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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

RuleBuilder

RuleBuilder is a tool for drawing graphs represented by the BioNetGen rule-based modeling language (BNGL), a domain-specific language used to formulate mathematical models of biochemical interaction networks. This framework provides an intuitive plain-text representation of such networks using a graph-based formalism. The interactions between the model's components are graph-rewriting rules that denote the minimum context needed for the interaction to occur. In some cases the context required for reactions to occur can grow quite large, and the lengthy textual description of the interaction can be difficult to interpret or debug. RuleBuilder is designed to make this process simpler through visualization of individual rule or pattern strings. The software assists in the reading of existing models by converting the BNGL strings of interest into a graph-based representation composed of nodes and edges. RuleBuilder also enables the user to build the visual representation directly from its interface, composing the BNGL string as components and operators are added to the graphical object.

Fricke, Matthew↗
DOE OSTI2015

An integrated insight into the response of sedimentary microbial communities to heavy metal contamination

Response of biological communities to environmental stresses is a critical issue in ecology, but how microbial communities shift across heavy metal gradients remain unclear. To explore the microbial response to heavy metal contamination (e.g., Cr, Mn, Zn), the composition, structure and functional potential of sedimentary microbial community were investigated by sequencing of 16S rRNA gene amplicons and a functional gene microarray. Analysis of 16S rRNA sequences revealed that the composition and structure of sedimentary microbial communities changed significantly across a gradient of heavy metal contamination and the relative abundances were higher for Firmicutes, Chloroflexi and Crenarchaeota, but lower for Proteobacteria and Actinobacteria in highly contaminated samples. Also, molecular ecological network analysis of sequencing data indicated that their possible interactions might be enhanced in highly contaminated communities. Correspondently, key functional genes involved in metal homeostasis (e.g., chrR, metC, merB), carbon metabolism and organic remediation showed a higher abundance in highly contaminated samples, indicating that bacterial communities in contaminated areas may modulate their energy consumption and organic remediation ability. This study indicated that the sedimentary indigenous microbial community may shift the composition and structure as well as function priority and interaction network to increase their adaptability and/or resistance to environmental contamination.

Science & Technology - Other Topics↗
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 OSTI2023

PathTracer Comprehensively Identifies Hypoxia-Induced Dormancy Adaptations in Mycobacterium tuberculosis

Mining large-scale data to discover biologically relevant information remains a challenge despite the rapid development of bioinformatics tools. Here, we have developed a new tool, PathTracer, to identify biologically relevant information flows by mining genome-wide protein–protein interaction networks following integration of gene expression data. PathTracer successfully mines interactions between genes and traces the most perturbed paths of perceived activities under the conditions of the study. Here, we further demonstrated the utility of this tool by identifying adaptation mechanisms of hypoxia-induced dormancy in Mycobacterium tuberculosis (Mtb).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Kindlin Is Mechanosensitive: Force-Induced Conformational Switch Mediates Cross-Talk among Integrins

Mechanical stresses directly regulate the function of several proteins of the integrin-mediated focal adhesion complex as they experience intra- and extracellular forces. Kindlin is a largely overlooked member of the focal adhesion complex whose roles in cellular mechanotransduction are only recently being identified. Recent crystallographic experiments have revealed that kindlins can form dimers that bind simultaneously to two integrins, providing a mechanistic explanation of how kindlins may promote integrin activation and clustering. In this study, using the newly identified molecular structure, we modeled the response of the kindlin2 dimer in complex with integrin β1 to mechanical cytoskeletal forces on integrins. Using molecular dynamics simulations, we show that forces on integrins are directly transmitted to the kindlin2 dimerization site, resulting in a shift in an R577-S550/E553 interaction network at this site. Under force, R577 on one protomer switches from interacting with S550 to forming new hydrogen bonds with E553 on the neighboring protomer, resulting in the strengthening of the kindlin2 dimer in complex with integrin β1. This force-induced strengthening is similar to the catch-bond mechanisms that have previously been observed in other adhesion molecules. So, based on our results, we propose that the kindlin2 dimer is mechanosensitive and can strengthen integrin-mediated focal adhesions under force by shifting the interactions at its dimerization sites.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Protein–Protein Interaction Regulates the Direction of Catalysis and Electron Transfer in a Redox Enzyme Complex

