A complex systems approach to Arabidopsis root stem-cell niche developmental mechanisms: from molecules, to networks, to morphogenesis

Gene Regulatory Network Dynamical Logical Models for Plant Development

Mathematical and computational approaches that integrate and model the concerted action of multiple genetic and nongenetic components holding highly nonlinear interactions are fundamental for the study of developmental processes. Among these, gene regulatory network (GRN) dynamical models are very useful to understand how diverse types of regulatory constraints restrict the multigene expression patterns that characterize different cell fates. In this chapter we present a hands-on approach to model GRN dynamics, taking as a working example a well-curated and experimentally grounded GRN developmental module proposed by our group: the flower organ specification gene regulatory network (FOS-GRN). We demonstrate how to build and analyze a GRN model according to the following steps: (1) integration of molecular genetic data and formulation of logical rules specifying the dynamic behavior of each gene; (2) determination of steady states (attractors) corresponding to each cell type; (3) validation of the GRN model; and (4) extension of the deterministic model with the inclusion of stochasticity in order to model cell-state transitions dependent on noise due to fluctuations of the involved gen products. The methodologies explained here in detail can be applied to any other developmental module.

Hormonal Regulation of Stem Cell Proliferation at the Arabidopsis thaliana Root Stem Cell Niche

The root stem cell niche (SCN) of Arabidopsis thaliana consists of the quiescent center (QC) cells and the surrounding initial stem cells that produce progeny to replenish all the tissues of the root. The QC cells divide rather slowly relative to the initials, yet most root tissues can be formed from these cells, depending on the requirements of the plant. Hormones are fundamental cues that link such needs with the cell proliferation and differentiation dynamics at the root SCN. Nonetheless, the crosstalk between hormone signaling and the mechanisms that regulate developmental adjustments is still not fully understood. Developmental transcriptional regulatory networks modulate hormone biosynthesis, metabolism, and signaling, and conversely, hormonal responses can affect the expression of transcription factors involved in the spatiotemporal patterning at the root SCN. Hence, a complex genetic-hormonal regulatory network underlies root patterning, growth, and plasticity in response to changing environmental conditions. In this review, we summarize the scientific literature regarding the role of hormones in the regulation of QC cell proliferation and discuss how hormonal signaling pathways may be integrated with the gene regulatory network that underlies cell fate in the root SCN. The conceptual framework we present aims to contribute to the understanding of the mechanisms by which hormonal pathways act as integrators of environmental cues to impact on SCN activity.

Genetic control of tracheid properties in Norway spruce wood

Through the use of genome-wide association studies (GWAS) mapping it is possible to establish the genetic basis of phenotypic trait variation. Our GWAS study presents the first such effort in Norway spruce (Picea abies (L). Karst.) for the traits related to wood tracheid characteristics. The study employed an exome capture genotyping approach that generated 178 101 Single Nucleotide Polymorphisms (SNPs) from 40 018 probes within a population of 517 Norway spruce mother trees. We applied a least absolute shrinkage and selection operator (LASSO) based association mapping method using a functional multi-locus mapping approach, with a stability selection probability method as the hypothesis testing approach to determine significant Quantitative Trait Loci (QTLs). The analysis has provided 30 significant associations, the majority of which show specific expression in wood-forming tissues or high ubiquitous expression, potentially controlling tracheids dimensions, their cell wall thickness and microfibril angle. Among the most promising candidates based on our results and prior information for other species are: Picea abies BIG GRAIN 2 (PabBG2) with a predicted function in auxin transport and sensitivity, and MA_373300g0010 encoding a protein similar to wall-associated receptor kinases, which were both associated with cell wall thickness. The results demonstrate feasibility of GWAS to identify novel candidate genes controlling industrially-relevant tracheid traits in Norway spruce.

KUP9 maintains root meristem activity by regulating K+ and auxin homeostasis in response to low K

Potassium (K) is essential for plant growth and development. Here, we show that the KUP/HAK/KT K⁺ transporter KUP9 controls primary root growth in Arabidopsis thaliana. Under low-K⁺ conditions, kup9 mutants displayed a short-root phenotype that resulted from reduced numbers of root cells. KUP9 was highly expressed in roots and specifically expressed in quiescent center (QC) cells in root tips. The QC acts to maintain root meristem activity, and low-K⁺ conditions induced QC cell division in kup9 mutants, resulting in impaired root meristem activity. The short-root phenotype and enhanced QC cell division in kup9 mutants could be rescued by exogenous auxin treatment or by specifically increasing auxin levels in QC cells, suggesting that KUP9 affects auxin homeostasis in QC cells. Further studies showed that KUP9 mainly localized to the endoplasmic reticulum (ER), where it mediated K⁺ and auxin efflux from the ER lumen to the cytoplasm in QC cells under low-K⁺ conditions. These results demonstrate that KUP9 maintains Arabidopsis root meristem activity and root growth by regulating K⁺ and auxin homeostasis in response to low-K⁺ stress.

