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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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27 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Wounding rapidly alters transcription factor expression, hormonal signaling, and phenolic compound metabolism in harvested sugarbeet roots

Injuries sustained by sugarbeet (Beta vulgaris L.) roots during harvest and postharvest operations seriously reduce the yield of white sugar produced from stored roots. Although wound healing is critically important to reduce losses, knowledge of these processes is limited for this crop as well as for roots in other species. To better understand the metabolic signals and changes that occur in wounded roots, dynamic changes in gene expression were determined by RNA sequencing and the activity of products from key genes identified in this analysis were determined in the 0.25 to 24 h following injury. Nearly five thousand differentially expressed genes that contribute to a wide range of cellular and molecular functions were identified in wounded roots. Highly upregulated genes included transcription factor genes, as well as genes involved in ethylene and jasmonic acid (JA) biosynthesis and signaling and phenolic compound biosynthesis and polymerization. Enzyme activities for key genes in ethylene and phenolic compound biosynthesis and polymerization also increased due to wounding. Results indicate that wounding causes a major reallocation of metabolism in sugarbeet taproots. Although both ethylene and JA are likely involved in triggering wound responses, the greater and more sustained upregulation of ethylene biosynthesis and signaling genes relative to those of JA, suggest a preeminence of ethylene signaling in wounded sugarbeet roots. Changes in gene expression and enzymes involved in phenolic compound metabolism additionally indicate that barriers synthesized to seal off wounds, such as suberin or lignin, are initiated within the first 24 h after injury.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Editorial: Phenylpropanoid Systems Biology and Biotechnology

Phenylpropanoids are specialized metabolites involved in several aspects of plant growth and development and in the responses of plants to environmental stimuli. These compounds are synthesized from key intermediates of the shikimate pathway, which are structurally modified by the combined activities of lyases, transferases, ligases, reductases and oxygenases, resulting in the organ- and developmental-specific synthesis and accumulation of diverse metabolites (Vogt, 2010). The phenylpropanoid pathway provides the building blocks for lignin, suberin, and condensed tannins that play a role in structural support and mechanical strength. Lignin is a major contributor to feedstock recalcitrance and negatively affects the conversion of plant biomass into downstream products in biorefineries (Liu et al., 2021). Further, this pathway is key for the production of anthocyanins for organ pigmentation, flavonols and flavones for UV protection, various flavonoids and isoflavonoids for plant-microbe interactions, and antimicrobial phytoalexins for protection against pathogens (Deng and Lu, 2017). In addition to their biological functions in planta, phenylpropanoids are economically important metabolites. They constitute important components in the human diet, acting as nutraceutical compounds with antioxidant, chemopreventive, antimitotic, neuroprotective, cardioprotective, and anti-inflammatory activities. Several phenylpropanoids are considered high-value biochemicals employed in the production of fragrances, pharmaceuticals and biopolymers (Lin and Eudes, 2020).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Morpho-physiological and transcriptomic responses of field pennycress to waterlogging

Field pennycress (Thlaspi arvense) is a new biofuel winter annual crop with extreme cold hardiness and a short life cycle, enabling off-season integration into corn and soybean rotations across the U.S. Midwest. Pennycress fields are susceptible to winter snow melt and spring rainfall, leading to waterlogged soils. The objective of this research was to determine the extent to which waterlogging during the reproductive stage affected gene expression, morphology, physiology, recovery, and yield between two pennycress lines (SP32-10 and MN106). In a controlled environment, total pod number, shoot/root dry weight, and total seed count/weight were significantly reduced in SP32-10 in response to waterlogging, whereas primary branch number, shoot dry weight, and single seed weight were significantly reduced in MN106. This indicated waterlogging had a greater negative impact on seed yield in SP32-10 than MN106. We compared the transcriptomic response of SP32-10 and MN106 to determine the gene expression patterns underlying these different responses to seven days of waterlogging. The number of differentially expressed genes (DEGs) between waterlogged and control roots were doubled in MN106 (3,424) compared to SP32-10 (1,767). Functional enrichment analysis of upregulated DEGs revealed Gene Ontology (GO) terms associated with hypoxia and decreased oxygen, with genes in these categories encoding proteins involved in alcoholic fermentation and glycolysis. Additionally, downregulated DEGs revealed GO terms associated with cell wall biogenesis and suberin biosynthesis, indicating suppressed growth and energy conservation. Interestingly, MN106 waterlogged roots exhibited significant stronger regulation of these genes than SP32-10, displaying a more robust transcriptomic response overall. Together, these results reveal the reconfiguration of cellular and metabolic processes in response to the severe energy crisis invoked by waterlogging in pennycress.

