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Results for “root genomics”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Phenome‐to‐genome insights for evaluating root system architecture in field studies of maize

Abstract Understanding the genetic basis of root system architecture (RSA) in crops requires innovative approaches that enable both high‐throughput and precise phenotyping in field conditions. In this study, we evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field‐grown maize in three field experiments. We used forward and reverse genetic approaches to evaluate >1700 maize root crowns, including a diversity panel, a biparental mapping population, and maize mutant and wild‐type alleles at two known RSA genes,DEEPER ROOTING 1(DRO1) andRootless1(Rt1). We show the utility of increasing the dimensionality of traditional two‐dimensional (2D) techniques, referred to as the “2D multi‐view” method, to improve the capture of whole root system information for mapping genetic variation influencing RSA. Comparison of univariate and multivariate genome‐wide association study (GWAS) approaches revealed that multivariate traits were effective at dissecting complex RSA phenotypes and identifying pleiotropic quantitative trait loci (QTLs). Overall, three‐dimensional (3D) root models generated from X‐ray computed tomography and digital phenotyping captured a larger proportion of RSA trait variations compared to other methods of root phenotyping, as evidenced by both genome‐wide and single‐gene analyses. Among the individual root traits, root pulling force emerged as a highly heritable estimate of RSA that identified the largest number of shared QTLs with 3D phenotypes. Our study shows that integrating complementary phenotyping technologies helps to provide a more comprehensive understanding of the genetic architecture of RSA in field‐grown maize.

Genetics & Heredity↗
DOE OSTI2022

Divergence in the ABA gene regulatory network underlies differential growth control

The phytohormone abscisic acid (ABA) is a central regulator of acclimation to environmental stress; however, its contribution to differences in stress tolerance between species is unclear. To establish a comparative framework for understanding how stress hormone signalling pathways diverge across species, we studied the growth response of four Brassicaceae species to ABA treatment and generated transcriptomic and DNA affinity purification and sequencing datasets to construct a cross-species gene regulatory network (GRN) for ABA. In this work, comparison of genes bound directly by ABA-responsive element binding factors suggests that cis-factors are most important for determining the target loci represented in the ABA GRN of a particular species. Using this GRN, we reveal how rewiring of growth hormone subnetworks contributes to stark differences in the response to ABA in the extremophyte Schrenkiella parvula. Our study provides a model for understanding how divergence in gene regulation can lead to species-specific physiological outcomes in response to hormonal cues.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Genome-sequenced bacterial collection from sorghum aerial root mucilage

ABSTRACT A collection of 47 bacteria isolated from the mucilage of aerial roots of energy sorghum is available at the Great Lakes Bioenergy Research Center, Michigan State University, Michigan, USA. We enriched bacteria with putative plant-beneficial phenotypes and included information on phenotypic diversity, taxonomy, and whole genome sequences.

Mechan-Llontop, Marco E.↗
DOE OSTI2022

Root system architecture in cereals: progress, challenges and perspective

We report roots are essential multifunctional plant organs involved in water and nutrient uptake, metabolite storage, anchorage, mechanical support, and interaction with the soil environment. Understanding of this ‘hidden half’ provides potential for manipulation of root system architecture (RSA) traits to optimize resource use efficiency and grain yield in cereal crops. Unfortunately, root traits are highly neglected in breeding due to the challenges of phenotyping, but could have large rewards if the variability in RSA traits can be fully exploited. Until now, a plethora of genes have been characterized in detail for their potential role in improving RSA. The use of forward genetics approaches to find sequence variations in genes underpinning desirable RSA would be highly beneficial. Advances in computer vision applications have allowed image-based approaches for high-throughput phenotyping of RSA traits that can be used by any laboratory worldwide to make progress in understanding root function and dissection of the genetics. At the same time, the frontiers of root measurement include non-invasive methods like X-ray computer tomography and magnetic resonance imaging that facilitate new types of temporal studies. Root physiology and ecology are further supported by spatiotemporal root simulation modeling. The discovery of component traits providing improved resilience and yield advantage in target environments is a key necessity for mainstreaming root-based cereal breeding. The integrated use of pan-genome resources, now available in most cereals, coupled with new in-field phenotyping platforms has the potential for precise selection of superior genotypes with improved RSA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Identification of genetic and environmental factors influencing aerial root traits that support biological nitrogen fixation in sorghum

