Meta Gene Regulatory Networks in Maize Highlight Functionally Relevant Regulatory Interactions[OPEN]

• Published in: The Plant cell Vol. 32; no. 5; pp. 1377 - 1396
• Main Authors: Zhou, Peng, Li, Zhi, Magnusson, Erika, Gomez Cano, Fabio, Crisp, Peter A., Noshay, Jaclyn M., Grotewold, Erich, Hirsch, Candice N., Briggs, Steven P., Springer, Nathan M.
• Format: Journal Article
• Language: English
• Published: England American Society of Plant Biologists 01.05.2020
• Subjects: Arabidopsis - genetics; Databases, Genetic; Gene Expression Regulation, Plant; Gene Ontology; Gene Regulatory Networks; Large-Scale Biology; Molecular Sequence Annotation; Phylogeny; Quantitative Trait Loci - genetics; Transcription Factors - metabolism; Zea mays - genetics
• ISSN: 1040-4651; 1532-298X
• DOI: 10.1105/tpc.20.00080

The generation of gene regulatory networks from thousands of maize transcriptome data sets identifies putative transcription factor targets and candidate regulators of important metabolic pathways. Abstract 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.