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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A novel bacterial protein family that catalyses nitrous oxide reduction

Nitrous oxide (N 2 O), a driver of global warming and climate change, has reached unprecedented concentrations in Earth’s atmosphere. Current N 2 O sources outpace N 2 O sinks, emphasizing the need for comprehensive understanding of processes that consume N 2 O. Microbes that express the enzyme N 2 O reductase (N 2 OR) convert N 2 O to climate change-neutral dinitrogen (N 2 ). Known N 2 ORs belong to the canonical clade I and clade II NosZ reductases and are considered key enzymes for N 2 O reduction. Here we report a previously unrecognized protein family with a role in N 2 O reduction, clade III lactonase-type N 2 OR (L-N 2 OR), which diverges in sequence from canonical NosZ but conserves three-dimensional protein structural features. Integrated physiological, metagenomic, proteomic and structural modelling studies demonstrate that L-N 2 ORs catalyse N 2 O reduction. L-N 2 OR genes occur in several phyla, predominantly in uncultured taxa with broad geographic distribution. Our findings expand the known diversity of N 2 ORs and implicate previously unrecognized taxa (for example, Nitrospinota) in N 2 O consumption. In conclusion, the expansion of N 2 OR diversity and the identification of a novel type of catalyst for N 2 O reduction advances the understanding of N 2 O sinks, has implications for greenhouse gas emission and climate change modelling, and expands opportunities for innovative biotechnologies aimed at curbing N 2 O emissions.

He, Guang 何广 [Univ. of Tennessee, Knoxville, TN (U↗
DOE OSTI2023

Snow-corrected vegetation indices for improved gross primary productivity assessment in North American evergreen forests

North American evergreen forests cover large areas and influence the global carbon cycle. Satellite remote sensing has been used to track the phenology of ecosystem photosynthesis of these forests by detecting variation in vegetation optical properties associated with physiological and structural features, and most of these methods have been closely tied to vegetation greenness. However, in evergreens, the application of satellite data to monitor photosynthetic phenology is often limited by the lack of sensitivity of greenness-based indices. In this study, we identified 47 evergreen forest flux sites in North America that had MODIS observation overlapping with the flux tower records. We then calculated four vegetation indices using MODIS MAIAC data (MCD19A1), including NDVI, CCI, NIRv, and kNDVI, for the 47 flux sites and evaluated relationships between gross primary productivity (GPP) and vegetation indices across the North American evergreen forests. Our results showed that snow had substantial effects on the performance of all vegetation indices in tracking GPP phenology, particularly in the early spring when rapid changes occurred to both GPP and snow cover. Furthermore, different vegetation indices were affected differently, indicating contradictory and confounding effects of snow on these indices. After correcting for the snow effects, both CCI and NIRv performed well in tracking GPP phenology, albeit for different reasons. CCI is sensitive to seasonal changes in the relative levels of chlorophyll and carotenoid pigments, which are closely tied to GPP phenology in evergreens. NIRv is sensitive to the absorbed photosynthetically active radiation and to the contribution of deciduous components to the overall optical properties. We also found that correlations between GPP and vegetation indices varied among ecoregions and climate classes. In general, regions with pronounced seasonal GPP patterns had stronger correlations between GPP and greenness-based indices than regions with weaker seasonal GPP patterns. These biome differences were less pronounced for CCI. The snow artifacts and complementary vegetation index effects reported here should be considered in any large-scale studies of GPP using reflectance-based indices from optical satellites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Ecohydrological decoupling under changing disturbances and climate

