Glutaredoxin regulation of primary root growth is associated with early drought stress tolerance in pearl millet.

Seedling root traits impact plant establishment under challenging environments. Pearl millet is one of the most heat and drought tolerant cereal crops that provides a vital food source across the sub-Saharan Sahel region. Pearl millet's early root system features a single fast-growing primary root which we hypothesize is an adaptation to the Sahelian climate. Using crop modeling, we demonstrate that early drought stress is an important constraint in agrosystems in the Sahel where pearl millet was domesticated. Furthermore, we show that increased pearl millet primary root growth is correlated with increased early water stress tolerance in field conditions. Genetics including genome-wide association study and quantitative trait loci (QTL) approaches identify genomic regions controlling this key root trait. Combining gene expression data, re-sequencing and re-annotation of one of these genomic regions identified a glutaredoxin-encoding gene PgGRXC9 as the candidate stress resilience root growth regulator. Functional characterization of its closest Arabidopsis homolog AtROXY19 revealed a novel role for this glutaredoxin (GRX) gene clade in regulating cell elongation. In summary, our study suggests a conserved function for GRX genes in conferring root cell elongation and enhancing resilience of pearl millet to its Sahelian environment.

Pearl millet is a staple food for over 90 million people living in regions of Africa and India that typically experience high temperatures and little rainfall. It was domesticated about 4,500 years ago in the Sahel region of West Africa and is one of the most heat and drought tolerant cereal crops worldwide. In most plants, organs known as roots absorb water and essential nutrients from the soil. Young pearl millet plants develop a fast-growing primary root, but it is unclear how this unique feature helps the crop to grow in hot and dry conditions. Using weather data collected from the Sahel over a 20-year period, Fuente, Grondin et al. predicted by modelling that early drought stress is the major factor limiting pearl millet growth and yield in this region. Field experiments found that plants with primary roots that grow faster within soil were better at tolerating early drought than those with slower growing roots. Further work using genetic approaches revealed that a gene known as PgGRXC9 promotes the growth of the primary root. To better understand how this gene works, the team examined a very similar gene in a well-studied model plant known as Arabidopsis. This suggested that PgGRXC9 helps the primary root to grow by stimulating cell elongation within the root. Since it is well adapted to dry conditions, pearl millet is expected to play an important role in helping agriculture adjust to climate change. The findings of Fuente, Grondin et al. may be used by plant breeders to create more resilient and productive varieties of pearl millet.

Chlorophyll fluorescence-based high-throughput phenotyping facilitates the genetic dissection of photosynthetic heat tolerance in African (Oryza glaberrima) and Asian (Oryza sativa) rice.

Rising temperatures and extreme heat events threaten rice production. Half of the global population relies on rice for basic nutrition, and therefore developing heat-tolerant rice is essential. During vegetative development, reduced photosynthetic rates can limit growth and the capacity to store soluble carbohydrates. The photosystem II (PSII) complex is a particularly heat-labile component of photosynthesis. We have developed a high-throughput chlorophyll fluorescence-based screen for photosynthetic heat tolerance capable of screening hundreds of plants daily. Through measuring the response of maximum PSII efficiency to increasing temperature, this platform generates data for modelling the PSII-temperature relationship in large populations in a small amount of time. Coefficients from these models (photosynthetic heat tolerance traits) demonstrated high heritabilities across African (Oryza glaberrima) and Asian (Oryza sativa, Bengal Assam Aus Panel) rice diversity sets, highlighting valuable genetic variation accessible for breeding. Genome-wide association studies were performed across both species for these traits, representing the first documented attempt to characterize the genetic basis of photosynthetic heat tolerance in any species to date. A total of 133 candidate genes were highlighted. These were significantly enriched with genes whose predicted roles suggested influence on PSII activity and the response to stress. We discuss the most promising candidates for improving photosynthetic heat tolerance in rice.

Loss of ancestral function in duckweed roots is accompanied by progressive anatomical reduction and a re-distribution of nutrient transporters.

Organ loss occurs frequently during plant and animal evolution. Sometimes, non-functional organs are retained through evolution. Vestigial organs are defined as genetically determined structures that have lost their ancestral (or salient) function.1,2,3 Duckweeds, an aquatic monocot family, exhibit both these characteristics. They possess a uniquely simple body plan, variably across five genera, two of which are rootless. Due to the existence of closely related species with a wide diversity in rooting strategies, duckweed roots represent a powerful system for investigating vestigiality. To explore this, we employed a panel of physiological, ionomic, and transcriptomic analyses, with the main goal of elucidating the extent of vestigiality in duckweed roots. We uncovered a progressive reduction in root anatomy as genera diverge and revealed that the root has lost its salient ancestral function as an organ required for supplying nutrients to the plant. Accompanying this, nutrient transporter expression patterns have lost the stereotypical root biased localization observed in other plant species. While other examples of organ loss such as limbs in reptiles4 or eyes in cavefish5 frequently display a binary of presence/absence, duckweeds provide a unique snapshot of an organ with varying degrees of vestigialization in closely related neighbors and thus provide a unique resource for exploration of how organs behave at different stages along the process of loss.

