In the plant meristem, tissue-wide maturation gradients are coordinated with specialized cell networks to establish various developmental phases required for indeterminate growth. Here, we used single-cell transcriptomics to reconstruct the protophloem developmental trajectory from the birth of cell progenitors to terminal differentiation in the Arabidopsis thaliana root. PHLOEM EARLY DNA-BINDING-WITH-ONE-FINGER (PEAR) transcription factors mediate lineage bifurcation by activating guanosine triphosphatase signaling and prime a transcriptional differentiation program. This program is initially repressed by a meristem-wide gradient of PLETHORA transcription factors. Only the dissipation of PLETHORA gradient permits activation of the differentiation program that involves mutual inhibition of early versus late meristem regulators. Thus, for phloem development, broad maturation gradients interface with cell-type-specific transcriptional regulators to stage cellular differentiation.

The mechanisms that allow cells in the plant meristem to coordinate tissue-wide maturation gradients with specialized cell networks are critical for indeterminate growth. Here, we reconstructed the protophloem developmental trajectory of 19 cells from cell birth to terminal differentiation at single cell resolution in the Arabidopsis root. We found that cellular specification is mediated near the stem cell niche by PHLOEM EARLY DOF (PEAR) transcription factors. However, the PEAR dependent differentiation program is repressed by a broad gradient of PLETHORA (PLT) transcription factors, which directly inhibit PEARs’ own direct target ALTERED PHLOEM DEVELOPMENT (APL) . The dissipation of PLT gradient around 7 cells from the stem cell activates APL expression, and a subsequent transitional network that results in a “seesaw” pattern of mutual inhibition over developmental time. Together, we provide a mechanistic understanding of how morphogen-like maturation gradients interface with cell-type specific transcriptional regulators to stage cellular differentiation.

The double fertilization of the female gametophyte initiates embryogenesis and endosperm development in seeds via the activation of genes involved in cell differentiation, organ patterning, and growth. A subset of genes expressed in endosperm exhibit imprinted expression, and the correct balance of gene expression between parental alleles is critical for proper endosperm and seed development. We use a transcriptional time series analysis to identify genes that are associated with key shifts in seed development, including genes associated with secondary cell wall synthesis, mitotic cell cycle, chromatin organization, auxin synthesis, fatty acid metabolism, and seed maturation. We relate these genes to morphological changes in Mimulus seeds. We also identify four endosperm-expressed transcripts that display imprinted (paternal) expression bias. The imprinted status of these four genes is conserved in other flowering plants, suggesting that they are functionally important in endosperm development. Our study explores gene regulatory dynamics in a species with ab initio cellular endosperm development, broadening the taxonomic focus of the literature on gene expression in seeds. Moreover, it is the first to validate genes with imprinted endosperm expression in Mimulus guttatus, and will inform future studies on the genetic causes of seed failure in this model system.

SummaryPredicting 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.

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 [3-7]. 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.

Stem cells are responsible for generating all of the differentiated cells, tissues, and organs in a multicellular organism and, thus, play a crucial role in cell renewal, regeneration, and organization. A number of stem cell type-specific genes have a known role in stem cell maintenance, identity, and/or division. Yet, how genes expressed across different stem cell types, referred to here as stem-cell-ubiquitous genes, contribute to stem cell regulation is less understood. Here, we find that, in the Arabidopsis root, a stem-cell-ubiquitous gene, TESMIN-LIKE CXC2 (TCX2), controls stem cell division by regulating stem cell-type specific networks. Development of a mathematical model of TCX2 expression allows us to show that TCX2 orchestrates the coordinated division of different stem cell types. Our results highlight that genes expressed across different stem cell types ensure cross-communication among cells, allowing them to divide and develop harmonically together.

Abstract FaMADS9 is the strawberry (Fragaria x ananassa) gene that exhibits the highest homology to the tomato (Solanum lycopersicum) RIN gene. Transgenic lines were obtained in which FaMADS9 was silenced. The fruits of these lines did not show differences in basic parameters, such as fruit firmness or colour, but exhibited lower Brix values in three of the four independent lines. The gene ontology MapMan category that was most enriched among the differentially expressed genes in the receptacles at the white stage corresponded to the regulation of transcription, including a high percentage of transcription factors and regulatory proteins associated with auxin action. In contrast, the most enriched categories at the red stage were transport, lipid metabolism and cell wall. Metabolomic analysis of the receptacles of the transformed fruits identified significant changes in the content of maltose, galactonic acid‐1,4‐lactone, proanthocyanidins and flavonols at the green/white stage, while isomaltose, anthocyanins and cuticular wax metabolism were the most affected at the red stage. Among the regulatory genes that were differentially expressed in the transgenic receptacles were several genes previously linked to flavonoid metabolism, such as MYB10, DIV, ZFN1, ZFN2, GT2, and GT5, or associated with the action of hormones, such as abscisic acid, SHP, ASR, GTE7 and SnRK2.7. The inference of a gene regulatory network, based on a dynamic Bayesian approach, among the genes differentially expressed in the transgenic receptacles at the white and red stages, identified the genes KAN1, DIV, ZFN2 and GTE7 as putative targets of FaMADS9. A MADS9‐specific CArG box was identified in the promoters of these genes.

