Summary To create a three-dimensional structure, plants rely on oriented cell divisions and cell elongation. Oriented cell divisions are specifically important in procambium cells of the root to establish the different vascular cell types [ 1 , 2 ]. These divisions are in part controlled by the auxin-controlled TARGET OF MONOPTEROS5 (TMO5) and LONESOME HIGHWAY (LHW) transcription factor complex [ 3 , 4 , 5 , 6 , 7 ]. Loss-of-function of tmo5 or lhw clade members results in strongly reduced vascular cell file numbers, whereas ectopic expression of both TMO5 and LHW can ubiquitously induce periclinal and radial cell divisions in all cell types of the root meristem. TMO5 and LHW interact only in young xylem cells, where they promote expression of two direct target genes involved in the final step of cytokinin (CK) biosynthesis, LONELY GUY3 ( LOG3 ) and LOG4 [ 8 , 9 ] Therefore, CK was hypothesized to act as a mobile signal from the xylem to trigger divisions in the neighboring procambium cells [ 3 , 6 ]. To unravel how TMO5/LHW-dependent cytokinin regulates cell proliferation, we analyzed the transcriptional responses upon simultaneous induction of both transcription factors. Using inferred network analysis, we identified AT2G28510/DOF2.1 as a cytokinin-dependent downstream target gene. We further showed that DOF2.1 controls specific procambium cell divisions without inducing other cytokinin-dependent effects such as the inhibition of vascular differentiation. In summary, our results suggest that DOF2.1 and its closest homologs control vascular cell proliferation, thus leading to radial expansion of the root.

Identifying the transcription factors (TFs) and associated networks involved in stem cell regulation is essential for understanding the initiation and growth of plant tissues and organs. Although many TFs have been shown to have a role in the Arabidopsis root stem cells, a comprehensive view of the transcriptional signature of the stem cells is lacking. In this work, we used spatial and temporal transcriptomic data to predict interactions among the genes involved in stem cell regulation. To accomplish this, we transcriptionally profiled several stem cell populations and developed a gene regulatory network inference algorithm that combines clustering with dynamic Bayesian network inference. We leveraged the topology of our networks to infer potential major regulators. Specifically, through mathematical modeling and experimental validation, we identified PERIANTHIA (PAN) as an important molecular regulator of quiescent center function. The results presented in this work show that our combination of molecular biology, computational biology, and mathematical modeling is an efficient approach to identify candidate factors that function in the stem cells.root stem cell | root development | cell-type expression profile | gene regulatory network | modeling I dentifying the transcriptional signature underlying stem cell regulation is fundamental to understanding the initiation and growth of plant tissues and organs. The Arabidopsis thaliana root provides a tractable system to study stem cells since they are spatially confined at the tip of the root, in the stem cell niche (SCN), and are anatomically well characterized. The SCN contains several stem cell populations that include the cortexendodermis initials (CEIs), vascular initials [including phloem and xylem (XYL)], columella initials, and epidermal/lateral root cap initials. These stem cell populations divide asymmetrically to replenish the stem cell and produce a daughter cell that later differentiates into the different tissues of the root. In the center of all of these stem cell populations is the quiescent center (QC), which acts as the organizing center and maintains the surrounding stem cells in an undifferentiated state (1). Major players in stemcell regulation have been previously identified, such as BABY BOOM (BBM) and PLETHORA1-3 (PLT1, PLT2, PLT3/AIL6), which are important for proper root formation and maintenance (2). Additionally in the QC, the homeodomain transcription factor WUSCHEL-RELATED HOMEOBOX 5 acts noncell autonomously to maintain the columella stem cells (3-5). In the endodermal and cortical layers, the GRAS family transcription factors SHORTROOT (SHR) and SCARECROW (SCR) activate the transcription of the cell-cycle gene CYCLIN D6 (CYCD6) to trigger the asymmetric cell division of the CEI (6, 7). Additional TFs, such as REVOLUTA (REV) and PHABULOSA (PHB), have been shown to regulate tissue specification and differentiation in the vascular stem cells (8-11). Despite these findings, a transcriptional signature within and across each of these diff...

Root meristem controls The plant meristem, a small cluster of stem cells generates all of the cell types necessary for the plant’s indeterminate growth pattern. Roszak et al . use single-cell analyses to follow development from the stem cell to the enucleated cell of the phloem vasculature. In the root of the small mustard plant Arabidopsis , this process takes just over 3 days, and the developmental trajectory spans more than a dozen different cell states. A transcriptional program initially held under repressive control is released as those initial repressors dissipate. Reciprocal repression by regulators early and late in the developmental trajectory control a rapid switch in the differentiation program. —PJH

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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.