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Arch Toxicol. 2017 Apr;91(4):1749-1762. doi: 10.1007/s00204-016-1824-6. Epub 2016 Sep 3.

Information-dependent enrichment analysis reveals time-dependent transcriptional regulation of the estrogen pathway of toxicity.

Author information

1
The Hamner Institutes for Health Sciences, Research Triangle Park, NC, USA.
2
ScitoVation, LLC, 6 Davis Drive, PO Box 110566, Research Triangle Park, NC, 27709, USA.
3
Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health Sciences, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
4
Agilent Technologies, Inc., Santa Clara, CA, USA.
5
Department of Pathology and Laboratory Medicine, Brown University, Providence, RI, USA.
6
Department of Biochemistry and Molecular and Cellular Biology, and Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA.
7
Department of Environmental Health Sciences, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
8
Center for Alternatives to Animal Testing-Europe, University of Konstanz, Constance, Germany.
9
The Hamner Institutes for Health Sciences, Research Triangle Park, NC, USA. pmcmullen@scitovation.com.
10
ScitoVation, LLC, 6 Davis Drive, PO Box 110566, Research Triangle Park, NC, 27709, USA. pmcmullen@scitovation.com.

Abstract

The twenty-first century vision for toxicology involves a transition away from high-dose animal studies to in vitro and computational models (NRC in Toxicity testing in the 21st century: a vision and a strategy, The National Academies Press, Washington, DC, 2007). This transition requires mapping pathways of toxicity by understanding how in vitro systems respond to chemical perturbation. Uncovering transcription factors/signaling networks responsible for gene expression patterns is essential for defining pathways of toxicity, and ultimately, for determining the chemical modes of action through which a toxicant acts. Traditionally, transcription factor identification is achieved via chromatin immunoprecipitation studies and summarized by calculating which transcription factors are statistically associated with up- and downregulated genes. These lists are commonly determined via statistical or fold-change cutoffs, a procedure that is sensitive to statistical power and may not be as useful for determining transcription factor associations. To move away from an arbitrary statistical or fold-change-based cutoff, we developed, in the context of the Mapping the Human Toxome project, an enrichment paradigm called information-dependent enrichment analysis (IDEA) to guide identification of the transcription factor network. We used a test case of activation in MCF-7 cells by 17β estradiol (E2). Using this new approach, we established a time course for transcriptional and functional responses to E2. ERα and ERβ were associated with short-term transcriptional changes in response to E2. Sustained exposure led to recruitment of additional 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 24 h. IDEA should be useful for identifying candidate pathways of toxicity. IDEA outperforms gene set enrichment analysis (GSEA) and provides similar results to weighted gene correlation network analysis, a platform that helps to identify genes not annotated to pathways.

KEYWORDS:

Bioinformatics; Endocrine Disruption; Enrichment Analysis; Gene Expression; Pathways of Toxicity

PMID:
27592001
PMCID:
PMC5364265
DOI:
10.1007/s00204-016-1824-6
[Indexed for MEDLINE]
Free PMC Article

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