Abstract Plant breeding and genetics play a major role in the adaptation of plants to meet human needs. The current requirement to make agriculture more sustainable can be partly met by a greater reliance on biological nitrogen fixation by symbiotic diazotrophic microorganisms that provide crop plants with ammonium. Select accessions of the cereal crop sorghum (Sorghum bicolor (L.) Moench) form mucilage-producing aerial roots that harbor nitrogen-fixing bacteria. Breeding programs aimed at developing sorghum varieties that support diazotrophs will benefit from a detailed understanding of the genetic and environmental factors contributing to aerial root formation. A genome-wide association study of the sorghum minicore, a collection of 242 landraces, and 30 accessions from the sorghum association panel was conducted in Florida and Wisconsin and under 2 fertilizer treatments to identify loci associated with the number of nodes with aerial roots and aerial root diameter. Sequence variation in genes encoding transcription factors that control phytohormone signaling and root system architecture showed significant associations with these traits. In addition, the location had a significant effect on the phenotypes. Concurrently, we developed F2 populations from crosses between bioenergy sorghums and a landrace that produced extensive aerial roots to evaluate the mode of inheritance of the loci identified by the genome-wide association study. Furthermore, the mucilage collected from aerial roots contained polysaccharides rich in galactose, arabinose, and fucose, whose composition displayed minimal variation among 10 genotypes and 2 fertilizer treatments. These combined results support the development of sorghums with the ability to acquire nitrogen via biological nitrogen fixation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Viruses of Nitrogen-Fixing Mesorhizobium Bacteria in Globally Distributed Chickpea Root Nodules

Legume nodules are specialized environments on plant roots that are induced and dominated by nitrogen-fixing bacteria. Bacteriophages (phages) in these nodules could potentially provide top-down controls on the population size and, therefore, the function of nitrogen-fixing symbionts. Here we sought to characterize the diversity and biogeographical patterns of phages that infect nitrogen-fixing Mesorhizobium symbionts isolated from root nodules, leveraging 266 genomes of Mesorhizobium isolated from nodules and 648 nodule metagenomes collected from three species of chickpea plants ( Cicer spp.) under different agricultural management practices, spanning eight countries on five continents. We identified 106 phage populations (viral operational taxonomic units [vOTUs]) in Mesorhizobium draft genomes, 37% of which were confirmed as likely prophages. These vOTUs were detected in 64% of the Mesorhizobium-dominated nodule metagenomes and 58% of the Mesorhizobium isolates. Per metagenome, 1 to 16 putative Mesorhizobium vOTUs were detected, with more than half of the nodules containing only one such vOTU. The majority of vOTUs were detected exclusively in Ethiopia, followed by India and Morocco, with the lowest richness of putative Mesorhizobium phages in countries that applied industrial Mesorhizobium inoculants to crops. Two vOTUs were identified in five or more countries and in nodules dominated by different strains of Mesorhizobium, suggesting infection of diverse Mesorhizobium hosts and long-term interactions. Beta-diversity of these Mesorhizobium phage assemblages was significantly correlated with the dominant Mesorhizobium strain, but not with measured environmental parameters. Our findings indicate that nitrogen-fixing nodules in chickpea plants can contain distinct viral assemblages, with potential impacts on the nodule microbiome that bear further exploration.

Microbiology↗
NASA NTRS2012

BRIC-17 Mapping Spaceflight-Induced Hypoxic Signaling and Response in Plants

Goals of this work are: (1) Define global changes in gene expression patterns in Arabidopsis plants grown in microgravity using whole genome microarrays (2) Compare to mutants resistant to low oxygen challenge using whole genome microarrays Also measuring root and shoot size Outcomes from this research are: (1) Provide fundamental information on plant responses to the stresses inherent in spaceflight (2) Potential for informing on genetic strategies to engineer plants for optimal growth in space

Gilroy, Simon↗
DOE OSTI2022

Root Pulling Force Across Drought in Maize Reveals Genotype by Environment Interactions and Candidate Genes

