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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Probe-Enabled Approaches for Function-Dependent Cell Sorting and Characterization of Microbiome Subpopulations

Understanding the roles that individual species or communities play within a microbiome is a significant challenge. The complexity and heterogeneity of microbiomes presents a challenge to researchers looking to unravel the function that microbiomes serve within larger environments. While identification of the species and proteins present in a microbiome can be accomplished through genomics approaches, strategies that report on enzyme activity are limited. In this chapter, we describe the application of small molecule chemical probes in the isolation and subsequent characterization of microbiome subpopulations based on enzymatic function. We will cover protocols for labeling microbes with appropriate probes, microbiome sample preparation, and using fluorescence-activated cell sorting to isolate subpopulations based on function. We hope that the strategies outlined here will serve as a resource for researchers studying the functional role that microbiomes play in the gut and soil.

Steiger, Andrea K.↗
NASA NTRS1989

Automatic labeling and characterization of objects using artificial neural networks

Existing NASA supported scientific data bases are usually developed, managed and populated in a tedious, error prone and self-limiting way in terms of what can be described in a relational Data Base Management System (DBMS). The next generation Earth remote sensing platforms, i.e., Earth Observation System, (EOS), will be capable of generating data at a rate of over 300 Mbs per second from a suite of instruments designed for different applications. What is needed is an innovative approach that creates object-oriented databases that segment, characterize, catalog and are manageable in a domain-specific context and whose contents are available interactively and in near-real-time to the user community. Described here is work in progress that utilizes an artificial neural net approach to characterize satellite imagery of undefined objects into high-level data objects. The characterized data is then dynamically allocated to an object-oriented data base where it can be reviewed and assessed by a user. The definition, development, and evolution of the overall data system model are steps in the creation of an application-driven knowledge-based scientific information system.

Campbell, William J.↗
DOE OSTI2020

Sequential Ammonia and Carbon Dioxide Adsorption on Pyrolyzed Biomass to Recover Waste Stream Nutrients

Untreated fecal waste in unsewered communities and reactive nitrogen (N) loading from wastewater and urine disposal are important environmental challenges. Through pyrolysis, untreated fecal waste can be converted into amine-rich carbon dioxide (CO 2 ) sorbents, the chemisorption of which tunes the surface chemistry toward ammonia (NH 3 ) uptake. We compared the N-loading potential within amine-rich pyrolyzed human solid waste through exposure to CO 2 followed by NH 3 with the N-loading potential on carboxyl-rich, pyrolyzed oxidized wood using sequential adsorption of isotopically labeled CO 2 and NH 3 . The total N content of pyrolyzed oxidized wood exposed once to NH3 was similar to native, pyrolyzed human solid waste (3.7% compared to 4.6%). However, sorption kinetics and spectroscopic evidence revealed distinct N forms between the two materials affecting subsequent CO 2 and NH 3 uptake. Tertiary amines comprised 70% of N in pyrolyzed waste, compared to only 53% in pyrolyzed oxidized wood. The high heat of adsorption of CO 2 on pyrolyzed human solid waste (50 kJ mol-1) at low concentrations (0.05 mmol CO 2 g -1 ) was indicative of CO 2 reactivity with tertiary amines, consistent with the formation of carbamic acid (Figure S1). Conversely, after exposure to NH 3 , pyrolyzed oxidized wood chemisorbed CO 2 at much higher concentrations (0.7 mmol CO 2 g-1), pointing to primary amines as the active functional moieties. Adsorption kinetics of pyrolyzed oxidized wood revealed continued, albeit diminishing NH 3 uptake with each consecutive CO 2 treatment, averaging 5.9 mmol NH 3 g -1 for the first NH 3 exposure event, and still 3.5 and 2.9 mmol NH 3 g -1 for the second and third. Penetration of 15 NH 3 and 13 CO 2 measured by NanoSIMS reached over 7 µm deep into both materials, explaining the large NH 3 capture. The significantly lower NH 3 adsorption compared to pyrolyzed oxidized wood may be a result of potassium (K) carbonate precipitation precluding a Lewis base interaction between NH 3 and chemisorbed CO 2 , suggested by a 2-fold increase in surface K concentrations after exposure of pyrolyzed human solid waste to CO 2 +NH 3 detected by XPS.

