Is it good noise? The role of developmental instability in the shaping of a root system

Physical obstacles in the substrate cause maize root growth trajectories to switch from vertical to oblique

Hard pans, soil compaction, soil aggregation, and stones create physical barriers that can affect the development of a root system. Roots are known to exploit paths of least resistance to avoid such obstacles, but the mechanism through which this is achieved is not well understood. Here, we used a combination of 3D-printed substrates with a high-throughput live-imaging platform to study the responses of maize roots to a range of physical barriers. Using image analysis algorithms, we determined the properties of growth trajectories and identified how the presence of rigid circular obstacles affects the ability of a primary root to maintain its vertical trajectory. The results showed that the types of growth responses were limited, with both vertical and oblique trajectories being found to be stable and influenced by the size of the obstacles. When obstacles were of intermediate sizes, trajectories were unstable and changed in nature through time. We formalized the conditions required for root trajectory to change from vertical to oblique, linking the angle at which the root detaches from the obstacle to the root curvature due to gravitropism. Exploitation of paths of least resistance by a root might therefore be constrained by the ability of the root to curve and respond to gravitropic signals.

Island plants with newly discovered reproductive traits have higher capacity for uniparental reproduction, supporting Baker's law

Uniparental reproduction is advantageous when lack of mates limits outcrossing opportunities in plants. Baker's law predicts an enrichment of uniparental reproduction in habitats colonized via long-distance dispersal, such as volcanic islands. To test it, we analyzed reproductive traits at multiple hierarchical levels and compared seed-set after selfing and crossing experiments in both island and mainland populations of Limonium lobatum, a widespread species that Baker assumed to be self-incompatible because it had been described as pollen-stigma dimorphic, i.e., characterized by floral morphs differing in pollen-surface morphology and stigma-papillae shape that are typically self-incompatible. We discovered new types and combinations of pollen and stigma traits hitherto unknown in the literature on pollen-stigma dimorphism and a lack of correspondence between such combinations and pollen compatibility. Contrary to previous reports, we conclude that Limonium lobatum comprises both self-compatible and self-incompatible plants characterized by both known and previously undescribed combinations of reproductive traits. Most importantly, plants with novel combinations are overrepresented on islands, selfed seed-set is higher in islands than the mainland, and insular plants with novel pollen-stigma trait-combinations disproportionally contribute to uniparental reproduction on islands. Our results thus support Baker's law, connecting research on reproductive and island biology.

OPT-ing out: Root-shoot dynamics are caused by local resource capture and biomass allocation, not optimal partitioning

Combining plant growth analysis with a simple model of local resource capture and biomass allocation applied to exemplary experimental data, showed that dynamic changes in allocation to roots when nutrients are scarce is caused by disparities in growth rates between roots and shoots. Whole-plant growth rates also change but are not caused by an adaptive allocation response. Allocation and whole-plant growth rate are interdependent, not independent, traits. Following a switch in nutrient availability or partial biomass removal, convergence of allocation and growth rate trajectories does not reflect goal-seeking behaviour, but the constraints imposed by finite resource availability. Optimal root-shoot allocations are unnecessary to maximise whole-plant growth rate. Similar growth rates are attainable with different allocations. Changes in allocation cannot maintain or restore a superior whole-plant growth rate. Roots and shoots do not have to be tightly coordinated but can operate semiautonomously, as their modular construction permits. These findings question the plausibility of the prevailing general explanation of plants' root-shoot allocation responses, 'optimal partitioning theory' (OPT). Local allocation models, not OPT, explain the origins of variability in root-shoot trade-offs in individuals, the allocation of biomass at global and ecosystem scales and inform selection for allocation plasticity in crop breeding.

Rhizosphere engineering for sustainable crop production: entropy-based insights

There is a growing interest in exploring interactions at root-soil interface in natural and agricultural ecosystems, but an entropy-based understanding of these dynamic rhizosphere processes is lacking. We have developed a new conceptual model of rhizosphere regulation by localized nutrient supply using thermodynamic entropy. Increased nutrient-use efficiency is achieved by rhizosphere management based on self-organization and minimized entropy via equilibrium attractors comprising (i) optimized root strategies for nutrient acquisition and (ii) improved information exchange related to root-soil-microbe interactions. The cascading effects through different hierarchical levels amplify the underlying processes in plant-soil system. We propose a strategy for manipulating rhizosphere dynamics and improving nutrient-use efficiency by localized nutrient supply with minimization of entropy to underpin sustainable food/feed/fiber production.

Objective Phenotyping of Root System Architecture Using Image Augmentation and Machine Learning in Alfalfa (Medicago sativa L.)

Active breeding programs specifically for root system architecture (RSA) phenotypes remain rare; however, breeding for branch and taproot types in the perennial crop alfalfa is ongoing. Phenotyping in this and other crops for active RSA breeding has mostly used visual scoring of specific traits or subjective classification into different root types. While image-based methods have been developed, translation to applied breeding is limited. This research is aimed at developing and comparing image-based RSA phenotyping methods using machine and deep learning algorithms for objective classification of 617 root images from mature alfalfa plants collected from the field to support the ongoing breeding efforts. Our results show that unsupervised machine learning tends to incorrectly classify roots into a normal distribution with most lines predicted as the intermediate root type. Encouragingly, random forest and TensorFlow-based neural networks can classify the root types into branch-type, taproot-type, and an intermediate taproot-branch type with 86% accuracy. With image augmentation, the prediction accuracy was improved to 97%. Coupling the predicted root type with its prediction probability will give breeders a confidence level for better decisions to advance the best and exclude the worst lines from their breeding program. This machine and deep learning approach enables accurate classification of the RSA phenotypes for genomic breeding of climate-resilient alfalfa.

