Cell-type-specific transcriptomics uncovers spatial regulatory networks in bioenergy sorghum stems.

Bioenergy sorghum is a low-input, drought-resilient, deep-rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum's 4-5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about cell-type gene expression and regulation in stems was available to enable engineering. To obtain this information, laser capture microdissection was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell-type-specific and cell-preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell-type-specific gene regulatory networks (GRNs) revealed that unique transcription factor families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell-specific transcriptome data was combined with known secondary cell wall (SCW) networks to identify the GRNs that differentially activate SCW formation in vascular sclerenchyma and epidermal cells. The spatial transcriptomic dataset provides a valuable source of information about the function of different sorghum cell types and GRNs that will enable the engineering of bioenergy sorghum stems, and an interactive web application developed during this project will allow easy access and exploration of the data (https://mc-lab.shinyapps.io/lcm-dataset/).

Genome-Wide Association Studies across Environmental and Genetic Contexts Reveal Complex Genetic Architecture of Symbiotic Extended Phenotypes.

A goal of modern biology is to develop the genotype-phenotype (G→P) map, a predictive understanding of how genomic information generates trait variation that forms the basis of both natural and managed communities. As microbiome research advances, however, it has become clear that many of these traits are symbiotic extended phenotypes, being governed by genetic variation encoded not only by the host's own genome, but also by the genomes of myriad cryptic symbionts. Building a reliable G→P map therefore requires accounting for the multitude of interacting genes and even genomes involved in symbiosis. Here, we use naturally occurring genetic variation in 191 strains of the model microbial symbiont Sinorhizobium meliloti paired with two genotypes of the host Medicago truncatula in four genome-wide association studies (GWAS) to determine the genomic architecture of a key symbiotic extended phenotype-partner quality, or the fitness benefit conferred to a host by a particular symbiont genotype, within and across environmental contexts and host genotypes. We define three novel categories of loci in rhizobium genomes that must be accounted for if we want to build a reliable G→P map of partner quality; namely, (i) loci whose identities depend on the environment, (ii) those that depend on the host genotype with which rhizobia interact, and (iii) universal loci that are likely important in all or most environments. IMPORTANCE Given the rapid rise of research on how microbiomes can be harnessed to improve host health, understanding the contribution of microbial genetic variation to host phenotypic variation is pressing, and will better enable us to predict the evolution of (and select more precisely for) symbiotic extended phenotypes that impact host health. We uncover extensive context-dependency in both the identity and functions of symbiont loci that control host growth, which makes predicting the genes and pathways important for determining symbiotic outcomes under different conditions more challenging. Despite this context-dependency, we also resolve a core set of universal loci that are likely important in all or most environments, and thus, serve as excellent targets both for genetic engineering and future coevolutionary studies of symbiosis.

Positive selection and heat-response transcriptomes reveal adaptive features of the Brassicaceae desert model, Anastatica hierochuntica.

Plant adaptation to a desert environment and its endemic heat stress is poorly understood at the molecular level. The naturally heat-tolerant Brassicaceae species Anastatica hierochuntica is an ideal extremophyte model to identify genetic adaptations that have evolved to allow plants to tolerate heat stress and thrive in deserts. We generated an A. hierochuntica reference transcriptome and identified extremophyte adaptations by comparing Arabidopsis thaliana and A. hierochuntica transcriptome responses to heat, and detecting positively selected genes in A. hierochuntica. The two species exhibit similar transcriptome adjustment in response to heat and the A. hierochuntica transcriptome does not exist in a constitutive heat 'stress-ready' state. Furthermore, the A. hierochuntica global transcriptome as well as heat-responsive orthologs, display a lower basal and higher heat-induced expression than in A. thaliana. Genes positively selected in multiple extremophytes are associated with stomatal opening, nutrient acquisition, and UV-B induced DNA repair while those unique to A. hierochuntica are consistent with its photoperiod-insensitive, early-flowering phenotype. We suggest that evolution of a flexible transcriptome confers the ability to quickly react to extreme diurnal temperature fluctuations characteristic of a desert environment while positive selection of genes involved in stress tolerance and early flowering could facilitate an opportunistic desert lifestyle.

Bioenergy sorghum stem growth regulation: intercalary meristem localization, development, and gene regulatory network analysis.

