Dataset Information

Conditioned place avoidance of zebrafish (Danio rerio) to three chemicals used for euthanasia and anaesthesia.

ABSTRACT: Zebrafish are becoming one of the most used vertebrates in developmental and biomedical research. Fish are commonly killed at the end of an experiment with an overdose of tricaine methanesulfonate (TMS, also known as MS-222), but to date little research has assessed if exposure to this or other agents qualifies as euthanasia (i.e. a "good death"). Alternative agents include metomidate hydrochloride and clove oil. We use a conditioned place avoidance paradigm to compare aversion to TMS, clove oil, and metomidate hydrochloride. Zebrafish (n = 51) were exposed to the different anaesthetics in the initially preferred side of a light/dark box. After exposure to TMS zebrafish spent less time in their previously preferred side; aversion was less pronounced following exposure to metomidate hydrochloride and clove oil. Nine of 17 fish exposed to TMS chose not to re-enter the previously preferred side, versus 2 of 18 and 3 of 16 refusals for metomidate hydrochloride and clove oil, respectively. We conclude that metomidate hydrochloride and clove oil are less aversive than TMS and that these agents be used as humane alternatives to TMS for killing zebrafish.

SUBMITTER: Wong D

PROVIDER: S-EPMC3913715 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Conditioned place avoidance of zebrafish (Danio rerio) to three chemicals used for euthanasia and anaesthesia.

Wong Devina D von Keyserlingk Marina A G MA Richards Jeffrey G JG Weary Daniel M DM

PloS one 20140204 2

Zebrafish are becoming one of the most used vertebrates in developmental and biomedical research. Fish are commonly killed at the end of an experiment with an overdose of tricaine methanesulfonate (TMS, also known as MS-222), but to date little research has assessed if exposure to this or other agents qualifies as euthanasia (i.e. a "good death"). Alternative agents include metomidate hydrochloride and clove oil. We use a conditioned place avoidance paradigm to compare aversion to TMS, clove oil ...[more]

PMID: 24505365

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Project description:BackgroundDevelopment and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of biomedical sciences. Many such classifiers discovered thus far lack vigorous statistical and experimental validations. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors was used in this study to search for classifiers of endocrine-disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and tissue-combined datasets, either across the entire transcriptome or within individual transcription factor (TF) networks previously linked to EDC effects. Candidate classifiers were evaluated by gene set enrichment analysis (GSEA) on both the original training data and a dedicated validation dataset.ResultsMulti-tissue dataset yielded no classifiers. Among the 19 chemical-tissue conditions evaluated, the transcriptome-wide searches yielded classifiers for six of them, each having approximately 20 to 30 gene features unique to a condition. Searches within individual TF networks produced classifiers for 15 chemical-tissue conditions, each containing 100 or fewer top-ranked gene features pooled from those of multiple TF networks and also unique to each condition. For the training dataset, 10 out of 11 classifiers successfully identified the gene expression profiles (GEPs) of their targeted chemical-tissue conditions by GSEA. For the validation dataset, classifiers for prochloraz-ovary and flutamide-ovary also correctly identified the GEPs of corresponding conditions while no classifier could predict the GEP from prochloraz-brain.ConclusionsThe discrepancies in the performance of these classifiers were attributed in part to varying data complexity among the conditions, as measured to some degree by Fisher's discriminant ratio statistic. This variation in data complexity could likely be compensated by adjusting sample size for individual chemical-tissue conditions, thus suggesting a need for a preliminary survey of transcriptomic responses before launching a full scale classifier discovery effort. Classifier discovery based on individual TF networks could yield more mechanistically-oriented biomarkers. GSEA proved to be a flexible and effective tool for application of gene classifiers but a similar and more refined algorithm, connectivity mapping, should also be explored. The distribution characteristics of classifiers across tissues, chemicals, and TF networks suggested a differential biological impact among the EDCs on zebrafish transcriptome involving some basic cellular functions.

Dataset Information

Conditioned place avoidance of zebrafish (Danio rerio) to three chemicals used for euthanasia and anaesthesia.

Publications

Conditioned place avoidance of zebrafish (Danio rerio) to three chemicals used for euthanasia and anaesthesia.

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