ERF-VII↗
DOE OSTI2025

HAMscope: a snapshot Hyperspectral Autofluorescence Miniscope for real-time molecular imaging

We introduce HAMscope, a compact, snapshot hyperspectral autofluorescence miniscope that enables real-time, label-free molecular imaging in a wide range of biological systems. By integrating a thin polymer diffuser into a widefield miniscope, HAMscope spectrally encodes each frame and employs a probabilistic deep learning framework to reconstruct 30-channel hyperspectral stacks (452-703 nm) or directly infer molecular composition maps from single images. A scalable multi-pass U-Net architecture with transformer-based attention and per pixel uncertainty estimation enables high spatio-spectral fidelity (mean absolute error ∼0.0048) at video rates. While initially demonstrated in plant systems, including lignin, chlorophyll, and suberin imaging in intact poplar and cork tissues, the platform is readily adaptable to other applications such as neural activity mapping, metabolic profiling, and histopathology. We show that the system generalizes to out-of-distribution tissue types and supports direct molecular mapping without the need for spectral unmixing. HAMscope establishes a general framework for compact, uncertainty-aware spectral imaging that combines minimal optics with advanced deep learning, offering broad utility for real-time biochemical imaging across neuroscience, environmental monitoring, and biomedicine.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1994

Plant metabolism and cell wall formation in space (microgravity) and on Earth

Variations in cell wall chemistry provide vascular plants with the ability to withstand gravitational forces, as well as providing facile mechanisms for correctional responses to various gravitational stimuli, e.g., in reaction wood formation. A principal focus of our current research is to precisely and systematically dissect the essentially unknown mechanism(s) of vascular plant cell wall assembly, particularly with respect to formation of its phenolic constituents, i.e., lignins and suberins, and how gravity impacts upon these processes. Formation of these phenolic polymers is of particular interest, since it appears that elaboration of their biochemical pathways was essential for successful land adaptation. By extrapolation, we are also greatly intrigued as to how the microgravity environment impacts upon 'normal' cell wall assembly mechanisms/metabolism.

Lewis, Norman G.↗
NASA NTRS1998

The macromolecular aromatic domain in suberized tissue: a changing paradigm

As a structural feature of specialized cell walls, suberization remains an enigma, despite its obvious importance both during normal growth and development and as a stress response in plants. While it is clear that suberized tissues contain both polyaromatic and polyaliphatic domains, and that each of these has its own unique characteristics, whether there is a contiguous macromolecule that can be called suberin is an open question. From a structural perspective, the aromatic domain is unique and distinct from lignin, and is apparently comprised primarily of (poly)hydroxycinnamates, such as amides (e.g., feruloyltyramine). The aliphatic domain is also unique, being quite distinct from cutin in terms of both its chemical composition and cellular location. In the present paper, histochemical, structural and biochemical data, particularly, regarding the polyaromatic domain of suberized tissues, are critically reviewed. A revised description of the polyaromatic domain of suberized tissues, based on the consensus that is emerging from the current data, is presented and especially includes a spatially distinct (poly)hydroxycinnamoyl-containing macromolecule.

