OpenSimRoot: widening the scope and application of root architectural models

Research Conducted and Rationale: OpenSimRoot is an open sourced, functional- structural plant model and mathematical description of root growth and function. We describe OpenSimRoot and its functionality to broaden the benefits of root modeling to the plant science community. Description: OpenSimRoot is an extended version of SimRoot, established to simulate root system architecture, nutrient acquisition, and plant growth. OpenSimRoot has a plugin, modular infrastructure, coupling single plant and crop stands to soil nutrient, and water transport models. It estimates the value of root traits for water and nutrient acquisition in environments and plant species. Key results and unique features: The flexible OpenSimRoot design allows upscaling from root anatomy to plant community to estimate 1) resource costs of developmental and anatomical traits, 2) trait synergisms, 3) (inter species) root competition. OpenSimRoot can model 3D images from MRI and X-ray CT of roots in soil. New modules include: 1) soil water dependent water uptake and xylem flow, 2) tiller formation, 3) evapotranspiration, 4) simultaneous simulation of mobile solutes, 5) mesh refinement, and 6) root growth plasticity. Conclusion: OpenSimRoot integrates plant phenotypic data with environmental metadata to support experimental designs and gain mechanistic understanding at system scales

Accelerating root system phenotyping of seedlings through a computer-assisted processing pipeline

Background: There are numerous systems and techniques to measure the growth of plant roots. However, phenotyping large numbers of plant roots for breeding and genetic analyses remains challenging. One major difficulty is to achieve high throughput and resolution at a reasonable cost per plant sample. Here we describe a cost-effective root phenotyping pipeline, on which we perform time and accuracy benchmarking to identify bottlenecks in such pipelines and strategies for their acceleration. Results: Our root phenotyping pipeline was assembled with custom software and low cost material and equipment. Results show that sample preparation and handling of samples during screening are the most time consuming task in root phenotyping. Algorithms can be used to speed up the extraction of root traits from image data, but when applied to large numbers of images, there is a trade-off between time of processing the data and errors contained in the database. Conclusions: Scaling-up root phenotyping to large numbers of genotypes will require not only automation of sample preparation and sample handling, but also efficient algorithms for error detection for more reliable replacement of manual interventions

Transcription factor bHLH121 regulates root cortical aerenchyma formation in maize

Root anatomical phenotypes present a promising yet underexploited avenue to deliver major improvements in yield and climate resilience of crops by improving water and nutrient uptake. For instance, the formation of root cortical aerenchyma (RCA) significantly increases soil exploration and resource capture by reducing the metabolic costs of root tissue. A key bottleneck in studying such phenotypes has been the lack of robust high-throughput anatomical phenotyping platforms. We exploited a phenotyping approach based on laser ablation tomography, termed Anatomics, to quantify variation in RCA formation of 436 diverse maize lines in the field. Results revealed a significant and heritable variation for RCA formation. Genome-wide association studies identified a single-nucleotide polymorphism mapping to a root cortex-expressed gene-encoding transcription factor bHLH121. Functional studies identified that the bHLH121 Mu transposon mutant line and CRISPR/Cas9 loss-of-function mutant line showed reduced RCA formation, whereas an overexpression line exhibited significantly greater RCA formation when compared to the wild-type line. Characterization of these lines under suboptimal water and nitrogen availability in multiple soil environments revealed that bHLH121 is required for RCA formation developmentally as well as under studied abiotic stress. Overall functional validation of the bHLH121 gene’s importance in RCA formation provides a functional marker to select varieties with improved soil exploration and thus yield under suboptimal conditions

Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics

Plant root systems are key drivers of plant function and yield. They are also under-explored targets to meet global food and energy demands. Many new technologies have been developed to characterize crop root system architecture (CRSA). These technologies have the potential to accelerate the progress in understanding the genetic control and environmental response of CRSA. Putting this potential into practice requires new methods and algorithms to analyze CRSA in digital images. Most prior approaches have solely focused on the estimation of root traits from images, yet no integrated platform exists that allows easy and intuitive access to trait extraction and analysis methods from images combined with storage solutions linked to metadata. Automated high-throughput phenotyping methods are increasingly used in laboratory-based efforts to link plant genotype with phenotype, whereas similar field-based studies remain predominantly manual low-throughput.DescriptionHere, we present an open-source phenomics platform “DIRT”, as a means to integrate scalable supercomputing architectures into field experiments and analysis pipelines. DIRT is an online platform that enables researchers to store images of plant roots, measure dicot and monocot root traits under field conditions, and share data and results within collaborative teams and the broader community. The DIRT platform seamlessly connects end-users with large-scale compute “commons” enabling the estimation and analysis of root phenotypes from field experiments of unprecedented size.ConclusionDIRT is an automated high-throughput computing and collaboration platform for field based crop root phenomics. The platform is accessible at http://​dirt.​iplantcollaborat​ive.​org/​ and hosted on the iPlant cyber-infrastructure using high-throughput grid computing resources of the Texas Advanced Computing Center (TACC). DIRT is a high volume central depository and high-throughput RSA trait computation platform for plant scientists working on crop roots. It enables scientists to store, manage and share crop root images with metadata and compute RSA traits from thousands of images in parallel. It makes high-throughput RSA trait computation available to the community with just a few button clicks. As such it enables plant scientists to spend more time on science rather than on technology. All stored and computed data is easily accessible to the public and broader scientific community. We hope that easy data accessibility will attract new tool developers and spur creative data usage that may even be applied to other fields of science

Opportunities and challenges in the subsoil: pathways to deeper rooted crops

Greater exploitation of subsoil resources by annual crops would afford multiple benefits, including greater water and N acquisition in most agroecosystems, and greater sequestration of atmospheric C. Constraints to root growth in the subsoil include soil acidity (an edaphic stress complex consisting of toxic levels of Al, inadequate levels of P and Ca, and often toxic levels of Mn), soil compaction, hypoxia, and suboptimal temperature. Multiple root phenes under genetic control are associated with adaptation to these constraints, opening up the possibility of breeding annual crops with root traits improving subsoil exploration. Adaptation to Al toxicity, hypoxia, and P deficiency are intensively researched, adaptation to soil hardness and suboptimal temperature less so, and adaptations to Ca deficiency and Mn toxicity are poorly understood. The utility of specific phene states may vary among soil taxa and management scenarios, interactions which in general are poorly understood. These traits and issues merit research because of their potential value in developing more productive, sustainable, benign, and resilient agricultural systems