Hotspots of root-exuded amino acids are created within a rhizosphere-on-a-chip

The rhizosphere is a challenging ecosystem to study from a systems biology perspective due to its diverse chemical, physical, and biological characteristics. In the past decade, microfluidic platforms (e.g. plant-on-a-chip) have created an alternative way to study whole rhizosphere organisms, like plants and microorganisms, under reduced-complexity conditions. However, in reducing the complexity of the environment, it is possible to inadvertently alter organism phenotype, which biases laboratory data compared to in situ experiments. To build back some of the complexity of the rhizosphere in a fully-defined, parameterized approach we have developed a rhizosphere-on-a-chip platform that mimics the physical structure of soil. We demonstrate, through computational simulation, how this synthetic soil structure can influence the emergence of molecular "hotspots" and "hotmoments" that arise naturally from the plant's exudation of labile carbon compounds. We establish the amenability of the rhizosphere-on-a-chip for long-term culture of Brachypodium distachyon, and experimentally validate the presence of exudate hotspots within the rhizosphere-on-a-chip pore spaces using liquid microjunction surface sampling probe mass spectrometry.

Root angle in maize influences nitrogen capture and is regulated by calcineurin B-like protein (CBL)-interacting serine/threonine-protein kinase 15 (ZmCIPK15)

Crops with reduced nutrient and water requirements are urgently needed in global agriculture. Root growth angle plays an important role in nutrient and water acquisition. A maize diversity panel of 481 genotypes was screened for variation in root angle employing a high-throughput field phenotyping platform. Genome-wide association mapping identified several single nucleotide polymorphisms (SNPs) associated with root angle, including one located in the root expressed CBL-interacting serine/threonine-protein kinase 15 (ZmCIPK15) gene (LOC100285495). Reverse genetic studies validated the functional importance of ZmCIPK15, causing a approximately 10° change in root angle in specific nodal positions. A steeper root growth angle improved nitrogen capture in silico and in the field. OpenSimRoot simulations predicted at 40 days of growth that this change in angle would improve nitrogen uptake by 11% and plant biomass by 4% in low nitrogen conditions. In field studies under suboptimal N availability, the cipk15 mutant with steeper growth angles had 18% greater shoot biomass and 29% greater shoot nitrogen accumulation compared to the wild type after 70 days of growth. We propose that a steeper root growth angle modulated by ZmCIPK15 will facilitate efforts to develop new crop varieties with optimal root architecture for improved performance under edaphic stress.

The Right-Skewed Distribution of Fine-Root Size in Three Temperate Forests in Northeastern China

Trees can build fine-root systems with high variation in root size (e.g., fine-root diameter) and root number (e.g., branching pattern) to optimize belowground resource acquisition in forest ecosystems. Compared with leaves, which are visible above ground, information about the distribution and inequality of fine-root size and about key associations between fine-root size and number is still limited. We collected 27,573 first-order fine-roots growing out of 3,848 second-order fine-roots, covering 51 tree species in three temperate forests (Changbai Mountain, CBS; Xianrendong, XRD; and Maoershan, MES) in Northeastern China. We investigated the distribution and inequality of fine-root length, diameter and area (fine-root size), and their trade-off with fine-root branching intensity and ratio (fine-root number). Our results showed a strong right-skewed distribution in first-order fine-root size across various tree species. Unimodal frequency distributions were observed in all three of the sampled forests for first-order fine-root length and area and in CBS and XRD for first-order fine-root diameter, whereas a marked bimodal frequency distribution of first-order fine-root diameter appeared in MES. Moreover, XRD had the highest and MES had the lowest inequality values (Gini coefficients) in first-order fine-root diameter. First-order fine-root size showed a consistently linear decline with increasing root number. Our findings suggest a common right-skewed distribution with unimodality or bimodality of fine-root size and a generalized trade-off between fine-root size and number across the temperate tree species. Our results will greatly improve our thorough understanding of the belowground resource acquisition strategies of temperate trees and forests.

Bioassays for the Effects of Strigolactones and Other Small Molecules on Root and Root Hair Development

Growth and development of plant roots are highly dynamic and adaptable to environmental conditions. They are under the control of several plant hormone signaling pathways, and therefore root developmental responses can be used as bioassays to study the action of plant hormones and other small molecules. In this chapter, we present different procedures to measure root traits of the model plant Arabidopsis thaliana. We explain methods for phenotypic analysis of lateral root development, primary root length, root skewing and straightness, and root hair density and length. We describe optimal growth conditions for Arabidopsis seedlings for reproducible root and root hair developmental outputs; and how to acquire images and measure the different traits using image analysis with relatively low-tech equipment. We provide guidelines for a semiautomatic image analysis of primary root length, root skewing, and root straightness in Fiji and a script to automate the calculation of root angle deviation from the vertical and root straightness. By including mutants defective in strigolactone (SL) or KAI2 ligand (KL) synthesis and/or signaling, these methods can be used as bioassays for different SLs or SL-like molecules. In addition, the techniques described here can be used for studying seedling root system architecture, root skewing, and root hair development in any context.

