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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A WRKY transcription factor confers broad-spectrum resistance to biotic stresses and yield stability in rice

Plants are subject to attack by diverse pests and pathogens. Few genes conferring broad-spectrum resistance to both insects and pathogens have been identified. Because of the growth–defense tradeoff, it is often challenging to balance biotic stress resistance and yield for crops. Here, we report thatOsWRKY36suppresses the resistance to insects and pathogens via transcriptional repression ofPhenylalanine Ammonia Lyases(PALs), a key enzyme in phenylpropanoid pathway in rice. Knocking outOsWRKY36causes elevated lignin biosynthesis and increased sclerenchyma thickness of leaf sheath, leading to enhanced resistance to multiple pests and pathogens. Additionally, loss ofOsWRKY36also derepresses the transcription ofIdeal Plant Architecture 1(IPA1) andMONOCULM2(MOC2), resulting in increased spikelet number per panicle and tiller number. These findings provide mechanistic insights into biotic stress tolerance in rice and offer a promising strategy to breed rice cultivars with broad-spectrum resistance to insects and pathogens while maintaining stable yield.

Science & Technology - Other Topics↗
DOE OSTI2020

SNPeffect: identifying functional roles of SNPs using metabolic networks

Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome-wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype-to-phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme-coding genes. To this end, we also constructed a genome-scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth-determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Future roots for future soils

Abstract Mechanical impedance constrains root growth in most soils. Crop cultivation changed the impedance characteristics of native soils, through topsoil erosion, loss of organic matter, disruption of soil structure and loss of biopores. Increasing adoption of Conservation Agriculture in high‐input agroecosystems is returning cultivated soils to the soil impedance characteristics of native soils, but in the low‐input agroecosystems characteristic of developing nations, ongoing soil degradation is generating more challenging environments for root growth. We propose that root phenotypes have evolved to adapt to the altered impedance characteristics of cultivated soil during crop domestication. The diverging trajectories of soils under Conservation Agriculture and low‐input agroecosystems have implications for strategies to develop crops to meet global needs under climate change. We present several root ideotypes as breeding targets under the impedance regimes of both high‐input and low‐input agroecosystems, as well as a set of root phenotypes that should be useful in both scenarios. We argue that a ‘whole plant in whole soil’ perspective will be useful in guiding the development of future crops for future soils.

Lynch, Jonathan P.↗
DOE OSTI2021

Optimisation of root traits to provide enhanced ecosystem services in agricultural systems: A focus on cover crops

Roots are the interface between the plant and the soil and play a central role in multiple ecosystem processes. With intensification of agricultural practices, rhizosphere processes are being disrupted and are causing degradation of the physical, chemical and biotic properties of soil. However, cover crops, a group of plants that provide ecosystem services, can be utilised during fallow periods or used as an intercrop to restore soil health. The effectiveness of ecosystem services provided by cover crops varies widely as very little breeding has occurred in these species. Improvement of ecosystem service performance is rarely considered as a breeding trait due to the complexities and challenges of belowground evaluation. Advancements in root phenotyping and genetic tools are critical in accelerating ecosystem service improvement in cover crops. In this study, we provide an overview of the range of belowground ecosystem services provided by cover crop roots: (1) soil structural remediation, (2) capture of soil resources and (3) maintenance of the rhizosphere and building of organic matter content. Based on the ecosystem services described, we outline current and promising phenotyping technologies and breeding strategies in cover crops that can enhance agricultural sustainability through improvement of root traits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Wavelet Analysis of GPR Data for Belowground Mass Assessment of Sorghum Hybrid for Soil Carbon Sequestration

