Kim Kenobi

Lateral root morphogenesis is dependent on the mechanical properties of the overlaying tissues

In Arabidopsis, lateral root primordia (LRPs) originate from pericycle cells located deep within the parental root and have to emerge through endodermal, cortical, and epidermal tissues. These overlaying tissues place biomechanical constraints on the LRPs that are likely to impact their morphogenesis. This study probes the interplay between the patterns of cell division, organ shape, and overlaying tissues on LRP morphogenesis by exploiting recent advances in live plant cell imaging and image analysis. Our 3D/4D image analysis revealed that early stage LRPs exhibit tangential divisions that create a ring of cells corralling a population of rapidly dividing cells at its center. The patterns of division in the latter population of cells during LRP morphogenesis are not stereotypical. In contrast, statistical analysis demonstrated that the shape of new LRPs is highly conserved. We tested the relative importance of cell division pattern versus overlaying tissues on LRP morphogenesis using mutant and transgenic approaches. The double mutant aurora1 (aur1) aur2 disrupts the pattern of LRP cell divisions and impacts its growth dynamics, yet the new organ’s dome shape remains normal. In contrast, manipulating the properties of overlaying tissues disrupted LRP morphogenesis. We conclude that the interaction with overlaying tissues, rather than the precise pattern of divisions, is most important for LRP morphogenesis and optimizes the process of lateral root emergence.

Accelerating the domestication of a bioenergy crop: identifying and modelling morphological targets for sustainable yield increase in Miscanthus

To accelerate domestication of Miscanthus, an important energy crop, 244 replicated genotypes, including two different species and their hybrids, were analysed for morphological traits and biomass yield over three growing seasons following an establishment phase of 2 years in the largest Miscanthus diversity trial described to date. Stem and leaf traits were selected that contributed both directly and indirectly to total harvested biomass yield, and there was variation in all traits measured. Morphological diversity within the population was correlated with dry matter yield (DMY) both as individual traits and in combination, in order to determine the respective contributions of the traits to biomass accumulation and to identify breeding targets for yield improvement. Predictive morphometric analysis was possible at year 3 within Miscanthus sinensis genotypes but not between M. sinensis, Miscanthus sacchariflorus, and interspecific hybrids. Yield is a complex trait, and no single simple trait explained more than 33% of DMY, which varied from 1 to 5297g among genotypes within this trial. Associating simple traits increased the power of the morphological data to predict yield to 60%. Trait variety, in combination, enabled multiple ideotypes, thereby increasing the potential diversity of the crop for multiple growth locations and end uses. Both triploids and interspecific hybrids produced the highest mature yields, indicating that there is significant heterosis to be exploited within Miscanthus that might be overlooked in early selection screens within years 1–3. The potential for optimizing biomass yield by selecting on the basis of morphology is discussed.

Xyloglucan endotransglucosylase/hydrolase (XTH) overexpression affects growth and cell wall mechanics in etiolated Arabidopsis hypocotyls

Growth and biomechanics of etiolated hypocotyls from Arabidopsis thaliana lines overexpressing xyloglucan endotransglucosylase/hydrolase AtXTH18, AtXTH19, AtXTH20, and PttXET16-34 were studied. Overexpression of AtXTH18, AtXTH19, and AtXTH20 stimulated growth of hypocotyls, while PttXET16-34 overexpression did not show this effect. In vitro extension of frozen/thawed hypocotyls measured by a constant-load extensiometer started from a high-amplitude initial deformation followed by a slow time-dependent creep. Creep of growing XTH-overexpressing (OE) hypocotyls was more linear in time compared with the wild type at pH 5.0, reflecting their higher potential for long-term extension. XTH-OE plants deposited 65-84% more cell wall material per hypocotyl cross-sectional area than wild-type plants. As a result, their wall stress under each external load was lower than in the wild-type. Growing XTH-OE hypocotyls had higher values of initial deformation·stress(-1) compared with the wild type. Plotting creep rates for each line under different loads against the respective wall stress values gave straight lines. Their slopes and intercepts with the abscissa correspond to ϕ (in vitro cell wall extensibility) and y (in vitro cell wall yield threshold) values characterizing cell wall material properties. The wall material in XTH-OE lines was more pliant than in the wild type due to lower y values. In contrast, the acid-induced wall extension in vitro resulted from increasing ϕ values. Thus, three factors contributed to the XTH-OE-stimulated growth in Arabidopsis hypocotyls: their more linear creep, higher values of initial deformation·stress(-1), and lower y values.

