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 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.Electronic supplementary materialThe online version of this article (doi:10.1007/s00204-016-1824-6) contains supplementary material, which is available to authorized users.

Weighted Gene Correlation Network Analysis (WGCNA) Reveals Novel Transcription Factors Associated With Bisphenol A Dose-Response

“…ZNF217 has previously been shown by our work ( Pendse et al, 2017 ) and others ( Frietze et al, 2014 ) to be a critical component of estrogen signaling and an important prognostic factor for breast cancer ( Vendrell et al, 2012 ). Similarly, TFAP2C is known to modulate ESR1 and GPR30 expression, and attenuate the expression of several estrogen-targeted genes ( Woodfield et al, 2007 ).…”

“…In our previous work for the Mapping the Human Toxome project ( Kleensang et al, 2014 ; Bouhifd et al, 2015 ), we demonstrated that using non-inferential statistical methods that did not depend on existing annotations such as IDEA ( Pendse et al, 2017 ) and WGCNA ( Maertens et al, 2015 ) offered a powerful method to untangle possible regulatory mechanisms and providing insight into possible Pathways of Toxicity compared to either inferential-based methods or approaches such as pathway enrichment analysis that depend exclusively on annotations. Building upon our previous work using WGCNA applied to in vitro transcriptomic data to more fully understand the transcription factors that are driving the biology of estradiol ( Pendse et al, 2017 ), we used WGCNA to examine a previously published transcriptomic dataset ( Shioda et al, 2013 ). Briefly, Shioda et al (2013) aimed to study the sensitivities of estrogen responsive genes to various endocrine disrupting chemicals (EDCs) based on the transcriptomic profile of MCF-7 cells exposed to either estrogen or several xenoestrogens (including BPA) over a dose-response curve ranging from picomolar to micromolar concentrations for a 48 h time period.…”

Gene co-regulation and co-expression in the aryl hydrocarbon receptor-mediated transcriptional regulatory network in the mouse liver

“…Finally, we found that coregulated gene clusters activated distinct groups of downstream biological processes, with the AHR-bound genomic cluster enriched for metabolic processes and the AHRunbound non-genomic cluster primarily activating immune processes. This work, together with the other recent studies of the peroxisome proliferator-activated receptor alpha (PPARα) and estrogen receptor pathways (McMullen et al 2014;Pendse et al 2016), illustrates the application of bioinformatic and statistical tools for reconstruction and analysis of the transcriptional regulatory cascades underlying cellular stress response. While these network reconstructions are species, tissue, and condition-specific, we anticipate that next-generation models that use machine learning to predict network structure and dynamics from genomic sequence and epigenomic features will soon be available.…”

An Effect of Culture Media on Epithelial Differentiation Markers in Breast Cancer Cell Lines MCF7, MDA-MB-436 and SkBr3

“…The impact of individual nutrients, hormones and factors on the transcriptional expression of various markers in culture of breast cancer and/or mammary cells has been studied before, e.g., reports on the effect of estrogen on transcriptional activity in various cell lines [ 42 , 43 , 44 , 45 , 46 , 47 ], but none of these studies have explored the long-term effects of supplementation with complex set of hormones/growth factors on transcriptomic profile of phenotype defining genes in breast cancer cell lines for exposure longer than five days [ 45 ]. Therefore, this is the first characterization of the long-term effects of complex hormone/growth factor set on the expression of breast cancer/mammary markers in breast cancer cell lines.…”