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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

LncRNA DLEU2 promotes cervical cancer cell proliferation by regulating cell cycle and NOTCH pathway

Highlights: • LncRNA DLEU2 is upregulated and related to tumor topography in cervical cancer. • LncRNA DLEU2 promotes cervical cancer cell proliferation. • LncRNA DLEU2 promotes the progression of the cell cycle. • LncRNA DLEU2 inhibits the activity of the Notch signaling pathway. Long noncoding RNAs (lncRNAs) are known to play a crucial role in the onset and progression of cervical cancer (CC). Here, the results of RNA microarray and RNA-sequencing dataset analysis showed that lncRNA DLEU2 was significantly upregulated in CC tissues. Clinicopathologic analysis indicated that lncRNA DLEU2 was closely related to tumor topography. Functional experiments and bioinformatics analysis revealed that lncRNA DLEU2 promoted CC cell proliferation and accelerated the cell cycle. Mechanistically, lncRNA DLEU2 promoted the progression of the cell cycle and inhibited the activity of the Notch signaling pathway by inhibiting p53 expression. Additionally, lncRNA DLEU2 probably interacted with ZFP36 Ring Finger Protein (ZFP36) to inhibit the expression of p53. In conclusion, this study revealed the function of lncRNA DLEU2 in CC tumorigenesis, suggesting new therapeutic targets in CC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Competing endogenous RNA network associated with oxygen-induced retinopathy: Expression of the network and identification of the MALAT1/miR-124–3p/EGR1 regulatory axis

Retinopathy of prematurity (ROP) is a severe retinal dysfunction in prematurely born babies. The relationship between non-coding RNAs and retinopathy of prematurity (ROP) remain unclear. Microarray analysis of lncRNAs, miRNAs, and mRNAs was conducted in a mouse model of ROP. A competing endogenous RNA (ceRNA) network was constructed. The relationship among MALAT1, miR-124–3p, and Early growth response protein 1 (EGR1) was assessed in hypoxia-induced primary human umbilical vein endothelial cells (HUVECs) and ROP mouse model. In the study, we found 2252 lncRNAs, 1239 mRNAs, and 36 miRNAs were differentially regulated. ceRNA network consisting of 21 lncRNAs, 10 miRNAs, and 19 mRNAs was established. Of the most down-regulated miRNAs, miR-124–3p was selected for additional study. miR-124–3p ceased the migration and proliferation of primary HUVECs in hypoxic conditions, and directly suppressed EGR1. Additionally, MALAT1 directly sponged miR-124–3p. Knockdown of MALAT1 decreased EGR1 expression and inhibited the migration and proliferation of primary HUVECs in hypoxia. Furthermore, these changes were rescued by depletion of miR-124–3p. In vivo, intravitreal injection of miR-124–3p, shMALAT1 decreased EGR1 expression and markedly suppressed retinal neovascularization in OIR models. Intravitreal injection of shMALAT1 and miR-124–3p antagomir at the same time can promote retinal neovascularization, which reversed the suppression of retinal neovascularization functioned by shMALAT1. In conclusion, the expression profiles of lncRNAs and miRNAs and the ceRNA network in a mouse model of ROP may be indicative of the underlying mechanisms of retinal angiogenesis and neural activity. The MALAT1/miR-124–3p/EGR1 regulatory axis is partly responsible for retinal neovascularization, which may provide a novel theoretical basis for the pathogenesis of ROP.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Long noncoding RNA AK023096 interacts with hnRNP-K and contributes to the maintenance of self-renewal in bladder cancer stem-like cells

Highlights: • BCSLCs play a critical role for the high risk of recurrence and we identified that lncRNA-AK023096 was required for controls stemness of BCSLCs. • LncRNA-AK023096 recruits hnRNP-K and epigenetically upregulated SOX2 expression by modulating H3K4 trimethylation of SOX2 promoter. • Overexpression of lncRNA-AK023096 expression in the tumor tissue was significantly associated with high risk of recurrence following TUR-Bt. LncRNA contribution to self-renewal of bladder cancer stem-like cells (CSLCs) remains largely unknown. We investigated the expression profile and biological function of lncRNAs in urothelial CSLCs by microarray analysis. Among these, lncRNA-AK023096 was identified as potentially playing a role in maintaining self-renewal of CSLCs. Knockdown of this transcript inhibited spheroid formation and tumor formation. We found that AK023096 mediates recruitment of hnRNP-K to SOX2 promoter and increases H3K4 trimethylation status on SOX2 promoter, leading to a robust change in SOX2 mRNA and protein levels. Moreover, AK023096 expression in primary tumors was found to be a powerful predictor of recurrence following transurethral resection in patients with nonmuscle-invasive bladder cancer, highlighting the critical role of lncRNA in the bladder cancer regulatory network.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Intermittent fasting enhances long-term memory consolidation, adult hippocampal neurogenesis, and expression of longevity gene Klotho