Protein-protein complexes are fundamental to life where they are key to processes ranging from central metabolism to cell signaling. Transient protein-protein interactions generally underpin the electron-transfer (ET) pathways of respiration.1 One of the many well-characterized examples of a transient ET complex is that between cytochrome c and cytochrome c oxidase.2-5 The interaction between these partner proteins is weak and dynamic. This ensures the frequent exchange of partner proteins as required to support electron flux in cases where the sole function of one of the proteins is to shuttle electrons between redox partners.1 While it is generally assumed that such transient protein-protein interactions are specific, for Paracoccus denitrificans it has recently been shown that seven proteins in a respiratory network interact in a seemingly ill-defined manner. This results in an intricate electron-transfer network that may be better suited to successful colonization of habitats with changing resources.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Protein Network Signatures Associated with Exogenous Biofuels Treatments in Cyanobacterium Synechocystis sp. PCC 6803

Although recognized as a promising microbial cell factory for producing biofuels, current productivity in cyanobacterial systems is low. To make the processes economically feasible, one of the hurdles, which need to be overcome is the low tolerance of hosts to toxic biofuels. Meanwhile, little information is available regarding the cellular responses to biofuels stress in cyanobacteria, which makes it challenging for tolerance engineering. Using large proteomic datasets of Synechocystis under various biofuels stress and environmental perturbation, a protein co-expression network was first constructed and then combined with the experimentally determined protein–protein interaction network. Proteins with statistically higher topological overlap in the integrated network were identified as common responsive proteins to both biofuels stress and environmental perturbations. In addition, a weighted gene co-expression network analysis was performed to distinguish unique responses to biofuels from those to environmental perturbations and to uncover metabolic modules and proteins uniquely associated with biofuels stress. The results showed that biofuel-specific proteins and modules were enriched in several functional categories, including photosynthesis, carbon fixation, and amino acid metabolism, which may represent potential key signatures for biofuels stress responses in Synechocystis. Network-based analysis allowed determination of the responses specifically related to biofuels stress, and the results constituted an important knowledge foundation for tolerance engineering against biofuels in Synechocystis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Alterations of circular RNAs in hyperglycemic human endothelial cells

Circular RNA (circRNA), a family of RNA generated by RNA circularization, is ubiquitously expressed in tissues and possesses increasingly important biological functions. Hyperglycemia-induced endothelial dysfunction is an initiating event in the pathogenesis of diabetes-associated cardiovascular complications. How high glucose may affect circRNAs is unknown. To address this issue, human endothelial cells were exposed to high glucose treatment and the changes of circRNAs were measured by RNA sequencing. A total 3686 circRNAs, including 1040 previously unrecorded circRNAs, were detected; and 95 different expression (DE) circRNAs were observed. The host genes of these DE circRNAs were further studied by function enrichment analyses. These analyses revealed genes of phosphoproteins, transferases, and zine finger proteins. Since circRNAs can function as a microRNA (miRNA) sponge, circRNAs-miRNAs interaction networks were explored by bioinformatics. These analyses identified a number of miRNAs, which might interact with DE circRNAs and play roles in the actions of high glucose on endothelial cells. These results demonstrate that high glucose exposure profoundly changes circRNA expression in endothelial cells. Altered circRNA expression may contribute to the effects of high glucose on endothelial function in diabetes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Proteomic analysis of trichloroethylene-induced alterations in expression, distribution, and interactions of SET/TAF-Iα and two SET/TAF-Iα-binding proteins, eEF1A1 and eEF1A2, in hepatic L-02 cells