Co-expression Networks From Gene Expression Variability Between Genetically Identical Seedlings Can Reveal Novel Regulatory Relationships

Co-expression networks are a powerful tool to understand gene regulation. They have been used to identify new regulation and function of genes involved in plant development and their response to the environment. Up to now, co-expression networks have been inferred using transcriptomes generated on plants experiencing genetic or environmental perturbation, or from expression time series. We propose a new approach by showing that co-expression networks can be constructed in the absence of genetic and environmental perturbation, for plants at the same developmental stage. For this, we used transcriptomes that were generated from genetically identical individual plants that were grown under the same conditions and for the same amount of time. Twelve time points were used to cover the 24-h light/dark cycle. We used variability in gene expression between individual plants of the same time point to infer a co-expression network. We show that this network is biologically relevant and use it to suggest new gene functions and to identify new targets for the transcriptional regulators GI, PIF4, and PRR5. Moreover, we find different co-regulation in this network based on changes in expression between individual plants, compared to the usual approach requiring environmental perturbation. Our work shows that gene co-expression networks can be identified using variability in gene expression between individual plants, without the need for genetic or environmental perturbations. It will allow further exploration of gene regulation in contexts with subtle differences between plants, which could be closer to what individual plants in a population might face in the wild.

Does Gene Expression Noise Play a Functional Role in Plants?

Gene expression in individual cells can be surprisingly noisy. In unicellular organisms this noise can be functional; for example, by allowing a subfraction of the population to prepare for environmental stress. The role of gene expression noise in multicellular organisms has, however, remained unclear. In this review, we discuss how new techniques are revealing an unexpected level of variability in gene expression between and within genetically identical plants. We describe recent progress as well as speculate on the function of transcriptional noise as a mechanism for generating functional phenotypic diversity in plants.

Computer vision and machine learning enabled soybean root phenotyping pipeline

BACKGROUND

Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis.

RESULTS

This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle.

CONCLUSIONS

This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts.

Computer vision and machine learning enabled soybean root phenotyping pipeline

BACKGROUND

Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis.

RESULTS

This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle.

CONCLUSIONS

This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts.

Lateral Roots: Random Diversity in Adversity

Lateral roots are essential for soil foraging and uptake of minerals and water. They feature a large morphological diversity that results from divergent primordia or root growth and development patterns. Besides a structured diversity, resulting from the hierarchical and developmental organization of root systems, there exists a random diversity, occurring between roots of similar age, of the same hierarchical order, and exposed to uniform conditions. The physiological bases and functional consequences of this random diversity are largely ignored. Here we review the evidence for such random diversity throughout the plant kingdom, present innovative approaches based on statistical modeling to account for such diversity, and set the list of its potential benefits in front of a variable and unpredictable soil environment.

Demystifying roots: A need for clarification and extended concepts in root phenotyping

Plant roots have major roles in plant anchorage, resource acquisition and offer environmental benefits including carbon sequestration and soil erosion mitigation. As such, the study of root system architecture, anatomy and functional properties is of crucial interest to plant breeding, with the aim of sustainable yield production and environmental stewardship. Due to the importance of the root system studies, there is a need for clarification of terms and concepts in the root phenotyping community. In particular in this contribution, we advocate for the use of a reference naming system (ontologies) for roots and root phenes. Such uniformity would not only allow better understanding of research results, but would also enable a better sharing of data. In addition, we highlight the need to incorporate the concept of plasticity in breeding programs, as it is an essential component of root system development in heterogeneous environments.

Root growth and force chains in a granular soil

Roots provide basic functions to plants such as water and nutrient uptake and anchoring in soil. The growth and development of root systems contribute to colonizing the surrounding soil and optimizing the access to resources. It is generally known that the variability of plant root architecture results from the combination of genetic, physiological, and environmental factors, in particular soil mechanical resistance. However, this last factor has never been investigated at the soil grain scale for roots. In this paper, we are interested in the effect of the disordered texture of granular soils on the evolution of forces experienced by the root cap during its growth. We introduce a numerical model in which the root is modeled as a flexible self-elongating tube that probes a soil composed of solid particles. By means of extensive simulations, we show that the forces exerted on the root cap reflect interparticle force chains. Our simulations also show that the mean force declines exponentially with root flexibility, the highest force corresponding to the soil hardness. Furthermore, we find that this functional dependence is characterized by a single dimensionless parameter that combines granular structure and root bending stiffness. This finding will be useful to further address the biological issues of mechanosensing and thigmomorphogenesis in plant roots.

Widespread inter-individual gene expression variability in Arabidopsis thaliana

A fundamental question in biology is how gene expression is regulated to give rise to a phenotype. However, transcriptional variability is rarely considered although it could influence the relationship between genotype and phenotype. It is known in unicellular organisms that gene expression is often noisy rather than uniform, and this has been proposed to be beneficial when environmental conditions are unpredictable. However, little is known about inter-individual transcriptional variability in multicellular organisms. Using transcriptomic approaches, we analysed gene expression variability between individual Arabidopsis thaliana plants growing in identical conditions over a 24-h time course. We identified hundreds of genes that exhibit high inter-individual variability and found that many are involved in environmental responses, with different classes of genes variable between the day and night. We also identified factors that might facilitate gene expression variability, such as gene length, the number of transcription factors regulating the genes and the chromatin environment. These results shed new light on the impact of transcriptional variability in gene expression regulation in plants.

Root Phenotyping for Drought Tolerance: A Review

Plant roots play a significant role in plant growth by exploiting soil resources via the uptake of water and nutrients. Root traits such as fine root diameter, specific root length, specific root area, root angle, and root length density are considered useful traits for improving plant productivity under drought conditions. Therefore, understanding interactions between roots and their surrounding soil environment is important, which can be improved through root phenotyping. With the advancement in technologies, many tools have been developed for root phenotyping. Canopy temperature depression (CTD) has been considered a good technique for field phenotyping of crops under drought and is used to estimate crop yield as well as root traits in relation to drought tolerance. Both laboratory and field-based methods for phenotyping root traits have been developed including soil sampling, mini-rhizotron, rhizotrons, thermography and non-soil techniques. Recently, a non-invasive approach of X-ray computed tomography (CT) has provided a break-through to study the root architecture in three dimensions (3-D). This review summarizes methods for root phenotyping. On the basis of this review, it can be concluded that root traits are useful characters to be included in future breeding programs and for selecting better cultivars to increase crop yield under water-limited environments.

