Dysregulation of the neuregulin‐1–<scp>E</scp>rb<scp>B</scp> network modulates endogenous oligodendrocyte differentiation and preservation after spinal cord injury
Bibliographic record
Abstract
Spinal cord injury (SCI) results in degeneration of oligodendrocytes that leads to demyelination and axonal dysfunction. Replacement of oligodendrocytes is impaired after SCI, owing to the improper endogenous differentiation and maturation of myelinating oligodendrocytes. Here, we report that SCI-induced dysregulation of neuregulin-1 (Nrg-1)-ErbB signaling may underlie the poor replacement of oligodendrocytes. Nrg-1 and its receptors, ErbB-2, ErbB-3, and ErbB-4, play essential roles in several aspects of oligodendrocyte development and physiology. In rats with SCI, we demonstrate that the Nrg-1 level is dramatically reduced at 1 day after injury, with no restoration at later time-points. Our characterisation shows that Nrg-1 is mainly expressed by neurons, axons and oligodendrocytes in the adult spinal cord, and the robust and lasting decrease in its level following SCI reflects the permanent loss of these cells. Neural precursor cells (NPCs) residing in the spinal cord ependyma express ErbB receptors, suggesting that they are responsive to Nrg-1 availability. In vitro, exogenous Nrg-1 enhanced the proliferation and differentiation of spinal NPCs into oligodendrocytes while reducing astrocyte differentiation. In rats with SCI, recombinant human Nrg-1β1 treatment resulted in a significant increase in the number of new oligodendrocytes and the preservation of existing ones after injury. Nrg-1β1 administration also enhanced axonal preservation and attenuated astrogliosis, tumor necrosis factor-α release and tissue degeneration after SCI. The positive effects of Nrg-1β1 treatment were reversed by inhibiting its receptors. Collectively, our data provide strong evidence to suggest an impact of Nrg-1-ErbB signaling on endogenous oligodendrocyte replacement and maintenance in the adult injured spinal cord, and its potential as a therapeutic target for SCI.
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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 | 0.001 |
| 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".