Search NASA⌕ Search

SEARCH · Search NASA

Results for “omics data”

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.

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

At least 523 records · Page 29

2024 NMDC Ambassador Training Materials [Slides]

The NMDC is a sustainable data discovery platform that promotes open science and shared-ownership across a broad and diverse community of researchers, funders, publishers, societies, and other collaborators. The NMDC aims to enable multi-omic microbiome research to accelerate scientific discovery. The NMDC is a Department of Energy funded program that is a collaboration between 3 National Laboratories: Lawrence Berkeley National Laboratory (LBNL), Los Alamos National Laboratory (LANL), and Pacific Northwest National Laboratory (PNNL).

54 ENVIRONMENTAL SCIENCES↗

Transcriptomics and Proteomics Discussion

This presentation will cover the the basic pipelines for transcriptomics and proteomics that the GeneLab Analysis Working Groups (AWGs) have so far determined to be optimal. Basic transcriptomic pipelines will first be presented from primary analysis to higher-order systems analysis. Examples of how the data has been analyzed will be presented. Proteomics pipelines will also be presented compiled from various AWG members. Discussion will be generated from the AWG members to reach a consensus for each omic type.

Transcriptomics↗

Omics-Lethal Human Viruses, Influenza A Experiment ICL106

The purpose of this experiment was to evaluate the human host epigenetic response to Influenza A H5N1 virus and MERS-CoV viral infection from human lung adenocarcinoma cell (Calu-3) high throughput sequencing (MeDIP-Seq) sample data. Host-associated viral downloads contain one or more primary host-pathogen experimental study metadata design workbooks (supporting metadata only).

59 BASIC BIOLOGICAL SCIENCES↗

RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species

Abstract We introduce RWRtoolkit, a multiplex generation, exploration, and statistical package built for R and command-line users. RWRtoolkit enables the efficient exploration of large and highly complex biological networks generated from custom experimental data and/or from publicly available datasets, and is species agnostic. A range of functions can be used to find topological distances between biological entities, determine relationships within sets of interest, search for topological context around sets of interest, and statistically evaluate the strength of relationships within and between sets. The command-line interface is designed for parallelization on high-performance cluster systems, which enables high-throughput analysis such as permutation testing. Several tools in the package have also been made available for use in reproducible workflows via the KBase web application.

Kainer, David (ORCID:0000000172714676)↗

Soybean Knowledge Base (SoyKB): a Web Resource for Soybean Translational Genomics

Background: Soybean Knowledge Base (SoyKB) is a comprehensive all-inclusive web resource for soybean translational genomics. SoyKB is designed to handle the management and integration of soybean genomics, transcriptomics, proteomics and metabolomics data along with annotation of gene function and biological pathway. It contains information on four entities, namely genes, microRNAs, metabolites and single nucleotide polymorphisms (SNPs). Methods: SoyKB has many useful tools such as Affymetrix probe ID search, gene family search, multiple gene/ metabolite search supporting co-expression analysis, and protein 3D structure viewer as well as download and upload capacity for experimental data and annotations. It has four tiers of registration, which control different levels of access to public and private data. It allows users of certain levels to share their expertise by adding comments to the data. It has a user-friendly web interface together with genome browser and pathway viewer, which display data in an intuitive manner to the soybean researchers, producers and consumers. Conclusions: SoyKB addresses the increasing need of the soybean research community to have a one-stop-shop functional and translational omics web resource for information retrieval and analysis in a user-friendly way. SoyKB can be publicly accessed at http://soykb.org/.

Environmental Molecular Sciences Laboratory↗

BioNutrients-1: Development of an On-Demand Nutrient Production System for Long-Duration Missions