Among many agricultural practices proposed to cut carbon emissions in the next 30 years is the deposition of carbon in soils as plant matter. Adding rooting traits as part of a sequestration strategy would result in significantly increased carbon sequestration. Integrating these traits into production agriculture requires a belowground phenotyping method compatible with high-throughput breeding (i.e., rapid, inexpensive, reliable, and non-destructive). However, methods that fulfill these criteria currently do not exist. We hypothesized that ground-penetrating radar (GPR) could fill this need as a phenotypic selection tool. In this study, we employed a prototype GPR antenna array to scan and discriminate the root and rhizome mass of the perennial sorghum hybrid PSH09TX15. B-scan level time/discrete frequency analyses using continuous wavelet transform were utilized to extract features of interest that could be correlated to the biomass of the subsurface roots and rhizome. Time frequency analysis yielded strong correlations between radar features and belowground biomass (max R −0.91 for roots and −0.78 rhizomes, respectively) These results demonstrate that continued refinement of GPR data analysis workflows should yield an applicable phenotyping tool for breeding efforts in contexts where selection is otherwise impractical.

Wolfe, Matthew↗
DOE OSTI2023

A 30% reduction in switchgrass rhizome reserves did not decrease biomass yield

Abstract A long‐standing question in perennial grass breeding and physiology is whether yield improvement strategies could compromise winter survival. Since perennial grasses rely on stored carbohydrates for winter maintenance and spring regrowth, yield improvement strategies could reduce winter survival if they increase biomass and grain yields at the expense of carbon allocation to storage. Therefore, it is crucial to comprehend the dependence of regrowth on storage reserves. We experimentally depleted switchgrass ( Panicum virgatum L.) rhizome reserves by storing rhizomes for 2 weeks at 5°C (control treatment) and 25°C (reserve‐depleted treatment). During the storage period rhizome respiration was 5.3× higher at 25°C (0.010 μmol CO 2 g −1 min −1 at 5°C vs. 0.054 μmol CO 2 g −1 min −1 at 25°C; p < 0.0001) and the starch content was depleted by 30% by the end of storage. Surprisingly, reserve‐depleted switchgrass had 60% larger leaf area (LA; LA control = 149 cm 2 pot −1 vs. LA depleted = 239 cm 2 pot −1 ; p = 0.013) and produced ~40% more aboveground biomass than control plants (9.46 g pot −1 vs. 6.63 g pot −1 ; p = 0.112). In addition, reserve‐depleted switchgrass restored its rhizome starch reserves to pre‐storage levels. Switchgrass showed a large plasticity among its source‐sink components to buffer the imposed reserve depletion. It increased plant photosynthesis by increasing the photosynthetic leaf area while keeping photosynthesis constant on a leaf area basis and readjusted the timing and activity of sink organs. These results suggest that switchgrass, and potentially other perennial grasses, largely over‐invest in storage reserves. Therefore, current breeding strategies in perennial grasses aimed to extend the aboveground growing season should not compromise crop persistence. Our study also has implications on long‐term yield dynamics as it highlights sink limitations as potential driver of the yield decline commonly observed in perennial grasses 5+ years after cultivation.

Tejera‐Nieves, Mauricio↗
DOE OSTI2022

Evaluation of artificial neural network performance for classification of potato plants infected with potato virus Y using spectral data on multiple varieties and genotypes

Potato virus Y (Potyviridae, PVY) is a plant virus that poses a significant threat to potato producers on a global basis. The pathogen has disrupted seed potato supplies and negatively impacted yield and quality of commercial potato crops. The potato industry currently manages PVY infection levels via insecticide applications, regional seed certification programs that rely on field scouting to visually assess individual plants for infection status, and destructive and costly tissue sampling coupled with laboratory assays. Despite these efforts, PVY continues to confound potato industry stakeholders resulting in economic harm. Remote sensing and machine learning provide for the development of new tools to more accurately detect and spatially quantify PVY-infected plants versus the current state of the art. However, there is a need to understand how the occurrence of many different potato varieties impact the dynamics of developing models to detect potato plants impacted with PVY and their potential effectiveness. This study evaluates classification modelling outcomes using spectral datasets collected in different temporal and spatial environments (greenhouse and a production field) on multiple potato varieties consisting of labelled instances of plants infected with PVY and those not infected with the virus. A modelling framework was developed to support iterative modelling runs using artificial neural network (ANN) architectures configured as binary classifiers to develop sample populations to support statistical analysis on model performance using specific spectral subsets. When using spectral data to detect PVY-infected plants, ANN models achieved the highest mean accuracy of 0.894 on a single variety. Conversely, the same ANN model architecture only achieved a mean accuracy of 0.575 on a spectral data set representing 29 potato breeding lines. Additionally, statistical analysis indicates spectral regions including the red edge, near infrared and shortwave infrared contain more important spectral features for the ANN classifier introduced in this research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Fusion Fuel Cycle Inventory Reduction Studies Using a Processing-Time–Based Discrete-Time Interval Model

