Data From EMSL Project 51710: Opportunities linking omics and structural biology at PNNL: Excelling at Cryo-EM
Explore the source record for details and available documents.
Evans, James↗
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.
RhizoGrid Indexed Sorghum Rhizosphere Multi-Omics
PerCon SFA project data dentification of spatially resolved biomarkers of drought in Sorghum bicolor rhizosphere molecular-microbe interactions using a novel root cartography "RhizoGrid" system for sampling plants under drought and control conditions across 10 equally sized root zone environments (4 quadrants each). Each quadrant was sampled and processed for 16S amplicon, metabolomics, and X-ray computed tomography (XCT). Data download includes experimental metadata and results files for 16S rRNA sequence analysis of microbial community assembly (processed data files), liquid chromatography mass spectrometry (LC-MS) metabolomics analysis of microbial community root exudates (processed data files), X-ray computed tomography (XCT) spatial gradient analysis (raw and processed data files) of microbial community composition, and related computational modeling outputs.
59 BASIC BIOLOGICAL SCIENCES↗
Systemic Microgravity Response: Utilizing GeneLab to Develop Hypotheses for Spaceflight Risks
Biological risks associated with microgravity is a major concern for space travel. Although determination of risk has been a focus for NASA research, data examining systemic (i.e., multi- or pan-tissue) responses to space flight are sparse. The overall goal of our work is to identify potential master regulators responsible for such responses to microgravity conditions. To do this we utilized the NASA GeneLab database which contains a wide array of omics experiments, including data from: 1) different flight conditions (space shuttle (STS) missions vs. International Space Station (ISS); 2) different tissues; and 3) different types of assays that measure epigenetic, transcriptional, and protein expression changes. We have performed meta-analysis identifying potential master regulators involved with systemic responses to microgravity. The analysis used 7 different murine and rat data sets, examining the following tissues: liver, kidney, adrenal gland, thymus, mammary gland, skin, and skeletal muscle (soleus, extensor digitorum longus, tibialis anterior, quadriceps, and gastrocnemius). Using a systems biology approach, we were able to determine that p53 and immune related pathways appear central to pan-tissue microgravity responses. Evidence for a universal response in the form of consistency of change across tissues in regulatory pathways was observed in both STS and ISS experiments with varying durations; while degree of change in expression of these master regulators varied across species and strain, some change in these master regulators was universally observed. Interestingly, certain skeletal muscle (gastrocnemius and soleus) show an overall down-regulation in these genes, while in other types (extensor digitorum longus, tibialis anterior and quadriceps) they are up-regulated, suggesting certain muscle tissues may be compensating for atrophy responses caused by microgravity. Studying these organtissue-specific perturbations in molecular signaling networks, we demonstrate the value of GeneLab in characterizing potential master regulators associated with biological risks for spaceflight.
Microgravity↗
PNNL DataHub NIAID Program Project: Modeling Host Responses to Understand Severe Human Virus Infections, Multi-Omic Viral Dataset Catalog Collection
The National Institute of Allergy and Infectious Diseases (NIAID) "Modeling Host Responses to Understand Severe Human Virus Infections" program project was a highly integrated and comprehensive systems biology research core, funded by the National Institute of Health (U19AI106772) from 2013-06-01 to 2018-05-31, investigating the complex host response to category A, B, and C priority pathogen infections. Resulting project deliverables include an extensive comprehensive collections of linked primary and secondary transformation viral experimental infection data. Here we provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open access to viral Omics lifecycle datasets and project metadata. Using a highly integrated and multidisciplinary approach, linked primary data and metadata supporting secondary normalization datasets, provide critical information necessary for research reproducibility and long-term preservation. Enabling on-demand data access for research community consumption and developer reuse, serves to support new mechanistic insights and discoveries into host-pathogen interactions for aiding future biohazard data preparedness efforts in emergency response to global health crises involving viral infection.
