Flower development as an interplay between dynamical physical fields and genetic networks

The genetic mechanisms underlying the convergent evolution of pollination syndromes in the Neotropical radiation of Costus L

Selection together with variation in floral traits can act to mold floral form, often driven by a plant's predominant or most effective pollinators. To investigate the evolution of traits associated with pollination, we developed a phylogenetic framework for evaluating tempo and mode of pollination shifts across the genus Costus L., known for its evolutionary toggle between traits related to bee and bird pollination. Using a target enrichment approach, we obtained 957 loci for 171 accessions to expand the phylogenetic sampling of Neotropical Costus. In addition, we performed whole genome resequencing for a subset of 20 closely related species with contrasting pollination syndromes. For each of these 20 genomes, a high-quality assembled transcriptome was used as reference for consensus calling of candidate loci hypothesized to be associated with pollination-related traits of interest. To test for the role these candidate genes may play in evolutionary shifts in pollinators, signatures of selection were estimated as dN/dS across the identified candidate loci. We obtained a well-resolved phylogeny for Neotropical Costus despite conflict among gene trees that provide evidence of incomplete lineage sorting and/or reticulation. The overall topology and the network of genome-wide single nucleotide polymorphisms (SNPs) indicate that multiple shifts in pollination strategy have occurred across Costus, while also suggesting the presence of previously undetected signatures of hybridization between distantly related taxa. Traits related to pollination syndromes are strongly correlated and have been gained and lost in concert several times throughout the evolution of the genus. The presence of bract appendages is correlated with two traits associated with defenses against herbivory. Although labellum shape is strongly correlated with overall pollination syndrome, we found no significant impact of labellum shape on diversification rates. Evidence suggests an interplay of pollination success with other selective pressures shaping the evolution of the Costus inflorescence. Although most of the loci used for phylogenetic inference appear to be under purifying selection, many candidate genes associated with functional traits show evidence of being under positive selection. Together these results indicate an interplay of phylogenetic history with adaptive evolution leading to the diversification of pollination-associated traits in Neotropical Costus.

The flowering transition pathways converge into a complex gene regulatory network that underlies the phase changes of the shoot apical meristem in Arabidopsis thaliana

Post-embryonic plant development is characterized by a period of vegetative growth during which a combination of intrinsic and extrinsic signals triggers the transition to the reproductive phase. To understand how different flowering inducing and repressing signals are associated with phase transitions of the Shoot Apical Meristem (SAM), we incorporated available data into a dynamic gene regulatory network model for Arabidopsis thaliana. This Flowering Transition Gene Regulatory Network (FT-GRN) formally constitutes a dynamic system-level mechanism based on more than three decades of experimental data on flowering. We provide novel experimental data on the regulatory interactions of one of its twenty-three components: a MADS-box transcription factor XAANTAL2 (XAL2). These data complement the information regarding flowering transition under short days and provides an example of the type of questions that can be addressed by the FT-GRN. The resulting FT-GRN is highly connected and integrates developmental, hormonal, and environmental signals that affect developmental transitions at the SAM. The FT-GRN is a dynamic multi-stable Boolean system, with 2²³ possible initial states, yet it converges into only 32 attractors. The latter are coherent with the expression profiles of the FT-GRN components that have been experimentally described for the developmental stages of the SAM. Furthermore, the attractors are also highly robust to initial states and to simulated perturbations of the interaction functions. The model recovered the meristem phenotypes of previously described single mutants. We also analyzed the attractors landscape that emerges from the postulated FT-GRN, uncovering which set of signals or components are critical for reproductive competence and the time-order transitions observed in the SAM. Finally, in the context of such GRN, the role of XAL2 under short-day conditions could be understood. Therefore, this model constitutes a robust biological module and the first multi-stable, dynamical systems biology mechanism that integrates the genetic flowering pathways to explain SAM phase transitions.