Developing a Fusion Pilot Plant (FPP) design that minimizes risks due to tritium in-process inventory (IPI) is an important concern for the operation of commercial devices. This becomes even more of concern since an FPP will be breeding more tritium than is burned in the reactor for sustainability. The IPI is the tritium moving through the system that is not in the storage and delivery subsystem. Here a process model that solves time-dependent differential equations based on processing times was used to investigate the reduction of the IPI of a potential fuel cycle design. The impact of new and more efficient technologies such as direct internal recycling (DIR), metal foil pumps, continuous pumping, improved isotope separation, and hydrogen separating continuous pumps on IPI was investigated by adjusting subsystem processing times and material flow streams. It was shown that any of the insertions of DIR studied in this paper caused a reduction in the total IPI of the system and proved to be the optimal way to reduce the IPI in the system. Fuel cycle modifications near the torus, such as a coupled DIR and improved pumping systems, produced the largest reductions in tritium inventory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

The Melampsora americana Population on Salix purpurea in the Great Lakes Region Is Highly Diverse with a Contributory Influence of Clonality

Shrub willows (Salix spp.) are emerging as a viable lignocellulosic, second-generation bioenergy crop with many growth characteristics favorable for marginal lands in New York State and surrounding areas. Willow rust, caused by members of the genus Melampsora, is the most limiting disease of shrub willow in this region and remains extremely understudied. In this study, genetic diversity, genetic structure, and pathogen clonality were examined in Melampsora americana over two growing seasons via genotyping-by-sequencing to identify single-nucleotide polymorphism markers. In conjunction with this project, a reference genome of rust isolate R15-033-03 was generated to aid in variant discovery. Sampling between years allowed regional and site-specific investigation into population dynamics, in the context of both wild and cultivated hosts within high-density plantings. This work revealed that this pathogen is largely panmictic over the sampled areas, with few sites showing moderate genetic differentiation. These data support the hypothesis of sexual recombination between growing seasons because no genotype persisted across the two years of sampling. Additionally, clonality was determined as a driver of pathogen populations within cultivated fields and single shrubs; however, there is also evidence of high genetic diversity of rust isolates in all settings. This work provides a framework for M. americana population structure in the Great Lakes region, providing crucial information that can aid in future resistance breeding efforts.

Plant Sciences↗
DOE OSTI2021

Capturing in-field root system dynamics with RootTracker

Abstract Optimizing root system architecture offers a promising approach to developing stress tolerant cultivars in the face of climate change, as root systems are critical for water and nutrient uptake as well as mechanical stability. However, breeding for optimal root system architecture has been hindered by the difficulty in measuring root growth in the field. Here, we describe the RootTracker, a technology that employs impedance touch sensors to monitor in-field root growth over time. Configured in a cylindrical, window shutter-like fashion around a planted seed, 264 electrodes are individually charged multiple times over the course of an experiment. Signature changes in the measured capacitance and resistance readings indicate when a root has touched or grown close to an electrode. Using the RootTracker, we have measured root system dynamics of commercial maize (Zea mays) hybrids growing in both typical Midwest field conditions and under different irrigation regimes. We observed rapid responses of root growth to water deficits and found evidence for a “priming response” in which an early water deficit causes more and deeper roots to grow at later time periods. Genotypic variation among hybrid maize lines in their root growth in response to drought indicated a potential to breed for root systems adapted for different environments. Thus, the RootTracker is able to capture changes in root growth over time in response to environmental perturbations.