59 BASIC BIOLOGICAL SCIENCES↗
Bleach Rescues Nannochloropsis from an Obligate Parasite and Alters Microbial and Metabolite Signatures of Outdoor Cultures
Chemical agents are commonly used to protect algal crops. Yet, few studies have characterized the effects of these agents on associated microbial communities to understand effects on microbial functions relevant to algal crop production and protection. Here, we used shotgun metagenomic sequencing and untargeted exometabolite profiling to link the application of bleach, a -cidal agent used to protect algae from pests, to changes in community composition, metabolic pathways, and exometabolies - at a whole community level. Bleach protected the algal crop from crashing but altered bacterial diversity. Analysis of metagenome-assembled genomes (MAGs) revealed a classic predator-prey cycle between Oligoflexus and our target alga Nannochloropsis. Olifoflexus genomes from our study were notably similar to a previously identified BALO (Bdellovibrio and like organism), FD111, known to kill Nannochloropsis cultures, providing strong evidence that an FD111-like organism was responsible for the crash. Metabolic pathway composition differed between bleached and unbleached ponds, with abundance of twelve pathways related to stress tolerance, including the superpathway of methylglyoxal degradation, lipid IVA biosynthesis, and ectoine biosynthesis, greater in bleached ponds compared to unbleached ponds. Virulence factors related to adherence, biofilm formation, motility, and pathogenicity increased dramatically in bleached ponds with time, although this increase was not coupled with an increase in pathogens - algal or otherwise - or a decline in algal health. Our study highlights the importance of coupling 16S rRNA gene sequencing with whole genome data and other -omics tools to sketch a larger picture of community structure and function in crop systems. Moreover, our results highlight that continued long-term bleaching may lead to negative effects to crop health or downstream adverse health effects to humans or animals, depending on the algal product (i.e. human supplements or animal feedstocks). Future work on alternative treatment methods that would reduce resistance is necessary in the field.
09 BIOMASS FUELS↗
A robust gene expression-based prognostic risk score predicts overall survival of lung adenocarcinoma patients
Identification of reliable predictive biomarkers and new therapeutic targets is a critical step for significant improvement in patient outcomes. Here, we developed a multi-step bioinformatics analytic strategy to mine large omics and clinical data to build a prognostic scoring system for predicting the overall survival (OS) of lung adenocarcinoma (LuADC) patients. In latter we first identified 1327 significantly and robustly deregulated genes, 600 of which were significantly associated with the OS of LuADC patients. Gene co-expression network analysis revealed the biological functions of these 600 genes in normal lung and LuADCs, which were found to be enriched for cell cycle-related processes, blood vessel development, cell-matrix adhesion and metabolic processes. Finally, we implemented a multiple resampling method combined with Cox regression analysis to identify a 27-gene signature associated with OS, and then created a prognostic scoring system based on this signature. This scoring system robustly predicted OS of LuADC patients in 100 sampling test sets and was further validated in four independent LuADC cohorts. In addition, in comparison to other existing prognostic gene signatures published in the literature, our signature was significantly superior in predicting OS of LuADC patients. In summary, our multiomics and clinical data integration study created a 27-gene prognostic risk score that can predict OS of LuADC patients independent of age, gender and clinical stage. This score could guide therapeutic selection and allow stratification in clinical trials.
59 BASIC BIOLOGICAL SCIENCES↗
Technical Report Department of Energy Grant #SC0004335 “Tracking Down Cheaters. Molecular Analysis of Carbon Consumption by Organisms That Do Not Contribute to Extracellular Enzyme Pools”
The overriding objective of our work is to integrate physiological and community ecology of belowground organisms into understanding of soil carbon dynamics to improve predictions of terrestrial ecosystem models. This includes using metagenomics and metatranscriptomics-based methods to understand microbial interactions affecting decomposition and soil carbon dynamics. The focus of the majority of the work directly related to this project was on “cheating”, a poorly understood microbial interaction with a potentially large effect on decomposition. Model organisms were used to determine the types of organisms that cheat based on their known niche and genomic characteristics. In addition, we study plant and microbial traits and plant-microbe interactions that affect species distributions and soil carbon, and also develop bioinformatics tools to increase the power of ecological inferences that can be obtained from omics-based sequence data.
54 ENVIRONMENTAL SCIENCES↗
Processes in DNA damage response from a whole-cell multi-omics perspective
Technological advances have made it feasible to collect multi-condition multi-omic time courses of cellular response to perturbation, but the complexity of these datasets impedes discovery due to challenges in data management, analysis, visualization, and interpretation. Here, we report a whole-cell mechanistic analysis of HL-60 cellular response to bendamustine. We integrate both enrichment and network analysis to show the progression of DNA damage and programmed cell death over time in molecular, pathway, and process-level detail using an interactive analysis framework for multi-omics data. Our framework, Mechanism of Action Generator Involving Network analysis (MAGINE), automates network construction and enrichment analysis across multiple samples and platforms, which can be integrated into our annotated gene-set network to combine the strengths of networks and ontology-driven analysis. Taken together, our work demonstrates how multi-omics integration can be used to explore signaling processes at various resolutions and demonstrates multi-pathway involvement beyond the canonical bendamustine mechanism.
