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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 OSTI2022

A structural homology approach to identify potential cross-reactive antibody responses following SARS-CoV-2 infection

Abstract The emergence of the novel SARS-CoV-2 virus is the most important public-health issue of our time. Understanding the diverse clinical presentations of the ensuing disease, COVID-19, remains a critical unmet need. Here we present a comprehensive listing of the diverse clinical indications associated with COVID-19. We explore the theory that anti-SARS-CoV-2 antibodies could cross-react with endogenous human proteins driving some of the pathologies associated with COVID-19. We describe a novel computational approach to estimate structural homology between SARS-CoV-2 proteins and human proteins. Antibodies are more likely to interrogate 3D-structural epitopes than continuous linear epitopes. This computational workflow identified 346 human proteins containing a domain with high structural homology to a SARS-CoV-2 Wuhan strain protein. Of these, 102 proteins exhibit functions that could contribute to COVID-19 clinical pathologies. We present a testable hypothesis to delineate unexplained clinical observations vis-à-vis COVID-19 and a tool to evaluate the safety-risk profile of potential COVID-19 therapies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Enhanced terrestrial Fe(II) mobilization identified through a novel mechanism of microbially driven cave formation in Fe(III)-rich rocks

Most cave formation requires mass separation from a host rock in a process that operates outward from permeable pathways to create the cave void. Given the poor solubility of Fe(III) phases, such processes are insufficient to account for the significant iron formation caves (IFCs) seen in Brazilian banded iron formations (BIF) and associated rock. Here in this study we demonstrate that microbially-mediated reductive Fe(III) dissolution is solubilizing the poorly soluble Fe(III) phases to soluble Fe(II) in the anoxic zone behind cave walls. The resultant Fe(III)-depleted material (termed sub muros) is unable to maintain the structural integrity of the walls and repeated rounds of wall collapse lead to formation of the cave void in an active, measurable process. This mechanism may move significant quantities of Fe(II) into ground water and may help to explain the mechanism of BIF dissolution and REE enrichment in the generation of canga. The role of Fe(III) reducing microorganism and mass separation behind the walls (outward-in, rather than inward-out) is not only a novel mechanism of speleogenesis, but it also may identify a previously overlooked source of continental Fe that may have contributed to Archaean BIF formation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Identifying the secondary electron cutoff in ultraviolet photoemission spectra for work function measurements of non-ideal surfaces

Abstract Absolute values of work functions can be determined in ultraviolet photoemission spectroscopy (UPS) by measuring the minimum kinetic energy of secondary electrons generated by a known photon energy. However, some samples can produce spectra that are difficult to interpret due to additional intensity below the true secondary electron cutoff. Disordered absorbates on elemental metals add small intensity below the onset for the transition metal surfaces studied, which can be attributed to energy losses after photoelectrons are generated. In contrast, spectra from WO 3−x films can produce multiple onsets with comparable intensity which do not fit this model. False onsets (in the context of work function measurements) can be minimized by optimizing experimental detection parameters including limiting analyzer acceptance angles and pass energy. True work functions can be identified by examining the onsets as the sample bias is varied.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

scRNA seq of an F1 cross of Marek’s disease resistant and susceptible chickens identifies allele specific expression signatures enriched in transcription modulators

