Identification of plant transcriptional activation domains.

Gene expression in Arabidopsis is regulated by more than 1,900 transcription factors (TFs), which have been identified genome-wide by the presence of well-conserved DNA-binding domains. Activator TFs contain activation domains (ADs) that recruit coactivator complexes; however, for nearly all Arabidopsis TFs, we lack knowledge about the presence, location and transcriptional strength of their ADs1. To address this gap, here we use a yeast library approach to experimentally identify Arabidopsis ADs on a proteome-wide scale, and find that more than half of the Arabidopsis TFs contain an AD. We annotate 1,553 ADs, the vast majority of which are, to our knowledge, previously unknown. Using the dataset generated, we develop a neural network to accurately predict ADs and to identify sequence features that are necessary to recruit coactivator complexes. We uncover six distinct combinations of sequence features that result in activation activity, providing a framework to interrogate the subfunctionalization of ADs. Furthermore, we identify ADs in the ancient AUXIN RESPONSE FACTOR family of TFs, revealing that AD positioning is conserved in distinct clades. Our findings provide a deep resource for understanding transcriptional activation, a framework for examining function in intrinsically disordered regions and a predictive model of ADs.

Gene regulatory networks for compatible versus incompatible grafts identify a role for SlWOX4 during junction formation.

Grafting has been adopted for a wide range of crops to enhance productivity and resilience; for example, grafting of Solanaceous crops couples disease-resistant rootstocks with scions that produce high-quality fruit. However, incompatibility severely limits the application of grafting and graft incompatibility remains poorly understood. In grafts, immediate incompatibility results in rapid death, but delayed incompatibility can take months or even years to manifest, creating a significant economic burden for perennial crop production. To gain insight into the genetic mechanisms underlying this phenomenon, we developed a model system using heterografting of tomato (Solanum lycopersicum) and pepper (Capsicum annuum). These grafted plants express signs of anatomical junction failure within the first week of grafting. By generating a detailed timeline for junction formation, we were able to pinpoint the cellular basis for this delayed incompatibility. Furthermore, we inferred gene regulatory networks for compatible self-grafts and incompatible heterografts based on these key anatomical events, which predict core regulators for grafting. Finally, we examined the role of vascular development in graft formation and uncovered SlWOX4 as a potential regulator of graft compatibility. Following this predicted regulator up with functional analysis, we show that Slwox4 homografts fail to form xylem bridges across the junction, demonstrating that indeed, SlWOX4 is essential for vascular reconnection during grafting, and may function as an early indicator of graft failure.

The ALOG family members OsG1L1 and OsG1L2 regulate inflorescence branching in rice.

The architecture of the rice inflorescence is an important determinant of crop yield. The length of the inflorescence and the number of branches are among the key factors determining the number of spikelets, and thus grains, that a plant will develop. In particular, the timing of the identity transition from indeterminate branch meristem to determinate spikelet meristem governs the complexity of the inflorescence. In this context, the ALOG gene TAWAWA1 (TAW1) has been shown to delay the transition to determinate spikelet development in Oryza sativa (rice). Recently, by combining precise laser microdissection of inflorescence meristems with RNA-seq, we observed that two ALOG genes, OsG1-like 1 (OsG1L1) and OsG1L2, have expression profiles similar to that of TAW1. Here, we report that osg1l1 and osg1l2 loss-of-function CRISPR mutants have similar phenotypes to the phenotype of the previously published taw1 mutant, suggesting that these genes might act on related pathways during inflorescence development. Transcriptome analysis of the osg1l2 mutant suggested interactions of OsG1L2 with other known inflorescence architecture regulators and the data sets were used for the construction of a gene regulatory network (GRN), proposing interactions among genes potentially involved in controlling inflorescence development in rice. In this GRN, we selected the homeodomain-leucine zipper transcription factor encoding the gene OsHOX14 for further characterization. The spatiotemporal expression profiling and phenotypical analysis of CRISPR loss-of-function mutants of OsHOX14 suggests that the proposed GRN indeed serves as a valuable resource for the identification of new proteins involved in rice inflorescence development.