Bioenergy sorghum is a highly productive drought tolerant C4 grass that accumulates 80% of its harvestable biomass in approximately 4 m length stems. Stem internode growth is regulated by development, shading, and hormones that modulate cell proliferation in intercalary meristems (IMs). In this study, sorghum stem IMs were localized above the pulvinus at the base of elongating internodes using magnetic resonance imaging, microscopy, and transcriptome analysis. A change in cell morphology/organization occurred at the junction between the pulvinus and internode where LATERAL ORGAN BOUNDARIES (SbLOB), a boundary layer gene, was expressed. Inactivation of an AGCVIII kinase in DDYM (dw2) resulted in decreased SbLOB expression, disrupted IM localization, and reduced internode cell proliferation. Transcriptome analysis identified approximately 1000 genes involved in cell proliferation, hormone signaling, and other functions selectively upregulated in the IM compared with a non-meristematic stem tissue. This cohort of genes is expressed in apical dome stem tissues before localization of the IM at the base of elongating internodes. Gene regulatory network analysis identified connections between genes involved in hormone signaling and cell proliferation. The results indicate that gibberellic acid induces accumulation of growth regulatory factors (GRFs) known to interact with ANGUSTIFOLIA (SbAN3), a master regulator of cell proliferation. GRF:AN3 was predicted to induce SbARF3/ETT expression and regulate SbAN3 expression in an auxin-dependent manner. GRFs and ARFs regulate genes involved in cytokinin and brassinosteroid signaling and cell proliferation. The results provide a molecular framework for understanding how hormone signaling regulates the expression of genes involved in cell proliferation in the stem IM.

Gene regulation network analyses of pistil development in papaya.

BACKGROUND: The pistil is an essential part of flowers that functions in the differentiation of the sexes and reproduction in plants. The stigma on the pistil can accept pollen to allow fertilization and seed development. Papaya (Carica papaya L.) is a dioecious plant, where female flowers exhibit normal pistil, while the male flowers exhibit aborted pistil at a late stage of pistil development. RESULTS: The developmental stages of papaya pistil were analyzed after first dividing it into slices representing the primordium stage 1 (S1), the pre-meiotic stages S2, post-meiotic stage S3, and the mitotic stage S4. The SS scoring algorithm analysis of genes preferentially expressed at different stages revealed differentially expressed genes between male and female flowers. A transcription factor regulatory network for each stage based on the genes that are differentially expressed between male and female flowers was constructed. Some transcription factors related to pistil development were revealed based on the analysis of regulatory networks such as CpAGL11, CpHEC2, and CpSUPL. Based on the specific expression of genes, constructed a gene regulatory subnetwork with CpAGL11-CpSUPL-CpHEC2 functioning as the core. Analysis of the functionally enriched terms in this network reveals several differentially expressed genes related to auxin/ brassinosteroid signal transduction in the plant hormone signal transduction pathway. At the same time, significant differences in the expression of auxin and brassinosteroid synthesis-related genes between male and female flowers at different developmental stages were detected. CONCLUSIONS: The pistil abortion of papaya might be caused by the lack of expression or decreased expression of some transcription factors and hormone-related genes, affecting hormone signal transduction or hormone biosynthesis. Analysis of aborted and normally developing pistil in papaya provided new insights into the molecular mechanism of pistil development and sex differentiation in dioecious papaya.

Overexpression of arogenate dehydratase reveals an upstream point of metabolic control in phenylalanine biosynthesis.

Out of the three aromatic amino acids, the highest flux in plants is directed towards phenylalanine, which is utilized to synthesize proteins and thousands of phenolic metabolites contributing to plant fitness. Phenylalanine is produced predominantly in plastids via the shikimate pathway and subsequent arogenate pathway, both of which are subject to complex transcriptional and post-transcriptional regulation. Previously, it was shown that allosteric feedback inhibition of arogenate dehydratase (ADT), which catalyzes the final step of the arogenate pathway, restricts flux through phenylalanine biosynthesis. Here, we show that in petunia (Petunia hybrida) flowers, which typically produce high phenylalanine levels, ADT regulation is relaxed, but not eliminated. Moderate expression of a feedback-insensitive ADT increased flux towards phenylalanine, while high overexpression paradoxically reduced phenylalanine formation. This reduction could be partially, but not fully, recovered by bypassing other known metabolic flux control points in the aromatic amino acid network. Using comparative transcriptomics, reverse genetics, and metabolic flux analysis, we discovered that transcriptional regulation of the d-ribulose-5-phosphate 3-epimerase gene in the pentose phosphate pathway controls flux into the shikimate pathway. Taken together, our findings reveal that regulation within and upstream of the shikimate pathway shares control over phenylalanine biosynthesis in the plant cell.