Daily calorie restriction (CR) and intermittent fasting (IF) enhance longevity and cognition but the effects and mechanisms that differentiate these two paradigms are unknown. We examined whether IF in the form of every-other-day feeding enhances cognition and adult hippocampal neurogenesis (AHN) when compared to a matched 10% daily CR intake and ad libitum conditions. After 3 months under IF, female C57BL6 mice exhibited improved long-term memory retention. IF increased the number of BrdU-labeled cells and neuroblasts in the hippocampus, and microarray analysis revealed that the longevity gene Klotho (Kl) was upregulated in the hippocampus by IF only. Furthermore, we found that downregulating Kl in human hippocampal progenitor cells led to decreased neurogenesis, whereas Kl overexpression increased neurogenesis. Finally, histological analysis of Kl knockout mice brains revealed that Kl is required for AHN, particularly in the dorsal hippocampus. These data suggest that IF is superior to 10% CR in enhancing memory and identifies Kl as a novel candidate molecule that regulates the effects of IF on cognition likely via AHN enhancement.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Long-term warming in a Mediterranean-type grassland affects soil bacterial functional potential but not bacterial taxonomic composition

Abstract Climate warming is known to impact ecosystem composition and functioning. However, it remains largely unclear how soil microbial communities respond to long-term, moderate warming. In this study, we used Illumina sequencing and microarrays (GeoChip 5.0) to analyze taxonomic and functional gene compositions of the soil microbial community after 14 years of warming (at 0.8–1.0 °C for 10 years and then 1.5–2.0 °C for 4 years) in a Californian grassland. Long-term warming had no detectable effect on the taxonomic composition of soil bacterial community, nor on any plant or abiotic soil variables. In contrast, functional gene compositions differed between warming and control for bacterial, archaeal, and fungal communities. Functional genes associated with labile carbon (C) degradation increased in relative abundance in the warming treatment, whereas those associated with recalcitrant C degradation decreased. A number of functional genes associated with nitrogen (N) cycling (e.g., denitrifying genes encoding nitrate-, nitrite-, and nitrous oxidereductases) decreased, whereas nifH gene encoding nitrogenase increased in the warming treatment. These results suggest that microbial functional potentials are more sensitive to long-term moderate warming than the taxonomic composition of microbial community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A genomic data archive from the Network for Pancreatic Organ donors with Diabetes

The Network for Pancreatic Organ donors with Diabetes (nPOD) is the largest biorepository of human pancreata and associated immune organs from donors with type 1 diabetes (T1D), maturity-onset diabetes of the young (MODY), cystic fibrosis-related diabetes (CFRD), type 2 diabetes (T2D), gestational diabetes, islet autoantibody positivity (AAb+), and without diabetes. nPOD recovers, processes, analyzes, and distributes high-quality biospecimens, collected using optimized standard operating procedures, and associated de-identified data/metadata to researchers around the world. Herein describes the release of high-parameter genotyping data from this collection. 372 donors were genotyped using a custom precision medicine single nucleotide polymorphism (SNP) microarray. Data were technically validated using published algorithms to evaluate donor relatedness, ancestry, imputed HLA, and T1D genetic risk score. Additionally, 207 donors were assessed for rare known and novel coding region variants via whole exome sequencing (WES). These data are publicly-available to enable genotype-specific sample requests and the study of novel genotype:phenotype associations, aiding in the mission of nPOD to enhance understanding of diabetes pathogenesis to promote the development of novel therapies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Epidermal growth factor receptor cascade prioritizes the maximization of signal transduction