Future long-duration missions beyond low-Earth orbit will require advances in food technologies to address the documented problem of degradation of vitamins and nutrients in supplied foods stored long-term. To begin to address the issue of nutrient degradation, we are developing and flight-testing a platform biomanufacturing technology for in situ production of target nutrients. This technology is being tested over a five-year duration on the International Space Station (ISS). As part of the BioNutrients-1 project we have developed an on-demand system for the production of two carotenoids, β-carotene and zeaxanthin, by genetically engineering distinct strains of Saccharomyces cerevisiae, more commonly known as baker’s yeast. The on-orbit nutrient production packs contain a desiccated yeast strain and edible growth substrate. Once hydrated, the contents of the production packs are intended to grow and produce a desired amount of ready-to-consume nutrients. In this current version the production packs will not be consumed and future missions will require an inactivation of microorganisms before consumption. In addition to the on-orbit hydration of the production packs a series of valuable microorganisms are currently being stored in stasis packs on the ISS including probiotics organisms, bacterial strains used in yogurt production, and organisms with potential use for future biomanufacturing. Analysis of returned ISS stasis packs and ground controls will include multi-omics studies and provide insight into long-term survival of organisms stored in a space environment. Both stasis packs and hydrated production packs will be intermittently returned to Earth for analysis. Preliminary data from long-term storage studies of stasis packs stored on the ISS for 47 days versus their ground control counterparts have shown no significant difference in viability. Currently no production packs have been processed.

Yeast↗

Nitrogen starvation causes lipid remodeling in Rhodotorula toruloides

Abstract Background The oleaginous yeast Rhodotorula toruloides is a promising chassis organism for the biomanufacturing of value-added bioproducts. It can accumulate lipids at a high fraction of biomass. However, metabolic engineering efforts in this organism have progressed at a slower pace than those in more extensively studied yeasts. Few studies have investigated the lipid accumulation phenotype exhibited by R. toruloides under nitrogen limitation conditions. Consequently, there have been only a few studies exploiting the lipid metabolism for higher product titers. Results We performed a multi-omic investigation of the lipid accumulation phenotype under nitrogen limitation. Specifically, we performed comparative transcriptomic and lipidomic analysis of the oleaginous yeast under nitrogen-sufficient and nitrogen deficient conditions. Clustering analysis of transcriptomic data was used to identify the growth phase where nitrogen-deficient cultures diverged from the baseline conditions. Independently, lipidomic data was used to identify that lipid fractions shifted from mostly phospholipids to mostly storage lipids under the nitrogen-deficient phenotype. Through an integrative lens of transcriptomic and lipidomic analysis, we discovered that R. toruloides undergoes lipid remodeling during nitrogen limitation, wherein the pool of phospholipids gets remodeled to mostly storage lipids. We identify specific mRNAs and pathways that are strongly correlated with an increase in lipid levels, thus identifying putative targets for engineering greater lipid accumulation in R. toruloides . One surprising pathway identified was related to inositol phosphate metabolism, suggesting further inquiry into its role in lipid accumulation. Conclusions Integrative analysis identified the specific biosynthetic pathways that are differentially regulated during lipid remodeling. This insight into the mechanisms of lipid accumulation can lead to the success of future metabolic engineering strategies for overproduction of oleochemicals.

59 BASIC BIOLOGICAL SCIENCES↗

Towards yeast taxogenomics: lessons from novel species descriptions based on complete genome sequences

In recent years, ‘multi-omic’ sciences have affected all aspects of fundamental and applied biological research. Yeast taxonomists, though somewhat timidly, have begun to incorporate complete genomic sequences into the description of novel taxa, taking advantage of these powerful data to calculate more reliable genetic distances, construct more robust phylogenies, correlate genotype with phenotype and even reveal cryptic sexual behaviors. Furthermore, the use of genomic data in formal yeast species descriptions is far from widespread. The present review examines published examples of genome-based species descriptions of yeasts, highlights relevant bioinformatic approaches, provides recommendations for new users and discusses some of the challenges facing the genome-based systematics of yeasts.

59 BASIC BIOLOGICAL SCIENCES↗

Space Flown Rodent Liver RNA Sequencing Data for Machine Learning in Space Biology Research

High-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. Data analysis has been accelerated in recent years by the adoption of artificial intelligence (AI) and machine learning (ML) techniques by biomedical researchers. In space biology research, RNAseq datasets from space-flown experimental samples are critical for characterizing the gene expression aberrations associated with exposure to spaceflight stressors. However, space biological experiments tend to be very low sample size, so identifying proper AI/ML algorithms for sequencing data analysis is an ongoing challenge since these algorithms typically require large sample size. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML”, focused on creating datasets meant for three main applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. These scientific benchmarks consist of an AI-ready dataset and a reference implementation on a specific scientific question. In this work, we focused on generating standardized datasets to allow the scientific community to benchmark AI/ML algorithms in the domain of space biology. We present here a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data as a collaboration between the NASA AI4LS (Artificial Intelligence for Life Sciences) working group. and NASA’s SMD. This dataset consists of space-flown and ground control mouse liver found in the NASA GeneLab omics database. However, to amplify the small sample number (n=112 samples) for ML purposes, we employ Gaussian noise and a generative adversarial network to extend this dataset to 6,000 synthetic samples, matching the original gene expression characteristics.

