TARGET: a transient transformation system for genome-wide transcription factor target discovery

Strigolactone induces D14-dependent large-scale changes in gene expression requiring SWI/SNF chromatin remodellers

Strigolactones (SL) function as plant hormones in control of multiple aspects of plant development, mostly via the regulation of gene expression. Immediate early-gene regulation by SL remains unexplored due to difficulty in dissecting early from late gene expression responses to SL. We used synthetic SL, rac-GR24 treatment of protoplasts and RNA-seq to explore early SL-induced changes in gene expression over time (5-180 minutes) and discovered rapid, dynamic and SL receptor D14-dependent regulation of gene expression in response to rac-GR24. Importantly, we discovered a significant dependence of SL signalling on chromatin remodelling processes, as the induction of a key SL-induced transcription factor BRANCHED1 requires the SWI/SNF chromatin remodelling ATPase SPLAYED (SYD) and leads to upregulation of a homologue SWI/SNF ATPase BRAHMA. ATAC-seq profiling of genome-wide changes in chromatin accessibility in response to rac-GR24 identified large-scale changes, with over 1400 differentially accessible regions. These changes in chromatin accessibility often precede transcriptional changes and are likely to harbour SL cis-regulatory elements. Importantly, we discovered that this early and extensive modification of the chromatin landscape also requires SYD. This study, therefore, provides evidence that SL signalling requires regulation of chromatin accessibility, and it identifies genomic locations harbouring likely SL cis-regulatory sequences.

Exploring the frontier of rapid prototyping technologies for plant synthetic biology and what could lie beyond

Realizing the full potential of plant synthetic biology both to elucidate the relationship between genotype and phenotype and to apply these insights to engineer traits requires rapidly iterating through design-build-test cycles. However, the months-long process of transgenesis, the long generation times, and the size-based limitations on experimentation have stymied progress by limiting the speed and scale of these cycles. Herein, we review a representative sample of recent studies that demonstrate a variety of rapid prototyping technologies that overcome some of these bottlenecks and accelerate progress. However, each of them has caveats that limit their broad utility. Their complementary strengths and weaknesses point to the intriguing possibility that these strategies could be combined in the future to enable rapid and scalable deployment of synthetic biology in plants.

Nitrogen sensing and regulatory networks: it's about time and space

A plant's response to external and internal nitrogen signals/status relies on sensing and signaling mechanisms that operate across spatial and temporal dimensions. From a comprehensive systems biology perspective, this involves integrating nitrogen responses in different cell types and over long distances to ensure organ coordination in real time and yield practical applications. In this prospective review, we focus on novel aspects of nitrogen (N) sensing/signaling uncovered using temporal and spatial systems biology approaches, largely in the model Arabidopsis. The temporal aspects span: transcriptional responses to N-dose mediated by Michaelis-Menten kinetics, the role of the master NLP7 transcription factor as a nitrate sensor, its nitrate-dependent TF nuclear retention, its "hit-and-run" mode of target gene regulation, and temporal transcriptional cascade identified by "network walking." Spatial aspects of N-sensing/signaling have been uncovered in cell type-specific studies in roots and in root-to-shoot communication. We explore new approaches using single-cell sequencing data, trajectory inference, and pseudotime analysis as well as machine learning and artificial intelligence approaches. Finally, unveiling the mechanisms underlying the spatial dynamics of nitrogen sensing/signaling networks across species from model to crop could pave the way for translational studies to improve nitrogen-use efficiency in crops. Such outcomes could potentially reduce the detrimental effects of excessive fertilizer usage on groundwater pollution and greenhouse gas emissions.

Insights into molecular links and transcription networks integrating drought stress and nitrogen signaling

Drought and the availability of nitrate, the predominant source of nitrogen (N) in agriculture, are major factors limiting plant growth and crop productivity. The dissection of the transcriptional networks' components integrating droght stress and nitrate responses provides valuable insights into how plants effectively balance stress response with growth programs. Recent evidence in Arabidopsis thaliana indicates that transcription factors (TFs) involved in abscisic acid (ABA) signaling affect N metabolism and nitrate responses, and reciprocally, components of nitrate signaling might affect ABA and drought gene responses. Advances in understanding regulatory circuits of nitrate and drought crosstalk in plant tissues empower targeted genetic modifications to enhance plant development and stress resistance, critical traits for optimizing crop yield and promoting sustainable agriculture.

Functional investigation of five R2R3-MYB transcription factors associated with wood development in Eucalyptus using DAP-seq-ML

A multi-tiered transcriptional network regulates xylem differentiation and secondary cell wall (SCW) formation in plants, with evidence of both conserved and lineage-specific SCW network architecture. We aimed to elucidate the roles of selected R2R3-MYB transcription factors (TFs) linked to Eucalyptus wood formation by identifying genome-wide TF binding sites and direct target genes through an improved DAP-seq protocol combined with machine learning for target gene assignment (DAP-seq-ML). We applied this to five TFs including a well-studied SCW master regulator (EgrMYB2; homolog of AtMYB83), a repressor of lignification (EgrMYB1; homolog of AtMYB4), a TF affecting SCW thickness and vessel density (EgrMYB137; homolog of PtrMYB074) and two TFs with unclear roles in SCW regulation (EgrMYB135 and EgrMYB122). Each DAP-seq TF peak set (average 12,613 peaks) was enriched for canonical R2R3-MYB binding motifs. To improve the reliability of target gene assignment to peaks, a random forest classifier was developed from Arabidopsis DAP-seq, RNA-seq, chromatin, and conserved noncoding sequence data which demonstrated significantly higher precision and recall to the baseline method of assigning genes to proximal peaks. EgrMYB1, EgrMYB2 and EgrMYB137 predicted targets showed clear enrichment for SCW-related biological processes. As validation, EgrMYB137 overexpression in transgenic Eucalyptus hairy roots increased xylem lignification, while its dominant repression in transgenic Arabidopsis and Populus reduced xylem lignification, stunted growth, and caused downregulation of SCW genes. EgrMYB137 targets overlapped significantly with those of EgrMYB2, suggesting partial functional redundancy. Our results show that DAP-seq-ML identified biologically relevant R2R3-MYB targets supported by the finding that EgrMYB137 promotes SCW lignification in planta.