The circadian clock rephases during lateral root organ initiation in Arabidopsis thaliana

The endogenous circadian clock enables organisms to adapt their growth and development to environmental changes. Here we describe how the circadian clock is employed to coordinate responses to the key signal auxin during lateral root (LR) emergence. In the model plant, Arabidopsis thaliana, LRs originate from a group of stem cells deep within the root, necessitating that new organs emerge through overlying root tissues. We report that the circadian clock is rephased during LR development. Metabolite and transcript profiling revealed that the circadian clock controls the levels of auxin and auxin-related genes including the auxin response repressor IAA14 and auxin oxidase AtDAO2. Plants lacking or overexpressing core clock components exhibit LR emergence defects. We conclude that the circadian clock acts to gate auxin signalling during LR development to facilitate organ emergence.

Inference of the Arabidopsis Lateral Root Gene Regulatory Network Suggests a Bifurcation Mechanism That Defines Primordia Flanking and Central Zones

A new correlation algorithm infers the Arabidopsis lateral root gene regulatory network from time-course transcriptomic data and provides mechanistic insight into primordia patterning. A large number of genes involved in lateral root (LR) organogenesis have been identified over the last decade using forward and reverse genetic approaches in Arabidopsis thaliana. Nevertheless, how these genes interact to form a LR regulatory network largely remains to be elucidated. In this study, we developed a time-delay correlation algorithm (TDCor) to infer the gene regulatory network (GRN) controlling LR primordium initiation and patterning in Arabidopsis from a time-series transcriptomic data set. The predicted network topology links the very early-activated genes involved in LR initiation to later expressed cell identity markers through a multistep genetic cascade exhibiting both positive and negative feedback loops. The predictions were tested for the key transcriptional regulator AUXIN RESPONSE FACTOR7 node, and over 70% of its targets were validated experimentally. Intriguingly, the predicted GRN revealed a mutual inhibition between the ARF7 and ARF5 modules that would control an early bifurcation between two cell fates. Analyses of the expression pattern of ARF7 and ARF5 targets suggest that this patterning mechanism controls flanking and central zone specification in Arabidopsis LR primordia.

Mechanical modelling quantifies the functional importance of outer tissue layers during root elongation and bending

Root elongation and bending require the coordinated expansion of multiple cells of different types. These processes are regulated by the action of hormones that can target distinct cell layers. We use a mathematical model to characterise the influence of the biomechanical properties of individual cell walls on the properties of the whole tissue. Taking a simple constitutive model at the cell scale which characterises cell walls via yield and extensibility parameters, we derive the analogous tissue-level model to describe elongation and bending. To accurately parameterise the model, we take detailed measurements of cell turgor, cell geometries and wall thicknesses. The model demonstrates how cell properties and shapes contribute to tissue-level extensibility and yield. Exploiting the highly organised structure of the elongation zone (EZ) of the Arabidopsis root, we quantify the contributions of different cell layers, using the measured parameters. We show how distributions of material and geometric properties across the root cross-section contribute to the generation of curvature, and relate the angle of a gravitropic bend to the magnitude and duration of asymmetric wall softening. We quantify the geometric factors which lead to the predominant contribution of the outer cell files in driving root elongation and bending.

