Deciphering the Combined Effects of Environmental Stressors on Gene Transcription: A Conceptual Approach

The
use of classical mixture toxicity models to predict the combined
effects of environmental stressors based on toxicogenomics (OMICS)
data is still in its infancy. Although several studies have made attempts
to implement mixture modeling in OMICS analysis to understand the
low-dose interactions of stressors, it is not clear how interactions
occur at the molecular level and how results generated from such approaches
can be better used to inform future studies and cumulative hazard
assessment of multiple stressors. The present work was therefore conducted
to propose a conceptual approach for combined effect assessment using
global gene expression data, as illustrated by a case study on assessment
of combined effects of gamma radiation and depleted uranium (DU) on
Atlantic salmon (Salmo salar). Implementation of
the independent action (IA) model in reanalysis of a previously published
microarray gene expression dataset was performed to describe gene
expression patterns of combined effects and identify key gene sets
and pathways that were relevant for understanding the interactive
effects of these stressors. By using this approach, 3120 differentially
expressed genes (DEGs) were found to display additive effects, whereas
279 (273 synergistic, 6 antagonistic) were found to deviate from additivity.
Functional analysis further revealed that multiple toxicity pathways,
such as oxidative stress responses, cell cycle regulation, lipid metabolism,
and immune responses were enriched by DEGs showing synergistic gene
expression. A key toxicity pathway of DNA damage leading to enhanced
tumorigenesis signaling is highlighted and discussed in detail as
an example of how to take advantage of the approach. Furthermore,
a conceptual workflow describing the integration of combined effect
modeling, OMICS analysis, and bioinformatics is proposed. The present
study presents a conceptual framework for utilizing OMICS data in
combined effect assessment and may provide novel strategies for dealing
with data analysis and interpretation of molecular responses of multiple
stressors.