervous system (PNS). In Erbin null mice, myelinated axons were hypomyelinated with reduced expression of P0, a marker of mature myelinating Schwann cells (SCs), whereas unmyelinated axons were aberrantly ensheathed in Remak bundles, with increased numbers of axons in the bundles and in pockets. The morphological deficits were associated with decreased nerve conduction velocity and increased sensory threshold to mechanistic stimulation. These phenotypes were duplicated in erbin(DeltaC/DeltaC) mice, in which Erbin lost the PDZ domain to interact with ErbB2. Moreover, ErbB2 was reduced at protein levels in both Erbin mutant sciatic nerves, and ErbB2 became unstable and NRG1 signaling compromised when Erbin expression was suppressed. These observations indicate a critical role of Erbin in myelination and identify a regulatory mechanism of NRG1 signaling. Our results suggest that Erbin, via the PDZ domain, binds to and stabilizes ErbB2, which is necessary for NRG1 signaling that has been implicated in tumorigenesis, heart development, and neural function.
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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".