SEARCH · Engineering Papers

Results for “Expression systems”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,351 records · Page 75

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Cellular, molecular, and therapeutic characterization of pilocarpine-induced temporal lobe epilepsy

Animal models have expanded our understanding of temporal lobe epilepsy (TLE). However, translating these to cell-specific druggable hypotheses is not explored. Herein, we conducted an integrative insilico-analysis of an available transcriptomics dataset obtained from animals with pilocarpine-induced-TLE. A set of 119 genes with subtle-to-moderate impact predicted most forms of epilepsy with ~ 97% accuracy and characteristically mapped to upregulated homeostatic and downregulated synaptic pathways. The deconvolution of cellular proportions revealed opposing changes in diverse cell types. The proportion of nonneuronal cells increased whereas that of interneurons, except for those expressing vasoactive intestinal peptide (Vip), decreased, and pyramidal neurons of the cornu-ammonis (CA) subfields showed the highest variation in proportion. A probabilistic Bayesian-network demonstrated an aberrant and oscillating physiological interaction between nonneuronal cells involved in the blood–brain-barrier and Vip interneurons in driving seizures, and their role was evaluated insilico using transcriptomic changes induced by valproic-acid, which showed opposing effects in the two cell-types. Additionally, we revealed novel epileptic and antiepileptic mechanisms and predicted drugs using causal inference, outperforming the present drug repurposing approaches. These well-powered findings not only expand the understanding of TLE and seizure oscillation, but also provide predictive biomarkers of epilepsy, cellular and causal micro-circuitry changes associated with it, and a drug-discovery method focusing on these events.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

The anaerobic fungus Caecomyces churrovis produces H 2 via a non-bifurcating NADH-dependent enzyme complex

ABSTRACT Hydrogenosomes are mitochondria-derived organelles that produce ATP and H 2 to support energy metabolism in anaerobic eukaryotes. H 2 production allows reoxidation of reduced cofactors generated during fermentative metabolism; however, the metabolic mechanisms for H 2 production in anaerobic eukaryotes remains incompletely understood. In particular, it remains unclear whether anaerobic fungi (AF) hydrogenosomes use a ferredoxin-dependent pathway or a distinct mechanism to regenerate NAD(P) + and link electron transfer to H 2 formation. Here, by combining genomic search, proteomic analysis, and enzymology, we reveal the molecular mechanism for H 2 production in the AF strain Caecomyces churrovis . Our enzyme assays on the organelle fraction of C. churrovis revealed the activity of H 2 :NAD + oxidoreductase but not pyruvate:ferredoxin oxidoreductase, which is usually linked to H 2 formation. We identified genes encoding [FeFe] hydrogenase (Hyd) and NADH dehydrogenase subunits E and F (NuoE, NuoF) in C. churrovis , and confirmed their expression in the isolated hydrogenosomal fractions by proteomic analysis. Combining the individually purified enzymes, we found Hyd and NuoEF proteins formed H 2 directly from NADH independently of ferredoxin, functioning as a non-bifurcating NADH-dependent enzyme rather than an electron-bifurcating enzyme. We identified homologs of hydrogenosomal NuoE, NuoF, and Hyd in many other AF, indicating this pathway is commonly shared among the AF. This work demonstrates the existence of a non-bifurcating NADH-dependent enzyme complex in eukaryotes. Moreover, this complex could potentially be exploited as a target for controlling AF H 2 production and altering fungal metabolism. IMPORTANCE H 2 production is a prominent feature of anaerobic energy metabolism, yet our understanding of eukaryotic mechanisms remains limited. Anaerobic fungi (AF) are key decomposers of lignocellulose and contribute to hydrogen flux in anaerobic environments. Although it has been more than 40 years since the H 2 production from AF was first reported, the molecular mechanism for hydrogenosomal H 2 production and redox balance remains unclear. We demonstrate that AF produce H 2 from NADH utilizing a non-bifurcating NADH-dependent enzyme complex rather than an electron-bifurcating, ferredoxin-dependent variant. We show that this enzyme complex is conserved across multiple AF lineages and thus demonstrate the occurrence of a non-bifurcating NADH-dependent enzyme in eukaryotes. This discovery expands our understanding of eukaryotic hydrogenosomal metabolism, reveals a previously unknown strategy for redox balancing, and highlights potential targets for manipulating H 2 production. These insights have broad implications for microbial energy metabolism, anaerobic ecosystems, and bioengineering of H 2 -producing systems.

