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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Comparative proteomics of a versatile, marine, iron-oxidizing chemolithoautotroph

This study conducted a comparative proteomic analysis to identify potential genetic markers for the biological function of chemolithoautotrophic iron oxidation in the marine bacterium Ghiorsea bivora. To date, this is the only characterized species in the class Zetaproteobacteria that is not an obligate iron-oxidizer, providing a unique opportunity to investigate differential protein expression to identify key genes involved in iron-oxidation at circumneutral pH. Over 1000 proteins were identified under both iron- and hydrogen-oxidizing conditions, with differentially expressed proteins found in both treatments. Notably, a gene cluster upregulated during iron oxidation was identified. This cluster contains genes encoding for cytochromes that share sequence similarity with the known iron-oxidase, Cyc2. Interestingly, these cytochromes, conserved in both Bacteria and Archaea, do not exhibit the typical β-barrel structure of Cyc2. This cluster potentially encodes a biological nanowire-like transmembrane complex containing multiple redox proteins spanning the inner membrane, periplasm, outer membrane, and extracellular space. The upregulation of key genes associated with this complex during iron-oxidizing conditions was confirmed by quantitative reverse transcription-PCR. These findings were further supported by electromicrobiological methods, which demonstrated negative current production by G. bivora in a three-electrode system poised at a cathodic potential. This research provides significant insights into the biological function of chemolithoautotrophic iron oxidation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Methods, systems, and compositions for studying solvent accessibility and three-dimensional structure of biological molecules

This disclosure provides methods, systems, and compositions of matter for studying solvent accessibility and three-dimensional structure of biological molecules. A plasma can be used to generate marker radicals, which can interact with a biological molecule and mark the solvent-accessible portions of the biological molecule.

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

Signal Differentiation of Moving Magnetic Nanoparticles for Enhanced Biodetection and Diagnostics

Magnetic nanoparticles are extensively utilized as markers/signal labelling in various biomedical applications. Detecting and distinguishing magnetic signals from similarly sized moving magnetic nanoparticles in microfluidic systems is crucial yet challenging for biosensing. In this study, we have developed an original method to detect and differentiate magnetic signals from moving superparamagnetic (SPM) and ferrimagnetic (FM) nanoparticles of comparable sizes. Our approach utilizes a highly sensitive magnetic-coil-based sensor that harnesses the combined effects of giant magnetoimpedance (GMI) and an LC-resonance circuit, offering performance superior to that of conventional GMI sensors. Iron oxide nanoparticles, which have similar particle sizes but differing coercivities (zero for SPM and non-zero for FM) or similar zero coercivities but differing particle sizes, flow through the magnetic coil at controlled velocities. Their distinct effects are analyzed through changes in the complex impedance of the sensing system. Our findings provide a unique pathway for utilizing SPM and FM nanoparticles as innovative magnetic markers to identify specific biological entities, thereby expanding their potential applications.

Hwang, Kee Young↗
DOE OSTI2023

Discovering useful genetic variation in the seed parent gene pool for sorghum improvement

Multi-parent populations contain valuable genetic material for dissecting complex, quantitative traits and provide a unique opportunity to capture multi-allelic variation compared to the biparental populations. A multi-parent advanced generation inter-cross (MAGIC) B-line (MBL) population composed of 708 F 6 recombinant inbred lines (RILs), was recently developed from four diverse founders. These selected founders strategically represented the four most prevalent botanical races (kafir, guinea, durra, and caudatum) to capture a significant source of genetic variation to study the quantitative traits in grain sorghum [Sorghum bicolor (L.) Moench]. MBL was phenotyped at two field locations for seven yield-influencing traits: panicle type (PT), days to anthesis (DTA), plant height (PH), grain yield (GY), 1000-grain weight (TGW), tiller number per meter (TN) and yield per panicle (YPP). High phenotypic variation was observed for all the quantitative traits, with broad-sense heritabilities ranging from 0.34 (TN) to 0.84 (PH). The entire population was genotyped using Diversity Arrays Technology (DArTseq), and 8,800 single nucleotide polymorphisms (SNPs) were generated. A set of polymorphic, quality-filtered markers (3,751 SNPs) and phenotypic data were used for genome-wide association studies (GWAS). We identified 52 marker-trait associations (MTAs) for the seven traits using BLUPs generated from replicated plots in two locations. We also identified desirable allelic combinations based on the plant height loci (Dw1, Dw2, and Dw3), which influences yield related traits. Additionally, two novel MTAs were identified each on Chr1 and Chr7 for yield traits independent of dwarfing genes. We further performed a multi-variate adaptive shrinkage analysis and 15 MTAs with pleiotropic effect were identified. The five best performing MBL progenies were selected carrying desirable allelic combinations. Since the MBL population was designed to capture significant diversity for maintainer line (B-line) accessions, these progenies can serve as valuable resources to develop superior sorghum hybrids after validation of their general combining abilities via crossing with elite pollinators. Further, newly identified desirable allelic combinations can be used to enrich the maintainer germplasm lines through marker-assisted backcross breeding.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

