Pharmacologic inhibition of reactive gliosis blocks TNF-α-mediated neuronal apoptosis
Bibliographic record
Abstract
Reactive gliosis is an early pathological feature common to most neurodegenerative diseases, yet its regulation and impact remain poorly understood. Normally astrocytes maintain a critical homeostatic balance. After stress or injury they undergo rapid parainflammatory activation, characterized by hypertrophy, and increased polymerization of type III intermediate filaments (IFs), particularly glial fibrillary acidic protein and vimentin. However, the consequences of IF dynamics in the adult CNS remains unclear, and no pharmacologic tools have been available to target this mechanism in vivo. The mammalian retina is an accessible model to study the regulation of astrocyte stress responses, and their influence on retinal neuronal homeostasis. In particular, our work and others have implicated p38 mitogen-activated protein kinase (MAPK) signaling as a key regulator of glutamate recycling, antioxidant activity and cytokine secretion by astrocytes and related Müller glia, with potent influences on neighboring neurons. Here we report experiments with the small molecule inhibitor, withaferin A (WFA), to specifically block type III IF dynamics in vivo. WFA was administered in a model of metabolic retinal injury induced by kainic acid, and in combination with a recent model of debridement-induced astrocyte reactivity. We show that WFA specifically targets IFs and reduces astrocyte and Müller glial reactivity in vivo. Inhibition of glial IF polymerization blocked p38 MAPK-dependent secretion of TNF-α, resulting in markedly reduced neuronal apoptosis. To our knowledge this is the first study to demonstrate that pharmacologic inhibition of IF dynamics in reactive glia protects neurons in vivo.
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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 | 0.000 |
| 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".