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Investigating Opioid‐induced Respiratory Depression and Analgesia Using Larval Zebrafish

2020· article· en· W3016752785 on OpenAlexaff
Shenhab Zaig, Carolina da Silveira Scarpellini, Gaspard Montandon

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsZebrafishOpioidRespiratory systemFentanylSerotonergicPharmacologyAnesthesiaMedicineDepression (economics)BiologyInternal medicineReceptorSerotoninGeneBiochemistry

Abstract

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Introduction Opioid drugs present the severe side‐effect of respiratory depression which can be lethal. In fact, every year, in North America, over 70,000 people die of opioid overdose. Our current understanding is limited due to a lack of simple and amenable animal models to study the effects of opioid drugs on the nervous system. We aim to understand respiratory depression and analgesia by opioid drugs using novel zebrafish models, allowing for high throughput drug screening and gene editing. Using these models, we will demonstrate whether respiratory depression can be reversed by serotonergic agonists, glutamatergic modulators and calcium channel activators. Methods We have established phenotype‐based approaches using in‐vivo zebrafish models of respiratory depression and analgesia by fentanyl, a clinically relevant opioid analgesic. To quantify respiratory depression, we measured respiratory activity by recording mandible movements in 12–14 day post‐fertilization larvae. To quantify analgesia, we measured the escape swimming response to nociceptive stimuli such as formalin and administered fentanyl to induce analgesia. To show that μ‐opioid receptors (MORs) are expressed in respiratory circuits in the brainstem, we used in‐situ hybridization for Oprm1 (mRNA of the MOR). Results In comparing respiratory sensitivity to opioids between zebrafish strains, we showed that Tübingen (TU) fish did not respond to fentanyl (1μM, P=0.4240 , n=9), whereas AB fish showed pronounced respiratory rate depression (54±7.7% decrease, P<0.001 , n=11). Zebrafish crossed between AB and TU did not show respiratory depression ( P=0.104 , n=7). In AB zebrafish, a dose‐dependent decrease in respiratory rate was observed, with 1μM fentanyl showing the most potent respiratory depression ( P=0.01 , n=11). The opioid receptor antagonist naloxone (5μM) or the selective MOR antagonist CTAP (4μM) reversed respiratory depression to baseline levels ( P=0.002 , n=8). Similarly, respiratory depression was reversed by the positive allosteric modulator of AMPA receptors CX614 (50μM, P=0.187 , n=10) or the serotonergic 5‐HT 4 agonist BIMU8 (10μM, P=0.024 , n=6). To test the role of N‐type calcium channels, we showed that nefiracetam, a calcium channel activator, reversed respiratory depression by fentanyl (57±19%, P=0.002 , n=9). Formalin (0.05%) significantly increased swimming velocity (320±313% of baseline, P=0.003 , n=24–30), which was reduced by fentanyl (3μM, 117±151%, n=10). The effect of fentanyl was blocked by naloxone (5μM, P=0.001 , n=16) and CTAP (4μM, P=0.029 , n=16). MOR mRNA was expressed in the medulla where respiratory circuits are located. Discussion Our models show that respiratory depression and analgesia by opioids can be mimicked in zebrafish larvae. The strain differences suggest a potential genetic protection against respiratory depression by opioids which can be uncovered using gene screening and knockout animals. Our novel zebrafish models are powerful models to investigate opioid‐induced respiratory depression and analgesia. Using them, we identified potential molecular targets to develop safe opioid pain therapies, such as calcium channels. Support or Funding Information St. Michael’s Hospital Foundation and the J.P. Bicknell Foundation Biomedical Grant

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.048
GPT teacher head0.312
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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