CORN (Crop Optimization Realized through Neuralnets)

Traditional models of predicting plant traits are limited because they often rely on linear assumptions that do not fully capture the complexity of biological interactions and DNA-based markers which are static across environments. This project generated a set of RNA-based data from large multi-environment field trials and combined it with advanced machine learning techniques to account for these complex interactions and improve the accuracy of predictions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Deep graph representations embed network information for robust disease marker identification

We report that the accurate disease diagnosis and prognosis based on omics data rely on the effective identification of robust prognostic and diagnostic markers that reflect the states of the biological processes underlying the disease pathogenesis and progression. In this article, we present GCNCC, a Graph Convolutional Network-based approach for Clustering and Classification, that can identify highly effective and robust network-based disease markers. Based on a geometric deep learning framework, GCNCC learns deep network representations by integrating gene expression data with protein interaction data to identify highly reproducible markers with consistently accurate prediction performance across independent datasets possibly from different platforms. GCNCC identifies these markers by clustering the nodes in the protein interaction network based on latent similarity measures learned by the deep architecture of a graph convolutional network, followed by a supervised feature selection procedure that extracts clusters that are highly predictive of the disease state. By benchmarking GCNCC based on independent datasets from different diseases (psychiatric disorder and cancer) and different platforms (microarray and RNA-seq), we show that GCNCC outperforms other state-of-the-art methods in terms of accuracy and reproducibility.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Three Photon Excited Image Scanning Microscopy for in-Depth Super-Resolution Studies of Biological Samples

Multiphoton laser scanning microscopy is a powerful tool for deep imaging of thick biological samples. Image scanning microscopy (ISM) has demonstrated significant improvements in the signal-to-noise ratio in confocal laser scanning microscopy, while at the same time improving upon the effectively attainable resolution. Two-photon excitation (2PE), combined with ISM, has been shown to allow for deep tissue imaging with enhanced resolution compared to 2PE microscopy. Three-photon excitation (3PE) has enabled record imaging depth and contrast for multiphoton imaging, due to the superior suppression of out-of-focus signal generation. In this paper, we demonstrate super-resolution 3PE ISM. This is achieved using a single-photon avalanche detector array, and 1040-nm pulses for 3PE of blue fluorescence. This method enables subdiffraction limited resolution imaging of biological samples stained with blue fluorescent markers, such as mouse myocardial and spinal cord tissues stained with 4 ′ , 6 -diamidino-2-phenylindole. Deconvolution improves the resolving power further and allows for imaging with better than λ / 8 resolution with respect to the 3PE wavelength λ . With the ISM pixel reassignment procedure, we demonstrate a resolution enhancement of ∼ 1.6 laterally, compared to the resolution attained using a photomultiplier tube in a non-descanned detection arrangement, and a factor of ∼ 1.8 enhancement in axial resolution. The experimentally measured three-dimensional point spread function volume is shrunk ∼ 4.4 -fold, which is close to the theoretically expected enhancement. Published by the American Physical Society 2024

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Real-Time Atomic-Scale Structural Analysis Resolves the Amorphous to Crystalline CaCO 3 Mechanism Controversy

