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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Molecular remodeling in Populus PdKOR RNAi roots profiled using LC-MS/MS proteomics

Plant endo-β-1,4-glucanases belonging to the Glycoside Hydrolase Family 9 have functional roles in cell wall biosynthesis and remodeling via endohydrolysis of (1→4)-β-D-glucosidic linkages. Modification of cell wall chemistry via RNAi-mediated downregulation of Populus deltoides KOR1 (PdKOR), a endo-β-1,4-glucanase gene, in Populus deltoides has been shown to have functional consequences for the composition of secondary metabolome and the ability of modified roots to interact with beneficial microbes. The molecular remodeling that underlies the observed differences at metabolic, physiological, and morphological levels in roots is not well understood. Here we used a LC-MS/MS-based proteome profiling approach to survey the molecular remodeling in root tissues of PdKOR and control plants. A total of 14316 peptides were identified and these mapped to 7139 P. deltoides proteins. Based on 90% sequence identity, the measured protein accessions represent 1187 functional protein groups. Analysis of GO categories and specific individual proteins showed differential expression of proteins relevant to plant-microbe interactions, cell wall chemistry, and metabolism. The new proteome dataset serves as a useful resource for deriving new hypotheses and empirical testing pertaining to functional roles of proteins and pathways in differential priming of plant roots to interactions with microbes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Implementation of Ion Mobility Spectrometry-Based Separations in Structures for Lossless Ion Manipulations (SLIM)

Structures for Lossless Ion Manipulations (SLIM) is a powerful variant of traveling wave ion mobility spectrometry (TW-IMS) that uses a serpentine pattern of microelectrodes deposited onto printed circuit boards to achieve ultralong ion path lengths (13.5 m). Ions are propelled through SLIM platforms via arrays of TW electrodes while RF and DC electrodes provide radial confinement, establishing near lossless transmission. The recent ability to cycle ions multiple times through a SLIM has allowed ion path lengths to exceed 1000 m, providing unprecedented separation power and the ability to observe ion structural conformations unobtainable with other IMS technologies. The combination of high separation power, high signal intensity, and the ability to couple with mass spectrometry places SLIM in the unique position of being able to address longstanding proteomics and metabolomics challenges by allowing the characterization of isomeric mixtures containing low abundance analytes.

Hollerbach, Adam L.↗
DOE OSTI2021

Using diverse potentials and scoring functions for the development of improved machine-learned models for protein–ligand affinity and docking pose prediction

The advent of computational drug discovery holds the promise of significantly reducing the effort of experimentalists, along with monetary cost. More generally, predicting the binding of small organic molecules to biological macromolecules has far-reaching implications for a range of problems, including metabolomics. However, problems such as predicting the bound structure of a protein–ligand complex along with its affinity have proven to be an enormous challenge. In recent years, machine learning-based methods have proven to be more accurate than older methods, many based on simple linear regression. Nonetheless, there remains room for improvement, as these methods are often trained on a small set of features, with a single functional form for any given physical effect, and often with little mention of the rationale behind choosing one functional form over another. Moreover, it is not entirely clear why one machine learning method is favored over another. Here, we endeavor to undertake a comprehensive effort towards developing high-accuracy, machine-learned scoring functions, systematically investigating the effects of machine learning method and choice of features, and, when possible, providing insights into the relevant physics using methods that assess feature importance. Here, we show synergism among disparate features, yielding adjusted R 2 with experimental binding affinities of up to 0.871 on an independent test set and enrichment for native bound structures of up to 0.913. When purely physical terms that model enthalpic and entropic effects are used in the training, we use feature importance assessments to probe the relevant physics and hopefully guide future investigators working on this and other computational chemistry problems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

How exudates production along a phosphorus gradient influences mineral dissolution across contrasting soil development stages