Signaling pathways underlying nitrogen transport and metabolism in plants

Nitrogen (N) is an essential macronutrient required for plant growth and crop production. However, N in soil is usually insufficient for plant growth. Thus, chemical N fertilizer has been extensively used to increase crop production. Due to negative effects of N rich fertilizer on the environment, improving N usage has been a major issue in the field of plant science to achieve sustainable production of crops. For that reason, many efforts have been made to elucidate how plants regulate N uptake and utilization according to their surrounding habitat over the last 30 years. Here, we provide recent advances focusing on regulation of N uptake, allocation of N by N transporting system, and signaling pathway controlling N responses in plants. [BMB Reports 2023; 56(2): 56-64].

DamID-seq: A Genome-Wide DNA Methylation Method that Captures Both Transient and Stable TF-DNA Interactions in Plant Cells

Capturing the dynamic and transient interactions of a transcription factor (TF) with its genome-wide targets whose regulation leads to plants' adaptation to their changing environment is a major technical challenge. This is a widespread problem with biochemical methods such as chromatin immunoprecipitation-sequencing (ChIP-seq) which are biased towards capturing stable TF-target gene interactions. Herein, we describe how DNA adenine methyltransferase identification and sequencing (DamID-seq) can be used to capture both transient and stable TF-target interactions by DNA methylation. The DamID technique uses a TF protein fused to a DNA adenine methyltransferase (Dam) from E. coli. When expressed in a plant cell, the Dam-TF fusion protein will methylate adenine (A) bases near the sites of TF-DNA interactions. In this way, DamID results in a permanent, stable DNA methylation mark on TF-target gene promoters, even if the target gene is only transiently "touched" by the Dam-TF fusion protein. Here we provide a step-by-step protocol to perform DamID-seq experiments in isolated plant cells for any Dam-TF fusion protein of interest. We also provide information that will enable researchers to analyze DamID-seq data to identify TF-binding sites in the genome. Our protocol includes instructions for vector cloning of the Dam-TF fusion proteins, plant cell protoplast transfections, DamID preps, library preparation, and sequencing data analysis. The protocol outlined in this chapter is performed in Arabidopsis thaliana, however, the DamID-seq workflow developed in this guide is broadly applicable to other plants and organisms.

The TARGET System: Rapid Identification of Direct Targets of Transcription Factors by Gene Regulation in Plant Cells

The TARGET system allows for the rapid identification of direct regulated gene targets of transcription factors (TFs). It employs the transient transformation of plant protoplasts with inducible nuclear entry of the TF and subsequent transcriptomic and/or ChIP-seq analysis. The ability to separate direct TF-target gene regulatory interactions from indirect downstream responses and the significantly shorter amount of time required to perform the assay, compared to the generation of transgenics, make this plant cell-based approach a valuable tool for a higher throughput approach to identify the genome-wide targets of multiple TFs, to build validated transcriptional networks in plants. Here, we describe the use of the TARGET system in Arabidopsis seedling root protoplasts to map the gene regulatory network downstream of transcription factors-of-interest.

Glutamate: A multifunctional amino acid in plants

Glutamate (Glu) is a versatile metabolite and a signaling molecule in plants. Glu biosynthesis is associated with the primary nitrogen assimilation pathway. The conversion between Glu and 2-oxoglutarate connects Glu metabolism to the tricarboxylic acid cycle, carbon metabolism, and energy production. Glu is the predominant amino donor for transamination reactions in the cell. In addition to protein synthesis, Glu is a building block for tetrapyrroles, glutathione, and folate. Glu is the precursor of γ-aminobutyric acid that plays an important role in balancing carbon/nitrogen metabolism and various cellular processes. Glu can conjugate to the major auxin indole 3-acetic acid (IAA), and IAA-Glu is destined for oxidative degradation. Glu also conjugates with isochorismate for the production of salicylic acid. Accumulating evidence indicates that Glu functions as a signaling molecule to regulate plant growth, development, and defense responses. The ligand-gated Glu receptor-like proteins (GLRs) mediate some of these responses. However, many of the Glu signaling events are GLR-independent. The receptor perceiving extracellular Glu as a danger signal is still unknown. In addition to GLRs, Glu may act on receptor-like kinases or receptor-like proteins to trigger immune responses. Glu metabolism and Glu signaling may entwine to regulate growth, development, and defense responses in plants.

High-throughput methods for genome editing: the more the better

During the last decade, targeted genome-editing technologies, especially clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein (Cas) technologies, have permitted efficient targeting of genomes, thereby modifying these genomes to offer tremendous opportunities for deciphering gene function and engineering beneficial traits in many biological systems. As a powerful genome-editing tool, the CRISPR/Cas systems, combined with the development of next-generation sequencing and many other high-throughput techniques, have thus been quickly developed into a high-throughput engineering strategy in animals and plants. Therefore, here, we review recent advances in using high-throughput genome-editing technologies in animals and plants, such as the high-throughput design of targeted guide RNA (gRNA), construction of large-scale pooled gRNA, and high-throughput genome-editing libraries, high-throughput detection of editing events, and high-throughput supervision of genome-editing products. Moreover, we outline perspectives for future applications, ranging from medication using gene therapy to crop improvement using high-throughput genome-editing technologies.

The biology of time: dynamic responses of cell types to developmental, circadian and environmental cues

As sessile organisms, plants are finely tuned to respond dynamically to developmental, circadian and environmental cues. Genome-wide studies investigating these types of cues have uncovered the intrinsically different ways they can impact gene expression over time. Recent advances in single-cell sequencing and time-based bioinformatic algorithms are now beginning to reveal the dynamics of these time-based responses within individual cells and plant tissues. Here, we review what these techniques have revealed about the spatiotemporal nature of gene regulation, paying particular attention to the three distinct ways in which plant tissues are time sensitive. (i) First, we discuss how studying plant cell identity can reveal developmental trajectories hidden in pseudotime. (ii) Next, we present evidence that indicates that plant cell types keep their own local time through tissue-specific regulation of the circadian clock. (iii) Finally, we review what determines the speed of environmental signaling responses, and how they can be contingent on developmental and circadian time. By these means, this review sheds light on how these different scales of time-based responses can act with tissue and cell-type specificity to elicit changes in whole plant systems.