Root system size and root hair length are key phenes for nitrate acquisition and biomass production across natural variation in Arabidopsis.

The role of root phenes in nitrogen (N) acquisition and biomass production was evaluated in 10 contrasting natural accessions of Arabidopsis thaliana L. Seedlings were grown on vertical agar plates with two different nitrate supplies. The low N treatment increased the root to shoot biomass ratio and promoted the proliferation of lateral roots and root hairs. The cost of a larger root system did not impact shoot biomass. Greater biomass production could be achieved through increased root length or through specific root hair characteristics. A greater number of root hairs may provide a low-resistance pathway under elevated N conditions, while root hair length may enhance root zone exploration under low N conditions. The variability of N uptake and the expression levels of genes encoding nitrate transporters were measured. A positive correlation was found between root system size and high-affinity nitrate uptake, emphasizing the benefits of an exploratory root organ in N acquisition. The expression levels of NRT1.2/NPF4.6, NRT2.2, and NRT1.5/NPF7.3 negatively correlated with some root morphological traits. Such basic knowledge in Arabidopsis demonstrates the importance of root phenes to improve N acquisition and paves the way to design eudicot ideotypes.

Systems approaches reveal that ABCB and PIN proteins mediate co-dependent auxin efflux.

Members of the B family of membrane-bound ATP-binding cassette (ABC) transporters represent key components of the auxin efflux machinery in plants. Over the last two decades, experimental studies have shown that modifying ATP-binding cassette sub-family B (ABCB) expression affects auxin distribution and plant phenotypes. However, precisely how ABCB proteins transport auxin in conjunction with the more widely studied family of PIN-formed (PIN) auxin efflux transporters is unclear, and studies using heterologous systems have produced conflicting results. Here, we integrate ABCB localization data into a multicellular model of auxin transport in the Arabidopsis thaliana root tip to predict how ABCB-mediated auxin transport impacts organ-scale auxin distribution. We use our model to test five potential ABCB-PIN regulatory interactions, simulating the auxin dynamics for each interaction and quantitatively comparing the predictions with experimental images of the DII-VENUS auxin reporter in wild-type and abcb single and double loss-of-function mutants. Only specific ABCB-PIN regulatory interactions result in predictions that recreate the experimentally observed DII-VENUS distributions and long-distance auxin transport. Our results suggest that ABCBs enable auxin efflux independently of PINs; however, PIN-mediated auxin efflux is predominantly through a co-dependent efflux where co-localized with ABCBs.

Dual expression and anatomy lines allow simultaneous visualization of gene expression and anatomy.

Studying the developmental genetics of plant organs requires following gene expression in specific tissues. To facilitate this, we have developed dual expression anatomy lines, which incorporate a red plasma membrane marker alongside a fluorescent reporter for a gene of interest in the same vector. Here, we adapted the GreenGate cloning vectors to create two destination vectors showing strong marking of cell membranes in either the whole root or specifically in the lateral roots. This system can also be used in both embryos and whole seedlings. As proof of concept, we follow both gene expression and anatomy in Arabidopsis (Arabidopsis thaliana) during lateral root organogenesis for a period of over 24 h. Coupled with the development of a flow cell and perfusion system, we follow changes in activity of the DII auxin sensor following application of auxin.

Non-invasive hydrodynamic imaging in plant roots at cellular resolution.

A key impediment to studying water-related mechanisms in plants is the inability to non-invasively image water fluxes in cells at high temporal and spatial resolution. Here, we report that Raman microspectroscopy, complemented by hydrodynamic modelling, can achieve this goal - monitoring hydrodynamics within living root tissues at cell- and sub-second-scale resolutions. Raman imaging of water-transporting xylem vessels in Arabidopsis thaliana mutant roots reveals faster xylem water transport in endodermal diffusion barrier mutants. Furthermore, transverse line scans across the root suggest water transported via the root xylem does not re-enter outer root tissues nor the surrounding soil when en-route to shoot tissues if endodermal diffusion barriers are intact, thereby separating 'two water worlds'.

Positioning the Root Elongation Zone Is Saltatory and Receives Input from the Shoot.