Harnessing rhizosphere processes offers a valuable opportunity to optimize nutrient use efficiency in agroecosystems. In nutrient-limited soils, plants discharge part of photosynthate surplus via root exudation, including carboxylates, which may enhance mineral dissolution and nutrient mobilization. We aimed to assess how plant responses to nutrient limitation translated into changes in exudate profiles, and how these exudates, in turn, drive bioweathering processes across soils of contrasting mineralogy and weathering degree. We conducted a hydroponic experiment with Lupinus albus grown under five phosphorus (P) concentrations (5, 10, 20, 30, and 50 µM) over seven weeks. We measured plant biomass and root traits, performed a metabolomics analysis and quantified seven carboxylates in root exudates using gas chromatography-mass spectrometry. To assess bioweathering processes across contrasted soil domains, we conducted batch dissolution tests with exudates using three soil horizons—35 each with distinct physicochemical properties: enriched in organic matter, iron oxides, or primary silicates. At the intermediate level of P supply, shoot biomass was comparable to that under high P, but plants produced more root biomass and a higher total carboxylate exudation rate. Despite low carboxylate concentrations (<100 ppb), exudates promoted the dissolution of Ca, Mg, Si, Fe, P and K in all horizons. Yet, the degree of element released varied among horizons. These findings highlight the importance of root exudates in enhancing mineral dissolution, with effects dependent on soil physicochemical properties. The results suggest that managing agroecosystems under moderate nutrient limitation could be a sustainable strategy to increase root-to-shoot ratios, enhance bioweathering processes and nutrient release in soil solution.

Pollet, Sasha L.↗
DOE OSTI2022

Mass Spectrometry-Based Approaches for Clinical Biomarker Discovery in Traumatic Brain Injury

Purpose of Review: Precision treatments to address the multifaceted pathophysiology of traumatic brain injury (TBI) are desperately needed, which has led to the intense study of fluid-based protein biomarkers in TBI. Mass Spectrometry (MS) is increasingly being applied to biomarker discovery and quantification in neurological disease to explore the proteome, allowing for more flexibility in biomarker discovery than commonly encountered antibody-based assays. In this narrative review, we will provide specific examples of how MS technology has advanced translational research in traumatic brain injury (TBI) focusing on clinical studies, and looking ahead to promising emerging applications of MS to the field of Neurocritical Care. Recent Findings: Proteomic biomarker discovery using MS technology in human subjects has included the full range of injury severity in TBI, though critically ill patients can offer more options to biofluids given the need for invasive monitoring. Blood, urine, cerebrospinal fluid, brain specimens, and cerebral extracellular fluid have all been sources for analysis. Emerging evidence suggests there are distinct proteomic profiles in radiographic TBI subtypes, and that biomarkers may be used to distinguish patients sustaining TBI from healthy controls. Metabolomics may offer a window into the perturbations of ongoing cerebral insults in critically ill patients after severe TBI. Summary: Emerging MS technologies may offer biomarker discovery and validation opportunities not afforded by conventional means due to its ability to handle the complexities associated with the proteome. In conclusion, while MS techniques are relatively early in development in the neurosciences space, the potential applications to TBI and neurocritical care are likely to accelerate in the coming decade.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Tag you're it: Application of stable isotope labeling and LC-MS to identify the precursors of specialized metabolites in plants

