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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

New high-throughput endstation to accelerate the experimental optimization pipeline for synchrotron X-ray footprinting

Synchrotron X-ray footprinting (XF) is a growing structural biology technique that leverages radiation-induced chemical modifications via X-ray radiolysis of water to produce hydroxyl radicals that probe changes in macromolecular structure and dynamics in solution states of interest. The X-ray Footprinting of Biological Materials (XFP) beamline at the National Synchrotron Light Source II provides the structural biology community with access to instrumentation and expert support in the XF method, and is also a platform for development of new technological capabilities in this field. Hee, the design and implementation of a new high-throughput endstation device based around use of a 96-well PCR plate form factor and supporting diagnostic instrumentation for synchrotron XF is described. This development enables a pipeline for rapid comprehensive screening of the influence of sample chemistry on hydroxyl radical dose using a convenient fluorescent assay, illustrated here with a study of 26 organic compounds. The new high-throughput endstation device and sample evaluation pipeline now available at the XFP beamline provide the worldwide structural biology community with a robust resource for carrying out well optimized synchrotron XF studies of challenging biological systems with complex sample compositions.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer

Deep learning has contributed to major advances in the prediction of protein structure from sequence, a fundamental problem in structural bioinformatics. With predictions now approaching the accuracy of crystallographic experiments, and with accelerators like GPUs and TPUs making inference using large models rapid, genome-level structure prediction becomes an obvious aim. Leadership-class computing resources can be used to perform genome-scale protein structure prediction using state-of-the-art deep learning models, providing a wealth of new data for systems biology applications. Here we describe our efforts to efficiently deploy the AlphaFold v.2 program, for full-proteome structure prediction, at scale on the Oak Ridge Leadership Computing Facility's resources, including the Summit supercomputer. We performed inference to produce the predicted structures for 40,526 protein sequences, corresponding to four prokaryotic proteomes and one plant proteome, using under 4,400 total Summit node hours, equivalent to using the majority of the supercomputer for a little over one hour. We also designed an optimized structure refinement that reduced the time for the relaxation stage of the AlphaFold pipeline by over 10X for longer sequences. We demonstrate the types of analyses that can be performed on proteome-scale collections of sequences, including a search for novel quaternary structures and implications for functional annotation.

Gao, Mu↗
DOE OSTI2021

Shear wave generation by mode conversion in picosecond ultrasonics: Impact of grain orientation and material properties

Abstract Picosecond ultrasonics has seen wide application in the investigation of interfaces in semiconductors and 3D imaging in biological systems. Shear ultrasonic waves are important for elastic constant and grain orientation measurement in ceramic materials. In this study, we investigate the impact of grain orientation and material's elastic properties on the generation efficiency of shear waves by mode conversion at a transducer thin film/substrate interface. The solution of acoustic wave equations suggests that crystal grain orientation has a strong impact on the generated shear wave amplitude. This dependence is found to be closely related to the magnitude of longitudinal component of wave displacements. The applicability of analytical model is validated by the experimental results from time‐domain Brillouin scattering. Moreover, material properties determine acoustic wave amplitudes based on the acoustic mismatch model and, particularly, large elastic anisotropy defined by Zener ratio favors strong shear waves. In the light of this analysis, several recommendations on suitable grain orientations and film materials are made to facilitate shear wave detection.

Wang, Yuzhou↗
DOE OSTI2021

Efficient chito–oligosaccharide utilization requires two TonB–dependent transporters and one hexosaminidase in Cellvibrio japonicus

Chitin utilization by microbes plays a significant role in biosphere carbon and nitrogen cycling, and studying the microbial approaches used to degrade chitin will facilitate our understanding of bacterial strategies to degrade a broad range of recalcitrant polysaccharides. The early stages of chitin depolymerization by the bacterium Cellvibrio japonicus have been characterized and are dependent on one chitin-specific lytic polysaccharide monooxygenase and non-redundant glycoside hydrolases from the family GH18 to generate chito-oligosaccharides for entry into metabolism. Here, we describe the mechanisms for the latter stages of chitin utilization by C. japonicus with an emphasis on the fate of chito-oligosaccharides. Here, our systems biology approach combined transcriptomics and bacterial genetics using ecologically relevant substrates to determine the essential mechanisms for chito-oligosaccharide transport and catabolism in Cellvibrio japonicus. Using RNAseq analysis we found a coordinated expression of genes that encode polysaccharide-degrading enzymes. Mutational analysis determined that the hex20B gene product, predicted to encode a hexosaminidase, was required for efficient utilization of chito-oligosaccharides. Furthermore, two gene loci (CJA_0353 and CJA_1157), which encode putative TonB-dependent transporters, were also essential for chito-oligosaccharides utilization. This study further develops our model of C. japonicus chitin metabolism and may be predictive for other environmentally or industrially important bacteria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Galactomannan utilization by Cellvibrio japonicus relies on a single essential α‐galactosidase encoded by the aga27A gene

