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

Regulation of root growth and elongation in wheat

Currently, the control of rhizosphere selection on farms has been applied to achieve enhancements in phenotype, extending from improvements in single root characteristics to the dynamic nature of entire crop systems. Several specific signals, regulatory elements, and mechanisms that regulate the initiation, morphogenesis, and growth of new lateral or adventitious root species have been identified, but much more work remains. Today, phenotyping technology drives the development of root traits. Available models for simulation can support all phenotyping decisions (root trait improvement). The detection and use of markers for quantitative trait loci (QTLs) are effective for enhancing selection efficiency and increasing reproductive genetic gains. Furthermore, QTLs may help wheat breeders select the appropriate roots for efficient nutrient acquisition. Single-nucleotide polymorphisms (SNPs) or alignment of sequences can only be helpful when they are associated with phenotypic variation for root development and elongation. Here, we focus on major root development processes and detail important new insights recently generated regarding the wheat genome. The first part of this review paper discusses the root morphology, apical meristem, transcriptional control, auxin distribution, phenotyping of the root system, and simulation models. In the second part, the molecular genetics of the wheat root system, SNPs, TFs, and QTLs related to root development as well as genome editing (GE) techniques for the improvement of root traits in wheat are discussed. Finally, we address the effect of omics strategies on root biomass production and summarize existing knowledge of the main molecular mechanisms involved in wheat root development and elongation.

Phototropin2 3'UTR overlaps with the AT5G58150 gene encoding an inactive RLK kinase

BACKGROUND

This study examines the biological implications of an overlap between two sequences in the Arabidopsis genome, the 3'UTR of the PHOT2 gene and a putative AT5G58150 gene, encoded on the complementary strand. AT5G58150 is a probably inactive protein kinase that belongs to the transmembrane, leucine-rich repeat receptor-like kinase family. Phot2 is a membrane-bound UV/blue light photoreceptor kinase. Thus, both proteins share their cellular localization, on top of the proximity of their loci.

RESULTS

The extent of the overlap between 3'UTR regions of AT5G58150 and PHOT2 was found to be 66 bp, using RACE PCR. Both the at5g58150 T-DNA SALK_093781C (with insertion in the promoter region) and 35S::AT5G58150-GFP lines overexpress the AT5G58150 gene. A detailed analysis did not reveal any substantial impact of PHOT2 or AT5G58150 on their mutual expression levels in different light and osmotic stress conditions. AT5G58150 is a plasma membrane protein, with no apparent kinase activity, as tested on several potential substrates. It appears not to form homodimers and it does not interact with PHOT2. Lines that overexpress AT5G58150 exhibit a greater reduction in lateral root density due to salt and osmotic stress than wild-type plants, which suggests that AT5G58150 may participate in root elongation and formation of lateral roots. In line with this, mass spectrometry analysis identified proteins with ATPase activity, which are involved in proton transport and cell elongation, as putative interactors of AT5G58150. Membrane kinases, including other members of the LRR RLK family and BSK kinases (positive regulators of brassinosteroid signalling), can also act as partners for AT5G58150.

CONCLUSIONS

AT5G58150 is a membrane protein that does not exhibit measurable kinase activity, but is involved in signalling through interactions with other proteins. Based on the interactome and root architecture analysis, AT5G58150 may be involved in plant response to salt and osmotic stress and the formation of roots in Arabidopsis.

Seedling and field assessment of wheat (Triticum aestivum L.) dwarfing genes and their influence on root traits in multiple genetic backgrounds

Deployment of the Rht-B1b and Rht-D1b dwarfing genes helped facilitate the Green Revolution to increase wheat yields globally. Much is known of the influence of these genes on plant height and agronomic performance, but not of their effects on root architecture. We assessed 29 near-isogenic lines (NILs) representing 11 Green Revolution and alternative dwarfing genes across multiple genetic backgrounds for root architecture characteristics in controlled and field environments. Genetic background did not influence plant height, but had a small and significant (P<0.05) effect on root architecture. All dwarfing gene NILs were significantly (P<0.01) shorter compared with tall controls. The Green Revolution Rht-B1b and Rht-D1b sometimes had longer seedling roots but were not different from their respective tall controls for root depth in the field. The Rht8, Rht12, and Rht18 dwarfing gene NILs produced long seminal roots in seedling pouches, and a greater maximum rooting depth (MRD) and root penetration rate (RPR) in the field. Genotypic increases in MRD and RPR were strongly correlated with increased harvest index and grain yield, particularly in dry environments. Careful root phenotyping highlights the potential of novel dwarfing genes for wheat genetic improvement under water-limited conditions.