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

National User Resource for Biological Accelerator Mass Spectrometry Annual Report

The National User Resource for Biological Accelerator Mass Spectrometry (User Resource) will provide isotopic analysis (primarily radiocarbon or 14C) by accelerator mass spectrometry (AMS) for NIH- funded researchers across the United States and will be the only User Resource of its type in the United States. The User Resource will provide measurement capability and expertise to a research community that requires highly sensitive, quantitative isotope analyses. Since commissioning a new accelerator mass spectrometer in June 2014, we have measured over 4000 samples a year for collaborators and service users. The User Resource will enable us to continue to meet these research needs, as well as provide for new users whose research programs would benefit from AMS as a measurement tool. The User Resource’s forte will be ultra-high sensitivity quantitation of radiocarbon and selected other radioisotopes for research studies where isotopes are required. Radioisotope labeling studies have been and will continue to be an important tool for addressing many complex biomedical science problems. AMS is a specialized and unique type of mass spectrometry that provides absolute quantitation of radiocarbon and other relevant radioisotopes with extreme sensitivity, having limits of detection in real samples on the order of a few attomol/mg of sample at measurement precisions of ~3%. It is the only instrumental method capable of quantifying radioisotope-labeled agents routinely in real-world samples with such precision and sensitivity. The sensitivity of AMS allows for the quantification of radiolabeled metabolites in extremely complex matrices of cells and organisms at very low concentrations and in small samples. AMS allows studies to be conducted without perturbing metabolism leading to more relevant quantification of metabolic rates and pathways. In addition, it enables quantification of pharmacokinetic and metabolic properties of toxicants at environmentally relevant concentrations in model systems as well as the ability to quantify pharmacokinetics and other molecular endpoints directly in humans. Such quantitative assessments can 1) improve risk assessment for toxicants, 2) address safety and efficacy considerations for therapeutic entities, 3) deepen understanding of xenobiotic and intermediary metabolism, 4) help understand the interactions between critical molecular pathways, and 5) improve efforts to model and predict various metabolic and biological states. These capabilities have been applied in a number of areas including research in carcinogenesis, toxicology, nutrition, pharmacology/drug development and basic biological science.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

