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 (FAIR data are data which meet principles of findability, accessibility, interoperability, and reusability), while also summarizing key meta-data attributes and (2) use ExpressAnalyst (including the Seq2Fun 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" subgroup 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 in the present study 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.