Emerging evidence indicates that trichloroethylene (TCE) exposure causes severe hepatotoxicity. However, the mechanisms of TCE hepatotoxicity remain unclear. Recently, we reported that TCE exposure up-regulated the expression of the oncoprotein SET/TAF-Iα and SET knockdown attenuated TCE-induced cytotoxicity in hepatic L-02 cells. To decipher the function of SET/TAF-Iα and its contributions to TCE-induced hepatotoxicity, we employed a proteomic analysis of SET/TAF-Iα with tandem affinity purification to identify SET/TAF-Iα-binding proteins. We identified 42 novel Gene Ontology co-annotated SET/TAF-Iα-binding proteins. The identifications of two of these proteins (eEF1A1, elongation factor 1-alpha 1; eEF1A2, elongation factor 1-alpha 2) were confirmed by Western blot analysis and co-immunoprecipitation (Co-IP). Furthermore, we analyzed the effects of TCE on the expression, distribution and interactions of eEF1A1, eEF1A2 and SET in L-02 cells. Western blot analysis reveals a significant up-regulation of eEF1A1, eEF1A2 and two isoforms of SET, and immunocytochemical analysis reveals that eEF1A1 and SET is redistributed by TCE. SET is redistributed from the nucleus to the cytoplasm, while eFE1A1 is translocated from the cytoplasm to the nucleus. Moreover, we find by Co-IP that TCE exposure significantly increases the interaction of SET with eEF1A2. Our data not only provide insights into the physiological functions of SET/TAF-Iα and complement the SET interaction networks, but also demonstrate that TCE exposure induces alterations in the expression, distribution and interactions of SET and its binding partners. These alterations may constitute the mechanisms of TCE cytotoxicity. -- Highlights: ► Identify 62 SET/TAF-Iα-associated proteins in human L-02 cells ► Trichloroethylene (TCE) alters the interaction of SET with eEF1A1 and eEF1A2. ► TCE induces the translocation and up-regulation of SET. ► TCE induces the translocation and up-regulation of eEF1A.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

petal: Co-expression network modelling in R

Background: Networks provide effective models to study complex biological systems, such as gene and protein interaction networks. With the advent of new sequencing technologies, many life scientists are grasping for user-friendly methods and tools to examine biological components at the whole-systems level. Gene co-expression network analysis approaches are frequently used to successfully associate genes with biological processes and demonstrate great potential to gain further insights into the functionality of genes, thus becoming a standard approach in Systems Biology. Here the objective is to construct biologically meaningful and statistically strong co-expression networks, the identification of research dependent subnetworks, and the presentation of self-contained results. Results: We introduce petal, a novel approach to generate gene co-expression network models based on experimental gene expression measures. petal focuses on statistical, mathematical, and biological characteristics of both, input data and output network models. Often over-looked issues of current co-expression analysis tools include the assumption of data normality, which is seldom the case for hight-throughput expression data obtained from RNA-seq technologies. petal does not assume data normality, making it a statistically appropriate method for RNA-seq data. Also, network models are rarely tested for their known typical architecture: scale-free and small-world. petal explicitly constructs networks based on both these characteristics, thereby generating biologically meaningful models. Furthermore, many network analysis tools require a number of user-defined input variables, these often require tuning and/or an understanding of the underlying algorithm; petal requires no user input other than experimental data. This allows for reproducible results, and simplifies the use of petal. Lastly, this approach is specifically designed for very large high-throughput datasets; this way, petal’s network models represent as much of the entire system as possible to provide a whole-system approach. Conclusion: petal is a novel tool for generating co-expression network models of whole-genomics experiments. It is implemented in R and available as a library. Its application to several whole-genome experiments has generated novel meaningful results and has lead the way to new testing hypothesizes for further biological investigation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response

Cells respond to stressful conditions by coordinating a complex, multi-faceted response that spans many levels of physiology. Much of the response is coordinated by changes in protein phosphorylation. Although the regulators of transcriptome changes during stress are well characterized in Saccharomyces cerevisiae , the upstream regulatory network controlling protein phosphorylation is less well dissected. Here, we developed a computational approach to infer the signaling network that regulates phosphorylation changes in response to salt stress. We developed an approach to link predicted regulators to groups of likely co-regulated phospho-peptides responding to stress, thereby creating new edges in a background protein interaction network. We then use integer linear programming (ILP) to integrate wild type and mutant phospho-proteomic data and predict the network controlling stress-activated phospho-proteomic changes. The network we inferred predicted new regulatory connections between stress-activated and growth-regulating pathways and suggested mechanisms coordinating metabolism, cell-cycle progression, and growth during stress. We confirmed several network predictions with co-immunoprecipitations coupled with mass-spectrometry protein identification and mutant phospho-proteomic analysis. Results show that the cAMP-phosphodiesterase Pde2 physically interacts with many stress-regulated transcription factors targeted by PKA, and that reduced phosphorylation of those factors during stress requires the Rck2 kinase that we show physically interacts with Pde2. Conclusively, our work shows how a high-quality computational network model can facilitate discovery of new pathway interactions during osmotic stress.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Quantification of the transferability of a designed protein specificity switch reveals extensive epistasis in molecular recognition

