To Örebro University

Zinc is an essential trace metal required for the maintenance of multiple physiological functions. Due to this, organisms can experience both zinc deficiency and toxicity. Hardness is recognized as one of the main modifying physiochemical factors regulating zinc bioavailability. Therefore, the present study analyzed the effect of hardness on zinc toxicity using Daphnia magna. Endpoint parameters were acute-toxicity, development, reproduction, and expression data for genes involved in metal regulation and oxidative stress. In addition, the temporal expression profiles of genes during the initiation of reproduction and molting were investigated. Water hardness influenced the survival in response to exposures to zinc. A zinc concentration of 50μg/L in soft water (50 mg CaCO₃ /L) caused 73% mortality after 96h exposure, whereas the same zinc concentration in the hardest water did not cause any significant mortality. Moreover, increasing water hardness from 100 to 200mg CaCO₃ /L resulted in a reduced number of offspring. Fecundity was higher at first brood for groups exposed to higher Zn concentrations. The survival data was used to assess the precision of the bioavailability models (Bio-met) and the geochemical model (Visual MINTEQ). As the Bio-met risk predictions overestimated the Zn toxicity, a competition-based model to describe the effects of hardness on zinc toxicity is proposed. This approach can be used to minimize differences in setting environmental quality standards. Moreover, gene expression data showed that using the toxicogenomic approach was more sensitive than the physiological endpoints. Therefore, data presented in the study can be used to improve risk assessment for zinc toxicity.

Zinc (Zn) is an essential element that influences many cellular functions. Depending on bioavailability, Zn can cause both deficiency and toxicity. Zn bioavailability is influenced by water hardness. Therefore, water quality analysis for health-risk assessment should consider both Zn concentration and water hardness. However, exposure media selection for traditional toxicology tests are set to defined hardness levels and do not represent the diverse water chemistry compositions observed in nature. Moreover, these tests commonly use whole organism endpoints, such as survival and reproduction, which require high numbers of test animals and are labor intensive. Gene expression stands out as a promising alternative to provide insight into molecular events that can be used for risk assessment. In this work, we apply machine learning techniques to classify the Zn concentrations and water hardness from Daphnia magna gene expression by using quantitative PCR. A method for gene ranking was explored using techniques from game theory, namely, Shapley values. The results show that standard machine learning classifiers can classify both Zn concentration and water hardness simultaneously, and that Shapley values are a versatile and useful alternative for gene ranking that can provide insight about the importance of individual genes.