Spatio-Temporal Dynamics of the Patterning of Arabidopsis Flower Meristem

The qualitative model presented in this work recovers the onset of the four fields that correspond to those of each floral organ whorl of Arabidopsis flower, suggesting a mechanism for the generation of the positional information required for the differential expression of the A, B, and C identity genes according to the ABC model for organ determination during early stages of flower development. Our model integrates a previous model for the emergence of WUS pattern in the floral meristem, and shows that this pre-pattern is a necessary but not sufficient condition for the posterior information of the four fields predicted by the ABC model. Furthermore, our model predicts that LFY diffusion along the L1 layer of cells is not a necessary condition for the patterning of the floral meristem.

Hierarchical graphical model reveals HFR1 bridging circadian rhythm and flower development in Arabidopsis thaliana

To study systems-level properties of the cell, it is necessary to go beyond individual regulators and target genes to study the regulatory network among transcription factors (TFs). However, it is difficult to directly dissect the TFs mediated genome-wide gene regulatory network (GRN) by experiment. Here, we proposed a hierarchical graphical model to estimate TF activity from mRNA expression by building TF complexes with protein cofactors and inferring TF's downstream regulatory network simultaneously. Then we applied our model on flower development and circadian rhythm processes in Arabidopsis thaliana. The computational results show that the sequence specific bHLH family TF HFR1 recruits the chromatin regulator HAC1 to flower development master regulator TF AG and further activates AG's expression by histone acetylation. Both independent data and experimental results supported this discovery. We also found a flower tissue specific H3K27ac ChIP-seq peak at AG gene body and a HFR1 motif in the center of this H3K27ac peak. Furthermore, we verified that HFR1 physically interacts with HAC1 by yeast two-hybrid experiment. This HFR1-HAC1-AG triplet relationship may imply that flower development and circadian rhythm are bridged by epigenetic regulation and enrich the classical ABC model in flower development. In addition, our TF activity network can serve as a general method to elucidate molecular mechanisms on other complex biological regulatory processes.

Curvature-driven spatial patterns in growing 3D domains: A mechanochemical model for phyllotaxis

Here we discuss the formation of phyllotactic patterns in the shoot apical meristem (SAM) of plants, where the spatial distribution of the phytohormone auxin determines phyllotaxis in a domain that is growing and changing in time. We assume that the concentration of auxin modifies the mechanical properties of the domain and that the mechanical stress field in the SAM orients the flux of auxin. To study this problem we propose a mechanism for pattern formation in growing domains with variable curvature. The dynamics of chemicals is modeled by a reaction-diffusion system that produces a three dimensional pattern of chemical concentrations that changes the stress field in the domain while growing. The growth process is modeled by a phase-field order parameter which determines the location of the boundaries of the domain. This field is coupled to the chemical concentration through a curvature term that affects the local mechanical stress in the domain. The local stress changes in turn modify the chemical patterns. Our model constitutes a useful and novel approach in theoretical biology, as many developmental processes in organisms seem to be affected by the changes of curvature, size, mechanical stress and other physical aspects. Several patterns seen in many plants are reproduced under certain conditions by our model.

Medical Systems Biology

The aim of this volume is to encourage the use of systems-level methodologies to contribute to the improvement of human-health . We intend to motivate biomedical researchers to complement their current theoretical and empirical practice with up-to-date systems biology conceptual approaches. Our perspective is based on the deep understanding of the key biomolecular regulatory mechanisms that underlie health, as well as the emergence and progression of human-disease . We strongly believe that the contemporary systems biology perspective opens the door to the effective development of novel methodologies to the improvement of prevention . This requires a deeper and integrative understanding of the involved underlying systems-level mechanisms. In order to explain our proposal in a simple way, in this chapter we privilege the conceptual exposition of our chosen framework over formal considerations. The formal exposition of our proposal will be expanded and discussed later in the next chapters.