Untargeted liquid chromatography/mass spectrometry (LC-MS) can contribute a comprehensive and unbiased picture of the metabolic space of plants. These data can be used to quantify natural metabolite variation for genome wide association studies, to compare global metabolic responses from environmental or genetic perturbations, and to identify previously undescribed metabolites in Nature. A major limitation with untargeted metabolomics is the classification and identification of the thousands of metabolite features that can be detected in a single analytical run. Isotopic labeling improves the informational value of these datasets by categorizing metabolites as being derived from specific upstream precursors and/or to known metabolic pathways. When a 13 C-labeled precursor is fed to either a plant or tissue, the downstream metabolites produced from it have a higher m/z value than the molecules in the pre-existing pool, generating an m/z peak pair that can be specifically identified within the MS data. In this paper, we outline methods and principles to consider when supplementing untargeted MS data with isotopic labeling, including how to choose the appropriate isotopic label, grow and feed plant tissues to maximize label uptake and incorporation into derivatives, optimize LC-MS methods, and interpret the resulting labeling data. Although the focus here is on annotation of amino acid-derived metabolites using LC-MS, we anticipate that the methods are generally adaptable to other precursors, plant species, and chromatographic approaches.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Mapping the Path Forward to Next Generation Algal Technologies: Workshop on Understanding the Rules of Life and Complexity in Algal Systems

Algal technology offers enormous potential as a source of renewable biomass and bioproducts for fuels, biochemicals, nutrients, and numerous other applications. While tremendous opportunities exist, significant challenges are also present in harnessing and utilizing this valuable and potentially sustainable bioproduction platform. Funded by a joint partnership between the Bioenergy Technologies Office (BETO) at the Department of Energy (DOE) and the National Science Foundation (NSF), the recent virtual workshop, "Understanding the Rules of Life: Complexity in Algal Systems Workshop," was organized to bring together thought leaders from academia, national laboratories and other government agencies, and industry to identify key challenges and propose research strategies to overcome these barriers. Two parallel sessions were undertaken: 1) algae strain and toolkit development and 2) algal ecosystem and microbiome dynamics, in order to advance algae engineering and cultivation efforts. In each session, attendees discussed the most pressing challenges from the perspective of model systems and omics analysis. Themes such as genetic engineering toolboxes, translation from the laboratory to outdoor cultivation, functional genomics, metabolomics, algal ecology, consortial design, microbiome dynamics, mathematical models, and data analytics emerged from the discussion. Overall, this workshop has served to elucidate the key hurdles and many opportunities present as we continue to advance algal science and technology from the laboratory and into the commercial realm.

09 BIOMASS FUELS↗
DOE OSTI2022

Applications of targeted proteomics in metabolic engineering: advances and opportunities

Optimization of metabolically engineered organisms requires good understanding of producing balanced level of pathway proteins. Targeted proteomics via selected-reaction monitoring (SRM) has been increasingly used in metabolic engineering studies to detect and quantify sets of proteins with high selectivity, multiplexity, and reproducibility. In combination with metabolomics and other omics tools, targeted proteomics has helped optimize the production of many bio-based chemicals in various metabolic engineering cell factories. In this review, we present recent applications of targeted proteomics in metabolic engineering studies and highlight several successful cases of targeted proteomics in boosting production of commodity and high value chemicals. Additionally, we also discuss challenges and limitations of current targeted proteomics and map opportunities for future research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Pharmacological Inhibition of HSP90 Radiosensitizes Head and Neck Squamous Cell Carcinoma Xenograft by Inhibition of DNA Damage Repair, Nucleotide Metabolism, and Radiation-Induced Tumor Vasculogenesis

Recent preclinical studies suggest combining the HSP90 inhibitor AT13387 (Onalespib) with radiation (IR) against colon cancer and head and neck squamous cell carcinoma (HNSCC). These studies emphasized that AT13387 downregulates HSP90 client proteins involved in oncogenic signaling and DNA repair mechanisms as major drivers of enhanced radiosensitivity. Given the large array of client proteins HSP90 directs, we hypothesized that other key proteins or signaling pathways may be inhibited by AT13387 and contribute to enhanced radiosensitivity. Metabolomic analysis of HSP90 inhibition by AT13387 was conducted to identify metabolic biomarkers of radiosensitization and whether modulations of key proteins were involved in IR-induced tumor vasculogenesis, a process involved in tumor recurrence.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Impact of Pelvic Radiation Therapy for Prostate Cancer on Global Metabolic Profiles and Microbiota-Driven Gastrointestinal Late Side Effects: A Longitudinal Observational Study