Spatiotemporal analysis identifies ABF2 and ABF3 as key hubs of endodermal response to nitrate

Nitrate is a nutrient and a potent signal that impacts global gene expression in plants. However, the regulatory factors controlling temporal and cell type-specific nitrate responses remain largely unknown. We assayed nitrate-responsive transcriptome changes in five major root cell types of the Arabidopsis thaliana root as a function of time. We found that gene-expression response to nitrate is dynamic and highly localized and predicted cell type-specific transcription factor (TF)-target interactions. Among cell types, the endodermis stands out as having the largest and most connected nitrate-regulatory gene network. ABF2 and ABF3 are major hubs for transcriptional responses in the endodermis cell layer. We experimentally validated TF-target interactions for ABF2 and ABF3 by chromatin immunoprecipitation followed by sequencing and a cell-based system to detect TF regulation genome-wide. Validated targets of ABF2 and ABF3 account for more than 50% of the nitrate-responsive transcriptome in the endodermis. Moreover, ABF2 and ABF3 are involved in nitrate-induced lateral root growth. Our approach offers an unprecedented spatiotemporal resolution of the root response to nitrate and identifies important components of cell-specific gene regulatory networks.

TARGETing Transcriptional Regulation in the Medicinal Plant Catharanthus roseus

Transcriptional regulation is a central piece of the highly valuable monoterpenoid indole alkaloid pathway of C. roseus , and the ultimate tool for its understanding and manipulation. Here, we describe the adaptation of the TARGET methodology to identify specific and genome-wide leaf targets of C. roseus candidate transcription factors (TFs).

Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice

Nitrogen (N) and Water (W) - two resources critical for crop productivity - are becoming increasingly limited in soils globally. To address this issue, we aim to uncover the gene regulatory networks (GRNs) that regulate nitrogen use efficiency (NUE) - as a function of water availability - in Oryza sativa, a staple for 3.5 billion people. In this study, we infer and validate GRNs that correlate with rice NUE phenotypes affected by N-by-W availability in the field. We did this by exploiting RNA-seq and crop phenotype data from 19 rice varieties grown in a 2x2 N-by-W matrix in the field. First, to identify gene-to-NUE field phenotypes, we analyzed these datasets using weighted gene co-expression network analysis (WGCNA). This identified two network modules ("skyblue" & "grey60") highly correlated with NUE grain yield (NUEg). Next, we focused on 90 TFs contained in these two NUEg modules and predicted their genome-wide targets using the N-and/or-W response datasets using a random forest network inference approach (GENIE3). Next, to validate the GENIE3 TF→target gene predictions, we performed Precision/Recall Analysis (AUPR) using nine datasets for three TFs validated in planta. This analysis sets a precision threshold of 0.31, used to "prune" the GENIE3 network for high-confidence TF→target gene edges, comprising 88 TFs and 5,716 N-and/or-W response genes. Next, we ranked these 88 TFs based on their significant influence on NUEg target genes responsive to N and/or W signaling. This resulted in a list of 18 prioritized TFs that regulate 551 NUEg target genes responsive to N and/or W signals. We validated the direct regulated targets of two of these candidate NUEg TFs in a plant cell-based TF assay called TARGET, for which we also had in planta data for comparison. Gene ontology analysis revealed that 6/18 NUEg TFs - OsbZIP23 (LOC_Os02g52780), Oshox22 (LOC_Os04g45810), LOB39 (LOC_Os03g41330), Oshox13 (LOC_Os03g08960), LOC_Os11g38870, and LOC_Os06g14670 - regulate genes annotated for N and/or W signaling. Our results show that OsbZIP23 and Oshox22, known regulators of drought tolerance, also coordinate W-responses with NUEg. This validated network can aid in developing/breeding rice with improved yield on marginal, low N-input, drought-prone soils.

Isolation of Specialized Plant Cells by Fluorescence-Activated Cell Sorting

Plant organs are built of different cell types, characterized by specific transcription programs and metabolic profiles. The possibility of isolation of such cell types to perform differential transcriptomic, proteomic and metabolomic analyses is highly important to understand many aspects of plant physiology, namely, the structure and regulation of economically valuable specialized metabolic pathways. Here, we describe the isolation of idioblast leaf protoplasts of the medicinal plant Catharanthus roseus by fluorescence-activated cell sorting, taking advantage of the differential autofluorescence properties of those specialized cells.

A genome-scale TF-DNA interaction network of transcriptional regulation of Arabidopsis primary and specialized metabolism

Plant metabolism is more complex relative to individual microbes. In single-celled microbes, transcriptional regulation by single transcription factors (TFs) is sufficient to shift primary metabolism. Corresponding genome-level transcriptional regulatory maps of metabolism reveal the underlying design principles responsible for these shifts as a model in which master regulators largely coordinate specific metabolic pathways. Plant primary and specialized metabolism occur within innumerable cell types, and their reactions shift depending on internal and external cues. Given the importance of plants and their metabolites in providing humanity with food, fiber, and medicine, we set out to develop a genome-scale transcriptional regulatory map of Arabidopsis metabolic genes. A comprehensive set of protein-DNA interactions between Arabidopsis thaliana TFs and gene promoters in primary and specialized metabolic pathways were mapped. To demonstrate the utility of this resource, we identified and functionally validated regulators of the tricarboxylic acid (TCA) cycle. The resulting network suggests that plant metabolic design principles are distinct from those of microbes. Instead, metabolism appears to be transcriptionally coordinated via developmental- and stress-conditional processes that can coordinate across primary and specialized metabolism. These data represent the most comprehensive resource of interactions between TFs and metabolic genes in plants.

Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks

All aspects of transcription and its regulation involve dynamic events. However, capturing these dynamic events in gene regulatory networks (GRNs) offers both a promise and a challenge. The promise is that capturing and modeling the dynamic changes in GRNs will allow us to understand how organisms adapt to a changing environment. The ability to mount a rapid transcriptional response to environmental changes is especially important in nonmotile organisms such as plants. The challenge is to capture these dynamic, genome-wide events and model them in GRNs. In this review, we cover recent progress in capturing dynamic interactions of transcription factors with their targets-at both the local and genome-wide levels-and how they are used to learn how GRNs operate as a function of time. We also discuss recent advances that employ time-based machine learning approaches to forecast gene expression at future time points, a key goal of systems biology.

CRISPR screens in plants: approaches, guidelines, and future prospects

Clustered regularly interspaced short palindromic repeat (CRISPR)-associated systems have revolutionized genome engineering by facilitating a wide range of targeted DNA perturbations. These systems have resulted in the development of powerful new screens to test gene functions at the genomic scale. While there is tremendous potential to map and interrogate gene regulatory networks at unprecedented speed and scale using CRISPR screens, their implementation in plants remains in its infancy. Here we discuss the general concepts, tools, and workflows for establishing CRISPR screens in plants and analyze the handful of recent reports describing the use of this strategy to generate mutant knockout collections or to diversify DNA sequences. In addition, we provide insight into how to design CRISPR knockout screens in plants given the current challenges and limitations and examine multiple design options. Finally, we discuss the unique multiplexing capabilities of CRISPR screens to investigate redundant gene functions in highly duplicated plant genomes. Combinatorial mutant screens have the potential to routinely generate higher-order mutant collections and facilitate the characterization of gene networks. By integrating this approach with the numerous genomic profiles that have been generated over the past two decades, the implementation of CRISPR screens offers new opportunities to analyze plant genomes at deeper resolution and will lead to great advances in functional and synthetic biology.

