@article{broeck_bhosale_song_lima_ashley_zhu_zhu_cotte_neyt_ortiz_et al._2023, title={Functional annotation of proteins for signaling network inference in non-model species}, volume={14}, ISSN={["2041-1723"]}, DOI={10.1038/s41467-023-40365-z}, abstractNote={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.}, number={1}, journal={NATURE COMMUNICATIONS}, author={Broeck, Lisa and Bhosale, Dinesh Kiran and Song, Kuncheng and Lima, Cassio Flavio and Ashley, Michael and Zhu, Tingting and Zhu, Shanshuo and Cotte, Brigitte and Neyt, Pia and Ortiz, Anna C. and et al.}, year={2023}, month={Aug} }
 @article{beretta_franchini_din_lacchini_broeck_sozzani_orozco-arroyo_caporali_adam_jouannic_et al._2023, title={The ALOG family members OsG1L1 and OsG1L2 regulate inflorescence branching in rice}, volume={4}, ISSN={["1365-313X"]}, DOI={10.1111/tpj.16229}, abstractNote={SUMMARY 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.}, journal={PLANT JOURNAL}, author={Beretta, Veronica M. and Franchini, Emanuela and Din, Israr Ud and Lacchini, Elia and Broeck, Lisa and Sozzani, Rosangela and Orozco-Arroyo, Gregorio and Caporali, Elisabetta and Adam, Helene and Jouannic, Stefan and et al.}, year={2023}, month={Apr} }
 @article{broeck_schwartz_krishnamoorthy_spurney_tahir_melvin_gobble_peters_muhammad_li_et al._2022, title={Establishing a reproducible approach for the controllable deposition and maintenance of plants cells with 3D bioprinting}, volume={3}, url={https://doi.org/10.1101/2022.03.25.485804}, DOI={10.1101/2022.03.25.485804}, abstractNote={Capturing cell-to-cell and cell-to-environment signals in a defined 3 dimensional (3D) microenvironment is key to study cellular functions, including cellular reprogramming towards tissue regeneration. A major challenge in current culturing methods is that these methods cannot accurately capture this multicellular 3D microenvironment. In this study, we established the framework of 3D bioprinting with plant cells to study cell viability, cell division, and cell identity. We established long-term cell viability for bioprinted Arabidopsis root cells and soybean meristematic cells. To analyze the large image datasets generated during these long-term viability studies, we developed an open source high-throughput image analysis pipeline. Furthermore, we showed the cell cycle re-entry of the isolated Arabidopsis and soybean cells leading to the formation of microcalli. Finally, we showed that the identity of isolated cells of Arabidopsis roots expressing endodermal markers maintained longer periods of time. The framework established in this study paves the way for a general use of 3D bioprinting for studying cellular reprogramming and cell cycle re-entry towards tissue regeneration.}, publisher={Cold Spring Harbor Laboratory}, author={Broeck, Lisa Van and Schwartz, Michael F and Krishnamoorthy, Srikumar and Spurney, Ryan J and Tahir, Maimouna Abderamane and Melvin, Charles and Gobble, Mariah and Peters, Rachel and Muhammad, Atiyya and Li, Baochun and et al.}, year={2022}, month={Mar} }
 @article{broeck_schwartz_krishnamoorthy_tahir_spurney_madison_melvin_gobble_nguyen_peters_et al._2022, title={Establishing a reproducible approach to study cellular functions of plant cells with 3D bioprinting}, url={https://doi.org/10.1126/sciadv.abp9906}, DOI={10.1126/sciadv.abp9906}, abstractNote={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.}, journal={Science Advances}, author={Broeck, Lisa Van and Schwartz, Michael F. and Krishnamoorthy, Srikumar and Tahir, Maimouna Abderamane and Spurney, Ryan J. and Madison, Imani and Melvin, Charles and Gobble, Mariah and Nguyen, Thomas and Peters, Rachel and et al.}, year={2022}, month={Oct} }
 @article{thomas_broeck_spurney_sozzani_frank_2022, title={Gene regulatory networks for compatible versus incompatible grafts identify a role for SlWOX4 during junction formation}, volume={34}, ISSN={["1532-298X"]}, url={https://doi.org/10.1093/plcell/koab246}, DOI={10.1093/plcell/koab246}, abstractNote={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.}, number={1}, journal={PLANT CELL}, publisher={Oxford University Press (OUP)}, author={Thomas, Hannah and Broeck, Lisa and Spurney, Ryan and Sozzani, Rosangela and Frank, Margaret}, year={2022}, month={Jan}, pages={535–556} }
 @article{broeck_spurney_fisher_schwartz_clark_nguyen_madison_gobble_long_sozzani_2021, title={A hybrid model connecting regulatory interactions with stem cell divisions in the root}, volume={2}, url={https://doi.org/10.1017/qpb.2021.1}, DOI={10.1017/qpb.2021.1}, abstractNote={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.}, journal={Quantitative Plant Biology}, publisher={Cambridge University Press (CUP)}, author={Broeck, Lisa Van and Spurney, Ryan J. and Fisher, Adam P. and Schwartz, Michael and Clark, Natalie M. and Nguyen, Thomas T. and Madison, Imani and Gobble, Mariah and Long, Terri and Sozzani, Rosangela}, year={2021} }
 @article{kim_van den broeck_karre_choi_christensen_wang_jo_cho_balint‐kurti_2021, title={Analysis of the transcriptomic, metabolomic, and gene regulatory responses to
            <i>Puccinia sorghi
            in maize}, volume={22}, ISSN={1464-6722 1364-3703}, url={http://dx.doi.org/10.1111/mpp.13040}, DOI={10.1111/mpp.13040}, abstractNote={Abstract Common rust, caused by Puccinia sorghi , is a widespread and destructive disease of maize. The Rp1‐D gene confers resistance to the P. sorghi IN2 isolate, mediating a hypersensitive cell death response (HR). To identify differentially expressed genes (DEGs) and metabolites associated with the compatible (susceptible) interaction and with Rp1‐D ‐mediated resistance in maize, we performed transcriptomics and targeted metabolome analyses of P. sorghi IN2‐infected leaves from the near‐isogenic lines H95 and H95:Rp1‐D, which differed for the presence of Rp1‐D . We observed up‐regulation of genes involved in the defence response and secondary metabolism, including the phenylpropanoid, flavonoid, and terpenoid pathways. Metabolome analyses confirmed that intermediates from several transcriptionally up‐regulated pathways accumulated during the defence response. We identified a common response in H95:Rp1‐D and H95 with an additional H95:Rp1‐D‐specific resistance response observed at early time points at both transcriptional and metabolic levels. To better understand the mechanisms underlying Rp1‐D ‐mediated resistance, we inferred gene regulatory networks occurring in response to P. sorghi infection. A number of transcription factors including WRKY53, BHLH124, NKD1, BZIP84, and MYB100 were identified as potentially important signalling hubs in the resistance‐specific response. Overall, this study provides a novel and multifaceted understanding of the maize susceptible and resistance‐specific responses to P. sorghi .}, number={4}, journal={Molecular Plant Pathology}, publisher={Wiley}, author={Kim, Saet‐Byul and Van den Broeck, Lisa and Karre, Shailesh and Choi, Hoseong and Christensen, Shawn A. and Wang, Guan‐Feng and Jo, Yeonhwa and Cho, Won Kyong and Balint‐Kurti, Peter}, year={2021}, month={Feb}, pages={465–479} }
 @article{thomas_broeck_spurney_sozzani_frank_2021, title={Gene regulatory networks for compatible versus incompatible grafts identify a role for SlWOX4 during junction formation}, volume={2}, url={https://doi.org/10.1101/2021.02.26.433082}, DOI={10.1101/2021.02.26.433082}, abstractNote={Abstract Graft incompatibility is a poorly understood phenomenon that presents a serious agricultural challenge. Unlike immediate incompatibility that results in rapid death, 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 with Solanum lycopersicum ‘tomato’ and Capsicum annuum ‘pepper’ heterografting, which expresses 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 infer gene regulatory networks for compatible self-grafts versus incompatible heterografts based on these key anatomical events, which predict core regulators for grafting. Finally, we delve into the role of vascular development in graft formation and validate SlWOX4 as a regulator for grafting in tomato. Notably, SlWOX4 is the first gene to be functionally implicated in vegetable crop grafting.}, publisher={Cold Spring Harbor Laboratory}, author={Thomas, Hannah and Broeck, Lisa Van and Spurney, Ryan and Sozzani, Rosangela and Frank, Margaret}, year={2021}, month={Feb} }
 @article{franchini_beretta_din_lacchini_broeck_sozzani_orozco-arroyo_adam_jouannic_gregis_et al._2021, title={The ALOG family members OsG1L1 and OsG1L2 regulate inflorescence branching in rice}, volume={5}, url={https://doi.org/10.1101/2021.05.03.442460}, DOI={10.1101/2021.05.03.442460}, abstractNote={ABSTRACT The architecture of the rice inflorescence is an important determinant of seed yield. The length of the inflorescence and the number of branches are among the key factors determining the amount of spikelets, and thus seeds, that will develop. Especially the timing of the identity transition from indeterminate branch meristem to determinate spikelet meristem regulates 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 rice. Recently, by combining precise laser microdissection of inflorescence meristems with RNA-seq we observed that two ALOG genes, Oryza sativa OsG1-like 1 ( OsG1L1 ) and OsG1L2 , have an expression profile similar to TAW1 . Here we report that osg1l1 and osg1l2 loss-of-function CRISPR mutants have similar phenotypes as the 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 datasets were also used for the construction of a gene regulatory network (GRN) proposing interactions between genes potentially involved in controlling inflorescence development in rice. The spatio-temporal expression profiling and phenotypical analysis of CRISPR loss-of-function mutants of the homeodomain-leucine zipper transcription factor gene OsHOX14 suggest that the proposed GRN indeed serves as a valuable resource for the identification of new players involved in rice inflorescence development. One-sentence summary OsG1L1 and OsG1L2 control panicle architecture through delaying the transition from indeterminate branch- to determinate spikelet-meristem identity.}, publisher={Cold Spring Harbor Laboratory}, author={Franchini, Emanuela and Beretta, Veronica M. and Din, Israr Ud and Lacchini, Elia and Broeck, Lisa Van and Sozzani, Rosangela and Orozco-Arroyo, Gregorio and Adam, Hélène and Jouannic, Stefan and Gregis, Veronica and et al.}, year={2021}, month={May} }