James Casaletto↗

Biosystems Design by Machine Learning

Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. Yet, the intricate connectivity and complexity of biosystems pose a major hurdle in designing biosystems with desired features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find non-obvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocess. Lastly, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.

59 BASIC BIOLOGICAL SCIENCES↗

Synthetic Soil Aggregates: Bioprinted Habitats for High-Throughput Microbial Metaphenomics

The dynamics of microbial processes are difficult to study in natural soil, owing to the small spatial scales on which microorganisms operate and to the opacity and chemical complexity of the soil habitat. To circumvent these challenges, we have created a 3D-bioprinted habitat that mimics aspects of natural soil aggregates while providing a chemically defined and translucent alternative culturing method for soil microorganisms. Our Synthetic Soil Aggregates (SSAs) retain the porosity, permeability, and patchy resource distribution of natural soil aggregates—parameters that are expected to influence emergent microbial community interactions. We demonstrate the printability and viability of several different microorganisms within SSAs and show how the SSAs can be integrated into a multi-omics workflow for single SSA resolution genomics, metabolomics, proteomics, lipidomics, and biogeochemical assays. We study the impact of the structured habitat on the distribution of a model co-culture microbial community and find that it is significantly different from the spatial organization of the same community in liquid culture, indicating a potential for SSAs to reproduce naturally occurring emergent community phenotypes. The SSAs have the potential as a tool to help researchers quantify microbial scale processes in situ and achieve high-resolution data from the interplay between environmental properties and microbial ecology.

59 BASIC BIOLOGICAL SCIENCES↗

Lipidomics reveals dramatic lipid compositional changes in the maturing postnatal lung

Lung immaturity is a major cause of morbidity and mortality in premature infants. Understanding the molecular mechanisms driving normal lung development could provide insights on how to ameliorate disrupted development. While transcriptomic and proteomic analyses of normal lung development have been previously reported, characterization of changes in the lipidome is lacking. Lipids play significant roles in the lung, such as dipalmitoylcholine in pulmonary surfactant; however, many of the roles of specific lipid species in normal lung development, as well as in disease states, are not well defined. In this study, we used liquid chromatography-mass spectrometry (LC-MS/MS) to investigate the murine lipidome during normal postnatal lung development. Lipidomics analysis of lungs from post-natal day 7, day 14 and 6-8 week mice (adult) identified 928 unique lipids across 21 lipid subclasses, with dramatic alterations in the lipidome across developmental stages. Our data confirmed previously recognized aspects of post-natal lung development and revealed several insights, including in sphingolipid-mediated apoptosis, inflammation and energy storage/usage. Complementary proteomics, metabolomics and chemical imaging corroborated these observations. Finally, this multi-omic view provides a unique resource and deeper insight into normal pulmonary development.

59 BASIC BIOLOGICAL SCIENCES↗

Mechanism Across Scales: A Holistic Modeling Framework Integrating Laboratory and Field Studies for Microbial Ecology

Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.

59 BASIC BIOLOGICAL SCIENCES↗

Multi-omics approach identifies molecular mechanisms of plant-fungus mycorrhizal interaction