GARP transcription factors repress Arabidopsis nitrogen starvation response via ROS-dependent and -independent pathways

Plants need to cope with strong variations of nitrogen availability in the soil. Although many molecular players are being discovered concerning how plants perceive NO3- provision, it is less clear how plants recognize a lack of nitrogen. Following nitrogen removal, plants activate their nitrogen starvation response (NSR), which is characterized by the activation of very high-affinity nitrate transport systems (NRT2.4 and NRT2.5) and other sentinel genes involved in N remobilization such as GDH3. Using a combination of functional genomics via transcription factor perturbation and molecular physiology studies, we show that the transcription factors belonging to the HHO subfamily are important regulators of NSR through two potential mechanisms. First, HHOs directly repress the high-affinity nitrate transporters, NRT2.4 and NRT2.5. hho mutants display increased high-affinity nitrate transport activity, opening up promising perspectives for biotechnological applications. Second, we show that reactive oxygen species (ROS) are important to control NSR in wild-type plants and that HRS1 and HHO1 overexpressors and mutants are affected in their ROS content, defining a potential feed-forward branch of the signaling pathway. Taken together, our results define the relationships of two types of molecular players controlling the NSR, namely ROS and the HHO transcription factors. This work (i) up opens perspectives on a poorly understood nutrient-related signaling pathway and (ii) defines targets for molecular breeding of plants with enhanced NO3- uptake.

ConnecTF: A platform to integrate transcription factor-gene interactions and validate regulatory networks

Deciphering gene regulatory networks (GRNs) is both a promise and challenge of systems biology. The promise lies in identifying key transcription factors (TFs) that enable an organism to react to changes in its environment. The challenge lies in validating GRNs that involve hundreds of TFs with hundreds of thousands of interactions with their genome-wide targets experimentally determined by high-throughput sequencing. To address this challenge, we developed ConnecTF, a species-independent, web-based platform that integrates genome-wide studies of TF-target binding, TF-target regulation, and other TF-centric omic datasets and uses these to build and refine validated or inferred GRNs. We demonstrate the functionality of ConnecTF by showing how integration within and across TF-target datasets uncovers biological insights. Case study 1 uses integration of TF-target gene regulation and binding datasets to uncover TF mode-of-action and identify potential TF partners for 14 TFs in abscisic acid signaling. Case study 2 demonstrates how genome-wide TF-target data and automated functions in ConnecTF are used in precision/recall analysis and pruning of an inferred GRN for nitrogen signaling. Case study 3 uses ConnecTF to chart a network path from NLP7, a master TF in nitrogen signaling, to direct secondary TF2s and to its indirect targets in a Network Walking approach. The public version of ConnecTF (https://ConnecTF.org) contains 3,738,278 TF-target interactions for 423 TFs in Arabidopsis, 839,210 TF-target interactions for 139 TFs in maize (Zea mays), and 293,094 TF-target interactions for 26 TFs in rice (Oryza sativa). The database and tools in ConnecTF will advance the exploration of GRNs in plant systems biology applications for model and crop species.

An efficient protocol for functional studies of apple transcription factors using a glucocorticoid receptor fusion system

PREMISE

We report a protocol for studying the function of apple (Malus ×domestica) transcription factors based on the glucocorticoid receptor (GR) system, which allows the dexamethasone (DEX)-mediated activation of plant transcription factors to monitor the expression levels of their potential target genes.

METHODS AND RESULTS

Apple leaves are transformed with a vector that allows the expression of the studied transcription factor (i.e., FLOWERING LOCUS C [MdFLC]) fused to GR. Calli derived from the transformed leaves are treated with DEX and cycloheximide, a protein synthesis inhibitor. Compared with other methods, combining the GR system with cycloheximide treatments enables the differentiation between direct and indirect transcription factor target genes. Finally, the expression levels of putative MdFLC target genes are quantified using quantitative reverse transcription PCR.

CONCLUSIONS

We demonstrate the efficiency of our GR system to study the function of apple transcription factors. This method is accessible to any laboratory familiar with basic molecular cloning procedures and the apple leaf-mediated agro-transformation technique.

Direct and indirect targets of the arabidopsis seed transcription factor ABSCISIC ACID INSENSITIVE3

Arabidopsis thaliana ABSCISIC ACID INSENSITIVE3 (ABI3) is a transcription factor in the B3 domain family. ABI3, along with B3 domain transcription factors LEAFY COTYLEDON2 (LEC2) and FUSCA3 (FUS3), and LEC1, a subunit of the CCAAT box-binding complex, form the so-called LAFL network to control various aspects of seed development and maturation. ABI3 also contributes to the abscisic acid (ABA) response. We report on chromatin immunoprecipitation-tiling array experiments to map binding sites for ABI3 globally. We also assessed transcriptomes in response to ABI3 by comparing developing abi3-5 and wild-type seeds and combined this information to ascertain direct and indirect responsive ABI3 target genes. ABI3 can induce and repress its transcription of target genes directly and some intriguing differences exist in cis motifs between these groups of genes. Directly regulated targets reflect the role of ABI3 in seed maturation, desiccation tolerance, entry into a quiescent state and longevity. Interestingly, ABI3 directly represses a gene encoding a microRNA (MIR160B) that targets AUXIN RESPONSE FACTOR (ARF)10 and ARF16 that are involved in establishment of dormancy. In addition, ABI3, like FUS3, regulates genes encoding MIR156 but while FUS3 only induces genes encoding this product, ABI3 induces these genes during the early stages of seed development, but represses these genes during late development. The interplay between ABI3, the other LAFL genes, and the VP1/ABI3-LIKE (VAL) genes, which are involved in the transition to seedling development are examined and reveal complex interactions controlling development.