Identifying biological landmarks using a novel cell measuring image analysis tool: Cell-o-Tape

BackgroundThe ability to quantify the geometry of plant organs at the cellular scale can provide novel insights into their structural organization. Hitherto manual methods of measurement provide only very low throughput and subjective solutions, and often quantitative measurements are neglected in favour of a simple cell count.ResultsWe present a tool to count and measure individual neighbouring cells along a defined file in confocal laser scanning microscope images. The tool allows the user to extract this generic information in a flexible and intuitive manner, and builds on the raw data to detect a significant change in cell length along the file. This facility can be used, for example, to provide an estimate of the position of transition into the elongation zone of an Arabidopsis root, traditionally a location sensitive to the subjectivity of the experimenter.ConclusionsCell-o-tape is shown to locate cell walls with a high degree of accuracy and estimate the location of the transition feature point in good agreement with human experts. The tool is an open source ImageJ/Fiji macro and is available online.

Bayesian Matching of Unlabeled Point Sets Using Procrustes and Configuration Models

The problem of matching unlabelled point sets using Bayesian inference is considered. Two recently proposed models for the likelihood are compared, based on the Procrustes size-and-shape and the full configuration. Bayesian inference is carried out for matching point sets using Markov chain Monte Carlo simulation. An improvement to the existing Procrustes algorithm is proposed which improves convergence rates, using occasional large jumps in the burn-in period. The Procrustes and configuration methods are compared in a simulation study and using real data, where it is of interest to estimate the strengths of matches between protein binding sites. The performance of both methods is generally quite similar, and a connection between the two models is made using a Laplace approximation.

Tissue-level segmentation and tracking of cells in growing plant roots

With the spread of systems approaches to biological research, there is increasing demand for methods and tools capable of extracting quantitative measurements of biological samples from individual and time-based sequences of microscope images. To this end, we have developed a software tool for tissue level segmentation and automatic tracking of a network of cells in confocal images of the roots of the model plant Arabidopsis thaliana. The tool implements a novel hybrid technique, which is a combination of the recently developed Network Snakes technique and MCMC-based particle filters and incorporates automatic initialisation of the network snakes. A novel method of evaluation of network-structured multi-target tracking is also presented, and is used to evaluate the developed tracking framework for accuracy and robustness against several timelapse sequences of Arabidopsis roots. Evaluation results are presented, along with a comparison between the results of the component techniques and the hybrid approach. The results show that the hybrid approach performed consistently well at all levels of complexity and better than the component methods alone.

Statistical evaluation of transcriptomic data generated using the Affymetrix one-cycle, two-cycle and IVT-Express RNA labelling protocols with the Arabidopsis ATH1 microarray

BackgroundMicroarrays are a powerful tool used for the determination of global RNA expression. There is an increasing requirement to focus on profiling gene expression in tissues where it is difficult to obtain large quantities of material, for example individual tissues within organs such as the root, or individual isolated cells. From such samples, it is difficult to produce the amount of RNA required for labelling and hybridisation in microarray experiments, thus a process of amplification is usually adopted. Despite the increasing use of two-cycle amplification for transcriptomic analyses on the Affymetrix ATH1 array, there has been no report investigating any potential bias in gene representation that may occur as a result.ResultsHere we compare transcriptomic data generated using Affymetrix one-cycle (standard labelling protocol), two-cycle (small-sample protocol) and IVT-Express protocols with the Affymetrix ATH1 array using Arabidopsis root samples. Results obtained with each protocol are broadly similar. However, we show that there are 35 probe sets (of a total of 22810) that are misrepresented in the two-cycle data sets. Of these, 33 probe sets were classed as mis-amplified when comparisons of two independent publicly available data sets were undertaken.ConclusionsGiven the unreliable nature of the highlighted probes, we caution against using data associated with the corresponding genes in analyses involving transcriptomic data generated with two-cycle amplification protocols. We have shown that the Affymetrix IVT-E labelling protocol produces data with less associated bias than the two-cycle protocol, and as such, would recommend this kit for new experiments that involve small samples.