In mycorrhizal symbiosis, plant roots form close, mutually beneficial interactions with soil fungi. Before this mycorrhizal interaction can be established however, plant roots must be capable of detecting potential beneficial fungal partners and initiating the gene expression patterns necessary to begin symbiosis. To predict a plant root – mycorrhizal fungi sensor systems, we analyzed in vitro experiments of Populus tremuloides (aspen tree) and Laccaria bicolor (mycorrhizal fungi) interaction and leveraged over 200 previously published transcriptomic experimental data sets, 159 experimentally validated plant transcription factor binding motifs, and more than 120-thousand experimentally validated protein-protein interactions to generate models of pre-mycorrhizal sensor systems in aspen root. These sensor mechanisms link extracellular signaling molecules with gene regulation through a network comprised of membrane receptors, signal cascade proteins, transcription factors, and transcription factor biding DNA motifs. Modeling predicted four pre-mycorrhizal sensor complexes in aspen that interact with fifteen transcription factors to regulate the expression of 1184 genes in response to extracellular signals synthesized by Laccaria. Predicted extracellular signaling molecules include common signaling molecules such as phenylpropanoids, salicylate, and, jasmonic acid. Lastly, this multi-omic computational modeling approach for predicting the complex sensory networks yielded specific, testable biological hypotheses for mycorrhizal interaction signaling compounds, sensor complexes, and mechanisms of gene regulation.

59 BASIC BIOLOGICAL SCIENCES↗

Analytics for Metabolic Engineering

Realizing the promise of metabolic engineering has been slowed by challenges related to moving beyond proof-of-concept examples to robust and economically viable systems. Key to advancing metabolic engineering beyond trial-and-error research is access to parts with well-defined performance metrics that can be readily applied in vastly different contexts with predictable effects. As the field now stands, research depends greatly on analytical tools that assay target molecules, transcripts, proteins, and metabolites across different hosts and pathways. Screening technologies yield specific information for many thousands of strain variants, while deep omics analysis provides a systems-level view of the cell factory. Efforts focused on a combination of these analyses yield quantitative information of dynamic processes between parts and the host chassis that drive the next engineering steps. Overall, the data generated from these types of assays aid better decision-making at the design and strain construction stages to speed progress in metabolic engineering research.

59 BASIC BIOLOGICAL SCIENCES↗

Untargeted, tandem mass spectrometry (LC/MS-MS) metaproteomes from soil samples in control and warming plots in Blodgett Forest, CA (2014-2021)

The pathways of carbon transport and loss through and from soils—soil organic matter (SOM) depolymerization to dissolved organic carbon and mineralization to carbon dioxide (CO2)—are fundamentally driven by microbial activity, which is strongly regulated by environmental conditions. As part of Lawrence Berkeley National Laboratory (LBNL) Terrestrial Ecosystem Science (TES) Belowground Biogeochemistry Science Focus Area (SFA), we have established a novel whole-soil long-term warming experiment at the University of California (UC) Blodgett Forest Research Station (Sierra Nevada) in 2014, where we study the role of biogeochemical, microbial and geochemical process interactions in SOM decomposition and stabilization. This package contains soil metaproteomics data in the context of site specific metagenomes from soil depth profiles in three paired control and warming plots from a temperate mixed forest in Northern California. Each paired plot had been subjected to experimental warming since June 2014 to simulate a predicted climate change scenario for northern California. These metaproteomes were collected in 2018 after 4.5 years of warming from five depth intervals (0-10 cm, 10-30 cm, 30-45 cm, 45-60 cm, 60-80 cm). For protein identification, the collected spectra were searched following a target-decoy search strategy against a database of metagenome predicted proteins (covering 96 samples from 2014 to 2021) representing the complete sequence diversity at the site. Data was searched with mass spectrometry database search tool (MS-GF+) using Pacific Northwest National Laboratory (PNNL)'s Data Management System (DMS) Processing pipeline. The metagenomes are published as part of another data package. Raw metaproteomic data and the data products from MS-GF+ are deposited in the Mass Spectrometry Interactive Virtual Environment (MassIVE) database under accession no. MSV000097826. Here we present a dataset that includes spectral counts for the detected proteins across samples (EMSL50964_BrodieAllMAGs_Globals_SC.txt), the sequences of the detected proteins, and sample metadata file that contains site information for the soil metaproteome samples.

