Developing of a quantitative toxicogenomics-based approach for water quality monitoring and toxicity evaluation

• Online Access: http://hdl.handle.net/2047/D20211464

The recognized and unknown risks associated with the ever-increasing number of pollutants in our environment presents a serious threat to us all. This poses a pressing need for a breakthrough in toxicity-assessment technology because the currently available methods are neither feasible nor sufficient to provide the timely information needed for regulatory decision-making and technology development to eliminate these threats. This study developed a novel, feasible and cost-effective quantitative toxicogenomics-based toxicity assessment platform for high-throughput and effective chemical hazardous identification and environmental toxicity monitoring. We systematically optimized the assay platform, evaluated its robustness and performance, validated the assay output and demonstrated its wide applications. Compared with other main stream "omics' technologies, the proposed method greatly improves the feasibility and cost effectiveness as a result of its much simpler, faster, and more reliable assay procedures. Furthermore, it provides multi-dimensional transcriptional level effect information with a temporal dimension and therefore can more accurately reflect the chemical-induced time-dependent cell responses with higher sensitivity and specificity. We demonstrated that information-rich toxicogenomics data are powerful for evaluating toxic effects, understanding toxicity mechanisms, and obtaining pollutant-specific molecular fingerprints for compound /sample classification and identification. One of the main challenges in applying toxicogenomics for environmental monitoring is the lack of a quantitative method to convert the toxicogenomic information into a readily usable and transferable format that can be incorporated into ecological risk assessment and regulatory framework. We proposed a new transcriptional effect level index (TELI) that exhibited a dose-response relationship and allowed for linking the transcriptional level effects to conventional toxicity endpoints. In addition, we pioneered quantitative molecule toxicity modeling within the context of toxicogenomics and paved the road for further mixture toxicity identification and prediction. Cross-species comparison and extrapolation is another key aspect related to predictive and mechanistic toxicity assessment to overcome the limitation of data generation ability. We have compared three different species for variety of compounds and demonstrated the possibility of cross-species extrapolation with stress-response pathway ensemble based toxicity assessment. Finally, we demonstrated successful application of the novel assay for mechanistic CECs toxicity assessment, whole effluent toxicity monitoring and risk-based water treatment technologies efficacy evaluation.