National User Resource for Biological Accelerator Mass Spectrometry

The National User Resource for Biological Accelerator Mass Spectrometry (User Resource) will provide isotopic analysis (primarily radiocarbon or 14C) by accelerator mass spectrometry (AMS) for NIH- funded researchers across the United States and will be the only User Resource of its type in the United States. The User Resource will provide measurement capability and expertise to a research community that requires highly sensitive, quantitative isotope analyses. Since commissioning a new accelerator mass spectrometer in June 2014, we have measured over 4000 samples a year for collaborators and service users. The User Resource will enable us to continue to meet these research needs, as well as provide for new users whose research programs would benefit from AMS as a measurement tool. The User Resource’s forte will be ultra-high sensitivity quantitation of radiocarbon and selected other radioisotopes for research studies where isotopes are required. Radioisotope labeling studies have been and will continue to be an important tool for addressing many complex biomedical science problems. AMS is a specialized and unique type of mass spectrometry that provides absolute quantitation of radiocarbon and other relevant radioisotopes with extreme sensitivity, having limits of detection in real samples on the order of a few attomol/mg of sample at measurement precisions of ~3%. It is the only instrumental method capable of quantifying radioisotope-labeled agents routinely in real-world samples with such precision and sensitivity. The sensitivity of AMS allows for the quantification of radiolabeled metabolites in extremely complex matrices of cells and organisms at very low concentrations and in small samples. AMS allows studies to be conducted without perturbing metabolism leading to more relevant quantification of metabolic rates and pathways. In addition, it enables quantification of pharmacokinetic and metabolic properties of toxicants at environmentally relevant concentrations in model systems as well as the ability to quantify pharmacokinetics and other molecular endpoints directly in humans. Such quantitative assessments can 1) improve risk assessment for toxicants, 2) address safety and efficacy considerations for therapeutic entities, 3) deepen understanding of xenobiotic and intermediary metabolism, 4) help understand the interactions between critical molecular pathways, and 5) improve efforts to model and predict various metabolic and biological states. These capabilities have been applied in a number of areas including research in carcinogenesis, toxicology, nutrition, pharmacology/drug development and basic biological science. As a NIGMS National Resource the National User Resource for Biological Accelerator Mass Spectrometry will help NIH funded scientists achieve a deeper understanding of the etiology of human health concerns by (1) enabling the quantification of pharmacokinetics and other molecular endpoints directly in humans; (2) offering the ability to conduct quantitative studies using biologics such as proteins or lipids, and thereby reducing the amount of radioisotope usage in biomedical labs; and (3) enabling more relevant studies of metabolic pathways in health and disease through the use of much lower, more biologically-relevant, concentrations of metabolic substrates in cells and intact organisms. Such studies support NIGMS’s basic biomedical research areas that contribute to the understanding of fundamental cellular and physiological principles and enable research supported by the Biophysics, Biomedical Technology, and Computational Biosciences (BBCB); Genetics and Molecular, Cellular, and Developmental Biology (GMCDB); Pharmacology, Physiology, Biological Chemistry (PPBC) and Training, Workforce Development, and Diversity (TWD) Divisions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Priming mechanisms providing plants and microbes access to mineral-associated organic matter

Mineral-associated organic matter (MAOM) is considered a stable reservoir for soil nutrients that influences long-term soil carbon (C) and nitrogen (N) dynamics. However, recent experimental and theoretical evidence shows that root exudates may mobilize MAOM, thereby providing plants and microbes access to a large and N-rich pool. Given the mechanisms underlying MAOM C and N mobilization remain largely untested, we examined direct and indirect pathways by which root exudates destabilize this nutrient pool in laboratory mesocosms. We simulated root exudation with 13 C-labeled oxalic acid to test whether root exudates are directly capable of mobilizing MAOM from mineral surfaces; and with 13 C-labeled glucose to test whether indirect stimulation of microbial and extracellular enzyme activity leads to MAOM decomposition. We also tested the potential for oxalic acid and glucose to mobilize MAOM in an additional subset of sterilized soils to clarify the potential for non-microbial pathways of MAOM destabilization. Over the course of the 12-day MAOM incubation with and without simulated exudates, we measured C cycling (CO 2 respiration rates, 13 C–CO 2 efflux), N cycling (inorganic N pools, gross N mineralization) and related microbial processes (enzyme activities and microbial community composition via phospholipid fatty acid analysis). Both of the simulated root exudates enhanced MAOM-C mineralization, with cumulative respiration increasing 35–89% relative to the water-only control. Likewise, glucose additions enhanced the production of an exo-cellulase and a chitinase by up to 130% and 39%, respectively, while oxalic acid enhanced oxidative enzyme activities up to 91% greater than control rates. We observed a positive association between glucose-induced shifts in enzyme activities, MAOM-C mineralization, and gross ammonification. Oxalic acid additions were associated with initial increases in fungal relative abundance and in sterile soils appeared to stimulate the release of metals and dissolved organic nitrogen into exchangeable pools. Our results indicate that common root exudates, like glucose and oxalic acid, can significantly increase the turnover and potential release of C and N from MAOM through indirect (e.g., enzyme induction) and direct (e.g., mobilization of metal oxides) mechanisms.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

A 3D Citizen Science Video Game for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network aimed at accurately assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. We present here the active learning component of the project, which consists of an interactive video game prototype for tablet and mobile devices where players are able to intuitively label morphology classifications over mm-scale 3D coral reef imagery. Active learning applications present a novel methodology for engaging the public while efficiently providing large-scale training and test data for increasingly complex and data-intensive machine learning algorithms. NeMO-Net trains players on domain-specific knowledge through interactive tutorials and periodically checks players' input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide personalized classification training. Players can rate the classifications of other players, unlock rewards and join a global community as they explore and classify coral reefs and other shallow marine environments.