59 BASIC BIOLOGICAL SCIENCES↗
GeneLab: A Systems Biology Platform for Omics Analysis
NASA's GeneLab includes an open-access repository of some 200+ omics datasets generated by biological experiments relevant to spaceflight (including simulated cosmic radiation and microgravity). In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics knowledge, GeneLab is now transforming the data in the repository into actual biological and physiological knowledge of the genetic and proteomic signatures found in these samples. This processed data is being derived by establishing standard data analysis workflows vetted by 114 scientists who are members of the four GeneLab Analysis Working Groups (Animal AWG, Plant AWG, Microbe AWG, Multi-Omics AWG). AWG members from institutes spanning the U.S. and four other countries participate on a voluntary basis. The AWGs meet monthly to discuss data mining, compare results and interpretations, and test forthcoming releases of the GeneLab Data Systems (GLDS). GLDS version 3.0 has been available to the general public since October 1st 2018, and has been providing a professional state-of-the-art bioinformatics platform for everyone in the space biology community to upload their data into a space biology omics data commons, to process their data with vetted standard workflows and to compare to existing analyses. The user interface for the platform is being designed to be accessible to a broad variety of users including those with limited bioinformatics experience, including high school and college students who can use it to learn about omics data analysis and space biology. As such, Genelab will constitute a powerful general public outreach capability of NASA and the Space Biology community at large. Data mining of the GeneLab database by the AWG has already started generating very interesting findings, including reports linking specific spaceflight conditions such as radiation, microgravity or carbon dioxide levels to molecular changes seen across various species. In this presentation, we will report on the current and future objectives for GeneLab, and review recent studies reported by the various AWGs relating molecular changes observed in various animal models and tissue with microgravity, radiation, circadian rhythm, hydration and carbon dioxide conditions.
Omics↗
Visual Comparative Omics of Fungi for Plant Biomass Deconstruction
Wood-decay fungi contain the cellular mechanisms to decompose such plant cell wall components as cellulose, hemicellulose, and lignin. A multi-omics approach to the comparative analysis of wood-decay fungi gives not only new insights into their strategies for decomposing recalcitrant plant biomass, but also an understanding of how to exploit these mechanisms for biotechnological applications. We have developed an analytical workflow, Applied Biomass Conversion Design for Efficient Fungal Green Technology (ABCDEFGT), to simplify the analysis and interpretation of transcriptomic and secretomic data. ABCDEFGT utilizes self-organizing maps for grouping genes with similar transcription patterns, and an overlay with secreted proteins. The key feature of ABCDEFGT is simple graphic outputs of genome-wide transcriptomic and secretomic topographies, which enables visual inspection without a priori of the omics data and facilitates discoveries of co-regulated genes and proteins. Genome-wide omics landscapes were built with the newly sequenced fungal species Pycnoporus coccineus, Pycnoporus sanguineus, and Pycnoporus cinnabarinus grown on various carbon sources. Integration of the post-genomic data revealed a global overlap, confirming the pertinence of the genome-wide approach. ABCDEFGT was evaluated by comparison with the latest clustering method for ease of output interpretation, and ABCDEFGT gave a better biological representation of fungal behaviors. The genome-wide multi-omics strategy allowed us to determine the potential synergy of particular enzymes decomposing cellulose, hemicellulose, and lignin such as Lytic Polysaccharide Monooxygenases, modular enzymes associated with a cellulose binding module1, and Class II Peroxidase isoforms co-regulated with oxido-reductases. Overall, ABCDEFGT was capable of visualizing genome-wide transcriptional and secretomic profiles for intuitive interpretations and is suitable for exploration of newly-sequenced organisms.