Terrestrial disturbances are increasing in frequency and severity, perturbing the hydrologic cycle by altering vegetation-mediated water use and microclimate. Changes in the hydrological cycle alter vegetation succession, which is additionally influenced by changing climate and atmospheric CO 2 . Here, the interacting drivers and feedbacks between disturbances, climate, and vegetation cause uncertainty regarding the sustainable provision of freshwater through streamflow. Here we synthesize the literature on post-disturbance ecohydrological coupling, in particular, including the mechanistic relationship between vegetation and streamflow, under changing disturbance regimes, atmospheric CO 2 , and climate. Disturbance can cause decoupling between transpiration and streamflow by altering the connectivity, size, availability, and spatial distribution of their source pools. Decoupling increases when soil moisture is low due to reduced connectivity, and when soil moisture is high and Q and T are independent. Successional trajectories regulate the physiological and physical features influencing influence the dynamics of source water partitioning. Changing climate and disturbance regimes can alter succession and prolong decoupling, with potential moderation by CO 2 . Increasing rates, severity, and spread of disturbances along with warming could promote greater decoupling globally. From this review emerges a framework of testable hypotheses that identify the critical processes regulating ecohydrological coupling and provides a roadmap for future research. Accurate prediction of post-disturbance coupling and subsequent water availability for human consumption requires understanding the degree of hydraulic connectivity between source water pools for transpiration and streamflow and their response to succession under changing disturbance and climate regimes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

What are mycorrhizal traits?

Traits are inherent properties of organisms, but how are they defined for organismal networks such as mycorrhizal symbioses? Mycorrhizal symbioses are complex and diverse belowground symbioses between plants and fungi that have proved challenging to fit into a unified and coherent trait framework. Here we propose an inclusive mycorrhizal trait framework that classifies traits as morphological, physiological, and phenological features that have functional implications for the symbiosis. We further classify mycorrhizal traits by location – plant, fungus, or the symbiosis – which highlights new questions in trait-based mycorrhizal ecology designed to charge and challenge the scientific community. This new framework is an opportunity for researchers to interrogate their data to identify novel insights and gaps in our understanding of mycorrhizal symbioses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Ecological divergence of syntopic marine bacterial species is shaped by gene content and expression

Identifying mechanisms by which bacterial species evolve and maintain genomic diversity is particularly challenging for the uncultured lineages that dominate the surface ocean. A longitudinal analysis of bacterial genes, genomes, and transcripts during a coastal phytoplankton bloom revealed two co-occurring, highly related Rhodobacteraceae species from the deeply branching and uncultured NAC11-7 lineage. These have identical 16S rRNA gene amplicon sequences, yet their genome contents assembled from metagenomes and single cells indicate species-level divergence. Moreover, shifts in relative dominance of the species during dynamic bloom conditions over 7 weeks confirmed the syntopic species’ divergent responses to the same microenvironment at the same time. Genes unique to each species and genes shared but divergent in per-cell inventories of mRNAs accounted for 5% of the species’ pangenome content. These analyses uncover physiological and ecological features that differentiate the species, including capacities for organic carbon utilization, attributes of the cell surface, metal requirements, and vitamin biosynthesis. Such insights into the coexistence of highly related and ecologically similar bacterial species in their shared natural habitat are rare.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Association of Diet and Antimicrobial Resistance in Healthy U.S. Adults

Antimicrobial resistance (AMR) represents a significant source of morbidity and mortality worldwide, with expectations that AMR-associated consequences will continue to worsen throughout the coming decades. Since resistance to antibiotics is encoded in the microbiome, interventions aimed at altering the taxonomic composition of the gut might allow us to prophylactically engineer microbiomes that harbor fewer antibiotic resistant genes (ARGs). Diet is one method of intervention, and yet little is known about the association between diet and antimicrobial resistance. To address this knowledge gap, we examined diet using the food frequency questionnaire (FFQ; habitual diet) and 24-h dietary recalls (Automated Self-Administered 24-h [ASA24 ® ] tool) coupled with an analysis of the microbiome using shotgun metagenome sequencing in 290 healthy adult participants of the United States Department of Agriculture (USDA) Nutritional Phenotyping Study. We found that aminoglycosides were the most abundant and prevalent mechanism of AMR in these healthy adults and that aminoglycoside-O-phosphotransferases (aph3-dprime) correlated negatively with total calories and soluble fiber intake. Individuals in the lowest quartile of ARGs (low-ARG) consumed significantly more fiber in their diets than medium- and high-ARG individuals, which was concomitant with increased abundances of obligate anaerobes, especially from the family Clostridiaceae, in their gut microbiota. Finally, we applied machine learning to examine 387 dietary, physiological, and lifestyle features for associations with antimicrobial resistance, finding that increased phylogenetic diversity of diet was associated with low-ARG individuals. These data suggest diet may be a potential method for reducing the burden of AMR.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Genetically engineered mouse models of esophageal cancer

