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At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Microbial functional diversity across biogeochemical provinces in the central Pacific Ocean

Enzymes catalyze key reactions within Earth’s life-sustaining biogeochemical cycles. Here, we use metaproteomics to examine the enzymatic capabilities of the microbial community (0.2 to 3 µm) along a 5,000-km-long, 1-km-deep transect in the central Pacific Ocean. Eighty-five percent of total protein abundance was of bacterial origin, with Archaea contributing 1.6%. Over 2,000 functional KEGG Ontology (KO) groups were identified, yet only 25 KO groups contributed over half of the protein abundance, simultaneously indicating abundant key functions and a long tail of diverse functions. Vertical attenuation of individual proteins displayed stratification of nutrient transport, carbon utilization, and environmental stress. The microbial community also varied along horizontal scales, shaped by environmental features specific to the oligotrophic North Pacific Subtropical Gyre, the oxygen-depleted Eastern Tropical North Pacific, and nutrient-rich equatorial upwelling. Some of the most abundant proteins were associated with nitrification and C1 metabolisms, with observed interactions between these pathways. The oxidoreductases nitrite oxidoreductase (NxrAB), nitrite reductase (NirK), ammonia monooxygenase (AmoABC), manganese oxidase (MnxG), formate dehydrogenase (FdoGH and FDH), and carbon monoxide dehydrogenase (CoxLM) displayed distributions indicative of biogeochemical status such as oxidative or nutritional stress, with the potential to be more sensitive than chemical sensors. Enzymes that mediate transformations of atmospheric gases like CO, CO 2 , NO, methanethiol, and methylamines were most abundant in the upwelling region. Here, we identified hot spots of biochemical transformation in the central Pacific Ocean, highlighted previously understudied metabolic pathways in the environment, and provided rich empirical data for biogeochemical models critical for forecasting ecosystem response to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Pervasive alterations to snow-dominated ecosystem functions under climate change

Climate change projections consistently demonstrate that warming temperatures and dwindling seasonal snowpack will elicit cascading effects on ecosystem function and water resource availability. Despite this consensus, little is known about potential changes in the variability of ecohydrological conditions, which is also required to inform climate change adaptation and mitigation strategies. Considering potential changes in ecohydrological variability is critical to evaluating the emergence of trends, assessing the likelihood of extreme events such as floods and droughts, and identifying when tipping points may be reached that fundamentally alter ecohydrological function. Using a single-model Large Ensemble with sophisticated terrestrial ecosystem representation, we characterize projected changes in the mean state and variability of ecohydrological processes in historically snow-dominated regions of the Northern Hemisphere. Widespread snowpack reductions, earlier snowmelt timing, longer growing seasons, drier soils, and increased fire risk are projected for this century under a high-emissions scenario. In addition to these changes in the mean state, increased variability in winter snowmelt will increase growing-season water deficits and increase the stochasticity of runoff. Thus, with warming, declining snowpack loses its dependable buffering capacity so that runoff quantity and timing more closely reflect the episodic characteristics of precipitation. This results in a declining predictability of annual runoff from maximum snow water equivalent, which has critical implications for ecosystem stress and water resource management. So our results suggest that there is a strong likelihood of pervasive alterations to ecohydrological function that may be expected with climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Structural insights reveal interplay between LAG-3 homodimerization, ligand binding, and function