Plant mannans are a component of lignocellulose that can have diverse compositions in terms of its backbone and side-chain substitutions. Consequently, the degradation of mannan substrates requires a cadre of enzymes for complete reduction to substituent monosaccharides that can include mannose, galactose, and/or glucose. One bacterium that possesses this suite of enzymes is the Gram-negative saprophyte Cellvibrio japonicus, which has 10 predicted mannanases from the Glycoside Hydrolase (GH) families 5, 26, and 27. Here we describe a systems biology approach to identify and characterize the essential mannan-degrading components in this bacterium. The transcriptomic analysis uncovered significant changes in gene expression for most mannanases, as well as many genes that encode carbohydrate active enzymes (CAZymes) when mannan was actively being degraded. A comprehensive mutational analysis characterized 54 CAZyme-encoding genes in the context of mannan utilization. Growth analysis of the mutant strains found that the man26C, aga27A, and man5D genes, which encode a mannobiohydrolase, α-galactosidase, and mannosidase, respectively, were important for the deconstruction of galactomannan, with Aga27A being essential. Our updated model of mannan degradation in C. japonicus proposes that the removal of galactose sidechains from substituted mannans constitutes a crucial step for the complete degradation of this hemicellulose.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Endophyte‐induced systemic spatial reprogramming of metabolism in Populus trichocarpa roots under drought

Beneficial, facultative endophytes help plants thrive in challenging environments by altering their host's metabolism, but how these cellular scale metabolic changes propagate to the systems biology scale is unknown. In this work, we employed a high-resolution chemical imaging approach to map metabolic changes at the Populus trichocarpa root-zone and cell-type levels combined with machine learning (ML) models to identify root metabolites and exudates that have predictive power over treatment class. We found that a nine-strain consortium of beneficial endophytes differentially altered the metabolome of droughted root tissues in a manner specific to cell type and root zone, with endophyte abundance showing a clear correlation to individual metabolites. Our study demonstrates that integrating spatial metabolomics with ML can reveal localized metabolic patterns linked to root–microbe interactions and generate novel hypotheses about underlying biological mechanisms.

Drought↗
DOE OSTI2024

Development of vegetative oil sorghum: From lab‐to‐field

Summary Biomass crops engineered to accumulate energy‐dense triacylglycerols (TAG or ‘vegetable oils’) in their vegetative tissues have emerged as potential feedstocks to meet the growing demand for renewable diesel and sustainable aviation fuel (SAF). Unlike oil palm and oilseed crops, the current commercial sources of TAG, vegetative tissues, such as leaves and stems, only transiently accumulate TAG. In this report, we used grain (Texas430 or TX430) and sugar‐accumulating ‘sweet’ (Ramada) genotypes of sorghum, a high‐yielding, environmentally resilient biomass crop, to accumulate TAG in leaves and stems. We initially tested several gene combinations for a ‘push‐pull‐protect’ strategy. The top TAG‐yielding constructs contained five oil transgenes for a sorghum WRINKLED1 transcription factor (‘push’), a Cuphea viscosissima diacylglycerol acyltransferase (DGAT; ‘pull’), a modified sesame oleosin (‘protect’) and two combinations of specialized Cuphea lysophosphatidic acid acyltransferases and medium‐chain acyl‐acyl carrier protein thioesterases. Though intended to generate oils with medium‐chain fatty acids, engineered lines accumulated oleic acid‐rich oil to amounts of up to 2.5% DW in leaves and 2.0% DW in stems in the greenhouse, 36‐fold and 49‐fold increases relative to wild‐type (WT) plants, respectively. Under field conditions, the top‐performing event accumulated TAG to amount to 5.5% DW in leaves and 3.5% DW in stems, 78‐fold and 58‐fold increases, respectively, relative to WT TX430. Transcriptomic and fluxomic analyses revealed potential bottlenecks for increased TAG accumulation. Overall, our studies highlight the utility of a lab‐to‐field pipeline coupled with systems biology studies to deliver high vegetative oil sorghum for SAF and renewable diesel production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Conformer-specific photochemistry imaged in real space and time

Conformation-dependent dynamics play an important role in organic chemistry syntheses such as electrocyclic reactions, as well as in biological processes such as protein folding. However, current time-resolved experimental methods struggle to distinguish conformers from each other, and conformational isomerism is usually analyzed through reactant and product distributions. Using a combination of mega–electron volt ultrafast electron diffraction and quantum wave packet simulations, Champenois et al. directly followed the photochemical electrocyclic ring opening of the molecule α-phellandrene with femtosecond time resolution and confirmed that the transformation of a specific molecular conformer follows the famous Woodward-Hoffmann rules. The proposed method is potentially a powerful tool to follow conformer specificity in various organic and biological systems in real time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Tools for genetic engineering and gene expression control in Novosphingobium aromaticivorans and Rhodobacter sphaeroides

