Information-dependent Enrichment Analysis Reveals Time-dependent Transcriptional Regulation of the Estrogen Pathway of Toxicity

Abstract

The twenty-first century vision for toxicology involves a transition away from high-dose animal studies and into in vitro and computational models. This movement requires mapping pathways of toxicity through an understanding of how in vitro systems respond to chemical perturbation. Uncovering transcription factors responsible for gene expression patterns is essential for defining pathways of toxicity, and ultimately, for determining chemical mode of action, through which a toxicant acts. Traditionally this is achieved via chromatin immunoprecipitation studies and summarized by calculating, which transcription factors are statistically associated with the up-and down-regulated genes. These lists are commonly determined via statistical or fold-change cutoffs, a procedure that is sensitive to statistical power and may not be relevant to determining transcription factor associations. To move away from an arbitrary statistical or fold-change based cutoffs, we have developed in the context of the Mapping the Human Toxome project, a novel enrichment paradigm called Information Dependent Enrichment Analysis (IDEA) to guide identification of the transcription factor network. We used the test case of endocrine disruption of MCF-7 cells activated by 17{beta} estradiol (E2). Using this new approach, we were able to establish a time course for transcriptional and functional responses to E2. ER and ER{beta} are associated with short-term transcriptional changes in response to E2. Sustained exposure leads to the recruitment of an additional ensemble of transcription factors and alteration of cell-cycle machinery. TFAP2C and SOX2 were the transcription factors most highly correlated with dose. E2F7, E2F1 and Foxm1, which are involved in cell proliferation, were enriched only at 24h. IDEA is, therefore, a novel tool to identify candidate pathways of toxicity, clearly outperforming Gene-set Enrichment Analysis but with similar results as Weighted Gene Correlation Network Analysis, which helps to identify genes not annotated to pathways.