Citizen Science↗
DOE OSTI2025

Lipids represent a dynamic, yet stable pool of microbially-derived soil carbon

There is an emerging consensus that microorganisms are a primary source of persistent, slow-cycling soil organic matter (SOM), however which microbial residues contribute to SOM and what controls their accumulation remains unresolved. Lipids are a commonly overlooked biomolecular pool that could contribute significantly to stable SOM. While current estimates for phospholipid degradation in soils are rapid, lipids are structurally heterogeneous molecules that could turnover at different rates. Through a year-long soil incubation and compound-specific, stable isotope probing (SIP)-lipidomics, we were able to rigorously track the persistence of lipid compounds in silty and sandy switchgrass bioenergy crop soils. Here, we assessed the influence of lipid structure on soil lipid accrual and degradation as moderated by soil texture, since mineral association is presumed to be the primary mechanism for lipid persistence. After rapid incorporation of 13 C glucose into microbial biomass, we found 13 C label was retained broadly across chemically diverse lipid classes, even after one year. 13 C-labeled lipid profiles varied significantly with soil texture; however, we found no difference between sandy and silty soils in lipid retention, suggesting soil texture may only play a minor role in modulating lipid persistence. Only two lipid subclasses were found to be persistent (i.e., retention of 13 C label without significant degradation or production): phosphatidylinositol lipids and hydroxyceramide lipids, both of which are negatively charged, possibly facilitating stabilization by mineral complexation. However, several other subclasses displayed substantial ongoing production. In particular, the accumulation of triacylglycerol lipids across soil textures suggests that storage lipids may be an important component of SOC, highlighting a potential target for management strategies to promote C retention by lipid accrual. Overall, the retention for over a year of 13 C label in microbial intact lipid biomarkers reveals the importance of efficient biomass production and turnover when considering microbial C contributions to SOM.

compound-specific↗
NASA NTRS2001

The Living with a Star Program: NASA's Role in Assuring Performance in Space and Atmospheric Environments

NASA has initiated the Living with a Star (LWS) Program to develop the scientific understanding to address the aspects of the Connected Sun-Earth system that affect life and society. A goal of the program is to bridge the gap between science, engineering, and user application communities. This will enable future science, operational, and commercial objectives in space and atmospheric environments by improving engineering approaches to the accommodation and/or mitigation of the effects of solar variability on technological systems. A pre-formulation study determined the optimum combination of science missions, modeling, and technology infusion elements to accomplish this goal. The results of the study are described.

Barth, Janet L.↗
NASA NTRSDate not supplied

Identifying Emerging Safety Threats Through Topic Modeling in the Aviation Safety Reporting System: A Covid-19 Study

The NASA Aviation Safety Reporting System (ASRS) is a voluntary, confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants, and others involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts, and a short descriptive synopsis is written to describe the safety issue. The de-identified reports are then disseminated to the aviation community in many ways, including via an online database, Safety Alert Bulletins, For Your Information Notices, and the CALLBACK newsletter. In this work, we consider whether we can improve the grouping, linking, and understanding of safety concerns through topic modeling. Specifically, we use topic modeling as a building block to identify emerging safety threats over time. This unsupervised approach, we argue, offers the flexibility to identify new emerging themes in this large dataset by constructing different timelines based on the content similarity of ASRS report narratives. This method's unsupervised nature improves upon related research, which is limited to pre-defined labels and therefore can not fully capture emerging safety threats. We apply our method to all ASRS reports in 2020 to assess if the generated timelines can highlight COVID-19 as it is emerging as a safety threat in incoming ASRS reports. We perform both a quantitative and qualitative evaluation of the automatically constructed timelines. The qualitative evaluation is performed by describing the evolution of top terms in the timelines, generated by our method, which we found explicitly convey the themes of COVID-19. Separately, we use a set of 1,213 COVID-19 reports from 2020 that were manually identified by ASRS analysts to quantitatively evaluate the COVID-19 reports distribution across the timelines. Our results have shown that COVID-19 emergence can be identified using the top terms that were generated by topic modeling. The top terms in topic modeling therefore can serve as a summary alternative to manually inspecting reports. Moreover, leveraging the manually identified COVID-19 reports, we found the manually identified timelines accounted for over 70% of the COVID-19 reports curated by the ASRS analysts, which demonstrates the potential of this approach for facilitating the understanding of safety concerns as they emerge and evolve. This method shows great potential to understand aerospace safety threats and other narrative- driven incident report databases.