 @article{krishnamoorthy_schwartz_broeck_hunt_horn_sozzani_2021, title={Tissue Regeneration with Hydrogel Encapsulation: A Review of Developments in Plants and Animals}, url={https://doi.org/10.34133/2021/9890319}, DOI={10.34133/2021/9890319}, abstractNote={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.}, journal={BioDesign Research}, author={Krishnamoorthy, Srikumar and Schwartz, Michael F. and Broeck, Lisa Van and Hunt, Aitch and Horn, Timothy J. and Sozzani, Rosangela}, year={2021}, month={Dec} }
 @article{doydora_gatiboni_grieger_hesterberg_jones_mclamore_peters_sozzani_van den broeck_duckworth_2020, title={Accessing Legacy Phosphorus in Soils}, volume={4}, ISSN={2571-8789}, url={http://dx.doi.org/10.3390/soilsystems4040074}, DOI={10.3390/soilsystems4040074}, abstractNote={Repeated applications of phosphorus (P) fertilizers result in the buildup of P in soil (commonly known as legacy P), a large fraction of which is not immediately available for plant use. Long-term applications and accumulations of soil P is an inefficient use of dwindling P supplies and can result in nutrient runoff, often leading to eutrophication of water bodies. Although soil legacy P is problematic in some regards, it conversely may serve as a source of P for crop use and could potentially decrease dependence on external P fertilizer inputs. This paper reviews the (1) current knowledge on the occurrence and bioaccessibility of different chemical forms of P in soil, (2) legacy P transformations with mineral and organic fertilizer applications in relation to their potential bioaccessibility, and (3) approaches and associated challenges for accessing native soil P that could be used to harness soil legacy P for crop production. We highlight how the occurrence and potential bioaccessibility of different forms of soil inorganic and organic P vary depending on soil properties, such as soil pH and organic matter content. We also found that accumulation of inorganic legacy P forms changes more than organic P species with fertilizer applications and cessations. We also discuss progress and challenges with current approaches for accessing native soil P that could be used for accessing legacy P, including natural and genetically modified plant-based strategies, the use of P-solubilizing microorganisms, and immobilized organic P-hydrolyzing enzymes. It is foreseeable that accessing legacy P will require multidisciplinary approaches to address these limitations.}, number={4}, journal={Soil Systems}, publisher={MDPI AG}, author={Doydora, Sarah and Gatiboni, Luciano and Grieger, Khara and Hesterberg, Dean and Jones, Jacob L. and McLamore, Eric S. and Peters, Rachel and Sozzani, Rosangela and Van den Broeck, Lisa and Duckworth, Owen W.}, year={2020}, month={Dec}, pages={74} }
 @article{van den broeck_gordon_inzé_williams_sozzani_2020, title={Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling}, volume={11}, ISSN={1664-8021}, url={http://dx.doi.org/10.3389/fgene.2020.00457}, DOI={10.3389/fgene.2020.00457}, abstractNote={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.}, journal={Frontiers in Genetics}, publisher={Frontiers Media SA}, author={Van den Broeck, Lisa and Gordon, Max and Inzé, Dirk and Williams, Cranos and Sozzani, Rosangela}, year={2020}, month={May} }
 @article{clark_van den broeck_guichard_stager_tanner_blilou_grossmann_iyer-pascuzzi_maizel_sparks_et al._2020, title={Novel Imaging Modalities Shedding Light on Plant Biology: Start Small and Grow Big}, volume={71}, ISSN={1543-5008 1545-2123}, url={http://dx.doi.org/10.1146/annurev-arplant-050718-100038}, DOI={10.1146/annurev-arplant-050718-100038}, abstractNote={The acquisition of quantitative information on plant development across a range of temporal and spatial scales is essential to understand the mechanisms of plant growth. Recent years have shown the emergence of imaging methodologies that enable the capture and analysis of plant growth, from the dynamics of molecules within cells to the measurement of morphometricand physiological traits in field-grown plants. In some instances, these imaging methods can be parallelized across multiple samples to increase throughput. When high throughput is combined with high temporal and spatial resolution, the resulting image-derived data sets could be combined with molecular large-scale data sets to enable unprecedented systems-level computational modeling. Such image-driven functional genomics studies may be expected to appear at an accelerating rate in the near future given the early success of the foundational efforts reviewed here. We present new imaging modalities and review how they have enabled a better understanding of plant growth from the microscopic to the macroscopic scale.}, number={1}, journal={Annual Review of Plant Biology}, publisher={Annual Reviews}, author={Clark, Natalie M. and Van den Broeck, Lisa and Guichard, Marjorie and Stager, Adam and Tanner, Herbert G. and Blilou, Ikram and Grossmann, Guido and Iyer-Pascuzzi, Anjali S. and Maizel, Alexis and Sparks, Erin E. and et al.}, year={2020}, month={Apr}, pages={789–816} }
 @article{clark_fisher_berckmans_van den broeck_nelson_nguyen_bustillo-avendaño_zebell_moreno-risueno_simon_et al._2020, title={Protein complex stoichiometry and expression dynamics of transcription factors modulate stem cell division}, volume={117}, ISSN={0027-8424 1091-6490}, url={http://dx.doi.org/10.1073/pnas.2002166117}, DOI={10.1073/pnas.2002166117}, abstractNote={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.}, number={26}, journal={Proceedings of the National Academy of Sciences}, publisher={Proceedings of the National Academy of Sciences}, author={Clark, Natalie M. and Fisher, Adam P. and Berckmans, Barbara and Van den Broeck, Lisa and Nelson, Emily C. and Nguyen, Thomas T. and Bustillo-Avendaño, Estefano and Zebell, Sophia G. and Moreno-Risueno, Miguel A. and Simon, Rüdiger and et al.}, year={2020}, month={Jun}, pages={15332–15342} }
 @article{spurney_broeck_clark_fisher_balaguer_sozzani_2020, title={tuxnet: a simple interface to process RNA sequencing data and infer gene regulatory networks}, volume={101}, ISSN={["1365-313X"]}, url={https://doi.org/10.1111/tpj.14558}, DOI={10.1111/tpj.14558}, abstractNote={Summary 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.}, number={3}, journal={PLANT JOURNAL}, publisher={Wiley}, author={Spurney, Ryan J. and Broeck, Lisa and Clark, Natalie M. and Fisher, Adam P. and Balaguer, Maria A. de Luis and Sozzani, Rosangela}, year={2020}, month={Feb}, pages={716–730} }
 @article{n_broeck l_s_cotte b_m_k_d_i_2018, title={Early mannitol-triggered changes in the Arabidopsis leaf (phospho)proteome reveal growth regulators.}, volume={8}, url={http://europepmc.org/abstract/med/30010984}, DOI={10.1093/jxb/ery261}, abstractNote={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.}, journal={Journal of experimental botany}, author={N, Nikonorova and Broeck L, Van and S, Zhu and Cotte B and M, Dubois and K, Gevaert and D, Inzé and I, De Smet}, year={2018}, month={Aug} }
 @article{dubois_broeck l van_inzé_2018, title={The Pivotal Role of Ethylene in Plant Growth.}, volume={4}, url={http://europepmc.org/abstract/med/29428350}, DOI={10.1016/j.tplants.2018.01.003}, abstractNote={An increasing number of transcriptome studies in plants exposed to biotic or abiotic stress highlight a role for ethylene under a broad range of stresses. The role of ethylene under stress is dual: it regulates a defense response, mostly in full-grown leaves, and a growth response in young leaves. In young leaves, ethylene and the downstream ERFs emerge as central regulators of leaf growth inhibition, orchestrating both cell division and cell expansion. The knowledge of ethylene-mediated growth inhibition can be successfully implemented in crops to improve plant growth and stress tolerance. Being continuously exposed to variable environmental conditions, plants produce phytohormones to react quickly and specifically to these changes. The phytohormone ethylene is produced in response to multiple stresses. While the role of ethylene in defense responses to pathogens is widely recognized, recent studies in arabidopsis and crop species highlight an emerging key role for ethylene in the regulation of organ growth and yield under abiotic stress. Molecular connections between ethylene and growth-regulatory pathways have been uncovered, and altering the expression of ethylene response factors (ERFs) provides a new strategy for targeted ethylene-response engineering. Crops with optimized ethylene responses show improved growth in the field, opening new windows for future crop improvement. This review focuses on how ethylene regulates shoot growth, with an emphasis on leaves. Being continuously exposed to variable environmental conditions, plants produce phytohormones to react quickly and specifically to these changes. The phytohormone ethylene is produced in response