Lymphocyte activation gene-3 (LAG-3) is an inhibitory receptor expressed on activated T cells and an emerging immunotherapy target. Domain 1 (D1) of LAG-3, which has been purported to directly interact with major histocompatibility complex class II (MHCII) and fibrinogen-like protein 1 (FGL1), has been the major focus for the development of therapeutic antibodies that inhibit LAG-3 receptor-ligand interactions and restore T cell function. Here, we present a high-resolution structure of glycosylated mouse LAG-3 ectodomain, identifying that cis-homodimerization, mediated through a network of hydrophobic residues within domain 2 (D2), is critically required for LAG-3 function. Additionally, we found a previously unidentified key protein-glycan interaction in the dimer interface that affects the spatial orientation of the neighboring D1 domain. Mutation of LAG-3 D2 residues reduced dimer formation, dramatically abolished LAG-3 binding to both MHCII and FGL1 ligands, and consequentially inhibited the role of LAG-3 in suppressing T cell responses. Intriguingly, we showed that antibodies directed against D1, D2, and D3 domains are all capable of blocking LAG-3 dimer formation and MHCII and FGL-1 ligand binding, suggesting a potential allosteric model of LAG-3 function tightly regulated by dimerization. Furthermore, our work reveals unique epitopes, in addition to D1, that can be targeted for immunotherapy of cancer and other human diseases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Lanthanide-dependent alcohol dehydrogenases require an essential aspartate residue for metal coordination and enzymatic function

The lanthanide elements (Ln 3+ ), those with atomic numbers 57–63 (excluding promethium, Pm 3+ ), form a cofactor complex with pyrroloquinoline quinone (PQQ) in bacterial XoxF methanol dehydrogenases (MDHs) and ExaF ethanol dehydrogenases (EDHs), expanding the range of biological elements and opening novel areas of metabolism and ecology. Other MDHs, known as MxaFIs, are related in sequence and structure to these proteins, yet they instead possess a Ca 2+ -PQQ cofactor. An important missing piece of the Ln 3+ puzzle is defining what features distinguish enzymes that use Ln 3+ -PQQ cofactors from those that do not. In this work, using XoxF1 MDH from the model methylotrophic bacterium Methylorubrum extorquens AM1, we investigated the functional importance of a proposed lanthanide-coordinating aspartate residue. We report two crystal structures of XoxF1, one with and another without PQQ, both with La 3+ bound in the active-site region and coordinated by Asp 320 . Using constructs to produce either recombinant XoxF1 or its D320A variant, we show that Asp 320 is needed for in vivo catalytic function, in vitro activity, and La 3+ coordination. XoxF1 and XoxF1 D320A, when produced in the absence of La 3+ , coordinated Ca 2+ but exhibited little or no catalytic activity. We also generated the parallel substitution in ExaF to produce ExaF D319S and found that this variant loses the capacity for efficient ethanol oxidation with La 3+ . These results provide evidence that a Ln 3+ -coordinating aspartate is essential for the enzymatic functions of XoxF MDHs and ExaF EDHs, supporting the notion that sequences of these enzymes, and the genes that encode them, are markers for Ln 3+ metabolism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The structural and functional integrities of porcine myocardium are mostly preserved by cryopreservation

Structural and functional studies of heart muscle are important to gain insights into the physiological bases of cardiac muscle contraction and the pathological bases of heart disease. While fresh muscle tissue works best for these kinds of studies, this is not always practical to obtain, especially for heart tissue from large animal models and humans. Conversely, tissue banks of frozen human hearts are available and could be a tremendous resource for translational research. It is not well understood, however, how liquid nitrogen freezing and cryostorage may impact the structural integrity of myocardium from large mammals. In this study, we directly compared the structural and functional integrity of never-frozen to previously frozen porcine myocardium to investigate the consequences of freezing and cryostorage. X-ray diffraction measurements from hydrated tissue under near-physiological conditions and electron microscope images from chemically fixed porcine myocardium showed that prior freezing has only minor effects on structural integrity of the muscle. Furthermore, mechanical studies similarly showed no significant differences in contractile capabilities of porcine myocardium with and without freezing and cryostorage. These results demonstrate that liquid nitrogen preservation is a practical approach for structural and functional studies of myocardium.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Exploring metastable states in UO 2 using hybrid functionals and dynamical mean field theory

