Metal-induced metabolomics study on oysters Crassostrea hongkongensis

Abstract

With the rapid development of economics and industrialization, anthropogenic activities can contribute significantly to aquatic environment pollution. And potential health risks induced by high levels of trace metals have received much attention. In estuarine regions, metal toxicity is coupled with various non-chemical environmental stressors, increasing the difficulty to predict metal effects accurately. In this research, NMR-based metabolomics approach is applied to learn the biological responses of oysters Crassostrea hongkongensis induced by metals under various conditions. First of all, oysters were transplanted to multiple-metal-contaminated sites in Jiulong River estuary for six months. Oysters accumulated more osmolytes to cope with metal-induced osmotic stress. Energy storage compounds and amino acids were mobilized to compensate for the loss of energy. Secondly, to mimic the salinity variation of estuarine waters, laboratory exposure experiment was conducted under three salinities with 50 μg L^-1 copper for six weeks. Salinity effects could overwhelm metabolomics variation of oysters induced by copper exposure. Lower salinity treatments accumulated higher glycogen, and copper exposure enhances the synthesis from glycine to dimethylglycine to cope with severe osmotic stress. Thirdly, due to the variations of effluent discharge in the environment, organisms are more likely polluted by contaminants intermittently. Oysters were exposed in both continuous (3.3 μg/L-24h; 20 μg/L-24h) and intermittent (20 μg/L-4h; 120 μg/L-4h) copper exposure regimes for six weeks. Results suggest that the continuous and intermittent copper exposure led to similar metabolite variation at an equal dose. Continuous exposure can reflect the accumulation of intermittent exposure at a low equal dose, while it is not applicable for high dose regimes. NMR-based metabolomics provides an effective way to detect sensitive variation of oysters’ inner status under complex environment, providing valuable data for future metal risk assessment.

Date of Award	2016
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology