Unknown,Transcriptomics,Genomics,Proteomics

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Discovery and validation of gene classifiers for endocrine disrupting chemicals in Zebrafish (Danio rerio)

ABSTRACT: 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.

ORGANISM(S): Danio rerio

SUBMITTER: Rong-Lin Wang

PROVIDER: E-GEOD-38070 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Discovery and validation of gene classifiers for endocrine-disrupting chemicals in zebrafish (danio rerio).

Wang Rong-Lin RL Bencic David D Biales Adam A Flick Robert R Lazorchak Jim J Villeneuve Daniel D Ankley Gerald T GT

BMC genomics 20120801

<h4>Background</h4>Development 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 bo ...[more]

PMID: 22849515

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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

Project description:Prochloraz is a fungicide known to cause endocrine disruption through effects on the hypothalamic-pituitary-gonadal (HPG) axis. To determine the short-term impacts of prochloraz on gene expression and steroid production, adult female fathead minnows (Pimephales promelas) were exposed to the chemical (0 or 300 M-NM-<g/L) for a time-course of 6, 12 and 24 h. Consistent with inhibition of cytochrome P450 17M-NM-1-hydroxylase/17,20-lyase (CYP17) and aromatase (CYP19), known molecular targets of prochloraz, plasma 17M-NM-2-estradiol (E2) was reduced within 6 hours. Ex vivo E2 production was significantly reduced at all time-points, while ex vivo testosterone (T) production remained unchanged. Consistent with the decrease in E2 levels, plasma concentrations of the estrogen-responsive protein vitellogenin were significantly reduced at 24 h. Genes coding for CYP19, CYP17, and steroidogenic acute regulatory protein were up-regulated in a compensatory manner in ovaries of the prochloraz-treated fish. In addition to targeted quantitative real-time polymerase chain reaction analyses, a 15k feature fathead minnow microarray was used to determine gene expression profiles in ovaries. From time-point to time-point, the microarray results showed a relatively rapid change in the differentially expressed gene (DEG) profiles associated with the chemical exposure. Functional analysis of the DEGs indicated changes in expression of genes associated with cofactor and coenzyme binding (GO: 0048037 and 0050662), fatty acid binding (GO:0005504) and organelle organization and biogenesis (GO: 0006996). Overall, the results from this study are consistent with compensation of the fish HPG axis to inhibition of steroidogenesis by prochloraz, and provide further insights into relatively rapid, system-wide, effects of a model chemical stressor on fish. RNA samples for each treatment/time point combination were used for microarray analysis (total of 23 arrays). For the microarray work, total RNA was isolated from ovary samples using RNeasy kits (Qiagen, Valencia, CA, USA). The RNA quality was assessed with an Agilent 2100 Bioanalyzer (Agilent, Wilmington, DE, USA) and quantity was determined using a NanodropM-BM-. ND-1000 spectrophotometer. Total RNA was stored at -80oC until analyzed with oligonucleotide microarrays. Microarray analysis was conducted at the US Army Engineer Research and Development Center (Vicksburg, MS, USA) using a 15k feature fathead minnow microarray (GEO: http://www.ncbi.nlm.nih.gov/geo/; Accession platform number GPL9248) designed by Dr. Nancy Denslow (University of Florida, Gainesville, FL, USA) and manufactured by Agilent Technologies (Palo Alto, CA, USA). The Agilent one-color microarray hybridization protocol (One-Color Microarray-Based Gene Expression Analysis, version 5.7, Agilent Technologies) was used for microarray hybridizations. One M-BM-5g of total RNA was used for all hybridizations. The cDNA synthesis, cRNA labeling, amplification and hybridization were performed following the manufacturerM-bM-^@M-^Ys kits and protocols (Quick Amp Labeling kit; Agilent Technologies). An Axon GenePixM-BM-. 4000B Microarray Scanner (Molecular Devices Inc., Sunnyvale, CA, USA) was used to scan microarray images at 5 M-NM-<m resolution. Data were resolved from microarray images using Agilent Feature Extraction software (Agilent Technologies). Statistical analyses were conducted using Statistica 8 (StatSoft Inc., Tulsa, OK, USA) and GraphPad Instat v. 3.01 (GraphPad Software, San Diego, CA, USA). Data were tested for normality and homogeneity of variance. When data conformed to parametric assumptions, data were analyzed using a parametric one-way ANOVA with chemical treatment as the independent variable. When data did not conform to parametric assumptions, they were either transformed (log 10) or analyzed using a non-parametric KruskallM-bM-^@M-^SWallis test (pM-bM-^IM-$ 0.05). The data are presented as means with standard errors of the mean. Differences were considered significant at p M-bM-^IM-$ 0.05. Microarray data were imported into the Rosetta Resolver 7.2 system for gene expression analysis (Rosetta Biosoftware, Kirkland, WA, USA). Data were normalized using the default, text loader, intensity profile builder settings in the Rosetta Resolver system. Significantly DEGs were identified using one-way ANOVA (p < 0.01) with no multiple testing correction

Project description:Sense-antisense transcript (SAT) pairs are pairs of transcripts that fully or partially overlap but are transcribed in opposite directions. Although SAT expression occurs in various species, most SAT pairs have not been examined in detail. Because our previous studies revealed some tissue specificity in SAT expression, SAT pairs might be involved in cell differentiation and the maintenance of tissue-specific gene expression programs. To analyze such tissue-specific SAT pairs, we assessed the expression profiles of SATs from 12 tissues of normal mice at a genome-wide scale and found that a considerable number of SAT pairs showed expression patterns unique to testis. This finding prompted us to study the relationship between SAT expression pattern and another epigenetic gene regulatory mechanism, DNA methylation. We conducted a comparative global analysis of the DNA methylation status of CpG island (CGI)-associated SAT loci from various tissues and found that in 99 of the 4911 SAT pairs studied, DNA methylation could cooperatively suppress its downstream “sense” transcription together with the “antisense” transcriptional activity in a tissue-specific manner. The tissue-specific differentially methylated regions (T-DMRs) of these SAT pairs mainly occurred outside of the defined CGIs. In addition, some of these T-DMRs are situated at the 5′ region of the antisense transcripts. This positioning implies a novel mechanism for DNA methylation to regulate gene transcription, in which DNA methylation inhibits an opposing transcriptional activity and therefore maintains stable expression of a tissue-specific gene. Twelve tissues (brain, thymus, heart, lung, liver, spleen, stomach, kidney, small intestine, testis, and placentae [10.5 and 13.5 days postcoitum (dpc)]) of C57BL/6J Jc1 and a mouse fibroblast cell line (SL10) were used for the mouse expression oligo-microarray. The expression array data were obtained by both oligo-dT and random priming methods, except for SL10 (olio-dT only). Twelve tissues (brain, thymus, heart, lung, liver, spleen, stomach, kidney, small intestine, testis, and placentae [10.5 and 13.5 days postcoitum (dpc)]) were used for DIP chip experiments. Nine of twelve tissues were separately analyzed classified on the basis of sex. A technical duplication of DIP chip analysis was performed by using the same genomic DNA of male kidney.

Dataset Information

Discovery and validation of gene classifiers for endocrine disrupting chemicals in Zebrafish (Danio rerio)

Publications

Discovery and validation of gene classifiers for endocrine-disrupting chemicals in zebrafish (danio rerio).

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