The regulation of gene expression is central to many biological processes. Gene regulatory networks (GRNs) link transcription factors (TFs) to their target genes and represent maps of potential transcriptional regulation. Here, we analyzed a large number of publically available maize (Zea mays) transcriptome data sets including >6000 RNA sequencing samples to generate 45 coexpression-based GRNs that represent potential regulatory relationships between TFs and other genes in different populations of samples (cross-tissue, cross-genotype, and tissue-and-genotype samples). While these networks are all enriched for biologically relevant interactions, different networks capture distinct TF-target associations and biological processes. By examining the power of our coexpression-based GRNs to accurately predict covarying TF-target relationships in natural variation data sets, we found that presence/absence changes rather than quantitative changes in TF gene expression are more likely associated with changes in target gene expression. Integrating information from our TF-target predictions and previous expression quantitative trait loci (eQTL) mapping results provided support for 68 TFs underlying 74 previously identified trans-eQTL hotspots spanning a variety of metabolic pathways. This study highlights the utility of developing multiple GRNs within a species to detect putative regulators of important plant pathways and provides potential targets for breeding or biotechnological applications.
Bibliographical noteFunding Information:
TF-Target Interactions Predicted by GRNs Are Supported by Experimentally Derived TF Targets and Knockout Mutant RNA-Seq Experiments.
We thank Sarah N. Anderson, Maria Katherine Mejía-Guerra, and Peter Hermanson for reading through the article and providing valuable feedback. We thank the Minnesota Supercomputing Institute at the University of Minnesota (http://www.msi.umn.edu) for providing resources that contributed to the research results reported within this article. This study was funded by the National Science Foundation (grants IOS-1546899 and IOS-1733633). This work is supported in part by Michigan State University and the National Science Foundation Research Traineeship Program, Division of Graduate Education (grant DGE-1828149 to F.A.G.C.).