Aguilar, Jeffrey J.↗
DOE OSTI2023

High-throughput, dynamic, multi-dimensional: an expanding repertoire of plant respiration measurements

A recent burst of technological innovation and adaptation has greatly improved our ability to capture respiration rate data from plant sources. At the tissue level, several independent respiration measurement options are now available, each with distinct advantages and suitability, including high-throughput sampling capacity. These advancements facilitate the inclusion of respiration rate data into large-scale biological studies such as genetic screens, ecological surveys, crop breeding trials, and multi-omics molecular studies. As a result, our understanding of the correlations of respiration with other biological and biochemical measurements is rapidly increasing. Difficult questions persist concerning the interpretation and utilization of respiration data; concepts such as allocation of respiration to growth versus maintenance, the unnecessary or inefficient use of carbon and energy by respiration, and predictions of future respiration rates in response to environmental change are all insufficiently grounded in empirical data. However, we emphasize that new experimental designs involving novel combinations of respiration rate data with other measurements will flesh-out our current theories of respiration. Furthermore, dynamic recordings of respiration rate, which have long been used at the scale of mitochondria, are increasingly being used at larger scales of size and time to reflect processes of cellular signal transduction and physiological response to the environment. Herein, we also highlight how respiratory methods are being better adapted to different plant tissues including roots and seeds, which have been somewhat neglected historically.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Genetics and Genomics of Pathogen Resistance in Switchgrass

This project investigates the genetic and genomic bases of resistance/tolerance to disease in switchgrass (Panicum virgatum) populations developed as bioenergy feedstock. The focus is placed populations adapted to the Central Great Plains derived from the hybridization of upland and lowland ecotypes, represented by cultivars Summer and Kanlow, respectively. Resistance was investigated against the rust disease caused by the fungus Puccinia novo-panici (syn. P emaculata) and mosaic disease caused by the virus complex of Panicum mosaic virus and its satellite. Objectives of the project were: 1) to elucidate the genetic and genomic bases of resistance/tolerance in locally adapted upland and lowland switchgrass breeding populations with contrasting disease symptoms under field conditions; 2) determine the molecular underpinnings of why cv Kanlow has broad resistance/tolerance to pathogens; and 3) discover the molecular differences that permit systemic viral infections in some switchgrass plants but not in other genetically-related plants.

09 BIOMASS FUELS↗
DOE OSTI2025

The mutation atlas of giant kelp (Macrocystis pyrifera): a mutation database resource for natural knockouts

Giant kelp (Macrocystis pyrifera) is a paramount species of immense ecological and economic importance. It forms dense underwater forests, providing crucial habitat and serving as a foundation species for diverse marine ecosystems. Understanding the genetics of giant kelp is essential for conservation and sustainable farming, safeguarding these valuable ecosystems and their benefits. By analyzing mutations based on their impact, we can gain insights into the potential functional consequences and implications for the organism, helping to identify critical genes or regions that may play a significant role in adaptation, development, and environmental response. To achieve this, we annotated the effects and impact of spontaneous mutations in 559 giant kelp individuals from four different populations. We found over 15.9 million mutations in genes of giant kelp, and classified them into modifier, low, moderate, and high impact depending on their predicted effects. The creation of this mutation effect database, attached to the seedbank of these individuals, offers several applications, including enhancing breeding programs, aiding genetic engineering with naturally occurring mutations, and developing strategies to mitigate the impact of environmental changes.