Esophageal cancer is the most common cause of cancer-related death worldwide with a diverse geographical distribution, poor prognosis, and diagnosis in advanced stages of the disease. Identification of the mechanisms involved in esophageal cancer development is evaluative to improve outcomes for patients. Genetically engineered mouse models (GEMMs) of cancer provide the physiologic, molecular, and histologic features of the human tumors to determine the pathogenesis and treatments for cancer, hence exhibiting a source of tremendous potential for oncology research. The advancement of cancer modeling in mice has improved to the extent that researchers can observe and manipulate the disease process in a specific manner. Despite the significant differences between mice and humans, mice can be great models for human oncology researches due to similarities between them at the molecular and physiological levels. Due to most of the existing esophageal cancer GEMMs do not propose an ideal system for pathogenesis of the disease, genetic risks, and microenvironment exposure, so identification of challenges in GEM modeling and well-developed technologies are required to obtain the most value for patients. In this review, we describe the biology of human and mouse, followed by the exciting esophageal cancer mouse models with a discussion of applicability and challenges of these models for generating new GEMMs in future studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Electron transport across the cell envelope via multiheme c -type cytochromes in Geobacter sulfurreducens

Extracellular electron transfer (EET) enables certain microorganisms to respire using soluble and insoluble extracellular electron acceptors by transporting electrons across the cell envelope. Among these, G. sulfurreducens serves as a model organism for understanding direct EET pathways, where multiheme c-type cytochromes mediate electron transport from intracellular redox carriers to extracellular acceptors such as Fe(III) oxides and electrodes. This review focuses on heme-dependent electron transfer in Geobacter sulfurreducens, detailing the roles of inner membrane cytochromes, periplasmic carriers, outer membrane conduits, and recently characterized extracellular nanowires formed by polymerized multiheme c-type cytochromes, including OmcS, OmcE, and OmcZ. We examine the state of understanding of their physiological function, their structural features, expression patterns, and essentiality under various respiratory conditions. These insights advance our understanding of microbial anaerobic respiration and have implications for biogeochemical cycling, bioenergy generation, and bioremediation. The molecular architecture, assembly mechanisms, and secretion pathways of multiheme c-type cytochrome nanowires remain active areas of investigation, offering promising directions for future research and biotechnological innovation in engineered microbial systems.

Chemistry↗
DOE OSTI2023

Acute stress reduces population-level metabolic and proteomic variation

Abstract Background Variation in omics data due to intrinsic biological stochasticity is often viewed as a challenging and undesirable feature of complex systems analyses. In fact, numerous statistical methods are utilized to minimize the variation among biological replicates. Results We demonstrate that the common statistics relative standard deviation (RSD) and coefficient of variation (CV), which are often used for quality control or part of a larger pipeline in omics analyses, can also be used as a metric of a physiological stress response. Using an approach we term Replicate Variation Analysis (RVA), we demonstrate that acute physiological stress leads to feature-wide canalization of CV profiles of metabolomes and proteomes across biological replicates. Canalization is the repression of variation between replicates, which increases phenotypic similarity. Multiple in-house mass spectrometry omics datasets in addition to publicly available data were analyzed to assess changes in CV profiles in plants, animals, and microorganisms. In addition, proteomics data sets were evaluated utilizing RVA to identify functionality of reduced CV proteins. Conclusions RVA provides a foundation for understanding omics level shifts that occur in response to cellular stress. This approach to data analysis helps characterize stress response and recovery, and could be deployed to detect populations under stress, monitor health status, and conduct environmental monitoring.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Feature Selection Techniques for a Machine Learning Model to Detect Autonomic Dysreflexia