Zhang, Bo [Department of Chemical Engineering, Uni↗

DOE OSTI2025

Effects of feruloyl-CoA 6'-hydroxylase 1 overexpression on lignin and cell wall characteristics in transgenic hybrid aspen

In plant cell walls, lignin, cellulose, and the hemicelluloses form intricate three-dimensional structures. Owing to its complexity, lignin often acts as a bottleneck for the efficient utilization of polysaccharide components as biochemicals and functional materials. A promising approach to mitigate and/or overcome lignin recalcitrance is the qualitative and quantitative modification of lignin by genetic engineering. Feruloyl-CoA 6'-hydroxylase (F6'H1) is a 2-oxoglutarate-dependent dioxygenase that catalyzes the conversion of feruloyl-CoA, one of the intermediates of the lignin biosynthetic pathway, into 6'-hydroxyferuloyl-CoA, the precursor of scopoletin (7-hydroxy-6-methoxycoumarin). In a previous study with Arabidopsis thaliana, we demonstrated that overexpression of F6'H1 under a xylem-preferential promoter led to scopoletin incorporation into the cell wall. This altered the chemical structure of lignin without affecting lignin content or saccharification efficiency. In the present study, the same F6'H1 construct was introduced into hybrid aspen (Populus tremula × tremuloides T89), a model woody plant, and its effects on plant morphology, lignin chemical structure, global gene expression, and phenolic metabolism were examined. The transgenic plants successfully overproduced scopoletin while exhibiting severe growth retardation, a phenotype not previously observed in Arabidopsis. Scopoletin accumulation was most pronounced in the secondary walls of tracheary elements and the compound middle lamella, with low levels in the fiber cell walls. Overexpression of F6'H1 also affected the metabolism of aromatics, including lignin precursors. Heteronuclear single-quantum coherence (HSQC) NMR spectroscopy revealed that scopoletin in cell walls was bound to lignin, leading to a reduction in lignin content and changes in its monomeric composition and molar mass distribution. Furthermore, the enzymatic saccharification efficiency of the transgenic cell walls was more than three times higher than that of the wild-type plants, even without pretreatment. Although addressing growth inhibition remains a priority, incorporating scopoletin into lignin demonstrates significant potential for improving woody biomass utilization.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Super Resolving Unrolled Neural Networks for Remote Sensing

In remote sensing systems, the capabilities of the system are constrained by the complex interactions between size, weight, and power (SWAP) of potential designs. In electro-optical (EO) systems, examples of these critical parameters include the system’s sensitivity and resolution. Those parameters can be increased by ever larger optical apertures and focal planes but at the cost of more SWAP. Multi-image super resolution (MISR) techniques allow resolution to be enhanced via computation rather than more sophisticated optical hardware. These algorithms combine multiple images together into a single, higher resolution image, trading temporal resolution and computation for spatial resolution. Fielded MISR techniques, such as Drizzle, can require several hundred images to create a single super resolved image, implying reduced temporal resolution, increased data acquisition load, and limiting mission applications. Iterative techniques, such as model-based image reconstruction and compressive sensing, have been shown to create super resolved images using fewer images than Drizzle. They do this by posing an optimization problem that balances accuracy between a highly accurate physical model and an image model. In the case of super resolution, the physical model is defined by the relation between low resolution input images and the desired high resolution output image. The image model encodes some assumptions about the super resolved image. These assumptions are meant to suppress reconstruction artifacts that arise due to deterministic physical model error, stochastic measurement noise, and potential undersampling. In practice, the performance of iterative methods are limited by imaging models compatible with optimization. Deep learning-based methods can effectively learn image models of arbitrary complexity, but lack the theoretical explainability and robustness of iterative techniques. Consensus equilibrium (CE) generalizes the iterative techniques beyond optimization, enabling blackbox algorithms such as traditional and neural image denoisers to be used as the image model. CE-based approaches retain much of the explainability and robustness of iterative techniques while allowing the expressiveness of machine learning image models to be used. Additionally, by unrolling iterations of CE with an embedded image denoiser, the image denoiser can be further trained and specialized to the specific application with potentially higher quality reconstructions. Under this project, we demonstrated the feasibility of training an unrolled neural network based upon CE. While we didn’t train one, we showed that the CE process is differentiable and its gradient can be tractably computed. We also explored the usage of a variants of CE akin to generative neural works. Most importantly, we applied the CE framework to a number of problems including non-blind deconvolution, upsampling, single-image super resolution, MISR, event-based sensing, and saturated deconvolution. Our MISR prototype creates high quality reconstructions with an order of magnitude fewer images than previous approaches and, critically, produces these reconstructions fast enough for practical usage.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Quantum Foundations of Classical Reversible Computing