Granger-causal testing for irregularly sampled time series with application to nitrogen signalling in Arabidopsis.

MOTIVATION: Identification of system-wide causal relationships can contribute to our understanding of long-distance, intercellular signalling in biological organisms. Dynamic transcriptome analysis holds great potential to uncover coordinated biological processes between organs. However, many existing dynamic transcriptome studies are characterized by sparse and often unevenly spaced time points that make the identification of causal relationships across organs analytically challenging. Application of existing statistical models, designed for regular time series with abundant time points, to sparse data may fail to reveal biologically significant, causal relationships. With increasing research interest in biological time series data, there is a need for new statistical methods that are able to determine causality within and between time series data sets. Here, a statistical framework was developed to identify (Granger) causal gene-gene relationships of unevenly spaced, multivariate time series data from two different tissues of Arabidopsis thaliana in response to a nitrogen signal. RESULTS: This work delivers a statistical approach for modelling irregularly sampled bivariate signals which embeds functions from the domain of engineering that allow to adapt the model's dependence structure to the specific sampling time. Using maximum-likelihood to estimate the parameters of this model for each bivariate time series, it is then possible to use bootstrap procedures for small samples (or asymptotics for large samples) in order to test for Granger-Causality. When applied to the A.thaliana data, the proposed approach produced 3078 significant interactions, in which 2012 interactions have root causal genes and 1066 interactions have shoot causal genes. Many of the predicted causal and target genes are known players in local and long-distance nitrogen signalling, including genes encoding transcription factors, hormones and signalling peptides. Of the 1007 total causal genes (either organ), 384 are either known or predicted mobile transcripts, suggesting that the identified causal genes may be directly involved in long-distance nitrogen signalling through intercellular interactions. The model predictions and subsequent network analysis identified nitrogen-responsive genes that can be further tested for their specific roles in long-distance nitrogen signalling. AVAILABILITY AND IMPLEMENTATION: The method was developed with the R statistical software and is made available through the R package 'irg' hosted on the GitHub repository https://github.com/SMAC-Group/irg where also a running example vignette can be found (https://smac-group.github.io/irg/articles/vignette.html). A few signals from the original data set are made available in the package as an example to apply the method and the complete A.thaliana data can be found at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97500. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Multiscale plant modeling: from genome to phenome and beyond.

Plants are complex organisms that adapt to changes in their environment using an array of regulatory mechanisms that span across multiple levels of biological organization. Due to this complexity, it is difficult to predict emergent properties using conventional approaches that focus on single levels of biology such as the genome, transcriptome, or metabolome. Mathematical models of biological systems have emerged as useful tools for exploring pathways and identifying gaps in our current knowledge of biological processes. Identification of emergent properties, however, requires their vertical integration across biological scales through multiscale modeling. Multiscale models that capture and predict these emergent properties will allow us to predict how plants will respond to a changing climate and explore strategies for plant engineering. In this review, we (1) summarize the recent developments in plant multiscale modeling; (2) examine multiscale models of microbial systems that offer insight to potential future directions for the modeling of plant systems; (3) discuss computational tools and resources for developing multiscale models; and (4) examine future directions of the field.

Towards a multiscale crop modelling framework for climate change adaptation assessment.

Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.

Multiscale computational models can guide experimentation and targeted measurements for crop improvement.

Computational models of plants have identified gaps in our understanding of biological systems, and have revealed ways to optimize cellular processes or organ-level architecture to increase productivity. Thus, computational models are learning tools that help direct experimentation and measurements. Models are simplifications of complex systems, and often simulate specific processes at single scales (e.g. temporal, spatial, organizational, etc.). Consequently, single-scale models are unable to capture the critical cross-scale interactions that result in emergent properties of the system. In this perspective article, we contend that to accurately predict how a plant will respond in an untested environment, it is necessary to integrate mathematical models across biological scales. Computationally mimicking the flow of biological information from the genome to the phenome is an important step in discovering new experimental strategies to improve crops. A key challenge is to connect models across biological, temporal and computational (e.g. CPU versus GPU) scales, and then to visualize and interpret integrated model outputs. We address this challenge by describing the efforts of the international Crops in silico consortium.

