Root branching plasticity: collective decision-making results from local and global signalling

Cells within tissues can be regarded as autonomous entities that respond to their local environment and to signals from neighbours. Coordination between cells is particularly important in plants, as the architecture of the plant adapts to environmental cues. To explain the architectural plasticity of the root, we propose to view it as a swarm of coupled multi-cellular structures, rhizomers, rather than a large set of autonomous cells. Each rhizomer contains a primed site with the potential to develop a single lateral root. Rhizomers are spaced through oscillatory genetic events that occur at the basal root tip. The decision whether or not to develop a lateral root primordium results from the interplay between local interactions of the rhizomer with its immediate environment, such as local nutrient availability, long-range interactions between the rhizomers and global cues, such as overall nutrient uptake. It can halt lateral root progression through its developmental stages, resulting in the observed complex root architecture

Systems Biology Approach Pinpoints Minimum Requirements for Auxin Distribution during Fruit Opening : Plant Systems Biology

The phytohormone auxin is implied in steering various developmental decisions during plant morphogenesis in a concentration-dependent manner. Auxin maxima have been shown to maintain meristematic activity, for example, of the root apical meristem, and position new sites of outgrowth, such as during lateral root initiation and phyllotaxis. More recently, it has been demonstrated that sites of auxin minima also provide positional information. In the developing Arabidopsis fruit, auxin minima are required for correct differentiation of the valve margin. It remains unclear, however, how this auxin minimum is generated and maintained. Here, we employ a systems biology approach to model auxin transport based on experimental observations. This allows us to determine the minimal requirements for its establishment. Our simulations reveal that two alternative processes—which we coin “flux-barrier” and “flux-passage”—are both able to generate an auxin minimum, but under different parameter settings. Both models are in principle able to yield similar auxin profiles but present qualitatively distinct patterns of auxin flux. The models were tested by tissue-specific inducible ablation, revealing that the auxin minimum in the fruit is most likely generated by a flux-passage process. Model predictions were further supported through 3D PIN localization imaging and implementing experimentally observed transporter localization. Through such an experimental–modeling cycle, we predict how the auxin minimum gradually matures during fruit development to ensure timely fruit opening and seed dispersal.Peer reviewe

Root System Architecture from Coupling Cell Shape to Auxin Transport

Lateral organ position along roots and shoots largely determines plant architecture, and depends on auxin distribution patterns. Determination of the underlying patterning mechanisms has hitherto been complicated because they operate during growth and division. Here, we show by experiments and computational modeling that curvature of the Arabidopsis root influences cell sizes, which, together with tissue properties that determine auxin transport, induces higher auxin levels in the pericycle cells on the outside of the curve. The abundance and position of the auxin transporters restricts this response to the zone competent for lateral root formation. The auxin import facilitator, AUX1, is up-regulated by auxin, resulting in additional local auxin import, thus creating a new auxin maximum that triggers organ formation. Longitudinal spacing of lateral roots is modulated by PIN proteins that promote auxin efflux, and pin2,3,7 triple mutants show impaired lateral inhibition. Thus, lateral root patterning combines a trigger, such as cell size difference due to bending, with a self-organizing system that mediates alterations in auxin transport

Pavement cells and the topology puzzle

D'Arcy Thompson emphasised the importance of surface tension as a potential driving force in establishing cell shape and topology within tissues. Leaf epidermal pavement cells grow into jigsaw-piece shapes, highly deviating from such classical forms. We investigate the topology of developing Arabidopsis leaves composed solely of pavement cells. Image analysis of around 50,000 cells reveals a clear and unique topological signature, deviating from previously studied epidermal tissues. This topological distribution is established early during leaf development, already before the typical pavement cell shapes emerge, with topological homeostasis maintained throughout growth and unaltered between division and maturation zones. Simulating graph models, we identify a heuristic cellular division rule that reproduces the observed topology. Our parsimonious model predicts how and when cells effectively place their division plane with respect to their neighbours. We verify the predicted dynamics through in vivo tracking of 800 mitotic events, and conclude that the distinct topology is not a direct consequence of the jigsaw piece-like shape of the cells, but rather owes itself to a strongly life history-driven process, with limited impact from cell-surface mechanics