Crop Root Responses to Drought Stress: Molecular Mechanisms, Nutrient Regulations, and Interactions with Microorganisms in the Rhizosphere

Roots play important roles in determining crop development under drought. Under such conditions, the molecular mechanisms underlying key responses and interactions with the rhizosphere in crop roots remain limited compared with model species such as Arabidopsis. This article reviews the molecular mechanisms of the morphological, physiological, and metabolic responses to drought stress in typical crop roots, along with the regulation of soil nutrients and microorganisms to these responses. Firstly, we summarize how root growth and architecture are regulated by essential genes and metabolic processes under water-deficit conditions. Secondly, the functions of the fundamental plant hormone, abscisic acid, on regulating crop root growth under drought are highlighted. Moreover, we discuss how the responses of crop roots to altered water status are impacted by nutrients, and vice versa. Finally, this article explores current knowledge of the feedback between plant and soil microbial responses to drought and the manipulation of rhizosphere microbes for improving the resilience of crop production to water stress. Through these insights, we conclude that to gain a more comprehensive understanding of drought adaption mechanisms in crop roots, future studies should have a network view, linking key responses of roots with environmental factors.

Semifield root phenotyping: Root traits for deep nitrate uptake

Deep rooting winter wheat genotypes can reduce nitrate leaching losses and increase N uptake. We aimed to investigate which deep root traits are correlated to deep N uptake and to estimate genetic variation in root traits and deep ¹⁵ N tracer uptake. In 2 years, winter wheat genotypes were grown in RadiMax, a semifield root-screening facility. Minirhizotron root imaging was performed three times during the main growing season. At anthesis, ¹⁵ N was injected via subsurface drip irrigation at 1.8 m depth. Mature ears from above the injection area were analysed for ¹⁵ N content. From minirhizotron image-based root length data, 82 traits were constructed, describing root depth, density, distribution and growth aspects. Their ability to predict ¹⁵ N uptake was analysed with the least absolute shrinkage and selection operator (LASSO) regression. Root traits predicted 24% and 14% of tracer uptake variation in 2 years. Both root traits and genotype showed significant effects on tracer uptake. In 2018, genotype and the three LASSO-selected root traits predicted 41% of the variation in tracer uptake, in 2019 genotype and one root trait predicted 48%. In both years, one root trait significantly mediated the genotype effect on tracer uptake. Deep root traits from minirhizotron images can predict deep N uptake, indicating the potential to breed deep-N-uptake-genotypes.

Identifying Developmental Patterns in Structured Plant Phenotyping Data

Technological breakthroughs concerning both sensors and robotized plant phenotyping platforms have totally renewed the plant phenotyping paradigm in the last two decades. This has impacted both the nature and the throughput of data with the availability of data at high-throughput from the tissular to the whole plant scale. Sensor outputs often take the form of 2D or 3D images or time series of such images from which traits are extracted while organ shapes, shoot or root system architectures can be deduced. Despite this change of paradigm, many phenotyping studies often ignore the structure of the plant and therefore loose the information conveyed by the temporal and spatial patterns emerging from this structure. The developmental patterns of plants often take the form of succession of well-differentiated phases, stages or zones depending on the temporal, spatial or topological indexing of data. This entails the use of hierarchical statistical models for their identification.The objective here is to show potential approaches for analyzing structured plant phenotyping data using state-of-the-art methods combining probabilistic modeling, statistical inference and pattern recognition. This approach is illustrated using five different examples at various scales that combine temporal and topological index parameters, and development and growth variables obtained using prospective or retrospective measurements.

