Quantifying the effect of metal interactions on growth rate in Saccharomyces cerevisiae

Abstract

Environmental stressors often co-occur, yet their combined effects on organisms remain poorly understood. As multifactorial anthropogenic changes intensify and alter environmental selection pressures, our understanding of stressor interactions is becoming increasingly important. However, established methods for characterizing the effects of stressor interactions can involve considerable experimental and computational labour. Here, we investigate how pairwise combinations of six divalent metal ions (Cd2+, Co2+, Cu2+, Mn2+, Ni2+, and Zn2+) affect the growth of Saccharomyces cerevisiae, using a high-throughput assay to generate concentration-response surfaces for all 15 combinations. We introduce{delta} , an easily calculable metric that quantifies the toxicity of a mixture relative to the toxicities of its individual components, and compare it to an established metric for synergism/antagonism, a, determined by calculating deviation from a null model of additivity, i.e., "concentration addition". Yeast growth rates reveal that metal mixture toxicity varies widely from the additive expectation with the effect of the combination ranging from greatly enhanced toxicity to greatly attenuated toxicity. Combinations with copper were more toxic than expected, and combinations with manganese or nickel were less. These trends correspond to known redox activities and metal-binding properties, pointing to possible mechanistic underpinnings rooted in oxidative stress and metal cofactor displacement. Furthermore, we find that a correlates with overlap in known metal resistance genes and similarity in redox potential, offering predictive insight into effects of metal interactions. While a provides a model-based measure of synergism or antagonism,{delta} serves as an intuitive, concentration-robust descriptor of ecological impact. Together, these metrics highlight the complexity of metal-metal interactions and the importance of accounting for nonadditive effects in ecotoxicological assessments. This framework provides a basis for evaluating mixture toxicity across diverse taxa and stressor types, with implications for both evolutionary biology and environmental risk assessment.