Re-engineering protein–protein recognition is an important route to dissecting and controlling complex interaction networks. Experimental approaches have used the strategy of “second-site suppressors,” where a functional interaction is inferred between two proteins if a mutation in one protein can be compensated by a mutation in the second. Mimicking this strategy, computational design has been applied successfully to change protein recognition specificity by predicting such sets of compensatory mutations in protein–protein interfaces. To extend this approach, it would be advantageous to be able to “transplant” existing engineered and experimentally validated specificity changes to other homologous protein–protein complexes. Here, we test this strategy by designing a pair of mutations that modulates peptide recognition specificity in the Syntrophin PDZ domain, confirming the designed interaction biochemically and structurally, and then transplanting the mutations into the context of five related PDZ domain–peptide complexes. We find a wide range of energetic effects of identical mutations in structurally similar positions, revealing a dramatic context dependence (epistasis) of designed mutations in homologous protein–protein interactions. To better understand the structural basis of this context dependence, we apply a structure-based computational model that recapitulates these energetic effects and we use this model to make and validate forward predictions. The context dependence of these mutations is captured by computational predictions, our results both highlight the considerable difficulties in designing protein–protein interactions and provide challenging benchmark cases for the development of improved protein modeling and design methods that accurately account for the context.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Temperature-dependent multi-k magnetic structure in multiferroic Co{sub 3}TeO{sub 6}

Highlights: Black-Right-Pointing-Pointer Co{sub 3}TeO{sub 6} with corundum-related structure orders antiferromagnetically below 26 K. Black-Right-Pointing-Pointer A field-driven electrical polarization was observed in the antiferromagnetic state. Black-Right-Pointing-Pointer NPD data was collected and refined for ceramics and crushed single crystals. Black-Right-Pointing-Pointer Successive magnetic phase transformations are observed as a function of temperature. Black-Right-Pointing-Pointer The complex magnetic structure reflects the competing interactions in Co{sub 3}TeO{sub 6}. -- Abstract: A complex magnetic order of the multiferroic compound Co{sub 3}TeO{sub 6} has been revealed by neutron powder diffraction studies on ceramics and crushed single crystals. The compound adopts a monoclinic structure (s.g. C2/c) in the studied temperature range 2-300 K but exhibits successive antiferromagnetic transitions at low temperature. Incommensurate antiferromagnetic order with the propagation vector k{sub 1} = (0, 0.485, 0.055) sets in at 26 K. A transition to a second antiferromagnetic structure with k{sub 2} = (0, 0, 0) takes place at 21.1 K. Moreover, a transition to a commensurate antiferromagnetic structure with k{sub 3} = (0, 0.5, 0.25) occurs at 17.4 K. The magnetic structures have been determined by neutron powder diffraction using group theory analysis as a preliminary tool. Different coordinations of the Co{sup 2+} ions involved in the low-symmetry C2/c structure of Co{sub 3}TeO{sub 6} render the exchange-interaction network very complex by itself. The observed magnetic phase transformations are interpreted as an evidence of competing magnetic interactions. The temperature dependent changes in the magnetic structure, derived from refinements of high-resolution neutron data, are discussed and possible mechanisms connected with the spin reorientations are described.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Next-generation yeast-two-hybrid analysis with Y2H-SCORES identifies novel interactors of the MLA immune receptor

Protein-protein interaction networks are one of the most effective representations of cellular behavior. In order to build these models, high-throughput techniques are required. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate interactome networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we present a computational framework, designated Y2H-SCORES, for analyzing high-throughput Y2H screens. Y2H-SCORES considers key aspects of NGIS experimental design and important characteristics of the resulting data that distinguish it from RNA-seq expression datasets. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we provide a quantitative assessment to predict interacting partners under a wide range of experimental scenarios, facilitating independent confirmation by one-to-one bait-prey tests. Simulation of Y2H-NGIS enabled us to identify conditions that maximize detection of true interactors, which can be achieved with protocols such as prey library normalization, maintenance of larger culture volumes and replication of experimental treatments. Y2H-SCORES can be implemented in different yeast-based interaction screenings, with an equivalent or superior performance than existing methods. Proof-of-concept was demonstrated by discovery and validation of novel interactions between the barley nucleotide-binding leucine-rich repeat (NLR) immune receptor MLA6, and fourteen proteins, including those that function in signaling, transcriptional regulation, and intracellular trafficking.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Protonic nickelate device networks for spatiotemporal neuromorphic computing