Dynamic network modelling to understand flowering transition and floral patterning

Differentiation and morphogenetic processes during plant development are particularly robust. At the cellular level, however, plants also show great plasticity in response to environmental conditions, and can even reverse apparently terminal differentiated states with remarkable ease. Can we understand and predict both robust and plastic systemic responses as a general consequence of the non-trivial interplay between intracellular regulatory networks, extrinsic environmental signalling, and tissue-level mechanical constraints? Flower development has become an ideal model system to study these general questions of developmental biology, which are especially relevant to understanding stem cell patterning in plants, animals, and human disease. Decades of detailed study of molecular developmental genetics, as well as novel experimental techniques for in vivo assays in both wild-type and mutant plants, enable the postulation and testing of experimentally grounded mathematical and computational network dynamical models. Research in our group aims to explain the emergence of robust transitions that occur at the shoot apical meristem, as well as flower development, as the result of the collective action of key molecular components in regulatory networks subjected to intra-organismal signalling and extracellular constraints. Here we present a brief overview of recent work from our group, and that of others, focusing on the use of simple dynamical models to address cell-fate specification and cell-state stochastic dynamics during flowering transition and cell-state transitions at the shoot apical meristem of Arabidopsis thaliana. We also focus on how our work fits within the general field of plant developmental modelling, which is being developed by many others.

Reshaping the epigenetic landscape during early flower development: induction of attractor transitions by relative differences in gene decay rates

BACKGROUND

Gene regulatory network (GRN) dynamical models are standard systems biology tools for the mechanistic understanding of developmental processes and are enabling the formalization of the epigenetic landscape (EL) model.

METHODS

In this work we propose a modeling framework which integrates standard mathematical analyses to extend the simple GRN Boolean model in order to address questions regarding the impact of gene specific perturbations in cell-fate decisions during development.

RESULTS

We systematically tested the propensity of individual genes to produce qualitative changes to the EL induced by modification of gene characteristic decay rates reflecting the temporal dynamics of differentiation stimuli. By applying this approach to the flower specification GRN (FOS-GRN) we uncovered differences in the functional (dynamical) role of their genes. The observed dynamical behavior correlates with biological observables. We found a relationship between the propensity of undergoing attractor transitions between attraction basins in the EL and the direction of differentiation during early flower development - being less likely to induce up-stream attractor transitions as the course of development progresses. Our model also uncovered a potential mechanism at play during the transition from EL basins defining inflorescence meristem to those associated to flower organs meristem. Additionally, our analysis provided a mechanistic interpretation of the homeotic property of the ABC genes, being more likely to produce both an induced inter-attractor transition and to specify a novel attractor. Finally, we found that there is a close relationship between a gene's topological features and its propensity to produce attractor transitions.

CONCLUSIONS

The study of how the state-space associated with a dynamical model of a GRN can be restructured by modulation of genes' characteristic expression times is an important aid for understanding underlying mechanisms occurring during development. Our contribution offers a simple framework to approach such problem, as exemplified here by the case of flower development. Different GRN models and the effect of diverse inductive signals can be explored within the same framework. We speculate that the dynamical role of specific genes within a GRN, as uncovered here, might give information about which genes are more likely to link a module to other regulatory circuits and signaling transduction pathways.

Modeling the epigenetic attractors landscape: toward a post-genomic mechanistic understanding of development

Robust temporal and spatial patterns of cell types emerge in the course of normal development in multicellular organisms. The onset of degenerative diseases may result from altered cell fate decisions that give rise to pathological phenotypes. Complex networks of genetic and non-genetic components underlie such normal and altered morphogenetic patterns. Here we focus on the networks of regulatory interactions involved in cell-fate decisions. Such networks modeled as dynamical non-linear systems attain particular stable configurations on gene activity that have been interpreted as cell-fate states. The network structure also restricts the most probable transition patterns among such states. The so-called Epigenetic Landscape (EL), originally proposed by C. H. Waddington, was an early attempt to conceptually explain the emergence of developmental choices as the result of intrinsic constraints (regulatory interactions) shaped during evolution. Thanks to the wealth of molecular genetic and genomic studies, we are now able to postulate gene regulatory networks (GRN) grounded on experimental data, and to derive EL models for specific cases. This, in turn, has motivated several mathematical and computational modeling approaches inspired by the EL concept, that may be useful tools to understand and predict cell-fate decisions and emerging patterns. In order to distinguish between the classical metaphorical EL proposal of Waddington, we refer to the Epigenetic Attractors Landscape (EAL), a proposal that is formally framed in the context of GRNs and dynamical systems theory. In this review we discuss recent EAL modeling strategies, their conceptual basis and their application in studying the emergence of both normal and pathological developmental processes. In addition, we discuss how model predictions can shed light into rational strategies for cell fate regulation, and we point to challenges ahead.