ASRS↗
NASA NTRSDate not supplied

Identifying Emerging Safety Threats Through Topic Modeling in the Aviation Safety Reporting System: A COVID-19 Study

The NASA Aviation Safety Reporting System (ASRS) is a voluntary, confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants, and others involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts, and a short descriptive synopsis is written to describe the safety issue. The de-identified reports are then disseminated to the aviation community in many ways, including via an online database, Safety Alert Bulletins, For Your Information Notices, and the CALLBACK newsletter. In this work, we consider whether we can improve the grouping, linking, and understanding of safety concerns through topic modeling. Specifically, we use topic modeling as a building block to identify emerging safety threats over time. This unsupervised approach, we argue, offers the flexibility to identify new emerging themes in this large dataset by constructing different timelines based on the content similarity of ASRS report narratives. This method's unsupervised nature improves upon related research, which is limited to pre-defined labels and therefore can not fully capture emerging safety threats. We apply our method to all ASRS reports in 2020 to assess if the generated timelines can highlight COVID-19 as it is emerging as a safety threat in incoming ASRS reports. We perform both a quantitative and qualitative evaluation of the automatically constructed timelines. The qualitative evaluation is performed by describing the evolution of top terms in the timelines, generated by our method, which we found explicitly convey the themes of COVID-19. Separately, we use a set of 1,213 COVID-19 reports from 2020 that were manually identified by ASRS analysts to quantitatively evaluate the COVID-19 reports distribution across the timelines. Our results have shown that COVID-19 emergence can be identified using the top terms that were generated by topic modeling. The top terms in topic modeling therefore can serve as a summary alternative to manually inspecting reports. Moreover, leveraging the manually identified COVID-19 reports, we found the manually identified timelines accounted for over 70% of the COVID-19 reports curated by the ASRS analysts, which demonstrates the potential of this approach for facilitating the understanding of safety concerns as they emerge and evolve. This method shows great potential to understand aerospace safety threats and other narrative- driven incident report databases.

ASRS↗
DOE OSTI2020

Modeling Plant Metabolism: From Network Reconstruction to Mechanistic Models

Mathematical modeling of plant metabolism allows the plant science community to understand the organization of plant metabolism, obtain quantitative insights into metabolic functions, and derive engineering strategies for manipulation of metabolism. Among the various modeling approaches, metabolic pathway analysis can dissect the basic functional modes of subsections of core metabolism, such as photorespiration, and reveal how classical definitions of metabolic pathways have overlapping functionality. In current research using constraint-based modeling in plants, numerous computational tools are currently available to analyze large-scale and genome-scale metabolic networks. For 13 C-metabolic flux analysis, principles of isotopic steady state have been used to study heterotrophic plant tissues, while nonstationary isotope labeling approaches are amenable to the study of photoautotrophic and secondary metabolism. Enzyme kinetic models explore pathways in mechanistic detail, and we discuss different approaches to determine or estimate kinetic parameters. In this review, we describe recent advances and challenges in modeling plant metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Pyrolyzed Substrates Induce Aromatic Compound Metabolism in the Post-fire Fungus, Pyronema domesticum

