Connecting Suborganismal Data to Bioenergetic Processes: Killifish Embryos Exposed to a Dioxin-Like Compound.

Area of Science:

• Ecological toxicology
• Environmental chemistry
• Computational biology

Background:

• Integrating molecular responses into ecological risk assessment remains a challenge.
• Bioenergetic theory offers a framework to link suborganismal responses to organismal and population dynamics.
• Adverse Outcome Pathways (AOPs) provide a structured approach to understand chemical toxicity mechanisms.

Purpose of the Study:

• To apply dynamic energy budget (DEB) theory within an AOP framework for quantitative prediction of chemical toxicity.
• To connect molecular-level damage from dioxin-like chemicals (DLCs) to organismal effects in Fundulus heteroclitus.
• To predict evolved tolerance to DLCs in wild fish populations based on altered DEB parameters.

Main Methods:

• Utilized early-life stage exposure of Fundulus heteroclitus to DLCs.
• Linked AOP key events to DEB processes, quantifying damage as a function of internal toxicant concentration.
• Employed transcriptomic data to translate molecular damage indicators into DEB parameter changes, specifically increased somatic maintenance costs.
• Developed DEB models to predict sublethal and lethal effects on young fish.

Main Results:

• Successfully connected molecular damage from DLCs to changes in DEB parameters, such as increased somatic maintenance costs.
• DEB models accurately predicted sublethal and lethal effects on young fish.
• Model parameter adjustments successfully predicted evolved tolerance to DLCs in wild Fundulus heteroclitus populations, without using this data for parameterization.
• Identified reduced sensitivity and altered damage repair dynamics as key factors in evolved resistance.

Conclusions:

• The novel application of DEB theory within an AOP framework provides a robust method for quantitative ecological risk assessment.
• This approach effectively links molecular responses to organismal and population-level outcomes, even predicting evolved tolerance.
• The methodology shows promise for extrapolating toxicity predictions to untested chemicals of ecological concern.