to multiple stresses. While the role of ethylene in defense responses to pathogens is widely recognized, recent studies in arabidopsis and crop species highlight an emerging key role for ethylene in the regulation of organ growth and yield under abiotic stress. Molecular connections between ethylene and growth-regulatory pathways have been uncovered, and altering the expression of ethylene response factors (ERFs) provides a new strategy for targeted ethylene-response engineering. Crops with optimized ethylene responses show improved growth in the field, opening new windows for future crop improvement. This review focuses on how ethylene regulates shoot growth, with an emphasis on leaves. The sessility of plants is undoubtedly their most disadvantageous feature compared to other living organisms, and implies that their survival can be threatened by environmental perturbations. However, plants have developed fascinating mechanisms enabling rapid detection of changing conditions accompanied by highly complex molecular responses, resulting in remarkable phenotypic plasticity. During the vegetative growth stage, one tightly controlled process is plant growth. Under favorable conditions, root and shoot growth is crucial to enable continuous nutrient uptake and energy production through photosynthesis, respectively. Leaf growth, for example, is controlled by no less than six different cellular mechanisms, including precise orchestration of the switch between cell division, that drives the growth of very young leaf primordia, and cell expansion and differentiation (reviewed in [1Gonzalez N. et al.Leaf size control: complex coordination of cell division and expansion.Trends Plant Sci. 2012; 17: 332-340Abstract Full Text Full Text PDF PubMed Scopus (182) Google Scholar]). By contrast, sustaining growth under unfavorable conditions could be detrimental. For example, growth under drought stress would increase the evaporative surface of the plant, rendering the plant even more susceptible. Plants thus constantly evaluate whether the environmental signals are favorable for growth or not, and redirect their resources either for growth or for stress defense. At the physiological level, the integration of environmental signals into proper phenotypic responses is orchestrated by phytohormones. Ethylene, the smallest phytohormone with the simple C2H4 structure, is gaseous and therefore enables plant-to-plant communication. Since its discovery around one century ago, the multiple facets of this hormone as a signaling molecule have fascinated scientists, and this led to the unraveling of its biosynthesis and signaling (Box 1 and Figure 1), and the identification of its various functions: regulation of leaf development, senescence, fruit ripening, stimulation of germination, etc. Importantly, ethylene is produced in response to multiple environmental stresses (Figure 1), both abiotic and biotic, suggesting that it acts as a bridge between a changing environment and developmental adaptation. The abiotic stress conditions that trigger ethylene synthesis include submergence, heat, shade, exposure to heavy metals and high salt, low nutrient availability, and water deficiency [2Skirycz A. et al.Pause-and-stop: the effects of osmotic stress on cell proliferation during early leaf development in Arabidopsis and a role for ethylene signaling in cell cycle arrest.Plant Cell. 2011; 23: 1876-1888Crossref PubMed Scopus (0) Google Scholar, 3Thao N.P. et al.Role of ethylene and its cross talk with other signaling molecules in plant responses to heavy metal stress.Plant Physiol. 2015; 169: 73-84Crossref PubMed Scopus (23) Google Scholar, 4Zhang M. et al.The regulatory roles of ethylene and reactive oxygen species (ROS) in plant salt stress responses.Plant Mol. Biol. 2016; 91: 651-659Crossref PubMed Scopus (11) Google Scholar, 5Dubois M. et al.Time of day determines Arabidopsis transcriptome and growth dynamics under mild drought.Plant Cell Environ. 2017; 40: 180-189Crossref PubMed Scopus (3) Google Scholar, 6Savada R.P. et al.Heat stress differentially modifies ethylene biosynthesis and signaling in pea floral and fruit tissues.Plant Mol. Biol. 2017; 95: 313-331Crossref PubMed Scopus (0) Google Scholar].Box 1Recent Advances in Ethylene Biosynthesis and SignalingThe ethylene biosynthesis pathway consists of a simple, three-step process: methionine is converted into S-adenosyl methionine (SAM; see Glossary), which is further converted by ACC-synthases (ACS) to ACC, the direct precursor of ethylene (Figure 1). Recycling of methylthioadenosine enables rapid ethylene biosynthesis when necessary [85Sauter M. et al.Methionine salvage and S-adenosylmethionine: essential links between sulfur, ethylene and polyamine biosynthesis.Biochem. J. 2013; 451: 145-154Crossref PubMed Scopus (90) Google Scholar]. Because the conversion from ACC to ethylene is an exothermic reaction that only requires oxygen, ethylene biosynthesis is regulated at the level of ACS enzymes, which are also under post-translational control: they can be phosphorylated before ubiquitin-mediated protein degradation by, for instance, ETO1 and CUL3 [86Thomann A. et al.Arabidopsis CULLIN3 genes regulate primary root growth and patterning by ethylene-dependent and -independent mechanisms.PLoS Genet. 2009; 5e1000328Crossref PubMed Scopus (0) Google Scholar, 87Yoon G.M. New insights into the protein turnover regulation in ethylene biosynthesis.Mol. Cells. 2015; 38: 597-603Crossref PubMed Google Scholar]. ACS induction and activation are responsive to environmental factors that trigger ethylene accumulation. As such, ACS genes are transcriptionally induced by drought [5Dubois M. et al.Time of day determines Arabidopsis transcriptome and growth dynamics under mild drought.Plant Cell Environ. 2017; 40: 180-189Crossref PubMed Scopus (3) Google Scholar] and by shade, under the control of PIF4 [58Nomoto Y. et al.Circadian clock- and PIF4-controlled plant growth: a coincidence mechanism directly integrates a hormone signaling network into the photoperiodic control of plant architectures in Arabidopsis thaliana.Plant Cell Physiol. 2012; 53: 1950-1964Crossref PubMed Scopus (52) Google Scholar]. ACS2 and ACS6 are post-translationally activated through phosphorylation by a MAPK-phosphorylation cascade involving MKK9 and MPK3/6 [88Xu J. Zhang S. Regulation of ethylene biosynthesis and signaling by protein kinases and phosphatases.Mol. Plant. 2014; 7: 939-942Abstract Full Text Full Text PDF Scopus (0) Google Scholar]. ACC levels are also regulated by conjugation and release from conjugates such as malonyl- or jasmonyl-ACC [89Van de Poel B. Van Der Straeten D. 1-Aminocyclopropane-1-carboxylic acid (ACC) in plants: more than just the precursor of ethylene!.Front. Plant Sci. 2014; 5: 640Crossref PubMed Scopus (35) Google Scholar]. The soluble ethylene precursor ACC can be taken up by the amino acid transporter LHT1 and further transported through the plant via the xylem (Figure 1) [90Shin K. et al.Genetic identification of ACC-RESISTANT2 reveals involvement of LYSINE HISTIDINE TRANSPORTER1 in the uptake of 1-aminocyclopropane-1-carboxylic acid in Arabidopsis thaliana.Plant Cell Physiol. 2015; 56: 572-582Crossref PubMed Scopus (15) Google Scholar].In the destination organ, ethylene triggers a signaling cascade initiated by ethylene receptors in the ER and Golgi membrane: ERS1 (ETHYLENE RESPONSE SENSOR 1), ERS2, ETR1 (ETHYLENE RESISTANCE 1), ETR2 and EIN4 (ETHYLENE INSENSITIVE 4). These receptors are active in the absence of ethylene, and their activity can be controlled by complex formation with RTE1 (REVERSION TO ETHYLENE SENSITIVITY) and ARGOS proteins: these are positive regulators of the ethylene receptors, and thus are negative regulators of ethylene sensitivity [11Rai M.I. et al.The ARGOS gene family functions in a negative feedback loop to desensitize plants to ethylene.BMC Plant Biol. 2015; 15: 157Crossref PubMed Scopus (12) Google Scholar, 91Resnick J.S. et al.REVERSION-TO-ETHYLENE SENSITIVITY1, a conserved gene that regulates ethylene receptor function in Arabidopsis.Proc. Natl. Acad. Sci. U. S. A. 2006; 103: 7917-7922Crossref PubMed Scopus (114) Google Scholar, 92Shi J. et al.Maize and Arabidopsis ARGOS proteins interact with ethylene receptor signaling complex, supporting a regulatory role for ARGOS in ethylene signal transduction.Plant Physiol. 2016; 171: 2783-2797Crossref PubMed Scopus (14) Google Scholar]. In the absence of ethylene, active receptors subsequently bind to and thereby activate the CTR1 protein [93Lacey R.F. Binder B.M. How plants sense ethylene gas – the ethylene receptors.J. Inorg. Biochem. 