Applying a mechanistic model to predict interacting effects of chemical exposure and food availability on fish populations

Influence of chemical dose and exposure duration on protein synthesis in green sea turtle primary cells

Understanding the impacts of chemical exposure in marine wildlife is challenging, due to practical and ethical constraints that preclude traditional toxicology research on these animals. This study addressed some of these limitations by presenting an ethical and high throughput cell-based approach to elucidate molecular-level effects of contaminants on sea turtles. The experimental design addressed basic questions of cell-based toxicology, including chemical dose and exposure time. Primary green turtle skin cells were exposed to polychlorinated biphenyl (PCB) 153 and perfluorononanoic acid (PFNA) for 24 and 48 h, at three sub-lethal, environmentally relevant concentrations (1, 10 and 100 μg/L). Sequential window acquisition of all theoretical mass spectra (SWATH-MS) identified over 1000 differentially abundant proteins within the 1% false discovery rate (FDR) threshold. The 24 h exposure resulted in a greater number of differentially abundant proteins, compared to 48 h exposure, for both contaminants. However, there were no statistically significant dose-response relationships for the number of differentially synthesised proteins, nor differences in the proportion of increased vs decreased proteins between or within exposure times. Known in vivo markers of contaminant exposure, superoxide dismutase and glutathione S-transferase, were differentially abundant following exposure to PCB153 and PFNA. SIGNIFICANCE: Cell-based (in vitro) proteomics provides an ethical and high throughput approach to understanding the impacts of chemical contamination on sea turtles. Through investigating effects of chemical dose and exposure duration on unique protein abundance in vitro, this study provides an optimised framework for conducting cell-based studies in wildlife proteomics, and highlights that proteins detected in vitro could act as biomarkers of chemical exposure and effect in vivo.

Temperature dependence of population responses to competition and metabolic stress: An agent-based model to inform ecological risk assessment in a changing climate

Understanding the interactions among multiple stressors is a crucial issue for ecological risk assessment and ecosystem management. However, it is often impractical, or impossible, to collect empirical data concerning all the interactions at any scale because the type of interaction differs across species and levels of biological organization. We applied an agent-based model to simulate the effects of a hypothetical chemical stressor and inter-specific competition (both alone and together) on greenback cutthroat trout (GCT), a listed species under the US Endangered Species Act, in two temperature scenarios. The trout life cycle is modeled using the Dynamic Energy Budget theory. The chemical stressor is represented by a reduction in ingestion efficiency, and competition is implemented by introducing a population of brown trout. Results show that chemical exposure is the major stressor in the colder temperature scenario, whereas competition mostly affected the GCT population in the warmer environment. Moreover, the effects of the stressors at the individual level were not predictive of the type of interactions between stressors (additive, antagonistic, synergistic) at the population level, which differed between the two-temperature scenarios. We conclude that mechanistic models can help to identify generalities about interactions among environmental and stressor properties, create in-silico experiments to provide different scenarios for conservation purposes, and explore multiple-exposure consequences at higher levels of biological organization. In this way they can provide useful tools for improving ecological risk assessment and informing management decisions.