Root System Architecture Plasticity of Bread Wheat in Response to Oxidative Burst under Extended Osmotic Stress

There is a demand for an increase in crop production because of the growing population, but water shortage hinders the expansion of wheat cultivation, one of the most important crops worldwide. Polyethylene glycol (PEG) was used to mimic drought stress due to its high osmotic potentials generated in plants subjected to it. This study aimed to determine the root system architecture (RSA) plasticity of eight bread wheat genotypes under osmotic stress in relation to the oxidative status and mitochondrial membrane potential of their root tips. Osmotic stress application resulted in differences in the RSA between the eight genotypes, where genotypes were divided into adapted genotypes that have non-significant decreased values in lateral roots number (LRN) and total root length (TRL), while non-adapted genotypes have a significant decrease in LRN, TRL, root volume (RV), and root surface area (SA). Accumulation of intracellular ROS formation in root tips and elongation zone was observed in the non-adapted genotypes due to PEG-induced oxidative stress. Mitochondrial membrane potential (∆Ψm) was measured for both stress and non-stress treatments in the eight genotypes as a biomarker for programmed cell death as a result of induced osmotic stress, in correlation with RSA traits. PEG treatment increased scavenging capacity of the genotypes from 1.4-fold in the sensitive genotype Gemmiza 7 to 14.3-fold in the adapted genotype Sakha 94. The adapted genotypes showed greater root trait values, ∆Ψm plasticity correlated with high scavenging capacity, and less ROS accumulation in the root tissue, while the non-adapted genotypes showed little scavenging capacity in both treatments, accompanied by mitochondrial membrane permeability, suggesting mitochondrial dysfunction as a result of oxidative stress.

Assessment of root phenotypes in mungbean mini-core collection (MMC) from the World Vegetable Center (AVRDC) Taiwan

Mungbean (Vigna radiata L.) is an important food grain legume, but its production capacity is threatened by global warming, which can intensify plant stress and limit future production. Identifying new variation of key root traits in mungbean will provide the basis for breeding lines with effective root characteristics for improved water uptake to mitigate heat and drought stress. The AVRDC mungbean mini core collection consisting of 296 genotypes was screened under modified semi-hydroponic screening conditions to determine the variation for fourteen root-related traits. The AVRDC mungbean mini core collection displayed wide variations for the primary root length, total surface area, and total root length, and based on agglomerative hierarchical clustering eight homogeneous groups displaying different root traits could be identified. Germplasm with potentially favorable root traits has been identified for further studies to identify the donor genotypes for breeding cultivars with enhanced adaptation to water-deficit stress and other stress conditions.

Phenotypic variability in bread wheat root systems at the early vegetative stage

BACKGROUND

Understanding root system morphology in bread wheat is critical for identifying root traits to breed cultivars with improved resource uptake and better adaptation to adverse environments. Variability in root morphological traits at early vegetative stages was examined among 184 bread wheat genotypes originating from 37 countries grown in a semi-hydroponic phenotyping system.

RESULTS

At the onset of tillering (Z2.1, 35 days after transplanting), plants had up to 42 cm in shoot height and 158 cm long in root depth. Phenotypic variation existed for both shoot and root traits, with a maximal 4.3-fold difference in total root length and 5-fold difference in root dry mass among the 184 genotypes. Of the 41 measured traits, 24 root traits and four shoot traits had larger coefficients of variation (CV ≥ 0.25). Strong positive correlations were identified for some key root traits (i.e., root mass, root length, and these parameters at different depths) and shoot traits (i.e., shoot mass and tiller number) (P ≤ 0.05). The selected 25 global traits (at whole-plant level) contributed to one of the five principal components (eigenvalues> 1) capturing 83.0% of the total variability across genotypes. Agglomerative hierarchical clustering analysis separated the 184 genotypes into four (at a rescaled distance of 15) or seven (at a rescaled distance of 10) major groups based on the same set of root traits. Strong relationships between performance traits (dry mass) with several functional traits such as specific root length, root length intensity and root tissue density suggest their linkage to plant growth and fitness strategies.

CONCLUSIONS

Large phenotypic variability in root system morphology in wheat genotypes was observed at the tillering stage using established semi-hydroponic phenotyping techniques. Phenotypic differences in and trait correlations among some interesting root traits may be considered for breeding wheat cultivars with efficient water acquisition and better adaptation to abiotic stress.