Morphological and genetic characterisation of the root system architecture of selected barley recombinant chromosome substitution lines using an integrated phenotyping approach

Discoveries on the genetics of resource acquisition efficiency are limited by the ability to measure plant roots in sufficient number and with adequate genotypic variability. This paper presents a root phenotyping study that explores ways to combine live imaging and computer algorithms for model-based extraction of root growth parameters. The study is based on a subset of barley Recombinant Chromosome Substitution Lines (RCSLs) and a combinatorial approach was designed for fast identification of the regions of the genome that contribute the most to variations in root system architecture (RSA). Results showed there was a strong genotypic variation in root growth parameters within the set of genotypes studied. The chromosomal regions associated with primary root growth differed from the regions of the genome associated with changes in lateral root growth. The concepts presented here are discussed in the context of identifying root QTL and its potential to assist breeding for novel crops with improved root systems.

Uncovering the contribution of epigenetics to plant phenotypic variation in Mediterranean ecosystems

Epigenetic signals can affect plant phenotype and fitness and be stably inherited across multiple generations. Epigenetic regulation plays a key role in the mechanisms of plant response to the environment, without altering DNA sequence. As plants cannot adapt behaviourally or migrate instantly, such dynamic epigenetic responses may be particularly crucial for survival of plants within changing and challenging environments, such as the Mediterranean-Type Ecosystems (MTEs). These ecosystems suffer recurrent stressful events (warm and dry summers with associated fire regimes) that have selected for plants with similar phenotypic complex traits, resulting in similar vegetation growth forms. However, the potential role of epigenetics in plant adaptation to recurrent stressful environments such as the MTEs has generally been ignored. To understand the full spectrum of adaptive processes in such contexts, it is imperative to prompt study of the causes and consequences of epigenetic variation in natural populations. With this purpose, we review here current knowledge on epigenetic variation in natural populations and the genetic and epigenetic basis of some key traits for plants in the MTEs, namely those traits involved in adaptation to drought, fire and oligotrophic soils. We conclude there is still much to be learned about 'plant epigenetics in the wild' and, thus, we propose future research steps in the study of natural epigenetic variation of key traits in the MTEs at different scales.

Six-Year Nitrogen-Water Interaction Shifts the Frequency Distribution and Size Inequality of the First-Order Roots of Fraxinus mandschurica in a Mixed Mature Pinus koraiensis Forest

The variation in fine root traits in terms of size inequality at the individual root level can be identified as a strategy for adapting to the drastic changes in soil water and nutrient availabilities. The Gini and Lorenz asymmetry coefficients have been applied to describe the overall degree of size inequality, which, however, are neglected when conventional statistical means are calculated. Here, we used the Gini coefficient, Lorenz asymmetry coefficient and statistical mean in an investigation of Fraxinus mandschurica roots in a mixed mature Pinus koraiensis forest on Changbai Mountain, China. We analyzed 967 individual roots to determine the responses of length, diameter and area of the first-order roots and of branching intensity to 6 years of nitrogen addition (N), rainfall reduction (W) and their combination (NW). We found that first-order roots had a significantly greater average length and area but had smaller Gini coefficients in NW plots compared to in control plots (CK). Furthermore, the relationship between first-order root length and branching intensity was negative in CK, N, and W plots but positive in NW plots. The Lorenz asymmetry coefficient was >1 for the first-order root diameter in NW and W plots as well as for branching intensity in N plots. The bimodal frequency distribution of the first-order root length in NW plots differed clearly from the unimodal one in CK, N, and W plots. These results demonstrate that not only the mean but also the variation and the distribution mode of the first-order roots of F. mandschurica respond to soil nitrogen and water availability. The changes in size inequality of the first-order root traits suggest that Gini and Lorenz asymmetry coefficients can serve as informative parameters in ecological investigations of roots to improve our ability to predict how trees will respond to a changing climate at the individual root level.

Differentiating Wheat Genotypes by Bayesian Hierarchical Nonlinear Mixed Modeling of Wheat Root Density

Ensuring future food security for a growing population while climate change and urban sprawl put pressure on agricultural land will require sustainable intensification of current farming practices. For the crop breeder this means producing higher crop yields with less resources due to greater environmental stresses. While easy gains in crop yield have been made mostly "above ground," little progress has been made "below ground"; and yet it is these root system traits that can improve productivity and resistance to drought stress. Wheat pre-breeders use soil coring and core-break counts to phenotype root architecture traits, with data collected on rooting density for hundreds of genotypes in small increments of depth. The measured densities are both large datasets and highly variable even within the same genotype, hence, any rigorous, comprehensive statistical analysis of such complex field data would be technically challenging. Traditionally, most attributes of the field data are therefore discarded in favor of simple numerical summary descriptors which retain much of the high variability exhibited by the raw data. This poses practical challenges: although plant scientists have established that root traits do drive resource capture in crops, traits that are more randomly (rather than genetically) determined are difficult to breed for. In this paper we develop a hierarchical nonlinear mixed modeling approach that utilizes the complete field data for wheat genotypes to fit, under the Bayesian paradigm, an "idealized" relative intensity function for the root distribution over depth. Our approach was used to determine heritability: how much of the variation between field samples was purely random vs. being mechanistically driven by the plant genetics? Based on the genotypic intensity functions, the overall heritability estimate was 0.62 (95% Bayesian confidence interval was 0.52 to 0.71). Despite root count profiles that were statistically very noisy, our approach led to denoised profiles which exhibited rigorously discernible phenotypic traits. Profile-specific traits could be representative of a genotype, and thus, used as a quantitative tool to associate phenotypic traits with specific genotypes. This would allow breeders to select for whole root system distributions appropriate for sustainable intensification, and inform policy for mitigating crop yield risk and food insecurity.