Plant Sciences↗
DOE OSTI2023

Tetratricopeptide repeat protein SlREC2 positively regulates cold tolerance in tomato

Abstract Cold stress is a key environmental constraint that dramatically affects the growth, productivity, and quality of tomato (Solanum lycopersicum); however, the underlying molecular mechanisms of cold tolerance remain poorly understood. In this study, we identified REDUCED CHLOROPLAST COVERAGE 2 (SlREC2) encoding a tetratricopeptide repeat protein that positively regulates tomato cold tolerance. Disruption of SlREC2 largely reduced abscisic acid (ABA) levels, photoprotection, and the expression of C-REPEAT BINDING FACTOR (CBF)-pathway genes in tomato plants under cold stress. ABA deficiency in the notabilis (not) mutant, which carries a mutation in 9-CIS-EPOXYCAROTENOID DIOXYGENASE 1 (SlNCED1), strongly inhibited the cold tolerance of SlREC2-silenced plants and empty vector control plants and resulted in a similar phenotype. In addition, foliar application of ABA rescued the cold tolerance of SlREC2-silenced plants, which confirms that SlNCED1-mediated ABA accumulation is required for SlREC2-regulated cold tolerance. Strikingly, SlREC2 physically interacted with β-RING CAROTENE HYDROXYLASE 1b (SlBCH1b), a key regulatory enzyme in the xanthophyll cycle. Disruption of SlBCH1b severely impaired photoprotection, ABA accumulation, and CBF-pathway gene expression in tomato plants under cold stress. Taken together, this study reveals that SlREC2 interacts with SlBCH1b to enhance cold tolerance in tomato via integration of SlNCED1-mediated ABA accumulation, photoprotection, and the CBF-pathway, thus providing further genetic knowledge for breeding cold-resistant tomato varieties.

Zhang, Ying (ORCID:0000000169841164)↗
DOE OSTI2023

Anti-fungal bioactive terpenoids in the bioenergy crop switchgrass (Panicum virgatum) may contribute to ecotype-specific microbiome composition

Abstract Plant derived bioactive small molecules have attracted attention of scientists across fundamental and applied scientific disciplines. We seek to understand the influence of these phytochemicals on rhizosphere and root-associated fungi. We hypothesize that – consistent with accumulating evidence that switchgrass genotype impacts microbiome assembly – differential terpenoid accumulation contributes to switchgrass ecotype-specific microbiome composition. An initial in vitro Petri plate-based disc diffusion screen of 18 switchgrass root derived fungal isolates revealed differential responses to upland- and lowland-isolated metabolites. To identify specific fungal growth-modulating metabolites, we tested fractions from root extracts on three ecologically important fungal isolates – Linnemania elongata, Trichoderma sp. and Fusarium sp. Saponins and diterpenoids were identified as the most prominent antifungal metabolites. Finally, analysis of liquid chromatography-purified terpenoids revealed fungal inhibition structure – activity relationships (SAR). Saponin antifungal activity was primarily determined by the number of sugar moieties – saponins glycosylated at a single core position were inhibitory whereas saponins glycosylated at two core positions were inactive. Saponin core hydroxylation and acetylation were also associated with reduced activity. Diterpenoid activity required the presence of an intact furan ring for strong fungal growth inhibition. These results inform future breeding and biotechnology strategies for crop protection with reduced pesticide application.

09 BIOMASS FUELS↗
DOE OSTI2022

Genomic prediction of hybrid performance for agronomic traits in sorghum

Hybrid breeding in sorghum [Sorghum bicolor (L.) Moench] utilizes the cytoplasmic-nuclear male sterility (CMS) system for seed production and subsequently harnesses heterosis. Since the cost of developing and evaluating inbred and hybrid lines in the CMS system is costly and time-consuming, genomic prediction of parental lines and hybrids is based on genetic data genotype. We generated 602 hybrids by crossing two female (A) lines with 301 diverse and elite male (R) lines from the sorghum association panel and collected phenotypic data for agronomic traits over two years. We genotyped the inbred parents using whole genome resequencing and used 2,687,342 high quality (minor allele frequency > 2%) single nucleotide polymorphisms for genomic prediction. For grain yield, the experimental hybrids exhibited an average mid-parent heterosis of 40%. Genomic best linear unbiased prediction (GBLUP) for hybrid performance yielded an average prediction accuracy of 0.76–0.93 under the prediction scenario where both parental lines in validation sets were included in the training sets (T2). However, when only female tester was shared between training and validation sets (T1F), prediction accuracies declined by 12–90%, with plant height showing the greatest decline. Mean accuracies for predicting the general combining ability of male parents ranged from 0.33 to 0.62 for all traits. Our results showed hybrid performance for agronomic traits can be predicted with high accuracy, and optimizing genomic relationship is essential for optimal training population design for genomic selection in sorghum breeding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Identification of pleiotropic loci mediating structural and non-structural carbohydrate accumulation within the sorghum bioenergy association panel using high-throughput markers