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.

Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities

Root systems determine the water and nutrients for photosynthesis and harvested products, underpinning agricultural productivity. We highlight 11 programs that integrated root traits into germplasm for breeding, relying on phenotyping. Progress was successful but slow. Today's phenotyping technologies will speed up root trait improvement. They combine multiple new alleles in germplasm for target environments, in parallel. Roots and shoots are detected simultaneously and nondestructively, seed to seed measures are automated, and field and laboratory technologies are increasingly linked. Available simulation models can aid all phenotyping decisions. This century will see a shift from single root traits to rhizosphere selections that can be managed dynamically on farms and a shift to phenotype-based improvement to accommodate the dynamic complexity of whole crop systems.

Genomic prediction of yield and root development in wheat under changing water availability

BACKGROUND

Deeper roots help plants take up available resources in deep soil ensuring better growth and higher yields under conditions of drought. A large-scale semi-field root phenotyping facility was developed to allow a water availability gradient and detect potential interaction of genotype by water availability gradient. Genotyped winter wheat lines were grown as rows in four beds of this facility, where indirect genetic effects from neighbors could be important to trait variation. The objective was to explore the possibility of genomic prediction for grain-related traits and deep root traits collected via images taken in a minirhizotron tube under each row of winter wheat measured.

RESULTS

The analysis comprised four grain-related traits: grain yield, thousand-kernel weight, protein concentration, and total nitrogen content measured on each half row that were harvested separately. Two root traits, total root length between 1.2 and 2 m depth and root length in four intervals on each tube were also analyzed. Two sets of models with or without the effects of neighbors from both sides of each row were applied. No interaction between genotypes and changing water availability were detected for any trait. Estimated genomic heritabilities ranged from 0.263 to 0.680 for grain-related traits and from 0.030 to 0.055 for root traits. The coefficients of genetic variation were similar for grain-related and root traits. The prediction accuracy of breeding values ranged from 0.440 to 0.598 for grain-related traits and from 0.264 to 0.334 for root traits. Including neighbor effects in the model generally increased the estimated genomic heritabilities and accuracy of predicted breeding values for grain yield and nitrogen content.

CONCLUSIONS

Similar relative amounts of additive genetic variance were found for both yield traits and root traits but no interaction between genotypes and water availability were detected. It is possible to obtain accurate genomic prediction of breeding values for grain-related traits and reasonably accurate predicted breeding values for deep root traits using records from the semi-field facility. Including neighbor effects increased the estimated additive genetic variance of grain-related traits and accuracy of predicting breeding values. High prediction accuracy can be obtained although heritability is low.

CRootBox: a structural-functional modelling framework for root systems

Background and Aims

Root architecture development determines the sites in soil where roots provide input of carbon and take up water and solutes. However, root architecture is difficult to determine experimentally when grown in opaque soil. Thus, root architecture models have been widely used and been further developed into functional-structural models that simulate the fate of water and solutes in the soil-root system. The root architecture model CRootBox presented here is a flexible framework to model root architecture and its interactions with static and dynamic soil environments.

Methods

CRootBox is a C++-based root architecture model with Python binding, so that CRootBox can be included via a shared library into any Python code. Output formats include VTP, DGF, RSML and a plain text file containing coordinates of root nodes. Furthermore, a database of published root architecture parameters was created. The capabilities of CRootBox for the unconfined growth of single root systems, as well as the different parameter sets, are highlighted in a freely available web application.

Key results

The capabilities of CRootBox are demonstrated through five different cases: (1) free growth of individual root systems; (2) growth of root systems in containers as a way to mimic experimental setups; (3) field-scale simulation; (4) root growth as affected by heterogeneous, static soil conditions; and (5) coupling CRootBox with code from the book Soil physics with Python to dynamically compute water flow in soil, root water uptake and water flow inside roots.

Conclusions

CRootBox is a fast and flexible functional-structural root model that is based on state-of-the-art computational science methods. Its aim is to facilitate modelling of root responses to environmental conditions as well as the impact of roots on soil. In the future, this approach will be extended to the above-ground part of the plant.