Transcriptome profiling of maize transcription factor mutants to probe gene regulatory network predictions

Abstract

AbstractTranscription factors (TFs) play important roles in regulation of gene expression and phenotype. A variety of approaches have been utilized to develop gene-regulatory networks (GRNs) to predict the regulatory targets for each TF, such as yeast-one-hybrid (Y1H) screens and gene co-expression network (GCN) analysis. Here we identified potential TF targets and used a reverse genetics approach to test the predictions of several GRNs in maize. Loss-of-function mutant alleles were isolated for 22 maize TFs. These mutants did not exhibit obvious morphological phenotypes. However, transcriptomic profiling identified differentially expressed genes in each of the mutant genotypes, and targeted metabolic profiling indicated variable phenolic accumulation in some mutants. An analysis of expression levels for predicted target genes based on Y1H screens identified a small subset of predicted targets that exhibit altered expression levels. The analysis of predicted targets from GCN-based methods found significant enrichments for prediction sets of some TFs, but most predicted targets did not exhibit altered expression. This could result from false-positive GCN predictions, a TF with a secondary regulatory role resulting in minor effects on gene regulation, or redundant gene regulation by other TFs. Collectively, these findings suggest that loss-of-function for single uncharacterized TFs might have limited phenotypic impacts but can reveal subsets of GRN predicted targets with altered expression.