The reversible computation paradigm aims to provide a new foundation for general classical digital computing that is capable of circumventing the thermodynamic limits to the energy efficiency of the conventional, non-reversible digital paradigm. However, to date, the essential rationale for, and analysis of, classical reversible computing (RC) has not yet been expressed in terms that leverage the modern formal methods of non-equilibrium quantum thermodynamics (NEQT). In this paper, we begin developing an NEQT-based foundation for the physics of reversible computing. We use the framework of Gorini-Kossakowski-Sudarshan-Lindblad dynamics (a.k.a. Lindbladians) with multiple asymptotic states, incorporating recent results from resource theory, full counting statistics and stochastic thermodynamics. Important conclusions include that, as expected: (1) Landauer’s Principle indeed sets a strict lower bound on entropy generation in traditional non-reversible architectures for deterministic computing machines when we account for the loss of correlations; and (2) implementations of the alternative reversible computation paradigm can potentially avoid such losses, and thereby circumvent the Landauer limit, potentially allowing the efficiency of future digital computing technologies to continue improving indefinitely. We also outline a research plan for identifying the fundamental minimum energy dissipation of reversible computing machines as a function of speed.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Multiomics and deep learning dissect regulatory syntax in human development

Transcription factors establish cell identity during development by binding regulatory DNA in a sequence-specific manner, often promoting local chromatin accessibility and regulating gene expression1. Mapping accessible chromatin offers critical insights into transcriptional control, but available datasets for human development are restricted to bulk tissue, single organs or single modalities2. Here we present the Human Development Multiomic Atlas, a single-cell atlas of chromatin accessibility and gene expression from 817,740 fetal cells across 12 organs, spanning 203 cell types and more than 1 million candidate cis-regulatory elements, many of which exhibit organ-specific in vivo enhancer activity. Deep learning models trained to predict accessibility from local DNA sequence unravel a comprehensive lexicon of motifs that influence accessibility, including composite motifs exhibiting distinct syntactic constraints that are predicted to mediate transcription factor cooperativity. We identify ‘hard’ syntactic rules requiring precise motif spacing and orientation, ‘soft’ rules allowing flexible motif arrangements, and ubiquitous motifs inhibiting accessibility. Model-based interpretation of genetic variants reveals that disruption of motifs with positive and negative effects is associated with concordant effects on gene expression. Our work delineates how motif syntax governs cell-type-specific chromatin accessibility and provides a foundational resource for decoding cis-regulatory logic and interpreting genetic variation during human development.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

PNNL DataHub NIAID Program Project: Modeling Host Responses to Understand Severe Human Virus Infections, Multi-Omic Viral Dataset Catalog Collection

The National Institute of Allergy and Infectious Diseases (NIAID) "Modeling Host Responses to Understand Severe Human Virus Infections" program project was a highly integrated and comprehensive systems biology research core, funded by the National Institute of Health (U19AI106772) from 2013-06-01 to 2018-05-31, investigating the complex host response to category A, B, and C priority pathogen infections. Resulting project deliverables include an extensive comprehensive collections of linked primary and secondary transformation viral experimental infection data. Here we provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open access to viral Omics lifecycle datasets and project metadata. Using a highly integrated and multidisciplinary approach, linked primary data and metadata supporting secondary normalization datasets, provide critical information necessary for research reproducibility and long-term preservation. Enabling on-demand data access for research community consumption and developer reuse, serves to support new mechanistic insights and discoveries into host-pathogen interactions for aiding future biohazard data preparedness efforts in emergency response to global health crises involving viral infection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

The Three Hundred Project: Modeling baryon and hot-gas fraction evolution in simulated clusters

