Predicting gene regulatory networks by combining spatial and temporal gene expression data inArabidopsisroot stem cells

Abstract

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...

Characterizing the involvement of FaMADS9 in the regulation of strawberry fruit receptacle development

“…This method uses transcriptional data as input and produces an inferred network showing the most likely regulatory pathways of the genes of interest based on the data. The availability of time course data allowed us to infer directed networks based on dynamic Bayesian networks (de Luis Balaguer et al , ) that infer statistical dependence among selected genes. Here, applied to the DE genes in the white and red stages of FaMADS9 ‐silenced receptacles, this method provided a subnetwork centred around FaMADS9 with connections to different regulatory genes.…”

“…To deduce the GRN, a set of 685 genes that were significantly differentially expressed in the receptacle of the FaMADS9 ‐silenced fruits compared to the untransformed fruits, both at white and red stages, were selected. For this set of genes, a computational pipeline (GENIST) (de Luis Balaguer et al , ) was used to infer their relationships from a combination of spatial (achene, receptacle and leaf) and temporal (green; white; turning; red) transcriptional data obtained by RNAseq (Sánchez‐Sevilla et al , ). Clustering of genes before the inference step by GENIST has been shown to improve GENIST performance (de Luis Balaguer et al , ), since it reduces the complexity of the inference steps for large networks.…”

“…The clustering data here used were the RNAseq expression data obtained from the receptacle of control and FaMADS9 ‐silenced fruits, at white and red stages (Table S1). Application of GENIST was performed as previously reported (de Luis Balaguer et al , ).…”

Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-specific gene networks

“…The Regression Tree Pipeline for Spatial, Temporal, and Replicate data (RTP-STAR) was used for all network inference. The pipeline consists of three parts: spatial clustering using the k -means method 41 , network inference using GENIE3 42 , and edge sign (positive/negative) inference using the first order Markov method 10 . An earlier version of this pipeline was used to infer GRNs of root hair development 43 .…”

“…Crucially, the movement of cells in the root is constrained due to cell walls, and cell-to-cell signals travel via the plasmodesmata, which are small channels in the cell walls 9 . This lack of cell movement coupled with well-defined marker lines that label specific cell populations 10,11 allows us to study stem cell identity, division, and maintenance in an isolated environment.…”