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.

SourceResearch connectionFrom yearThrough year

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

At least 649 records · Page 36

All sources NASA NTRS DOE OSTI

DOE OSTI2021

NASA GeneLab RNA-seq consensus pipeline: Standardized processing of short-read RNA-seq data

With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility, and reusability of pipeline data; to provide a template for data processing of future spaceflight-relevant datasets; and to encourage cross-analysis of data from other databases with the data available in GeneLab.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Improved Biofuel Production through Discovery and Engineering of Terpene Metabolism in Switchgrass

Project Objectives - Of the myriad specialized metabolites that plants deploy to adapt to environmental challenges, terpenes form the largest group. In many major crops, unique terpene blends serve as key stress defenses that directly impact plant fitness and yield. In addition, terpenes, such as bisabolene and pinene, are used for producing renewable biofuels. Essential to advancing a broader use of terpenes for biofuel feedstock engineering is a system-wide knowledge of the diverse biosynthetic machinery and defensive potential of often species-specific terpene blends. The proposed project would merge genome-wide enzyme discovery with comparative –omics, protein structural and plant microbiome studies to define the biosynthesis and stress-defensive functions of the switchgrass (Panicum virgatum) terpene network. These insights would be combined with developing and applying non-transgenic genome editing tools to design plants with desirable terpene blends for higher productivity and biofuel production on marginal lands. As a dedicated lignocellulosic feedstock for U.S. biofuel production with high net energy yield, stress tolerance, and available genome resources, switchgrass is well-suited for devising new avenues for biofuel production. Project Description – The diversity of plant terpene defenses is governed by species-specific families of terpene synthase (TPS) and cytochrome P450 monooxygenase (P450) enzymes. Mining of the switchgrass genome (genotype Alamo) identified ~100 TPS and P450 candidate genes, and combinatorial biochemical analysis of synthesized TPSs and P450s revealed more than a dozen enzymes with common and novel activities. In addition, several identified terpene metabolites and the corresponding transcripts were up-regulated in response to abiotic stressors. These findings demonstrate a unique switchgrass terpene network with probable importance to abiotic stress tolerance, thus providing a large chemical portfolio for optimizing crop resistance, yield, and biofuel composition. Leveraging these preliminary data, we propose to generate a genome-wide map of the switchgrass terpene metabolic network through multi-gene co-expression analyses that allow the efficient cross-validation of TPS and P450 functions. Key enzymes would further be applied to structure-function studies via X-ray protein crystallography, homology modeling and site-directed mutagenesis to gain mechanistic insight into the catalytic specificity of switchgrass terpene metabolism and provide gene and amino acid targets for genome editing. In tandem with terpene pathway discovery, system-wide metabolomics, transcriptomics and proteomics studies in switchgrass accessions of contrasting drought tolerance would define the role of switchgrass terpene metabolism in conferring abiotic stress resilience. Metabolic changes would be assessed in a combined approach of targeted (terpenes) and untargeted metabolite profiling using a high-resolution LC-MS/MS approach, differential gene expression analyses through multiplexed Illumina RNA sequencing, and quantitative analysis of high-priority pathway enzymes using multiple reaction monitoring (MRM). Drawing on these insights, knock-down/out mutants of stress-associated pathway nodes would be generated by optimizing transient virus-induced gene silencing (VIGS) and CRISPR/Cas9 systems under control of the Tobacco Rattle Virus (TRV). The resulting mutant lines would then be analyzed for stress susceptibility and the impact on the root microbiome to define gene functions in planta. Knowledge of terpene pathways, enzyme mechanisms and bioactivities would be applied to enhance switchgrass stress resilience and to tailor-make terpene blends for biofuel production. Here, TRV-enabled CRISPR/Cas9 genome editing, including allele-specific knock-out of redundant genes, engineering of enzyme specificity via structure-guided point mutations, and overexpression of terpene genes relevant to stress-protection or biofuel production, would be used to increase metabolic flux toward desired pathways. Broader Impacts - Integrating the system-wide discovery, mechanistic analysis and non-transgenic genome engineering of the switchgrass terpene network aligns the required steps to unlock the chemical potential of this important metabolite class to generate crops that are more resistant to stress and provide advanced biofuel production in light of rising climate pressures as foreseeable challenges for bioenergy crop cultivation. The proposed project would further offer interdisciplinary student training through active involvement in the project and integration of research concepts and outcomes into newly-developed graduate and undergraduate courses on Plant Biotechnology.