54 ENVIRONMENTAL SCIENCES↗
Open Science for Plants in Space: Data Sharing, Standards, and Informatics for Reuse and Knowledge Discovery
Upcoming deep space missions rely on plants and crops for crew and ecosystem health. Access to space plant data enables scientists to gain a deeper understanding of biological responses to ionizing radiation, altered gravity, low atmospheric pressure, elevated CO2, and altered photoperiods. Open Science is the practice of making research available to all, while respecting diverse cultures, fostering collaborations with equity. 2023 is the ‘Year of Open Science’, and NASA has a 5-year Transform to Open Science (TOPS) mission designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) developed by NASA’s Biological and Physical Sciences Division provides access to data from space-relevant biological experiments. OSDR combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. OSDR started in 2014 with the creation of the first space-relevant FAIR (Findable, Accessible, Interoperable, Reusable) biological ‘omics repository (GeneLab), providing detailed metadata on investigation, sample, and assay levels. Today, GeneLab hosts 62 plant datasets which have led to 5 published peer-reviewed meta-analysis publications. Most of these publications were collaboration efforts under the OSDR Analysis Working Groups (AWGs). AWGs provide great opportunities for investigators to collaborate and set new standards for space-relevant data and metadata. The AWGs are welcoming any ASPB members interested in providing plant expertise for space biology. The addition of ALSDA to OSDR is also expanding analysis capability beyond ‘omics. Now is the time to get involved as a Subject Matter Expert as we establish the framework for modern plant data archiving through the AWGs. Investigators are invited to submit their space-relevant plant datasets to OSDR and visit the site to learn about the tools OSDR has to offer (osdr.nasa.gov/bio).
FAIR↗
Unlocking the Secrets of Space Biology: From the Twin Study to Groundbreaking Open Science Discoveries
In this interdisciplinary lecture, we will delve into the fascinating world of Space Biology and the realm of Open Science at NASA. Join us as we explore the profound insights gained from the groundbreaking twin study, which was published several years ago and continues to shape our understanding of spaceflight's impact on human health. We will highlight the study's key findings and contextualize them alongside more recent research endeavors, including the remarkable Inspiration 4 mission and several comprehensive meta-analysis publications enabled by the NASA GeneLab Omics database and the Ames Life Science Data Archive for physiological and phenotypic data.
Omics↗
A Multi-Omics Approach Demonstrates that Spaceflight Leads to Lipid Accumulation in Mouse Livers
Spaceflight has several detrimental effects on the physiology of astronauts, many of which are recapitulated in rodent models. We analyzed liver transcriptomic and proteomic data from three mouse spaceflight experiments flown aboard the International Space Station (Rodent Research-1 NASA (RR-1 NASA), Rodent Research-1 CASIS (RR-1 CASIS), Rodent Research-3 (RR-3)), and one mouse experiment flown on the Space Shuttle (Commercial Biomedical Testing Module-3 (CBTM-3) aboard STS-135). Despite the differences in genetic background and time of exposure to microgravity it was shown through Oil Red staining and histology that increased lipid accumulation was occurring in the liver of all mice flown in space compared to the ground controls. This led to further pursue the existing GeneLab datasets related to liver omics data from these mice. We were able to discover key conserved pathways across all the mice independent of the flight conditions that were related to increased lipid metabolism, fatty acid metabolism, both lipid and fatty acid processing, lipid catabolic processing, and lipid localization. In addition, key upstream regulators were predicted to be commonly regulated across all conditions which include ESR1, GCG, and NR1I2 being inhibited and INS being activated. Interestingly, estrogen receptor alpha (ESR1) expression has been known to be heavily involved with lipoprotein metabolism. In addition, insulin (INS) is the primary driver for fat metabolism and increased INS has been associated with increased fatty acids in the liver. Through additional proteomic analysis we were able to identify the majority of the key proteins related to lipids for both the RR-1 and RR-3 rodents were being up-regulated in the livers when comparing flight to ground controls. This additional confirmation of the lipid associated activity also showed that the lipid related proteins are heavily involved with lipid metabolism, cholesterol binding, and cholesterol metabolism. Lastly, the analysis also revealed that the circadian clock related pathways in the liver are commonly being increased across all space flight conditions which has also been reported in the literature to potentially cause increased liver damage. The combination of the very strong lipid uptake in the liver and the transcriptomic/proteomic signatures (including the circadian clock pathways) following spaceflight are consistent with early onset of liver disease. Taken together, these data indicate that, activation of lipotoxic pathways could persist during longer duration spaceflight which might result in the development of liver disease
Lipids↗
Open Science for Plants in Space: Improvements in NASA's Open Science Data Repository
Upcoming deep space missions will rely on plants for crew and ecosystem health. Open access space biology data enables scientists to examine the biological responses of plants to ionizing radiation, altered gravity, elevated CO2, and many other abiotic stressors. NASA has declared 2023 as the ‘Year of Open Science’ and created a 5-year Transform to Open Science (TOPS) initiative designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. Current OSDR standards include the ISA (Investigation-Study-Assay) experiment model, assay metadata configurations, and standardized terminology and ontologies. In 2024 OSDR will include a new suite of features for improved FAIR compliance including downloadable plant metadata templates, data submission tools and overall improved AI-readiness of plant datasets. AI/ML methods can be helpful tools to overcome the inherent challenges of space biology research (small sample size, sparse and heterogeneous data etc.). However these methods are built on an assumption of normalized and well-curated data. OSDR’s new curation tools will improve users ability to leverage ML and AI methods to model space biology data and better understand the complex effects of spaceflight on living systems across hierarchical biological levels. We look forward to sharing our advances with the spaceflight community.
