Regulation of heart rate following genetic deletion of the ß1 adrenergic receptor in larval zebrafish
Bibliographic record
Abstract
Abstract Aim Although zebrafish are gaining popularity as biomedical models of cardiovascular disease, our understanding of their cardiac control mechanisms is fragmentary. Our goal was to clarify the controversial role of the ß1‐adrenergic receptor (AR) in the regulation of heart rate in zebrafish. Methods CRISPR‐Cas9 was used to delete the adrb1 gene in zebrafish allowing us to generate a stable adrb1 −/− line. Larval heart rates were measured during pharmacological protocols and with exposure to hypercapnia. Expression of the five zebrafish adrb genes were measured in larval zebrafish hearts using qPCR. Results Compared with genetically matched wild‐types ( adrb1 +/+ ), adrb1 −/− larvae exhibited ~20 beats min −1 lower heart rate, measured from 2 to 21 days post‐fertilization (dpf). Nevertheless, adrb1 −/− larvae exhibited preserved positive chronotropic responses to pharmacological treatment with AR agonists (adrenaline, noradrenaline, isoproterenol), which were blocked by propranolol (general ß‐AR antagonist). Regardless of genotype, larvae exhibited similar increases in heart rate in response to hypercapnia (1% CO 2 ) at 5 dpf, but tachycardia was blunted in adrb1 −/− larvae at 6 dpf. adrb1 gene expression was abolished in the hearts of adrb1 −/− larvae, confirming successful knockout. While gene expression of adrb2a and adrb3a was unchanged, adrb2b and adrb3b mRNA levels increased in adrb1 −/− larval hearts. Conclusion Despite adrb1 contributing to the setting of resting heart rate in larvae, it is not strictly essential for zebrafish, as we generated a viable and breeding adrb1 −/− line. The chronotropic effects of adrenergic stimulation persist in adrb1 −/− zebrafish, likely due to the upregulation of other ß‐AR subtypes.
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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) | 0.002 | 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".