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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Leveraging computational genomics to understand the molecular basis of metal homeostasis

Genome-based data is helping to reveal the diverse strategies plants and algae use to maintain metal homeostasis. In addition to acquisition, distribution and storage of metals, acclimating to feast or famine can involve a wealth of genes that we are just now starting to understand. The fast-paced acquisition of genome-based data, however, is far outpacing our ability to experimentally characterize protein function. Computational genomic approaches are needed to fill the gap between what is known and unknown. To avoid misconstruing bioinformatically derived data, which is the root cause of the inaccurate functional annotations that plague databases, functional inferences from diverse sources and contextualization of that evidence with a robust understanding of protein family evolution is needed. Phylogenomic- and comparative-genomic-based studies can aid in the interpretation of experimental data or provide a spark for the discovery of a new function. These analyses not only lead to novel insight into a target protein's function but can generate thought-provoking insights across protein families.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Packaging “vegetable oils”: Insights into plant lipid droplet proteins

Abstract Plant neutral lipids, also known as “vegetable oils”, are synthesized within the endoplasmic reticulum (ER) membrane and packaged into subcellular compartments called lipid droplets (LDs) for stable storage in the cytoplasm. The biogenesis, modulation, and degradation of cytoplasmic LDs in plant cells are orchestrated by a variety of proteins localized to the ER, LDs, and peroxisomes. Recent studies of these LD-related proteins have greatly advanced our understanding of LDs not only as steady oil depots in seeds but also as dynamic cell organelles involved in numerous physiological processes in different tissues and developmental stages of plants. In the past 2 decades, technology advances in proteomics, transcriptomics, genome sequencing, cellular imaging and protein structural modeling have markedly expanded the inventory of LD-related proteins, provided unprecedented structural and functional insights into the protein machinery modulating LDs in plant cells, and shed new light on the functions of LDs in nonseed plant tissues as well as in unicellular algae. Here, we review critical advances in revealing new LD proteins in various plant tissues, point out structural and mechanistic insights into key proteins in LD biogenesis and dynamic modulation, and discuss future perspectives on bridging our knowledge gaps in plant LD biology.

Cai, Yingqi (ORCID:0000000203575809)↗
DOE OSTI2020

Prediction of condition-specific regulatory genes using machine learning

Recent advances in genomic technologies have generated data on large-scale protein–DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5–25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A Sesquiterpene Synthase from the Endophytic Fungus Serendipita indica Catalyzes Formation of Viridiflorol

Interactions between plant-associated fungi and their hosts are characterized by a continuous crosstalk of chemical molecules. Specialized metabolites are often produced during these associations and play important roles in the symbiosis between the plant and the fungus, as well as in the establishment of additional interactions between the symbionts and other organisms present in the niche. Serendipita indica, a root endophytic fungus from the phylum Basidiomycota, is able to colonize a wide range of plant species, conferring many benefits to its hosts. The genome of S. indica possesses only few genes predicted to be involved in specialized metabolite biosynthesis, including a putative terpenoid synthase gene (SiTPS). In our experimental setup, SiTPS expression was upregulated when the fungus colonized tomato roots compared to its expression in fungal biomass growing on synthetic medium. Heterologous expression of SiTPS in Escherichia coli showed that the produced protein catalyzes the synthesis of a few sesquiterpenoids, with the alcohol viridiflorol being the main product. To investigate the role of SiTPS in the plant-endophyte interaction, an SiTPS-over-expressing mutant line was created and assessed for its ability to colonize tomato roots. Although overexpression of SiTPS did not lead to improved fungal colonization ability, an in vitro growth-inhibition assay showed that viridiflorol has antifungal properties. Addition of viridiflorol to the culture medium inhibited the germination of spores from a phytopathogenic fungus, indicating that SiTPS and its products could provide S. indica with a competitive advantage over other plant-associated fungi during root colonization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Viral delivery of recombinases activates heritable genetic switches in plants