Abstract Marek’s disease (MD), a T cell lymphoma disease in chickens, is caused by the Marek’s disease virus (MDV) found ubiquitously in the poultry industry. Genetically resistant Line 6 3 (L6) and susceptible Line 7 2 (L7) chickens have been instrumental to research on avian immune system response to MDV infection. In this study we characterized molecular signatures unique to splenic immune cell types across different genetic backgrounds 6 days after infection. Using three populations, L6, L7, and an F1 cross between L6xL7, we evaluated the immune cell transcriptome of responding cell types using single cell RNA sequencing. Several MDV genes were found expressed mainly in cytotoxic T cells while ICP4 and MEQ MDV genes were expressed across infected cell types. Using the F1 we quantified allele specific expression (ASE) of biallelic SNPs and found biased expression of parental alleles specific to immune cell subtypes. We identified 22 SNPs with ASE in response to MDV infection mapped to gene rich regions surrounding 59 genes of critical importance for chromatin remodeling and transcriptional regulation. Histone deacetylase genes (HDAC1 and HDAC8) had increased expression of L6 alleles, while small nuclear RNA genes (SNORA68 and SNORA72) expressed higher levels of L7 alleles with infection in T cell subsets. SNPs with ASE also mapped genes important for an adequate immune response including GNLY (cytotoxic activity) and PDIA3 (component of MHC class I peptide loading complex), and genes known to promote viral replication (MCM5 and EIF3M). These results show that functional variants associated with susceptibility to MD may have a bigger impact in subsets of immune cell types, and by characterizing the transcriptomes of these subtypes we can unravel molecular signatures specific to MD genomic resistance.

Science & Technology - Other Topics↗
DOE OSTI2018

Sociomarkers and biomarkers: predictive modeling in identifying pediatric asthma patients at risk of hospital revisits

The importance of social components of health has been emphasized both in epidemiology and public health. This paper highlights the significant impact of social components on health outcomes in a novel way. Introducing the concept of sociomarkers, which are measurable indicators of social conditions in which a patient is embedded, we employed a machine learning approach that uses both biomarkers and sociomarkers to identify asthma patients at risk of a hospital revisit after an initial visit with an accuracy of 66%. The analysis has been performed over an integrated dataset consisting of individual-level patient information such as gender, race, insurance type, and age, along with ZIP code-level sociomarkers such as poverty level, blight prevalence, and housing quality. Using this uniquely integrated database, we then compare the traditional biomarker-based risk model and the sociomarker-based risk model. A biomarker-based predictive model yields an accuracy of 65% and the sociomarkerbased model predicts with an accuracy of 61%. Without knowing specific symptom-related features, the sociomarker-based model can correctly predict two out of three patients at risk. We systematically show that sociomarkers play an important role in predicting health outcomes at the individual level in pediatric asthma cases. Additionally, by merging multiple data sources with detailed neighborhood-level data, we directly measure the importance of residential conditions for predicting individual health outcomes.

Shin, Eun Kyong↗
DOE OSTI2022

Advanced genomics identifies growth effectors for proteotoxic ER stress recovery in Arabidopsis thaliana

Abstract Adverse environmental and pathophysiological situations can overwhelm the biosynthetic capacity of the endoplasmic reticulum (ER), igniting a potentially lethal condition known as ER stress. ER stress hampers growth and triggers a conserved cytoprotective signaling cascade, the unfolded protein response (UPR) for ER homeostasis. As ER stress subsides, growth is resumed. Despite the pivotal role of the UPR in growth restoration, the underlying mechanisms for growth resumption are yet unknown. To discover these, we undertook a genomics approach in the model plant species Arabidopsis thaliana and mined the gene reprogramming roles of the UPR modulators, basic leucine zipper28 (bZIP28) and bZIP60, in ER stress resolution. Through a network modeling and experimental validation, we identified key genes downstream of the UPR bZIP-transcription factors (bZIP-TFs), and demonstrated their functional roles. Our analyses have set up a critical pipeline for functional gene discovery in ER stress resolution with broad applicability across multicellular eukaryotes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data