Amorphous calcium carbonate (ACC) occurs as a precursor to geological and biogenic calcium carbonate (CaCO 3 ), yet its transformation pathways and reaction mechanisms remain inconsistent and controversial. In this study, we investigated the transformation of ACC to calcite under both solution and dry conditions, in the presence and absence of impurity ions, utilizing operando time-resolved synchrotron X-ray diffraction (TRXRD) and reactive transport modeling. Results demonstrate that TRXRD techniques allow us to differentiate dissolution-reprecipitation versus solid-state transformation mechanisms for amorphous to crystalline phase transitions. Specifically, we observe that in environments with abundant water, ACC transforms to calcite through a dissolution-reprecipitation mechanism. This features an activation energy of 63 ± 2 kJ/mol and unit cell volume contraction during calcite crystal growth. Conversely, under water-limited conditions, ACC to calcite transformation proceeds via a solid-state transformation mechanism, with an activation energy of 210 ± 2 kJ/mol, three times greater than the dissolution-reprecipitation route, and a unit cell expansion during crystalline calcite growth. Further, to illustrate the magnitude of these effects, the rates of calcite growth were similar during dissolution-reprecipitation at 3 °C [0.00207(35) s –1 ] and solid-state transformation at 280 °C [0.00134(11) s –1 ]. Moreover, the incorporation of an impurity, strontium, significantly retards the rate of calcite growth while expanding its unit cell but whose incorporation is history dependent. Reactive transport modeling of the dissolution–precipitation kinetics suggests that ACC must be dissolving as compact aggregates. These various transformation mechanisms drive diverse geological and biological carbonate formations, impacting their use as paleoenvironmental markers and functional materials synthesis.

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

Hidden Markov Model: a shortest unique representative approach to detect the protein toxins, virulence factors and antibiotic resistance genes

Objective: Currently, next generation sequencing (NGS) is widely used to decode potential novel or variant pathogens both in emergent outbreaks and in routine clinical practice. However, the efficient identification of novel or diverged pathogenomic compositions remains a big challenge. It is especially true for short DNA sequence fragments from NGS, since sequence similarity searching is vulnerable to false negatives or false positives, as is mismatching or matching with unrelated proteins. Therefore, this study aimed to establish a bioinformatics approach that can generate unique motif sequences for profiling searching, resulting in high specificity and sensitivity. Results: In this study, we introduced a Shortest Unique Representative Hidden Markov Model (HMM) approach to identify bacterial toxin, virulence factor (VF), and antimicrobial resistance (AR) in short sequence reads. We first construct unique representative domain sequences of toxin genes, VFs, and ARs to avoid potential false positives, and then to use HMM models to accurately identify potential toxin, VF, and AR fragments. The benchmark shows this approach can achieve relatively high specificity and sensitivity if the appropriate cutoff value is applied. Our approach can be used to recognize the protein sequences of known toxins and pathogens, identifies their common characteristics and then searches for similar sequences in other organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Rat bronchoalveolar lavage proteome changes following e-cigarette aerosol exposures

E-cigarette liquids are complex mixtures of chemicals consisting of humectants, such as propylene glycol (PG) and vegetable glycerin (VG), with nicotine or flavorings added. Published literature emphasizes the toxicity of e-cigarette aerosols with flavorings whereas much less attention has been given to the biologic effects of humectants. The purpose of the current study was to provide a comprehensive view of the acute biologic effects of e-cigarette aerosols on rat bronchoalveolar lavage (BAL) using mass spectrometry-based global proteomics. Sprague–Dawley rats were exposed to e-cigarette aerosol for 3 h/day for three consecutive days. Groups included: PG/VG alone, PG/VG + 2.5% nicotine (N), or PG/VG + N + 3.3% vanillin (V). Right lung lobes were lavaged for BAL and supernatants prepared for proteomics. Extracellular BAL S100A9 concentrations and BAL cell staining for citrullinated histone H3 (citH3) were also performed. From global proteomics, ~2,100 proteins were identified from rat BAL. Overall, the greatest change in number of BAL proteins occurred with PG/VG exposures alone compared with controls with biological pathways enriched for acute phase responses, extracellular trap formation, and coagulation. Extracellular BAL S100A9 concentrations and the number of citH3 + BAL cells also increased significantly in PG/VG and PG/VG + 2.5% N. In contrast to PG/VG or PG/VG + N, the addition of vanillin to PG/VG + N increased BAL neutrophilia and downregulated lipid transport proteins. In summary, global proteomics support e-cigarette aerosol exposures to PG/VG alone as having a significant biologic effect on the lung independent of nicotine or flavoring with increased markers of extracellular trap formation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Cognitive analysis of metabolomics data for systems biology

Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. Additionally, the four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1–4 h or more to complete, depending on the processing time of the various software used.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Long-term, non-invasive FTIR detection of low-dose ionizing radiation exposure

Non-invasive methods of detecting radiation exposure show promise to improve upon current approaches to biological dosimetry in ease, speed, and accuracy. Here we developed a pipeline that employs Fourier transform infrared (FTIR) spectroscopy in the mid-infrared spectrum to identify a signature of low dose ionizing radiation exposure in mouse ear pinnae over time. Mice exposed to 0.1 to 2 Gy total body irradiation were repeatedly measured by FTIR at the stratum corneum of the ear pinnae. We found significant discriminative power for all doses and time-points out to 90 days after exposure. Classification accuracy was maximized when testing 14 days after exposure (specificity > 0.9 with a sensitivity threshold of 0.9) and dropped by roughly 30% sensitivity at 90 days. Infrared frequencies point towards biological changes in DNA conformation, lipid oxidation and accumulation and shifts in protein secondary structure. Since only hundreds of samples were used to learn the highly discriminative signature, developing human-relevant diagnostic capabilities is likely feasible and this non-invasive procedure points toward rapid, non-invasive, and reagent-free biodosimetry applications at population scales.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation

Our understanding of Alzheimer’s disease (AD) pathophysiology remains incomplete. Here, for this work, we used quantitative mass spectrometry and coexpression network analysis to conduct the largest proteomic study thus far on AD. A protein network module linked to sugar metabolism emerged as one of the modules most significantly associated with AD pathology and cognitive impairment. This module was enriched in AD genetic risk factors and in microglia and astrocyte protein markers associated with an anti-inflammatory state, suggesting that the biological functions it represents serve a protective role in AD. Proteins from this module were elevated in cerebrospinal fluid in early stages of the disease. In this study of >2,000 brains and nearly 400 cerebrospinal fluid samples by quantitative proteomics, we identify proteins and biological processes in AD brains that may serve as therapeutic targets and fluid biomarkers for the disease.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Local-scale panmixia in the lichenized fungus Xanthoria parietina contrasts with substantial genetic structure in its Trebouxia photobionts

Microsatellite markers can provide valuable information about gene flow and population history. We developed and tested new microsatellites for the nitrophilic lichenized fungus Xanthoria parietina and studied its genetic diversity and structure within the urban area of Munich, Bavaria. We compared its local genetic pattern with that of its photobiont partner Trebouxia decolorans, for which existing microsatellites were applied. For comparison, a reference site with clean air was included in the sampling. We found support for three genetic clusters in the fungus X. parietina, which occurred intermingled in collecting sites. There was a high degree of admixture within fungal populations and individuals, and analysis of molecular variance revealed a lack of population structure in the mycobiont. The Trebouxia photobiont, in contrast, exhibited structured populations which grouped into two to five genetic clusters, and individuals showed less admixture than in the mycobiont. This indicates that the two lichen partners differ in their ability to move around in the landscape. The microsatellite markers we report are polymorphic and are suitable for population genetic studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Chemometric optimisation of pressurised liquid extraction for the determination of alliin and S‐allyl‐cysteine in giant garlic ( Allium ampeloprasum L.) by liquid chromatography tandem mass spectrometry