Morphometrics of complex cell shapes: lobe contribution elliptic Fourier analysis (LOCO-EFA)

Quantifying cell morphology is fundamental to the statistical study of cell populations, and can help unravel mechanisms underlying cell and tissue morphogenesis. Current methods, however, require extensive human intervention, are highly parameter sensitive, or produce metrics that are difficult to interpret biologically. We therefore developed a method, lobe contribution elliptical Fourier analysis (LOCO-EFA), which generates from digitalised two-dimensional cell outlines meaningful descriptors that can be directly matched to morphological features. This is shown by studying well-defined geometric shapes as well as actual biological cells from plant and animal tissues. LOCO-EFA provides a tool to phenotype efficiently and objectively populations of cells, here demonstrated by applying it to the complex shaped pavement cells of Arabidopsis thaliana wild-type and speechless leaves, and Drosophila amnioserosa cells. To validate our method's applicability to large populations, we analysed computer-generated tissues. By controlling in silico cell shape, we explored the potential impact of cell packing on individual cell shape, quantifying through LOCO-EFA deviations between the specified shape of single cells in isolation and the resultant shape when they interact within a confluent tissue

Rapid transporter regulation prevents substrate flow traffic jams in boron transport

Nutrient uptake by roots often involves substrate-dependent regulated nutrient transporters. For robust uptake, the system requires a regulatory circuit within cells and a collective, coordinated behaviour across the tissue. A paradigm for such systems is boron uptake, known for its directional transport and homeostasis, as boron is essential for plant growth but toxic at high concentrations. In Arabidopsis thaliana, boron uptake occurs via diffusion facilitators (NIPs) and exporters (BORs), each presenting distinct polarity. Intriguingly, although boron soil concentrations are homogenous and stable, both transporters manifest strikingly swift boron-dependent regulation. Through mathematical modelling, we demonstrate that slower regulation of these transporters leads to physiologically detrimental oscillatory behaviour. Cells become periodically exposed to potentially cytotoxic boron levels, and nutrient throughput to the xylem becomes hampered. We conclude that, while maintaining homeostasis, swift transporter regulation within a polarised tissue context is critical to prevent intrinsic traffic-jam like behaviour of nutrient flow

Back to the future: evolution of computational models in plant morphogenesis

There has been a recent surge of studies in plant biology that combine experimental data with computational modeling. Here, we categorize a diversity of theoretical models and emphasize the need to tailor modeling approaches to the questions at hand. Models can start from biophysical or purely heuristic basic principles, and can focus at several levels of biological organization. Recent examples illustrate that this entire spectrum can be useful to understand plant development, and point to a future direction where more approaches are combined in fruitful ways — either by proving the same result with different basic principles or by exploring interactions across levels, in the so-called multilevel models

Resolving conflicts.

<p>Effect of phosphoinositide feedback () versus absent feedback () and cell shape dynamics on the response of cells to competing, conflicting, or noisy stimuli. (a) “V” shaped gradient with static cell shape. (See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002402#pcbi.1002402.s006" target="_blank">Video S6</a>). (b) “V” shaped gradient with dynamic shape changes. (See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002402#pcbi.1002402.s007" target="_blank">Video S7</a>). (c) Noise-induced fronts within motile cells. (See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002402#pcbi.1002402.s008" target="_blank">Video S8</a>). PIs allow the cell to rapidly resolve the competition between contradictory signals.</p

Parameter estimates relevant to (i) actin dynamics; (ii) Rho-proteins; (iii) phosphoinositide dynamics; and (iv) cell surface mechanics, with sources from which they were obtained.

<p>Note citations of our earlier works (i.e. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002402#pcbi.1002402-Dawes1" target="_blank">[25]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002402#pcbi.1002402-Mare1" target="_blank">[36]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002402#pcbi.1002402-Jilkine2" target="_blank">[91]</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002402#pcbi.1002402-Dawes3" target="_blank">[93]</a>), where many detailed derivations, explanations, and references can be found.</p