09 BIOMASS FUELS↗

DOE OSTI2016

petal: Co-expression network modelling in R

Background: Networks provide effective models to study complex biological systems, such as gene and protein interaction networks. With the advent of new sequencing technologies, many life scientists are grasping for user-friendly methods and tools to examine biological components at the whole-systems level. Gene co-expression network analysis approaches are frequently used to successfully associate genes with biological processes and demonstrate great potential to gain further insights into the functionality of genes, thus becoming a standard approach in Systems Biology. Here the objective is to construct biologically meaningful and statistically strong co-expression networks, the identification of research dependent subnetworks, and the presentation of self-contained results. Results: We introduce petal, a novel approach to generate gene co-expression network models based on experimental gene expression measures. petal focuses on statistical, mathematical, and biological characteristics of both, input data and output network models. Often over-looked issues of current co-expression analysis tools include the assumption of data normality, which is seldom the case for hight-throughput expression data obtained from RNA-seq technologies. petal does not assume data normality, making it a statistically appropriate method for RNA-seq data. Also, network models are rarely tested for their known typical architecture: scale-free and small-world. petal explicitly constructs networks based on both these characteristics, thereby generating biologically meaningful models. Furthermore, many network analysis tools require a number of user-defined input variables, these often require tuning and/or an understanding of the underlying algorithm; petal requires no user input other than experimental data. This allows for reproducible results, and simplifies the use of petal. Lastly, this approach is specifically designed for very large high-throughput datasets; this way, petal’s network models represent as much of the entire system as possible to provide a whole-system approach. Conclusion: petal is a novel tool for generating co-expression network models of whole-genomics experiments. It is implemented in R and available as a library. Its application to several whole-genome experiments has generated novel meaningful results and has lead the way to new testing hypothesizes for further biological investigation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Metagenome-assembled genomes from East River floodplain sediments near Crested Butte, CO, USA (June to September 2017)

Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here, we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken in 2017 in June (flooded conditions) and September (drained conditions) at two locations (MCB1 and MCB3) in an active meander (Meander C) of the East River. Sediment cores were collected from 2 depths, a near-surface, generally unsaturated depth (15-40 centimeter (cm) depth below surface) and a deeper depth influenced by flooding with redoximorphic features (50-88 cm depth below surface). Sediments were homogenized from the ~10 cm cores for microbial analyses. A total of 24 metagenomes were sequenced through the Joint genome institute (JGI) corresponding to 8 samples sequenced in triplicate. These metagenomes can be found under Genomes Online Database (GOLD) sequencing project: Gs0151851. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 405 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Integration of transcriptomic and proteomic data identifies biological functions in cell populations from human infant lung

Systems biology uses computational approaches to integrate diverse data types to understand cell and organ behavior. Data derived from complementary technologies, for example transcriptomic and proteomic analyses, are providing new insights into development and disease. We compared mRNA and protein profiles from purified endothelial, epithelial, immune, and mesenchymal cells from normal human lung tissue. Signatures for each cell type were identified and compared at both mRNA and protein levels. Cell specific biological processes and pathways were predicted by analysis of concordant and discordant RNA-protein pairs. Cell clustering and gene set enrichment comparisons identified shared versus unique processes associated with transcriptomic and/or proteomic data. Clear cell-cell correlations between mRNA and protein data were obtained from each cell type. Approximately 40% of RNA-protein pairs were coherently expressed. While the correlation between RNA and their protein products was relatively low (approximately 0.4), cell specific signature genes involved in functional processes characteristic of each cell type, were more highly correlated with their protein products. Consistency of cell specific RNA-protein signatures indicated an essential framework for the function of each cell type. Visualization and reutilization of the protein and RNA profiles are supported by a new web application, "LungProteomics," which is incorporated into the LGEA web portal and freely accessible at (https://research.cchmc.org/pbge/lunggens/lungProteomics/human_sorted_profiles.html).

human lung, sorted cells, integrated omics, Transc↗

DOE OSTI2025

Metagenome-assembled genomes from topsoils collected during NEON campaign in East River, CO (06/14/2018-06/28/2018)

The Watershed Function Science Focus Area (WF SFA) at Lawrence Berkeley National Lab is working to build a mechanistic understanding of the distribution and dynamics of biogeochemical processes in mountainous watersheds and their response to perturbation. In June 2018, the NEON (National Ecological Observatory Network) Airborne Observatory Platform (AOP) performed a taskable airborne imaging campaign to collect visible to shortwave infrared (VSWIR) imaging spectroscopy and LiDAR data across 330 km2 in the Upper East River at Crested Butte, CO. We conducted a parallel ground sampling campaign to sample vegetation traits, as well as soil physical, chemical, and microbiological characteristics. We collected these samples from 438 sites across 12 locations spanning much of the elevation, topographic, and geologic variability across the study area. A subset of 250 samples were used for soil metagenomics which is presented here. In addition, at each site, vegetation samples were collected to measure species-specific leaf water content and leaf mass area, foliar elemental composition and foliar CN stable isotope ratios. Soil samples were collected to measure soil physical properties which include bulk density and soil texture analysis. A suite of soil chemical properties was measured from the samples collected at each site, including pH, organic matter, concentrations exchangeable cations, total elemental composition, and the concentrations of extractable N pools (e.g. total free amino acids, ammonium, nitrate, dissolved organic N, and total dissolved N). Additionally, we have measured soil microbial biomass CN stoichiometry. Here, we present 1982 metagenome-assembled genomes (MAGs) for the bacterial and archaeal community from topsoil collected from during NEON 2018 campaign. All metagenomes were sequenced at JGI (Joint Genome Institute) (GOLD Study ID: Gs0149986). Metagenomes were assembled using JGI Metagenome Workflow (10.1128/mSystems.00804-20). The dataset includes (1) zip files for 1982 MAG fasta files (neon_genomes1-5.tar.gz, split into 5 tarballs to keep tarballs under 0.5 GB), (2) neon_Gs0149986_samples_soilproperties_metagenomes.csv: the sample information together with the accession numbers for the underlying metagenomes and the associated soil physical and chemical measurements in NMDC (National Microbiome Data Collaborative) compliant format, (3) neon_Gs0149986.kml: location bounding box file for the sampled locations, (4) samples.csv: sample metadata file used to register Internationall Generic Sample Numbers (IGSNs), (5) flmd.csv: file level metadata file, and (6) dd.csv: data dictionary file. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗

