抄録
Metabolomic approach to ecological risk assessments of chemicals(ecotoxicometabolomics) has great potential for facilitating a better understanding of toxicity pathways or mode of actions (MOAs). The purpose of this study is to establish a system that can detect the MOAs by capturing the change of etabolome in green algae (Pseudokirchneriella subcapitata), commonly used for the ecotoxicological test as a model organism in freshwater, treated with the bioactive compounds. P.subcapitata were cultured in accordance with the OECD test guideline No.201. Exponentially growing P.subcapitata was exposed to the bioactive compounds, as herbicides, with each different MOA at 50%-effective concentration (ErC50) level. The metabolite was extracted from the algae using d4-methanol, hence metabolome was measured by 1H-NMR. The 1H-NMR spectra were analyzed by pattern recognition method, known as principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA). The score plot of PCA showed clear classification between each group treated with and without chemicals. In addition, increases and decreases of the fraction that considerably contributed to separation of each PCA were also reflected on the spectrum of 1H-NMR. Moreover, the results of SIMCA gave the good presentation of classification reflecting the difference of MOA between with and without exposure to chemicals. It was suggested that MOA for bioactive compounds would be classified by ecotoxicometabolomic approach using 1H-NMR measurements coupled with pattern recognition method