Wildfires represent a fundamental and profound disturbance in many ecosystems, and their frequency and severity are increasing in many regions of the world. Fire affects soil by removing carbon in the form of CO2 and transforming remaining surface carbon into pyrolyzed organic matter (PyOM). Fires also generate substantial necromass at depths where the heat kills soil organisms but does not catalyze the formation of PyOM. Pyronema species strongly dominate soil fungal communities within weeks to months after fire. However, the carbon pool (i.e., necromass or PyOM) that fuels their rise in abundance is unknown. We used a Pyronema domesticum isolate from the catastrophic 2013 Rim Fire (CA, United States) to ask whether P. domesticum is capable of metabolizing PyOM. Pyronema domesticum grew readily on agar media where the sole carbon source was PyOM (specifically, pine wood PyOM produced at 750°C). Using RNAseq, we investigated the response of P. domesticum to PyOM and observed a comprehensive induction of genes involved in the metabolism and mineralization of aromatic compounds, typical of those found in PyOM. Lastly, we used 13 C-labeled 750°C PyOM to demonstrate that P. domesticum is capable of mineralizing PyOM to CO 2 . Collectively, our results indicate a robust potential for P. domesticum to liberate carbon from PyOM in post-fire ecosystems and return it to the bioavailable carbon pool.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Identifying Suspect Instrument Intervals Using Midnight Noise Time Histories

With an intense work ethic, and high levels of devotion to their craft, seismologists continue to battle the elements and wrestle with complex logistics in their efforts to field instrumentation over the entire globe. We are fortunate to see data volumes accelerating in size, and quality continuing to improve; for example, universal timing issues can now be considered rare. However, instrument response issues are still common, and can hinder research that seeks to understand amplitudes of seismic wavefields. We are interested in using the accumulations of seismic data to develop models that will predict high frequency (0.2-20 Hz, and higher) signal amplitudes for explosion monitoring purposes over broad areas, which requires large data sets and extensive quality control. To identify response and station health issues, we have collected noise time histories for global seismic data, focusing on measurements near (but not restricted to) midnight to eliminate diurnal variations, and have manually determined time intervals that appear inconsistent with background behavior. We assign descriptive labels, but do not attempt to diagnose causes. We use these intervals to discard data. To date, we have examined 39,260 channels from 11,105 stations, heavily weighted toward IRIS holdings, dates through 2017 (depending on station, roughly the date we started our manual review), bands between 1 and 8 Hz, finding 24,733 anomalous time intervals. The great majority (90%) of these intervals appear to be shifts of constant offset, often bounded by times of known instrument changes, likely the result of poor documentation of response parameters at one of many stages between the field and the plotter. We hope these results can be of use to our colleagues, and would encourage community efforts to diagnose anomalous behavior, and fix poor responses. We also hope these results will support automation efforts, including application of supervised learning techniques.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Hydra: Computer Vision for Data Quality Monitoring

Hydra: Computer Vision for Data Quality Monitoring Mar 13, 2024, 4:15 PM 30m Charles B. Wang Center, Stony Brook University Poster Track 1: Computing Technology for Physics Research Poster session with coffee break Speaker Ayan Roy Description Hydra is an extensible framework for training, managing, and deploying machine learning models for near real-time data quality monitoring. It is designed to take some of the burden off of shift crews by providing ‘round-the-clock’ monitoring of plots representing the data being collected. The Hydra system is backed by a database which is leveraged for near push button training and is primarily controlled and viewed through a set of web interfaces. This web interface contains a simple-to-use web based GUI for labeling of the datasets used in training; making it possible to label thousands of images quickly and efficiently. To aid in the analysis of Hydra inferences gradCAM, a method of interpretability, is performed and overlaid on the target image, highlighting regions of interest, making diagnosis of problems much faster. Development began in 2019 for the GlueX Experiment in Hall-D, the Hydra system has grown to encompass all of the experimental halls at Jefferson Laboratory. This talk will focus on the features of Hydra as well as provide details of the challenges present with deploying to disparate experimental halls. With a roadmap of development established, Hydra aims to continue to grow in richness of feature set and expand to encompass other monitoring tasks in differing communities.