2014; 133: 58-62Crossref PubMed Scopus (12) Google Scholar]. The levels of the receptors are regulated by ethylene and CTR1: slightly increasing ethylene levels stimulate the transcription of the receptors and stabilization of CTR1, whereas higher ethylene levels push the receptor/CTR1 towards proteasome-mediated degradation [94Shakeel S.N. et al.Ethylene regulates levels of ethylene receptor/CTR1 signaling complexes in Arabidopsis thaliana.J. Biol. Chem. 2015; 290: 12415-12424Crossref PubMed Scopus (15) Google Scholar]. CTR1 is a kinase that represses EIN2, an ER-located membrane protein. When this repression is released in the presence of ethylene, EIN2 is dephosphorylated and cleaved, releasing a C-terminal fragment that either moves to P-bodies or to the nucleus [95Li W. et al.EIN2-directed translational regulation of ethylene signaling in Arabidopsis.Cell. 2015; 163: 670-683Abstract Full Text Full Text PDF PubMed Scopus (66) Google Scholar, 96Merchante C. et al.Gene-specific translation regulation mediated by the hormone-signaling molecule EIN2.Cell. 2015; 163: 684-697Abstract Full Text Full Text PDF PubMed Scopus (67) Google Scholar]. The downstream mode of action of the EIN2 fragment has long been a mystery, but recent studies have shown that it is involved in gene-specific regulation of translation [95Li W. et al.EIN2-directed translational regulation of ethylene signaling in Arabidopsis.Cell. 2015; 163: 670-683Abstract Full Text Full Text PDF PubMed Scopus (66) Google Scholar, 96Merchante C. et al.Gene-specific translation regulation mediated by the hormone-signaling molecule EIN2.Cell. 2015; 163: 684-697Abstract Full Text Full Text PDF PubMed Scopus (67) Google Scholar]. The EIN2 fragment binds to the 3'-untranslated regions (3'-UTRs) of EBF1 and EBF2 transcripts, thereby repressing their translation. EBF1 and EBF2 are two central F-box proteins that target the primary ethylene-responsive TFs EIN3 and EIN3-LIKE 1 (EIL1) for protein degradation in the absence of ethylene [97Guo H. Ecker J.R. Plant responses to ethylene gas are mediated by SCFEBF1/EBF2-dependent proteolysis of EIN3 transcription factor.Cell. 2003; 115: 667-677Abstract Full Text Full Text PDF PubMed Scopus (495) Google Scholar, 98Potuschak T. et al.EIN3-dependent regulation of plant ethylene hormone signaling by two Arabidopsis F box proteins: EBF1 and EBF2.Cell. 2003; 115: 679-689Abstract Full Text Full Text PDF PubMed Scopus (420) Google Scholar]. In the presence of ethylene, EIN3 and EIL1 induce the expression of numerous secondary transcription factors (TFs), the ERFs [99Nakano T. et al.Identification of genes of the plant-specific transcription-factor families cooperatively regulated by ethylene and jasmonate in Arabidopsis thaliana.J. Plant Res. 2006; 119: 407-413Crossref PubMed Scopus (0) Google Scholar]. The activity of some ERFs has been reported to be increased by phosphorylation through the MPK3/6-cascade that also regulates ethylene biosynthesis, providing dual-level regulation of the ERF-mediated response [24Meng X. et al.Phosphorylation of an ERF transcription factor by Arabidopsis MPK3/MPK6 regulates plant defense gene induction and fungal resistance.Plant Cell. 2013; 25: 1126-1142Crossref PubMed Scopus (143) Google Scholar, 100Yoo S.-D. Sheen J. MAPK signaling in plant hormone ethylene signal transduction.Plant Signal. Behav. 2008; 3: 848-849Crossref PubMed Google Scholar]. The ethylene biosynthesis pathway consists of a simple, three-step process: methionine is converted into S-adenosyl methionine (SAM; see Glossary), which is further converted by ACC-synthases (ACS) to ACC, the direct precursor of ethylene (Figure 1). Recycling of methylthioadenosine enables rapid ethylene biosynthesis when necessary [85Sauter M. et al.Methionine salvage and S-adenosylmethionine: essential links between sulfur, ethylene and polyamine biosynthesis.Biochem. J. 2013; 451: 145-154Crossref PubMed Scopus (90) Google Scholar]. Because the conversion from ACC to ethylene is an exothermic reaction that only requires oxygen, ethylene biosynthesis is regulated at the level of ACS enzymes, which are also under post-translational control: they can be phosphorylated before ubiquitin-mediated protein degradation by, for instance, ETO1 and CUL3 [86Thomann A. et al.Arabidopsis CULLIN3 genes regulate primary root growth and patterning by ethylene-dependent and -independent mechanisms.PLoS Genet. 2009; 5e1000328Crossref PubMed Scopus (0) Google Scholar, 87Yoon G.M. New insights into the protein turnover regulation in ethylene biosynthesis.Mol. Cells. 2015; 38: 597-603Crossref PubMed Google Scholar]. ACS induction and activation are responsive to environmental factors that trigger ethylene accumulation. As such, ACS genes are transcriptionally induced by drought [5Dubois M. et al.Time of day determines Arabidopsis transcriptome and growth dynamics under mild drought.Plant Cell Environ. 2017; 40: 180-189Crossref PubMed Scopus (3) Google Scholar] and by shade, under the control of PIF4 [58Nomoto Y. et al.Circadian clock- and PIF4-controlled plant growth: a coincidence mechanism directly integrates a hormone signaling network into the photoperiodic control of plant architectures in Arabidopsis thaliana.Plant Cell Physiol. 2012; 53: 1950-1964Crossref PubMed Scopus (52) Google Scholar]. ACS2 and ACS6 are post-translationally activated through phosphorylation by a MAPK-phosphorylation cascade involving MKK9 and MPK3/6 [88Xu J. Zhang S. Regulation of ethylene biosynthesis and signaling by protein kinases and phosphatases.Mol. Plant. 2014; 7: 939-942Abstract Full Text Full Text PDF Scopus (0) Google Scholar]. ACC levels are also regulated by conjugation and release from conjugates such as malonyl- or jasmonyl-ACC [89Van de Poel B. Van Der Straeten D. 1-Aminocyclopropane-1-carboxylic acid (ACC) in plants: more than just the precursor of ethylene!.Front. Plant Sci. 2014; 5: 640Crossref PubMed Scopus (35) Google Scholar]. The soluble ethylene precursor ACC can be taken up by the amino acid transporter LHT1 and further transported through the plant via the xylem (Figure 1) [90Shin K. et al.Genetic identification of ACC-RESISTANT2 reveals involvement of LYSINE HISTIDINE TRANSPORTER1 in the uptake of 1-aminocyclopropane-1-carboxylic acid in Arabidopsis thaliana.Plant Cell Physiol. 2015; 56: 572-582Crossref PubMed Scopus (15) Google Scholar]. In the destination organ, ethylene triggers a signaling cascade initiated by ethylene receptors in the ER and Golgi membrane: ERS1 (ETHYLENE RESPONSE SENSOR 1), ERS2, ETR1 (ETHYLENE RESISTANCE 1), ETR2 and EIN4 (ETHYLENE INSENSITIVE 4). These receptors are active in the absence of ethylene, and their activity can be controlled by complex formation with RTE1 (REVERSION TO ETHYLENE SENSITIVITY) and ARGOS proteins: these are positive regulators of the ethylene receptors, and thus are negative regulators of ethylene sensitivity [11Rai M.I. et al.The ARGOS gene family functions in a negative feedback loop to desensitize plants to ethylene.BMC Plant Biol. 2015; 15: 157Crossref PubMed Scopus (12) Google Scholar, 91Resnick J.S. et al.REVERSION-TO-ETHYLENE SENSITIVITY1, a conserved gene that regulates ethylene receptor function in Arabidopsis.Proc. Natl. Acad. Sci. U. S. A. 2006; 103: 7917-7922Crossref PubMed Scopus (114) Google Scholar, 92Shi J. et al.Maize and Arabidopsis ARGOS proteins interact with ethylene receptor signaling complex, supporting a regulatory role for ARGOS in ethylene signal transduction.Plant Physiol. 2016; 171: 2783-2797Crossref PubMed Scopus (14) Google Scholar]. In the absence of ethylene, active receptors subsequently bind to and thereby activate the CTR1 protein [93Lacey R.F. Binder B.M. How plants sense ethylene gas – the ethylene receptors.J. Inorg. Biochem. 2014; 133: 58-62Crossref PubMed Scopus (12) Google Scholar]. The levels of the receptors are regulated by ethylene and CTR1: slightly increasing ethylene levels stimulate the transcription of the receptors and stabilization of CTR1, whereas higher ethylene levels push the receptor/CTR1 towards proteasome-mediated degradation [94Shakeel S.N. et al.Ethylene regulates levels of ethylene receptor/CTR1 signaling complexes in Arabidopsis thaliana.J. Biol. Chem. 2015; 290: 12415-12424Crossref PubMed Scopus (15) Google Scholar]. CTR1 is a kinase that represses EIN2, an ER-located membrane protein. When this repression is released in the presence of ethylene, EIN2 is dephosphorylated and cleaved, releasing a C-terminal fragment that either moves to P-bodies or to the nucleus [95Li W. et al.EIN2-directed translational regulation of ethylene signaling in Arabidopsis.Cell. 2015; 163: 670-683Abstract Full Text Full Text PDF PubMed Scopus (66) Google Scholar, 96Merchante C. et al.Gene-specific translation regulation mediated by the hormone-signaling molecule EIN2.Cell. 2015; 163: 684-697Abstract Full Text Full Text PDF PubMed Scopus (67) Google Scholar]. The downstream mode of action of the EIN2 fragment has long been a mystery, but recent studies have shown that it is involved in gene-specific regulation of translation [95Li W. et al.EIN2-directed translational regulation of ethylene signaling in Arabidopsis.Cell. 2015; 163: 670-683Abstract Full Text Full Text PDF PubMed Scopus (66) Google Scholar, 96Merchante C. et al.Gene-specific translation regulation mediated by the hormone-signaling molecule EIN2.Cell. 2015; 163: 684-697Abstract Full Text Full Text PDF PubMed Scopus (67) Google Scholar]. The EIN2 fragment binds to the 3'-untranslated regions (3'-UTRs) of EBF1 and EBF2 transcripts, thereby repressing their translation. EBF1 and EBF2 are two central F-box proteins that target the primary ethylene-responsive TFs EIN3 and EIN3-LIKE 1 (EIL1) for protein degradation in the absence of ethylene [97Guo H. Ecker J.R. Plant responses to ethylene gas are mediated by SCFEBF1/EBF2-dependent proteolysis of EIN3 transcription factor.Cell. 2003; 115: 667-677Abstract Full Text Full Text PDF PubMed Scopus (495) Google Scholar, 98Potuschak T. et al.EIN3-dependent regulation of plant ethylene hormone signaling by two Arabidopsis F box proteins: EBF1 and EBF2.Cell. 2003; 115: 679-689Abstract Full Text Full Text PDF PubMed Scopus (420) Google Scholar]. In the presence of ethylene, EIN3 and EIL1 induce the expression of numerous secondary transcription factors (TFs), the ERFs [99Nakano T. et al.Identification of genes of the plant-specific transcription-factor families cooperatively regulated by ethylene and jasmonate in Arabidopsis thaliana.J. Plant Res. 2006; 119: 407-413Crossref PubMed Scopus (0) Google Scholar]. The activity of some ERFs has been reported to be increased by phosphorylation through the MPK3/6-cascade that also regulates ethylene biosynthesis, providing dual-level regulation of the ERF-mediated response [24Meng X. et al.Phosphorylation of an ERF transcription factor by Arabidopsis MPK3/MPK6 regulates plant defense gene induction and fungal resistance.Plant Cell. 2013; 25: 1126-1142Crossref PubMed Scopus (143) Google Scholar, 100Yoo S.