A detailed exploration of the f-atomic orbital occupancy space for UO2 is performed using a first principles approach based on density functional theory (DFT), employing a full hybrid functional within a systematic basis set. Specifically, the PBE0 functional is combined with an occupancy biasing scheme implemented in a wavelet-based algorithm which is adapted to large supercells. The results are compared with previous DFT + U calculations reported in the literature, while dynamical mean field theory is also performed to provide a further base for comparison. This work shows that the computational complexity of the energy landscape of a correlated f-electron oxide is much richer than has previously been demonstrated. The resulting calculations provide evidence of the existence of multiple previously unexplored metastable electronic states of UO 2 , including those with energies which are lower than previously reported ground states.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Aemulus ν: precision halo mass functions in wνCDM cosmologies

Precise and accurate predictions of the halo mass function for cluster mass scales in wνCDM cosmologies are crucial for extracting robust and unbiased cosmological information from upcoming galaxy cluster surveys. Here, we present a halo mass function emulator for cluster mass scales (≳ 1013 M ⊙/h) up to redshift z = 2 with comprehensive support for the parameter space of wνCDM cosmologies allowed by current data. Based on the Aemulus ν suite of simulations, the emulator marks a significant improvement in the precision of halo mass function predictions by incorporating both massive neutrinos and non-standard dark energy equation of state models. This allows for accurate modeling of the cosmology dependence in large-scale structure and galaxy cluster studies. We show that the emulator, designed using Gaussian Process Regression, has negligible theoretical uncertainties compared to dominant sources of error in future cluster abundance studies. Our emulator is publicly available (https://github.com/DelonShen/aemulusnu_hmf), providing the community with a crucial tool for upcoming cosmological surveys such as LSST and Euclid.

cluster counts↗
DOE OSTI2026

Monte Carlo simulation methodology for characterizing the energy response function and resolving power of a digital Flat Panel Detector (FPD) assembly

The use of high-resolution, digital flat panel detectors (FPDs) makes quantitative assessments of radiographic and 3D tomographic representations feasible. However, the energy response function and spatial resolving power of the detector must be known. Here, we present a methodology for characterizing an FPD assembly using Monte Carlo simulations. The detector assembly modeled here was fielded in the 3-Ring Flash X-ray Imaging System designed and built at Lawrence Livermore National Laboratory (LLNL), which employs fifteen Carestream ® HPX-DR3543 FPDs enclosed in protective housings. Detailed Monte Carlo (MCNP6) simulations of an as-fielded detector assembly were performed to estimate its energy response function in terms of analog-to-digital unit (ADU) “counts” per pixel per incident x-ray as a function of energy up to ∼ 500 keV and its theoretical (idealized) resolving power. The practical resolving power of the FPD assembly in a realistic scenario was then estimated by simulating radiographic images of a variant of the United Kingdom (U.K.) “Kaleidoscope” resolution target.

Physics↗
DOE OSTI2021

Differentiable programming for online training of a neural artificial viscosity function within a staggered grid Lagrangian hydrodynamics scheme

Lagrangian methods to solve the inviscid Euler equations produce numerical oscillations near shock waves. A common approach to reducing these oscillations is to add artificial viscosity (AV) to the discrete equations. The AV term acts as a dissipative mechanism that attenuates oscillations by smearing the shock across a finite number of computational cells. However, AV introduces several control parameters that are not determined by the underlying physical model, and hence, in practice are tuned to the characteristics of a given problem. We seek to improve the standard quadratic-linear AV form by replacing it with a learned neural function that reduces oscillations relative to exact solutions of the Euler equations, resulting in a hybrid numerical-neural hydrodynamic solver. Because AV is an artificial construct that exists solely to improve the numerical properties of a hydrodynamic code, there is no offline ‘viscosity data’ against which a neural network can be trained before inserting into a numerical simulation, thus requiring online training. We achieve this via differentiable programming, i.e. end-to-end backpropagation or adjoint solution through both the neural and differential equation code, using automatic differentiation of the hybrid code in the Julia programming language to calculate the necessary loss function gradients. A novel offline pre-training step accelerates training by initializing the neural network to the default numerical AV scheme, which can be learned rapidly by space-filling sampling over the AV input space. We find that online training over early time steps of simulation is sufficient to learn a neural AV function that reduces numerical oscillations in long-term hydrodynamic shock simulations. These results offer an early proof-of-principle that online differentiable training of hybrid numerical schemes with novel neural network components can improve certain performance aspects existing in purely numerical schemes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Predicting nonequilibrium Green’s function dynamics and photoemission spectra via nonlinear integral operator learning

