11 research outputs found

    Mechanical Stress Induces Remodeling of Vascular Networks in Growing Leaves

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    International audienceDifferentiation into well-defined patterns and tissue growth are recognized as key processes in organismal development. However, it is unclear whether patterns are passively, homogeneously dilated by growth or whether they remodel during tissue expansion. Leaf vascu-lar networks are well-fitted to investigate this issue, since leaves are approximately two-dimensional and grow manyfold in size. Here we study experimentally and computationally how vein patterns affect growth. We first model the growing vasculature as a network of viscoelastic rods and consider its response to external mechanical stress. We use the so-called texture tensor to quantify the local network geometry and reveal that growth is heterogeneous , resembling non-affine deformations in composite materials. We then apply mechanical forces to growing leaves after veins have differentiated, which respond by anisotropic growth and reorientation of the network in the direction of external stress. External mechanical stress appears to make growth more homogeneous, in contrast with the model with viscoelastic rods. However, we reconcile the model with experimental data by incorporating randomness in rod thickness and a threshold in the rod growth law, making the rods viscoelastoplastic. Altogether, we show that the higher stiffness of veins leads to their reorientation along external forces, along with a reduction in growth heterogeneity. This process may lead to the reinforcement of leaves against mechanical stress. More generally , our work contributes to a framework whereby growth and patterns are coordinated through the differences in mechanical properties between cell types

    JAGGED controls Arabidopsis petal growth and shape by interacting with a divergent polarity field

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    A flowering plant generates many different organs such as leaves, petals, and stamens, each with a particular function and shape. These types of organ are thought to represent variations on a common underlying developmental program. However, it is unclear how this program is modulated under different selective constraints to generate the diversity of forms observed. Here we address this problem by analysing the development of Arabidopsis petals and comparing the results to models of leaf development. We show that petal development involves a divergent polarity field with growth rates perpendicular to local polarity increasing towards the distal end of the petal. The hypothesis is supported by the observed pattern of clones induced at various stages of development and by analysis of polarity markers, which show a divergent pattern. We also show that JAGGED (JAG) has a key role in promoting distal enhancement of growth rates and influences the extent of the divergent polarity field. Furthermore, we reveal links between the polarity field and auxin function: auxin-responsive markers such as DR5 have a broader distribution along the distal petal margin, consistent with the broad distal organiser of polarity, and PETAL LOSS (PTL), which has been implicated in the control of auxin dynamics during petal initiation, is directly repressed by JAG. By comparing these results with those from studies on leaf development, we show how simple modifications of an underlying developmental system may generate distinct forms, providing flexibility for the evolution of different organ functions

    Modeling Plant Tissue Growth and Cell Division

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    Morphogenesis is the creation of form, a complex process requiring the integration of genetics, mechanics, and geometry. Patterning processes driven by molecular regulatory and signaling networks interact with growth to create organ shape, often in unintuitive ways. Computer simulation modeling is becoming an increasingly important tool to aid our understanding of these complex interactions. In this chapter we introduce computational approaches for studying these processes on spatial, multicellular domains. For some problems, such as the exploration of many patterning processes, simulation can be done on static (non-growing) templates. These can range from abstract idealized cells, such as rectangular or hex grids, to more realistic shapes such as Voronoi regions, or even shapes extracted from bio-imaging data. More dynamic processes like phyllotaxis involve the interaction of growth and patterning, and require the simulation of growing domains. In the simplest case growth can be modeled descriptively, provided as an input to the model. Growth is specified globally, and must be designed carefully to avoid conflicts (growing cells must fit together). We present several methods for this that can be applied to shoots, roots, leaves, and other plant organs. However when shape is an emergent property of the model, different cells or areas of the tissue need to specify their growth locally, and physically-based methods (mechanics) are required to resolve conflicts. Among these are mass-spring, finite element, and Hamiltonian-based approaches

    Tailored immune responses: novel effector helper T cell subsets in protective immunity.

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    Differentiation of naïve CD4⁺ cells into functionally distinct effector helper T cell subsets, characterised by distinct "cytokine signatures," is a cardinal strategy employed by the mammalian immune system to efficiently deal with the rapidly evolving array of pathogenic microorganisms encountered by the host. Since the T(H)1/T(H)2 paradigm was first described by Mosmann and Coffman, research in the field of helper T cell biology has grown exponentially with seven functionally unique subsets having now been described. In this review, recent insights into the molecular mechanisms that govern differentiation and function of effector helper T cell subsets will be discussed in the context of microbial infections, with a focus on how these different helper T cell subsets orchestrate immune responses tailored to combat the nature of the pathogenic threat encountered

    The Therapeutic Potential of Epigenetics in Autoimmune Diseases

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