Establishing the Architecture of Plant Gene Regulatory Networks

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

Transcription factors play important roles in regulation of gene expression and phenotype. A variety of approaches have been utilized to develop gene regulatory networks to predict the regulatory targets for each transcription factor, such as yeast-1-hybrid screens and gene co-expression network analysis. Here we identified potential transcription factor targets and used a reverse genetics approach to test the predictions of several gene regulatory networks in maize. Loss-of-function mutant alleles were isolated for 22 maize transcription factors. 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 yeast-1-hybrid screens identified a small subset of predicted targets that exhibit altered expression levels. The analysis of predicted targets from gene co-expression network-based methods found significant enrichments for prediction sets of some transcription factors, but most predicted targets did not exhibit altered expression. This could result from false-positive gene co-expression network predictions, a transcription factor with a secondary regulatory role resulting in minor effects on gene regulation, or redundant gene regulation by other transcription factors. Collectively, these findings suggest that loss-of-function for single uncharacterized transcription factors might have limited phenotypic impacts but can reveal subsets of gene regulatory network predicted targets with altered expression.

Experimental approaches to investigate biophysical interactions between homeodomain transcription factors and DNA

Homeodomain transcription factors (TFs) bind to specific DNA sequences to regulate the expression of target genes. Structural work has provided insight into molecular identities and aided in unraveling structural features of these TFs. However, the detailed affinity and specificity by which these TFs bind to DNA sequences is still largely unknown. Qualitative methods, such as DNA footprinting, Electrophoretic Mobility Shift Assays (EMSAs), Systematic Evolution of Ligands by Exponential Enrichment (SELEX), Bacterial One Hybrid (B1H) systems, Surface Plasmon Resonance (SPR), and Protein Binding Microarrays (PBMs) have been widely used to investigate the biochemical characteristics of TF-DNA binding events. In addition to these qualitative methods, bioinformatic approaches have also assisted in TF binding site discovery. Here we discuss the advantages and limitations of these different approaches, as well as the benefits of utilizing more quantitative approaches, such as Mechanically Induced Trapping of Molecular Interactions (MITOMI), Microscale Thermophoresis (MST) and Isothermal Titration Calorimetry (ITC), in determining the biophysical basis of binding specificity of TF-DNA complexes and improving upon existing computational approaches aimed at affinity predictions.

Evolutionary trajectory of transcription factors and selection of targets for metabolic engineering

Transcription factors (TFs) provide potentially powerful tools for plant metabolic engineering as they often control multiple genes in a metabolic pathway. However, selecting the best TF for a particular pathway has been challenging, and the selection often relies significantly on phylogenetic relationships. Here, we offer examples where evolutionary relationships have facilitated the selection of the suitable TFs, alongside situations where such relationships are misleading from the perspective of metabolic engineering. We argue that the evolutionary trajectory of a particular TF might be a better indicator than protein sequence homology alone in helping decide the best targets for plant metabolic engineering efforts. This article is part of the theme issue 'The evolution of plant metabolism'.

Prioritizing Maize Metabolic Gene Regulators through Multi-Omic Network Integration

Elucidating gene regulatory networks is a major area of study within plant systems biology. Phenotypic traits are intricately linked to specific gene expression profiles. These expression patterns arise primarily from regulatory connections between sets of transcription factors (TFs) and their target genes. Here, we integrated 46 co-expression networks, 283 protein-DNA interaction (PDI) assays, and 16 million SNPs used to identify expression quantitative trait loci (eQTL) to construct TF-target networks. In total, we analyzed ∼4.6M interactions to generate four distinct types of TF-target networks: co-expression, PDI, trans -eQTL, and cis -eQTL combined with PDIs. To functionally annotate TFs based on their target genes, we implemented three different network integration strategies. We evaluated the effectiveness of each strategy through TF loss-of function mutant inspection and random network analyses. The multi-network integration allowed us to identify transcriptional regulators of several biological processes. Using the topological properties of the fully integrated network, we identified potential functionally redundant TF paralogs. Our findings retrieved functions previously documented for numerous TFs and revealed novel functions that are crucial for informing the design of future experiments. The approach here-described lays the foundation for the integration of multi-omic datasets in maize and other plant systems.