ABSTRACT Alphaproteobacteria have a variety of cellular and metabolic features that provide important insights into biological systems and enable biotechnologies. For example, some species are capable of converting plant biomass into valuable biofuels and bioproducts that have the potential to contribute to the sustainable bioeconomy. Among the Alphaproteobacteria, Novosphingobium aromaticivorans , Rhodobacter sphaeroides , and Zymomonas mobilis show promise as organisms that can be engineered to convert extracted plant lignin or sugars into bioproducts and biofuels. Genetic manipulation of these bacteria is needed to introduce engineered pathways and modulate expression of native genes with the goal of enhancing bioproduct output. Although recent work has expanded the genetic toolkit for Z. mobilis , N. aromaticivorans and R. sphaeroides still need facile, reliable approaches to deliver genetic payloads to the genome and to control gene expression. Here, we expand the platform of genetic tools for N. aromaticivorans and R. sphaeroides to address these issues. We demonstrate that Tn 7 transposition is an effective approach for introducing engineered DNA into the chromosome of N. aromaticivorans and R. sphaeroides . We screen a synthetic promoter library to identify isopropyl β-D-1-thiogalactopyranoside-inducible promoters with regulated activity in both organisms (up to ~15-fold induction in N. aromaticivorans and ~5-fold induction in R. sphaeroides ). Combining Tn 7 integration with promoters from our library, we establish CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) interference systems for N. aromaticivorans and R. sphaeroides (up to ~10-fold knockdown in N. aromaticivorans and R. sphaeroides ) that can target essential genes and modulate engineered pathways. We anticipate that these systems will greatly facilitate both genetic engineering and gene function discovery efforts in these species and other Alphaproteobacteria. IMPORTANCE It is important to increase our understanding of the microbial world to improve health, agriculture, the environment, and biotechnology. For example, building a sustainable bioeconomy depends on the efficient conversion of plant material to valuable biofuels and bioproducts by microbes. One limitation in this conversion process is that microbes with otherwise promising properties for conversion are challenging to genetically engineer. Here we report genetic tools for Novosphingobium aromaticivorans and Rhodobacter sphaeroides that add to the burgeoning set of tools available for genome engineering and gene expression in Alphaproteobacteria. Our approaches allow straightforward insertion of engineered pathways into the N. aromaticivorans or R. sphaeroides genome and control of gene expression by inducing genes with synthetic promoters or repressing genes using CRISPR interference. These tools can be used in future work to gain additional insight into these and other Alphaproteobacteria and to aid in optimizing yield of biofuels and bioproducts.

Hall, Ashley N.↗
DOE OSTI2025

Engineering the hyperthermophilic archaeon Pyrococcus furiosus for 1-propanol production

ABSTRACT Society relies heavily on chemicals traditionally produced through the refinement of fossil fuels. The conversion of renewable biomass to value-added chemicals by microbes, particularly hyperthermophiles (T opt ≥80°C), offers a renewable alternative to this traditional approach. Herein, we describe the engineering of the hyperthermophilic archaeon Pyrococcus furiosus , which grows optimally (T opt ) at 100°C, for the conversion of sugar to 1-propanol. This was accomplished by constructing a hybrid metabolic pathway consisting of two native and seven heterologously produced enzymes to convert acetyl-CoA from carbohydrate metabolism to 1-propanol. A total of eleven foreign genes from two other organisms were utilized, one from the thermophilic bacterium Thermoanaerobacter sp. strain X514 and 10 from the thermoacidophilic archaeon Metallosphaera sedula, both of which grow optimally near 70°C. The recombinant P. furiosus strain produced 1-propanol at similar concentrations (up to ~1 mM) when incubated at 75°C to activate the gene products of Thermoanaerobacter sp. strain X514 and M. sedula and by initially incubating at 95°C for P. furiosus growth and then subsequently returning to 75°C to promote 1-propanol formation. Note that 1-propanol was not produced if the culture was grown only at 95°C. This work has the potential for future optimization through harnessing the genome-scale metabolic model of P. furiosus that was used herein to identify engineering targets to increase 1-propanol titer. IMPORTANCE As petroleum reserves become increasingly strained, the development of renewable alternatives to traditional chemical synthesis becomes more important. In this work, a high-temperature biological system for sugar to 1-propanol conversion was demonstrated by metabolic engineering of the hyperthermophilic archaeon Pyrococcus furiosus (T opt 100°C). The engineered strain produced 1-propanol by temperature shifting from 75°C to 95°C and then back to 75°C to accommodate the temperature ranges for native and foreign proteins associated with 1-propanol biosynthesis. Genome-scale metabolic modeling informed the carbon and reductant flux in the system, identified potential factors limiting 1-propanol production, and revealed potential optimization targets.