In vivo assessments of influenza A virus (IAV) pathogenicity and transmissibility in ferrets represent a crucial component of many pandemic risk assessment rubrics, but few systematic efforts to identify which data from in vivo experimentation are most useful for predicting pathogenesis and transmission outcomes have been conducted. To this aim, we aggregated viral and molecular data from 125 contemporary IAV (H1, H2, H3, H5, H7, and H9 subtypes) evaluated in ferrets under a consistent protocol. Three overarching predictive classification outcomes (lethality, morbidity, transmissibility) were constructed using machine learning (ML) techniques, employing datasets emphasizing virological and clinical parameters from inoculated ferrets, limited to viral sequence-based information, or combining both data types. Among 11 different ML algorithms tested and assessed, gradient boosting machines and random forest algorithms yielded the highest performance, with models for lethality and transmission consistently better performing than models predicting morbidity. Comparisons of feature selection among models was performed, and highest performing models were validated with results from external risk assessment studies. Our findings show that ML algorithms can be used to summarize complex in vivo experimental work into succinct summaries that inform and enhance risk assessment criteria for pandemic preparedness that take in vivo data into account.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Identifying chemically similar multiphase nanoprecipitates in compositionally complex non-equilibrium oxides via machine learning

Abstract Characterizing oxide nuclear fuels is difficult due to complex fission products, which result from time-evolving system chemistry and extreme operating environments. Here, we report a machine learning-enhanced approach that accelerates the characterization of spent nuclear fuels and improves the accuracy of identifying nanophase fission products and bubbles. We apply this approach to commercial, high-burnup, irradiated light-water reactor fuels, demonstrating relationships between fission product precipitates and gases. We also gain understanding of the fission versus decay pathways of precipitates across the radius of a fuel pellet. An algorithm is provided for quantifying the chemical segregation of the fission products with respect to the high-burnup structure, which enhances our ability to process large amounts of microscopy data, including approaching the atomistic-scale. This may provide a faster route for achieving physics-based fuel performance modeling.

36 MATERIALS SCIENCE↗
DOE OSTI2022

TbasCO: trait-based comparative ‘omics identifies ecosystem-level and niche-differentiating adaptations of an engineered microbiome

A grand challenge in microbial ecology is disentangling the traits of individual populations within complex communities. Various cultivation-independent approaches have been used to infer traits based on the presence of marker genes. However, marker genes are not linked to traits with complete fidelity, nor do they capture important attributes, such as the timing of gene expression or coordination among traits. To address this, we present an approach for assessing the trait landscape of microbial communities by statistically defining a trait attribute as a shared transcriptional pattern across multiple organisms. Leveraging the KEGG pathway database as a trait library and the Enhanced Biological Phosphorus Removal (EBPR) model microbial ecosystem, we demonstrate that a majority (65%) of traits present in 10 or more genomes have niche-differentiating expression attributes. For example, while many genomes containing high-affinity phosphorus transporter pstABCS display a canonical attribute (e.g. up-regulation under phosphorus starvation), we identified another attribute shared by many genomes where transcription was highest under high phosphorus conditions. Taken together, we provide a novel framework for unravelling the functional dynamics of uncultivated microorganisms by assigning trait-attributes through genome-resolved time-series metatranscriptomics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Host cell and viral protease targets of human SERPINs identified by in silico docking

Serine protease inhibitors (SERPINs) are involved in various physiological processes and diseases, such as inflammation, cancer metastasis, and neurodegeneration. Their role in viral infections is poorly understood, as their expression patterns during infection and the range of proteases they target have yet to be fully characterized. Here, we show widespread expression of human SERPINs in response to respiratory virus infections, both in bronchioalveolar lavages from COVID-19 patients and in polarized human airway epithelial cultures. Using in silico docking of 10 SERPINs to 48 host proteases, we confirm known targets and predict new interactions. Protease activity assays validated selected interactions, confirming the newly predicted host targets for PAI-1 (SERPINE1) and PAI-2 (SERPINB2). PAI-1 inhibits cathepsin L, essential for SARS-CoV-2 maturation, and suppresses multi-cycle replication of both ancestral SARS-CoV-2 WA-1 and its variant Omicron BA.1. In addition, we identify PAI-2 as an antiviral SERPIN that reduces infectivity of human adenovirus 5 by directly inhibiting the adenoviral protease. Our study leverages in silico docking using full-length 3D protein structures to uncover new SERPIN targets, offering a range of candidate targets for therapeutic interventions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Structural Characterization of a Newly Identified Component of α-Carboxysomes: The AAA+ Domain Protein CsoCbbQ

