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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

High-yield recombinant bacterial expression of 13 C-, 15 N-labeled, serine-16 phosphorylated, murine amelogenin using a modified third generation genetic code expansion protocol

Amelogenin constitutes ~ 90% of the enamel matrix in the secretory stage of amelogenesis, a still poorly understood process that results in the formation of the hardest and most mineralized tissue in vertebrates – enamel. Most biophysical research with amelogenin uses recombinant protein expressed in Escherichia coli. In addition to providing copious amounts of protein, recombinant expression allows 13 C- and 15 N-labelling for detailed structural studies using NMR spectroscopy. However, native amelogenin is phosphorylated at one position, Ser-16 in murine amelogenin, and a lingering question overshadowing decades of enamel research with recombinant amelogenin is the effect of phosphorylation on its biophysical properties. Using a modified genetic code expansion protocol we have expressed and purified uniformly 13 C-, 15 N-labeled murine amelogenin (pS16M179) with ~95% of the protein being correctly phosphorylated. Homogeneous phosphorylation was achieved using commercially available, enriched, 13 C-, 15 N-labeled media and protein expression was induced with isopropyl β-D-1-thiogalactopyranoside at 310 K. Phosphoserine incorporation was verified from one-dimensional 31P NMR spectra, comparison of 1 H- 15 N HSQC spectra, Phos-tag SDS PAGE, and mass spectrometry. Phosphorus-31 NMR spectra for pS16M179 under conditions known to trigger amelogenin self-assembly into nanospheres confirm nanosphere models with buried N-termini. Lambda phosphatase treatment of these nanospheres results in the dephosphorylation of pS16M179, confirming that smaller oligomers and monomers with exposed N-termini are in equilibrium with nanospheres. Such 13 C-, 15 N-labeling of amelogenin with accurately encoded phosphoserine incorporation will accelerate biomineralization research to understand amelogenesis and stimulate the expanded use of genetic code expansion protocols to introduce phosphorylated amino acids into proteins.

31P-NMR↗
DOE OSTI2018

A genetic meta-algorithm-assisted inversion approach: hydrogeological study for the determination of volumetric rock properties and matrix and fluid parameters in unsaturated formations

An evolutionary inversion approach is suggested for the interpretation of nuclear and resistivity logs measured by direct-push tools in shallow unsaturated sediments. The efficiency of formation evaluation is improved by estimating simultaneously (1) the petrophysical properties that vary rapidly along a drill hole with depth and (2) the zone parameters that can be treated as constant, in one inversion procedure. In the workflow, the fractional volumes of water, air, matrix and clay are estimated in adjacent depths by linearized inversion, whereas the clay and matrix properties are updated using a float-encoded genetic meta-algorithm. The proposed inversion method provides an objective estimate of the zone parameters that appear in the tool response equations applied to solve the forward problem, which can significantly increase the reliability of the petrophysical model as opposed to setting these parameters arbitrarily. The global optimization meta-algorithm not only assures the best fit between the measured and calculated data but also gives a reliable solution, practically independent of the initial model, as laboratory data are unnecessary in the inversion procedure. The feasibility test uses engineering geophysical sounding logs observed in an unsaturated loessy-sandy formation in Hungary. The multi-borehole extension of the inversion technique is developed to determine the petrophysical properties and their estimation errors along a profile of drill holes. The genetic meta-algorithmic inversion method is recommended for hydrogeophysical logging applications of various kinds to automatically extract the volumetric ratios of rock and fluid constituents as well as the most important zone parameters in a reliable inversion procedure.

