Differential TGF-β Signaling in Glial Subsets Underlies IL-6–Mediated Epileptogenesis in Mice
Bibliographic record
Abstract
TGF-β1 is a master cytokine in immune regulation, orchestrating both pro- and anti-inflammatory reactions. Recent studies show that whereas TGF-β1 induces a quiescent microglia phenotype, it plays a pathogenic role in the neurovascular unit and triggers neuronal hyperexcitability and epileptogenesis. In this study, we show that, in primary glial cultures, TGF-β signaling induces rapid upregulation of the cytokine IL-6 in astrocytes, but not in microglia, via enhanced expression, phosphorylation, and nuclear translocation of SMAD2/3. Electrophysiological recordings show that administration of IL-6 increases cortical excitability, culminating in epileptiform discharges in vitro and spontaneous seizures in C57BL/6 mice. Intracellular recordings from layer V pyramidal cells in neocortical slices obtained from IL-6 -: treated mice show that during epileptogenesis, the cells respond to repetitive orthodromic activation with prolonged after-depolarization with no apparent changes in intrinsic membrane properties. Notably, TGF-β1 -: induced IL-6 upregulation occurs in brains of FVB/N but not in brains of C57BL/6 mice. Overall, our data suggest that TGF-β signaling in the brain can cause astrocyte activation whereby IL-6 upregulation results in dysregulation of astrocyte -: neuronal interactions and neuronal hyperexcitability. Whereas IL-6 is epileptogenic in C57BL/6 mice, its upregulation by TGF-β1 is more profound in FVB/N mice characterized as a relatively more susceptible strain to seizure-induced cell death.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".