Positive selection on the K domain of the AGAMOUS protein in the Zingiberales suggests a mechanism for the evolution of androecial morphology

BACKGROUND

The ABC model of flower development describes the molecular basis for specification of floral organ identity in model eudicots such as Arabidopsis and Antirrhinum. According to this model, expression of C-class genes is linked to stamen and gynoecium organ identity. The Zingiberales is an order of tropical monocots in which the evolution of floral morphology is characterized by a marked increase in petaloidy in the androecium. Petaloidy is a derived characteristic of the ginger families and seems to have arisen in the common ancestor of the ginger clade. We hypothesize that duplication of the C-class AGAMOUS (AG) gene followed by divergence of the duplicated AG copies during the diversification of the ginger clade lineages explains the evolution of petaloidy in the androecium. In order to address this hypothesis, we carried out phylogenetic analyses of the AG gene family across the Zingiberales and investigated patterns of gene expression within the androecium.

RESULTS

Phylogenetic analysis supports a scenario in which Zingiberales-specific AG genes have undergone at least one round of duplication. Gene duplication was immediately followed by divergence of the retained copies. In particular, we detect positive selection in the third alpha-helix of the K domain of Zingiberales AGAMOUS copy 1 (ZinAG-1). A single fixed amino acid change is observed in ZinAG-1 within the ginger clade when compared to the banana grade. Expression analyses of AG and APETALA1/FRUITFULL (AP1/FUL) in Musa basjoo is similar to A- and C-class gene expressions in the Arabidopsis thaliana model, while Costus spicatus exhibits simultaneous expression of AG and AP1/FUL in most floral organs. We propose that this novel expression pattern could be correlated with the evolution of androecial petaloidy within the Zingiberales.

CONCLUSIONS

Our results present an intricate story in which duplication of the AG lineage has lead to the retention of at least two diverged Zingiberales-specific copies, ZinAG-1 and Zingiberales AGAMOUS copy 2 (ZinAG-2). Positive selection on ZinAG-1 residues suggests a mechanism by which AG gene divergence may explain observed morphological changes in Zingiberales flowers. Expression data provides preliminary support for the proposed mechanism, although further studies are required to fully test this hypothesis.

Role of transcriptional regulation in the evolution of plant phenotype: A dynamic systems approach

A growing body of evidence suggests that alterations in transcriptional regulation of genes involved in modulating development are an important part of phenotypic evolution, and this can be documented among species and within populations. While the effects of differential transcriptional regulation in organismal development have been preferentially studied in animal systems, this phenomenon has also been addressed in plants. In this review, we summarize evidence for cis-regulatory mutations, trans-regulatory changes and epigenetic modifications as molecular events underlying important phenotypic alterations, and thus shaping the evolution of plant development. We postulate that a mechanistic understanding of why such molecular alterations have a key role in development, morphology and evolution will have to rely on dynamic models of complex regulatory networks that consider the concerted action of genetic and nongenetic components, and that also incorporate the restrictions underlying the genotype to phenotype mapping process. Developmental Dynamics 244:1074-1095, 2015. © 2015 Wiley Periodicals, Inc.

