Gomez-Cano, L. | Search bioRxiv

bioRxiv2024

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

Transcription 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.

plant biology↗

bioRxiv2024

Prioritizing Metabolic Gene Regulators through Multi-Omic Network Integration in Maize

Gene regulatory networks (GRNs) link transcription factors (TFs) to the biological processes they control. Assembling them remains difficult because the relevant data types (gene expression, protein-DNA interactions, and genetic variation) are large, heterogeneous, and rarely combined. Here, we developed and benchmarked a framework that integrates these data into TF-function predictions in maize. We assembled four complementary TF-target gene network layers, based on expression, protein-DNA interaction, trans-expression quantitative trait loci (eQTL), and cis-eQTL-supported interaction, from 46 Random Forest (RF)-inferred regulatory networks, 283 protein-DNA interaction assays, and eQTLs derived from 16 million SNPs across 304 inbred lines. Together these layers comprised ~4.6 million interactions. We then compared three strategies for integrating them, benchmarking each against published TF knockout data. A network-based approach, which represents every gene as a low-dimensional vector (embedding) learned from the combined network, outperformed the two overlap-based strategies, annotating over eight times more TFs (~3,000), agreeing most closely with gene knockout responses where predictions existed, and remaining robust when individual layers lacked data. The predictions recovered TF functions and predicted new regulators of hormone, developmental, and metabolic processes, which we prioritized per process and mapped to specific conditions. Using similarity on the low-dimensional vector representation (embedding), we further identified candidate functionally redundant or diverged TF paralogs. Because it relies only on data types now common across species, the framework provides a generalizable template for prioritizing regulatory genes in maize and other plants.

plant biology↗