Branching patterns of root systems: comparison of monocotyledonous and dicotyledonous species

BACKGROUND AND AIMS

Acropetal root branching is a major process which increases the number of growing tips and distributes their growth potential within the whole root system.

METHODS

Using a method presented in a recent paper, the defined branching traits were estimated in 140 different species, and the branching patterns of monocots (45 species) and dicots (95 species) were compared.

KEY RESULTS

It was checked that the method also applied to monocots (not considered in the previous paper), and that all traits could be estimated in each species. Variations of most traits were even larger for monocots than for dicots. Systematic differences appeared between these two groups: monocots tended to have a larger range in apical diameters (stronger heterorhizy), with both finer and thicker roots; the diameters of their lateral roots were also more variable; their roots exerted a stronger dominance over lateral branches. Altogether, species exhibited two main dependencies among their traits that were illustrated using two axes: (1) the 'fineness-density' axis separated the species which develop very fine roots and branch densely, from species without fine roots which space out their branches; and (2) the 'dominance-heterorhizy' axis separated the species according to the range in their apical diameter which was positively correlated to the level of dominance of mother roots over their branches. Both axes and correlations were remarkably similar for monocots and dicots.

CONCLUSIONS

Beyond the overall typology, this study went on to validate the phenotyping method in Natura, and showed its potential to characterize the differences in groups of species.

Fine-tuning of root elongation by ethylene: a tool to study dynamic structure-function relationships between root architecture and nitrate absorption

Background Recently developed genetic and pharmacological approaches have been used to explore NO3-/ethylene signalling interactions and how the modifications in root architecture by pharmacological modulation of ethylene biosynthesis affect nitrate uptake. Key Results Structure-function studies combined with recent approaches to chemical genomics highlight the non-specificity of commonly used inhibitors of ethylene biosynthesis such as AVG (l-aminoethoxyvinylglycine). Indeed, AVG inhibits aminotransferases such as ACC synthase (ACS) and tryptophan aminotransferase (TAA) involved in ethylene and auxin biosynthesis but also some aminotransferases implied in nitrogen (N) metabolism. In this framework, it can be assumed that the products of nitrate assimilation and hormones may interact through a hub in carbon (C) and N metabolism to drive the root morphogenetic programme (RMP). Although ethylene/auxin interactions play a major role in cell division and elongation in root meristems, shaping of the root system depends also on energetic considerations. Based on this finding, the analysis is extended to nutrient ion-hormone interactions assuming a fractal or constructal model for root development. Conclusion Therefore, the tight control of root structure-function in the RMP may explain why over-expressing nitrate transporter genes to decouple structure-function relationships and improve nitrogen use efficiency (NUE) has been unsuccessful.

Modelling the vasculature of the stem of Cyperus involucratus Rottb.: evidence for three patterns of vascular bundles

Three independent patterns of vein formation in Cyperus involucratus Rottb. were identified based on rare spontaneous interruptions of scape vein development. A number of developmental anomalies of vascular bundles in Cyperus involucratus Rottb. were identified and they include "turnabout", "absent", "twins", "doublet", amphivasal and various stages of "arrested". These were used to develop a computer program to explain the three vasculature patterns of the scape of (a) ordered deployment of vascular bundles, (b) arrangement of tissues within vascular bundles and (c) orientation of vascular bundles with respect to stem edge. The computer model is a cell-by-cell determination of cell types and facet states.

Developmental mechanisms underlying variable, invariant and plastic phenotypes

BACKGROUND

Discussions of phenotypic robustness often consider scenarios where invariant phenotypes are optimal and assume that developmental mechanisms have evolved to buffer the phenotypes of specific traits against stochastic and environmental perturbations. However, plastic plant phenotypes that vary between environments or variable phenotypes that vary stochastically within an environment may also be advantageous in some scenarios.

SCOPE

Here the conditions under which invariant, plastic and variable phenotypes of specific traits may confer a selective advantage in plants are examined. Drawing on work from microbes and multicellular organisms, the mechanisms that may give rise to each type of phenotype are discussed.

CONCLUSION

In contrast to the view of robustness as being the ability of a genotype to produce a single, invariant phenotype, changes in a phenotype in response to the environment, or phenotypic variability within an environment, may also be delivered consistently (i.e. robustly). Thus, for some plant traits, mechanisms have probably evolved to produce plasticity or variability in a reliable manner.