Phosphate starvation: response mechanisms and solutions.

Phosphorus is essential to plant growth and agricultural crop yields, yet the challenges associated with phosphorus fertilization in agriculture, such as aquatic runoff pollution and poor phosphorus bioavailability, are increasingly difficult to manage. Comprehensively understanding the dynamics of phosphorus uptake and signaling mechanisms will inform the development of strategies to address these issues. This review describes regulatory mechanisms used by specific tissues in the root apical meristem to sense and take up phosphate from the rhizosphere. The major regulatory mechanisms and related hormone crosstalk underpinning phosphate starvation responses, cellular phosphate homeostasis, and plant adaptations to phosphate starvation are also discussed, along with an overview of the major mechanism of plant systemic phosphate starvation responses. Finally, this review discusses recent promising genetic engineering strategies for improving crop phosphorus use and computational approaches that may help further design strategies for improved plant phosphate acquisition. The mechanisms and approaches presented include a wide variety of species including not only Arabidopsis but also crop species such as Oryza sativa (rice), Glycine max (soybean), and Triticum aestivum (wheat) to address both general and species-specific mechanisms and strategies. The aspects of phosphorus deficiency responses and recently employed strategies of improving phosphate acquisition that are detailed in this review may provide insights into the mechanisms or phenotypes that may be targeted in efforts to improve crop phosphorus content and plant growth in low phosphorus soils.

Protein complex stoichiometry and expression dynamics of transcription factors modulate stem cell division.

Stem cells divide and differentiate to form all of the specialized cell types in a multicellular organism. In the Arabidopsis root, stem cells are maintained in an undifferentiated state by a less mitotically active population of cells called the quiescent center (QC). Determining how the QC regulates the surrounding stem cell initials, or what makes the QC fundamentally different from the actively dividing initials, is important for understanding how stem cell divisions are maintained. Here we gained insight into the differences between the QC and the cortex endodermis initials (CEI) by studying the mobile transcription factor SHORTROOT (SHR) and its binding partner SCARECROW (SCR). We constructed an ordinary differential equation model of SHR and SCR in the QC and CEI which incorporated the stoichiometry of the SHR-SCR complex as well as upstream transcriptional regulation of SHR and SCR. Our model prediction, coupled with experimental validation, showed that high levels of the SHR-SCR complex are associated with more CEI division but less QC division. Furthermore, our model prediction allowed us to propose the putative upstream SHR regulators SEUSS and WUSCHEL-RELATED HOMEOBOX 5 and to experimentally validate their roles in QC and CEI division. In addition, our model established the timing of QC and CEI division and suggests that SHR repression of QC division depends on formation of the SHR homodimer. Thus, our results support that SHR-SCR protein complex stoichiometry and regulation of SHR transcription modulate the division timing of two different specialized cell types in the root stem cell niche.

tuxnet: a simple interface to process RNA sequencing data and infer gene regulatory networks.

Predicting gene regulatory networks (GRNs) from expression profiles is a common approach for identifying important biological regulators. Despite the increased use of inference methods, existing computational approaches often do not integrate RNA-sequencing data analysis, are not automated or are restricted to users with bioinformatics backgrounds. To address these limitations, we developed tuxnet, a user-friendly platform that can process raw RNA-sequencing data from any organism with an existing reference genome using a modified tuxedo pipeline (hisat 2 + cufflinks package) and infer GRNs from these processed data. tuxnet is implemented as a graphical user interface and can mine gene regulations, either by applying a dynamic Bayesian network (DBN) inference algorithm, genist, or a regression tree-based pipeline, rtp-star. We obtained time-course expression data of a PERIANTHIA (PAN) inducible line and inferred a GRN using genist to illustrate the use of tuxnet while gaining insight into the regulations downstream of the Arabidopsis root stem cell regulator PAN. Using rtp-star, we inferred the network of ATHB13, a downstream gene of PAN, for which we obtained wild-type and mutant expression profiles. Additionally, we generated two networks using temporal data from developmental leaf data and spatial data from root cell-type data to highlight the use of tuxnet to form new testable hypotheses from previously explored data. Our case studies feature the versatility of tuxnet when using different types of gene expression data to infer networks and its accessibility as a pipeline for non-bioinformaticians to analyze transcriptome data, predict causal regulations, assess network topology and identify key regulators.