Many studies have been performed to quantify cell signaling. Cell signaling molecules are phosphorylated in response to extracellular stimuli, with the phosphorylation sequence forming a signal cascade. The information gain during a signal event is given by the logarithm of the phosphorylation molecule ratio. The average information gain can be regarded as the signal transduction quantity (ST), which is identical to the Kullback–Leibler divergence (KLD), a relative entropy. We previously reported that if the total ST value in a given signal cascade is maximized, the ST rate (STR) of each signaling molecule per signal duration (min) approaches a constant value. To experimentally verify this theoretical conclusion, we measured the STR of the epidermal growth factor (EGF)-related cascade in A431 skin cancer cells following stimulation with EGF using antibody microarrays against phosphorylated signal molecules. The results were consistent with those from the theoretical analysis. Thus, signaling transduction systems may adopt a strategy that prioritizes the maximization of ST. Furthermore, signal molecules with similar STRs may form a signal cascade. In conclusion, ST and STR are promising properties for quantitative analysis of signal transduction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Novel lamprey antibody recognizes terminal sulfated galactose epitopes on mammalian glycoproteins

The terminal galactose residues of N- and O-glycans in animal glycoproteins are often sialylated and/or fucosylated, but sulfation, such as 3-O-sulfated galactose (3-O-SGal), represents an additional, but poorly understood modification. To this end, we have developed a novel sea lamprey variable lymphocyte receptor (VLR) termed O6 to explore 3-O-SGal expression. O6 was engineered as a recombinant murine IgG chimera and its specificity and affinity to the 3-O-SGal epitope was defined using a variety of approaches, including glycan and glycoprotein microarray analyses, isothermal calorimetry, ligand-bound crystal structure, FACS, and immunohistochemistry of human tissue macroarrays. 3-O-SGal is expressed on N-glycans of many plasma and tissue glycoproteins, but recognition by O6 is often masked by sialic acid and thus exposed by treatment with neuraminidase. O6 recognizes many human tissues, consistent with expression of the cognate sulfotransferases (GAL3ST-2 and GAL3ST-3). The availability of O6 for exploring 3-O-SGal expression could lead to new biomarkers for disease and aid in understanding the functional roles of terminal modifications of glycans and relationships between terminal sulfation, sialylation and fucosylation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Community RNA-Seq: multi-kingdom responses to living versus decaying roots in soil

Abstract Roots are a primary source of organic carbon input in most soils. The consumption of living and detrital root inputs involves multi-trophic processes and multiple kingdoms of microbial life, but typical microbial ecology studies focus on only one or two major lineages. We used Illumina shotgun RNA sequencing to conduct PCR-independent SSU rRNA community analysis (“community RNA-Seq”) and simultaneously assess the bacteria, archaea, fungi, and microfauna surrounding both living and decomposing roots of the annual grass, Avena fatua. Plants were grown in 13CO2-labeled microcosms amended with 15N-root litter to identify the preferences of rhizosphere organisms for root exudates (13C) versus decaying root biomass (15N) using NanoSIMS microarray imaging (Chip-SIP). When litter was available, rhizosphere and bulk soil had significantly more Amoebozoa, which are potentially important yet often overlooked top-down drivers of detritusphere community dynamics and nutrient cycling. Bulk soil containing litter was depleted in Actinobacteria but had significantly more Bacteroidetes and Proteobacteria. While Actinobacteria were abundant in the rhizosphere, Chip-SIP showed Actinobacteria preferentially incorporated litter relative to root exudates, indicating this group’s more prominent role in detritus elemental cycling in the rhizosphere. Our results emphasize that decomposition is a multi-trophic process involving complex interactions, and our methodology can be used to track the trajectory of carbon through multi-kingdom soil food webs.

Nuccio, Erin E. (ORCID:000000030189183X)↗
DOE OSTI2023

DNA-caged nanoparticles via electrostatic self-assembly

DNA-modified nanoparticles enable DNA sensing and therapeutics in nanomedicine and are also crucial for nanoparticle self-assembly with DNA-based materials. However, methods to conjugate DNA to nanoparticle surfaces are limited, inefficient, and lack control. Inspired by DNA tile nanotechnology, we demonstrate a new approach to nanoparticle modification based on electrostatic attraction between negatively charged DNA tiles and positively charged nanoparticles. This approach does not disrupt nanoparticle surfaces and leverages the programmability of DNA nanotechnology to control DNA presentation. We demonstrated this approach using a variety of nanoparticles, including polymeric micelles, polystyrene beads, gold nanoparticles, and superparamagnetic iron oxide nanoparticles with sizes ranging from 5–20 nm in diameter. DNA cage formation was confirmed through transmission electron microscopy (TEM), neutralization of zeta potential, and a series of fluorescence experiments. DNA cages present “handle” sequences that can be used for reversible target attachment or self-assembly. Handle functionality was verified in solution, at the solid–liquid interface, and inside fixed cells, corresponding to applications in biosensing, DNA microarrays, and erasable immunocytochemistry. Finally, these experiments demonstrate the versatility of the electrostatic DNA caging approach and provide a new pathway to nanoparticle modification with DNA that will empower further applications of these materials in medicine and materials science.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