Radiation therapy to the prostate and pelvic lymph nodes (PLNRT) is part of the curative treatment of high-risk prostate cancer. Yet, the broader influence of radiation therapy on patient physiology is poorly understood. We conducted comprehensive global metabolomic profiling of urine, plasma, and stools sampled from patients undergoing PLNRT for high-risk prostate cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Itaconic acid production is regulated by LaeA in Aspergillus pseudoterreus

The global regulator LaeA controls secondary metabolism in diverse Aspergillus species. Here we explored its role in regulation of itaconic acid production in Aspergillus pseudoterreus. To understand its role in regulating metabolism, we deleted and overexpressed laeA, and assessed the transcriptome, proteome, and secreted metabolome prior to and during initiation of phosphate limitation induced itaconic acid production. We found that secondary metabolite clusters, including the itaconic acid biosynthetic gene cluster, are regulated by laeA and that laeA is required for high yield production of itaconic acid. Overexpression of laeA improves itaconic acid yield at the expense of biomass by increasing the expression of key biosynthetic pathway enzymes and attenuating the expression of genes involved in phosphate acquisition and scavenging. Increased yield was observed in optimized conditions as well as conditions containing excess nutrients that may be present in inexpensive sugar containing feedstocks such as excess phosphate or complex nutrient sources. This suggests that global regulators of metabolism may be useful targets for engineering metabolic flux that is robust to environmental heterogeneity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Microbial diversity analysis of two full-scale seawater desalination treatment trains provides insights into detrimental biofilm formation

Detrimental biofilms on RO membranes remain a crucial challenge for seawater desalination. Comparative analysis of 16S rRNA gene amplicon sequencing data revealed differences and commonalities of biofilm communities associated with unit operations in the two largest seawater desalination facilities in the U.S., the Claude "Bud" Lewis Carlsbad Desalination Plant and the Tampa Bay Seater Desalination facility. At both plants, feedwater collected at a single time point was a poor indicator of the RO membrane communities, which showed far greater taxa diversity. The analysis of prefilter cartridges from the Carlsbad plant revealed similarly high taxon diversity as the RO module biofilms, with relevant differences. Algal sequences were enriched on the prefilter cartridges as were sequences representing Bdellovibrionota, which are predatory bacteria. Sequences representing opportunistic Gammaproteobacteria (i.e., Shewanella, Woesia) were present in significantly higher relative abundance on the RO membranes than in the prefilter cartridges, suggesting growth of certain taxa in the RO modules. Untargeted metabolomics distinguished intra- and inter-desalination plant biofilm samples, highlighting the potential value of this tool for biofilm monitoring. These findings underscore the value of omics tools for effective microbial monitoring, to understand biofouling dynamics within RO desalination plants, and to provide insight for the development of ecologically-informed biofilm control measures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Persistent urinary metabolic signatures in children with type 1 diabetes

There are an estimated 3.7 million people with undiagnosed type 1 diabetes (T1D), living primarily in poor areas of the globe. Therefore, there is a need for non-invasive, affordable tests to provide accurate diagnosis despite the time post-disease onset and fasting state. Here, we studied persistent urinary T1D biomarkers that can be used to develop such tests. Here, we analyzed the urine metabolomes of three independent cohorts of samples collected within 48 h (from Indiana University), and 1 year (from University of Colorado) and 1–10 years (6 years in average) (from Children’s National Medical Center) post-diagnosis. Samples were submitted to gas chromatography-mass spectrometry and machine learning an0alyses to determine diagnostic metabolite panels. The data were also mapped into a metabolic pathway to understand persistently regulated processes in T1D. Seven metabolites showed consistent increases in all three cohorts: d-glucose, d-mannose, myo-inositol, 3-hydroxyisobutyric acid, gluconolactone, d-gluconic acid, and d-glucuronic acid. A combination of machine learning analysis and metabolite ratios as biomarker candidates diagnosed T1D with high sensitivity and specificity across different cohorts and times. Mapping the regulated metabolites into a pathway showed impairment in glycolysis and overflow of glucose towards other pathways in subjects with T1D that was persistent over time. We identified and cross-validated highly specific and sensitive urinary biomarkers. This opens opportunities to develop affordable, robust, and non-invasive tests. The results also show that most of the biomarkers were signatures of dysregulated glucose metabolism.