58 GEOSCIENCES↗
DOE OSTI2017

Evaluation of phenotype stability and ecological risk of a genetically engineered alga in open pond production

Genetically engineered (GE) algae offer the promise of producing food, fuel, and other valuable products with reduced requirements for land and fresh water. While the gains in productivity measured in GE terrestrial crops are predicted to be mirrored in GE algae, the stability of phenotypes and ecological risks posed by GE algae in large-scale outdoor cultivation remain unknown. In this paper, we describe the first US Environmental Protection Agency (EPA)-sanctioned experiment aimed at understanding how GE algae perform in outdoor cultivation. Acutodesmus dimorphus was genetically engineered by the addition of two genes, one for enhanced fatty acid biosynthesis, and one for recombinant green fluorescence protein (GFP) expression; both the genes and their associated phenotypes were maintained during fifty days of outdoor cultivation. We also observed that while the GE algae dispersed from the cultivation ponds, colonization of the trap ponds by the GE strain declined rapidly with increasing distance from the source cultivation ponds. In contrast, many species of indigenous algae were found in every trap pond within a few days of starting the experiment. When inoculated in water from five local lakes, the GE algae's effect on biodiversity, species composition, and biomass of native algae was indiscernible from those of the wild-type (wt) progenitor algae, and neither the GE nor wt algae were able to outcompete native strains. We conclude that GE algae can be successfully cultivated outdoors while maintaining GE traits, and that for the specific GE algal strain tested here they did not outcompete or adversely impact native algae populations when grown in water taken from local lakes. Finally, this study provides an initial evaluation of GE algae in outdoor cultivation and a framework to evaluate GE algae risks associated with outdoor GE algae production.

09 BIOMASS FUELS↗
DOE OSTI2024

Successful post-translocation reproduction and genetic integration of eastern box turtles

Translocation is a conservation tool increasingly used in the recovery of at-risk species, including turtles, which are one of the world's most imperiled taxa. Post-release monitoring is essential to determine the outcomes of a given intervention and inform future efforts. However, monitoring typically focuses on post-release survival and spatial ecology whereas few studies assess the genetic and demographic outcomes. The eastern box turtle (Terrapene carolina carolina) is in decline throughout its range and is increasingly likely to be subject to translocations, including efforts to repatriate animals confiscated from the illegal wildlife trade. In 2019–2021, we translocated two groups of box turtles to the Savannah River Site in South Carolina, USA, including confiscated turtles (n = 208) and surrendered long-term captive turtles (LTC; n = 35). In 2022, we monitored a subset of confiscated (n = 12), LTC (n = 15), and sympatric resident (n = 8) females for reproductive output and genotyped their offspring and candidate sires to assign parentage. We found that all groups of females produced eggs at a similar rate and produced viable offspring but that the most recently translocated group (LTCs) displayed lower hatching success. Parentage assignment revealed that all groups sired offspring and mated with each other. Furthermore, our results broadly indicate that confiscated and LTC box turtles can successfully reproduce and genetically integrate following their release into wild populations, and that translocation may serve as a valuable tool for local population recovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Cross-kingdom expression of synthetic genetic elements promotes discovery of metabolites in the human microbiome

Small molecules encoded by biosynthetic pathways mediate cross-species interactions and harbor untapped potential, which has provided valuable compounds for medicine and biotechnology. Since studying biosynthetic gene clusters in their native context is often difficult, alternative efforts rely on heterologous expression, which is limited by host-specific metabolic capacity and regulation. Here, in this work, we describe a computational-experimental technology to redesign genes and their regulatory regions with hybrid elements for cross-species expression in Gram-negative and -positive bacteria and eukaryotes, decoupling biosynthetic capacity from host-range constraints to activate silenced pathways. These synthetic genetic elements enabled the discovery of a class of microbiome-derived nucleotide metabolites—tyrocitabines—from Lactobacillus iners. Tyrocitabines feature a remarkable orthoester-phosphate, inhibit translational activity, and invoke unexpected biosynthetic machinery, including a class of “Amadori synthases” and “abortive” tRNA synthetases. Our approach establishes a general strategy for the redesign, expression, mobilization, and characterization of genetic elements in diverse organisms and communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework

Goal-oriented microstructure design in metallic materials is a challenging task due to complex structure-property relationships. Traditional experimental and computational approaches are time-intensive and economically inefficient, limiting their applicability for large-scale design space exploration. Here, in this work, we propose an end-to-end framework that integrates deep learning models with genetic optimization to design microstructures with targeted mechanical properties. Deep learning models enable accurate forward design, while their integration with genetic optimization enables efficient inverse design within a few hours, compared to days or weeks using conventional finite element simulations. The framework combines experimental characterization and finite element modeling to analyze the influence of microstructural features on the mechanical behavior of hypoeutectoid steels. Data from both experiments and simulations are used to train the deep learning models. To demonstrate its effectiveness, we apply the framework to 0.63% carbon steel with proeutectoid ferrite and pearlite phases, commonly used in industrial applications. In this study, 2D microstructures were used for modeling, selected primarily for computational efficiency and to establish proof of concept. The framework successfully optimizes microstructures for targeted yield strength, ultimate strength, and stress concentration factors while significantly reducing computational time. Beyond hypoeutectoid steels, this scalable framework can be extended to other material systems and integrated with additive manufacturing, offering an efficient approach for accelerating microstructure design for specific engineering applications.

ConvLSTM↗
DOE OSTI2018

Arsenic methylation capacity in relation to nutrient intake and genetic polymorphisms in one-carbon metabolism

Highlights: • FOLH1 CC, MTR GG, and MTHFD1 GG carriers had better iAs methylation capacity. • Vitamin B12 interacted with FOLH1 c.223 T > C on iAs metabolism. • One-carbon nutrients and genetic variants may jointly influence iAs metabolism. Nutrients and genetic polymorphisms participating in one-carbon metabolism may explain interindividual differences in inorganic arsenic (iAs) methylation capacity, which in turn may account for variations in susceptibility to iAs-induced diseases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

High-Throughput Genetic Identification of Functionally Important Regions of the Yeast DEAD-Box Protein Mss116p

The Saccharomyces cerevisiae DEAD-box protein Mss116p is a general RNA chaperone that functions in splicing mitochondrial group I and group II introns. Recent X-ray crystal structures of Mss116p in complex with ATP analogs and single-stranded RNA show that the helicase core induces a bend in the bound RNA, as in other DEAD-box proteins, while a C-terminal extension (CTE) induces a second bend, resulting in RNA crimping. Here, we illuminate these structures by using high-throughput genetic selections, unigenic evolution, and analyses of in vivo splicing activity to comprehensively identify functionally important regions and permissible amino acid substitutions throughout Mss116p. The functionally important regions include those containing conserved sequence motifs involved in ATP and RNA binding or interdomain interactions, as well as previously unidentified regions, including surface loops that may function in protein-protein interactions. The genetic selections recapitulate major features of the conserved helicase motifs seen in other DEAD-box proteins but also show surprising variations, including multiple novel variants of motif III (SAT). Patterns of amino acid substitutions indicate that the RNA bend induced by the helicase core depends on ionic and hydrogen-bonding interactions with the bound RNA; identify a subset of critically interacting residues; and indicate that the bend induced by the CTE results primarily from a steric block. Finally, we identified two conserved regions - one the previously noted post II region in the helicase core and the other in the CTE - that may help displace or sequester the opposite RNA strand during RNA unwinding.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Machine learning-guided discovery of polymer membranes for CO 2 separation with genetic algorithm

Designing polymer membranes with high gas permeability and selectivity is a difficult multi-task constrained problem due to the trade-off between these two properties. In this work, we present a machine learning (ML) driven genetic algorithm to tackle the design problem of polymer membranes for CO 2 separation from N 2 and O 2 . Using literature data of permeability for three gases, we constructed multiple ML models with different fingerprinting featurization schemes to predict gas permeabilities. Then, we employed a genetic algorithm to design new polymers and evaluated their performance using our ML models. We were able to identify new polymer membranes that are promising for both CO 2 /N 2 and CO 2 /O 2 separations. Further, the top discovered polymers are predicted to have high glass transition temperatures. Similarly, the pyridine functionality was found in ≈20% of the predicted polymers. This framework can be used to design polymers for any application involving constrained optimization. Finally, we outlined the challenges and opportunities with using ML guided data-driven inverse design of polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Development of a reverse genetics system to generate a recombinant Ebola virus Makona expressing a green fluorescent protein

