Adenosine A<sub>2a</sub> receptor‐mediated inhibition of rod opsin mRNA expression in tiger salamander
Bibliographic record
Abstract
The neuromodulator adenosine mediates dark-adaptive changes in retinal photoreceptors through A(2a) receptors. In cold-blooded vertebrates, opsin mRNA expression is lower at night than during the day. In the present study, we tested whether adenosine could inhibit opsin mRNA expression in cultured rod cells and if endogenous adenosine acts to suppress opsin mRNA in the intact retina at night. Semi-quantitative in situ hybridization showed that treatment with 100 nm of the A(2a)/A(2b) agonist N(6)-[2-(3,5-dimethoxyphenyl)-2-(2-methylphenyl)-ethyl]adenosine (DPMA) reduced opsin mRNA 41% in cultured rod cells. The effect of DPMA was blocked by 10 microm of the A(2a) antagonist 8-(3-chlorostyryl)caffeine (CSC) but not by 10 microm of the A(2b) antagonist alloxazine. One micromolar adenosine alone had no effect on opsin mRNA. However, in the presence of the adenosine deaminase inhibitor erythro-9-(2-hydroxy-3-nonyl)adenine hydrochloride (EHNA), 1 microm adenosine reduced opsin mRNA 61%. EHNA alone reduced opsin mRNA by 26%. Consistent with an A(2a) receptor mechanism, 100 nm forskolin (adenylate cyclase agonist) decreased opsin mRNA 34%. Finally, northern blots showed that intravitreal injection of 10 microm CSC at night increased opsin I mRNA 38%. Thus, endogenous adenosine suppresses rod opsin I mRNA expression at night; in vitro results indicate this reduction occurs through A(2a)-like receptor binding and stimulation of adenylate cyclase activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".