In the root, meristem and elongation zone lengths remain stable, despite growth and division of cells. To gain insight into zone stability, we imaged individual Arabidopsis thaliana roots through a horizontal microscope and used image analysis to obtain velocity profiles. For a root, velocity profiles obtained every 5 min over 3 h coincided closely, implying that zonation is regulated tightly. However, the position of the elongation zone saltated, by on average 17 µm every 5 min. Saltation was apparently driven by material elements growing faster and then slower, while moving through the growth zone. When the shoot was excised, after about 90 min, growth zone dynamics resembled those of intact roots, except that the position of the elongation zone moved, on average, rootward, by several hundred microns in 24 h. We hypothesize that mechanisms determining elongation zone position receive input from the shoot.

Low-Cost Automated Vectors and Modular Environmental Sensors for Plant Phenotyping.

High-throughput plant phenotyping in controlled environments (growth chambers and glasshouses) is often delivered via large, expensive installations, leading to limited access and the increased relevance of "affordable phenotyping" solutions. We present two robot vectors for automated plant phenotyping under controlled conditions. Using 3D-printed components and readily-available hardware and electronic components, these designs are inexpensive, flexible and easily modified to multiple tasks. We present a design for a thermal imaging robot for high-precision time-lapse imaging of canopies and a Plate Imager for high-throughput phenotyping of roots and shoots of plants grown on media plates. Phenotyping in controlled conditions requires multi-position spatial and temporal monitoring of environmental conditions. We also present a low-cost sensor platform for environmental monitoring based on inexpensive sensors, microcontrollers and internet-of-things (IoT) protocols.

Auxin fluxes through plasmodesmata modify root-tip auxin distribution.

Auxin is a key signal regulating plant growth and development. It is well established that auxin dynamics depend on the spatial distribution of efflux and influx carriers on the cell membranes. In this study, we employ a systems approach to characterise an alternative symplastic pathway for auxin mobilisation via plasmodesmata, which function as intercellular pores linking the cytoplasm of adjacent cells. To investigate the role of plasmodesmata in auxin patterning, we developed a multicellular model of the Arabidopsis root tip. We tested the model predictions using the DII-VENUS auxin response reporter, comparing the predicted and observed DII-VENUS distributions using genetic and chemical perturbations designed to affect both carrier-mediated and plasmodesmatal auxin fluxes. The model revealed that carrier-mediated transport alone cannot explain the experimentally determined auxin distribution in the root tip. In contrast, a composite model that incorporates both carrier-mediated and plasmodesmatal auxin fluxes re-capitulates the root-tip auxin distribution. We found that auxin fluxes through plasmodesmata enable auxin reflux and increase total root-tip auxin. We conclude that auxin fluxes through plasmodesmata modify the auxin distribution created by efflux and influx carriers.

Preparation, Scanning and Analysis of Duckweed Using X-Ray Computed Microtomography.

Quantification of anatomical and compositional features underpins both fundamental and applied studies of plant structure and function. Relatively few non-invasive techniques are available for aquatic plants. Traditional methods such as sectioning are low-throughput and provide 2-dimensional information. X-ray Computed Microtomography (µCT) offers a non-destructive method of three dimensional (3D) imaging in planta, but has not been widely used for aquatic species, due to the difficulties in sample preparation and handling. We present a novel sample handling protocol for aquatic plant material developed for µCT imaging, using duckweed plants and turions as exemplars, and compare the method against existing approaches. This technique allows for previously unseen 3D volume analysis of gaseous filled spaces, cell material, and sub-cellular features. The described embedding method, utilizing petrolatum gel for sample mounting, was shown to preserve sample quality during scanning, and to display sufficiently different X-ray attenuation to the plant material to be easily differentiated by image analysis pipelines. We present this technique as an improved method for anatomical structural analysis that provides novel cellular and developmental information.

Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling.

Plant phenotyping is the quantitative description of a plant's physiological, biochemical, and anatomical status which can be used in trait selection and helps to provide mechanisms to link underlying genetics with yield. Here, an active vision- based pipeline is presented which aims to contribute to reducing the bottleneck associated with phenotyping of architectural traits. The pipeline provides a fully automated response to photometric data acquisition and the recovery of three-dimensional (3D) models of plants without the dependency of botanical expertise, whilst ensuring a non-intrusive and non-destructive approach. Access to complete and accurate 3D models of plants supports computation of a wide variety of structural measurements. An Active Vision Cell (AVC) consisting of a camera-mounted robot arm plus combined software interface and a novel surface reconstruction algorithm is proposed. This pipeline provides a robust, flexible, and accurate method for automating the 3D reconstruction of plants. The reconstruction algorithm can reduce noise and provides a promising and extendable framework for high throughput phenotyping, improving current state-of-the-art methods. Furthermore, the pipeline can be applied to any plant species or form due to the application of an active vision framework combined with the automatic selection of key parameters for surface reconstruction.

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures.