Carboxysomes are bacterial microcompartments that enhance carbon fixation by concentrating ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) and its substrate CO 2 within a proteinaceous shell. They are found in all cyanobacteria, some purple photoautotrophs and many chemoautotrophic bacteria. Carboxysomes consist of a protein shell that encapsulates several hundred molecules of RuBisCO, and contain carbonic anhydrase and other accessory proteins. Genes coding for carboxysome shell components and the encapsulated proteins are typically found together in an operon. The α-carboxysome operon is embedded in a cluster of additional, conserved genes that are presumably related to its function. In many chemoautotrophs, products of the expanded carboxysome locus include CbbO and CbbQ, a member of the AAA+ domain superfamily. We bioinformatically identified subtypes of CbbQ proteins and show that their genes frequently co-occur with both Form IA and Form II RuBisCO. The α-carboxysome-associated ortholog, CsoCbbQ, from Halothiobacillus neapolitanus forms a hexamer in solution and hydrolyzes ATP. The crystal structure shows that CsoCbbQ is a hexamer of the typical AAA+ domain; the additional C-terminal domain, diagnostic of the CbbQ subfamily, structurally fills the inter-monomer gaps, resulting in a distinctly hexagonal shape. Finally, we show that CsoCbbQ interacts with CsoCbbO and is a component of the carboxysome shell, the first example of ATPase activity associated with a bacterial microcompartment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Powder diffraction and crystal structure prediction identify four new coumarin polymorphs

Coumarin, a simple, commodity chemical isolated from beans in 1820, has, to date, only yielded one solid state structure. Here, we report a rich polymorphism of coumarin grown from the melt. Four new metastable forms were identified and their crystal structures were solved using a combination of computational crystal structure prediction algorithms and X-ray powder diffraction. With five crystal structures, coumarin has become one of the few rigid molecules showing extensive polymorphism at ambient conditions. We demonstrate the crucial role of advanced electronic structure calculations including many-body dispersion effects for accurate ranking of the stability of coumarin polymorphs and the need to account for anharmonic vibrational contributions to their free energy. As such, coumarin is a model system for studying weak intermolecular interactions, crystallization mechanisms, and kinetic effects.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Ultrasensitive single-cell proteomics workflow identifies >1000 protein groups per mammalian cell

Here, we report on the combination of nanodroplet sample preparation, ultra-low-flow nanoLC, high-field asymmetric ion mobility spectrometry (FAIMS), and the latest-generation Orbitrap Eclipse Tribrid mass spectrometer for greatly improved single-cell proteome profiling. FAIMS effectively filtered out singly charged ions for more effective MS analysis of multiply charged peptides, resulting in an average of 1056 protein groups identified from single HeLa cells without MS1-level feature matching. This is 2.3 times more identifications than without FAIMS and a far greater level of proteome coverage for single mammalian cells than has been previously reported for a label-free study. Differential analysis of single microdissected motor neurons and interneurons from human spinal tissue indicated a similar level of proteome coverage, and the two subpopulations of cells were readily differentiated based on single-cell label-free quantification.

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

On the use of vibronic coherence to identify reaction coordinates for ultrafast excited-state dynamics of transition metal-based chromophores

