Neuregulin-1 Fosters Supportive Interactions between Microglia and Neural Stem/Progenitor Cells
Bibliographic record
Abstract
Microglia play diverse roles in homeostasis and pathology of the central nervous system (CNS). Their response to injury or insult is critical for initiating neuroinflammation and tissue damage as well as resolution of inflammation and wound healing. Changes to the microenvironment of microglia appear to be a key determinant of their phenotype and their role in the endogenous repair process in the injured or diseased CNS. Our recent findings have identified a positive role for neuregulin-1 (Nrg-1) in regulating immune response in spinal cord injury and focal demyelinating lesions. We show that increasing the tissue availability of Nrg-1 after injury can promote endogenous repair by modulating neuroinflammation. In the present study, we sought to elucidate the specific role of Nrg-1 in regulating microglial activity and more importantly their influence on the behavior of neural stem/progenitor cells (NPCs). Using injury-relevant in vitro systems, we demonstrate that Nrg-1 attenuates the expression of proinflammatory mediators in activated microglia. Moreover, we provide novel evidence that availability of Nrg-1 can restore the otherwise suppressed phagocytic ability of proinflammatory microglia. Interestingly, the presence of Nrg-1 in the microenvironment of proinflammatory microglia mitigates their inhibitory effects on NPC proliferation. Nrg-1 treated proinflammatory microglia also augment mobilization of NPCs, while they had no influence on their suppressive effects on NPC differentiation. Mechanistically, we show that Nrg-1 enhances the interactions of proinflammatory microglia and NPCs, at least in part, through reduction of TNF- α expression in microglia. These findings provide new insights into the endogenous regulation of microglia-NPC interactions and identify new potential targets for optimizing this important crosstalk during the regenerative process after CNS injury and neuroinflammatory conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".