From network to phenotype: the dynamic wiring of an Arabidopsis transcriptional network induced by osmotic stress.

Plants have established different mechanisms to cope with environmental fluctuations and accordingly fine-tune their growth and development through the regulation of complex molecular networks. It is largely unknown how the network architectures change and what the key regulators in stress responses and plant growth are. Here, we investigated a complex, highly interconnected network of 20 Arabidopsis transcription factors (TFs) at the basis of leaf growth inhibition upon mild osmotic stress. We tracked the dynamic behavior of the stress-responsive TFs over time, showing the rapid induction following stress treatment, specifically in growing leaves. The connections between the TFs were uncovered using inducible overexpression lines and were validated with transient expression assays. This study resulted in the identification of a core network, composed of ERF6, ERF8, ERF9, ERF59, and ERF98, which is responsible for most transcriptional connections. The analyses highlight the biological function of this core network in environmental adaptation and its redundancy. Finally, a phenotypic analysis of loss-of-function and gain-of-function lines of the transcription factors established multiple connections between the stress-responsive network and leaf growth.

Early mannitol-triggered changes in the Arabidopsis leaf (phospho)proteome reveal growth regulators.

Leaf growth is a complex, quantitative trait, controlled by a plethora of regulatory mechanisms. Diverse environmental stimuli inhibit leaf growth to cope with the perceived stress. In plant research, mannitol is often used to impose osmotic stress and study the underlying growth-repressing mechanisms. In growing leaf tissue of plants briefly exposed to mannitol-induced stress, a highly interconnected gene regulatory network is induced. However, early signalling and associated protein phosphorylation events that probably precede part of these transcriptional changes and that potentially act at the onset of mannitol-induced leaf size reduction are largely unknown. Here, we performed a proteome and phosphoproteome analysis on growing leaf tissue of Arabidopsis thaliana plants exposed to mild mannitol-induced stress and captured the fast (within the first half hour) events associated with this stress. Based on this in-depth data analysis, 167 and 172 differentially regulated proteins and phosphorylated sites were found. We provide these data sets as a community resource and we flag differentially phosphorylated proteins with described growth-regulatory functions, but we also illustrate potential novel regulators of shoot growth.

Time of day determines Arabidopsis transcriptome and growth dynamics under mild drought.

Drought stress is a major problem for agriculture worldwide, causing significant yield losses. Plants have developed highly flexible mechanisms to deal with drought, including organ- and developmental stage-specific responses. In young leaves, growth is repressed as an active mechanism to save water and energy, increasing the chances of survival but decreasing yield. Despite its importance, the molecular basis for this growth inhibition is largely unknown. Here, we present a novel approach to explore early molecular mechanisms controlling Arabidopsis leaf growth inhibition following mild drought. We found that growth and transcriptome responses to drought are highly dynamic. Growth was only repressed by drought during the day, and our evidence suggests that this may be due to gating by the circadian clock. Similarly, time of day strongly affected the extent, specificity, and in certain cases even direction of drought-induced changes in gene expression. These findings underscore the importance of taking into account diurnal patterns to understand stress responses, as only a small core of drought-responsive genes are affected by drought at all times of the day. Finally, we leveraged our high-resolution data to demonstrate that phenotypic and transcriptome responses can be matched to identify putative novel regulators of growth under mild drought.