The baryon fraction of galaxy clusters, expressed as the ratio between the mass in baryons (including both stars and cold or hot gas) and the total mass, is a powerful tool to provide information on the cosmological parameters, while the hot-gas fraction provides indications on the physics of the intracluster plasma and its interplay with the processes that drive galaxy formation. Using cosmological hydrodynamical simulations of about 300 simulated massive galaxy clusters with a median mass M 500 ≈ 7 × 10 14 M ⊙ at z = 0, we model the relations between total mass and either baryon fraction or the hot gas fractions at overdensities Δ = 2500, 500, and 200 with respect to the cosmic critical density, and their evolution from z ∼ 0 to z ∼ 1.3. We utilized the simulated galaxy clusters from the Three Hundred project, which include star formation and feedback from both supernovae and active galactic nuclei. We fit the simulation results for such scaling relations against three analytic forms (linear, quadratic, and logarithmic in a logarithmic plane) and three forms for the redshift dependence, and we considered as a variable both the inverse of the cosmic scale factor, (1 + z), and the Hubble expansion rate, E(z). We show that power-law dependencies on cluster mass poorly describe the investigated relations. A power law fails to simultaneously capture the flattening of the total baryon and gas fractions at high masses, their drop at low masses, and the transition between these two regimes. The other two functional forms provide a more accurate description of the curvature in mass scaling. The fractions measured within smaller radii exhibit a stronger evolution than those measured within larger radii. From the analysis of these simulations, we evince that as long as we include systems in the mass range herein investigated, the baryon or gas fraction can be accurately related to the total mass through either a parabola or a logarithm in the logarithmic plane. The trends are common to all modern hydro simulations, although the amplitude of the drop at low masses might differ. Being able to observationally determine the gas fraction in groups will thus provide constraints on the baryonic physics.

galaxy clusters↗

DOE OSTI2022

Expanding the Solar Workforce through the Illinois Community College System

The Illinois Green Economy Network (IGEN) is a consortium of all 39 Illinois community college districts working to grow the green economy in the state. The network aims to build capacity in sustainability and transform education by connecting business/industry and educators to resources, curating information and tools, cultivating partnerships, and advocating for change. Led by IGEN, the goal of the “Expanding the Solar Workforce through the Illinois Community College System” project was to create and sustain a comprehensive, statewide strategy that equips education and training providers, workers, businesses, and communities with resources and tools to prepare a solar workforce. A cohort of five Illinois community colleges participated in the program based on their capacity and interest, as well as their potential for and proximity to industry growth in the state: City Colleges of Chicago, John A. Logan College, Kankakee Community College, Lewis and Clark Community College, and Lincoln Land Community College. The project had three Objectives, 1) Address an unmet need by increasing the pool of skilled solar workers, 2) Keep pace with evolving needs of the solar workforce by improving programs to be more accessible and industry-driven, 3) Strengthen IGEN’s capacity as a statewide resource for solar education, training, and workforce initiatives. Overall, over 400 students were trained in solar installation practices, with over 40 businesses engaged. Additionally. the project spurred a number of innovations and the development of effective practices related to establishing and maintaining a solar installation program at a community college. This project sought to identify effective and scalable practices for developing a solar installation program which included the following components: enrolling students into aligned programs; developing curriculum and courses which meet industry needs; placing students in solar installation or related employment; and sustaining programming past grant funding. Through this project- IGEN was able to identify critical components of a solar installation program. Detailed further in this report these components describe 1) Necessary conditions for establishing a solar installation program, 2) Program structures use cases for different contexts, 3) Effective practices related to student pipeline development and program throughout. As a result of this project- all five participating community colleges have expressed support for continuing to offer solar installation training to their students. Additionally, three nonparticipating colleges in IGEN have expressed interest in adopting practice, courses, or curriculum developed through this program. IGEN has also committed to making the curriculum, course structure and other materials available on their website for accelerated adoption of effective practices both state and nationwide.

