Meta Gene Regulatory Networks in Maize Highlight Functionally Relevant Regulatory Interactions.

Peng Zhou,Zhi Li,Erich Grotewold,Erika Magnusson,Fabio Gomez Cano,Jaclyn M Noshay,Nathan M Springer,Steven P Briggs,Peter A Crisp,Candice N Hirsch

doi:10.1105/tpc.20.00080

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.

Highlights

A central goal in linking genotype to phenotype is to understand how a limited number of transcription factors (TFs) drive dynamic gene-expression changes in different cell types and environmental conditions
We used these datasets to generate putative Gene regulatory networks (GRNs) based upon the expression patterns of TFs and their target genes
A total of 45 putative GRNs were developed for 25 different maize RNA-Seq expression datasets that were all aligned to the B73v4 genome and normalized using consistent methods (Figure 1, Method)

Summary

Introduction

A central goal in linking genotype to phenotype is to understand how a limited number of transcription factors (TFs) drive dynamic gene-expression changes in different cell types and environmental conditions. Based on the genomes of well-characterized model systems, multicellular organisms dedicate a substantial portion of their proteincoding genes (6-8%) to the expression of TFs (Babu et al, 2004). Characterizing the TF regulatory landscape within an organism is critical to expanding our knowledge of complex phenotypic traits and gene expression networks. A specific TF can regulate multiple genes that are part of the same biochemical pathway, providing higher-level control of cellular functions. This can make TFs an attractive target for modulation through breeding or biotechnology approaches

Methods

Results

Conclusion