-D. Sheen J. MAPK signaling in plant hormone ethylene signal transduction.Plant Signal. Behav. 2008; 3: 848-849Crossref PubMed Google Scholar]. Arabidopsis (Arabidopsis thaliana) plants overproducing ethylene are generally dwarfed, and plant growth is reduced by exposure to ethylene [7Burg S.P. Burg E.A. Ethylene formation in pea seedlings; its relation to the inhibition of bud growth caused by indole-3-acetic acid.Plant Physiol. 1968; 43: 1069-1074Crossref PubMed Google Scholar, 8Vogel J.P. et al.Recessive and dominant mutations in the ethylene biosynthetic gene ACS5 of Arabidopsis confer cytokinin insensitivity and ethylene overproduction, respectively.Proc. Natl. Acad. Sci. U. S. A. 1998; 95: 4766-4771Crossref PubMed Scopus (0) Google Scholar, 9Qu X. et al.A strong constitutive ethylene-response phenotype conferred on Arabidopsis plants containing null mutations in the ethylene receptors ETR1 and ERS1.BMC Plant Biol. 2007; 7: 3Crossref PubMed Scopus (0) Google Scholar]. Consequently, when the positive regulators of the ethylene signaling pathway (Box 1 and Figure 1) are mutated, plants are generally found to have larger rosettes with larger leaves in comparison to control plants. Increased growth has, for example, been observed upon mutation the endoplasmic reticulum (ER)- anchored protein EIN2 [10Feng G. et al.The Arabidopsis EIN2 restricts organ growth by retarding cell expansion.Plant Signal. Behav. 2015; 10e1017169Crossref PubMed Scopus (2) Google Scholar]. Conversely, mutants of negative regulators of ethylene signaling, such as the receptors ETR1 and ERS1 (Box 1), show a growth decrease [9Qu X. et al.A strong constitutive ethylene-response phenotype conferred on Arabidopsis plants containing null mutations in the ethylene receptors ETR1 and ERS1.BMC Plant Biol. 2007; 7: 3Crossref PubMed Scopus (0) Google Scholar]. Accordingly, overexpression of the negative regulators ARGOS or ARGOS-LIKE (ARL) stimulates leaf growth in arabidopsis [11Rai M.I. et al.The ARGOS gene family functions in a negative feedback loop to desensitize plants to ethylene.BMC Plant Biol. 2015; 15: 157Crossref PubMed Scopus (12) Google Scholar, 12Shi J. et al.Overexpression of ARGOS genes modifies plant sensitivity to ethylene, leading to improved drought tolerance in both Arabidopsis and maize.Plant Physiol. 2015; 169: 266-282Crossref PubMed Scopus (43) Google Scholar]. Moreover, plant lines in which the ethylene sensitivity is reduced, or treatments reducing sensitivity to ethylene, cause larger leaves. For instance, plants overexpressing NEIP2 or TCTP, genes encoding proteins interacting with the Nicotiana tabacum ethylene receptor, show decreased ethylene sensitivity but improved growth [13Cao Y.-R. et al.Tobacco ankyrin protein NEIP2 interacts with ethylene receptor NTHK1 and regulates plant growth and stress responses.Plant Cell Physiol. 2015; 56: 803-818Crossref PubMed Scopus (8) Google Scholar, 14Tao J.-J. et al.Tobacco translationally controlled tumor protein interacts with ethylene receptor tobacco histidine kinase1 and enhances plant growth through promotion of cell proliferation.Plant Physiol. 2015; 169: 96-114Crossref PubMed Scopus (18) Google Scholar]. Similarly, Pseudomonas frederiksbergensis, a soil bacterium that reduces plant sensitivity to ethylene, promotes the growth of red pepper plants [15Chatterjee P. et al.Beneficial soil bacterium Pseudomonas frederiksbergensis OS261 augments salt tolerance and promotes red pepper plant growth.Front. Plant Sci. 2017; 8: 705Crossref PubMed Scopus (0) Google Scholar]. Finally, some rhizosphere bacteria that promote plant growth do so by expressing ACC-DEAMINASE, decreasing the levels of 1-aminocyclopropane-1-carboxylic acid (ACC) in plants exposed to stress, and this has a positive effect on growth [16Chen L. et al.The rhizobacterium Variovorax paradoxus 5C-2, containing ACC deaminase, promotes growth and development of Arabidopsis thaliana via an ethylene-dependent pathway.J. Exp. Bot. 2013; 64: 1565-1573Crossref PubMed Scopus (30) Google Scholar]. Exceptionally, ethylene has been reported to stimulate leaf growth. In the presence of very low ethylene concentrations, Poa alpina and Poa compressa show increased leaf elongation rates [17Fiorani F. et al.Ethylene emission and responsiveness to applied ethylene vary among Poa species that inherently differ in leaf elongation rates.Plant Physiol. 2002; 129: 1382-1390Crossref PubMed Scopus (0) Google Scholar], and also the primary leaves of sunflower (Helianthus annuus) are enlarged [18Lee S.H. Reid D.M. The role of endogenous ethylene in the expansion of Helianthus annuus leaves.Can. J. Bot. 1997; 75: 501-508Crossref PubMed Google Scholar]. However, the opposite effect was observed as soon as ethylene levels are increased to concentrations higher than this low growth-promoting optimum. This general negative correlation between ethylene sensitivity and leaf growth has led to the classification of ethylene as a growth-repressing hormone. In plants, where growth mainly occurs post-embryonically through well-orchestrated cell divisions, the progression through the cell cycle is tightly governed by more than 70 core cell-cycle proteins (reviewed in [19Polyn S. et al.Cell cycle entry, maintenance, and exit during plant development.Curr. Opin. Plant Biol. 2015; 23: 1-7Crossref PubMed Scopus (46) Google Scholar]). Controlled by endogenous cues and environmental signals, cell-cycle progression and regulation vary depending on the plant organ, and the effect of ethylene is similarly organ-dependent. For instance, during the early development of the apical hook, ethylene participates in stimulating cell divisions, although its contribution is not crucial for curving of the apical hook [20Raz V. Koornneef M. Cell division activity during apical hook development.Plant Physiol. 2001; 125: 219-226Crossref PubMed Scopus (0) Google Scholar]. Moreover, ethylene and the downstream transcription factors (TFs) ERF018 and ERF109 promote cell division during vasculature development in arabidopsis stems [21Etchells J.P. et al.Plant vascular cell division is maintained by an interaction between PXY and ethylene signalling.PLoS Genet. 2012; 8e1002997Crossref PubMed Scopus (0) Google Scholar]. Thus, in these specific developmental contexts, ethylene can have a positive effect on cell division. In leaves of plants exposed to environmental stress, ethylene appears to have a negative effect on the cell cycle. When plants are exposed to less than 10 h of osmotic stress, ethylene mediates a temporary and reversible stop of the cell cycle. This is likely to occur through the inactivation of the CDKA by phosphorylation, possibly through the MPK3/6 pathway but independently from EIN3/EIL1 (Figure2) [2Skirycz A. et al.Pause-and-stop: the effects of osmotic stress on cell proliferation during early leaf development in Arabidopsis and a role for ethylene signaling in cell cycle arrest.Plant Cell. 2011; 23: 1876-1888Crossref PubMed Scopus (0) Google Scholar]. Moreover, at least four mechanisms in leaves link ethylene to the exit of cell division and a shift to endoreduplication and differentiation. First, accumulation of ethylene and induction of the BOLITA TF (an ERF, Table 1) triggers the activation of type II TEOSINTE BRANCHED 1/CYCLOIDEA/PCF (TCP) genes (Figure 2) [22Marsch-Martinez N. et al.BOLITA, an Arabidopsis AP2/ERF-like transcription factor that affects cell expansion and proliferation/differentiation pathways.Plant Mol. Biol. 2006; 62: 825-843Crossref PubMed Scopus (48) Google Scholar]. These TCP proteins bind to the promoter of RETINOBLASTOMA RELATED 1 (RBR1), and the encoded protein phosphorylates E2Fa and thus represses the transcription of the E2F target genes, thereby inhibiting progression into the S-phase and cell division. Second, ethylene induces the expression of ERF5 and ERF6, two closely related TFs, in actively growing leaves of plants exposed to stress [2Skirycz A. et al.Pause-and-stop: the effects of osmotic stress on cell proliferation during early leaf development in Arabidopsis and a role for ethylene signaling in cell cycle arrest.Plant Cell. 2011; 23: 1876-1888Crossref PubMed Scopus (0) Google Scholar, 23Dubois M. et al.ETHYLENE RESPONSE FACTOR6 acts as a central regulator of leaf growth under water-limiting conditions in Arabidopsis.Plant Physiol. 2013; 162: 319-332Crossref PubMed Scopus (97) Google Scholar, 24Meng X. et al.Phosphorylation of an ERF transcription factor by Arabidopsis MPK3/MPK6 regulates pl}, journal={Trends in plant science}, author={Dubois, M and Broeck L Van and Inzé, D}, year={2018}, month={Apr} }
 @misc{e-mtab-6205 - the expression profiles of 31 transcription factors in wild-type col-0 plants upon mild osmotic stress, including 10 time points and different leaf tissues_2017, url={https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6205}, year={2017} }
 @misc{e-mtab-6209 - the expression profiles of 30 transcription factors in 17 inducible gain-of-function lines after activation of overexpression, including 5 time points_2017, url={https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6209}, year={2017} }
