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Record W2174106028 · doi:10.18192/uojm.v5i2.1413

Rotenone Neurotoxicity Causes Dopamine Neuron Loss in Zebrafish

2015· article· en· W2174106028 on OpenAlexaffvenue
Simon Martel, Jonathan Keow, Marc Ekker

Bibliographic record

VenueUniversity of Ottawa Journal of Medicine · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRotenoneNeurotoxicityDopamineZebrafishDopamine transporterOxidative stressBiologyNeuronCell biologyMolecular biologyNeuroscienceChemistryToxicityEndocrinologyInternal medicineDopaminergicBiochemistryMedicineMitochondrionGene

Abstract

fetched live from OpenAlex

ABSTRACT:Objectives: We sought to determine whether rotenone, a commonly used pesticide, exhibits neurotoxicity in zebrafish by causing dopamine neuron loss through rotenone-induced oxidative damage. Methods: We exposed transgenic zebrafish embryos expressing green fluorescent protein under the control of the cis-regulatory elements of dopamine transporter (dat) to rotenone to determine the neurotoxic effects of rotenone on dopamine neuron abundance and pattern distribution, as well as the presence of apoptotic markers. The oxidative stress potential of rotenone on embryos was assessed using a live MitoSOX Red assay, and behavioural testing on adult zebrafish was assessed using video recordings of midline crossing events. Results: Zebrafish embryos treated with rotenone displayed a 50% reduction in dopamine neurons in the ventral diencephalon when exposed to 30µM rotenone (n=6, p<0.001), and rotenone-exposed zebrafish raised to adulthood demonstrate an anxiety-like behaviour (n=5, p<0.01). Furthermore embryos exposed to rotenone also demonstrated a logarithmic increase in markers of oxidative damage (n=3, p<0.001) and apoptotic activity in their diencephalic neurons. Conclusions: These results show that rotenone can induce dopamine neuron loss in zebrafish, providing a useful model for studying the environmental causes of Parkinson’s disease. RÉSUMÉ:Objectif: Nous cherchons à déterminer si la roténone, un élément commun dans les pesticides, démontre de la neurotoxicité dans les poissons-zèbres en causant une perte de dopamine dans leurs neurones à travers le dommage oxydatif induit par la roténone. Méthode: Nous avons exposé des embryons de poissons-zèbres transgéniques qui expriment la protéine fluorescente verte sous le contrôle d’éléments cis-régulateurs des transporteurs sélectifs de dopamine (dat) à la roténone pour déterminer les effets neurotoxiques de ce dernier sur les niveaux dopaminergiques dans leurs neurones. De plus, nous avons évalué la présence de marqueurs apoptotiques. Le stress oxydatif potentiel de la roténone sur les embryons a été analysé par le « live MitoSOX Red assay » et les tests comportementaux sur les poissons-zèbres adultes furent analysés en utilisant des enregistrements vidéo. Résultats: Les embryons de poissons-zèbres qui ont été traités avec la roténone ont démontré une réduction de dopamine de 50% dans les neurones localisés dans le diencéphale ventral, quand exposés à 30µM de roténone (n=6, p<0.001). Ils ont également illustré une augmentation logarithmique dans les marqueurs de dommage oxydatif (n=3, p<0.001) et une activité apoptotique dans les neurones du diencéphale. Les poissons-zèbres exposés à de la roténone qui ont atteint l’âge adulte ont démontré des comportements d’anxiété (n=5, p<0.01). Conclusion: Les résultats démontrent que la roténone peut induire une perte dopaminergique dans les neurones des poissons-zèbres. Ces résultats s’avèrent utiles pour étudier davantage les causes environnementales reliées à la maladie de Parkinson.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.020
GPT teacher head0.267
Teacher spread0.247 · 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 designObservational
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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Citations10
Published2015
Admission routes2
Has abstractyes

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