High-throughput, field-based characterization of root systems for hundreds of genotypes in thousands of plots is necessary for breeding and identifying loci underlying variation in root traits and their plasticity. We designed a large-scale sampling of root pulling force, the vertical force required to extract the root system from the soil, in a maize diversity panel under differing irrigation levels for two growing seasons. We then characterized the root system architecture of the extracted root crowns. We found consistent patterns of phenotypic plasticity for root pulling force for a subset of genotypes under differential irrigation, suggesting that root plasticity is predictable. Using genome-wide association analysis, we identified 54 SNPs as statistically significant for six independent root pulling force measurements across two irrigation levels and four developmental timepoints. For every significant GWAS SNP for any trait in any treatment and timepoint we conducted post hoc tests for genotype-by-environment interaction, using a mixed model ANOVA. We found that 8 of the 54 SNPs showed significant GxE. Candidate genes underlying variation in root pulling force included those involved in nutrient transport. Although they are often treated separately, variation in the ability of plant roots to sense and respond to variation in environmental resources including water and nutrients may be linked by the genes and pathways underlying this variation. While functional validation of the identified genes is needed, our results expand the current knowledge of root phenotypic plasticity at the whole plant and gene levels, and further elucidate the complex genetic architecture of maize root systems.

Woods, Patrick↗
DOE OSTI2024

Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits

Abstract Plant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency of genetic improvement in future breeding cycles. However, generating the genetic marker data required to apply genomic prediction at the early stages of a breeding program remains costly for many public‐sector breeding programs as well as for many plant breeders operating in developing countries. As the pace of climate change intensifies, the time lag of developing and deploying new crop varieties requires plant breeders to make selection decisions without knowing the future environments those crop varieties will encounter in farmers’ fields. Therefore, both lower cost and higher accuracy methods for prediction of crop performance are essential for creating and maintaining resilient agricultural systems in the latter half of the 21 st century. To address this challenge, we conducted linked yield trials of 752 public maize ( Zea mays ) genotypes in two distinct environments. We developed and trained a phenotypic prediction model to predict yield from manually scored plant traits. The phenotypic prediction approach we employed outperformed genomic prediction in predicting yields in a second environment, with 8.7%–63% higher R 2 and 4%–13% less root mean square error than the genomic prediction. The phenotypic prediction has the potential to be applied to a wider range of breeding programs, including those that lack the resources to genotype large populations, such as programs in the developing world, breeding programs for specialty crops, and public sector programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Dissecting the metabolic reprogramming of maize root under nitrogen-deficient stress conditions

The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through the root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen stress conditions. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants were used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N-) condition. Model-predicted flux-sum variability analysis achieved 70% accuracy compared with the experimental change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (l-methionine, l-asparagine, l-lysine, cholesterol, and l-pipecolate) playing a regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidylcholine and phosphatidylglycerol metabolites which, even though not coupled with biomass production, played a key role in the increased biomass production under N-deficient conditions. Overall, the omics-integrated GSM provides a promising tool to facilitate stress condition analysis for maize root and engineer better stress-tolerant maize genotypes.

Plant Sciences↗
DOE OSTI2022

DEEPER: An Intergrated Platform for Deeper Roots

Crops with deeper roots would have multiple benefits, including better drought tolerance, reduced requirement for nitrogen fertilizer, and better sequestration of atmospheric CO 2 . DEEPER is an integrated platform of phenomic, genomic, and in silico technologies to generate maize lines with deeper roots. DEEPER is: LEADER (Leaf Elemental Accumulation from Deep Roots) is a breakthrough technology to nondestructively measure rooting depth by using the plant itself as a sensor. LEADER uses handheld X-ray Fluorescence spectrometry to quantify foliar accumulation of elements that are differentially distributed in the soil profile. LEADER is nondestructive and is orders of magnitude cheaper, faster, and more precise than any competing assay of rooting depth in the field. LEADER is able to distinguish deep-rooted from shallow-rooted maize lines in the field without the need for costly and noisy soil coring. RootRobot/DIRT3D, to automatically phenotype root architecture in any field, combining RootRobot, a mechatronics platform to excavate, clean, section, and image mature root crowns, with DIRT3D, software to quantify architectural traits in 3D. Anatomics, a high-throughput platform to phenotype root anatomy, combining LAT 2.0, a technology for 3D imaging of root anatomy and composition, with RootScan3D, software to automatically extract 3D anatomical and cell wall composition metrics from LAT 2.0 output. Using this platform we discovered two novel root traits, parenchyma cell wall thickness and multiseriate cortical sclerenchyma, that improve rooting depth and drought tolerance in maize and wheat. OpenSimRoot/Deep, software to simulate root interaction with hard subsoils. Using this platform we discovered novel concepts regarding how to increase crop rooting depth by modulating how individual root axes respond to hard soil. DeepGenes, a toolkit of genes, parent lines, and genomic selection strategies to enable breeding hybrids with deeper roots. We discovered 3 novel root genes that increase rooting depth in maize and wheat. DEEPER discovered novel root phenotypes for deeper rooting, and delivered validated ideotypes for deeper-rooted maize; novel technologies to rapidly assess root depth, root architecture and anatomy in field-grown plants; novel software tools for root modeling and 3D image analysis of root architecture and anatomy; and validated genes and genomic selection models to deploy traits for deeper rooting in maize breeding. Each DEEPER technology is transformative in its own right, and exceeds existing technologies. They are mutually synergistic, deployable for field-grown plants, and are ready for application. The phenotyping and modeling technologies are readily applicable to many crops, and genetic leads in maize may have utility in other grasses. Taken as a whole they represent a transformative platform to develop deeper-rooted crops, with greater drought tolerance, reduced fertilizer requirement, and greater carbon sequestration.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Genome-Wide Association Study Reveals Complex Genetic Architecture of Cadmium and Mercury Accumulation and Tolerance Traits in Medicago truncatula