 @article{broeck_dubois_vermeersch_storme_matsui_inzé_2017, title={From network to phenotype: the dynamic wiring of an Arabidopsis transcriptional network induced by osmotic stress}, volume={13}, url={https://doi.org/10.15252/msb.20177840}, DOI={10.15252/msb.20177840}, abstractNote={Article21 December 2017Open Access Source DataTransparent process From network to phenotype: the dynamic wiring of an Arabidopsis transcriptional network induced by osmotic stress Lisa Van den Broeck Lisa Van den Broeck orcid.org/0000-0003-0226-0757 Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium VIB Center for Plant Systems Biology, Ghent, Belgium Search for more papers by this author Marieke Dubois Marieke Dubois orcid.org/0000-0002-5190-2130 Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium VIB Center for Plant Systems Biology, Ghent, Belgium Search for more papers by this author Mattias Vermeersch Mattias Vermeersch Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium VIB Center for Plant Systems Biology, Ghent, Belgium Search for more papers by this author Veronique Storme Veronique Storme Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium VIB Center for Plant Systems Biology, Ghent, Belgium Search for more papers by this author Minami Matsui Minami Matsui RIKEN Center for Sustainable Resource Science, Kanagawa, Japan Search for more papers by this author Dirk Inzé Corresponding Author Dirk Inzé [email protected] orcid.org/0000-0002-3217-8407 Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium VIB Center for Plant Systems Biology, Ghent, Belgium Search for more papers by this author Lisa Van den Broeck Lisa Van den Broeck orcid.org/0000-0003-0226-0757 Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium VIB Center for Plant Systems Biology, Ghent, Belgium Search for more papers by this author Marieke Dubois Marieke Dubois orcid.org/0000-0002-5190-2130 Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium VIB Center for Plant Systems Biology, Ghent, Belgium Search for more papers by this author Mattias Vermeersch Mattias Vermeersch Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium VIB Center for Plant Systems Biology, Ghent, Belgium Search for more papers by this author Veronique Storme Veronique Storme Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium VIB Center for Plant Systems Biology, Ghent, Belgium Search for more papers by this author Minami Matsui Minami Matsui RIKEN Center for Sustainable Resource Science, Kanagawa, Japan Search for more papers by this author Dirk Inzé Corresponding Author Dirk Inzé [email protected] orcid.org/0000-0002-3217-8407 Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium VIB Center for Plant Systems Biology, Ghent, Belgium Search for more papers by this author Author Information Lisa Van den Broeck1,2,‡, Marieke Dubois1,2,4,‡, Mattias Vermeersch1,2, Veronique Storme1,2, Minami Matsui3 and Dirk Inzé *,1,2 1Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium 2VIB Center for Plant Systems Biology, Ghent, Belgium 3RIKEN Center for Sustainable Resource Science, Kanagawa, Japan 4Present address: Institut de Biologie Moléculaire des Plantes, CNRS, Strasbourg, France ‡These authors contributed equally to this work *Corresponding author. Tel: +32 9 331 38 06; E-mail: [email protected] Molecular Systems Biology (2017)13:961https://doi.org/10.15252/msb.20177840 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract 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. Synopsis This study unravels a transcriptional network controlling Arabidopsis leaf growth inhibition in response to osmotic stress. The network consists of 20 transcription factors, whose complex and redundant patterns of interconnections enable robust adaptation to environmental changes. Linear pathways are a simplification. Multiple transcription factors can regulate the same target genes and, in some cases more than one transcription factor is necessary to induce the expression of a target gene. The network is robust because regulatory redundancy is built in, making the network less susceptible to mutations. ERF6 and ERF98 are both induced in the first induction group and can transcriptionally activate a large part of the network. They have an overlap of 6 target genes. ERF8 and ERF9 are both induced in the third induction group and can transcriptionally repress a large part of the network, showing an overlap of 9 target genes. The network is efficient for environmental adaption to a stress signal. The repressing activities in the network after 2 h of stress enables the network to return to its prestimulus state. The network is highly responsive to a range of input signals and might be part of a general stress response. However, the need for two transcription factors to transactivate target genes prevents stochastic activation of the network. The random induction of the network would lead to a needless stress response which is disadvantageous for the plant. Introduction Plant growth is a very complex quantitative trait and depends on both the genetic background and environmental conditions that can stimulate or adversely affect growth (Doust et al, 2014; Saïdou et al, 2014). Each environmental stimulus causes a specific response established by multiple regulatory components forming an interconnected network rather than a linear pathway (Vermeirssen et al, 2014; Miao et al, 2015; Luo et al, 2016). In addition, environmental changes are often multifactorial, such as heat and drought often occurring simultaneously. The combination of different environmental signals thus leads to complex responses, which are integrated by gene regulatory networks (GRNs) that enable the regulation of complex traits such as growth. It is therefore necessary to study these genetic networks as one entity in addition to studying the role of their individual components in order to get insights into the arising phenotype. A GRN can be defined as a combination of regulatory proteins such as transcription factors (TFs) that function together to regulate a specific set of output genes. A very well-known example of a GRN is the circadian clock regulatory network (Nagel & Kay, 2012; Pokhilko et al, 2012; Seaton et al, 2015; Hernando et al, 2017). This network consists of a core oscillator module of three TFs (CIRCADIAN CLOCK ASSOCIATED1 (CCA1), LATE HYPOCOTYL (LHY) and TIMING OF CAB1 (TOC1)) that forms the base of a larger interconnected network regulating circadian rhythms, hypocotyl growth, and flowering of Arabidopsis plants through transcriptional but also post-translational regulation, chromatin remodeling, and alternative splicing (Nakamichi, 2011; Malapeira et al, 2012; Perez-Santángelo et al, 2013; Wang & Ma, 2013). The core circadian clock network in Arabidopsis has even been extrapolated to crops such as rice, maize, soybean, and Brassica rapa (Murakami et al, 2007; Liu et al, 2009; Xu et al, 2010; Wang et al, 2011). A more specific example of a smaller GRN is the BRASSINAZOLE RESISTANT(BZR)—PHYTOCHROME INTERACTING FACTOR 4 (PIF4)—DELLA module that integrates brassinosteroid, light, and gibberellin signals to regulate cell elongation (Bai et al, 2012; Claeys et al, 2014a; Zhiponova et al, 2014). Environmental signals disturb the molecular steady state of GRNs by changing the gene expression levels or by post-translational modifications triggering the (de)activation of a protein. Under such changing conditions, networks dynamically evolve to reach a new steady state in which the components are in balance. At the phenotypic level, the modifications in the GRN ultimately lead to a particular output, for example, growth stimulation or inhibition. The existence of such complex networks facilitates the fine-tuning of the response to a continuously varying input, such as heat or drought stress. The compound mannitol is used in plant research as a molecule to induce osmotic stress and interfere with plant growth (Claeys et al, 2014b). Low concentrations of mannitol (25 mM) induce mild stress, triggering a decrease in Arabidopsis rosette size of approximately 50% without affecting the development or survival. Therefore, this setup can be used to investigate the molecular mechanisms underlying leaf growth inhibition (Skirycz et al, 2011; Claeys et al, 2014b). During Arabidopsis leaf development, the growth of an emerging leaf primordium is first solely driven by cell proliferation, resulting in an increased cell number. After a few days, cells at the distal end of the leaf exit the mitotic cell cycle and start to expand and subsequently differentiate (Donnelly et al, 1999; Andriankaja et al, 2012). At this point, growth is merely driven by cell expansion and, in the epidermis, by the division activity of meristemoid cells (White, 2006; Andriankaja et al, 2012; Gonzalez et al, 2015). Both cell proliferation and cell expansion can be adversely affected by mild osmotic stress conditions (Skirycz et al, 2011; Huber et al, 2014). Mannitol-induced stress inhibits the cell cycle by a two-step process called the “pause-and-stop” mechanism (Skirycz et al, 2011). In the first phase, the “pause” phase, the cells are kept in a latent state allowing rapid resumption of the cell cycle when conditions are again favorable. When the osmotic stress persists, the cells permanently exit the cell cycle and differentiate, called the “stop” phase. Previously, a transcriptome analysis on microdissected, actively growing leaf tissue exposed to low concentrations of mannitol was performed to identify putative molecular players orchestrating the observed growth arrest (Skirycz et al, 2011). Upon short-term exposure to mannitol, a gradually increasing number of genes encoding TFs is significantly upregulated, suggesting that a transcriptional cascade initiates the early response to mannitol. Few members of this transcriptional cascade have been studied previously, such as the rapidly induced ETHYLENE