GRAPHICAL ABSTRACT

Methodology for Constructing a Knowledgebase for Plant Gene Regulation Information

The amount of biological data is growing at a rapid pace as many high-throughput omics technologies and data pipelines are developed. This is resulting in the growth of databases for DNA and protein sequences, gene expression, protein accumulation, structural, and localization information. The diversity and multi-omics nature of such bioinformatic data requires well-designed databases for flexible organization and presentation. Besides general-purpose online bioinformatic databases, users need narrowly focused online databases to quickly access a meaningful collection of related data for their research. Here, we describe the methodology used to implement a plant gene regulatory knowledgebase, with data, query, and tool features, as well as the ability to expand to accommodate future datasets. We exemplify this methodology for the GRASSIUS knowledgebase, but it is applicable to developing and updating similar plant gene regulatory knowledgebases. GRASSIUS organizes and presents gene regulatory data from grass species with a central focus on maize (Zea mays). The main class of data presented include not only the families of transcription factors (TFs) and co-regulators (CRs) but also protein-DNA interaction data, where available.

Cis-regulatory sequences in plants: Their importance, discovery, and future challenges

The identification and characterization of cis-regulatory DNA sequences and how they function to coordinate responses to developmental and environmental cues is of paramount importance to plant biology. Key to these regulatory processes are cis-regulatory modules (CRMs), which include enhancers and silencers. Despite the extraordinary advances in high-quality sequence assemblies and genome annotations, the identification and understanding of CRMs, and how they regulate gene expression, lag significantly behind. This is especially true for their distinguishing characteristics and activity states. Here, we review the current knowledge on CRMs and breakthrough technologies enabling identification, characterization, and validation of CRMs; we compare the genomic distributions of CRMs with respect to their target genes between different plant species, and discuss the role of transposable elements harboring CRMs in the evolution of gene expression. This is an exciting time to study cis-regulomes in plants; however, significant existing challenges need to be overcome to fully understand and appreciate the role of CRMs in plant biology and in crop improvement.

Improved Functional Expression of Cytochrome P450s in Saccharomyces cerevisiae Through Screening a cDNA Library From Arabidopsis thaliana

Cytochrome P450 enzymes (P450s) are a superfamily of heme-thiolate proteins widely existing in various organisms and play a key role in the metabolic network and secondary metabolism. However, the low expression levels and activities have become the biggest challenge for P450s studies. To improve the functional expression of P450s in Saccharomyces cerevisiae, an Arabidopsis thaliana cDNA library was expressed in the betaxanthin-producing yeast strain, which functioned as a biosensor for high throughput screening. Three new target genes AtGRP7, AtMSBP1, and AtCOL4 were identified to improve the functional expression of CYP76AD1 in yeast, with accordingly the accumulation of betaxanthin increased for 1.32-, 1.86-, and 1.10-fold, respectively. In addition, these three targets worked synergistically/additively to improve the production of betaxanthin, representing a total of 2.36-fold improvement when compared with the parent strain. More importantly, these genes were also determined to effectively increase the activity of another P450 enzyme (CYP736A167), catalyzing the hydroxylation of α-santalene to produce Z-α-santalol. Simultaneous overexpression of AtGRP7, AtMSBP1, and AtCOL4 increased α-santalene to Z-α-santalol conversion rate for more than 2.97-fold. The present study reported a novel strategy to improve the functional expression of P450s in S. cerevisiae and promises the construction of platform yeast strains for the production of natural products.

Challenges of Translating Gene Regulatory Information into Agronomic Improvements

Improvement of agricultural species has exploited the genetic variation responsible for complex quantitative traits. Much of the functional variation is regulatory, in cis-regulatory elements and trans-acting factors that ultimately contribute to gene expression differences. However, the identification of gene regulatory network components that, when modulated, will increase plant productivity or resilience, is challenging, yet essential to provide increased predictive power for genome engineering approaches that are likely to benefit useful traits. Here, we discuss the opportunities and limitations of using data obtained from gene coexpression, transcription factor binding, and genome-wide association mapping analyses to predict regulatory interactions that impact crop improvement. It is apparent that a combination of information from these data types is necessary for the reliable identification and utilization of important regulatory interactions that underlie complex agronomic traits.