Previous studies have demonstrated the potential application of reverse genetics technology in studying a broad range of aspects of viral biology, including gene regulation, protein function, cell entry, and pathogenesis. Here, we describe a highly efficient reverse genetics system used to generate recombinant Ebola virus (EBOV) based on a recent isolate from a human patient infected during the 2014–2015 outbreak in Western Africa. We also rescued a recombinant EBOV expressing a fluorescent reporter protein from a cleaved VP40 protein fusion. Using this virus and an inexpensive method to quantitate the expression of the foreign gene, we demonstrate its potential usefulness as a tool for screening antiviral compounds and measuring neutralizing antibodies. - Highlights: • Recombinant Ebola virus (EBOV) derived from Makona variant was rescued. • New protocol for viral rescue allows 100% efficiency. • Modified EBOV expresses a green fluorescent protein from a VP40-fused protein. • Modified EBOV was tested as tool to screen antiviral compounds and measure neutralizing antibodies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Human adipose-derived mesenchymal stromal cells from face and abdomen undergo replicative senescence and loss of genetic integrity after long-term culture

Body fat depots are heterogeneous concerning their embryonic origin, structure, exposure to environmental stressors, and availability. Thus, investigating adipose-derived mesenchymal stromal cells (ASCs) from different sources is essential to standardization for future therapies. In vitro amplification is also critical because it may predispose cell senescence and mutations, reducing regenerative properties and safety. Here, we evaluated long-term culture of human facial ASCs (fASCs) and abdominal ASCs (aASCs) and showed that both met the criteria for MSCs characterization but presented differences in their immunophenotypic profile, and differentiation and clonogenic potentials. The abdominal tissue yielded more ASCs, and these had higher proliferative potential, but facial cells displayed fewer mitotic errors at higher passages. However, both cell types reduced clonal efficiency over time and entered replicative senescence around P12, as evaluated by progressive morphological alterations, reduced proliferative capacity, and SA-β-galactosidase expression. Loss of genetic integrity was detected by a higher proportion of cells showing nuclear alterations and γ-H2AX expression. Our findings indicate that the source of ASCs can substantially influence their phenotype and therefore should be carefully considered in future cell therapies, avoiding, however, long-term culture to ensure genetic stability.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Genetically engineered mouse models of esophageal cancer

Esophageal cancer is the most common cause of cancer-related death worldwide with a diverse geographical distribution, poor prognosis, and diagnosis in advanced stages of the disease. Identification of the mechanisms involved in esophageal cancer development is evaluative to improve outcomes for patients. Genetically engineered mouse models (GEMMs) of cancer provide the physiologic, molecular, and histologic features of the human tumors to determine the pathogenesis and treatments for cancer, hence exhibiting a source of tremendous potential for oncology research. The advancement of cancer modeling in mice has improved to the extent that researchers can observe and manipulate the disease process in a specific manner. Despite the significant differences between mice and humans, mice can be great models for human oncology researches due to similarities between them at the molecular and physiological levels. Due to most of the existing esophageal cancer GEMMs do not propose an ideal system for pathogenesis of the disease, genetic risks, and microenvironment exposure, so identification of challenges in GEM modeling and well-developed technologies are required to obtain the most value for patients. In this review, we describe the biology of human and mouse, followed by the exciting esophageal cancer mouse models with a discussion of applicability and challenges of these models for generating new GEMMs in future studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

System-Level Optimization to Improve Biofuel Potential via Genetic Engineering and Hydrothermal Liquefaction