The ETHYLENE RESPONSE FACTORs ERF6 and ERF11 Antagonistically Regulate Mannitol-Induced Growth Inhibition in Arabidopsis.

Leaf growth is a tightly regulated and complex process, which responds in a dynamic manner to changing environmental conditions, but the mechanisms that reduce growth under adverse conditions are rather poorly understood. We previously identified a growth inhibitory pathway regulating leaf growth upon exposure to a low concentration of mannitol and characterized the ETHYLENE RESPONSE FACTOR (ERF)/APETALA2 transcription factor ERF6 as a central activator of both leaf growth inhibition and induction of stress tolerance genes. Here, we describe the role of the transcriptional repressor ERF11 in relation to the ERF6-mediated stress response in Arabidopsis (Arabidopsis thaliana). Using inducible overexpression lines, we show that ERF6 induces the expression of ERF11. ERF11 in turn molecularly counteracts the action of ERF6 and represses at least some of the ERF6-induced genes by directly competing for the target gene promoters. As a phenotypical consequence of the ERF6-ERF11 antagonism, the extreme dwarfism caused by ERF6 overexpression is suppressed by overexpression of ERF11. Together, our data demonstrate that dynamic mechanisms exist to fine-tune the stress response and that ERF11 counteracts ERF6 to maintain a balance between plant growth and stress defense.

RALFL34 regulates formative cell divisions in Arabidopsis pericycle during lateral root initiation.

In plants, many signalling molecules, such as phytohormones, miRNAs, transcription factors, and small signalling peptides, drive growth and development. However, very few small signalling peptides have been shown to be necessary for lateral root development. Here, we describe the role of the peptide RALFL34 during early events in lateral root development, and demonstrate its specific importance in orchestrating formative cell divisions in the pericycle. Our results further suggest that this small signalling peptide acts on the transcriptional cascade leading to a new lateral root upstream of GATA23, an important player in lateral root formation. In addition, we describe a role for ETHYLENE RESPONSE FACTORs (ERFs) in regulating RALFL34 expression. Taken together, we put forward RALFL34 as a new, important player in lateral root initiation.

Insights into multilevel spatial regulation within the root stem cell niche.

All differentiated root cells derive from stem cells spatially organized within the stem cell niche (SCN), a microenvironment located within the root tip. Here, we compiled recent advances in the understanding of how the SCN drives the establishment and maintenance of cell types. The quiescent center (QC) is widely recognized as the primary driver of cell fate determination, but it is recently considered a convergence center of multiple signals. Cell identity of the cortex endodermis initials is mainly driven by the regulatory feedback loops between transcription factors (TFs), acting as mobile signals between neighboring cells, including the QC. As exemplified in the vascular initials, the precise spatial expression of these regulatory TFs is connected with a dynamic hormonal interplay. Thus, stem cell maintenance and cell differentiation are regulated by a plethora of signals forming a complex, multilevel regulatory network. Integrating the transcriptional and post-translational regulations, protein-protein interactions, and mobile signals into models will be fundamental for the comprehensive understanding of SCN maintenance and differentiation.

A Data-Driven Signaling Network Inference Approach for Phosphoproteomics.

Proteins are rapidly and dynamically post-transcriptionally modified as cells respond to changes in their environment. For example, protein phosphorylation is mediated by kinases while dephosphorylation is mediated by phosphatases. Quantifying and predicting interactions between kinases, phosphatases, and target proteins over time will aid the study of signaling cascades under a variety of environmental conditions. Here, we describe methods to statistically analyze label-free phosphoproteomic data and infer posttranscriptional regulatory networks over time. We provide an R-based method that can be used to normalize and analyze label-free phosphoproteomic data using variance stabilizing normalization and a linear mixed model across multiple time points and conditions. We also provide a method to infer regulator-target interactions over time using a discretization scheme followed by dynamic Bayesian modeling computations to validate our conclusions. Overall, this pipeline is designed to perform functional analyses and predictions of phosphoproteomic signaling cascades.

