Respiratory depression and analgesia by opioid drugs in freely-behaving larval zebrafish
An opioid epidemic is spreading in North America with millions of opioid overdoses annually. Opioid drugs, like fentanyl, target the mu opioid receptor system and induce potentially lethal respiratory depression. The challenge in opioid research is to find a safe pain therapy with analgesic properties but no respiratory depression. Current discoveries are limited by lack of amenable animal models to screen candidate drugs. Zebrafish (Danio rerio) is an emerging animal model with high reproduction and fast development, which shares remarkable similarity in their physiology and genome to mammals. However, it is unknown whether zebrafish possesses similar opioid system, respiratory and analgesic responses to opioids than mammals. In freely-behaving larval zebrafish, fentanyl depresses the rate of respiratory mandible movements and induces analgesia, effects reversed by mu-opioid receptor antagonists. Zebrafish presents evolutionary conserved mechanisms of action of opioid drugs, also found in mammals, and constitute amenable models for phenotype-based drug discovery.
All data generated during this study are included in the manuscript. Source files are available.
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St. Michael's Hospital Foundation (RIC)
- Gaspard Montandon
J. P. Bickel Foundation (Medical Grant)
- Gaspard Montandon
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Animal experimentation: The protocol was approved by the Animal Care Committee of St. Michael's Hospital. Protocol: ACC-811
- Allan Basbaum, University of California San Francisco, United States
- Received: September 23, 2020
- Accepted: March 11, 2021
- Accepted Manuscript published: March 15, 2021 (version 1)
- Version of Record published: April 21, 2021 (version 2)
© 2021, Zaig et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Key Technological Projects of Guangdong Province grant 2018B030335001.
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