The rate of new chemical development in commerce
Application of Gene Set Enrichment Analysis for Identification of Critical Gene Expression Networks and Application in Human Health Risk Assessment.
The rate of new chemical development in commerce combined with a paucity of toxicity data for legacy chemicals presents a unique challenge for human health risk assessment. There is a clear need to develop new technologies and incorporate novel data streams to more efficiently inform derivation of toxicity values. One avenue of exploitation lies in the field of transcriptomics and the application of gene expression analysis to characterize biological responses to chemical exposures. In this context, gene set enrichment analysis (GSEA) was employed to evaluate tissue-specific, dose-response gene expression data generated following exposure to multiple chemicals for various durations. Patterns of transcriptional
enrichment were evident across time and with increasing dose, and coordinated enrichment plausibly linked to the etiology of the biological responses was observed. GSEA was able to capture both transient and sustained transcriptional enrichment events facilitating differentiation between adaptive versus longer term molecular responses. When combined with benchmark dose (BMD) modeling of gene expression data from key drivers of biological enrichment, GSEA facilitated characterization of dose ranges required for enrichment of biologically relevant molecular signaling pathways, and promoted comparison of the activation dose ranges required for individual pathways. Median transcriptional BMD values were calculated for the most sensitive enriched pathway as well as the overall median BMD value for key gene members of significantly enriched pathways, and both were observed to be good estimates of the most sensitive apical endpoint BMD value. Together, these efforts support the application of GSEA to qualitative and quantitative human health risk assessment.
Jeffry L. Dean (1), Q. Jay Zhao (1), Jason C. Lambert (1), Belinda S. Hawkins (1), Russell S. Thomas (2) and Scott C. Wesselkamper (1)
1) U.S. EPA, National Center for Environmental Assessment , Cincinnati, OH; 2) U.S. EPA, National Center for Computational Toxicology, RTP, NC
The views expressed in this abstract are those of the authors and do not necessarily reflect the views and policies of the U.S. EPA.