ROS Detoxification and Proinflammatory Cytokines Are Linked by p38 MAPK Signaling in a Model of Mature Astrocyte Activation
Bibliographic record
Abstract
Astrocytes are the most abundant glial cell in the retinal nerve fiber layer (NFL) and optic nerve head (ONH), and perform essential roles in maintaining retinal ganglion cell (RGC) detoxification and homeostasis. Mature astrocytes are relatively quiescent, but rapidly undergo a phenotypic switch in response to insult, characterized by upregulation of intermediate filament proteins, loss of glutamate buffering, secretion of pro-inflammatory cytokines, and increased antioxidant production. These changes result in both positive and negative influences on RGCs. However, the mechanism regulating these responses is still unclear, and pharmacologic strategies to modulate select aspects of this switch have not been thoroughly explored. Here we describe a system for rapid culture of mature astrocytes from the adult rat retina that remain relatively quiescent, but respond robustly when challenged with oxidative damage, a key pathogenic stress associated with inner retinal injury. When primary astrocytes were exposed to reactive oxygen species (ROS) we consistently observed characteristic changes in activation markers, along with increased expression of detoxifying genes, and secretion of proinflammatory cytokines. This in vitro model was then used for a pilot chemical screen to target specific aspects of this switch. Increased activity of p38α and β Mitogen Activated Protein Kinases (MAPKs) were identified as a necessary signal regulating expression of MnSOD, and heme oxygenase 1 (HO-1), with consequent changes in ROS-mediated injury. Additionally, multiplex cytokine profiling detected p38 MAPK-dependent secretion of IL-6, MCP-1, and MIP-2α, which are proinflammatory signals recently implicated in damage to the inner retina. These data provide a mechanism to link increased oxidative stress to proinflammatory signaling by astrocytes, and establish this assay as a useful model to further dissect factors regulating the reactive switch.
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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) | 0.000 | 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".