Non-NASA Center↗
NASA NTRS1996

Nitrogen metabolism in Lignifying Pinus taeda cell cultures

The primary metabolic fate of phyenylalanine, following its deamination in plants, is conscription of its carbon skeleton for lignin, suberin, flavonoid, and related metabolite formation. Since this accounts for approximately 30-40% of all organic carbon, an effective means of recycling the liberated ammonium ion must be operative. In order to establish how this occurs, the uptake and metabolism of various 15N-labeled precursors (15N-Phe, 15NH4Cl, 15N-Gln, and 15N-Glu) in lignifying Pinus taeda cell cultures was investigated, using a combination of high performance liquid chromatography, 15N NMR, and gas chromatograph-mass spectrometry analyses. It was found that the ammonium ion released during active phenylpropanoid metabolism was not made available for general amino acid/protein synthesis. Rather it was rapidly recycled back to regenerate phenylalanine, thereby providing an effective means of maintaining active phenylpropanoid metabolism with no additional nitrogen requirement. These results strongly suggest that, in lignifying cells, ammonium ion reassimilation is tightly compartmentalized.

NASA Discipline Number 40-50↗
DOE OSTI2023

Benzene polycarboxylic acids, lignin and hydrolysable lipids data from the whole-soil warming experiment at Blodgett Forest, CA, 2018, from: "Rapid loss of complex polymers and pyrogenic carbon in subsoils under whole soil warming"

This data package contains benzene polycarboxylic acids, lignin and hydrolysable lipids data used for the paper: “Rapid loss of subsoil plant-derived polymers and pyrogenic carbon with whole-soil warming”.On April 2018 (after 4.5 years of warming), we collected soil cores down to 90 cm depth from the whole soil profile warming experiment at the University of California Blodgett Forest Research Station, in the Sierra Nevada foothills near Georgetown, CA at 1370 m above sea level. Cores were collected from 6 experimental plots (three replicated blocks); samples were collected in 10 cm increments from 0 to 90 cm depth. This data package includes a file in .csv format and a file in .xlsx format, which can be accessed and processed using MS Excel or R. Benzene polycarboxylic acid, lignin and hydrolysable lipid concentrations are provided as processed data files.The aim of the measurements was to assess how +4°C of whole-soil warming for 4.5 years will affect structurally complex polymeric compounds at different soil depths. The research was supported by Swiss National Science Foundation (SNF) project 172744 (DEEP C) and U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under contract number DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data

High-throughput analysis of biomass is necessary to ensure consistent and uniform feedstocks for agricultural and bioenergy applications and is needed to inform genomics and systems biology models. Pyrolysis followed by mass spectrometry such as molecular beam mass spectrometry (py-MBMS) analyses are becoming increasingly popular for the rapid analysis of biomass cell wall composition and typically require the use of different data analysis tools depending on the need and application. Here, the authors report the py-MBMS analysis of several types of lignocellulosic biomass to gain an understanding of spectral patterns and variation with associated biomass composition and use machine learning approaches to classify, differentiate, and predict biomass types on the basis of py-MBMS spectra. Py-MBMS spectra were also corrected for instrumental variance using generalized linear modeling (GLM) based on the use of select ions relative abundances as spike-in controls. Machine learning classification algorithms e.g., random forest, k-nearest neighbor, decision tree, Gaussian Naïve Bayes, gradient boosting, and multilayer perceptron classifiers were used. The k-nearest neighbors (k-NN) classifier generally performed the best for classifications using raw spectral data, and the decision tree classifier performed the worst. After normalization of spectra to account for instrumental variance, all the classifiers had comparable and generally acceptable performance for predicting the biomass types, although the k-NN and decision tree classifiers were not as accurate for prediction of specific sample types. Gaussian Naïve Bayes (GNB) and extreme gradient boosting (XGB) classifiers performed better than the k-NN and the decision tree classifiers for the prediction of biomass mixtures. The data analysis workflow reported here could be applied and extended for comparison of biomass samples of varying types, species, phenotypes, and/or genotypes or subjected to different treatments, environments, etc. to further elucidate the sources of spectral variance, patterns, and to infer compositional information based on spectral analysis, particularly for analysis of data without a priori knowledge of the feedstock composition or identity.

59 BASIC BIOLOGICAL SCIENCES↗