RESPONSE FACTOR 6 (ERF6), which activates the expression of GIBBERELLIN2-OXIDASE6 (GA2-OX6), a gene encoding a gibberellin-inactivating enzyme (Rieu et al, 2008; Dubois et al, 2013). Because of the resulting lower levels of gibberellin, DELLA proteins are stabilized, which ensures that cells permanently exit the cell division phase and are pushed to cell differentiation (Claeys et al, 2012). The transcriptional repressor ERF11 also has been characterized and could counteract the effect of ERF6 both on molecular and phenotypic level (Dubois et al, 2015). In this study, we investigated a subset of mannitol-responsive TFs and show that they form a dense GRN that is very rapidly induced upon mannitol treatment. We demonstrate the transcriptional connections between these individual components and give new insights into their regulatory capacities on the expression of target genes. Using this systems biology approach, we identified a hub of five TFs (ERF6, ERF8, ERF9, ERF59, and ERF98) that drives most regulatory connections. Finally, we studied the role of the 20 TFs in the regulation of leaf growth under standard conditions and when exposed to mild osmotic stress, leading to the identification of multiple growth-regulating TFs. Results A GRN of 20 TFs is specifically activated in growing leaves exposed to mannitol A previous transcriptome analysis upon short-term exposure of Arabidopsis seedlings to mannitol has identified genes that are rapidly induced upon osmotic stress in young proliferating leaves (Skirycz et al, 2011). Among them, ERF6 appeared to play a key role in this early stress response, enabling the inhibition of leaf growth and the simultaneous activation of stress-inducible genes. Based on the identified mannitol-responsive genes (Skirycz et al, 2011) and the ERF6 target genes (Dubois et al, 2013), we selected 28 genes encoding TFs with a putative role in the mannitol-mediated growth retardation. To measure the transcriptional induction of these 28 genes by mannitol, 15-day-old plants grown on half-strength Murashige and Skoog (1/2 MS) medium covered with a nylon mesh were transferred to medium containing 25 mM mannitol or control medium (Skirycz et al, 2011). After 4 h, the third leaf was harvested for transcript profiling. At this stage, the third leaf is actively growing and mostly contains expanding cells. Because the transcriptional induction was confirmed for 20 genes (Appendix Fig S1), we hypothesized that these 20 TFs could act together in a transcriptional network to regulate growth upon stress. Half of the TFs of the putative mannitol-responsive GRN belong to the ERF family (Appendix Table S1) (Nakano et al, 2006; Skirycz et al, 2011; Phukan et al, 2017), containing a single AP2/ERF domain that is responsible for the specific binding to GCC boxes in the promoter of their target genes (Fujimoto et al, 2000; Yang et al, 2009). Three ERF proteins, ERF8, ERF9, and ERF11, belong to group VIII and are putative transcriptional repressors, because they contain an ERF-associated amphiphilic repression (EAR) domain (Nakano et al, 2006). Six other stress-induced ERFs belong to group IX: ERF-1, ERF2, ERF5, ERF6, ERF59, and ERF98. ERF5 and ERF6 contain an additional motif, CMIX-5, which is a predicted phosphorylation site (Fujimoto et al, 2000; Nakano et al, 2006). The last ERF protein part of the putative mannitol-induced network, RAP2.6L, belongs to group X (Nakano et al, 2006). Seven members of the proposed GRN are part of the WRKY TF family: WRKY6, WRKY15, WRKY28, WRKY30, WRKY33, WRKY40, and WRKY48, which contain a conserved sequence (WRKYGQK) followed by a zinc finger motif, enabling the binding to DNA at the position of a W-box TTGAC(C/T) (Wu et al, 2005). Finally, three other TFs, ZAT6 and STZ, belonging to the Zinc Finger TF family (Englbrecht et al, 2004; Ciftci-Yilmaz & Mittler, 2008; Kiełbowicz-Matuk, 2012), and MYB51 (Stracke et al, 2001; Dubos et al, 2010), are part of the proposed mannitol-inducible network (Appendix Table S1). To investigate the developmental timing of the putative GRN into more detail, we measured the expression level of the 20 genes upon stress in the third leaf of wild-type plants during the proliferating (9 days after stratification [DAS]), expanding (15 DAS) and mature (22 DAS) developmental stage (Dataset EV1). With the exception of ERF8 and ERF9, which were most probably only transiently induced by mannitol, all other 18 TFs were significantly upregulated under stress conditions (Student's t-test, FDR < 0.05) in proliferating or expanding tissue (Fig 1). For about half of these genes, the level of induction in proliferating and expanding tissue was similar. Three genes, ERF5, ERF6, and ERF11, were induced more highly in expanding leaf tissue, whereas six genes, ERF59, ERF98, MYB51, WRKY6, WRKY30, and WRKY40, were induced more strongly in proliferating leaf tissue. Interestingly, none of the TFs were significantly upregulated in mature leaf tissue (Fig 1), suggesting that the putative stress-responsive GRN is only induced in growing leaves, because these tissues are prone to growth inhibition upon mild stress. Figure 1. Mannitol-induced transcriptional changes of the selected TFs in proliferating, expanding, and mature leaf tissue The expression of the 20 genes encoding TFs was measured 24 h after mannitol treatment during the proliferating (n = 192 plants), expanding (n = 16 plants), and mature (n = 16) leaf developmental stage. Expression levels in wild-type plants transferred to mannitol-induced stress were compared to those transferred to control conditions at the same developmental stage. Data information: Data are presented as mean ± SEM, n = 4 independent experiments. FC = fold change. *FDR < 0.05, unpaired two-sided Student's t-test. Download figure Download PowerPoint The GRN shows the sequential activation of four TF groups Because expression analysis has previously shown the early upregulation of these genes upon mannitol treatment (Skirycz et al, 2011), the young developing third leaf (15 DAS) was harvested at a high temporal resolution (20 min, 40 min, 1 h, 2 h, 4 h, 8 h, 12 h, 16 h, 24 h, and 48 h) after transfer to control or 25 mM mannitol-containing medium. RNA was extracted, and a detailed expression pattern over time for each gene of the putative GRN was generated with the nCounter Nanostring® technology (Dataset EV1). This technology enables the determination of the expression level of multiple genes in parallel without losing sensitivity. Within 1 h upon stress, nine of the 20 TF-encoding genes were significantly upregulated (Table EV1; Student's t-test, FDR < 0.05) and most genes reached a maximum expression level after 2 h, demonstrating the very rapid response of this regulatory network. When considering the earliest time points in more detail, the initial upregulation was not equally fast but instead occurred in a sequential manner (Fig 2). The TFs could be classified into four different groups based on the initial time point at which their expression exceeded the threshold of log2(fold change [FC]) > 1 (Fig 2). The first group included seven genes (ERF5, ERF6, ERF11, ERF98, WRKY40, STZ and ZAT6). All genes showed a fast and strong induction, exceeding the threshold already at 40 min (Fig 2A). The second group, including ERF-1, ERF2, WRKY30, WRKY33, and MYB51, was upregulated from 1 h onward (Fig 2B). However, the induction of these genes, except for WRKY30, was not as strong as that of the first group; the genes of the second group reached a maximum of approximately log2(FC) 4 compared to a maximum of approximately log2(FC) 6 in the first group. The third group, which passed the threshold at 2 h, contained WRKY6, WRKY15, WRKY28, WRKY48, ERF59, and notably two genes encoding the repressors ERF8 and ERF9 (Fig 2C). The induction was even less strong than that of the second group; most genes reached a maximum around log2(FC) 3. In the fourth group, the expression of the activator RAP2.6L was upregulated only 4 h after mannitol treatment with a maximum of approximately log2(FC) 5 (Fig 2D). Figure 2. Four groups of transcriptional induction upon exposure to mannitol A–D. Based on a threshold of log2(FC) > 1, the 20 TFs were categorized into four groups. The first group contains TFs that reached the log2(FC) threshold 40 min after mannitol treatment (A), the second group reached the threshold after 1 h (B), the third group after 2 h (C), and the fourth group after 4 h (D). The arrow indicates the initial upregulation of every group. Data information: Data are presented as mean ± SEM. n = 4 independent experiments. FC = fold change. FDR values are available in Table EV1. Download figure Download PowerPoint During later time points (12 h, 16 h, 24 h, and 48 h), three scenarios could be observed (Appendix Fig S2). Following the initial induction, the expression of the TF either (i) gradually decreased to the expression level in control conditions and was not significantly upregulated at 48 h (Appendix Fig S2A), (ii) reached a minimum and increased again (Appendix Fig S2B), or (iii) remained induced until at least 48 h after stress (Appendix Fig S2C). In total, 11 TFs were significantly upregulated upon 48 h of stress. In conclusion, the 20 selected TFs were rapidly upregulated upon mannitol treatment and, interestingly, their induction could be divided into four groups of initial transcriptional activation. For most TFs, the maximum expression level was reached after 2 h. Remarkably, the expression of 11 genes remained higher even after 48 h of mannitol treatment, suggesting that these TFs also play a role in the long-term response to osmotic stress. The GRN is highly interconnected