14 SOLAR ENERGY↗

DOE OSTI2025

Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study

ABSTRACT Context-specific genome-scale model (CS-GSM) reconstruction is becoming an efficient strategy for integrating and cross-comparing experimental multi-scale data to explore the relationship between cellular genotypes, facilitating fundamental or applied research discoveries. However, the application of CS modeling for non-conventional microbes is still challenging. Here, we present a graphical user interface that integrates COBRApy, EscherPy, and RIPTiDe, Python-based tools within the BioUML platform, and streamlines the reconstruction and interrogation of the CS genome-scale metabolic frameworks via Jupyter Notebook. The approach was tested using -omics data collected for Methylotuvimicrobium alcaliphilum 20Z R , a prominent microbial chassis for methane capturing and valorization. We optimized the previously reconstructed whole genome-scale metabolic network by adjusting the flux distribution using gene expression data. The outputs of the automatically reconstructed CS metabolic network were comparable to manually optimized i IA409 models for Ca-growth conditions. However, the CS model questions the reversibility of the phosphoketolase pathway and suggests higher flux via primary oxidation pathways. The model also highlighted unresolved carbon partitioning between assimilatory and catabolic pathways at the formaldehyde-formate node. Only a very few genes and only one enzyme with a predicted function in C1 metabolism, a homolog of the formaldehyde oxidation enzyme ( fae1-2 ), showed a significant change in expression in La-growth conditions. The CS-GSM predictions agreed with the experimental measurements under the assumption that the Fae1-2 is a part of the tetrahydrofolate-linked pathway. The cellular roles of the tungsten (W)-dependent formate dehydrogenase ( fdhAB ) and fae homologs ( fae1-2 and fae3 ) were investigated via mutagenesis. The phenotype of the f dhAB mutant followed the model prediction. Furthermore, a more significant reduction of the biomass yield was observed during growth in La-supplemented media, confirming a higher flux through formate. M. alcaliphilum 20Z R mutants lacking fae1-2 did not display any significant defects in methane or methanol-dependent growth. However, contrary to fae1, the fae1-2 homolog failed to restore the formaldehyde-activating enzyme function in complementation tests. Overall, the presented data suggest that the developed computational workflow supports the reconstruction and validation of CS-GSM networks of non-model microbes. IMPORTANCE The interrogation of various types of data is a routine strategy to explore the relationship between genotype and phenotype. An efficient approach for integrating and cross-comparing experimental multi-scale data in the context of whole-genome-based metabolic network reconstruction becomes a powerful tool that facilitates fundamental and applied research discoveries. The present study describes the reconstruction of a context-specific (CS) model for the methane-utilizing bacterium, Methylotuvimicrobium alcaliphilum 20Z R . M. alcaliphilum 20Z R is becoming an attractive microbial platform for the production of biofuels, chemicals, pharmaceuticals, and bio-sorbents for capturing atmospheric methane. We demonstrate that this pipeline can help reconstruct metabolic models that are similar to manually curated networks. Furthermore, the model is able to highlight previously overlooked pathways, thus advancing fundamental knowledge of non-model microbial systems or promoting their development toward biotechnological or environmental implementations.

Kulyashov, M. A.↗

DOE OSTI2023

Cryo-EM structures reveal native GABA A receptor assemblies and pharmacology

Type A γ-aminobutyric acid receptors (GABA A Rs) are the principal inhibitory receptors in the brain and the target of a wide range of clinical agents, including anaesthetics, sedatives, hypnotics and antidepressants. However, our understanding of GABA A R pharmacology has been hindered by the vast number of pentameric assemblies that can be derived from 19 different subunits and the lack of structural knowledge of clinically relevant receptors. Here, we isolate native murine GABA A R assemblies containing the widely expressed α1 subunit and elucidate their structures in complex with drugs used to treat insomnia (zolpidem (ZOL) and flurazepam) and postpartum depression (the neurosteroid allopregnanolone (APG)). Using cryo-electron microscopy (cryo-EM) analysis and single-molecule photobleaching experiments, we uncover three major structural populations in the brain: the canonical α1β2γ2 receptor containing two α1 subunits, and two assemblies containing one α1 and either an α2 or α3 subunit, in which the single α1-containing receptors feature a more compact arrangement between the transmembrane and extracellular domains. Interestingly, APG is bound at the transmembrane α/β subunit interface, even when not added to the sample, revealing an important role for endogenous neurosteroids in modulating native GABA A Rs. Together with structurally engaged lipids, neurosteroids produce global conformational changes throughout the receptor that modify the ion channel pore and the binding sites for GABA and insomnia medications. Our data reveal the major α1-containing GABA A R assemblies, bound with endogenous neurosteroid, thus defining a structural landscape from which subtype-specific drugs can be developed.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

CD8 Lymphocyte Depletion Enhances the Latency Reversal Activity of the SMAC Mimetic AZD5582 in ART-Suppressed Simian Immunodeficiency Virus-Infected Rhesus Macaques

