Toxicogenomic Assessment of Complex Chemical Signatures in Double-Crested Cormorant Embryos from Variably Contaminated Great Lakes Sites.
Pu XiaDoug CrumpSuzanne ChiuTiff-Annie KennyJason M O'BrienPublished in: Environmental science & technology (2020)
Using omics approaches to monitor complex environmental mixtures is challenging. Previously, we evaluated in vitro transcriptomic effects of complex organic extracts derived from avian eggs. However, there is a lack of studies using wild species that are naturally exposed to contaminant mixtures. Here, we examined polychlorinated biphenyl (PCB) and polybrominated diphenyl ether (PBDE) residues and gene expression in embryonic liver tissue of double-crested cormorants (Phalacrocorax auritus) collected from six variably contaminated colonies. Colonies near industrialized areas were distinguished from less contaminated sites based on their PCB and PBDE concentrations. The most variably expressed genes between sites were involved in pathways including, xenobiotic metabolism (e.g., Cyp1a4), lipid/bile acid homeostasis (e.g., Lbfabp), and oxidative stress (e.g., Mt4). Hierarchical clustering, based on relative gene expression, revealed a grouping pattern similar to chemical residue concentrations. Further, partial least squares regression analysis was used to estimate chemical concentrations from transcriptomics data. PCB 155 and BDE 47 showed the highest slopes (0.77 and 0.69, respectively) fitted by linear regression of measured and estimated chemical concentrations. The application of transcriptomics to a wild avian species, naturally exposed to complex chemical mixtures and other stressors, represents a promising means to distinguish and prioritize variably contaminated sites.
Keyphrases
- single cell
- gene expression
- heavy metals
- drinking water
- ionic liquid
- oxidative stress
- rna seq
- dna methylation
- genome wide
- dna damage
- ischemia reperfusion injury
- electronic health record
- fatty acid
- big data
- induced apoptosis
- climate change
- human health
- signaling pathway
- artificial intelligence
- data analysis
- bioinformatics analysis