FAIR↗
Missing microbial eukaryotes and misleading meta-omic conclusions
Meta-omics is commonly used for large-scale analyses of microbial eukaryotes, including species or taxonomic group distribution mapping, gene catalog construction, and inference on the functional roles and activities of microbial eukaryotes in situ. Here, we explore the potential pitfalls of common approaches to taxonomic annotation of protistan meta-omic datasets. We re-analyze three environmental datasets at three levels of taxonomic hierarchy in order to illustrate the crucial importance of database completeness and curation in enabling accurate environmental interpretation. We show that taxonomic membership of sequence clusters estimates community composition more accurately than returning exact sequence labels, and overlap between clusters can address database shortcomings. Clustering approaches can be applied to diverse environments while continuing to exploit the wealth of annotation data collated in databases, and selecting and evaluating these databases is a critical part of correctly annotating protistan taxonomy in environmental datasets. We argue that ongoing curation of genetic resources is crucial in accurately annotating protists in in situ meta-omic datasets. Moreover, we propose that precise taxonomic annotation of meta-omic data is a clustering problem rather than a feasible alignment problem.
59 BASIC BIOLOGICAL SCIENCES↗
Sexual dimorphism and the multi-omic response to exercise training in rat subcutaneous white adipose tissue
Subcutaneous white adipose tissue (scWAT) is a dynamic storage and secretory organ that regulates systemic homeostasis, yet the impact of endurance exercise training (ExT) and sex on its molecular landscape is not fully established. Utilizing an integrative multi-omics approach, and leveraging data generated by the Molecular Transducers of Physical Activity Consortium (MoTrPAC), we show profound sexual dimorphism in the scWAT of sedentary rats and in the dynamic response of this tissue to ExT. Specifically, the scWAT of sedentary females displays -omic signatures related to insulin signaling and adipogenesis, whereas the scWAT of sedentary males is enriched in terms related to aerobic metabolism. These sex-specific -omic signatures are preserved or amplified with ExT. Integration of multi-omic analyses with phenotypic measures identifies molecular hubs predicted to drive sexually distinct responses to training. Overall, this study underscores the powerful impact of sex on adipose tissue biology and provides a rich resource to investigate the scWAT response to ExT.
59 BASIC BIOLOGICAL SCIENCES↗
Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery
Molecular-omics, physiological-phenotypic-behavioral, and environmental-radiation telemetry data from spaceflight biological and health studies are increasingly being made findable, accessible, interoperable, and reusable for the scientific public. These data, as well as space science-relevant biospecimens, are available through NASA’s Open Science Data Repository (OSDR), which is the new umbrella grouping of NASA GeneLab, the Ames Life Sciences Data Archive (ALSDA), and the NASA Biological Institutional Scientific Collection (NBISC). The quality of data is underpinned by datasets having rich metadata (determined through Analysis Working Group members), processing pipelines to enable data reuse standards, and ontologies specifying terminology semantics (e.g., the Radiation Biology Ontology).
space biology↗
Metabolic Pathway Engineering
Modern microbial and enzyme engineering and their advancement are increasingly dependent on the marriage of a wide range of sophisticated technologies. For students entering the field of biotechnology, the outlook is indeed daunting. Expertise at levels beyond that of simple familiarity will be needed to conduct competitive research. It goes without saying that to be competitive, all new researchers in this field will need basic preparation in molecular biology, biochemistry, and genetics. Further, experience working with concepts and experimental tools in enzyme biochemistry and kinetics, gene editing, computational metabolic pathway modeling, experimental pathway flux analysis, and computational clustering tools to process complex data sets will be vital for success. We speculate that most biotechnology researchers in early career at this time will build teams of collaborators to address these disparate science fields rather than attempt to become experts in one lab.
BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS↗