NLRP3 inflammasome activation drives bystander cone photoreceptor cell death in a P23H rhodopsin model of retinal degeneration
Bibliographic record
Abstract
The molecular signaling leading to cell death in hereditary neurological diseases such as retinal degeneration is incompletely understood. Previous neuroprotective studies have focused on apoptotic pathways; however, incomplete suppression of cell death with apoptosis inhibitors suggests that other mechanisms are at play. Here, we report that different signaling pathways are activated in rod and cone photoreceptors in the P23H rhodopsin mutant rat, a model representing one of the commonest forms of retinal degeneration. Up-regulation of the RIP1/RIP3/DRP1 axis and markedly improved survival with necrostatin-1 treatment highlighted necroptosis as a major cell-death pathway in degenerating rod photoreceptors. Conversely, up-regulation of NLRP3 and caspase-1, expression of mature IL-1β and IL-18 and improved cell survival with N-acetylcysteine treatment suggested that inflammasome activation and pyroptosis was the major cause of cone cell death. This was confirmed by generation of the P23H mutation on an Nlrp3-deficient background, which preserved cone viability. Furthermore, Brilliant Blue G treatment inhibited inflammasome activation, indicating that the 'bystander cell death' phenomenon was mediated through the P2RX7 cell-surface receptor. Here, we identify a new pathway in cones for bystander cell death, a phenomenon important in development and disease in many biological systems. In other retinal degeneration models different cell-death pathways are activated, which suggests that the particular pathways that are triggered are to some extent genotype-specific. This also implies that neuroprotective strategies to limit retinal degeneration need to be customized; thus, different combinations of inhibitors will be needed to target the specific pathways in any given disease.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".