WRKY1 Mediates Transcriptional Regulation of Light and Nitrogen Signaling Pathways.

Plant responses to multiple environmental stimuli must be integrated to enable them to adapt their metabolism and development. Light and nitrogen (N) are two such stimuli whose downstream signaling pathways must be intimately connected to each other to control plant energy status. Here, we describe the functional role of the WRKY1 transcription factor in controlling genome-wide transcriptional reprogramming of Arabidopsis (Arabidopsis thaliana) leaves in response to individual and combined light and N signals. This includes a cross-regulatory network consisting of 724 genes regulated by WRKY1 and involved in both N and light signaling pathways. The loss of WRKY1 gene function has marked effects on the light and N response of genes involved in N uptake and assimilation (primary metabolism) as well as stress response pathways (secondary metabolism). Our results at the transcriptome and at the metabolite analysis level support a model in which WRKY1 enables plants to activate genes involved in the recycling of cellular carbon resources when light is limiting but N is abundant and upregulate amino acid metabolism when both light and N are limiting. In this potential energy conservation mechanism, WRKY1 integrates information about cellular N and light energy resources to trigger changes in plant metabolism.

Plant Networks as Traits and Hypotheses: Moving Beyond Description.

Biology relies on the central thesis that the genes in an organism encode molecular mechanisms that combine with stimuli and raw materials from the environment to create a final phenotypic expression representative of the genomic programming. While conceptually simple, the genotype-to-phenotype linkage in a eukaryotic organism relies on the interactions of thousands of genes and an environment with a potentially unknowable level of complexity. Modern biology has moved to the use of networks in systems biology to try to simplify this complexity to decode how an organism's genome works. Previously, biological networks were basic ways to organize, simplify, and analyze data. However, recent advances are allowing networks to move beyond description and become phenotypes or hypotheses in their own right. This review discusses these efforts, like mapping responses across biological scales, including relationships among cellular entities, and the direct use of networks as traits or hypotheses.

Directions for research and training in plant omics: Big Questions and Big Data.

A key remit of the NSF-funded "Arabidopsis Research and Training for the 21st Century" (ART-21) Research Coordination Network has been to convene a series of workshops with community members to explore issues concerning research and training in plant biology, including the role that research using Arabidopsis thaliana can play in addressing those issues. A first workshop focused on training needs for bioinformatic and computational approaches in plant biology was held in 2016, and recommendations from that workshop have been published (Friesner et al., Plant Physiology, 175, 2017, 1499). In this white paper, we provide a summary of the discussions and insights arising from the second ART-21 workshop. The second workshop focused on experimental aspects of omics data acquisition and analysis and involved a broad spectrum of participants from academics and industry, ranging from graduate students through post-doctorates, early career and established investigators. Our hope is that this article will inspire beginning and established scientists, corporations, and funding agencies to pursue directions in research and training identified by this workshop, capitalizing on the reference species Arabidopsis thaliana and other valuable plant systems.

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

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.

Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security.

Sustainable crop production is a contributing factor to current and future food security. Innovative technologies are needed to design strategies that will achieve higher crop yields on less land and with fewer resources. Computational modeling coupled with advanced scientific visualization enables researchers to explore and interact with complex agriculture, nutrition, and climate data to predict how crops will respond to untested environments. These virtual observations and predictions can direct the development of crop ideotypes designed to meet future yield and nutritional demands. This review surveys modeling strategies for the development of crop ideotypes and scientific visualization technologies that have led to discoveries in "big data" analysis. Combined modeling and visualization approaches have been used to realistically simulate crops and to guide selection that immediately enhances crop quantity and quality under challenging environmental conditions. This survey of current and developing technologies indicates that integrative modeling and advanced scientific visualization may help overcome challenges in agriculture and nutrition data as large-scale and multidimensional data become available in these fields.