Nitrate in 2020: Thirty Years from Transport to Signaling Networks

Nitrogen (N) is an essential macronutrient for plants and a major limiting factor for plant growth and crop production. Nitrate is the main source of N available to plants in agricultural soils and in many natural environments. Sustaining agricultural productivity is of paramount importance in the current scenario of increasing world population, diversification of crop uses, and climate change. Plant productivity for major crops around the world, however, is still supported by excess application of N-rich fertilizers with detrimental economic and environmental impacts. Thus, understanding how plants regulate nitrate uptake and metabolism is key for developing new crops with enhanced N use efficiency and to cope with future world food demands. The study of plant responses to nitrate has gained considerable interest over the last 30 years. This review provides an overview of key findings in nitrate research, spanning biochemistry, molecular genetics, genomics, and systems biology. We discuss how we have reached our current view of nitrate transport, local and systemic nitrate sensing/signaling, and the regulatory networks underlying nitrate-controlled outputs in plants. We hope this summary will serve not only as a timeline and information repository but also as a baseline to define outstanding questions for future research.

Prediction of condition-specific regulatory genes using machine learning

Recent advances in genomic technologies have generated data on large-scale protein-DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5-25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.

Towards understanding the hierarchical nitrogen signalling network in plants

Nitrogen (N) is the most abundant mineral elements in plants, and the application of inorganic N fertilizer makes huge contribution to the crop production and global food security. However, low N use efficiency (NUE) and overapplication of N fertilizers causes ever-growing environmental problems. Understanding the molecular mechanisms of N sensing and signalling in plants will provide molecular basis for NUE improvement of crops. Forward genetics screening and functional analysis have characterized the NRT1.1-NLP centered N signalling pathway at the cellular level. With the incorporation of systems biology approaches, a preliminary N regulatory network has been delineated. Meanwhile, long-distance N signalling has also been unveiled at the whole plant level. This review highlights most recent understanding of the N signalling network in plants, and also discusses how to further integrate hierarchical regulation of N signalling in plants.

Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling

Plant responses to environmental and intrinsic signals are tightly controlled by multiple transcription factors (TFs). These TFs and their regulatory connections form gene regulatory networks (GRNs), which provide a blueprint of the transcriptional regulations underlying plant development and environmental responses. This review provides examples of experimental methodologies commonly used to identify regulatory interactions and generate GRNs. Additionally, this review describes network inference techniques that leverage gene expression data to predict regulatory interactions. These computational and experimental methodologies yield complex networks that can identify new regulatory interactions, driving novel hypotheses. Biological properties that contribute to the complexity of GRNs are also described in this review. These include network topology, network size, transient binding of TFs to DNA, and competition between multiple upstream regulators. Finally, this review highlights the potential of machine learning approaches to leverage gene expression data to predict phenotypic outputs.

Nutrient dose-responsive transcriptome changes driven by Michaelis-Menten kinetics underlie plant growth rates

An increase in nutrient dose leads to proportional increases in crop biomass and agricultural yield. However, the molecular underpinnings of this nutrient dose-response are largely unknown. To investigate, we assayed changes in the Arabidopsis root transcriptome to different doses of nitrogen (N)-a key plant nutrient-as a function of time. By these means, we found that rate changes of genome-wide transcript levels in response to N-dose could be explained by a simple kinetic principle: the Michaelis-Menten (MM) model. Fitting the MM model allowed us to estimate the maximum rate of transcript change (V _max), as well as the N-dose at which one-half of V _max was achieved (K _m) for 1,153 N-dose-responsive genes. Since transcription factors (TFs) can act in part as the catalytic agents that determine the rates of transcript change, we investigated their role in regulating N-dose-responsive MM-modeled genes. We found that altering the abundance of TGA1, an early N-responsive TF, perturbed the maximum rates of N-dose transcriptomic responses (V _max), K _m, as well as the rate of N-dose-responsive plant growth. We experimentally validated that MM-modeled N-dose-responsive genes included both direct and indirect TGA1 targets, using a root cell TF assay to detect TF binding and/or TF regulation genome-wide. Taken together, our results support a molecular mechanism of transcriptional control that allows an increase in N-dose to lead to a proportional change in the rate of genome-wide expression and plant growth.

Transient genome-wide interactions of the master transcription factor NLP7 initiate a rapid nitrogen-response cascade

Dynamic reprogramming of gene regulatory networks (GRNs) enables organisms to rapidly respond to environmental perturbation. However, the underlying transient interactions between transcription factors (TFs) and genome-wide targets typically elude biochemical detection. Here, we capture both stable and transient TF-target interactions genome-wide within minutes after controlled TF nuclear import using time-series chromatin immunoprecipitation (ChIP-seq) and/or DNA adenine methyltransferase identification (DamID-seq). The transient TF-target interactions captured uncover the early mode-of-action of NIN-LIKE PROTEIN 7 (NLP7), a master regulator of the nitrogen signaling pathway in plants. These transient NLP7 targets captured in root cells using temporal TF perturbation account for 50% of NLP7-regulated genes not detectably bound by NLP7 in planta. Rapid and transient NLP7 binding activates early nitrogen response TFs, which we validate to amplify the NLP7-initiated transcriptional cascade. Our approaches to capture transient TF-target interactions genome-wide can be applied to validate dynamic GRN models for any pathway or organism of interest.

Conventional methods cannot reveal transient transcription factors (TFs) and targets interactions. Here, Alvarez et al. capture both stable and transient TF-target interactions by time-series ChIP-seq and/or DamID-seq in a cell-based TF perturbation system and show NLP7 as a master TF to initiate a rapid nitrogen-response cascade.

SDG8-Mediated Histone Methylation and RNA Processing Function in the Response to Nitrate Signaling

Chromatin modification has gained increased attention for its role in the regulation of plant responses to environmental changes, but the specific mechanisms and molecular players remain elusive. Here, we show that the Arabidopsis (Arabidopsis thaliana) histone methyltransferase SET DOMAIN GROUP8 (SDG8) mediates genome-wide changes in H3K36 methylation at specific genomic loci functionally relevant to nitrate treatments. Moreover, we show that the specific H3K36 methyltransferase encoded by SDG8 is required for canonical RNA processing, and that RNA isoform switching is more prominent in the sdg8-5 deletion mutant than in the wild type. To demonstrate that SDG8-mediated regulation of RNA isoform expression is functionally relevant, we examined a putative regulatory gene, CONSTANS, CO-like, and TOC1 101 (CCT101), whose nitrogen-responsive isoform-specific RNA expression is mediated by SDG8. We show by functional expression in shoot cells that the different RNA isoforms of CCT101 encode distinct regulatory proteins with different effects on genome-wide transcription. We conclude that SDG8 is involved in plant responses to environmental nitrogen supply, affecting multiple gene regulatory processes including genome-wide histone modification, transcriptional regulation, and RNA processing, and thereby mediating developmental and metabolic processes related to nitrogen use.