ABSTRACT Inducing latency reversal to reveal infected cells to the host immune system represents a potential strategy to cure human immunodeficiency virus (HIV) infection. In separate studies, we have shown previously that CD8 + T cells may contribute to the maintenance of viral latency and have identified a novel SMAC (second mitochondrial activator of caspases) mimetic/IAP (inhibitor of apoptosis protein) inhibitor (AZD5582) capable of reversing HIV/simian immunodeficiency virus (SIV) latency in vivo by activating the noncanonical (nc) NF-κB pathway. Here, we use AZD5582 in combination with antibody (Ab)-mediated depletion of CD8α + cells to further evaluate the role of CD8 + T cells in the maintenance of viral latency. Six rhesus macaques (RMs) were infected with SIV mac239 and treated with antiretroviral therapy (ART) starting at week 8 postinfection. After 84 to 85 weeks of ART, all animals received a single dose of the anti-CD8α depleting Ab MT807R1 (50 mg/kg of body weight, administered subcutaneously [s.c.]), followed by five weekly doses of AZD5582 (0.1 mg/kg, administered intravenously [i.v.]). Following combined treatment with CD8α depletion and AZD5582, 100% of RMs experienced on-ART viremia levels above 60 copies per ml of plasma. In comparator groups of SIV-infected, ART-suppressed RMs treated with AZD5582 only or CD8α depletion only, on-ART viremia was experienced by 56% and 57% of the animals, respectively. Furthermore, the increased frequency of viremic episodes during the treatment period was greater in the group treated with CD8α depletion plus AZD5582 than in the other groups. Mathematical modeling of virus reactivation suggested that in addition to viral dynamics during acute infection, CD8α depletion influenced the response to AZD5582. This work suggests that the latency reversal induced by activation of the ncNF-κB signaling pathway with AZD5582 can be enhanced by CD8α + cell depletion. IMPORTANCE A favored approach to curing HIV infection aims at inducing viral expression using latency-reversing agents (LRAs) to allow the elimination of infected cells. Here, we tested a combination of two recently identified LRAs, the SMAC mimetic/IAP inhibitor AZD5582, which activates the noncanonical NF-κB pathway, and the antibody (Ab) MT807R1, which depletes CD8α + cells, in SIV-infected rhesus macaques (RMs) on ART. Latency reversal, as defined by increased on-ART viremia, was observed in all six SIV-infected, ART-treated RMs that received this combined treatment. Furthermore, comparison of viral reactivation between these animals and groups of SIV-infected, ART-treated RMs treated with AZD5582 only or CD8α + cell depletion only showed more frequent increases in viremic episodes when the two treatments were combined. This study provides additional evidence that CD8 + T cells may contribute to the maintenance of HIV/SIV latency on ART and potentially inhibit latency reversal during HIV cure approaches.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Potential State Regulatory Pathways to Facilitate Low-Carbon Fuels

States and the federal government are increasingly engaged in the challenges around decarbonizing the electric grid. In particular, regulators, consumers, stakeholders, and utilities recognize the need to carefully consider the role natural gas will play in a decarbonized future. A variety of technology and policy options to reduce greenhouse gas emissions associated with natural gas use are available, including energy efficiency programs, demand reduction tools, strategic electrification, and strategies to reduce emissions from natural gas production, transportation, and consumption. Low-carbon fuels – mainly renewable natural gas (RNG) and clean hydrogen – are being considered an important component of decarbonization goals. RNG and hydrogen may be able to meaningfully reduce emissions from processes independent of geologic natural gas, displacing emissions of methane, a powerful greenhouse gas. Although RNG and hydrogen are not cost-competitive today with geologic natural gas and are smaller in scale and potential than other decarbonization options, they can be explored as potential critical tools to decarbonize sectors that are difficult to electrify or shift off of natural gas entirely, such as air travel, industrial processes, maritime transport, long-distance trucking, space heating on cold days, and railroads (Nadel, 2022). The role of this report is to provide informational context for state utility regulators to understand the impacts of and challenges associated with broader integration of low-carbon fuels, followed by examples of state regulatory actions taken to date to facilitate the development of low-carbon fuels. Setting clear guidance to calculate the environmental benefits of low-carbon fuels and continuing federal and state investments in research and development to reduce costs relative to fossil fuels will be important steps to take to signal the desire to grow the market for these fuels. State public utility commissions may play a key role in setting regulatory frameworks for low-carbon fuels and ensuring that ratepayer funds, if utilized, are done so to further the public interest. This report is intended to summarize decisions that states have made to date on low-carbon fuels. In the spirit of understanding the current market and sharing information, this report provides success stories, and lessons learned across states as regulators implement varying strategies to achieve decarbonization objectives while maintaining their focus on affordability, safety, and reliability of the energy system. The report begins with an introduction of the role of natural gas in the U.S. economy (Section I) and background information on natural gas use, decarbonization, and low-carbon fuels (Section II). Next, the report describes the current market by discussing the scale of current production, emissions intensity, resource potential, and costs of low-carbon fuels compared to geologic natural gas (Section III). Following these sections, the report describes four strategies states have employed to facilitate low-carbon fuels: opening exploratory dockets, approving voluntary tariffs for customers, approving interconnection tariffs for producers, and considering portfolio-wide procurement targets (Section IV). This section lists states that have taken actions in each category, citing utility filings, commission decisions, stakeholder comments, and other relevant sources. Finally, the report concludes with suggested questions regulators may wish to consider regarding low-carbon fuels, in the interest of preparing to make decisions in the future (Section V). These questions include: Are there existing regulatory or technical barriers to voluntary purchases of low-carbon fuels? Can customers work with utilities to procure low-carbon fuels; are producers able to interconnect projects without significant barriers to entry? Should the infrastructure and/or commodity costs of low-carbon fuels be socialized among all ratepayers, or borne solely by the large commercial and industrial (C&I) customers currently driving the market? Should regulated natural gas and/or electric utilities own and operate low-carbon fuel production? How should regulators consider the unique decarbonization potential of low-carbon fuels, particularly for hard-to-abate sectors, in decision-making? Is additional direction or clarity from state policymakers needed? What no-regrets approaches can help facilitate both near-term RNG development and long-term development of hydrogen and other zero-carbon fuels? We collectively wish to express our gratitude to the U.S. Department of Energy, Office of Fossil Energy and Carbon Management, for supporting this report and other technical assistance resources for state regulators on natural gas topics. State regulators operate under a variety of policy environments, and states have vastly different types of energy resources, infrastructure, and customers. While there is no optimal regulatory, policy, or technological solution that will be successful in every state, state regulators can benefit by exchanging lessons learned with their peers across the country. We look forward to continued engagement with our fellow commissioners, commission staff, NARUC, the U.S. Department of Energy, and other stakeholders to develop sound regulation in the public interest.