High-throughput Single-Cell Proteomics and Transcriptomics from the Same Cells with a Nanoliter-Scale Spin-Transfer Approach

Single-cell multiomic platforms provide a comprehensive snapshot of cellular states and cell types by offering critical insights into the spatiotemporal regulation of biomolecular networks at a systems level, thereby defining the basis of multicellularity. Here, we introduce nanoSPINS, an advanced platform that enables high-throughput profiling and integrative analysis of the transcriptome and proteome from the same single cells using RNA sequencing and isobaric labeling LC-MS-based proteomics, respectively. NanoSPINS can efficiently transfer mRNA-containing droplets across two microarrays via a centrifugation-based approach, while proteins are retained on the initial platform. Benchmarking of nanoSPINS on two cell lines demonstrates its ability to generate global proteomic and transcriptomic profiles that align well with previously established methodologies/platforms. The incorporation of isobaric TMTpro labeling into this single-cell multiomics platform significantly enhances the throughput of single-cell proteomic analyses. Through the high-throughput quantification of the proteome and transcriptome, nanoSPINS not only facilitates the identification of molecular features at both mRNA and protein level but also provides larger sample sizes for improved statistical power in clustering and differential abundance. Given the broad applicability of single-cell multiomics in biological research and clinical settings, we believe nanoSPINS represents a powerful platform for the characterization of heterogeneous cell populations.

multi 'omics↗
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 OSTI2020

Winter warming rapidly increases carbon degradation capacities of fungal communities in tundra soil: Potential consequences on carbon stability

We report high-latitude tundra ecosystems are increasingly affected by climate warming. As an important fraction of soil microorganisms, fungi play essential roles in carbon degradation, especially the old, chemically recalcitrant carbon. However, it remains obscure how fungi respond to climate warming and whether fungi, in turn, affect carbon stability of tundra. In a 2-year winter soil warming experiment of 2°C by snow fences, we investigated responses of fungal communities to warming in the active layer of an Alaskan tundra. Although fungal community composition, revealed by the 28S rRNA gene amplicon sequencing, remained unchanged (p > .05), fungal functional gene composition, revealed by a microarray named GeoChip, was altered (p < .05). Changes in functional gene composition were linked to winter soil temperature, thaw depth, soil moisture, and gross primary productivity (canonical correlation analysis, p < .05). Specifically, relative abundances of fungal genes encoding invertase, xylose reductase and vanillin dehydrogenase significantly increased (p < .05), indicating higher carbon degradation capacities of fungal communities under warming. Accordingly, we detected changes in fungal gene networks under warming, including higher average path distance, lower average clustering coefficient and lower percentage of negative links, indicating that warming potentially changed fungal interactions. Together, our study reveals higher carbon degradation capacities of fungal communities under short-term warming and highlights the potential impacts of fungal communities on tundra ecosystem respiration, and consequently future carbon stability of high-latitude tundra.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A common resequencing‐based genetic marker data set for global maize diversity

SUMMARY Maize ( Zea mays ssp. mays ) populations exhibit vast ranges of genetic and phenotypic diversity. As sequencing costs have declined, an increasing number of projects have sought to measure genetic differences between and within maize populations using whole‐genome resequencing strategies, identifying millions of segregating single‐nucleotide polymorphisms (SNPs) and insertions/deletions (InDels). Unlike older genotyping strategies like microarrays and genotyping by sequencing, resequencing should, in principle, frequently identify and score common genetic variants. However, in practice, different projects frequently employ different analytical pipelines, often employ different reference genome assemblies and consistently filter for minor allele frequency within the study population. This constrains the potential to reuse and remix data on genetic diversity generated from different projects to address new biological questions in new ways. Here, we employ resequencing data from 1276 previously published maize samples and 239 newly resequenced maize samples to generate a single unified marker set of approximately 366 million segregating variants and approximately 46 million high‐confidence variants scored across crop wild relatives, landraces as well as tropical and temperate lines from different breeding eras. We demonstrate that the new variant set provides increased power to identify known causal flowering‐time genes using previously published trait data sets, as well as the potential to track changes in the frequency of functionally distinct alleles across the global distribution of modern maize.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Scalable fabrication of Chip-integrated 3D-nanostructured electronic devices via DNA-programmable assembly