Understanding the dynamics of nonequilibrium quantum many-body systems is an important research topic in a wide range of fields across condensed matter physics, quantum optics, and high-energy physics. However, numerical studies of large-scale nonequilibrium phenomena in realistic materials face serious challenges due to intrinsic high-dimensionality of quantum many-body problems and the absence of time-invariance. The nonequilibrium properties of many-body systems can be described by the dynamics of the correlator, or the Green's function of the system, whose time evolution is given by a high-dimensional system of integro-differential equations, known as the Kadanoff–Baym equations (KBEs). The time-convolution term in KBEs, which needs to be recalculated at each time step, makes it difficult to perform long-time numerical simulation. In this paper, we develop an operator-learning framework based on recurrent neural networks (RNNs) to address this challenge. We utilize RNNs to learn the nonlinear mapping between Green's functions and convolution integrals in KBEs. By using the learned operators as a surrogate model in the KBE solver, we obtain a general machine-learning scheme for predicting the dynamics of nonequilibrium Green's functions. Besides significant savings per each time step, the new methodology reduces the temporal computational complexity from $O(N_t^3)$ to $O(N_t)$ where N t is the number of steps taken in a simulation, thereby making it possible to study large many-body problems which are currently infeasible with conventional KBE solvers. Through various numerical examples, we demonstrate the effectiveness of the operator-learning based approach in providing accurate predictions of physical observables such as the reduced density matrix and time-resolved photoemission spectra. Moreover, our framework exhibits clear numerical convergence and can be easily parallelized, thereby facilitating many possible further developments and applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

FEBIP for functional nanolithography of 2D nanomaterials

Lithographic patterning on emerging nanomaterials is one of essential ingredients for the development of new functional nanomaterial and device systems. Focused electron beam induced processing (FEBIP) in combination with an on-demand reconfigurable precursor injection system enables the highest resolution and controllability of nanolithography on two-dimensional (2D) nanomaterials. FEBIP allows for localized atomic-to-nanoscale engineering of nanomaterials and devices in a multi-mode format including geometrical patterning, functionalization/defect formation/doping, metal/metal oxide deposition, interface fusion and activation, active channel cleaning. This chapter discusses the scientific bases and showcases the state-of-the-art FEBIP technologies with a specific focus on functional lithography of 2D nanomaterials and provides future prospects of FEBIP technologies to address real industrial needs for the development of high-performance, next-generation functional devices.

2D materials, nanolithography, focused electron be↗
DOE OSTI2023

Integrating functional scoring and regulatory data to predict the effect of non-coding SNPs in a complex neurological disease

Abstract Most SNPs associated with complex diseases seem to lie in non-coding regions of the genome; however, their contribution to gene expression and disease phenotype remains poorly understood. Here, we established a workflow to provide assistance in prioritising the functional relevance of non-coding SNPs of candidate genes as susceptibility loci in polygenic neurological disorders. To illustrate the applicability of our workflow, we considered the multifactorial disorder migraine as a model to follow our step-by-step approach. We annotated the overlap of selected SNPs with regulatory elements and assessed their potential impact on gene expression based on publicly available prediction algorithms and functional genomics information. Some migraine risk loci have been hypothesised to reside in non-coding regions and to be implicated in the neurotransmission pathway. In this study, we used a set of 22 non-coding SNPs from neurotransmission and synaptic machinery-related genes previously suggested to be involved in migraine susceptibility based on our candidate gene association studies. After prioritising these SNPs, we focused on non-reported ones that demonstrated high regulatory potential: (1) VAMP2_rs1150 (3′ UTR) was predicted as a target of hsa-mir-5010-3p miRNA, possibly disrupting its own gene expression; (2) STX1A_rs6951030 (proximal enhancer) may affect the binding affinity of zinc-finger transcription factors (namely ZNF423) and disturb TBL2 gene expression; and (3) SNAP25_rs2327264 (distal enhancer) expected to be in a binding site of ONECUT2 transcription factor. This study demonstrated the applicability of our practical workflow to facilitate the prioritisation of potentially relevant non-coding SNPs and predict their functional impact in multifactorial neurological diseases.