Viral vectors provide an increasingly versatile platform for transformation-free reagent delivery to plants. RNA viral vectors can be used to induce gene silencing, overexpress proteins, or introduce gene editing reagents; however, they are often constrained by carrying capacity or restricted tropism in germline cells. Site-specific recombinases that catalyze precise genetic rearrangements are powerful tools for genome engineering that vary in size and, potentially, efficacy in plants. In this work, we show that viral vectors based on tobacco rattle virus (TRV) deliver and stably express four recombinases ranging in size from ~0.6kb to ~1.5kb and achieve simultaneous marker removal and reporter activation through targeted excision in transgenic Nicotiana benthamiana lines. TRV vectors with Cre, FLP, CinH, and Integrase13 efficiently mediated recombination in infected somatic tissue and led to heritable modifications at high frequency. Here, an excision-activated Ruby reporter enabled simple and high-resolution tracing of infected cell lineages without the need for molecular genotyping. Together, our experiments broaden the scope of viral recombinase delivery and offer insights into infection dynamics that may be useful in developing future viral vectors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Integration of physiologically relevant photosynthetic energy flows into whole genome models of light‐driven metabolism

SUMMARY Characterizing photosynthetic productivity is necessary to understand the ecological contributions and biotechnology potential of plants, algae, and cyanobacteria. Light capture efficiency and photophysiology have long been characterized by measurements of chlorophyll fluorescence dynamics. However, these investigations typically do not consider the metabolic network downstream of light harvesting. By contrast, genome‐scale metabolic models capture species‐specific metabolic capabilities but have yet to incorporate the rapid regulation of the light harvesting apparatus. Here, we combine chlorophyll fluorescence parameters defining photosynthetic and non‐photosynthetic yield of absorbed light energy with a metabolic model of the pennate diatom Phaeodactylum tricornutum. This integration increases the model predictive accuracy regarding growth rate, intracellular oxygen production and consumption, and metabolic pathway usage. Through the quantification of excess electron transport, we uncover the sequential activation of non‐radiative energy dissipation processes, cross‐compartment electron shuttling, and non‐photochemical quenching as the rapid photoacclimation strategy in P. tricornutum. Interestingly, the photon absorption thresholds that trigger the transition between these mechanisms were consistent at low and high incident photon fluxes. We use this understanding to explore engineering strategies for rerouting cellular resources and excess light energy towards bioproducts in silico . Overall, we present a methodology for incorporating a common, informative data type into computational models of light‐driven metabolism and show its utilization within the design–build–test–learn cycle for engineering of photosynthetic organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

3D reconstruction identifies loci linked to variation in angle of individual sorghum leaves

Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.

3D reconstruction↗
DOE OSTI2024

Elemental profiling and genome-wide association studies reveal genomic variants modulating ionomic composition in Populus trichocarpa leaves

The ionome represents elemental composition in plant tissues and can be an indicator of nutrient status as well as overall plant performance. Thus, identifying genetic determinants governing elemental uptake and storage is an important goal for breeding and engineering biomass feedstocks with improved performance. In this study, we coupled high-throughput ionome characterization of leaf tissues with high-resolution genome-wide association studies (GWAS) to uncover genetic loci that modulate ionomic composition in leaves of poplar ( Populus trichocarpa ). Significant agreement was observed across the three ionomic profiling platforms tested: inductively coupled plasma-mass spectrometry (ICP-MS), neutron activation analysis (NAA) and laser-induced breakdown spectroscopy (LIBS). Relative quantification of 20 elements using ICP-MS across a population of 584 genotypes, revealed larger variation in micro-nutrients and trace elements content than for macro-nutrients across genotypes. The GWAS performed using a set of high-density (>8.2 million) single nucleotide polymorphisms, identified over 600 loci significantly associated with variations in these mineral elements, pointing to numerous uncharacterized candidate genes. A significant enrichment for genes related to ion homeostasis and transport was observed, including several members of the cation-proton antiporters (CPA) family and MATE efflux transporters, previously reported to be critical for plant growth and fitness in other species. Our results also included a polymorphic copy of the high-affinity molybdenum transporter MOT1 found directly associated to molybdenum content. For the first time in a perennial plant, our results provide evidence of genetic control of mineral content in a model tree species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Symbiont-Mediated Digestion of Plant Biomass in Fungus-Farming Insects