Gene regulatory network (GRN) models have been shown to predict and represent interactions among sets of genes. Here, we first show the basic steps to implement a simple but computationally efficient algorithm to infer GRNs based on dynamic Bayesian networks (DBNs), and we then explain how to approximate DBN-based GRN models with continuous models. In addition, we show a MATLAB implementation of the key steps of this method, which we use to infer an Arabidopsis root GRN.

Identifying the transcription factors (TFs) and associated networks involved in stem cell regulation is key 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. For this, we transcriptionally profiled several stem cell populations and developed a gene regulatory network (GRN) inference algorithm that combines clustering with Dynamic Bayesian Network (DBN) inference. We leveraged the topology of our networks to infer potential key regulators. The results presented in this work show that our combination of molecular biology approaches, computational biology and mathematical modeling was key to identify candidate factors that function in the stem cells. Specifically, through experimental validation and mathematical modeling, we identified PERIANTHIA (PAN) as an important molecular regulator of quiescent center (QC) function.

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.

Thicker leaves allow plants to grow in water-limited conditions. However, our understanding of the genetic underpinnings of this highly functional leaf shape trait is poor. We used a custom-built confocal profilometer to directly measure leaf thickness in a set of introgression lines (ILs) derived from the desert tomato Solanum pennellii and identified quantitative trait loci. We report evidence of a complex genetic architecture of this trait and roles for both genetic and environmental factors. Several ILs with thick leaves have dramatically elongated palisade mesophyll cells and, in some cases, increased leaf ploidy. We characterized the thick IL2-5 and IL4-3 in detail and found increased mesophyll cell size and leaf ploidy levels, suggesting that endoreduplication underpins leaf thickness in tomato. Next, we queried the transcriptomes and inferred dynamic Bayesian networks of gene expression across early leaf ontogeny in these lines to compare the molecular networks that pattern leaf thickness. We show that thick ILs share S. pennellii-like expression profiles for putative regulators of cell shape and meristem determinacy as well as a general signature of cell cycle-related gene expression. However, our network data suggest that leaf thickness in these two lines is patterned at least partially by distinct mechanisms. Consistent with this hypothesis, double homozygote lines combining introgression segments from these two ILs show additive phenotypes, including thick leaves, higher ploidy levels, and larger palisade mesophyll cells. Collectively, these data establish a framework of genetic, anatomical, and molecular mechanisms that pattern leaf thickness in desert-adapted tomato.

Time-course imaging experiments on live organisms are critical for understanding the dynamics of growth and development. Light-sheet microscopy has advanced the field of long-term imaging of live specimens by significantly reducing photo-toxicity and allowing fast acquisition of three-dimensional data over time. However, current light-sheet technology does not allow the imaging of multiple plant specimens in parallel. To achieve higher throughput, we have developed a Multi-sample Arabidopsis Growth and Imaging Chamber (MAGIC) that provides near-physiological imaging conditions and allows high-throughput time-course imaging experiments in the ZEISS Lightsheet Z.1. Here, we illustrate MAGIC's imaging capabilities by following cell divisions, as an indicator of plant growth and development, over prolonged time periods. To automatically quantify the number of cell divisions in long-term experiments, we present a FIJI-based image processing pipeline. We demonstrate that plants imaged with our chamber undergo cell divisions for >16 times longer than those with the glass capillary system supplied by the ZEISS Z1.

Biological systems that are representative of regulatory, metabolic, or signaling pathways can be highly complex. Mathematical models that describe such systems inherit this complexity. As a result, these models can often fail to provide a path toward the intuitive comprehension of these systems. More coarse information that allows a perceptive insight of the system is sometimes needed in combination with the model to understand control hierarchies or lower level functional relationships. In this paper, we present a method to identify relationships between components of dynamic models of biochemical pathways that reside in different functional groups. We find primary relationships and secondary relationships. The secondary relationships reveal connections that are present in the system, which current techniques that only identify primary relationships are unable to show. We also identify how relationships between components dynamically change over time. This results in a method that provides the hierarchy of the relationships among components, which can help us to understand the low level functional structure of the system and to elucidate potential hierarchical control. As a proof of concept, we apply the algorithm to the epidermal growth factor signal transduction pathway, and to the C3 photosynthesis pathway. We identify primary relationships among components that are in agreement with previous computational decomposition studies, and identify secondary relationships that uncover connections among components that current computational approaches were unable to reveal.

