Senescence-associated secretory phenotype contributes to pathological angiogenesis in retinopathy
Bibliographic record
Abstract
Pathological angiogenesis is the hallmark of diseases such as cancer and retinopathies. Although tissue hypoxia and inflammation are recognized as central drivers of vessel growth, relatively little is known about the process that bridges the two. In a mouse model of ischemic retinopathy, we found that hypoxic regions of the retina showed only modest rates of apoptosis despite severely compromised metabolic supply. Using transcriptomic analysis and inducible loss-of-function genetics, we demonstrated that ischemic retinal cells instead engage the endoplasmic reticulum stress inositol-requiring enzyme 1α (IRE1α) pathway that, through its endoribonuclease activity, induces a state of senescence in which cells adopt a senescence-associated secretory phenotype (SASP). We also detected SASP-associated cytokines (plasminogen activator inhibitor 1, interleukin-6, interleukin-8, and vascular endothelial growth factor) in the vitreous humor of patients suffering from proliferative diabetic retinopathy. Therapeutic inhibition of the SASP through intravitreal delivery of metformin or interference with effectors of senescence (semaphorin 3A or IRE1α) in mice reduced destructive retinal neovascularization in vivo. We conclude that the SASP contributes to pathological vessel growth, with ischemic retinal cells becoming prematurely senescent and secreting inflammatory cytokines that drive paracrine senescence, exacerbate destructive angiogenesis, and hinder reparative vascular regeneration. Reversal of this process may be therapeutically beneficial.
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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 | 0.000 |
| 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".