Feature selection plays a crucial role in the development of machine learning algorithms. Understanding the impact of the features on a model, and their physiological relevance can improve the performance. This is particularly helpful in the healthcare domain wherein disease states need to be identified with relatively small quantities of data. Autonomic Dysreflexia (AD) is one such example, wherein mismanagement of this neurological condition could lead to severe consequences for individuals with spinal cord injuries. We explore different methods of feature selection needed to improve the performance of a machine learning model in the detection of the onset of AD. We present different techniques used as well as the ideal metrics using a dataset of thirty-six features extracted from electrocardiograms, skin nerve activity, blood pressure and temperature. The best performing algorithm was a 5-layer neural network with five relevant features, which resulted in 93.4% accuracy in the detection of AD. The techniques in this paper can be applied to a myriad of healthcare datasets allowing forays into deeper exploration and improved machine learning model development. Through critical feature selection, it is possible to design better machine learning algorithms for detection of niche disease states using smaller datasets.

electrocardiography↗
DOE OSTI2021

Landscape rules predict optimal superhighways for the first peopling of Sahul

Archaeological data and demographic modelling suggest that the peopling of Sahul required substantial populations, occurred rapidly within a few thousand years and encompassed environments ranging from hyper-arid deserts to temperate uplands and tropical rainforests. How this migration occurred and how humans responded to the physical environments they encountered have, however, remained largely speculative. By constructing a high-resolution digital elevation model for Sahul and coupling it with fine-scale viewshed analysis of landscape prominence, least-cost pedestrian travel modelling and high-performance computing, here we create over 125 billion potential migratory pathways, whereby the most parsimonious routes traversed emerge. Our analysis revealed several major pathways—superhighways—transecting the continent, that we evaluated using archaeological data. These results suggest that the earliest Australian ancestors adopted a set of fundamental rules shaped by physiological capacity, attraction to visually prominent landscape features and freshwater distribution to maximize survival, even without previous experience of the landscapes they encountered.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Fabrication of a novel 3D-printed perfusion bioreactor for complex cell culture models

We introduce a novel fabrication method for developing a 3D-printed perfusion bioreactor (3D-PBR) to facilitate the in situ growth and differentiation of human bone marrow (BM)-derived mesenchymal stem cells (MSCs) while enabling coculture with vascular cells. To recapitulate human physiology, in vitro platforms must incorporate several key features of their native target organ. This often entails a supportive 3D architecture for growing and differentiating multiple human cell types in situ under perfusion. Other essential characteristics include reproducibility, ease of customization, and biocompatibility. Our 3D-PBR combines these features and was fabricated using a biocompatible resin-based polymer, which was 3D-printed, followed by the addition of a permeable membrane to create a coculture microenvironment. MSCs were encapsulated in a collagen-fibrin gel alongside human endothelium within the 3D-PBR. The physical cues that our 3D-PBR provided facilitated the differentiation of MSCs into specific lineages, such as adipocytes and osteoblasts. Immunohistochemistry images demonstrated that cells grown in the 3D-PBR exhibited more physiologically relevant BM perivascular niche markers compared to static culture models. Our method utilizes emerging 3D printing techniques and alternative materials, departing from traditional PDMS-based soft lithography. These advancements in fabrication further enhance in vitro platforms for diverse cell culture models and vascular permeability assays.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Deconstructing the root system of grasses through an exploration of development, anatomy and function

Abstract Well‐adapted root systems allow plants to grow under resource‐limiting environmental conditions and are important determinants of yield in agricultural systems. Important staple crops such as rice and maize belong to the family of grasses, which develop a complex root system that consists of an embryonic root system that emerges from the seed, and a postembryonic nodal root system that emerges from basal regions of the shoot after germination. While early seedling establishment is dependent on the embryonic root system, the nodal root system, and its associated branches, gains in importance as the plant matures and will ultimately constitute the bulk of below‐ground growth. In this review, we aim to give an overview of the different root types that develop in cereal grass root systems, explore the different physiological roles they play by defining their anatomical features, and outline the genetic networks that control their development. Through this deconstructed view of grass root system function, we provide a parts‐list of elements that function together in an integrated root system to promote survival and crop productivity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Quinoa Phenotyping Methodologies: An International Consensus

Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The aging proteostasis decline: From nematode to human