The economic success of biofuels and bioproducts depends on system-level optimization including biomass production and conversion. Hydrothermal liquefaction (HTL) can convert wet biomass such as microalgae into a biofuel intermediate (BFI) under elevated temperatures and pressure. An understanding of the impacts of biomass composition on BFI yield and quality can inform genetic engineering strategies in the improvement of biochemical composition for biofuel production. In this work, wild type cyanobacterium Synechocystis sp. PCC 6803 biomass was doped with various common cellular storage compounds in lab-scale HTL experiments. Doping with glycogen or polyhydroxybutyrate (PHB) significantly reduced BFI yields, while doping with triglycerides (TAG) or medium chain-length polyhydroxyalkanoate (mcl-PHA) increased BFI yield and quality. In light of these observations, a genetically engineered Synechocystis strain deficient in glycogen biosynthesis was cultivated to produce biomass for HTL, leading to a 17% increase in BFI yield. In addition, we built a multiphase component additivity (MCA) model that can predict BFI yield and quality with PHAs in the biomass. This work demonstrates an effective strategy to integrate strain development with downstream biomass conversion to maximize biofuel yield, with lessons applicable to microalgae as well as other biomass.

09 BIOMASS FUELS↗
DOE OSTI2025

RT-EZ: A Golden Gate Assembly Toolkit for Streamlined Genetic Engineering of Rhodotorula toruloides

For economic and sustainable biomanufacturing, the oleaginous yeast Rhodotorula toruloides has emerged as a promising platform for producing biofuels, pharmaceuticals, and other valuable chemicals. However, genetic manipulation of R. toruloides has been limited by its high GC content and the lack of a replicating plasmid, necessitating gene integration into the genome of the yeast. To address these challenges, we developed the RT-EZ (R. toruloides Efficient Zipper) toolkit, a versatile tool based on Golden Gate assembly, designed to streamline R. toruloides engineering with improved efficiency and flexibility. The RT-EZ toolkit simplifies vector construction by incorporating new features such as bidirectional promoters and 2A peptides, color-based screening using RFP, and sequences optimized for both Agrobacterium tumefaciens-mediated transformation (ATMT) and easy linearization, enabling straightforward selection and transformation. Notably, the RT-EZ kit can be used to construct an expression cassette with four different genes in one assembly reaction, significantly improving vector construction speed and efficiency. The utility of the RT-EZ toolkit was demonstrated through the successful synthesis of arachidonic acid in R. toruloides by coexpressing fatty acid elongases and desaturases. Furthermore, this result underscores the potential of the RT-EZ toolkit to advance synthetic biology in R. toruloides, providing a streamlined method for addressing genetic engineering challenges in the yeast.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Neuropathological correlates and genetic architecture of microglial activation in elderly human brain

Microglia, the resident immune cells of the brain, have important roles in brain health. However, little is known about the regulation and consequences of microglial activation in the aging human brain. Here we report that the proportion of morphologically activated microglia (PAM) in postmortem cortical tissue is strongly associated with ß-amyloid, tau-related neuropathology, and the rate of cognitive decline. Effect sizes for PAM measures are substantial, comparable to that of APOE e4, the strongest genetic risk factor for Alzheimer's disease, and mediation models support an upstream role for microglial activation in Alzheimer's disease via accumulation of tau. Further, we identify a common variant (rs2997325) influencing PAM that also affects in vivo microglial activation measured by [ 11 C]-PBR28 PET in an independent cohort. Thus, our analyses begin to uncover pathways regulating resident neuroinflammation and identify overlaps of PAM's genetic architecture with those of Alzheimer's disease and several other traits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Ecological trade-offs drive phenotypic and genetic differentiation of Arabidopsis thaliana in Europe