Type 1 diabetes↗
DOE OSTI2022

Computational prediction of plant metabolic pathways

Uncovering genes encoding enzymes responsible for the biosynthesis of diverse plant metabolites is essential for metabolic engineering and production of plant metabolite-derived medicine. With the availability of multi-omics data for an ever-increasing number of plant species and the development of computational approaches, the metabolic pathways of many important plant compounds can be predicted, complementing a more traditional genetic and/or biochemical approach. In this work, we summarize recent progress in predicting plant metabolic pathways using genome, transcriptome, proteome, interactome, and/or metabolome data, and the utility of integrating these data with machine learning to further improve metabolic pathway predictions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Comparative functional genomics identifies an iron-limited bottleneck in a Saccharomyces cerevisiae strain with a cytosolic-localized isobutanol pathway

Metabolic engineering strategies have been successfully implemented to improve the production of isobutanol, a next-generation biofuel, in Saccharomyces cerevisiae. Here, we explore how two of these strategies, pathway relocalization and redox cofactor-balancing, affect the performance and physiology of isobutanol producing strains. We equipped yeast with isobutanol cassettes which had either a mitochondrial or cytosolic localized isobutanol pathway and used either a redox-imbalanced (NADPH-dependent) or redox-balanced (NADHdependent) ketol-acid reductoisomerase enzyme. We then conducted transcriptomic, proteomic and metabolomic analyses to elucidate molecular differences between the engineered strains. Pathway localization had a large effect on isobutanol production with the strain expressing the mitochondrial-localized enzymes producing 3.8-fold more isobutanol than strains expressing the cytosolic enzymes. Cofactor-balancing did not improve isobutanol titers and instead the strain with the redox-imbalanced pathway produced 1.5-fold more isobutanol than the balanced version, albeit at low overall pathway flux. Functional genomic analyses suggested that the poor performances of the cytosolic pathway strains were in part due to a shortage in cytosolic Fe–S clusters, which are required cofactors for the dihydroxyacid dehydratase enzyme. We then demonstrated that this cofactor limitation may be partially recovered by disrupting iron homeostasis with a fra2 mutation, thereby increasing cellular iron levels. The resulting isobutanol titer of the fra2 null strain harboring a cytosolic-localized isobutanol pathway outperformed the strain with the mitochondrial-localized pathway by 1.3-fold, demonstrating that both localizations can support flux to isobutanol.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Diel dynamics of multi-omics in elkhorn fern provide new insights into weak CAM photosynthesis

Crassulacean acid metabolism (CAM) has high water-use efficiency (WUE) and is widely recognized to have evolved from C 3 photosynthesis. Different plant lineages have convergently evolved CAM, but the molecular mechanism that underlies C 3 -to-CAM evolution remains to be clarified. Platycerium bifurcatum (elkhorn fern) provides an opportunity to study the molecular changes underlying the transition from C 3 to CAM photosynthesis because both modes of photosynthesis occur in this species, with sporotrophophyll leaves (SLs) and cover leaves (CLs) performing C 3 and weak CAM photosynthesis, respectively. Here, we report that the physiological and biochemical attributes of CAM in weak CAM-performing CLs differed from those in strong CAM species. We investigated the diel dynamics of the metabolome, proteome, and transcriptome in these dimorphic leaves within the same genetic background and under identical environmental conditions. We found that multi-omic diel dynamics in P. bifurcatum exhibit both tissue and diel effects. Our analysis revealed temporal rewiring of biochemistry relevant to the energy-producing pathway (TCA cycle), CAM pathway, and stomatal movement in CLs compared with SLs. We also confirmed that PHOSPHOENOLPYRUVATE CARBOXYLASE KINASE (PPCK) exhibits convergence in gene expression among highly divergent CAM lineages. Gene regulatory network analysis identified candidate transcription factors regulating the CAM pathway and stomatal movement. Taken together, our results provide new insights into weak CAM photosynthesis and new avenues for CAM bioengineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Engineering Rhodosporidium toruloides for production of 3-hydroxypropionic acid from lignocellulosic hydrolysate