Adaptation in flower form: a comparative evodevo approach

Evolutionary developmental biology (evodevo) attempts to explain how the process of organismal development evolves, utilizing a comparative approach to investigate changes in developmental pathways and processes that occur during the evolution of a given lineage. Evolutionary genetics uses a population approach to understand how organismal changes in form or function are linked to underlying genetics, focusing on changes in gene and genotype frequencies within populations and the fixation of genotypic variation into traits that define species or evoke speciation events. Microevolutionary processes, including mutation, genetic drift, natural selection and gene flow, can provide the foundation for macroevolutionary patterns observed as morphological evolution and adaptation. The temporal element linking microevolutionary processes to macroevolutionary patterns is development: an organism's genotype is converted to phenotype by ontogenetic processes. Because selection acts upon the phenotype, the connection between evolutionary genetics and developmental evolution becomes essential to understanding adaptive evolution in organismal form and function. Here, we discuss how developmental genetic studies focused on key developmental processes could be linked within a comparative framework to study the developmental genetics of adaptive evolution, providing examples from research on two key processes of plant evodevo - floral symmetry and organ fusion - and their role in the adaptation of floral form.

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.

Gene regulatory network models for floral organ determination

Understanding how genotypes map unto phenotypes implies an integrative understanding of the processes regulating cell differentiation and morphogenesis, which comprise development. Such a task requires the use of theoretical and computational approaches to integrate and follow the concerted action of multiple genetic and nongenetic components that hold highly nonlinear interactions. Gene regulatory network (GRN) models have been proposed to approach such task. GRN models have become very useful to understand how such types of interactions restrict the multi-gene expression patterns that characterize different cell-fates. More recently, such temporal single-cell models have been extended to recover the temporal and spatial components of morphogenesis. Since the complete genomic GRN is still unknown and intractable for any organism, and some clear developmental modules have been identified, we focus here on the analysis of well-curated and experimentally grounded small GRN modules. One of the first experimentally grounded GRN that was proposed and validated corresponds to the regulatory module involved in floral organ determination. In this chapter we use this GRN as an example of the methodologies involved in: (1) formalizing and integrating molecular genetic data into the logical functions (Boolean functions) that rule gene interactions and dynamics in a Boolean GRN; (2) the algorithms and computational approaches used to recover the steady-states that correspond to each cell type, as well as the set of initial GRN configurations that lead to each one of such states (i.e., basins of attraction); (3) the approaches used to validate a GRN model using wild type and mutant or overexpression data, or to test the robustness of the GRN being proposed; (4) some of the methods that have been used to incorporate random fluctuations in the GRN Boolean functions and enable stochastic GRN models to address the temporal sequence with which gene configurations and cell fates are attained; (5) the methodologies used to approximate discrete Boolean GRN to continuous systems and their use in further dynamic analyses. The methodologies explained for the GRN of floral organ determination developed here in detail can be applied to any other functional developmental module.

Cell patterns emerge from coupled chemical and physical fields with cell proliferation dynamics: the Arabidopsis thaliana root as a study system

A central issue in developmental biology is to uncover the mechanisms by which stem cells maintain their capacity to regenerate, yet at the same time produce daughter cells that differentiate and attain their ultimate fate as a functional part of a tissue or an organ. In this paper we propose that, during development, cells within growing organs obtain positional information from a macroscopic physical field that is produced in space while cells are proliferating. This dynamical interaction triggers and responds to chemical and genetic processes that are specific to each biological system. We chose the root apical meristem of Arabidopsis thaliana to develop our dynamical model because this system is well studied at the molecular, genetic and cellular levels and has the key traits of multicellular stem-cell niches. We built a dynamical model that couples fundamental molecular mechanisms of the cell cycle to a tension physical field and to auxin dynamics, both of which are known to play a role in root development. We perform extensive numerical calculations that allow for quantitative comparison with experimental measurements that consider the cellular patterns at the root tip. Our model recovers, as an emergent pattern, the transition from proliferative to transition and elongation domains, characteristic of stem-cell niches in multicellular organisms. In addition, we successfully predict altered cellular patterns that are expected under various applied auxin treatments or modified physical growth conditions. Our modeling platform may be extended to explicitly consider gene regulatory networks or to treat other developmental systems.

