Adaptation of a Bioinformatics Microarray Analysis Workflow for a Toxicogenomic Study in Rainbow Trout

Abstract

Sex steroids play a key role in triggering sex differentiation in fish, the use of exogenous hormone treatment leading to partial or complete sex reversal. This phenomenon has attracted attention since the discovery that even low environmental doses of exogenous steroids can adversely affect gonad morphology (ovotestis development) and induce reproductive failure. Modern genomic-based technologies have enhanced opportunities to find out mechanisms of actions (MOA) and identify biomarkers related to the toxic action of a compound. However, high throughput data interpretation relies on statistical analysis, species genomic resources, and bioinformatics tools. The goals of this study are to improve the knowledge of feminisation in fish, by the analysis of molecular responses in the gonads of rainbow trout fry after chronic exposure to several doses (0.01, 0.1, 1 and 10 μg/L) of ethynylestradiol (EE2) and to offer target genes as potential biomarkers of ovotestis development. We successfully adapted a bioinformatics microarray analysis workflow elaborated on human data to a toxicogenomic study using rainbow trout, a fish species lacking accurate functional annotation and genomic resources. The workflow allowed to obtain lists of genes supposed to be enriched in true positive differentially expressed genes (DEGs), which were subjected to over-representation analysis methods (ORA). Several pathways and ontologies, mostly related to cell division and metabolism, sexual reproduction and steroid production, were found significantly enriched in our analyses. Moreover, two sets of potential ovotestis biomarkers were selected using several criteria. The first group displayed specific potential biomarkers belonging to pathways/ontologies highlighted in the experiment. Among them, the early ovarian differentiation gene foxl2a was overexpressed. The second group, which was highly sensitive but not specific, included the DEGs presenting the highest fold change and lowest p-value of the statistical workflow output. The methodology can be generalized to other (non-model) species and various types of microarray platforms.

Nanosecond pulsed electric field incorporation technique to predict molecular mechanisms of teratogenicity and developmental toxicity of estradiol‐17β on medaka embryos

“…In addition, our results showed that genes related to several metabolic‐related processes, such as the cellular metabolic process, the primary metabolic process, and the organic substance metabolic process were significantly enriched in embryos treated with nsPEF and E 2 . Previous transcriptomic and/or proteomic studies reported that the synthetic estrogen, ethynylestradiol, affected various metabolic processes in teleost fishes such as rainbow trout ( Oncorhynchus mykiss ) (Depiereux et al, ) and zebrafish ( D. rerio ) (De Wit et al, ). Moreover, previous surveys using medaka fish have also demonstrated that approximately one‐quarter of the up‐ or downregulated genes were categorized as “metabolism” in the larval stages when exposed to 100 ng l –1 E 2 for 7 days, as determined via a microarray analysis (Mizukami‐Murata & Kishi‐Kadota, ).…”

Meta-Analysis of Microarray Data of Rainbow Trout Fry Gonad Differentiation Modulated by Ethynylestradiol

“…The initial microarray analysis workflow [ 24 ] was adapted to handle another type of microarray platform (dataset 2) and to fit with the meta-analysis scheme and work hypothesis. Briefly, this workflow includes (i) the analysis of pre-treated data by (ii) ANOVA analysis followed by Scheffe’s post hoc pairwise comparisons.…”

“…Data analyses were performed using the R statistical software version 2.15.3 available on the R-Project website ( http://cran.r-project.org ) and a set of packages available in the Bioconductor repository ( http://www.bioconductor.org ). This methodology used for the first dataset [ 24 ] was also applied to re-analyse the dataset 2. Therefore, brief descriptions and specificities related to this dataset analysis are provided below.…”

“…Here, the meta-analysis consists of retrieving DEGs common to both analyses. The use of intersections between top gene lists has proven to be effective in filtering relevant genes [ 19 , 22 , 24 ]. Three intersections (Intersection I, II and III) were defined for further analyses ( Table 1 ).…”

“…Moreover, the meta-analysis of several datasets is a way to yet further enhance analytical performance [ 19 – 21 ]. In a previous work, we successfully adapted a microarray analysis workflow validated on human cancer data [ 19 , 22 ][ 23 ] to a toxicogenomic study using rainbow trout as model organism [ 24 ]. We measured gene expression in juvenile male fish exposed chronically to several doses of the potent xenoestrogen ethynylestradiol (EE2).…”