Analysis of root growth from a phenotyping data set using a density-based model

Major research efforts are targeting the improved performance of root systems for more efficient use of water and nutrients by crops. However, characterizing root system architecture (RSA) is challenging, because roots are difficult objects to observe and analyse. A model-based analysis of RSA traits from phenotyping image data is presented. The model can successfully back-calculate growth parameters without the need to measure individual roots. The mathematical model uses partial differential equations to describe root system development. Methods based on kernel estimators were used to quantify root density distributions from experimental image data, and different optimization approaches to parameterize the model were tested. The model was tested on root images of a set of 89 Brassica rapa L. individuals of the same genotype grown for 14 d after sowing on blue filter paper. Optimized root growth parameters enabled the final (modelled) length of the main root axes to be matched within 1% of their mean values observed in experiments. Parameterized values for elongation rates were within ±4% of the values measured directly on images. Future work should investigate the time dependency of growth parameters using time-lapse image data. The approach is a potentially powerful quantitative technique for identifying crop genotypes with more efficient root systems, using (even incomplete) data from high-throughput phenotyping systems.

Stochastic developmental variation, an epigenetic source of phenotypic diversity with far-reaching biological consequences

This article reviews the production of different phenotypes from the same genotype in the same environment by stochastic cellular events, nonlinear mechanisms during patterning and morphogenesis, and probabilistic self-reinforcing circuitries in the adult life. These aspects of phenotypic variation are summarized under the term 'stochastic developmental variation' (SDV) in the following. In the past, SDV has been viewed primarily as a nuisance, impairing laboratory experiments, pharmaceutical testing, and true-to-type breeding. This article also emphasizes the positive biological effects of SDV and discusses implications for genotype-to-phenotype mapping, biological individuation, ecology, evolution, and applied biology. There is strong evidence from experiments with genetically identical organisms performed in narrowly standardized laboratory set-ups that SDV is a source of phenotypic variation in its own right aside from genetic variation and environmental variation. It is obviously mediated by molecular and higher-order epigenetic mechanisms. Comparison of SDV in animals, plants, fungi, protists, bacteria, archaeans, and viruses suggests that it is a ubiquitous and phylogenetically old phenomenon. In animals, it is usually smallest for morphometric traits and highest for life history traits and behaviour. SDV is thought to contribute to phenotypic diversity in all populations but is particularly relevant for asexually reproducing and genetically impoverished populations, where it generates individuality despite genetic uniformity. In each generation, SDV produces a range of phenotypes around a well-adapted target phenotype, which is interpreted as a bet-hedging strategy to cope with the unpredictability of dynamic environments. At least some manifestations of SDV are heritable, adaptable, selectable, and evolvable, and therefore, SDV may be seen as a hitherto overlooked evolution factor. SDV is also relevant for husbandry, agriculture, and medicine because most pathogens are asexuals that exploit this third source of phenotypic variation to modify infectivity and resistance to antibiotics. Since SDV affects all types of organisms and almost all aspects of life, it urgently requires more intense research and a better integration into biological thinking.

Characterizing root response phenotypes by neural network analysis

Roots play an immediate role as the interface for water acquisition. To improve sustainability in low-water environments, breeders of major crops must therefore pay closer attention to advantageous root phenotypes; however, the complexity of root architecture in response to stress can be difficult to quantify. Here, the Sholl method, an established technique from neurobiology used for the characterization of neural network anatomy, was adapted to more adequately describe root responses to osmotic stress. This method was used to investigate the influence of in vitro osmotic stress on early root architecture and distribution in drought-resistant and -susceptible genotypes of winter oilseed rape. Interactive changes in root architecture can be easily captured by individual intersection profiles generated by Sholl analysis. Validation using manual measurements confirmed that the number of lateral roots decreased, while mean lateral root length was enhanced, under osmotic stress conditions. Both genotypes reacted to osmotic stress with a shift in their intersection patterns measured with Sholl analysis. Changes in interactive root architecture and distribution under stress were more pronounced in the drought-resistant genotype, indicating that these changes may contribute to drought resistance under mild osmotic stress conditions. The Sholl methodology is presented as a promising tool for selection of cultivars with advantageous root phenotypes under osmotic stress conditions.

Root Traits and Phenotyping Strategies for Plant Improvement

Roots are crucial for nutrient and water acquisition and can be targeted to enhance plant productivity under a broad range of growing conditions. A current challenge for plant breeding is the limited ability to phenotype and select for desirable root characteristics due to their underground location. Plant breeding efforts aimed at modifying root traits can result in novel, more stress-tolerant crops and increased yield by enhancing the capacity of the plant for soil exploration and, thus, water and nutrient acquisition. Available approaches for root phenotyping in laboratory, greenhouse and field encompass simple agar plates to labor-intensive root digging (i.e., shovelomics) and soil boring methods, the construction of underground root observation stations and sophisticated computer-assisted root imaging. Here, we summarize root architectural traits relevant to crop productivity, survey root phenotyping strategies and describe their advantages, limitations and practical value for crop and forage breeding programs.

Branching patterns of root systems: quantitative analysis of the diversity among dicotyledonous species

BACKGROUND AND AIMS

Root branching, and in particular acropetal branching, is a common and important developmental process for increasing the number of growing tips and defining the distribution of their meristem size. This study presents a new method for characterizing the results of this process in natura from scanned images of young, branched parts of excavated roots. The method involves the direct measurement or calculation of seven different traits.

METHODS

Young plants of 45 species of dicots were sampled from fields and gardens with uniform soils. Roots were separated, scanned and then measured using ImageJ software to determine seven traits related to root diameter and interbranch distance.

RESULTS

The traits exhibited large interspecific variations, and covariations reflecting trade-offs. For example, at the interspecies level, the spacing of lateral roots (interbranch distance along the parent root) was strongly correlated to the diameter of the finest roots found in the species, and showed a continuum between two opposite strategies: making dense and fine lateral roots, or thick and well-spaced laterals.