The emergence of tumors results from altered cell differentiation and proliferation during organ and tissue development. Understanding how such altered or normal patterns are established is still a challenge. Molecular genetic approaches to understanding pattern formation have searched for key central genetic controllers. However, biological patterns emerge as a consequence of coupled complex genetic and non-genetic sub-systems operating at various spatial and temporal scales and levels of organization. We present a two-dimensional model and simulation benchmark that considers the integrated dynamics of physical and chemical fields that result from cell proliferation. We aim at understanding how the cellular patterns of stem-cell niches emerge. In these, organizer cells with very low rates of proliferation are surrounded by stem cells with slightly higher proliferation rates that transit to a domain of active proliferation and then of elongation and differentiation. We quantified such cellular patterns in the Arabidopsis thaliana root to test our theoretical propositions. The results of our simulations closely mimic observed root cellular patterns, thus providing a proof of principle that coupled physical fields and chemical processes under active cell proliferation give rise to stem-cell patterns. Our framework may be extended to other developmental systems and to consider gene regulatory networks.

General theory of genotype to phenotype mapping: derivation of epigenetic landscapes from N-node complex gene regulatory networks

We propose a systematic methodology to construct a probabilistic epigenetic landscape of cell-fate attainment associated with N-node Boolean genetic regulatory networks. The general derivation proposed here is exemplified with an Arabidopsis thaliana network underlying floral organ determination grounded on qualitative experimental data.

Modeling parr-mark pattern formation during the early development of Amago trout

This paper studies the formation of the large dark patterns, known as parr marks, that form on the Amago trout as it grows from the early larval stages to adulthood. The Amago trout, known as Oncorhynchus masou ishikawa, exhibits stripes during the early stages of development that in turn evolve (through reorientation and peak insertion) to form zigzag spot patterns as the fish grows to adulthood. By considering a standard representation of the Turing model for biological self-organization via interacting and diffusing morphogens, we illustrate that a diffusively driven instability can generate transient patterns consistent with those experimentally observed during the process of parr-mark formation in the early development of the Amago trout. Surface evolution is modeled through an experimentally driven growth function. Our studies conclude that the surface evolution profile, the surface geometry, and the curvature are key factors that play a pivotal role in reaction-diffusion systems in a study motivated by observations of Amago trout parr-mark pattern formation.

"Immunetworks", intersecting circuits and dynamics

This paper proposes a study of biological regulation networks based on a multi-level strategy. Given a network, the first structural level of this strategy consists in analysing the architecture of the network interactions in order to describe it. The second dynamical level consists in relating the patterns found in the architecture to the possible dynamical behaviours of the network. It is known that circuits are the patterns that play the most important part in the dynamics of a network in the sense that they are responsible for the diversity of its asymptotic behaviours. Here, we pursue further this idea and argue that beyond the influence of underlying circuits, intersections of circuits also impact significantly on the dynamics of a network and thus need to be payed special attention to. For some genetic regulation networks involved in the control of the immune system ("immunetworks"), we show that the small number of attractors can be explained by the presence, in the underlying structures of these networks, of intersecting circuits that "inter-lock".

Arabidopsis paves the way: genomic and network analyses in crops

Arabidopsis genomic and network analyses have facilitated crop research towards the understanding of many biological processes of fundamental importance for agriculture. Genes that were identified through genomic analyses in Arabidopsis have been used to manipulate crop traits such as pathogen resistance, yield, water-use efficiency, and drought tolerance, with the effects being tested in field conditions. The integration of diverse Arabidopsis genome-wide datasets in probabilistic functional networks has been demonstrated as a feasible strategy to associate novel genes with traits of interest, and novel genomic methods continue to be developed. The combination of genome-wide location studies, using ChIP-Seq, with gene expression profiling data is affording a genome-wide view of regulatory networks previously delineated through genetic and molecular analyses, leading to the identification of novel components and of new connections within these networks.