Harmful algal blooms (HABs) expose aquatic organisms to multiple physical and chemical stressors during an acute time period. Algal toxins themselves may be altered by water chemistry parameters affecting their bioavailability and resultant toxicity. The purpose of this study was to determine the effects of two abiotic parameters (pH, inorganic metal salts) on the toxicity of fatty acid amides and fatty acids, two classes of lipids produced by harmful algae, including the golden alga, Prymnesium parvum, that are toxic to aquatic organisms. Rainbow trout gill cells were used as a model of the fish gill and exposed to single compounds and mixtures of compounds along with variations in pH level and concentration of inorganic metal salts. We employed artificial neural networks (ANNs) and standard ANOVA statistical analysis to examine and predict the effects of these abiotic parameters on the toxicity of fatty acid amides and fatty acids. Our results demonstrate that increasing pH levels increases the toxicity of fatty acid amides and inhibits the toxicity of fatty acids. This phenomenon is reversed at lower pH levels. Exposing gill cells to complex mixtures of chemical factors resulted in dramatic increases in toxicity compared to tests of single compounds for both the fatty acid amides and fatty acids. These findings highlight the potential of physicochemical factors to affect the toxicity of chemicals released during algal blooms and demonstrate drastic differences in the effect of pH on fatty acid amides and fatty acids.
Keywords: 3-4,5-dimethylthiazol; 50% lethal concentration; ANN; ANOVA; Artificial neural networks; CV; DHA; EPA; Fatty acid amides; Fatty acids; HAB; LC(50); MTT; Mixtures; Physicochemical; Prymnesium parvum; analysis of variance; artificial neural network; cross validation; docosahexaenoic acid; eicosapentaenoic acid; harmful algal bloom.
Published by Elsevier B.V.