Comparing Transcriptomic Responses to Chemicals Across Six Species using the EcoToxChip RNASeq database.

The EcoToxChip project includes RNA-sequencing data from experiments involving model (Japanese quail, fathead minnow, African clawed frog) and ecological (double-crested cormorant, rainbow trout, northern leopard frog) species, at multiple life stages (whole embryo and adult), exposed to eight chemicals of environmental concern known to perturb a wide range of biological systems (ethinyl estradiol, hexabromocyclododecane, lead, selenomethionine, 17β trenbolone, chlorpyrifos, fluoxetine, and benzo[a]pyrene). The objectives of this short communication were to: 1) present and make available this RNA-sequencing database (i.e., 724 samples from 49 experiments) under the FAIR principles, while also summarizing key meta-data attributes; and 2) use ExpressAnalyst (including the Seq. 2Fun algorithm and EcoOmicsDB) to perform a comparative transcriptomics analysis of this database focusing on baseline and differential transcriptomic changes across species-life stage-chemical combinations. The database is available in NCBI GEO under accession number GSE239776. Across all species, the number of raw reads per sample ranged between 13 and 58 million, with 30 to 79% of clean reads mapped to the 'vertebrate' sub-group database in EcoOmicsDB. Principal component analyses of the reads illustrated separation across the three taxonomic groups as well as some between tissue types. The most common differentially expressed gene was CYP1A1 followed by CTSE, FAM20CL, MYC, ST1S3, RIPK4, VTG1, and VIT2. The most common enriched pathways were metabolic pathways, biosynthesis of cofactors and biosynthesis of secondary metabolites, and chemical carcinogenesis, drug metabolism and metabolism of xenobiotics by Cytochrome P450. The RNA-sequencing database presented here may be used by the research community for multiple purposes, including for example, cross-species investigations, in depth analyses of a particular test compound, and transcriptomic meta-analyses.