03 NATURAL GAS↗

DOE OSTI2024

Annotation of DOM metabolomes with an ultrahigh resolution mass spectrometry molecular formula library

Current approaches to analyzing metabolomic data often rely on matching MS/MS fragmentation data to sparse libraries or databases. This approach results in limited identification of features, often with less than 10% of the dataset being annotated. A complementary approach is to assign molecular formula to features based on accurate mass measurements, but the platforms commonly used for metabolomics do not have the needed accuracy or resolving power to do this robustly, particularly for larger molecules. Using our newly modified analysis tool, CoreMS, we generated a library of molecular formula from pooled samples analyzed with LC-21T FT-ICR MS. This library successfully annotated approximately 53.2% of features identified from the exometabolome of marine diatom Phaeodactylum tricornutum – a nearly ten-fold increase over the 5.9% annotation rate achieved using a conventional MS/MS library matching approach. Using this FT-ICR MS library approach, we were able to differentiate differences in the exometabolome of P. tricornutum in iron replete and iron limited conditions, with 668 metabolites being differentially expressed (p < 0.05, 2 x intensity difference) under these conditions. The traditional MS/MS fragmentation-based annotation approach only annotated 61 of these metabolites, while our novel pipeline annotated 450 metabolites and revealed 12 metabolites that were significantly more abundant under low iron conditions. Our results demonstrate the utility of ultrahigh resolution mass spectrometry for generating more comprehensive and confident molecular annotations.

21T-FTICR-MS, CoreMS↗

DOE OSTI2025

Structure and mechanism of biosynthesis of Streptococcus mutans cell wall polysaccharide

Streptococcus mutans, the causative agent of human dental caries, expresses a cell wall attached Serotype c-specific Carbohydrate (SCC) that is critical for cell viability. SCC consists of a polyrhamnose backbone of →3)α-Rha(1 → 2)α-Rha(1→ repeats with glucose (Glc) side-chains and glycerol phosphate (GroP) decorations. This study reveals that SCC has one predominant and two more minor Glc modifications. The predominant Glc modification, α-Glc, attached to position 2 of 3-rhamnose, is installed by SccN and SccM glycosyltransferases and is the site of the GroP addition. The minor Glc modifications are β-Glc linked to position 4 of 3-rhamnose installed by SccP and SccQ glycosyltransferases, and α-Glc attached to position 4 of 2-rhamnose installed by SccN working in tandem with an unknown enzyme. Both the major and the minor β-Glc modifications control bacterial morphology, but only the GroP and major Glc modifications are critical for biofilm formation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Simultaneous Overexpression of FERULOYL‐CoA 6′‐HYDROXYLASE 1 and COUMARIN SYNTHASE Leads to Coumarin‐Enriched Lignin and Improved Saccharification in Greenhouse‐ and Field‐Grown Poplar