Feeding on living or dead plant material is widespread in insects. Seminal work on termites and aphids has provided profound insights into the critical nutritional role that microbes play in plant-feeding insects. Here, some ants, beetles, and termites, among others, have evolved the ability to use microbes to gain indirect access to plant substrate through the farming of a fungus on which they feed. Recent genomic studies, including studies of insect hosts and fungal and bacterial symbionts, as well as metagenomics and proteomics, have provided important insights into plant biomass digestion across insect–fungal mutualisms. Not only do advances in understanding of the divergent and complementary functions of complex symbionts reveal the mechanism of how these herbivorous insects catabolize plant biomass, but these symbionts also represent a promising reservoir for novel carbohydrate-active enzyme discovery, which is of considerable biotechnological interest.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Conservation of endo -glucanase 16 (EG16) activity across highly divergent plant lineages

Plant cell walls are highly dynamic structures that are composed predominately of polysaccharides. As such, endogenous carbohydrate active enzymes (CAZymes) are central to the synthesis and subsequent modification of plant cells during morphogenesis. The endo-glucanase 16 (EG16) members constitute a distinct group of plant CAZymes, angiosperm orthologs of which were recently shown to have dual β-glucan/xyloglucan hydrolase activity. Molecular phylogeny indicates that EG16 members comprise a sister clade with a deep evolutionary relationship to the widely studied apoplastic xyloglucan endo-transglycosylases/hydrolases (XTH). A cross-genome survey indicated that EG16 members occur as a single ortholog across species and are widespread in early diverging plants, including the non-vascular bryophytes, for which functional data were previously lacking. Remarkably, enzymological characterization of an EG16 ortholog from the model moss Physcomitrella patens (PpEG16) revealed that EG16 activity and sequence/structure are highly conserved across 500 million years of plant evolution, vis-à-vis orthologs from grapevine and poplar. Ex vivo biomechanical assays demonstrated that the application of EG16 gene products caused abrupt breakage of etiolated hypocotyls rather than slow extension, thereby indicating a mode-of-action distinct from endogenous expansins and microbial endo-glucanases. As a result, the biochemical data presented here will inform future genomic, genetic, and physiological studies of EG16 enzymes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Systematic identification of transcriptional activation domains from non-transcription factor proteins in plants and yeast

Transcription factors can promote gene expression through activation domains. Whole-genome screens have systematically mapped activation domains in transcription factors but not in non-transcription factor proteins (e.g., chromatin regulators and coactivators). To fill this knowledge gap, we employed the activation domain predictor PADDLE to analyze the proteomes of Arabidopsis thaliana and Saccharomyces cerevisiae. We screened 18,000 predicted activation domains from >800 non-transcription factor genes in both species, confirming that 89% of candidate proteins contain active fragments. Our work enables the annotation of hundreds of nuclear proteins as putative coactivators, many of which have never been ascribed any function in plants. Analysis of peptide sequence compositions reveals how the distribution of key amino acids dictates activity. Finally, we validated short, "universal" activation domains with comparable performance to state-of-the-art activation domains used for genome engineering. Our approach enables the genome-wide discovery and annotation of activation domains that can function across diverse eukaryotes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

EvoNet: A phylogenomic and systems biology approach to identify genes underlying plant survival in marginal, low‐N soils

