Neuregulin‐1 positively modulates glial response and improves neurological recovery following traumatic spinal cord injury
Bibliographic record
Abstract
Spinal cord injury (SCI) results in glial activation and neuroinflammation, which play pivotal roles in the secondary injury mechanisms with both pro- and antiregeneration effects. Presently, little is known about the endogenous molecular mechanisms that regulate glial functions in the injured spinal cord. We previously reported that the expression of neuregulin-1 (Nrg-1) is acutely and chronically declined following traumatic SCI. Here, we investigated the potential ramifications of Nrg-1 dysregulation on glial and immune cell reactivity following SCI. Using complementary in vitro approaches and a clinically-relevant model of severe compressive SCI in rats, we demonstrate that immediate delivery of Nrg-1 (500 ng/day) after injury enhances a neuroprotective phenotype in inflammatory cells associated with increased interleukin-10 and arginase-1 expression. We also found a decrease in proinflammatory factors including IL-1β, TNF-α, matrix metalloproteinases (MMP-2 and 9) and nitric oxide after injury. In addition, Nrg-1 modulates astrogliosis and scar formation by reducing inhibitory chondroitin sulfate proteoglycans after SCI. Mechanistically, Nrg-1 effects on activated glia are mediated through ErbB2 tyrosine phosphorylation in an ErbB2/3 heterodimer complex. Furthermore, Nrg-1 exerts its effects through downregulation of MyD88, a downstream adaptor of Toll-like receptors, and increased phosphorylation of Erk1/2 and STAT3. Nrg-1 treatment with the therapeutic dosage of 1.5 μg/day significantly improves tissue preservation and functional recovery following SCI. Our findings for the first time provide novel insights into the role and mechanisms of Nrg-1 in acute SCI and suggest a positive immunomodulatory role for Nrg-1 that can harness the beneficial properties of activated glia and inflammatory cells in recovery following 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) | 0.001 | 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".