Roy, Ayan↗
NASA NTRSDate not supplied

Human Specimen Repository: Sample Collection for the Future [#133-000445]

In early 2014, an executive memo was released directing the improvement of the management of and access to scientific collections funded by government resources. In the spirit of this memo, NASA’s Human Research Program (HRP) funded a project to review existing residual samples in long-term storage for viability, identification, and scientific significance. These samples were to be collected in a centralized location and a detailed inventory was to be conducted to create the Human Sample Repository (HSR). HSR would work to review data from over 27,000 samples, from flight research projects dating back to 1998. The samples themselves would be reviewed, re-labeled, and organized into easy to retrieve, long-term storage. In addition to the older residual samples, HSR would expand to include pristine samples from all consenting crew who have flown on International Space Station (ISS) missions. All samples inventoried and managed by HSR are intended for the same purpose; to support future research projects which will utilize advanced technologies or techniques, by providing one-of-a-kind sample collections that can not be replicated. With this mission in mind, HSR will continue to collect residual samples from HRP funded projects and pristine samples from consenting crew, to further build this unique collection and increase the diversity of flight samples available to the scientific community.

Human Specimen Repository↗
DOE OSTI2022

HT-SIP: a semi-automated stable isotope probing pipeline identifies cross-kingdom interactions in the hyphosphere of arbuscular mycorrhizal fungi

Abstract Background Linking the identity of wild microbes with their ecophysiological traits and environmental functions is a key ambition for microbial ecologists. Of many techniques that strive for this goal, Stable-isotope probing—SIP—remains among the most comprehensive for studying whole microbial communities in situ. In DNA-SIP, actively growing microorganisms that take up an isotopically heavy substrate build heavier DNA, which can be partitioned by density into multiple fractions and sequenced. However, SIP is relatively low throughput and requires significant hands-on labor. We designed and tested a semi-automated, high-throughput SIP (HT-SIP) pipeline to support well-replicated, temporally resolved amplicon and metagenomics experiments. We applied this pipeline to a soil microhabitat with significant ecological importance—the hyphosphere zone surrounding arbuscular mycorrhizal fungal (AMF) hyphae. AMF form symbiotic relationships with most plant species and play key roles in terrestrial nutrient and carbon cycling. Results Our HT-SIP pipeline for fractionation, cleanup, and nucleic acid quantification of density gradients requires one-sixth of the hands-on labor compared to manual SIP and allows 16 samples to be processed simultaneously. Automated density fractionation increased the reproducibility of SIP gradients compared to manual fractionation, and we show adding a non-ionic detergent to the gradient buffer improved SIP DNA recovery. We applied HT-SIP to 13 C-AMF hyphosphere DNA from a 13 CO 2 plant labeling study and created metagenome-assembled genomes (MAGs) using high-resolution SIP metagenomics (14 metagenomes per gradient). SIP confirmed the AMF Rhizophagus intraradices and associated MAGs were highly enriched (10–33 atom% 13 C), even though the soils’ overall enrichment was low (1.8 atom% 13 C). We assembled 212 13 C-hyphosphere MAGs; the hyphosphere taxa that assimilated the most AMF-derived 13 C were from the phyla Myxococcota, Fibrobacterota, Verrucomicrobiota, and the ammonia-oxidizing archaeon genus Nitrososphaera . Conclusions Our semi-automated HT-SIP approach decreases operator time and improves reproducibility by targeting the most labor-intensive steps of SIP—fraction collection and cleanup. We illustrate this approach in a unique and understudied soil microhabitat—generating MAGs of actively growing microbes living in the AMF hyphosphere (without plant roots). The MAGs’ phylogenetic composition and gene content suggest predation, decomposition, and ammonia oxidation may be key processes in hyphosphere nutrient cycling.

59 BASIC BIOLOGICAL SCIENCES↗