The NAC family transcription factor GmNAC42-1 regulates biosynthesis of the anticancer and neuroprotective glyceollins in soybean

BACKGROUND

Glyceollins are isoflavonoid-derived pathogen-inducible defense metabolites (phytoalexins) from soybean (Glycine max L. Merr) that have important roles in providing defense against pathogens. They also have impressive anticancer and neuroprotective activities in mammals. Despite their potential usefulness as therapeutics, glyceollins are not economical to synthesize and are biosynthesized only transiently and in low amounts in response to specific stresses. Engineering the regulation of glyceollin biosynthesis may be a promising approach to enhance their bioproduction, yet the transcription factors (TFs) that regulate their biosynthesis have remained elusive. To address this, we first aimed to identify novel abiotic stresses that enhance or suppress the elicitation of glyceollins and then used a comparative transcriptomics approach to search for TF gene candidates that may positively regulate glyceollin biosynthesis.

RESULTS

Acidity stress (pH 3.0 medium) and dehydration exerted prolonged (week-long) inductive or suppressive effects on glyceollin biosynthesis, respectively. RNA-seq found that all known biosynthetic genes were oppositely regulated by acidity stress and dehydration, but known isoflavonoid TFs were not. Systemic acquired resistance (SAR) genes were highly enriched in the geneset. We chose to functionally characterize the NAC (NAM/ATAF1/2/CUC2)-family TF GmNAC42-1 that was annotated as an SAR gene and a homolog of the Arabidopsis thaliana (Arabidopsis) indole alkaloid phytoalexin regulator ANAC042. Overexpressing and silencing GmNAC42-1 in elicited soybean hairy roots dramatically enhanced and suppressed the amounts of glyceollin metabolites and biosynthesis gene mRNAs, respectively. Yet, overexpressing GmNAC42-1 in non-elicited hairy roots failed to stimulate the expressions of all biosynthesis genes. Thus, GmNAC42-1 was necessary but not sufficient to activate all biosynthesis genes on its own, suggesting an important role in the glyceollin gene regulatory network (GRN). The GmNAC42-1 protein directly bound the promoters of biosynthesis genes IFS2 and G4DT in the yeast one-hybrid (Y1H) system.

CONCLUSIONS

Acidity stress is a novel elicitor and dehydration is a suppressor of glyceollin biosynthesis. The TF gene GmNAC42-1 is an essential positive regulator of glyceollin biosynthesis. Overexpressing GmNAC42-1 in hairy roots can be used to increase glyceollin yields > 10-fold upon elicitation. Thus, manipulating the expressions of glyceollin TFs is an effective strategy for enhancing the bioproduction of glyceollins in soybean.

A Maize Gene Regulatory Network for Phenolic Metabolism

The translation of the genotype into phenotype, represented for example by the expression of genes encoding enzymes required for the biosynthesis of phytochemicals that are important for interaction of plants with the environment, is largely carried out by transcription factors (TFs) that recognize specific cis-regulatory elements in the genes that they control. TFs and their target genes are organized in gene regulatory networks (GRNs), and thus uncovering GRN architecture presents an important biological challenge necessary to explain gene regulation. Linking TFs to the genes they control, central to understanding GRNs, can be carried out using gene- or TF-centered approaches. In this study, we employed a gene-centered approach utilizing the yeast one-hybrid assay to generate a network of protein-DNA interactions that participate in the transcriptional control of genes involved in the biosynthesis of maize phenolic compounds including general phenylpropanoids, lignins, and flavonoids. We identified 1100 protein-DNA interactions involving 54 phenolic gene promoters and 568 TFs. A set of 11 TFs recognized 10 or more promoters, suggesting a role in coordinating pathway gene expression. The integration of the gene-centered network with information derived from TF-centered approaches provides a foundation for a phenolics GRN characterized by interlaced feed-forward loops that link developmental regulators with biosynthetic genes.