CONCLUSIONS

A simple method is presented for classification of branching patterns in roots that allows relatively quick sampling and measurements to be undertaken. The feasibilty of the method is demonstrated for dicotyledonous species and it has the potential to be developed more broadly for other species and a wider range of enivironmental conditions.

An integrated method for quantifying root architecture of field-grown maize

BACKGROUND AND AIMS

A number of techniques have recently been developed for studying the root system architecture (RSA) of seedlings grown in various media. In contrast, methods for sampling and analysis of the RSA of field-grown plants, particularly for details of the lateral root components, are generally inadequate.

METHODS

An integrated methodology was developed that includes a custom-made root-core sampling system for extracting intact root systems of individual maize plants, a combination of proprietary software and a novel program used for collecting individual RSA information, and software for visualizing the measured individual nodal root architecture.

KEY RESULTS

Example experiments show that large root cores can be sampled, and topological and geometrical structure of field-grown maize root systems can be quantified and reconstructed using this method. Second- and higher order laterals are found to contribute substantially to total root number and length. The length of laterals of distinct orders varies significantly. Abundant higher order laterals can arise from a single first-order lateral, and they concentrate in the proximal axile branching zone.

CONCLUSIONS

The new method allows more meaningful sampling than conventional methods because of its easily opened, wide corer and sampling machinery, and effective analysis of RSA using the software. This provides a novel technique for quantifying RSA of field-grown maize and also provides a unique evaluation of the contribution of lateral roots. The method also offers valuable potential for parameterization of root architectural models.

A scanner system for high-resolution quantification of variation in root growth dynamics of Brassica rapa genotypes

The potential exists to breed for root system architectures that optimize resource acquisition. However, this requires the ability to screen root system development quantitatively, with high resolution, in as natural an environment as possible, with high throughput. This paper describes the construction of a low-cost, high-resolution root phenotyping platform, requiring no sophisticated equipment and adaptable to most laboratory and glasshouse environments, and its application to quantify environmental and temporal variation in root traits between genotypes of Brassica rapa L. Plants were supplied with a complete nutrient solution through the wick of a germination paper. Images of root systems were acquired without manual intervention, over extended periods, using multiple scanners controlled by customized software. Mixed-effects models were used to describe the sources of variation in root traits contributing to root system architecture estimated from digital images. It was calculated that between one and 43 replicates would be required to detect a significant difference (95% CI 50% difference between traits). Broad-sense heritability was highest for shoot biomass traits (>0.60), intermediate (0.25-0.60) for the length and diameter of primary roots and lateral root branching density on the primary root, and lower (<0.25) for other root traits. Models demonstrate that root traits show temporal variations of various types. The phenotyping platform described here can be used to quantify environmental and temporal variation in traits contributing to root system architecture in B. rapa and can be extended to screen the large populations required for breeding for efficient resource acquisition.

Stochastic noise reduction upon complexification: positively correlated birth-death type systems

Cell systems consist of a huge number of various molecules that display specific patterns of interactions, which have a determining influence on the cell׳s functioning. In general, such complexity is seen to increase with the complexity of the organism, with a concomitant increase of the accuracy and specificity of the cellular processes. The question thus arises how the complexification of systems - modeled here by simple interacting birth-death type processes - can lead to a reduction of the noise - described by the variance of the number of molecules. To gain understanding of this issue, we investigated the difference between a single system containing molecules that are produced and degraded, and the same system - with the same average number of molecules - connected to a buffer. We modeled these systems using Itō stochastic differential equations in discrete time, as they allow straightforward analytical developments. In general, when the molecules in the system and the buffer are positively correlated, the variance on the number of molecules in the system is found to decrease compared to the equivalent system without a buffer. Only buffers that are too noisy themselves tend to increase the noise in the main system. We tested this result on two model cases, in which the system and the buffer contain proteins in their active and inactive state, or protein monomers and homodimers. We found that in the second test case, where the interconversion terms are non-linear in the number of molecules, the noise reduction is much more pronounced; it reaches up to 20% reduction of the Fano factor with the parameter values tested in numerical simulations on an unperturbed birth-death model. We extended our analysis to two arbitrary interconnected systems, and found that the sum of the noise levels in the two systems generally decreases upon interconnection if the molecules they contain are positively correlated.

Dynamic root growth and architecture responses to limiting nutrient availability: linking physiological models and experimentation

In recent years the study of root phenotypic plasticity in response to sub-optimal environmental factors and the genetic control of these responses have received renewed attention. As a path to increased productivity, in particular for low fertility soils, several applied research projects worldwide target the improvement of crop root traits both in plant breeding and biotechnology contexts. To assist these tasks and address the challenge of optimizing root growth and architecture for enhanced mineral resource use, the development of realistic simulation models is of great importance. We review this research field from a modeling perspective focusing particularly on nutrient acquisition strategies for crop production on low nitrogen and low phosphorous soils. Soil heterogeneity and the dynamics of nutrient availability in the soil pose a challenging environment in which plants have to forage efficiently for nutrients in order to maintain their internal nutrient homeostasis throughout their life cycle. Mathematical models assist in understanding plant growth strategies and associated root phenes that have potential to be tested and introduced in physiological breeding programs. At the same time, we stress that it is necessary to carefully consider model assumptions and development from a whole plant-resource allocation perspective and to introduce or refine modules simulating explicitly root growth and architecture dynamics through ontogeny with reference to key factors that constrain root growth. In this view it is important to understand negative feedbacks such as plant-plant competition. We conclude by briefly touching on available and developing technologies for quantitative root phenotyping from lab to field, from quantification of partial root profiles in the field to 3D reconstruction of whole root systems. Finally, we discuss how these approaches can and should be tightly linked to modeling to explore the root phenome.

