Project description:Background: Development and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of human biomedical science. Many such classifiers discovered thus far lack vigorous statistical and experimental validations, with their stability and reliability unknown. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors were used in this study to search for classifiers of endocrine disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and all 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. Results: Multi-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 intended 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. Conclusions: The 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. While GSEA appeared to provide a flexible and effective tool for application of gene classifiers, a similar but more refined algorithm, connectivity mapping, should also be explored for ecotoxicological applications. 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. chemical abbreviations: EE2, 17α-ethynyl estradiol; FAD, fadrozole; TRB, 17 -trenbolone; FIP, fipronil; PRO, prochloraz; FLU, flutamide; MUS, muscimol; KET, ketoconazole; TRI, trilostane; VIN, vinclozolin Since this study was conducted in several phases, three different version of Agilent zebrafish two color microarrays were used based on their availability at the time. These include G2518A (designID 013223) and G2519F (designID 015064, 019161). There were a total of 58 treatment conditions with various combinations of chemical, tissue type, exposure time, and gender. Each condition contained eight to 12 independent samples, half from chemical-treated fish and half from water- control fish.

Project description:Background: Development and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of human biomedical science. Many such classifiers discovered thus far lack vigorous statistical and experimental validations, with their stability and reliability unknown. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors were used in this study to search for classifiers of endocrine disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and all 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. Results: Multi-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 intended 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. Conclusions: The 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. While GSEA appeared to provide a flexible and effective tool for application of gene classifiers, a similar but more refined algorithm, connectivity mapping, should also be explored for ecotoxicological applications. 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. chemical abbreviations: EE2, 17α-ethynyl estradiol; FAD, fadrozole; TRB, 17 -trenbolone; FIP, fipronil; PRO, prochloraz; FLU, flutamide; MUS, muscimol; KET, ketoconazole; TRI, trilostane; VIN, vinclozolin

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Danio rerio

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