A Common Pathway of Root Growth Control and Response to CLE Peptides Through Two Receptor Kinases in Arabidopsis

Cell-cell communication is essential for plants to integrate developmental programs with external cues that affect their growth. Recent advances in plant signaling have uncovered similar molecular mechanisms in shoot, root, and vascular meristem signaling that involve receptor-like kinases and small, secreted peptides. Here, we report that the receptor-like kinases TOAD2/RPK2 and RPK1 regulate root growth by controlling cell proliferation and affecting meristem size. Two types of developmental alterations were observed upon exogenous CLE peptide application. The first type was detected in all plants treated, and comprise increased proliferative activity of cells in the stem cell niche and a delay of progression in differentiation of daughter cells. The second type was changes specific to the genotypes that are sensitive to CLE-driven root meristem inhibition and include a large decrease in the occurrence of cell divisions in longitudinal files, correlating with shorter meristems and cessation of root growth. The root meristems of toad2/rpk2 mutant plants are insensitive to the inhibitory effect of CLE17 peptide treatment, consistent with TOAD2/RPK2 function as a receptor for CLE peptides. In addition, a strong reduction in the expression of RPK1 protein upon CLE treatment, dependent on TOAD2/RPK2, suggests that these two RLKs mediate CLE signaling in a common pathway to control root growth.

Descriptive vs. mechanistic network models in plant development in the post-genomic era

Network modeling is now a widespread practice in systems biology, as well as in integrative genomics, and it constitutes a rich and diverse scientific research field. A conceptually clear understanding of the reasoning behind the main existing modeling approaches, and their associated technical terminologies, is required to avoid confusions and accelerate the transition towards an undeniable necessary more quantitative, multidisciplinary approach to biology. Herein, we focus on two main network-based modeling approaches that are commonly used depending on the information available and the intended goals: inference-based methods and system dynamics approaches. As far as data-based network inference methods are concerned, they enable the discovery of potential functional influences among molecular components. On the other hand, experimentally grounded network dynamical models have been shown to be perfectly suited for the mechanistic study of developmental processes. How do these two perspectives relate to each other? In this chapter, we describe and compare both approaches and then apply them to a given specific developmental module. Along with the step-by-step practical implementation of each approach, we also focus on discussing their respective goals, utility, assumptions, and associated limitations. We use the gene regulatory network (GRN) involved in Arabidopsis thaliana Root Stem Cell Niche patterning as our illustrative example. We show that descriptive models based on functional genomics data can provide important background information consistent with experimentally supported functional relationships integrated in mechanistic GRN models. The rationale of analysis and modeling can be applied to any other well-characterized functional developmental module in multicellular organisms, like plants and animals.

Mechanical forces as information: an integrated approach to plant and animal development

Mechanical forces such as tension and compression act throughout growth and development of multicellular organisms. These forces not only affect the size and shape of the cells and tissues but are capable of modifying the expression of genes and the localization of molecular components within the cell, in the plasma membrane, and in the plant cell wall. The magnitude and direction of these physical forces change with cellular and tissue properties such as elasticity. Thus, mechanical forces and the mesoscopic fields that emerge from their local action constitute important sources of positional information. Moreover, physical and biochemical processes interact in non-linear ways during tissue and organ growth in plants and animals. In this review we discuss how such mechanical forces are generated, transmitted, and sensed in these two lineages of multicellular organisms to yield long-range positional information. In order to do so we first outline a potentially common basis for studying patterning and mechanosensing that relies on the structural principle of tensegrity, and discuss how tensegral structures might arise in plants and animals. We then provide some examples of morphogenesis in which mechanical forces appear to act as positional information during development, offering a possible explanation for ubiquitous processes, such as the formation of periodic structures. Such examples, we argue, can be interpreted in terms of tensegral phenomena. Finally, we discuss the hypothesis of mechanically isotropic points as a potentially generic mechanism for the localization and maintenance of stem-cell niches in multicellular organisms. This comparative approach aims to help uncovering generic mechanisms of morphogenesis and thus reach a better understanding of the evolution and development of multicellular phenotypes, focusing on the role of physical forces in these processes.

Finding Missing Interactions of the Arabidopsis thaliana Root Stem Cell Niche Gene Regulatory Network

Over the last few decades, the Arabidopsis thaliana root stem cell niche (RSCN) has become a model system for the study of plant development and stem cell niche dynamics. Currently, many of the molecular mechanisms involved in RSCN maintenance and development have been described. A few years ago, we published a gene regulatory network (GRN) model integrating this information. This model suggested that there were missing components or interactions. Upon updating the model, the observed stable gene configurations of the RSCN could not be recovered, indicating that there are additional missing components or interactions in the model. In fact, due to the lack of experimental data, GRNs inferred from published data are usually incomplete. However, predicting the location and nature of the missing data is a not trivial task. Here, we propose a set of procedures for detecting and predicting missing interactions in Boolean networks. We used these procedures to predict putative missing interactions in the A. thaliana RSCN network model. Using our approach, we identified three necessary interactions to recover the reported gene activation configurations that have been experimentally uncovered for the different cell types within the RSCN: (1) a regulation of PHABULOSA to restrict its expression domain to the vascular cells, (2) a self-regulation of WOX5, possibly by an indirect mechanism through the auxin signaling pathway, and (3) a positive regulation of JACKDAW by MAGPIE. The procedures proposed here greatly reduce the number of possible Boolean functions that are biologically meaningful and experimentally testable and that do not contradict previous data. We believe that these procedures can be used on any Boolean network. However, because the procedures were designed for the specific case of the RSCN, formal demonstrations of the procedures should be shown in future efforts.