Functional annotation of proteins for signaling network inference in non-model species.

Molecular biology aims to understand cellular responses and regulatory dynamics in complex biological systems. However, these studies remain challenging in non-model species due to poor functional annotation of regulatory proteins. To overcome this limitation, we develop a multi-layer neural network that determines protein functionality directly from the protein sequence. We annotate kinases and phosphatases in Glycine max. We use the functional annotations from our neural network, Bayesian inference principles, and high resolution phosphoproteomics to infer phosphorylation signaling cascades in soybean exposed to cold, and identify Glyma.10G173000 (TOI5) and Glyma.19G007300 (TOT3) as key temperature regulators. Importantly, the signaling cascade inference does not rely upon known kinase motifs or interaction data, enabling de novo identification of kinase-substrate interactions. Conclusively, our neural network shows generalization and scalability, as such we extend our predictions to Oryza sativa, Zea mays, Sorghum bicolor, and Triticum aestivum. Taken together, we develop a signaling inference approach for non-model species leveraging our predicted kinases and phosphatases.

Quantifying Intercellular Movement and Protein Stoichiometry for Computational Modeling.

Analyzing protein movement dynamics and their regulation has shown to be important in the study of cell fate decisions. Such analyses can be performed with scanning fluorescence correlation spectroscopy (scanning FCS), a versatile imaging methodology that has been applied in the animal kingdom and recently adapted to the plant kingdom. Specifically, scanning FCS allows for qualitatively capturing protein movement across barriers, such as the active transport through plasmodesmata, the analysis of protein movement rates, and the quantification of the stoichiometry of protein complexes, composed of one or more different proteins. Importantly, the quantifiable data generated with scanning FCS can be used to inform computational models, enhancing model simulations of in vivo events, such as cell fate decisions, during plant development.

Establishing a reproducible approach to study cellular functions of plant cells with 3D bioprinting.

Capturing cell-to-cell signals in a three-dimensional (3D) environment is key to studying cellular functions. A major challenge in the current culturing methods is the lack of accurately capturing multicellular 3D environments. In this study, we established a framework for 3D bioprinting plant cells to study cell viability, cell division, and cell identity. We established long-term cell viability for bioprinted Arabidopsis and soybean cells. To analyze the generated large image datasets, we developed a high-throughput image analysis pipeline. Furthermore, we showed the cell cycle reentry of bioprinted cells for which the timing coincides with the induction of core cell cycle genes and regeneration-related genes, ultimately leading to microcallus formation. Last, the identity of bioprinted Arabidopsis root cells expressing endodermal markers was maintained for longer periods. The framework established here paves the way for a general use of 3D bioprinting for studying cellular reprogramming and cell cycle reentry toward tissue regeneration.

Spatiotemporal Gene Expression Profiling and Network Inference: A Roadmap for Analysis, Visualization, and Key Gene Identification.

Gene expression data analysis and the prediction of causal relationships within gene regulatory networks (GRNs) have guided the identification of key regulatory factors and unraveled the dynamic properties of biological systems. However, drawing accurate and unbiased conclusions requires a comprehensive understanding of relevant tools, computational methods, and their workflows. The topics covered in this chapter encompass the entire workflow for GRN inference including: (1) experimental design; (2) RNA sequencing data processing; (3) differentially expressed gene (DEG) selection; (4) clustering prior to inference; (5) network inference techniques; and (6) network visualization and analysis. Moreover, this chapter aims to present a workflow feasible and accessible for plant biologists without a bioinformatics or computer science background. To address this need, TuxNet, a user-friendly graphical user interface that integrates RNA sequencing data analysis with GRN inference, is chosen for the purpose of providing a detailed tutorial.