Tyrosine-610 in the Receptor Kinase BAK1 Does Not Play a Major Role in Brassinosteroid Signaling or Innate Immunity.

The plasma membrane-localized BRI1-ASSOCIATED KINASE1 (BAK1) functions as a co-receptor with several receptor kinases including the brassinosteroid (BR) receptor BRASSINOSTEROID-INSENSITIVE 1 (BRI1), which is involved in growth, and the receptors for bacterial flagellin and EF-Tu, FLAGELLIN-SENSING 2 (FLS2) and EF-TU RECEPTOR (EFR), respectively, which are involved in immunity. BAK1 is a dual specificity protein kinase that can autophosphorylate on serine, threonine and tyrosine residues. It was previously reported that phosphorylation of Tyr-610 in the carboxy-terminal domain of BAK1 is required for its function in BR signaling and immunity. However, the functional role of Tyr-610 in vivo has recently come under scrutiny. Therefore, we have generated new BAK1 (Y610F) transgenic plants for functional studies. We first produced transgenic Arabidopsis lines expressing BAK1 (Y610F)-Flag in the homozygous bak1-4 bkk1-1 double null background. In a complementary approach, we expressed untagged BAK1 and BAK1 (Y610F) in the bak1-4 null mutant. Neither BAK1 (Y610F) transgenic line had any obvious growth phenotype when compared to wild-type BAK1 expressed in the same background. In addition, the BAK1 (Y610F)-Flag plants responded similarly to plants expressing BAK1-Flag in terms of brassinolide (BL) inhibition of root elongation, and there were only minor changes in gene expression between the two transgenic lines as monitored by microarray analysis and quantitative real-time PCR. In terms of plant immunity, there were no significant differences between plants expressing BAK1 (Y610F)-Flag and BAK1-Flag in the growth of the non-pathogenic hrpA- mutant of Pseudomonas syringae pv. tomato DC3000. Furthermore, untagged BAK1 (Y610F) transgenic plants were as responsive as plants expressing BAK1 (in the bak1-4 background) and wild-type Col-0 plants toward treatment with the EF-Tu- and flagellin-derived peptide epitopes elf18- and flg22, respectively, as measured by reactive oxygen species production, mitogen-activated protein kinase activation, and seedling growth inhibition. These new results do not support any involvement of Tyr-610 phosphorylation in either BR or immune signaling.

Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform.

Multi-scale models can facilitate whole plant simulations by linking gene networks, protein synthesis, metabolic pathways, physiology, and growth. Whole plant models can be further integrated with ecosystem, weather, and climate models to predict how various interactions respond to environmental perturbations. These models have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts. Outcomes will potentially accelerate improvement of crop yield, sustainability, and increase future food security. It is time for a paradigm shift in plant modeling, from largely isolated efforts to a connected community that takes advantage of advances in high performance computing and mechanistic understanding of plant processes. Tools for guiding future crop breeding and engineering, understanding the implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment are urgently needed. The purpose of this perspective is to introduce Crops in silico (cropsinsilico.org), an integrative and multi-scale modeling platform, as one solution that combines isolated modeling efforts toward the generation of virtual crops, which is open and accessible to the entire plant biology community. The major challenges involved both in the development and deployment of a shared, multi-scale modeling platform, which are summarized in this prospectus, were recently identified during the first Crops in silico Symposium and Workshop.

"Hit-and-Run" leaves its mark: catalyst transcription factors and chromatin modification.

Understanding how transcription factor (TF) binding is related to gene regulation is a moving target. We recently uncovered genome-wide evidence for a "Hit-and-Run" model of transcription. In this model, a master TF "hits" a target promoter to initiate a rapid response to a signal. As the "hit" is transient, the model invokes recruitment of partner TFs to sustain transcription over time. Following the "run", the master TF "hits" other targets to propagate the response genome-wide. As such, a TF may act as a "catalyst" to mount a broad and acute response in cells that first sense the signal, while the recruited TF partners promote long-term adaptive behavior in the whole organism. This "Hit-and-Run" model likely has broad relevance, as TF perturbation studies across eukaryotes show small overlaps between TF-regulated and TF-bound genes, implicating transient TF-target binding. Here, we explore this "Hit-and-Run" model to suggest molecular mechanisms and its biological relevance.