The presence of an auxin gradient in the Arabidopsis root is crucial for proper root development and importantly, for stem cell niche (SCN) maintenance. Subsequently, developmental pathways in the root SCN regulate the formation of the auxin gradient. Combinations of experimental data and computational modeling enable the identification of pathways involved in establishing and maintaining the auxin gradient. We describe how the predictive power of these computational models is used to find how genes and their interactions tightly control the formation of an auxin maximum in the SCN. In addition, we highlight known connections between signaling pathways involving auxin and controlling patterning and development in Arabidopsis.

Mathematical models of biological systems have traditionally described processes that occur at the gene, enzyme, and metabolic pathway levels. We are often unable to relate these low-level models to higher-level biological phenomena. For example, models that describe the photosynthetic pathway do not explicitly relate the components to changes in photosynthetic rate. Thus, there is a need for developing methods that can link low levels of biological organization and higher-level phenotypes. We present an approach to solve this bottleneck that combines 1) a decomposition algorithm, 2) machine learning tools and 3) sensitivity analysis. With this approach, we quantified the influence of key components and functional modules on specific phenotypes, such as the photosynthetic pathway in C3 plants. Our algorithm was able to predict the relationship between specific components and the photosynthetic rate, where these results were consistent with previous experimental data. With these results we demonstrate that computational methods can be used to identify key modules and/or components that influence a measurable output of a biological system.

The modeling of biological systems often involves the estimation of model parameters. Estimation methods have been developed to model these systems in a boundederror context due to the uncertainty involved in biological processes. This application of bounded methods to nonlinear and higher-dimensional systems is computationally expensive resulting in excessive simulation times. One possible solution to this problem is parallelizing the computations of bounded-error estimation approaches using multiple processor cores. In this paper, we developed a method for use on a single multi-core workstation using POSIX threads to process subsets of the parameter space while access to shared information was controlled by mutex-locked linked lists. This approach allows the parallelized algorithm to run on easily accessible multicore workstations and does not require utilization of large supercomputers or distributed computing. Initial results of this method using 8 threads on an 8-core machine show speedups of 7.59 and 7.86 when applied to bounded parameter estimation problems involving the nonlinear Lotka-Volterra predator-prey model and SEIR infectious disease model, respectively.

The estimation of state variables and parameters is an important and necessary step when modeling biological systems. Uncertainty has to be included to obtain results that are consistent with the true dynamics of the system. Classically, the estimation problem with uncertainty was addressed by methods such as the Kalman filter. In biological systems however, the probabilistic information about uncertainties is unknown, and bounded error estimation algorithms are better suited. The latter consider bounded uncertainties to produce results that are consistent with these uncertainties. These methods have been applied to a multitude of areas. To our knowledge, they have not been specifically developed to address the issues associated with state and parameter estimation of biological systems. The purpose of this work has been to generate an estimation algorithm that improves the amount of uncertainty that traditional methods can handle in scenarios typically encountered in biological systems. In those scenarios, knowledge about the system through measurements is rather small. The developed method is based on Interval Analysis. We present in this paper the developed method, followed by the results of applying it to a biological pathway. A comparison of our results with those from different methods are also presented.

Text: Ripening is a critical step for the development of flavor quality in fruits. This character has significantly declined in many fleshy fruits over recent decades. This is particularly significant in strawberry (Fragaria × ananassa), where current cultivars are derived from a narrow germplasm collection. Improving fruit quality requires two important breakthroughs: 1) a precise understanding of the fruit ripening process that will allow the targeting of relevant genes, and 2) the identification of novel alleles responsible for fruit quality traits. In our project (TRANSFR-Q, Starting Grant-ERC), we aim at the identification and characterization of key transcription factors involved in fruit ripening regulation and their target genes, in order to infer the Gene Regulatory Network controlling this process. On the other hand, we are carrying out a Genome-Wide Association Study using a germplasm collection of the woodland strawberry (Fragaria vesca) in order to identify loci involved in important traits such as aroma, fruit size, and resistance to pathogens. Finally, we have implemented the use of the genome-editing tool CRISPR/Cas9 in the cultivated strawberry, which we expect to open opportunities for engineering this species to improve traits of economic importance.