Microbial production of valuable bioproducts is a promising route towards green and sustainable manufacturing. The oleaginous yeast, Rhodosporidium toruloides, has emerged as an attractive host for the production of biofuels and bioproducts from lignocellulosic hydrolysates. 3-hydroxypropionic acid (3HP) is an attractive platform molecule that can be used to produce a wide range of commodity chemicals. This study focuses on establishing and optimizing the production of 3HP in R. toruloides. As R. toruloides naturally has a high metabolic flux towards malonyl-CoA, we exploited this pathway to produce 3HP. Upon finding the yeast capable of catabolizing 3HP, we then implemented functional genomics and metabolomic analysis to identify the catabolic pathways. Deletion of a putative malonate semialdehyde dehydrogenase gene encoding an oxidative 3HP pathway was found to significantly reduce 3HP degradation. We further explored monocarboxylate transporters to promote 3HP transport and identified a novel 3HP transporter in Aspergillus pseudoterreus by RNA-seq and proteomics. Combining these engineering efforts with media optimization in a fed-batch fermentation resulted in 45.4 g/L 3HP production. This represents one of the highest 3HP titers reported, and the highest titer produced from lignocellulosic hydrolysate to the best of our knowledge. This work demonstrates microbial production of 3HP in R. toruloides from lignocellulosic hydrolysate at high titers, and it represents a significant step toward enabling industrial production of 3HP in the future.

3-Hydroxypropionic acid↗
DOE OSTI2023

Metabolic engineering of Synechococcus elongatus 7942 for enhanced sucrose biosynthesis

The capability of cyanobacteria to produce sucrose from CO 2 and light has a remarkable societal and biotechnological impact since sucrose can serve as a carbon and energy source for a variety of heterotrophic organisms and can be converted into value-added products. However, most metabolic engineering efforts have focused on understanding local pathway alterations that drive sucrose biosynthesis and secretion in cyanobacteria rather than analyzing the global flux re-routing that occurs following induction of sucrose production by salt stress. Here, we investigated global metabolic flux alterations in a sucrose-secreting (cscB-overexpressing) strain relative to its wild-type Synechococcus elongatus 7942 parental strain. Here we used targeted metabolomics, 13 C metabolic flux analysis (MFA), and genome-scale modeling (GSM) as complementary approaches to elucidate differences in cellular resource allocation by quantifying metabolic profiles of three cyanobacterial cultures - wild-type S. elongatus 7942 without salt stress (WT), wild-type with salt stress (WT/NaCl), and the cscB-overexpressing strain with salt stress (cscB/NaCl) - all under photoautotrophic conditions. We quantified the substantial rewiring of metabolic fluxes in WT/NaCl and cscB/NaCl cultures relative to WT and identified a metabolic bottleneck limiting carbon fixation and sucrose biosynthesis. This bottleneck was subsequently mitigated through heterologous overexpression of glyceraldehyde-3-phosphate dehydrogenase in an engineered sucrose-secreting strain. Our study also demonstrates that combining 13 C-MFA and GSM is a useful strategy to both extend the coverage of MFA beyond central metabolism and to improve the accuracy of flux predictions provided by GSM.

09 BIOMASS FUELS↗