Evolution and divergence of teleost adrenergic receptors: why sometimes ‘the drugs don't work’ in fish
Bibliographic record
Abstract
Adrenaline and noradrenaline, released as hormones and/or neurotransmitters, exert diverse physiological functions in vertebrates, and teleost fishes are widely used as model organisms to study adrenergic regulation; however, such investigations often rely on receptor subtype-specific pharmacological agents (agonists and antagonists; see Glossary) developed and validated in mammals. Meanwhile, evolutionary (phylogenetic and comparative genomic) studies have begun to unravel the diversification of adrenergic receptors (ARs) and reveal that whole-genome duplications and pseudogenization events in fishes results in notable distinctions from mammals in their genomic repertoire of ARs, while lineage-specific gene losses within teleosts have generated significant interspecific variability. In this Review, we visit the evolutionary history of ARs (including α1-, α2- and β-ARs) to highlight the prominent interspecific differences in teleosts, as well as between teleosts and other vertebrates. We also show that structural modelling of teleost ARs predicts differences in ligand binding affinity compared with mammalian orthologs. To emphasize the difficulty of studying the roles of different AR subtypes in fish, we collate examples from the literature of fish ARs behaving atypically compared with standard mammalian pharmacology. Thereafter, we focus on specific case studies of the liver, heart and red blood cells, where our understanding of AR expression has benefited from combining pharmacological approaches with molecular genetics. Finally, we briefly discuss the ongoing advances in 'omics' technologies that, alongside classical pharmacology, will provide abundant opportunities to further explore adrenergic signalling in teleosts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".