and dynamic To validate our hypothesis that the 20 selected TFs act as a network rather than independently, we aimed to identify and visualize the putative GRN. The putative GRN consists of 20 nodes, representing the 20 TFs, and directed edges between the nodes, indicating the transcriptional regulatory connections. To determine these regulatory connections and thus the edges, we performed a large-scale expression analysis with gain-of-function (GOF) lines. We opted for inducible constructs in which a C-terminal fusion protein of the TF of interest and a glucocorticoid receptor (GR) domain is driven by a constitutive 35S promoter. Such fusion proteins reside in the cytosol and can only translocate to the nucleus in the presence of dexamethasone (DEX), enabling the TF to regulate its downstream target genes (Corrado & Karali, 2009). Per TF, two or three independent GOF lines with intermediate or high overexpression of the TF were obtained (Appendix Figs S3–S22), with the exception of three genes (WRKY6, WRKY30, and WRKY40) for which we could not obtain a proper overexpression line. To get an indication of which genes are direct or indirect targets of the induced TF, we opted for a time-course approach rather than an inhibition of translation by cycloheximide, because the latter already induced 18 of the 20 TFs by itself (Appendix Fig S23, Hruz et al, 2008). Therefore, one independent GOF line was selected for all 17 TFs and transferred at 15 DAS to DEX-containing medium and the third leaf was harvested at 1 h, 2 h, 4 h, 8 h, and 24 h after transfer (Appendix Table S2). The expression of each of the 19 other TFs was measured with nCounter Nanostring (Source Data for Fig 3) (Geiss et al, 2008). The time-course experiment gives an indication of whether a gene is putatively a direct, and thus induced during the early time points, or an indirect target of the induced TF. The expression analysis rendered, for each time point, a network of which the edges are based on the differentially expressed genes in every GOF line (Fig 3A). For example, a directed edge from ERF6 to STZ means that STZ was significantly differentially expressed (FDR < 0.1) in the ERF6-GR line at that specific time point and could thus be directly or indirectly regulated by ERF6; STZ is then defined as a target gene of ERF6. When considering all time points, we could observe that in nine GOF lines, ERF-1-GR, ERF2-GR, ERF6-GR, ERF8-GR, ERF9-GR, ERF59-GR, ERF98-GR, WRKY15-GR, and WRKY48-GR, the expression of at least half of the other TFs was affected (log[FC] > 1) (Appendix Figs S3–S22). The large amount of observed regulatory interactions clearly demonstrates that the selected TFs form a highly interconnected GRN. Figure 3. The regulatory connections of the osmotic stress-responsive GRN The significant regulatory interactions identified by nCounter Nanostring at 1 h, 2 h, 4 h, 8 h, and 24 h after induction of overexpression of a TF. The confirmed regulatory interactions between the 20 TFs part of the GRN, according to transient expression assays (n = 4 biological repeats). Green arrows represent activation and red arrows repression. Heatmap of the significant regulations upon induction of overexpression of the five members of the core network, the activators (green) ERF6, ERF59, ERF98, and the repressors (red) ERF8 and ERF9. Color code represents FDR-corrected P-values with thresholds at FDR = 0.01, 0.05 and 0.1. Data information: In (A), data are extrapolated from estimated averages, n = 3 independent experiments, FDR-corrected P < 0.1 (mixed model analysis, user-defined Wald tests). The thickness of the arrows represents the FDR value. In (B), data are presented as averages, n = 3 independent experiments. The intensity of the color of the arrows represents the strength of the regulation according to the TEA values and the thickness the FDR value of the nCounter Nanostring experiment. In (C), data are represented as FDR-corrected P-values, n = 3 independent experiments (mixed model analysis, user-defined Wald tests). Source data are available online for this figure. Source Data for Figure 3 [msb177840-sup-0009-SDataFig3.xlsx] Download figure Download PowerPoint The inclusion of multiple time points allowed to explore dynamic changes in regulatory connections. If we assume that every TF acts directly on its target genes without being influenced by other TFs, we could expect that the continuous induction of overexpression leads to a fast induction followed by a sigmoidal expression pattern of the target genes. For example, the strong activation of WRKY15 led to the gradually increased expression of part of its target genes such as STZ, WRKY6, WRKY30, WRKY40, ERF-1, and ERF11 (Fig EV1A). However, some genes showed an oscillating pattern upon WRKY15-induced activation, such as the target genes ERF6, RAP2.6L, and ZAT6 (Fig EV1B). The oscillation of some transcripts was also visible at the network level: most interactions were formed after 1 h of induction and decreased after 2 h or 4 h, but increased again after 8 h or 24 h (Fig 3A, Source Data for Fig 3). The oscillations further strengthen the hypothesis that multiple TFs regulate the expression of the same target gene, leading to multiple indirect effects. The highly fluctuating regulations also emphasize the need for short-term analysis because the steady state of the network masks these connections. Click here to expand this figure. Figure EV1. Expression profiles of WRKY15 target genesExpanding leaf tissue (third leaf – 15 DAS) of WRKY15-GR was harvested 1 h, 2 h, 4 h, 8 h, and 24 h after transfer to dexamethasone. Expression values were normalized against the control line. WRKY15 target genes (significantly differentially expressed during one time point) that showed a gradually increasing expression pattern. WRKY15 target genes that showed an oscillating expression pattern. Data information: data are presented as mean ± SEM, n = 3 independent experiments, *FDR < 0.1 (mixed model analysis, user-defined Wald tests). Download figure Download PowerPoint To analyze the transactivation capacities of the TFs on their target genes, the edges based on the transcriptome data at 1 h, 2 h, and 4 h (in total 81 edges) were further verified with transient expression assays (TEAs). Luciferase reporter genes were used to perform TEAs in tobacco (Nicotiana tabacum) Bright Yellow-2 (BY2) protoplasts (Vanden Bossche et al, 2013). The protoplasts were co-transformed with 35S::TF and pTF::fLUC (firefly luciferase) constructs to evaluate whether a TF can activate or repress a target promoter, here defined as the region upstream of the start codon until the next gene with a maximum of 2 kb. In total, 45 out of the 81 edges were confirmed (Appendix Table S3, Appendix Figs S3–S22) and were used to build a more robust GRN (Fig 3B). Two distinct types of edges are represented in the network: red arrows represent inhibition of the expression of the target gene, whereas green arrows represent activation. All TFs were exclusive activators or repressors. For example, ERF8 and ERF9 appeared to be strong repressors, because for all tested target genes, the co-transformation with ERF8 or ERF9 led to a decreased luminescence signal (Fig 3B, Appendix Figs S3–S22). However, it should be noted that the nature of the regulation was not always consistent between the DEX-inducible in planta system and the TEA experiments. The repressing function of the literature-described repressors ERF8 and ERF9 (Ohta et al, 2001; Nakano et al, 2006) seemed to be abolished by fusion with the GR domain, as observed in planta (Appendix Figs S3–S22) and in TEAs performed with ERF8-GR or ERF9-GR (Appendix Fig S24). The discrepancy is thus most likely due to the presence of the GR domain close to the EAR motif. The TEAs performed with the TFs without GR domain are thus more likely to represent the activity of the endogenous TF. Among the 45 confirmed regulatory connections, 39 were originating from only five ERF genes, ERF6, ERF8, ERF9, ERF59, and ERF98. We further refer to these TFs as the core network (Fig 3C). In conclusion, the large-scale expression analysis revealed a dense GRN with generally a strong induction of the network genes when one member is activated. More than half of the regulatory connections could be confirmed by an independent transactivation assay and led to the identification of a core network. Most TFs are involved in}, number={12}, journal={Molecular Systems Biology}, publisher={EMBO}, author={Broeck, Lisa Van and Dubois, Marieke and Vermeersch, Mattias and Storme, Veronique and Matsui, Minami and Inzé, Dirk}, year={2017}, month={Dec}, pages={961} }
 @article{time of day determines arabidopsis transcriptome and growth dynamics under mild drought._2017, volume={2}, url={http://europepmc.org/abstract/med/27479938}, DOI={10.1111/pce.12809}, abstractNote={Abstract 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.}, journal={Plant, cell & environment}, year={2017}, month={Feb} }
 @article{ralfl34 regulates formative cell divisions in arabidopsis pericycle during lateral root initiation._2016, volume={8}, url={http://europepmc.org/abstract/med/27521602}, DOI={10.1093/jxb/erw281}, abstractNote={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.}, journal={Journal of experimental botany}, year={2016}, month={Aug} }
 @article{the ethylene response factors erf6 and erf11 antagonistically regulate mannitol-induced growth inhibition in arabidopsis._2015, volume={9}, url={http://europepmc.org/abstract/med/25995327}, DOI={10.1104/pp.15.00335}, abstractNote={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.}, journal={Plant physiology}, year={2015}, month={Sep} }