Computation in biological neural circuits arises from the interplay of nonlinear temporal responses and spatially distributed dynamic network interactions. Replicating this richness in hardware has remained challenging, as most neuromorphic devices emulate only isolated neuron- or synapse-like functions. Here we introduce an integrated neuromorphic computing platform in which both nonlinear spatiotemporal processing and programmable memory are realized within a single perovskite nickelate material system. By engineering symmetric and asymmetric hydrogenated NdNiO 3 junction devices on the same wafer, we combine ultrafast, proton-mediated transient dynamics with stable multilevel resistance states. Networks of symmetric NdNiO 3 junctions exhibit emergent spatial interactions mediated by proton redistribution, while each node simultaneously provides short-term temporal memory, enabling nanosecond-scale operation with an energy cost of ~0.2 nJ per input. When interfaced with asymmetric output units serving as reconfigurable long-term weights, these networks allow both feature transformation and linear classification in the same material system. Leveraging these emergent interactions, the platform enables real-time pattern recognition and achieves high accuracy in spoken digit classification and early seizure detection, outperforming temporal-only or uncoupled architectures. These results position protonic nickelates as a compact, energy-efficient, CMOS-compatible platform that integrates processing and memory for scalable intelligent hardware.

Electrical and electronic engineering↗
DOE OSTI2014

Dynamics on modular networks with heterogeneous correlations

We develop a new ensemble of modular random graphs in which degree-degree correlations can be different in each module, and the inter-module connections are defined by the joint degree-degree distribution of nodes for each pair of modules. We present an analytical approach that allows one to analyze several types of binary dynamics operating on such networks, and we illustrate our approach using bond percolation, site percolation, and the Watts threshold model. The new network ensemble generalizes existing models (e.g., the well-known configuration model and Lancichinetti-Fortunato-Radicchi networks) by allowing a heterogeneous distribution of degree-degree correlations across modules, which is important for the consideration of nonidentical interacting networks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Community response of soil microorganisms to combined contamination of polycyclic aromatic hydrocarbons and potentially toxic elements in a typical coking plant

Both polycyclic aromatic hydrocarbons (PAHs) and potentially toxic elements (PTEs) of coking industries impose negative effects on the stability of soil ecosystem. Soil microbes are regarded as an essential moderator of biochemical processes and soil remediation, while their responses to PAHs-PTEs combined contamination are largely unknown. In the present study, soil microbial diversity and community composition in the typical coking plant under the chronic co-exposure of PAHs and PTEs were investigated and microbial interaction networks were built to reveal microbial co-occurrence patterns. The results indicated that the concentrations of PAHs in the soil inside the coking plant were significantly higher than those outside the plant. The mean concentration of ∑16PAHs was 2894.4 ng·g −1 , which is 5.58 times higher than that outside the plant. The average Hg concentration inside the coking plant was 22 times higher than the background value of Hebei province. The soil fungal community inside the coking plant showed lower richness compared with that of outside community, and there are significant difference in the bacterial and fungal community composition between inside and outside of coking plant ( p < 0.01). Predicted contribution of different environmental factors to each dominant species based on random forest identified 20 and 25 biomarkers in bacteria and fungi, respectively, that were highly sensitive to coking plant soil in operation, such as Betaproteobacteria，Sordariomycetes and Dothideomycetes . Bacterial and fungal communities were shaped by the soil chemical properties (pH), PTEs (Hg), and PAHs together in the coking plant soils. Furthermore, the bacterial and fungal interaction patterns were investigated separately or jointly by intradomain and interdomain networks. Competition is the main strategy based on the co-exclusion pattern in fungal community, and the competitive relationship inside the coking plant is more complex than that outside the plant. In contrast, cooperation is the dominant strategy in bacterial networks based on the co-occurrence pattern. The present study provided insights into microbial response strategies and the interactions between bacteria and fungi under long-term combined contamination.

Shen, Qihui↗