Heavy metals are an increasing problem due to contamination from human sources that and can enter the food chain by being taken up by plants. Understanding the genetic basis of accumulation and tolerance in plants is important for reducing the uptake of toxic metals in crops and crop relatives, as well as for removing heavy metals from soils by means of phytoremediation. Following exposure of Medicago truncatula seedlings to cadmium (Cd) and mercury (Hg), we conducted a genome-wide association study using relative root growth (RRG) and leaf accumulation measurements. Cd and Hg accumulation and RRG had heritability ranging 0.44 – 0.72 indicating high genetic diversity for these traits. The Cd and Hg trait associations were broadly distributed throughout the genome, indicated the traits are polygenic and involve several quantitative loci. For all traits, candidate genes included several membrane associated ATP-binding cassette transporters, P-type ATPase transporters, oxidative stress response genes, and stress related UDP-glycosyltransferases. The P-type ATPase transporters and ATP-binding cassette protein-families have roles in vacuole transport of heavy metals, and our findings support their wide use in physiological plant responses to heavy metals and abiotic stresses. We also found associations between Cd RRG with the genes CAX3 and PDR3, two linked adjacent genes, and leaf accumulation of Hg associated with the genes NRAMP6 and CAX9. When plant genotypes with the most extreme phenotypes were compared, we found significant divergence in genomic regions using population genomics methods that contained metal transport and stress response gene ontologies. Several of these genomic regions show high linkage disequilibrium (LD) among candidate genes suggesting they have evolved together. Minor allele frequency (MAF) and effect size of the most significant SNPs was negatively correlated with large effect alleles being most rare. This is consistent with purifying selection against alleles that increase toxicity and abiotic stress. Conversely, the alleles with large affect that had higher frequencies that were associated with the exclusion of Cd and Hg. Overall, macroevolutionary conservation of heavy metal and stress response genes is important for improvement of forage crops by harnessing wild genetic variants in gene banks such as the Medicago HapMap collection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

The ancestral environment of teosinte populations shapes their root microbiome

Summary Background The composition of the root microbiome affects the host’s growth, with variation in the host genome associated with microbiome variation. However, it is not known whether this intra-specific variation of root microbiomes is a consequence of plants performing targeted manipulations of them to adapt to their local environment or varying passively with other traits. To explore the relationship between the genome, environment and microbiome, we sampled seeds from teosinte populations across its native range in Mexico. We then grew teosinte accessions alongside two modern maize lines in a common garden experiment. Metabarcoding was performed using universal bacterial and fungal primers to profile their root microbiomes. Results The root microbiome varied between the two modern maize lines and the teosinte accessions. We further found that variation of the teosinte genome, the ancestral environment (temperature/elevation) and root microbiome were all correlated. Multiple microbial groups significantly varied in relative abundance with temperature/elevation, with an increased abundance of bacteria associated with cold tolerance found in teosinte accessions taken from high elevations. Conclusions Our results suggest that variation in the root microbiome is pre-conditioned by the genome for the local environment (i.e. non-random). Ultimately, these claims would be strengthened by confirming that these differences in the root microbiome impact host phenotype, for example, by confirming that the root microbiomes of high-elevation teosinte populations enhance cold tolerance.

Genetics & Heredity↗