DNA-based self-assembly methods have demonstrated powerful and unique capabilities to encode nanomaterial structures through the prescribed placement of inorganic and biological nanocomponents. However, the challenge of selectively growing DNA superlattices on specific locations of surfaces and their integration with conventional nanofabrication has hindered the fabrication of three-dimensional (3D) DNA-assembled functional devices. Here, we present a scalable nanofabrication technique that combines bottom-up and top-down approaches for selective growth of 3D DNA superlattices on gold microarrays. This approach allows for the fabrication of self-assembled 3D-nanostructured electronic devices. DNA strands are bound onto the gold arrays, which anchor DNA origami frames and promote ordered framework growth on the specific areas of the surface, enabling control of the lateral placement and orientation of superlattices. DNA frameworks selectively grown on the pads are subsequently templated to nanoscale silica and tin oxide (SnO x ) that follow the architecture, as confirmed by structural and chemical characterizations. The fabricated SnO x superlattices are integrated into devices that demonstrate photocurrent response.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Multi-strain analysis of Pseudomonas putida reveals the metabolic and genetic diversity of the species

Pseudomonas putida is a gram-negative bacterial species increasingly utilized in biotechnology due to its robust growth, ability to degrade aromatic compounds, solvent tolerance, and genetic tractability. In this study, we report a comprehensive multi-strain analysis of 164 P. putida strains based on the reconstruction of a pan-putida metabolic network and the formulation of strain-specific genome-scale metabolic models (GEMs). We performed whole-genome sequencing and hybrid assembly for 40 strains, contributing a ~8% increase to the available genomic data for P. putida . Furthermore, high-throughput phenotypic profiling using the Biolog phenotype microarray system for 24 strains on 190 unique carbon sources, along with 15 aromatic compounds not present on Biolog plates, yielded 4,920 unique strain-phenotype measurements. These data were leveraged to curate GEMs for 24 representative strains, including a refined model for strain KT2440, which comprised 1,480 genes and 2,191 metabolites, achieving a prediction accuracy of 91.2% in carbon utilization. Systematic comparison of genomes and GEMs revealed both conserved core pathways and significant allelic and functional divergence across strains, highlighting strain-specific variation in aromatic degradation. While pathways for protocatechuate and phenylacetate degradation were widely conserved, metabolic capabilities for compounds such as ferulate, phenol, and cresols varied markedly, suggesting adaptation to distinct ecological niches. Alleleome analysis of enzymes, such as PcaI and PcaJ, revealed distinct, functionally similar clades, indicating possible convergent evolution or horizontal gene transfer. These results provide computable resources and informative models for selecting P. putida strains with desired traits for biomanufacturing and bioremediation and offer insights into the evolution and phylogeny of the P. putida species.

aromatics utilization↗
DOE OSTI2026

Transplatformer: translating toxicogenomic profiles between generations of platforms

Background Transcriptomic profiling technologies have advanced the analysis of biological and toxicological responses. However, substantial differences in probe design, dynamic range, gene coverage, and preprocessing pipelines across platforms introduce artifacts that limit cross-study integration and hinder the reuse of historical datasets. We aim to develop computational methods for accurate cross-platform translation to maximize the value of legacy resources. Results We present TransPlatformer a deep learning framework for translating gene expression profiles across heterogeneous toxicogenomics platforms. TransPlatformer employs a novel attention-based architecture to map high-dimensional fold-change vectors from legacy microarray technologies to current platforms. Models are trained and evaluated using DrugMatrix, spanning three technological generations. We investigate mixed-tissue, single-tissue, and cross-tissue training paradigms and benchmark performance against multilayer perceptron and matrix-completion baselines. In mixed-tissue training, TransPlatformer achieves a greater than 50% reduction in mean absolute error (0.043 vs. 0.09) and nearly doubles Pearson correlation ( ≈ 0.71 vs. 0.37) relative to baseline methods. Importantly, TransPlatformer preserves rare but biologically meaningful over- and under-expressed signals, with mean absolute error below 0.22. Single-tissue models yield further improvements for well-represented organs, such as a 10% reduction in liver mean absolute error, while underscoring the need for data augmentation strategies in low-sample tissues.ra Conclusions TransPlatformer provides an effective and scalable computational solution for cross-platform transcriptomic translation. By enabling biologically faithful harmonization of gene expression data, the proposed approach facilitates the reuse of legacy toxicogenomics datasets, enhances downstream biomarker discovery, and supports more reproducible predictive modeling in toxicology.

59 BASIC BIOLOGICAL SCIENCES↗