Molecular mechanisms of robustness in plants

Robustness, the ability of organisms to buffer phenotypes against perturbations, has drawn renewed interest among developmental biologists and geneticists. A growing body of research supports an important role of robustness in the genotype to phenotype translation, with far-reaching implications for evolutionary processes and disease susceptibility. Similar to animals and fungi, plant robustness is a function of genetic network architecture. Most perturbations are buffered; however, perturbation of network hubs destabilizes many traits. Here, we review recent advances in identifying molecular robustness mechanisms in plants that have been enabled by a combination of classical genetics and population genetics with genome-scale data.

Phenotyping for drought tolerance of crops in the genomics era

Improving crops yield under water-limited conditions is the most daunting challenge faced by breeders. To this end, accurate, relevant phenotyping plays an increasingly pivotal role for the selection of drought-resilient genotypes and, more in general, for a meaningful dissection of the quantitative genetic landscape that underscores the adaptive response of crops to drought. A major and universally recognized obstacle to a more effective translation of the results produced by drought-related studies into improved cultivars is the difficulty in properly phenotyping in a high-throughput fashion in order to identify the quantitative trait loci that govern yield and related traits across different water regimes. This review provides basic principles and a broad set of references useful for the management of phenotyping practices for the study and genetic dissection of drought tolerance and, ultimately, for the release of drought-tolerant cultivars.

Genetic, environmental and stochastic factors in monozygotic twin discordance with a focus on epigenetic differences

Genetic-epidemiological studies on monozygotic (MZ) twins have been used for decades to tease out the relative contributions of genes and the environment to a trait. Phenotypic discordance in MZ twins has traditionally been ascribed to non-shared environmental factors acting after birth, however recent data indicate that this explanation is far too simple. In this paper, we review other reasons for discordance, including differences in the in utero environment, genetic mosaicism, and stochastic factors, focusing particularly on epigenetic discordance. Epigenetic differences are gaining increasing recognition. Although it is clear that in specific cases epigenetic alterations provide a causal factor in disease etiology, the overall significance of epigenetics in twin discordance remains unclear. It is also challenging to determine the causality and relative contributions of environmental, genetic, and stochastic factors to epigenetic variability. Epigenomic profiling studies have recently shed more light on the dynamics of temporal methylation change and methylome heritability, yet have not given a definite answer regarding their relevance to disease, because of limitations in establishing causality. Here, we explore the subject of epigenetics as another component in human phenotypic variability and its links to disease focusing particularly on evidence from MZ twin studies.

The Behavioral Ecology of Nutrient Foraging by Plants

Foraging for resources influences ecological interactions among individuals and species, regardless of taxonomic affiliation. Here we review studies of nutrient foraging in plants, with an emphasis on how nutritious and non-nutritious cues in the soil alter behavioral decisions and patterns of root placement. Three patterns emerge: (a) Plants alter root placement in response to many diverse cues; (b) species respond differently to these cues; and (c) there are nonadditive responses to multiple cues, indicating that plants exhibit complex multidimensional root foraging strategies. We suggest that this complexity calls for novel approaches to understanding nutrient foraging by plants. Resource selection functions are commonly used by animal behaviorists and may be useful to describe plant foraging strategies. Understanding such approaches may allow researchers to link individual behavior to population and community dynamics.

Novel temporal, fine-scale and growth variation phenotypes in roots of adult-stage maize (Zea mays L.) in response to low nitrogen stress

There is interest in discovering root traits associated with acclimation to nutrient stress. Large root systems, such as in adult maize, have proven difficult to be phenotyped comprehensively and over time, causing target traits to be missed. These challenges were overcome here using aeroponics, a system where roots grow in the air misted with a nutrient solution. Applying an agriculturally relevant degree of low nitrogen (LN) stress, 30-day-old plants responded by increasing lengths of individual crown roots (CRs) by 63%, compensated by a 40% decline in CR number. LN increased the CR elongation rate rather than lengthening the duration of CR growth. Only younger CR were significantly responsive to LN stress, a novel finding. LN shifted the root system architectural balance, increasing the lateral root (LR)-to-CR ratio, adding ∼70 m to LR length. LN caused a dramatic increase in second-order LR density, not previously reported in adult maize. Despite the near-uniform aeroponics environment, LN induced increased variation in the relative lengths of opposing LR pairs. Large-scale analysis of root hairs (RHs) showed that LN decreased RH length and density. Time-course experiments suggested the RH responses may be indirect consequences of decreased biomass/demand under LN. These results identify novel root traits for genetic dissection.

Links between root developmental traits and foraging performance

We designed a simple dynamic and stochastic architectural model with six parameters to link the foraging performance of root systems to their developmental processes. Soil foraging was quantified by the volume enveloping the roots until a given uptake distance. Many simulated architectures were obtained by combining four different values for each parameter. The rate of soil colonization was mainly defined by individual root elongation rates and interbranch distances. Less intuitively, we showed that differentiation of elongation rates among the roots increased this colonization rate. Uptake efficiency--the ratio of the actual colonized volume to the volume of a unique cylinder with the same length and a radius corresponding to the uptake distance--declined with root system size. Nevertheless, large variations in efficiency existed among root systems for a given size, typically in a 4- to 10-fold range. Therefore, the 'efficiency gain' was defined as the deviation from the average trend in efficiency versus size. Between-root differentiation in elongation rates increased this gain. The level of hierarchy between mother and lateral roots, as well as the variation of elongation rates among lateral roots, was also shown to contribute to this optimization. Several parameter combinations could lead to similar efficiency gains.