BACKGROUND: In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions. We present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task convolutional neural network architecture. The network also locates seeds, first order and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. RESULTS: We develop and train a novel deep network architecture to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. The proposed method was evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. Compared with semi-automatic analysis via the original RootNav tool, the proposed method demonstrated comparable accuracy, with a 10-fold increase in speed. The network was able to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. A final instance of transfer learning, to images of Brassica napus from a hydroponic assay, still demonstrated good accuracy despite many fewer training images. CONCLUSIONS: We present RootNav 2.0, a new approach to root image analysis driven by a deep neural network. The tool can be adapted to new image domains with a reduced number of images, and offers substantial speed improvements over semi-automatic and manual approaches. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools. The tool will provide researchers with the ability to analyse root systems at larget scales than ever before, at a time when large scale genomic studies have made this more important than ever.

Uncovering the hidden half of plants using new advances in root phenotyping.

Major increases in crop yield are required to keep pace with population growth and climate change. Improvements to the architecture of crop roots promise to deliver increases in water and nutrient use efficiency but profiling the root phenome (i.e. its structure and function) represents a major bottleneck. We describe how advances in imaging and sensor technologies are making root phenomic studies possible. However, methodological advances in acquisition, handling and processing of the resulting 'big-data' is becoming increasingly important. Advances in automated image analysis approaches such as Deep Learning promise to transform the root phenotyping landscape. Collectively, these innovations are helping drive the selection of the next-generation of crops to deliver real world impact for ongoing global food security efforts.

Plant Phenotyping: An Active Vision Cell for Three-Dimensional Plant Shoot Reconstruction.

Three-dimensional (3D) computer-generated models of plants are urgently needed to support both phenotyping and simulation-based studies such as photosynthesis modeling. However, the construction of accurate 3D plant models is challenging, as plants are complex objects with an intricate leaf structure, often consisting of thin and highly reflective surfaces that vary in shape and size, forming dense, complex, crowded scenes. We address these issues within an image-based method by taking an active vision approach, one that investigates the scene to intelligently capture images, to image acquisition. Rather than use the same camera positions for all plants, our technique is to acquire the images needed to reconstruct the target plant, tuning camera placement to match the plant's individual structure. Our method also combines volumetric- and surface-based reconstruction methods and determines the necessary images based on the analysis of voxel clusters. We describe a fully automatic plant modeling/phenotyping cell (or module) comprising a six-axis robot and a high-precision turntable. By using a standard color camera, we overcome the difficulties associated with laser-based plant reconstruction methods. The 3D models produced are compared with those obtained from fixed cameras and evaluated by comparison with data obtained by x-ray microcomputed tomography across different plant structures. Our results show that our method is successful in improving the accuracy and quality of data obtained from a variety of plant types.

Root Gravitropism: Quantification, Challenges, and Solutions.

Better understanding of root traits such as root angle and root gravitropism will be crucial for development of crops with improved resource use efficiency. This chapter describes a high-throughput, automated image analysis method to trace Arabidopsis (Arabidopsis thaliana) seedling roots grown on agar plates. The method combines a "particle-filtering algorithm with a graph-based method" to trace the center line of a root and can be adopted for the analysis of several root parameters such as length, curvature, and stimulus from original root traces.

Adding a Piece to the Leaf Epidermal Cell Shape Puzzle.

The jigsaw puzzle-shaped pavement cells in the leaf epidermis collectively function as a load-bearing tissue that controls organ growth. In this issue of Developmental Cell, Majda et al. (2017) shed light on how the jigsaw shape can arise from localized variations in wall stiffness between adjacent epidermal cells.

An Updated Protocol for High Throughput Plant Tissue Sectioning.

Quantification of the tissue and cellular structure of plant material is essential for the study of a variety of plant sciences applications. Currently, many methods for sectioning plant material are either low throughput or involve free-hand sectioning which requires a significant amount of practice. Here, we present an updated method to provide rapid and high-quality cross sections, primarily of root tissue but which can also be readily applied to other tissues such as leaves or stems. To increase the throughput of traditional agarose embedding and sectioning, custom designed 3D printed molds were utilized to embed 5-15 roots in a block for sectioning in a single cut. A single fluorescent stain in combination with laser scanning confocal microscopy was used to obtain high quality images of thick sections. The provided CAD files allow production of the embedding molds described here from a number of online 3D printing services. Although originally developed for roots, this method provides rapid, high quality cross sections of many plant tissue types, making it suitable for use in forward genetic screens for differences in specific cell structures or developmental changes. To demonstrate the utility of the technique, the two parent lines of the wheat (Triticum aestivum) Chinese Spring × Paragon doubled haploid mapping population were phenotyped for root anatomical differences. Significant differences in adventitious cross section area, stele area, xylem, phloem, metaxylem, and cortical cell file count were found.