RT Journal Article
SR Electronic
T1 Gene Coregulation and Coexpression in the Aryl Hydrocarbon Receptor-mediated Transcriptional Regulatory Network in the Mouse Liver
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 260018
DO 10.1101/260018
A1 Navya Josyula
A1 Melvin E. Andersen
A1 Norbert Kaminski
A1 Edward Dere
A1 Timothy R. Zacharewski
A1 Sudin Bhattacharya
YR 2018
UL http://biorxiv.org/content/early/2018/02/05/260018.abstract
AB Tissue-specific network models of chemical-induced gene perturbation can improve our mechanistic understanding of the intracellular events leading to adverse health effects resulting from chemical exposure. The aryl hydrocarbon receptor (AHR) is a ligand-inducible transcription factor (TF) that activates a battery of genes and produces a variety of species-specific adverse effects in response to the potent and persistent environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Here we assemble a global map of the AHR gene regulatory network in TCDD-treated mouse liver from a combination of previously published gene expression and genome-wide TF binding data sets. Using Kohonen selforganizing maps and subspace clustering, we show that genes co-regulated by common upstream TFs in the AHR network exhibit a pattern of co-expression. Specifically, directly-bound, indirectly-bound and non-genomic AHR target genes exhibit distinct patterns of gene expression, with the directly bound targets generally associated with highest median expression. Further, 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, with AHR-bound target genes enriched for metabolic processes and enrichment of immune responses among AHR-unbound target genes, likely reflecting infiltration of immune cells into the mouse liver upon TCDD treatment. This work describes an approach to the reconstruction and analysis of transcriptional regulatory cascades underlying cellular stress response using bioinformatic and statistical tools.