Felício, Daniela↗
DOE OSTI2023

New cyclophilin D inhibitor rescues mitochondrial and cognitive function in Alzheimer’s disease

Abstract Mitochondrial dysfunction is an early pathological feature of Alzheimer disease and plays a crucial role in the development and progression of Alzheimer’s disease. Strategies to rescue mitochondrial function and cognition remain to be explored. Cyclophilin D (CypD), the peptidylprolyl isomerase F (PPIase), is a key component in opening the mitochondrial membrane permeability transition pore, leading to mitochondrial dysfunction and cell death. Blocking membrane permeability transition pore opening by inhibiting CypD activity is a promising therapeutic approach for Alzheimer’s disease. However, there is currently no effective CypD inhibitor for Alzheimer’s disease, with previous candidates demonstrating high toxicity, poor ability to cross the blood–brain barrier, compromised biocompatibility and low selectivity. Here, we report a new class of non-toxic and biocompatible CypD inhibitor, ebselen, using a conventional PPIase assay to screen a library of ∼2000 FDA-approved drugs with crystallographic analysis of the CypD-ebselen crystal structure (PDB code: 8EJX). More importantly, we assessed the effects of genetic and pharmacological blockade of CypD on Alzheimer’s disease mitochondrial and glycolytic bioenergetics in Alzheimer’s disease-derived mitochondrial cybrid cells, an ex vivo human sporadic Alzheimer’s disease mitochondrial model, and on synaptic function, inflammatory response and learning and memory in Alzheimer’s disease mouse models. Inhibition of CypD by ebselen protects against sporadic Alzheimer’s disease- and amyloid-β-induced mitochondrial and glycolytic perturbation, synaptic and cognitive dysfunction, together with suppressing neuroinflammation in the brain of Alzheimer’s disease mouse models, which is linked to CypD-related membrane permeability transition pore formation. Thus, CypD inhibitors have the potential to slow the progression of neurodegenerative diseases, including Alzheimer’s disease, by boosting mitochondrial bioenergetics and improving synaptic and cognitive function.

Neurosciences & Neurology↗
DOE OSTI2020

Non-coding RNAs: update on mechanisms and therapeutic targets from the ESC Working Groups of Myocardial Function and Cellular Biology of the Heart

Abstract Vast parts of mammalian genomes are actively transcribed, predominantly giving rise to non-coding RNA (ncRNA) transcripts including microRNAs, long ncRNAs, and circular RNAs among others. Contrary to previous opinions that most of these RNAs are non-functional molecules, they are now recognized as critical regulators of many physiological and pathological processes including those of the cardiovascular system. The discovery of functional ncRNAs has opened up new research avenues aiming at understanding ncRNA-related disease mechanisms as well as exploiting them as novel therapeutics in cardiovascular therapy. In this review, we give an update on the current progress in ncRNA research, particularly focusing on cardiovascular physiological and disease processes, which are under current investigation at the ESC Working Groups of Myocardial Function and Cellular Biology of the Heart. This includes a range of topics such as extracellular vesicle-mediated communication, neurohormonal regulation, inflammation, cardiac remodelling, cardio-oncology as well as cardiac development and regeneration, collectively highlighting the wide-spread involvement and importance of ncRNAs in the cardiovascular system.

