Granzyme B degrades extracellular matrix and promotes inflammation and choroidal neovascularization
Bibliographic record
Abstract
Age-related macular degeneration (AMD) is a common retinal neurodegenerative disease among the elderly. Neovascular AMD (nAMD), a leading cause of AMD-related blindness, involves choroidal neovascularization (CNV), which can be suppressed by anti-angiogenic treatments. However, current CNV treatments do not work in all nAMD patients. Here we investigate a novel target for AMD. Granzyme B (GzmB) is a serine protease that promotes aging, chronic inflammation and vascular permeability through the degradation of the extracellular matrix (ECM) and tight junctions. Extracellular GzmB is increased in retina pigment epithelium (RPE) and mast cells in the choroid of the healthy aging outer retina. It is further increased in donor eyes exhibiting features of nAMD and CNV. Here, we show in RPE-choroidal explant cultures that exogenous GzmB degrades the RPE-choroid ECM, promotes retinal/choroidal inflammation and angiogenesis while diminishing anti-angiogenic factor, thrombospondin-1 (TSP-1). The pharmacological inhibition of either GzmB or mast-cell degranulation significantly reduces choroidal angiogenesis. In line with our in vitro data, GzmB-deficiency reduces the extent of laser-induced CNV lesions and the age-related deterioration of electroretinogram (ERG) responses in mice. These findings suggest that targeting GzmB, a serine protease with no known endogenous inhibitors, may be a potential novel therapeutic approach to suppress CNV in nAMD.
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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".