Local Changes in Chromatin Accessibility and Transcriptional Networks Underlying the Nitrate Response in Arabidopsis Roots

Transcriptional regulation, determined by the chromatin structure and regulatory elements interacting at promoter regions, is a key step in plant responses to environmental cues. Nitrate (NO₃^-) is a nutrient signal that regulates the expression of hundreds of genes in Arabidopsis thaliana. Here, we integrate mRNA sequencing, genome-wide RNA polymerase II (RNPII), chromatin immunoprecipitation sequencing, and DNase sequencing datasets to establish the relationship between RNPII occupancy and chromatin accessibility in response to NO₃^- treatments in Arabidopsis roots. Genomic footprinting allowed us to identify in vivo regulatory elements controlling gene expression in response to NO₃^- treatments. NO₃^--modulated transcription factor (TF) footprints are important for a rapid increase in RNPII occupancy and transcript accumulation over time. We mapped key TF regulatory interactions and functionally validated the role of NAP, an NAC-domain containing TF, as a new regulatory factor in NO₃^- transport. Taken together, our study provides a comprehensive view of transcriptional networks in response to a nutrient signal in Arabidopsis roots.

Evolutionary flexibility in flooding response circuitry in angiosperms

Flooding due to extreme weather threatens crops and ecosystems. To understand variation in gene regulatory networks activated by submergence, we conducted a high-resolution analysis of chromatin accessibility and gene expression at three scales of transcript control in four angiosperms, ranging from a dryland-adapted wild species to a wetland crop. The data define a cohort of conserved submergence-activated genes with signatures of overlapping cis regulation by four transcription factor families. Syntenic genes are more highly expressed than nonsyntenic genes, yet both can have the cis motifs and chromatin accessibility associated with submergence up-regulation. Whereas the flexible circuitry spans the eudicot-monocot divide, the frequency of specific cis motifs, extent of chromatin accessibility, and degree of submergence activation are more prevalent in the wetland crop and may have adaptive importance.

Network Walking charts transcriptional dynamics of nitrogen signaling by integrating validated and predicted genome-wide interactions

Charting a temporal path in gene networks requires linking early transcription factor (TF)-triggered events to downstream effects. We scale-up a cell-based TF-perturbation assay to identify direct regulated targets of 33 nitrogen (N)-early response TFs encompassing 88% of N-responsive Arabidopsis genes. We uncover a duality where each TF is an inducer and repressor, and in vitro cis-motifs are typically specific to regulation directionality. Validated TF-targets (71,836) are used to refine precision of a time-inferred root network, connecting 145 N-responsive TFs and 311 targets. These data are used to chart network paths from direct TF₁-regulated targets identified in cells to indirect targets responding only in planta via Network Walking. We uncover network paths from TGA1 and CRF4 to direct TF₂ targets, which in turn regulate 76% and 87% of TF₁ indirect targets in planta, respectively. These results have implications for N-use and the approach can reveal temporal networks for any biological system.

Temporal control of transcriptional networks enables organisms to adapt to changing environment. Here, the authors use a scaled-up cell-based assay to identify direct targets of nitrogen-early responsive transcription factors and validate a network path mediating dynamic nitrogen signaling in Arabidopsis.

Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants

Our study exploits time—the relatively unexplored fourth dimension of gene regulatory networks (GRNs)—to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. We introduce several conceptual innovations to the analysis of time-series data in the area of predictive GRNs. Our resulting network now provides the “transcriptional logic” for transcription factor perturbations aimed at improving N-use efficiency, an important issue for global food production in marginal soils and for sustainable agriculture. More broadly, the combination of the time-based approaches we develop and deploy can be applied to uncover the temporal “transcriptional logic” for any response system in biology, agriculture, or medicine.

This study exploits time, the relatively unexplored fourth dimension of gene regulatory networks (GRNs), to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. Our “just-in-time” analysis of time-series transcriptome data uncovered a temporal cascade of cis elements underlying dynamic N signaling. To infer transcription factor (TF)-target edges in a GRN, we applied a time-based machine learning method to 2,174 dynamic N-responsive genes. We experimentally determined a network precision cutoff, using TF-regulated genome-wide targets of three TF hubs (CRF4, SNZ, and CDF1), used to “prune” the network to 155 TFs and 608 targets. This network precision was reconfirmed using genome-wide TF-target regulation data for four additional TFs (TGA1, HHO5/6, and PHL1) not used in network pruning. These higher-confidence edges in the GRN were further filtered by independent TF-target binding data, used to calculate a TF “N-specificity” index. This refined GRN identifies the temporal relationship of known/validated regulators of N signaling (NLP7/8, TGA1/4, NAC4, HRS1, and LBD37/38/39) and 146 additional regulators. Six TFs—CRF4, SNZ, CDF1, HHO5/6, and PHL1—validated herein regulate a significant number of genes in the dynamic N response, targeting 54% of N-uptake/assimilation pathway genes. Phenotypically, inducible overexpression of CRF4 in planta regulates genes resulting in altered biomass, root development, and ¹⁵NO₃⁻ uptake, specifically under low-N conditions. This dynamic N-signaling GRN now provides the temporal “transcriptional logic” for 155 candidate TFs to improve nitrogen use efficiency with potential agricultural applications. Broadly, these time-based approaches can uncover the temporal transcriptional logic for any biological response system in biology, agriculture, or medicine.

The multi-faceted potential of plant-derived metabolites as antimicrobial agents against multidrug-resistant pathogens

Multidrug-resistant (MDR) pathogens are currently causing serious problems globally in the medical setting. Improper and extensive usage of antibiotics results in a selective pressure supporting the rise of antibiotic-resistant microbes. Many key cellular bacterial components, including enzymes and small noncoding RNAs (sRNAs), and their involvement in MDR have been well studied, but exploiting such components in eradicating these pathogens requires further study. Delineation of many mechanisms that underpin the known MDR pathways necessitates urgent development of new specific strategies to control the rise of MDR pathogens. Botanical derivatives are comparatively safer than currently used antibiotics and exert multiple therapeutic benefits associated with their high efficacy. Numerous plant-derived compounds display synergistic activity with antibiotics against many MDR pathogens. Such plant derivatives include alkaloids, flavonoids, terpenoids, and tannins. A synthetic biological approach, e.g., metabolic engineering of secondary metabolites, can be utilized to exploit the natural metabolic pathways against MDR microbes. In this review, we focused on the major threats of antibiotic resistance, and the utilization of plant-derived compounds as alternative therapeutic agents to limit the rise of MDR pathogens.

