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At the heart of all biological processes lies the control of nuclear gene expression, which is primarily achieved through the action of transcription factors (TFs) that generally contain a nuclear localization signal (NLS) to facilitate their transport into the nucleus. However, some TFs reside in the cytoplasm in a transcriptionally inactive state and only enter the nucleus in response to specific signals, which in plants include biotic or abiotic stresses. These extra-nuclear TFs can be found in the cytosol or associated with various membrane systems, including the endoplasmic reticulum and plasma membrane. They may be integral proteins with transmembrane domains or associate peripherally with the lipid bilayer via acylation or membrane-binding domains. Although over 30 plant TFs, most of them involved in stress responses, have been experimentally shown to reside outside the nucleus, computational predictions suggest that this number is much larger. Understanding how extra-nuclear TFs are trafficked into the nucleus is essential for reconstructing transcriptional regulatory networks that govern major cellular pathways in response to biotic and abiotic signals. Here, we provide a perspective on what is known on plant extranuclear-nuclear TF retention, nuclear trafficking, and the post-translational modifications that ultimately enable them to regulate gene expression upon entering the nucleus.

 

The highly active family of Mutator (Mu) DNA transposons has been widely used for forward and reverse genetics in maize. There are examples of Mu-suppressible alleles that result in conditional phenotypic effects based on the activity of Mu. Phenotypes from these Mu-suppressible mutations are observed in Mu-active genetic backgrounds, but absent when Mu activity is lost. For some Mu-suppressible alleles, phenotypic suppression likely results from an outward-reading promoter within Mu that is only active when the autonomous Mu element is silenced or lost. We isolated 35 Mu alleles from the UniformMu population that represent insertions in 24 different genes. Most of these mutant alleles are due to insertions within gene coding sequences, but several 5′ UTR and intron insertions were included. RNA-seq and de novo transcript assembly were utilized to document the transcripts produced from 33 of these Mu insertion alleles. For 20 of the 33 alleles, there was evidence of transcripts initiating within the Mu sequence reading through the gene. This outward-reading promoter activity was detected in multiple types of Mu elements and does not depend on the orientation of Mu. Expression analyses of Mu-initiated transcripts revealed the Mu promoter often provides gene expression levels and patterns that are similar to the wild-type gene. These results suggest the Mu promoter may represent a minimal promoter that can respond to gene cis-regulatory elements. Findings from this study have implications for maize researchers using the UniformMu population, and more broadly highlight a strategy for transposons to co-exist with their host.

 

Many plant species exhibit genetic variation for coping with environmental stress. However, there are still limited approaches to effectively uncover the genomic region that regulates distinct responsive patterns of the gene across multiple varieties within the same species under abiotic stress.

By analyzing the transcriptomes of more than 100 maize inbreds, we reveal manycis- andtrans-acting eQTLs that influence the expression response to heat stress. Thecis-acting eQTLs in response to heat stress are identified in genes with differential responses to heat stress between genotypes as well as genes that are only expressed under heat stress. Thecis-acting variants for heat stress-responsive expression likely result from distinct promoter activities, and the differential heat responses of the alleles are confirmed for selected genes using transient expression assays. Global footprinting of transcription factor binding is performed in control and heat stress conditions to document regions with heat-enriched transcription factor binding occupancies.

Footprints enriched near proximal regions of characterized heat-responsive genes in a large association panel can be utilized for prioritizing functional genomic regions that regulate genotype-specific responses under heat stress.

 

Plants respond to wounding stress by changing gene expression patterns and inducing the production of hormones including jasmonic acid. This wounding transcriptional response activates specialized metabolism pathways such as the glucosinolate pathways in Arabidopsis thaliana. While the regulatory factors and sequences controlling a subset of wound-response genes are known, it remains unclear how wound response is regulated globally. Here, we how these responses are regulated by incorporating putative cis-regulatory elements, known transcription factor binding sites, in vitro DNA affinity purification sequencing, and DNase I hypersensitive sites to predict genes with different wound-response patterns using machine learning. We observed that regulatory sites and regions of open chromatin differed between genes upregulated at early and late wounding time-points as well as between genes induced by jasmonic acid and those not induced. Expanding on what we currently know, we identified cis-elements that improved model predictions of expression clusters over known binding sites. Using a combination of genome editing, in vitro DNA-binding assays, and transient expression assays using native and mutated cis-regulatory elements, we experimentally validated four of the predicted elements, three of which were not previously known to function in wound-response regulation. Our study provides a global model predictive of wound response and identifies new regulatory sequences important for wounding without requiring prior knowledge of the transcriptional regulators.

 

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.

 

In biological research domains, liquid chromatography–mass spectroscopy (LC-MS) has prevailed as the preferred technique for generating high quality metabolomic data. However, even with advanced instrumentation and established data acquisition protocols, technical errors are still routinely encountered and can pose a significant challenge to unveiling biologically relevant information. In large-scale studies, signal drift and batch effects are how technical errors are most commonly manifested. We developed pseudoDrift, an R package with capabilities for data simulation and outlier detection, and a new training and testing approach that is implemented to capture and to optionally correct for technical errors in LC–MS metabolomic data. Using data simulation, we demonstrate here that our approach performs equally as well as existing methods and offers increased flexibility to the researcher. As part of our study, we generated a targeted LC–MS dataset that profiled 33 phenolic compounds from seedling stem tissue in 602 genetically diverse non-transgenic maize inbred lines. This dataset provides a unique opportunity to investigate the dynamics of specialized metabolism in plants. 

Abstract 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. 

Abstract Changes in gene expression are important for responses to abiotic stress. Transcriptome profiling of heat- or cold-stressed maize genotypes identifies many changes in transcript abundance. We used comparisons of expression responses in multiple genotypes to identify alleles with variable responses to heat or cold stress and to distinguish examples of cis- or trans-regulatory variation for stress-responsive expression changes. We used motifs enriched near the transcription start sites (TSSs) for thermal stress-responsive genes to develop predictive models of gene expression responses. Prediction accuracies can be improved by focusing only on motifs within unmethylated regions near the TSS and vary for genes with different dynamic responses to stress. Models trained on expression responses in a single genotype and promoter sequences provided lower performance when applied to other genotypes but this could be improved by using models trained on data from all three genotypes tested. The analysis of genes with cis-regulatory variation provides evidence for structural variants that result in presence/absence of transcription factor binding sites in creating variable responses. This study provides insights into cis-regulatory motifs for heat- and cold-responsive gene expression and defines a framework for developing models to predict expression responses across multiple genotypes.