The DOE‐BER “EvoNet” project investigates the genetic and molecular basis of plant resilience in extreme environments. We do this by identifying key genes that enable “extreme survivor” species to thrive in the nitrogen-poor soils of Chile’s hyper-arid Atacama Desert. Our collections focus on 32 Atacama extremophile species, including seven grass species with potential biofuel applications. To identify genes-of-importance to survival we compared genomic and transcriptomic profiles of extremophile species that thrive in the Atacama to those of closely related “sister” species from nitrogen-rich arid and mesic regions of California. Deep RNA sequencing and de novo transcriptome assembly across these triplet species sets supported a phylogenomic framework for identifying positively selected genes associated with adaptive divergence. Our integrative analysis combined ecological and environmental data, metagenomics, evolutionary and systems biology, and metabolomics. This enabled us to create an unprecedented framework for systematically understanding how non-model plants have adapted to survive in extreme conditions. Our resulting database of positively selected ortholog groups in the extremophile plants offers promising targets for engineering crop and biofuel species with enhanced resilience to drought and extreme weather. Additionally, our newest dataset explores and exploits a complementary metabolomic approach. This new aspect provides innovative strategies to manipulate plant cell metabolism, further supporting efforts to improve agricultural productivity in the face of extreme climates. Importantly, our combined evolutionary- and metabolomic-based strategies focused on convergent patterns of adaptation, providing a genetic and metabolomic toolkit for improving crop and biofuel resilience across diverse plant species. Finally, our novel exploration of ecological and evolutionary dynamics delivered to the community a phylogenomic computational pipeline called “PhyloGeneious.” Our continued adaptations of this pipeline are publicly available to expedite evolutionary genomic research for future scientific discoveries. In total, our DOE-BER has provided genomic, metabolomic, and computational strategies to understand how extremophile plants provide evolutionary and physiological targets for improving agricultural and biofuel production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Holo-Omics disentangle drought response and biotic interactions among plant, endophyte and pathogen

Holo-omics provide a novel opportunity to study the interactions among fungi from different functional guilds in host plants in field conditions. We address the entangled responses of plant pathogenic and endophytic fungi associated with sorghum when droughted through the assembly of the most abundant fungal, endophyte genome from rhizospheric metagenomic sequences followed by a comparison of its metatranscriptome with the host plant metabolome and transcriptome. The rise in relative abundance of endophytic Acremonium persicinum (operational taxonomic unit 5 (OTU5)) in drought co-occurs with a rise in fungal membrane dynamics and plant metabolites, led by ethanolamine, a key phospholipid membrane component. The negative association between endophytic A. persicinum (OTU5) and plant pathogenic fungi co-occurs with a rise in expression of the endophyte's biosynthetic gene clusters coding for secondary compounds. Endophytic A. persicinum (OTU5) and plant pathogenic fungi are negatively associated under preflowering drought but not under postflowering drought, likely a consequence of variation in fungal fitness responses to changes in the availability of water and niche space caused by plant maturation over the growing season. Our findings suggest that the dynamic biotic interactions among host, beneficial and harmful microbiota in a changing environment can be disentangled by a blending of field observation, laboratory validation, holo-omics and ecological modelling.

Chen, Peilin↗
DOE OSTI2026

Detailed Method for the Purification of Rhamnogalacturonan-I (RG-I) in Arabidopsis thaliana

The plant cell wall is a dynamic and complex extracellular matrix that not only provides structural integrity and determines cell shape but also mediates intercellular communication. Among its major components, pectins play essential roles in cell adhesion, wall porosity, hydration, and flexibility. Rhamnogalacturonan-I (RG-I), a structurally diverse pectic polysaccharide, remains one of the least understood components of the plant cell wall. Its backbone is substituted with arabinan, galactan, and arabinogalactan side chains that vary in length, branching, and composition across tissues, species, and developmental stages. In addition, RG-I can undergo modifications such as backbone acetylation, further contributing to its structural complexity and functional diversity. To advance understanding of RG-I, we present a detailed method for isolating RG-I from the model plant Arabidopsis thaliana . Leveraging Arabidopsis as a model system provides major advantages owing to its well-characterized genome and powerful molecular toolkit, enabling deeper investigation into the roles of RG-I in plant development and responses to environmental stress. Our method consists of two major steps: an initial chemical extraction using oxalate, followed by endo-polygalacturonase (EPG) digestion to fragment the pectic domains. An advantage of this approach is that it produces a dry material that can be stored at room temperature without special handling and does not introduce chemicals that may interfere with downstream analyses. The purified RG-I can be used for detailed compositional and structural analyses, as well as for functional studies of enzymes involved in pectin biosynthesis, modification, and degradation. Although this protocol was developed for isolating RG-I from Arabidopsis rosette leaves, it is also applicable to other Arabidopsis organs and other plant species.