O'Quinn, Hailey C. (ORCID:0009000274865755)↗
DOE OSTI2020

Cross-Cutting Software Solutions in Support of Experimental Analysis Challenges at National Scattering Facilities

Rapid improvements in instrumentation at neutron and light source combined with new capabilities in computing, data analytics, and machine learning offer unprecedented scientific capabilities to address fundamental and applied science questions in a wide area of subjects from quantum materials to biological systems. In order to make these capabilities accessible to the broader scientific community, significant developments are needed related to workflow spanning from experimental control to advanced materials simulations as well as the underpinning scientific software. In this chapter, we explore these needs and recent developments at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory. A central part of the workflow at the SNS is the data reduction framework Mantid which is developed as a partnership between European and US neutron-scattering facilities.

Proffen, Thomas↗
DOE OSTI2025

BioPac

BioPAC is the Biological and other Particles in Aerosols Classifier. It is a software to assist in the accurate measurement and identification of primary biological aerosol particles, which play a pivotal role in mediating the reciprocal relationships between biological systems and atmospheric conditions.

Rahman, Ashfiqur [Pacific Northwest National Labor↗
DOE OSTI2025

PyTUQ: Python Toolkit for Uncertainty Quantification

SAND2025-03661O PyTUQ is a user-friendly software toolkit designed to help researchers and professionals understand and manage uncertainty in several scientific fields. By providing tools for analyzing how uncertainties affect outcomes, PyTUQ can be applied in areas such as energy production, and biology. Its unique approach allows users to make more informed decisions by assessing risks and improving predictions. Whether you're studying combustion processes or exploring complex biological systems, PyTUQ empowers you to gain deeper insights and enhance the reliability of your results. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
DOE OSTI2025

Kinetic Deep Learning v0.1

Here, we present a method that uses protein levels to predict times series of metabolite concentrations. Understanding this type of pathway dynamics is important in order to predict the behavior of the pathway and, more pragmatically, to be able to design biological systems (such as strains bioengineered to produce chemical products) reliably. Typically, for this purpose, kinetic models consisting of differential equations based on the Michaelis-Menten dynamics have been used in the past. However, these methods can rarely produce good fits to measured data time series. Possibly, this happens because the kinetic constants are unknown or are different from the ones measured in vivo, or perhaps because Michaelis-Menten dynamics is not a satisfactory description. In order to improve the predictive nature of these kinetic models we have eliminated the Michaelis-Menten description of pathway dynamics and we have substituted it by algorithms that automatically learn these dynamics from previously obtained metabolomics and proteomics data using machine learning approaches. Specifically, kinetic deep learning uses deep learning to map proteomics time series to metabolite concentration time series, instead of learning the first metabolite derivative and integrating in (as in the first version of kinetic learning). This approach is shown to provide good to excellent results with a data set specifically collected for this purpose.

Garcia Martin, Hector [Joint BioEnergy Institute (↗
DOE OSTI2021

Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Single-cell proteomics of Arabidopsis leaf mesophyll reveals dynamic protein responses to water-deficit stress

Background The application of single-cell omics tools to biological systems can provide unique insights into diverse cellular populations and their heterogeneous responses to internal and external perturbations. Thus far, most single-cell studies in plant systems have been limited to RNA-sequencing approaches, which only provide indirect readouts of cellular functions. Results Here, we present a single-cell proteomics workflow for plant cells that integrates tape-sandwich protoplasting, piezoelectric cell sorting, nanoPOTS sample preparation, and ion mobility-based MS data acquisition method for label-free single-cell proteomics analysis of Arabidopsis leaf mesophyll cells. From a single leaf protoplast, over 3,000 proteins were quantified with high precision. The workflow is demonstrated to identify stress associated changes in protein abundance by analyzing 117 protoplasts from well-watered and water-deficit stressed plants. Additionally, we describe a new approach for constructing covarying protein networks at the single-cell level and demonstrate how single-cell protein covariation analysis can reveal previously unrecognized protein functions while also capturing stress-induced changes in protein–protein dynamics. Conclusions The label-free scProteomic approach presented here represents a significant advance through the demonstration of a facile protoplast isolation method combined with deep and precise proteomic coverage of Arabidopsis leaf mesophyll cell types. We believe this study will serve as an informative reference to future plant scProteomic investigations.

Arabidopsis↗