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

Navya Josyula,Melvin E Andersen,Timothy R Zacharewski,Norbert E Kaminski,Edward Dere,Sudin Bhattacharya

doi:10.1007/s00204-019-02620-5

Navya Josyula, Melvin E Andersen + Show 4 more

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https://doi.org/10.1007/s00204-019-02620-5

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Four decades after its discovery, the aryl hydrocarbon receptor (AHR), a ligand-inducible transcription factor (TF) activated by the persistent environmental contaminant 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD), remains an enigmatic molecule with a controversial endogenous role. Here, we have assembled a global map of the AHR gene regulatory network in female C57BL/6 mice orally gavaged with 30 µg/kg of TCDD from a combination of previously published gene expression and genome-wide TF-binding data sets. Using Kohonen self-organizing maps and subspace clustering, we show that genes co-regulated by common upstream TFs in the AHR network exhibit a pattern of co-expression. Directly bound, indirectly bound, and non-genomic AHR target genes exhibit distinct expression patterns, with the directly bound targets associated with highest median expression. Interestingly, among the directly bound AHR target genes, the expression level increases with the number of AHR-binding sites in the proximal promoter regions. Finally, we show that co-regulated genes in the AHR network activate distinct groups of downstream biological processes. Although the specific findings described here are restricted to hepatic effects under short-term TCDD exposure, this work describes a generalizable approach to the reconstruction and analysis of transcriptional regulatory cascades underlying cellular stress response, revealing network hierarchy and the nature of information flow from the initial signaling events to phenotypic outcomes. Such reconstructed networks can form the basis of a new generation of quantitative adverse outcome pathways.

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Intracellular signaling pathways, when sufficiently perturbed by exogenous chemicals, can lead to an adverse outcome at the cellular level, and potentially at the level of tissues and the whole organism
The selforganizing map (SOM) algorithm follows a clustering technique to group the target genes according to their transcription factor (TF)-binding patterns
Target genes with similar TF-binding patterns are grouped into the same cluster or adjacent clusters, referred to as ‘units’ (Supplementary Methods; Supplementary Code 4)

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Intracellular signaling pathways, when sufficiently perturbed by exogenous chemicals, can lead to an adverse outcome at the cellular level, and potentially at the level of tissues and the whole organism. A core set of master regulatory transcription factors (TFs) are central actors in most molecular pathways leading to altered expression of suites of genes in response to exposure to a variety of chemical compounds (Jennings et al 2013) These TFs, including the nuclear receptors, p53, nuclear factor erythroid 2-related factor (NRF2), nuclear factor-κB (NF-κB), the STAT (signal transducers and activators of transcription) family, and the aryl hydrocarbon receptor (AHR), typically coordinate a broad range of physiological processes like metabolism, oxidative stress response, differentiation, tumor suppression, reproduction, development, and homeostasis (Audet-Walsh and Giguére 2015; Evans and Mangelsdorf 2014; Ma 2013; Tyagi et al 2011; Wright et al 2017). They act as sentinels of normal biological activity, but their inappropriate activation or inhibition can lead to adverse outcomes at the cellular or tissue level (Andersen et al 2013)

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