Anjali S. Iyer-Pascuzzi

Imaging and Analysis Platform for Automatic Phenotyping and Trait Ranking of Plant Root Systems

The ability to nondestructively image and automatically phenotype complex root systems, like those of rice (Oryza sativa), is fundamental to identifying genes underlying root system architecture (RSA). Although root systems are central to plant fitness, identifying genes responsible for RSA remains an underexplored opportunity for crop improvement. Here we describe a nondestructive imaging and analysis system for automated phenotyping and trait ranking of RSA. Using this system, we image rice roots from 12 genotypes. We automatically estimate RSA traits previously identified as important to plant function. In addition, we expand the suite of features examined for RSA to include traits that more comprehensively describe monocot RSA but that are difficult to measure with traditional methods. Using 16 automatically acquired phenotypic traits for 2,297 images from 118 individuals, we observe (1) wide variation in phenotypes among the genotypes surveyed; and (2) greater intergenotype variance of RSA features than variance within a genotype. RSA trait values are integrated into a computational pipeline that utilizes supervised learning methods to determine which traits best separate two genotypes, and then ranks the traits according to their contribution to each pairwise comparison. This trait-ranking step identifies candidate traits for subsequent quantitative trait loci analysis and demonstrates that depth and average radius are key contributors to differences in rice RSA within our set of genotypes. Our results suggest a strong genetic component underlying rice RSA. This work enables the automatic phenotyping of RSA of individuals within mapping populations, providing an integrative framework for quantitative trait loci analysis of RSA.

3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture

Significance Improving the efficiency of root systems should result in crop varieties with better yields, requiring fewer chemical inputs, and that can grow in harsher environments. Little is known about the genetic factors that condition root growth because of roots’ complex shapes, the opacity of soil, and environmental influences. We designed a 3D root imaging and analysis platform and used it to identify regions of the rice genome that control several different aspects of root system growth. The results of this study should inform future efforts to enhance root architecture for agricultural benefit. Identification of genes that control root system architecture in crop plants requires innovations that enable high-throughput and accurate measurements of root system architecture through time. We demonstrate the ability of a semiautomated 3D in vivo imaging and digital phenotyping pipeline to interrogate the quantitative genetic basis of root system growth in a rice biparental mapping population, Bala × Azucena. We phenotyped >1,400 3D root models and >57,000 2D images for a suite of 25 traits that quantified the distribution, shape, extent of exploration, and the intrinsic size of root networks at days 12, 14, and 16 of growth in a gellan gum medium. From these data we identified 89 quantitative trait loci, some of which correspond to those found previously in soil-grown plants, and provide evidence for genetic tradeoffs in root growth allocations, such as between the extent and thoroughness of exploration. We also developed a multivariate method for generating and mapping central root architecture phenotypes and used it to identify five major quantitative trait loci (r2 = 24–37%), two of which were not identified by our univariate analysis. Our imaging and analytical platform provides a means to identify genes with high potential for improving root traits and agronomic qualities of crops.

GiA Roots: software for the high throughput analysis of plant root system architecture

BackgroundCharacterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks.ResultsWe have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user.ConclusionsWe demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa. We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis.

Cell Identity Regulators Link Development and Stress Responses in the Arabidopsis Root

Stress responses in plants are tightly coordinated with developmental processes, but interaction of these pathways is poorly understood. We used genome-wide assays at high spatiotemporal resolution to understand the processes that link development and stress in the Arabidopsis root. Our meta-analysis finds little evidence for a universal stress response. However, common stress responses appear to exist with many showing cell type specificity. Common stress responses may be mediated by cell identity regulators because mutations in these genes resulted in altered responses to stress. Evidence for a direct role for cell identity regulators came from genome-wide binding profiling of the key regulator SCARECROW, which showed binding to regulatory regions of stress-responsive genes. Coexpression in response to stress was used to identify genes involved in specific developmental processes. These results reveal surprising linkages between stress and development at cellular resolution, and show the power of multiple genome-wide data sets to elucidate biological processes.