The aging proteostasis decline manifests in a failure of aging cells and organisms to properly respond to proteotoxic challenges. This proteostasis collapse has long been considered a hallmark of aging in nematodes, and has recently been shown to occur also in human cells upon entry to senescence, opening the way to exploring the phenomenon in the broader context of human aging. Cellular senescence is part of the normal human physiology of aging, with senescent cell accumulation as a prominent feature of aged tissues. Being highly resistant to cell death, senescent cells, as they accumulate, become pro-inflammatory and promote disease. Here we discuss the causes of human senescence proteostasis decline, in view of the current literature on nematodes, on the one hand, and senescence, on the other hand. We review two major aspects of the phenomenon: (1) the decline in transcriptional activation of stress-response pathways, and (2) impairments in proteasome function. We further outline potential underlying mechanisms of transcriptional proteostasis decline, focusing on reduced chromatin dynamics and compromised nuclear integrity. Finally, we discuss potential strategies for reinforcing proteostasis as a means to improve organismal health and address the relationship to senolytics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Identifying sequence perturbations to an intrinsically disordered protein that determine its phase-separation behavior

Phase separation of intrinsically disordered proteins (IDPs) commonly underlies the formation of membraneless organelles, which compartmentalize molecules intracellularly in the absence of a lipid membrane. Identifying the protein sequence features responsible for IDP phase separation is critical for understanding physiological roles and pathological consequences of biomolecular condensation, as well as for harnessing phase separation for applications in bioinspired materials design. To expand our knowledge of sequence determinants of IDP phase separation, we characterized variants of the intrinsically disordered RGG domain from LAF-1, a model protein involved in phase separation and a key component of P granules. Based on a predictive coarse-grained IDP model, we identified a region of the RGG domain that has high contact probability and is highly conserved between species; deletion of this region significantly disrupts phase separation in vitro and in vivo. We determined the effects of charge patterning on phase behavior through sequence shuffling. We designed sequences with significantly increased phase separation propensity by shuffling the wild-type sequence, which contains well-mixed charged residues, to increase charge segregation. This result indicates the natural sequence is under negative selection to moderate this mode of interaction. We measured the contributions of tyrosine and arginine residues to phase separation experimentally through mutagenesis studies and computationally through direct interrogation of different modes of interaction using all-atom simulations. Finally, we show that despite these sequence perturbations, the RGG-derived condensates remain liquid-like. Together, these studies advance our fundamental understanding of key biophysical principles and sequence features important to phase separation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Parameterizing Perennial Bioenergy Crops in Version 5 of the Community Land Model Based on Site‐Level Observations in the Central Midwestern United States

Abstract With projected expansion of biofuel production at a global scale, there is a pressing need to develop adequate representation of bioenergy crops in land surface models to help effectively quantify the biogeophysical and biogeochemical effects of its associated land use changes. This study implements two new perennial bioenergy crops, Miscanthus and switchgrass, into the Community Land Model Version 5 based on site‐level observations from the midwestern United States by modifying parameters associated with photosynthesis, phenology, allocation, decomposition, and carbon cost of nitrogen uptake and integrating concomitantly land management practices. Sensitivity analyses indicate that carbon and energy fluxes of the perennial crops are most sensitive to photosynthesis and phenology parameters. Validation of simulated fluxes against site‐level measurements demonstrates that the model is capable of capturing the overall patterns of energy and carbon fluxes, as well as physiological transitions from leaf emergence to senescence. Compared to annual crops, perennial crops feature longer growing season, greater leaf areas, and higher productivity, leading to increased transpiration, lower annual runoff, and larger carbon uptake. The model simulations suggest that with higher CO 2 assimilation rates and lower demands for nutrients and water, high‐yielding perennial crops are promising alternatives of bioenergy feedstocks compared to traditional annual crops not only for mitigating climate change but also for environmental conservation purposes by reducing fertilizer application and therefore alleviating surface‐ and ground‐water contaminations. Although the local‐scale simulations shed light on potential benefits of using perennial grasses as bioenergy feedstocks, quantifying consequences of their plantations at larger scales warrants additional investigation.

Cheng, Yanyan↗