CD73 controls ocular adenosine levels and protects retina from light-induced phototoxicity
Bibliographic record
Abstract
Abstract ATP and adenosine have emerged as important signaling molecules involved in vascular remodeling, retinal functioning and neurovascular coupling in the mammalian eye. However, little is known about the regulatory mechanisms of purinergic signaling in the eye. Here, we used three-dimensional multiplexed imaging, in situ enzyme histochemistry, flow cytometric analysis, and single cell transcriptomics to characterize the whole pattern of purine metabolism in mouse and human eyes. This study identified ecto-nucleoside triphosphate diphosphohydrolase-1 (NTPDase1/CD39), NTPDase2, and ecto-5′-nucleotidase/CD73 as major ocular ecto-nucleotidases, which are selectively expressed in the photoreceptor layer (CD73), optic nerve head, retinal vasculature and microglia (CD39), as well as in neuronal processes and cornea (CD39, NTPDase2). Specifically, microglial cells can create a spatially arranged network in the retinal parenchyma by extending and retracting their branched CD39 high /CD73 low processes and forming local “purinergic junctions” with CD39 low /CD73 − neuronal cell bodies and CD39 high /CD73 − retinal blood vessels. The relevance of the CD73–adenosine pathway was confirmed by flash electroretinography showing that pharmacological inhibition of adenosine production by injection of highly selective CD73 inhibitor PSB-12489 in the vitreous cavity of dark-adapted mouse eyes rendered the animals hypersensitive to prolonged bright light, manifested as decreased a-wave and b-wave amplitudes. The impaired electrical responses of retinal cells in PSB-12489-treated mice were not accompanied by decrease in total thickness of the retina or death of photoreceptors and retinal ganglion cells. Our study thus defines ocular adenosine metabolism as a complex and spatially integrated network and further characterizes the critical role of CD73 in maintaining the functional activity of retinal cells.
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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".