Tissue Regeneration with Hydrogel Encapsulation: A Review of Developments in Plants and Animals.

Hydrogel encapsulation has been widely utilized in the study of fundamental cellular mechanisms and has been shown to provide a better representation of the complex in vivo microenvironment in natural biological conditions of mammalian cells. In this review, we provide a background into the adoption of hydrogel encapsulation methods in the study of mammalian cells, highlight some key findings that may aid with the adoption of similar methods for the study of plant cells, including the potential challenges and considerations, and discuss key findings of studies that have utilized these methods in plant sciences.

A hybrid model connecting regulatory interactions with stem cell divisions in the root.

Stem cells give rise to the entirety of cells within an organ. Maintaining stem cell identity and coordinately regulating stem cell divisions is crucial for proper development. In plants, mobile proteins, such as WUSCHEL-RELATED HOMEOBOX 5 (WOX5) and SHORTROOT (SHR), regulate divisions in the root stem cell niche. However, how these proteins coordinately function to establish systemic behaviour is not well understood. We propose a non-cell autonomous role for WOX5 in the cortex endodermis initial (CEI) and identify a regulator, ANGUSTIFOLIA (AN3)/GRF-INTERACTING FACTOR 1, that coordinates CEI divisions. Here, we show with a multi-scale hybrid model integrating ordinary differential equations (ODEs) and agent-based modeling that quiescent center (QC) and CEI divisions have different dynamics. Specifically, by combining continuous models to describe regulatory networks and agent-based rules, we model systemic behaviour, which led us to predict cell-type-specific expression dynamics of SHR, SCARECROW, WOX5, AN3 and CYCLIND6;1, and experimentally validate CEI cell divisions. Conclusively, our results show an interdependency between CEI and QC divisions.

RNA-Seq and Gene Regulatory Network Analyses Uncover Candidate Genes in the Early Defense to Two Hemibiotrophic Colletorichum spp. in Strawberry.

Two hemibiotrophic pathogens, Colletotrichum acutatum (Ca) and C. gloeosporioides (Cg), cause anthracnose fruit rot and anthracnose crown rot in strawberry (Fragaria × ananassa Duchesne), respectively. Both Ca and Cg can initially infect through a brief biotrophic phase, which is associated with the production of intracellular primary hyphae that can infect host cells without causing cell death and establishing hemibiotrophic infection (HBI) or quiescent (latent infections) in leaf tissues. The Ca and Cg HBI in nurseries and subsequent distribution of asymptomatic infected transplants to fruit production fields is the major source of anthracnose epidemics in North Carolina. In the absence of complete resistance, strawberry varieties with good fruit quality showing rate-reducing resistance have frequently been used as a source of resistance to Ca and Cg. However, the molecular mechanisms underlying the rate-reducing resistance or susceptibility to Ca and Cg are still unknown. We performed comparative transcriptome analyses to examine how rate-reducing resistant genotype NCS 10-147 and susceptible genotype 'Chandler' respond to Ca and Cg and identify molecular events between 0 and 48 h after the pathogen-inoculated and mock-inoculated leaf tissues. Although plant response to both Ca and Cg at the same timepoint was not similar, more genes in the resistant interaction were upregulated at 24 hpi with Ca compared with those at 48 hpi. In contrast, a few genes were upregulated in the resistant interaction at 48 hpi with Cg. Resistance response to both Ca and Cg was associated with upregulation of MLP-like protein 44, LRR receptor-like serine/threonine-protein kinase, and auxin signaling pathway, whereas susceptibility was linked to modulation of the phenylpropanoid pathway. Gene regulatory network inference analysis revealed candidate transcription factors (TFs) such as GATA5 and MYB-10, and their downstream targets were upregulated in resistant interactions. Our results provide valuable insights into transcriptional changes during resistant and susceptible interactions, which can further facilitate assessing candidate genes necessary for resistance to two hemibiotrophic Colletotrichum spp. in strawberry.