Abstract Introduction Giant garlic is a functional food that contains different kinds of bioactive molecules with beneficial effects on chronic noncommunicable diseases like diabetes and cardiovascular conditions. Considering biosynthesis pathways, abundance, and biological activity, alliin and S‐allyl‐cysteine were used as chemical markers of organosulphur compounds present in giant garlic. Objective To establish a chemometric optimisation of pressurised liquid extraction for the determination of alliin and S‐allyl‐cysteine in giant garlic by liquid chromatography tandem mass spectrometry (LC–MS/MS). Methodology Samples were blanched (ca. 90°C for 10 min) to inactivate alliinase and γ‐glutamyl transpeptidase enzymes and then freeze‐dried. Chemometric optimisation was performed via response surface methodology based on central composite design (CCD). Organosulphur compound yields were determined applying a validated LC–MS/MS method in multiple reaction monitoring (MRM) mode using the following transitions: for alliin m/z 178 → 74 and for S‐allyl‐cysteine m/z 162 → 41. Results According to CCD results, under constant conditions of pressure (1500 psi) and time (20 min), the optimal conditions for pressurised liquid extraction of alliin and S‐allyl‐cysteine were 70.75 and 68.97% v/v of ethanol in extraction solvent and 76.45 and 98.88°C as extraction temperature, respectively. Multiple response optimisation for the simultaneous extraction of both organosulphur compounds was established via desirability function. Under these conditions, 2.70 ± 0.27 mg g −1 dry weight (DW) of alliin and 2.79 ± 0.22 mg g −1 DW of S‐allyl‐cysteine were extracted. Conclusions These results clearly demonstrated that pressurised liquid extraction is an efficient green technique to extract bioactive organosulphur compounds from giant garlic. Extraction yields were significantly ( p < 0.05) higher than those obtained with conventional ultra‐turrax extraction.

Peterssen‐Fonseca, Darlene↗
DOE OSTI2026

Miscanthus giganteus Biolistic Transformation Using the Visible RUBY Red Marker Gene to Monitor Transformation Efficiency

Miscanthus × giganteus ( M × g ) is a high-yielding perennial C4 bioenergy crop, but genetic improvement by breeding is constrained by triploid sterility and clonal propagation. Improving genetic transformation methods for M × g would provide opportunities for advantageous trait introgression. Use of an easy to phenotype reporter gene is a promising strategy to improve transformation processes and efficiency. This study presents an efficient novel method for biolistic transformation of inflorescence-derived callus in M × g and demonstrates its efficacy using RUBY, a betalain-based noninvasive reporter that is visible throughout the transformation process. RUBY expression ( Zea mays codon optimized) was visible from callus stage through plantlet development into maturity. RUBY expressing independently transformed plants were confirmed by hygromycin phosphotransferase ELISA and by genomic PCR demonstrating that the RUBY phenotype is sufficient for screening transformants. The Zea mays codon optimized hygromycin selection marker was driven by previously established promoters for Miscanthus, ZmUBI and 2×35S, while the RUBY gene expression was controlled by a known Zea mays C4 promoter, Brachypodium UBI10, newly employed in Miscanthus. The construct containing the 2×35S promoter for hygromycin had a 15.1% transformation efficiency while the ZmUBI promoter had a 20.5% transformation efficiency. This study provides a novel, highly efficient protocol for successful biolistic transformation of M × g for stable expression. This study also demonstrates that RUBY expression can be used as a convenient and powerful monitor of transformation in ongoing and future work to engineer M × g into an improved bioproduct feedstock.

RUBY↗
DOE OSTI2022

Neutralization titer biomarker for antibody-mediated prevention of HIV-1 acquisition

The Antibody Mediated Prevention trials showed that the broadly neutralizing antibody (bnAb) VRC01 prevented acquisition of human immunodeficiency virus-1 (HIV-1) sensitive to VRC01. Using AMP trial data, here we show that the predicted serum neutralization 80% inhibitory dilution titer (PT 80 ) biomarker—which quantifies the neutralization potency of antibodies in an individual’s serum against an HIV-1 isolate—can be used to predict HIV-1 prevention efficacy. Similar to the results of nonhuman primate studies, an average PT 80 of 200 (meaning a bnAb concentration 200-fold higher than that required to reduce infection by 80% in vitro) against a population of probable exposing viruses was estimated to be required for 90% prevention efficacy against acquisition of these viruses. Based on this result, we suggest that the goal of sustained PT 80 <200 against 90% of circulating viruses can be achieved by promising bnAb regimens engineered for long half-lives. We propose the PT 80 biomarker as a surrogate endpoint for evaluatinon of bnAb regimens, and as a tool for benchmarking candidate bnAb-inducing vaccines.

59 BASIC BIOLOGICAL SCIENCES↗