ABSTRACT The urgent need for renewable resources has increased the interest in woody biomass to manufacture bio‐based products. However, lignin recalcitrance limits the enzymatic conversion of wood into fermentable sugars, posing a major challenge for biomass deconstruction. To address this problem, we aimed at incorporating the coumarin scopoletin into the lignin polymer of poplar ( Populus tremula × P . alba ) by expressing FERULOYL‐CoA 6′‐HYDROXYLASE 1 ( F6′H1 ) and COUMARIN SYNTHASE ( COSY ) in lignifying cells. Three constructs were evaluated: two bicistronic constructs, SCOP1 ( COSY followed by F6′H1 ) and SCOP2 ( F6′H1 followed by COSY ), and one monocistronic, SCOP3 (only F6′H1 ). SCOP1 poplars produced most free scopoletin without altering overall lignin, cellulose or hemicellulose content. SCOP2 poplars were overall less efficient in scopoletin production and most of these lines showed a severe biomass yield penalty, whereas SCOP3 caused plant lethality. NMR and metabolic analyses confirmed that scopoletin cross‐coupled with G and S monomers during lignification in SCOP1 lines. In addition to scopoletin, the detection of benzodioxane structures revealed the incorporation of dihydroxycoumarins. Overall coumarin incorporation in lignin amounted up to 2.3%. After alkaline pretreatment, wood from greenhouse‐grown SCOP1 poplars released up to 29% more glucose compared to the wild type upon limited saccharification. Field‐testing of three SCOP1 lines showed a 6 to 11% increase in saccharification efficiency, with the line containing the lowest scopoletin levels maintaining normal growth. These results demonstrate that engineering lignin composition in poplar can improve saccharification, and emphasize the importance of construct design, translational research and field validation.

alternative lignin monomers↗

DOE OSTI2020

Effective spin-mixing conductance of topological-insulator/ferromagnet and heavy-metal/ferromagnet spin-orbit-coupled interfaces: A first-principles Floquet-nonequilibrium Green function approach

The spin-mixing conductance (SMC) is a key quantity determining efficiency of spin transport across interfaces. Thus, knowledge of its precise value is required for accurate measurement of parameters quantifying numerous effects in spintronics, such as spin-orbit torque, spin Hall magnetoresistance, spin Hall effect, and spin pumping. However, the standard expression for SMC, provided by the scattering theory in terms of the reflection probability amplitudes, is inapplicable when strong spin-orbit coupling (SOC) is present directly at the interface. This is the precisely the case of topological-insulator/ferromagnet and heavy-metal/ferromagnet interfaces of great contemporary interest. We introduce an approach where first-principles Hamiltonian of these interfaces, obtained from noncollinear density functional theory (ncDFT) calculations, is combined with charge-conserving Floquet-nonequilibrium-Green-function formalism to compute directly the pumped spin current $I^{S_z}_L$ into semi-infinite left lead of two-terminal heterostructures Cu/X/Co/Cu or Y/Co/Cu—where X = Bi 2 Se 3 and Y = Pt or W—due to microwave-driven steadily precessing magnetization of the Co layer. This allows us to extract an effective SMC as a prefactor in $I^{S_z}_L$ versus precession cone angle θ dependence, as long as it remains the same, $I^{S_z}$ ∝ sin 2 θ , as in the case where SOC is absent. By comparing calculations where SOC in switched off versus switched on in ncDFT calculations, we find that SOC consistently reduces the pumped spin current and therefore the effective SMC.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Microsimulation of Energy and Flow Effects from Optimal Automated Driving in Mixed Traffic

In this paper we study the energy and traffic impact of a proposed Anticipative Cruise Controller in a PTV VISSIM microsimulation environment. We dissect our controller into two parts: 1. the unconnected mode, active when following a human-driven vehicle, and 2. the connected mode, active when following another automated vehicle equipped with connectivity. Probabilistic constraints balance safety considerations with inter-vehicle compactness, and vehicle constraints for acceleration capabilities are expressed through the use of powertrain maps. Emergent highway traffic scenarios are then modeled using time headway distributions from empirical traffic data. To study the impact of automation over a range of demands of free-flow to stop-and-go, we vary vehicle flux from low to high and vary automated vehicle penetration from low to high. When examining all-human driving scenarios, network capacity failed to meet demand in high-volume scenarios, such as rush-hour traffic. We further find that with connected automated vehicles introduced, network capacity was improved to support the high-volume scenarios. Finally, we examine energy efficiencies of the fleet for conventional, electric, and hybrid vehicles. We find that automated vehicles perform at a 10%-20% higher energy efficiency over human drivers when considering conventional powertrains, and find that automated vehicles perform at a 3%-9% higher energy efficiency over human drivers when considering electric and hybrid powertrains. Due to secondary effects of smoothing traffic flow and reducing unnecessary braking, energy benefits also apply to human-driven vehicles that interact with automated ones. Such simulated humans were found to drive up to 10% more energy-efficiently than they did in the baseline all-human scenario.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