μChIP-Seq for Genome-Wide Mapping of In Vivo TF-DNA Interactions in Arabidopsis Root Protoplasts

Chromatin immunoprecipitation (ChIP) is a widely used method to map the position of DNA-binding proteins such as histones and transcription factors (TFs) upon their interaction with particular regions of the genome. To examine the genomic distribution of a TF in specific cell types in response to a change in nitrogen concentration, we developed a micro-ChIP (μChIP) protocol that requires only ~5000 Arabidopsis cells transiently expressing the Arabidopsis TF Basic Leucine Zipper 1 (bZIP1) fused to the glucocorticoid receptor (GR) domain that mediates nuclear import in the presence of dexamethasone. The DNA fragments obtained from the immunoprecipitation of bZIP1-DNA complexes were analyzed by next-generation sequencing (ChIP-seq), which helped uncover genome-wide associations between a bZIP1 and its targets in plant cells upon fluctuations in nitrogen availability.

Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets

Statistical and machine learning (ML)-based methods have recently advanced in construction of gene regulatory network (GRNs) based on high-throughput biological datasets. GRNs underlie almost all cellular phenomena; hence, comprehensive GRN maps are essential tools to elucidate gene function, thereby facilitating the identification and prioritization of candidate genes for functional analysis. High-throughput gene expression datasets have yielded various statistical and ML-based algorithms to infer causal relationship between genes and decipher GRNs. This review summarizes the recent advancements in the computational inference of GRNs, based on large-scale transcriptome sequencing datasets of model plants and crops. We highlight strategies to select contextual genes for GRN inference, and statistical and ML-based methods for inferring GRNs based on transcriptome datasets from plants. Furthermore, we discuss the challenges and opportunities for the elucidation of GRNs based on large-scale datasets obtained from emerging transcriptomic applications, such as from population-scale, single-cell level, and life-course transcriptome analyses.

The world according to GARP transcription factors

Plant specific GARP transcription factor family (made of ARR-B and G2-like) contains genes with very diverse in planta functions: nutrient sensing, root and shoot development, floral transition, chloroplast development, circadian clock oscillation maintenance, hormonal transport and signaling. In this work we review: first, their structural but distant relationships with MYB transcription factors, second, their role in planta, third, the diversity of their Cis-regulatory elements, fourth, their potential protein partners. We conclude that the GARP family may hold keys to understand the interactions between nutritional signaling pathways (nitrogen and phosphate at least) and development. Understanding how plant nutrition and development are coordinated is central to understand how to adapt plants to an ever-changing environment. Consequently GARPs are likely to attract increasing research attentions, as they are likely at the crossroads of these fundamental processes.

TransDetect Identifies a New Regulatory Module Controlling Phosphate Accumulation

Identifying transcription factor (TFs) cooperation controlling target gene expression is still an arduous challenge. The accuracy of current methods at genome scale significantly drops with the increase in number of genes, which limits their applicability to more complex genomes, like animals and plants. Here, we developed an algorithm, TransDetect, able to predict TF combinations controlling the expression level of a given gene. TransDetect was used to identify novel TF modules regulating the expression of Arabidopsis (Arabidopsis thaliana) phosphate transporter PHO1;H3 comprising MYB15, MYB84, bHLH35, and ICE1. These TFs were confirmed to interact between themselves and with the PHO1;H3 promoter. Phenotypic and genetic analyses of TF mutants enable the organization of these four TFs and PHO1;H3 in a new gene regulatory network controlling phosphate accumulation in zinc-dependent manner. This demonstrates the potential of TransDetect to extract directionality in nondynamic transcriptomes and to provide a blueprint to identify gene regulatory network involved in a given biological process.

Reverse engineering highlights potential principles of large gene regulatory network design and learning

Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions. Here, we developed a reverse engineering approach based on mathematical and computer simulation to evaluate the impact that this prior knowledge on gene regulatory networks may have on training machine learning algorithms. First, we developed a gene regulatory networks-simulating engine called FRANK (Fast Randomizing Algorithm for Network Knowledge) that is able to simulate large gene regulatory networks (containing 10⁴ genes) with characteristics of gene regulatory networks observed in vivo. FRANK also generates stable or oscillatory gene expression directly produced by the simulated gene regulatory networks. The development of FRANK leads to important general conclusions concerning the design of large and stable gene regulatory networks harboring scale free properties (built ex nihilo). In combination with supervised (accepting prior knowledge) support vector machine algorithm we (i) address biologically oriented questions concerning our capacity to accurately reconstruct gene regulatory networks and in particular we demonstrate that prior-knowledge structure is crucial for accurate learning, and (ii) draw conclusions to inform experimental design to performed learning able to solve gene regulatory networks in the future. By demonstrating that our predictions concerning the influence of the prior-knowledge structure on support vector machine learning capacity holds true on real data (Escherichia coli K14 network reconstruction using network and transcriptomic data), we show that the formalism used to build FRANK can to some extent be a reasonable model for gene regulatory networks in real cells.

This work by Carré et al addresses central questions in biology, which are: how very large gene regulatory networks (GRNs) are organized, generate stable gene expression, and can be learnt using machine learning algorithms? In this work authors developed an algorithm able to simulate large GRNs. From these networks they simulate stable or oscillating gene expression and highlights some mathematical rules controlling such a collective (several thousands of genes) behavior. They discuss consequent hypothesis concerning the organization of GRNs in real cells. Using this simulation tool, authors also demonstrate that it’s likely possible to computationally learn GRNs from transcriptomic data and prior knowledge on the network (actual known connections issued from Yeast One Hybrid or ChIP Seq for instance). They particularly highlight the crucial importance of the prior knowledge structure in their capacity to learn large GRNs.

Nitrate signaling and early responses in Arabidopsis roots

Nitrogen (N) is an essential macronutrient that impacts many aspects of plant physiology, growth, and development. Besides its nutritional role, N nutrient and metabolites act as signaling molecules that regulate the expression of a wide range of genes and biological processes. In this review, we describe recent advances in the understanding of components of the nitrate signaling pathway. Recent evidence posits that in one nitrate signaling pathway, nitrate sensed by NRT1.1 activates a phospholipase C activity that is necessary for increased cytosolic calcium levels. The nitrate-elicited calcium increase presumably activates calcium sensors, kinases, or phosphatases, resulting in changes in expression of primary nitrate response genes. Consistent with this model, nitrate treatments elicit proteome-wide changes in phosphorylation patterns in a wide range of proteins, including transporters, metabolic enzymes, kinases, phosphatases, and other regulatory proteins. Identifying and characterizing the function of the different players involved in this and other nitrate signaling pathways and their functional relationships is the next step to understand N responses in plants.

