To Örebro University

Detection of various organic contaminants in the aquatic environment at low concentrations, has raised concerns for animal and human health. Structural similarities of these compounds to estrogens and ecdysones suggests that organic pollutants may interfere with hormonal system of aquatic organisms. Techniques of traditional toxicity tests that are based on observable physiological defects may fail to identify sub-lethal / subobservable effects. Under these techniques the mode of action of pollutants cannot be investigated. Thus, there is a need for more sensitive techniques that are capable to evaluate effects at molecular level and provide an early warning to environmental pollution. Toxicogenomic is proposed to be a useful tool to meet this goal. However, there is a limited data that compare toxicogenomic and traditional toxicity test approaches. The aim of this thesis is to evaluate the utility of qRT-PCR based toxicogenomic analysis in risk assessment. To achieve this goal, we first analyzed toxicogenomic and physiological responses of Daphnia manga exposed to three class of widely detected organic pollutant sphthalates, perfluorinated alkylated substances and brominated flame retardant in Paper I-III. Investigated endpoints included effects on hatching, acute toxicity, survival, developmental abnormalities, reproduction success, swimming and feeding behavior, fat metabolism, and life span. Result of these studies demonstrated a high degree of correlation between transcriptional data and the traditional physiological data. Besides, toxicogenomic approach was able to identify effects of sublethal concentrations. Thus, in Paper IV we analyzed environmental sample from Akaki river in Ethiopia using transcriptional approach and showed toxicogenomic tools utility in environmental risk assessment. Overall, the finding of these studies showed that the use of toxicogenomic can improve traditional physiological based toxicity assay to determine environmental risk assessment. Furthermore, toxicogenomic analysis can be performed with relatively short time and limited volume of samples.