Genotypic recognition and spatial responses by rice roots

Root system growth and development is highly plastic and is influenced by the surrounding environment. Roots frequently grow in heterogeneous environments that include interactions from neighboring plants and physical impediments in the rhizosphere. To investigate how planting density and physical objects affect root system growth, we grew rice in a transparent gel system in close proximity with another plant or a physical object. Root systems were imaged and reconstructed in three dimensions. Root–root interaction strength was calculated using quantitative metrics that characterize the extent to which the reconstructed root systems overlap each other. Surprisingly, we found the overlap of root systems of the same genotype was significantly higher than that of root systems of different genotypes. Root systems of the same genotype tended to grow toward each other but those of different genotypes appeared to avoid each other. Shoot separation experiments excluded the possibility of aerial interactions, suggesting root communication. Staggered plantings indicated that interactions likely occur at root tips in close proximity. Recognition of obstacles also occurred through root tips, but through physical contact in a size-dependent manner. These results indicate that root systems use two different forms of communication to recognize objects and alter root architecture: root-root recognition, possibly mediated through root exudates, and root-object recognition mediated by physical contact at the root tips. This finding suggests that root tips act as local sensors that integrate rhizosphere information into global root architectural changes.

Recessive Resistance Genes and the Oryza sativa-Xanthomonas oryzae pv. oryzae Pathosystem

Though recessive resistance is well-studied in viral systems, little is understood regarding the phenomenon in plant-bacterial interactions. The Oryza sativa-Xanthomonas oryzae pv. orzyae pathosystem provides an excellent opportunity to examine recessive resistance in plant-bacterial interactions, in which nine of 30 documented resistance (R) genes are recessively inherited. Infestations of X. oryzae pv. oryzae, the causal agent of bacterial blight, result in significant crop loss and damage throughout South and Southeast Asia. Two recently cloned novel recessive R genes, xa5 and xa13, have yielded insights to this system. Like their viral counterparts, these bacterial recessive R gene products do not conform to the five commonly described classes of R proteins. New findings suggest that such genes may more aptly be viewed as mutations in dominant susceptibility alleles and may also function in a gene-for-gene manner. In this review, we discuss recent accomplishments in the understanding of recessively inherited R genes in the rice-bacterial blight pathosystem and suggest a new model for the function of recessive resistance in plant-bacterial interactions.

Functional markers for xa5-mediated resistance in rice (Oryza sativa, L.)

The recent cloning of several agronomically important genes has facilitated the development of functional markers. These markers reside within the target genes themselves and can be used with great reliability and efficiency to identify favorable alleles in a breeding program. Bacterial blight (BB) is a severe rice disease throughout the world that is controlled primarily through use of resistant cultivars. xa5 is a race-specific, recessive gene mediating resistance to BB. It is widely used in rice breeding programs throughout the tropics. Due to its recessive nature, phenotypic selection for xa5-mediated resistance is both slow and costly. Previously, marker assisted selection (MAS) for this resistance gene was not efficient because it involved markers that were only indirectly linked to xa5 and ran the risk of being separated from the trait by recombination. Recently, the cloning of the gene underlying this trait made it possible to develop functional markers. Here we present a set of CAPS markers for easy, quick and direct identification of cultivars or progeny carrying xa5-mediated resistance and provide evidence that these markers are 100% predictive of the presence of the xa5 allele. These markers are expected to enhance the reliability and cost-effectiveness of MAS for xa5-mediated resistance.

Functional genomics of root growth and development in Arabidopsis.

Roots are vital for the uptake of water and nutrients, and for anchorage in the soil. They are highly plastic, able to adapt developmentally and physiologically to changing environmental conditions. Understanding the molecular mechanisms behind this growth and development requires knowledge of root transcriptomics, proteomics, and metabolomics. Genomics approaches, including the recent publication of a root expression map, root proteome, and environment-specific root expression studies, are uncovering complex transcriptional and post-transcriptional networks underlying root development. The challenge is in further capitalizing on the information in these datasets to understand the fundamental principles of root growth and development. In this review, we highlight progress researchers have made toward this goal.

Transcriptional networks in root cell fate specification

Cell fate in the Arabidopsis root is determined by positional information mediated by plant hormones and interpreted by transcriptional networks. In this review, we summarize recent advances in our understanding of the regulatory networks that control cell fate within the root meristem, and in the interplay of these networks with phytohormones. Recent work describing the importance of chromatin organization in tissue patterning is also highlighted. A new, high resolution root expression map detailing the transciptome of nearly all cell types in the Arabidopsis root across developmental timepoints will provide a framework for understanding these networks.

Fluorescence-Activated Cell Sorting in Plant Developmental Biology

Understanding the development of an organ requires knowledge of gene, protein, and metabolite expression in the specific cell types and tissues that comprise the organ. Fluorescence-activated cell sorting (FACS) is an efficient method to isolate specific cells of interest, and the information gained from this approach has been integral to plant developmental biology. The Benfey lab has developed this method to examine gene expression profiles of different cell types in the Arabidopsis root under both standard and stress conditions. In addition to gene expression, downstream applications of FACS include proteomic and metabolite analysis. This is a powerful method to examine biological functions of specific cell types and tissues with a systems biology approach.