Establishment of Expression in the SHORTROOT-SCARECROW Transcriptional Cascade through Opposing Activities of Both Activators and Repressors

Tissue-specific gene expression is often thought to arise from spatially restricted transcriptional cascades. However, it is unclear how expression is established at the top of these cascades in the absence of pre-existing specificity. We generated a transcriptional network to explore how transcription factor expression is established in the Arabidopsis thaliana root ground tissue. Regulators of the SHORTROOT-SCARECROW transcriptional cascade were validated in planta. At the top of this cascade, we identified both activators and repressors of SHORTROOT. The aggregate spatial expression of these regulators is not sufficient to predict transcriptional specificity. Instead, modeling, transcriptional reporters, and synthetic promoters support a mechanism whereby expression at the top of the SHORTROOT-SCARECROW cascade is established through opposing activities of activators and repressors.

A matter of time - How transient transcription factor interactions create dynamic gene regulatory networks

Dynamic reprogramming of transcriptional networks enables cells to adapt to a changing environment. Thus, it is crucial not only to understand what gene targets are regulated by a transcription factor (TF) but also when. This review explores the way TFs function with respect to time, paying particular attention to discoveries made in plants - where coordinated, genome-wide responses to environmental change is crucial to the survival of these sessile organisms. We investigate the molecular mechanisms that mediate transient TF-DNA binding, and assess how these rapid and dynamic interactions translate to long-term temporal regulation of genomes. We also discuss how current molecular techniques can catch, and sometimes miss, transient TF-target interactions that underlie dynamic cellular responses. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

Nitrate Transport, Sensing, and Responses in Plants

Nitrogen (N) is an essential macronutrient that affects plant growth and development. N is an important component of chlorophyll, amino acids, nucleic acids, and secondary metabolites. Nitrate is one of the most abundant N sources in the soil. Because nitrate and other N nutrients are often limiting, plants have developed sophisticated mechanisms to ensure adequate supply of nutrients in a variable environment. Nitrate is absorbed in the root and mobilized to other organs by nitrate transporters. Nitrate sensing activates signaling pathways that impinge upon molecular, metabolic, physiological, and developmental responses locally and at the whole plant level. With the advent of genomics technologies and genetic tools, important advances in our understanding of nitrate and other N nutrient responses have been achieved in the past decade. Furthermore, techniques that take advantage of natural polymorphisms present in divergent individuals from a single species have been essential in uncovering new components. However, there are still gaps in our understanding of how nitrate signaling affects biological processes in plants. Moreover, we still lack an integrated view of how all the regulatory factors identified interact or crosstalk to orchestrate the myriad N responses plants typically exhibit. In this review, we provide an updated overview of mechanisms by which nitrate is sensed and transported throughout the plant. We discuss signaling components and how nitrate sensing crosstalks with hormonal pathways for developmental responses locally and globally in the plant. Understanding how nitrate impacts on plant metabolism, physiology, and growth and development in plants is key to improving crops for sustainable agriculture.

Identification of plant transcription factor target sequences

Regulation of gene expression depends on specific cis-regulatory sequences located in the gene promoter regions. These DNA sequences are recognized by transcription factors (TFs) in a sequence-specific manner, and their identification could help to elucidate the regulatory networks that underlie plant physiological responses to developmental programs or to environmental adaptation. Here we review recent advances in high throughput methodologies for the identification of plant TF binding sites. Several approaches offer a map of the TF binding locations in vivo and of the dynamics of the gene regulatory networks. As an alternative, high throughput in vitro methods provide comprehensive determination of the DNA sequences recognized by TFs. These advances are helping to decipher the regulatory lexicon and to elucidate transcriptional network hierarchies in plants in response to internal or external cues. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

Mapping Transcriptional Networks in Plants: Data-Driven Discovery of Novel Biological Mechanisms

In plants, systems biology approaches have led to the generation of a variety of large data sets. Many of these data are created to elucidate gene expression profiles and their corresponding transcriptional regulatory mechanisms across a range of tissue types, organs, and environmental conditions. In an effort to map the complexity of this transcriptional regulatory control, several types of experimental assays have been used to map transcriptional regulatory networks. In this review, we discuss how these methods can be best used to identify novel biological mechanisms by focusing on the appropriate biological context. Translating network biology back to gene function in the plant, however, remains a challenge. We emphasize the need for validation and insight into the underlying biological processes to successfully exploit systems approaches in an effort to determine the emergent properties revealed by network analyses.

"Hit-and-Run" transcription: de novo transcription initiated by a transient bZIP1 "hit" persists after the "run"

BACKGROUND

Dynamic transcriptional regulation is critical for an organism's response to environmental signals and yet remains elusive to capture. Such transcriptional regulation is mediated by master transcription factors (TF) that control large gene regulatory networks. Recently, we described a dynamic mode of TF regulation named "hit-and-run". This model proposes that master TF can interact transiently with a set of targets, but the transcription of these transient targets continues after the TF dissociation from the target promoter. However, experimental evidence validating active transcription of the transient TF-targets is still lacking.

RESULTS

Here, we show that active transcription continues after transient TF-target interactions by tracking de novo synthesis of RNAs made in response to TF nuclear import. To do this, we introduced an affinity-labeled 4-thiouracil (4tU) nucleobase to specifically isolate newly synthesized transcripts following conditional TF nuclear import. Thus, we extended the TARGET system (Transient Assay Reporting Genome-wide Effects of Transcription factors) to include 4tU-labeling and named this new technology TARGET-tU. Our proof-of-principle example is the master TF Basic Leucine Zipper 1 (bZIP1), a central integrator of metabolic signaling in plants. Using TARGET-tU, we captured newly synthesized mRNAs made in response to bZIP1 nuclear import at a time when bZIP1 is no longer detectably bound to its target. Thus, the analysis of de novo transcripomics demonstrates that bZIP1 may act as a catalyst TF to initiate a transcriptional complex ("hit"), after which active transcription by RNA polymerase continues without the TF being bound to the gene promoter ("run").

CONCLUSION

Our findings provide experimental proof for active transcription of transient TF-targets supporting a "hit-and-run" mode of action. This dynamic regulatory model allows a master TF to catalytically propagate rapid and broad transcriptional responses to changes in environment. Thus, the functional read-out of de novo transcripts produced by transient TF-target interactions allowed us to capture new models for genome-wide transcriptional control.