Bär, Christian↗
DOE OSTI2022

A roadmap for the functional annotation of protein families: a community perspective

Over the last 25 years, biology has entered the genomic era and is becoming a science of ‘big data’. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3–4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Warming and disturbance alter soil microbiome diversity and function in a northern forest ecotone

ABSTRACT The response to global change by soil microbes is set to affect important ecosystem processes. These impacts could be most immediate in transitional zones, such as the temperate-boreal forest ecotone, yet previous work in these forests has primarily focused on specific subsets of microbial taxa. Here, we examined how bacterial and fungal communities respond to simulated above- and below-ground warming under realistic field conditions in closed and open canopy treatments in Minnesota, USA. Our results show that warming and canopy disturbance shifted bacterial and fungal community structure as dominant bacterial and fungal groups differed in the direction and intensity of their responses. Ectomycorrhizal and saprotrophic fungal communities with greater connectivity (higher prevalence of strongly interconnected taxa based on pairwise co-occurrence relationships) were more resistant to compositional change. Warming effects on soil enzymes involved in the hydrolytic and oxidative liberation of carbon from plant cell walls and nutrients from organic matter were most strongly linked to fungal community responses, although community structure–function relationships differed between fungal guilds. Collectively, these findings indicate that warming and disturbance will influence the composition and function of microbial communities in the temperate-boreal ecotone, and fungal responses are particularly important to understand for predicting future ecosystem functioning.

Microbiology↗
DOE OSTI2024

Potential for functional divergence in ectomycorrhizal fungal communities across a precipitation gradient

Abstract Functional traits influence the assembly of microbial communities, but identifying these traits in the environment has remained challenging. We studied ectomycorrhizal fungal (EMF) communities inhabiting Populus trichocarpa roots distributed across a precipitation gradient in the Pacific Northwest, USA. We profiled these communities using taxonomic (meta-barcoding) and functional (metagenomic) approaches. We hypothesized that genes involved in fungal drought-stress tolerance and fungal mediated plant water uptake would be most abundant in drier soils. We were unable to detect support for this hypothesis; instead, the abundance of genes involved in melanin synthesis, hydrophobins, aquaporins, trehalose-synthases, and other gene families exhibited no significant shifts across the gradient. Finally, we studied variation in sequence homology for certain genes, finding that fungal communities in dry soils are composed of distinct aquaporin and hydrophobin gene sequences. Altogether, our results suggest that while EMF communities exhibit significant compositional shifts across this gradient, coupled functional turnover, at least as inferred using community metagenomics is limited. Accordingly, the consequences of these distinct EMF communities on plant water uptake remain critically unknown, and future studies targeting the expression of genes involved in drought stress tolerance are required.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Functional team selection as a framework for local adaptation in plants and their belowground microbiomes

Abstract Multicellular organisms are hosts to diverse communities of smaller organisms known as microbiomes. Plants have distinctive microbiomes that can provide important functions related to nutrition, defense, and stress tolerance. Empirical studies provide convincing evidence that in some—but not all—circumstances, belowground microbiomes help plants adapt to their local environment. The purpose of this review is to develop functional team selection (FTS) as a framework to help predict the conditions necessary for root microbiomes to generate local adaptation for their plant hosts. FTS envisions plants and their microbiomes as complex adaptive systems, and plant adaptations as emergent properties of these systems. If plants have the capacity to recognize and cultivate beneficial microbes and suppress pathogens, then it is possible for plants to evolve the capacity to gain adaptations by curating their microbiome. In resource-limited and stressful environments, the emergent functions of complex microbial systems may contribute to positive feedback linked to plant vigor, and ultimately, local adaptation. The key factors in this process are: (i) selective force, (ii) host constitution, (iii) microbial diversity, and (iv) time. There is increasing interest in harnessing beneficial microbial interactions in agriculture and many microbial growth-promoting products are commercially available, but their use is controversial because a large proportion of these products fail to consistently enhance plant growth. The FTS framework may help direct the development of durable plant-microbiome systems that enhance crop production and diminish pathogens. It may also provide valuable insights for understanding and managing other kinds of host-microbe systems.

Environmental Sciences & Ecology↗