Belowground Biogeochemistry Science Focus Area↗

NanoSIP: NanoSIMS Applications for Microbial Biology

High-resolution imaging with secondary ion mass spectrometry (nanoSIMS) has become a standard method in systems biology and environmental biogeochemistry and is broadly used to decipher ecophysiological traits of environmental microorganisms, metabolic processes in plant and animal tissues, and cross-kingdom symbioses. When combined with stable isotope-labeling—an approach we refer to as nanoSIP—nanoSIMS imaging offers a distinctive means to quantify net assimilation rates and stoichiometry of individual cell-sized particles in both low- and high-complexity environments. While the majority of nanoSIP studies in environmental and microbial biology have focused on nitrogen and carbon metabolism (using 15 N and 13 C tracers), multiple advances have pushed the capabilities of this approach in the past decade. The development of a high-brightness oxygen ion source has enabled high-resolution metal analyses that are easier to perform, allowing quantification of metal distribution in cells and environmental particles. New preparation methods, tools for automated data extraction from large data sets, and analytical approaches that push the limits of sensitivity and spatial resolution have allowed for more robust characterization of populations ranging from marine archaea to fungi and viruses. Further, NanoSIMS studies continue to be enhanced by correlation with orthogonal imaging and ‘omics approaches; when linked to molecular visualization methods, such as in situ hybridization and antibody labeling, these techniques enable in situ function to be linked to microbial identity and gene expression. Here we present an updated description of the primary materials, methods, and calculations used for nanoSIP, with an emphasis on recent advances in nanoSIMS applications, key methodological steps, and potential pitfalls.

07 ISOTOPE AND RADIATION SOURCES↗

Accounting for hydrological and microbial processes on greenhouse gas budgets from river systems (Final Report)

River systems have a disproportionately large impact on carbon and nitrogen cycling on this planet. For instance, despite their much smaller size in area, it is estimated that global carbon dioxide emission from river systems exceed the emission from lakes and reservoirs, and are on par with the estimated oceanic and land carbon sinks. Closing carbon and nitrogen budgets in river systems requires knowledge of end member greenhouse gas (GHG) products (e.g. carbon dioxide, nitrous oxide, methane), which typically have not been measured in river systems. Moreover, while the production of these gasses is thought to be microbially derived, little is known about microbial activities in sediments of large rivers, despite their potentially high influence on biogeochemical budgets. Typically, when we think of rivers we visualize the channels that carve through the earth, but this only represents a small part of the hydrologic exchange that a river participates in. Rivers are more than conduits; they are dynamically exchanging water and materials with surrounding terrestrial and subsurface environments. In fact, 96% of the carbon dioxide produced within river systems occurs in hyporheic zones, or sediment regions beneath and alongside a river where groundwater and river water mix. In fact, it is widely recognized that microbial processes in hyporheic zones, also known as the river’s liver, strongly influence the fate of carbon, nutrients, and contaminants transported through watersheds. Despite this general knowledge, observational studies linking nitrogen and carbon gas production to microbial activity, and studies of overall gas emissions, are limited across river systems, but especially absent from larger rivers. To identify microbial processes responsible for GHG emissions from large rivers, and to quantify their environmental drivers and dependencies, we selected the Columbia River as a representative field site. This is the largest river west of the North American Continental Divide, and produces a drainage and discharge roughly double of the Nile in Egypt. This river is also an important source for agricultural irrigation and generates more hydroelectric power than any other North American river. Dams manage the release of water in the river, with these changes in river discharge patterns strongly impacting microbial activities in hyporheic zones. Specifically, we target a region down-river from the Priest Rapids Dam (WA) associated with the PNNL SFA, where water discharge from the dam is highly controlled, resulting in 1.5 m fluctuations in river stage daily. This dynamic river stage creates an ideal research opportunity for investigating hydrological perturbations in GHG production, uptake, and emission from large river systems. In this exploratory 1-year project, we conducted data-driven field research to provide observations needed to support predictions about GHG flux from river ecosystems. Using porewater dialysis and surface flux measurements, we observed where and when GHGs were produced and is the emission impacted by river stage. Using meta-omics tools, we determined what microbial processes control production and consumption of GHG in these systems. Finally, we provided key observations to parameterize models that could predict the effect of environmental conditions on GHG emission in the Columbia River near shore environment, at the scale that the modeled processes operate. Closing knowledge gaps in the linkages between river stage, microbial processes, and GHG production and emission, as well as the spatial distribution and ecosystem dependencies of these processes, is necessary to better parameterize land surface models. We provide some of the first, detailed GHG measurements paired microbial activity from large river systems. Our findings have direct relevance to many other large rivers with flows altered by hydroelectric energy, reservoirs, snowmelt, tides, or agriculture.

54 ENVIRONMENTAL SCIENCES↗