Molecular characterization of diverse germplasm can contribute to breeding programs by increasing genetic gain for sorghum [ Sorghum bicolor (L.) Moench] improvement. Identifying novel marker-trait associations and candidate genes enriches the existing genomic resources and can improve bioenergy-related traits using genomic-assisted breeding. In the current scenario, identifying the genetic loci underlying biomass and carbon partitioning is vital for ongoing efforts to maximize each carbon sink’s yield for bioenergy production. Here, we have processed a high-density genomic marker (22 466 550) data based on whole-genome sequencing (WGS) using a set of 365 accessions from the bioenergy association panel (BAP), which includes ~19.7 million (19 744 726) single nucleotide polymorphism (SNPs) and 2.7 million (~2 721 824) insertion deletions (indels). A set of high-quality filtered SNP (~5.48 million) derived markers facilitated the assessment of population structure, genetic diversity, and genome-wide association studies (GWAS) for various traits related to biomass and its composition using the BAP. The phenotypic traits for GWAS included seed color (SC), plant height (PH), days to harvest (DTH), fresh weight (FW), dry weight (DW), brix content % (BRX), neutral detergent fiber (NDF), acid detergent fiber (ADF), non-fibrous carbohydrate (NFC), and lignin content. Several novel loci and candidate genes were identified for bioenergy-related traits, and some well-characterized genes for plant height ( Dw1 and Dw2 ) and the YELLOW SEED1 locus ( Y1 ) were validated. We further performed a multi-variate adaptive shrinkage analysis to identify pleiotropic QTL, which resulted in several shared marker-trait associations among bioenergy and compositional traits. Significant marker-trait associations with pleiotropic effects can be used to develop molecular markers for trait improvement using a marker-assisted breeding approach. Significant nucleotide diversity and heterozygosity were observed between photoperiod-sensitive and insensitive individuals of the panel. This diverse bioenergy panel with genomic resources will provide an excellent opportunity for further genetic studies, including selecting parental lines for superior hybrid development to improve biomass-related traits in sorghum.

bioenergy association panel↗
DOE OSTI2023

Savannah River National Laboratory – General Fusion 2023 INFUSE Report (Rev.1)

This report describes the results from an INFUSE research project, where Savannah River National Laboratory (SRNL) in collaboration with General Fusion (GF) used process modeling to understand and optimize commercial power plant (CPP) fuel cycle designs based on parameters provided by GF. The study primarily focused on two candidate fuel cycles with different blanket materials, one with a lead lithium eutectic (LLE) blanket and the other with a pure lithium (Li) blanket. LLE benefits from a low melting point, favorable neutronics, and lower reactivity, but liquid lithium has the potential for higher tritium breeding ratios (TBR) and does not poison the plasma as a high Z contaminant. It was found that the main differences between LLE and Li designs are the extraction technologies required to remove tritium from the blanket and the amount of tritium and its distribution within the facility. More than 80% of the in-process tritium inventory for the LLE design is contained in the isotope separation system, while for the Li design, over 60% of the in-process tritium inventory is contained within the blanket material. This is due to significant tritium retention by Li. For the Li blanket, the burden of tritium processing rests on the blanket extraction technology rather than the traditional exhaust processing route. Thus, the blanket extraction technology is a main driver of tritium inventory in the Li system and determines the subsequent interface with the tritium processing plant.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