Plant diversity is shaped by trade-offs between traits related to competitive ability, propagule dispersal, and stress resistance. However, we still lack a clear understanding of how these trade-offs influence species distribution and population dynamics. In Arabidopsis thaliana , recent genetic analyses revealed a group of cosmopolitan genotypes that successfully recolonized Europe from its center after the last glaciation, excluding older (relict) lineages from the distribution except for their north and south margins. Here, we tested the hypothesis that cosmopolitans expanded due to higher colonization ability, while relicts persisted at the margins due to higher tolerance to competition and/or stress. We compared the phenotypic and genetic differentiation between 71 European genotypes originating from the center, and the south and north margins. We showed that a trade-off between plant fecundity and seed mass shapes the differentiation of A. thaliana in Europe, suggesting that the success of the cosmopolitan groups could be explained by their high dispersal ability. However, at both north and south margins, we found evidence of selection for alleles conferring low dispersal but highly competitive and stress-resistance abilities. This study sheds light on the role of ecological trade-offs as evolutionary drivers of the distribution and dynamics of plant populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Genetic background influences the effect of thirdhand smoke exposure on anxiety and memory in Collaborative Cross mice

Growing evidence indicates that thirdhand smoke (THS) exposure induces many adverse health effects. However, it is unclear how THS exposure affects behavior and how host genetic background modulates phenotypic changes. Here we used the Collaborative Cross (CC) mouse population-based model to assess behavioral alterations immediately after THS exposure from 4 to 9 weeks of age. We first measured anxiety-like behavior in six strains using light/dark box combined with a custom multivariate mouse tracking system. We developed an anxiety risk scoring system based on anxiety-related traits and then evaluated the THS impact on them. THS exposure significantly decreased anxiety risk in CC019 ( P = 0.002) and CC051 ( P = 0.009), but increased anxiety risk in CC036 ( P < 0.001), while the other three strains did not show significant changes in anxiety-related traits. Such differences were driven by female mice for the six measures of anxiety-like behavior. Memory potential was measured in the same cohort of mice using the passive avoidance assay. Both THS-exposed male and female CC019 mice displayed significant memory loss compared to controls while no significant changes were found in the other five strains. This study provides strong evidence that THS exposure leads to strain-dependent changes in anxiety-like behavior and memory, suggesting that host genetic variations play a critical role in individual susceptibility to THS-induced effects.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Design of lightweight BCC multi-principal element alloys with enhanced hydrogen storage using a machine learning-driven genetic algorithm

Body-centered cubic (BCC) based multi-principal element alloy (MPEA) hydrides have demonstrated significant potential for compact and efficient hydrogen storage. In this work, we first leverage machine learning (ML) models to predict the hydrogen affinity, storage capacity and phase stability of BCC MPEAs, creating a unique hydrogen-to-metal (H/M) predictor for materials with unprecedented performance. We developed a metaheuristic optimizer high-throughput framework by interfacing ML models with a genetic algorithm for the accelerated search of {Mg, Al, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Nb, Mo} based lightweight BCC MPEAs with improved hydrogen storage characteristics. We report five new MPEAs with a predicted gravimetric hydrogen storage capacity of around 3.5 wt% or more, including Cr 0.09 Mg 0.73 Ti 0.18 (4.25 wt% H) and Cr 0.21 Nb 0.11 Ti 0.35 V 0.33 (3.5 wt% H). The electronic structure of the top-performing composition, Cr 0.09 Mg 0.73 Ti 0.18 , was analyzed using density functional theory (DFT) to understand the reasons for its improved hydrogen storage properties compared to TiFe (1.90 wt% H), LaNi 5 (1.37 wt% H) or BCC MPEAs like TiVNbCr (3.70 wt% H). Temperature-dependent molecular dynamics (MD) studies were further performed on optimized BCC MPEAs to qualitatively study hydrogen mobility and analyze the effect of different elemental composition on bulk hydrogen diffusion. Our findings demonstrate how a ML assisted genetic algorithm framework can be used for efficient search of stable, lightweight and cost-effective MPEAs while minimizing the need for expensive ab initio calculations.

DFT↗