25 ENERGY STORAGE↗
DOE OSTI2025

Section-level genome sequencing and comparative genomics of Aspergillus sections Cavernicolus and Usti.

The genus Aspergillus is diverse, including species of industrial importance, human pathogens, plant pests, and model organisms. Aspergillus includes species from sections Usti and Cavernicolus, which until recently were joined in section Usti, but have now been proposed to be non-monophyletic and were split by section Nidulantes, Aenei and Raperi. To learn more about these sections, we have sequenced the genomes of 13 Aspergillus species from section Cavernicolus (A. cavernicola, A. californicus, and A. egyptiacus), section Usti (A. carlsbadensis, A. germanicus, A. granulosus, A. heterothallicus, A. insuetus, A. keveii, A. lucknowensis, A. pseudodeflectus and A. pseudoustus), and section Nidulantes (A. quadrilineatus, previously A. tetrazonus). We compared these genomes with 16 additional species from Aspergillus to explore their genetic diversity, based on their genome content, repeat-induced point mutations (RIPs), transposable elements, carbohydrate-active enzyme (CAZyme) profile, growth on plant polysaccharides, and secondary metabolite gene clusters (SMGCs). All analyses support the split of section Usti and provide additional insights: Analyses of genes found only in single species show that these constitute genes which appear to be involved in adaptation to new carbon sources, regulation to fit new niches, and bioactive compounds for competitive advantages, suggesting that these support species differentiation in Aspergillus species. Sections Usti and Cavernicolus have mainly unique SMGCs. Section Usti contains very large and information-rich genomes, an expansion partially driven by CAZymes, as section Usti contains the most CAZyme-rich species seen in genus Aspergillus. Section Usti is clearly an underutilized source of plant biomass degraders and shows great potential as industrial enzyme producers. Citation: Nybo JL, Vesth TC, Theobald S, Frisvad JC, Larsen TO, Kjaerboelling I, Rothschild-Mancinelli K, Lyhne EK, Barry K, Clum A, Yoshinaga Y, Ledsgaard L, Daum C, Lipzen A, Kuo A, Riley R, Mondo S, LaButti K, Haridas S, Pangalinan J, Salamov AA, Simmons BA, Magnuson JK, Chen J, Drula E, Henrissat B, Wiebenga A, Lubbers RJM, Müller A, dos Santos Gomes AC, Mäkelä MR, Stajich JE, Grigoriev IV, Mortensen UH, de Vries RP, Baker SE, Andersen MR (2025). Section-level genome sequencing and comparative genomics of Aspergillus sections Cavernicolus and Usti. Studies in Mycology 111: 101-114. doi: 10.3114/sim.2025.111.03.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Modeling Plant Metabolism: From Network Reconstruction to Mechanistic Models

Mathematical modeling of plant metabolism allows the plant science community to understand the organization of plant metabolism, obtain quantitative insights into metabolic functions, and derive engineering strategies for manipulation of metabolism. Among the various modeling approaches, metabolic pathway analysis can dissect the basic functional modes of subsections of core metabolism, such as photorespiration, and reveal how classical definitions of metabolic pathways have overlapping functionality. In current research using constraint-based modeling in plants, numerous computational tools are currently available to analyze large-scale and genome-scale metabolic networks. For 13 C-metabolic flux analysis, principles of isotopic steady state have been used to study heterotrophic plant tissues, while nonstationary isotope labeling approaches are amenable to the study of photoautotrophic and secondary metabolism. Enzyme kinetic models explore pathways in mechanistic detail, and we discuss different approaches to determine or estimate kinetic parameters. In this review, we describe recent advances and challenges in modeling plant metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
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↗