Microglia and Interleukin-1β in Ischemic Retinopathy Elicit Microvascular Degeneration Through Neuronal Semaphorin-3A
Bibliographic record
Abstract
OBJECTIVE: Proinflammatory cytokines contribute to the development of retinal vasculopathies. However, the role of these factors and the mechanisms by which they elicit their effects in retina are not known. We investigated whether activated microglia during early stages of ischemic retinopathy produces excessive interleukin-1β (IL-1β), which elicits retinal microvascular degeneration not directly but rather by triggering the release of the proapoptotic/repulsive factor semaphorin-3A (Sema3A) from neurons. APPROACH AND RESULTS: Sprague Dawley rats subjected to retinopathy induced by hyperoxia (80% O2; O2-induced retinopathy) exhibited retinal vaso-obliteration associated with microglial activation, NLRP3 upregulation, and IL-1β and Sema3A release; IL-1β was mostly generated by microglia. Intraperitoneal administration of IL-1 receptor antagonists (Kineret, or rytvela [101.10]) decreased these effects and enhanced retinal revascularization; knockdown of Sema3A resulted in microvessel preservation and, conversely, administration of IL-1β caused vaso-obliteration. In vitro, IL-1β derived from activated primary microglial cells, cultured under hyperoxia, stimulated the release of Sema3A in retinal ganglion cells-5, which in turn induced apoptosis of microvascular endothelium; antagonism of IL-1 receptor decreased microglial activation and on retinal ganglion cells-5 abolished the release of Sema3A inhibiting ensuing endothelial cell apoptosis. IL-1β was not directly cytotoxic to endothelial cells. CONCLUSIONS: Our findings suggest that in the early stages of O2-induced retinopathy, retinal microglia are activated to produce IL-1β, which sustains the activation of microglia and induces microvascular injury through the release of Sema3A from adjacent neurons. Interference with IL-1 receptor or Sema3A actions preserves the microvascular bed in ischemic retinopathies and, consequently, decreases ensued pathological preretinal neovascularization.
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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) | 0.000 | 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 | 0.000 |
| 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".