In this study, the question of whether one can use information from quantum coherence as a means of identifying vibrational degrees of freedom that are active along an excited-state reaction coordinate is discussed. Specifically, we are exploring the notion of whether quantum oscillations observed in single-wavelength kinetics data exhibiting coherence dephasing times that are intermediate between that expected for either pure electronic or pure vibrational dephasing are vibronic in nature and therefore may be coupled to electronic state-to-state evolution. In the case of a previously published Fe(ιι) polypyridyl complex, coherences observed subsequent to 1 A 1 → 1 MLCT excitation were linked to large-amplitude motion of a portion of the ligand framework; dephasing times on the order of 200–300 fs suggested that these degrees of freedom could be associated with ultrafast (~100 fs) conversion from the initially formed MLCT excited state to lower-energy, metal-centered ligand-field excited state(s) of the compound. Incorporation of an electronically benign but sterically restrictive Cu(ι) ion into the superstructure designed to interfere with this motion yielded a compound exhibiting a ~25-fold increase in the compound’s MLCT lifetime, a result that was interpreted as confirmation of the initial hypothesis. However, new data acquired on a different chemical system – Cr(acac') 3 (where acac' represents various derivatives of acetylacetonate) – yielded results that call into question this same hypothesis. Coherences observed subsequent to 4 A 2 → 4 T 2 ligand-field excitation on a series of molecules implicated similar vibrational degrees of freedom across the series, but exhibited dephasing times ranging from 340 fs to 2.5 ps without any clear correlation to the dynamics of excited-state evolution in the system. Taken together, the results obtained on both of these chemical platforms suggest that while identification of coherences can indeed point to degrees of freedom that should be considered as candidate modes for defining reaction trajectories, our understanding of the factors that determine the interplay across coherences, dephasing times, and electronic and geometric structure is insufficient at the present time to view this parameter as a robust metric for differentiating active versus spectator modes for ultrafast dynamics.

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

An activity-based probe library for identifying promiscuous amide hydrolases

A fluorogenic substrate library was developed to detect amide hydrolase activity in soil-derived chitin-degrading bacteria. Hit compounds were converted into pull-down probes for chemoproteomic enrichment, identifying previously unannotated proteins now linked to putative hydrolases. This approach prioritizes candidate hydrolases for further experimental validation with potential applications in the environment, biomanufacturing, and medicine.

Activity based probes↗
DOE OSTI2020

Structure based virtual screening identifies small molecule effectors for the sialoglycan binding protein Hsa

Infective endocarditis (IE) is a cardiovascular disease often caused by bacteria of the viridans group of streptococci, which includes Streptococcus gordonii and Streptococcus sanguinis. Previous research has found that serine-rich repeat (SRR) proteins on the S. gordonii bacterial surface play a critical role in pathogenesis by facilitating bacterial attachment to sialylated glycans displayed on human platelets. Despite their important role in disease progression, there are currently no anti-adhesive drugs available on the market. Here, we performed structure-based virtual screening using an ensemble docking approach followed by consensus scoring to identify novel small molecule effectors against the sialoglycan binding domain of the SRR adhesin protein Hsa from the S. gordonii strain DL1. The screening successfully predicted nine compounds which were able to displace the native ligand (sialyl-T antigen) in an in vitro assay and bind competitively to Hsa. Furthermore, hierarchical clustering based on the MACCS fingerprints showed that eight of these small molecules do not share a common scaffold with the native ligand. This study indicates that SRR family of adhesin proteins can be inhibited by diverse small molecules and thus prevent the interaction of the protein with the sialoglycans. This opens new avenues for discovering potential drugs against IE.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Dye aggregation identified by vibrational coupling using 2D IR spectroscopy

We report that a model dye, Re(CO){sub 3}(bypy)CO{sub 2}H, aggregates into clusters on TiO{sub 2} nanoparticles regardless of our preparation conditions. Using two-dimensional infrared (2D IR) spectroscopy, we have identified characteristic frequencies of monomers, dimers, and trimers. A comparison of 2D IR spectra in solution versus those deposited on TiO{sub 2} shows that the propensity to dimerize in solution leads to higher dimer formation on TiO{sub 2}, but that dimers are formed even if there are only monomers in solution. Aggregates cannot be washed off with standard protocols and are present even at submonolayer coverages. We observe cross peaks between aggregates of different sizes, primarily dimers and trimers, indicating that clusters consist of microdomains in close proximity. 2D IR spectroscopy is used to draw these conclusions from measurements of vibrational couplings, but if molecules are close enough to be vibrationally coupled, then they are also likely to be electronically coupled, which could alter charge transfer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