DOE OSTI2017

PCR and Omics Based Techniques to Study the Diversity, Ecology and Biology of Anaerobic Fungi: Insights, Challenges and Opportunities

Anaerobic fungi (phylum Neocallimastigomycota) are common inhabitants of the digestive tract of mammalian herbivores, and in the rumen, can account for up to 20% of the microbial biomass. Anaerobic fungi play a primary role in the degradation of lignocellulosic plant material. They also have a syntrophic interaction with methanogenic archaea, which increases their fiber degradation activity. To date, nine anaerobic fungal genera have been described, with further novel taxonomic groupings known to exist based on culture-independent molecular surveys. However, the true extent of their diversity may be even more extensively underestimated as anaerobic fungi continue being discovered in yet unexplored gut and non-gut environments. Additionally many studies are now known to have used primers that provide incomplete coverage of the Neocallimastigomycota. For ecological studies the internal transcribed spacer 1 region (ITS1) has been the taxonomic marker of choice, but due to various limitations the large subunit rRNA (LSU) is now being increasingly used. How the continued expansion of our knowledge regarding anaerobic fungal diversity will impact on our understanding of their biology and ecological role remains unclear; particularly as it is becoming apparent that anaerobic fungi display niche differentiation. As a consequence, there is a need to move beyond the broad generalization of anaerobic fungi as fiber-degraders, and explore the fundamental differences that underpin their ability to exist in distinct ecological niches. Application of genomics, transcriptomics, proteomics and metabolomics to their study in pure/mixed cultures and environmental samples will be invaluable in this process. To date the genomes and transcriptomes of several characterized anaerobic fungal isolates have been successfully generated. In contrast, the application of proteomics and metabolomics to anaerobic fungal analysis is still in its infancy. A central problem for all analyses, however, is the limited functional annotation of anaerobic fungal sequence data. There is therefore an urgent need to expand information held within publicly available reference databases. Finally, once this challenge is overcome, along with improved sample collection and extraction, the application of these techniques will be key in furthering our understanding of the ecological role and impact of anaerobic fungi in the wide range of environments they inhabit.

(meta) transcriptomics↗

DOE OSTI2016

From genomes to metabolomes: Understanding mechanisms of symbiosis and cell-cell signaling using the archaeal system Ignicoccus-Nanoarchaeum

The main objective of this project was to use symbiotic Nanoarchaeaota, a group of thermophilic Archaea that are obligate symbionts/parasites on other Archaea, to develop an integrated multi-omic approach to study inter-species interactions as well as to understand fundamental mechanism that enable such relationships. As part of this grant we have achieved a number of important milestone on both technical and scientific levels. On the technical side, we developed immunofluorescence labeling and tracking methods to follow Nanoarchaeota in cultures and in environmental samples, we applied such methods in conjunction with flow cytometry to quantify and isolate uncultured representatives from the environment and characterized them by single cell genomics. On the proteomics side, we developed a more efficient and sensitive method to recover and semi-quantitatively measure membrane proteins, while achieving high total cellular proteome coverage (70-80% of the predicted proteome). Metabolomic analyses used complementary NMR and LC/GC mass spectrometry and led to the identification of novel lipids in these organisms as well as quantification of some of the major metabolites. Importantly, using several informatics approaches we were also able to integrate the transcriptomic, proteomic and metabolomic datasets, revealing aspects of the interspecies interaction that were not evident in the single omic analyses (manuscript in review). On the science side we determined that N. equitans and I. hospitalis are metabolically coupled and that N. equitans is strictly dependent on its host both for metabolic precursors and energetic needs. The actual mechanism by which small molecules move across the cell membrane remains unknown. The Ignicoccus host responds to the metabolic and energetic burned by upregulating of key primary metabolism steps and ATP synthesis. The two species have co-evolved, aspect that we determined by comparative genomics with other species of Ignicoccus (manuscript in preparation) and by characterizing other similar Nanoarchaeota systems. Using a single cell genomics approach we characterized the first terrestrial geothermal Nanoarchaeota system, from Yellowstone National Park. That nanoarchaeon uses a different host, a species of Sulfolobales, and comparative genomics with N. equitans-Ignicoccus allowed us to come up with an evolutionary model for the evolution of this group of organisms across marine and terrestrial ecosystems. Based on metabolic inferences we were also able to isolate in culture the first such terrestrial nanoarchaeal system, also from Yellowstone, which involves a species of Acidilobus. The novel nanoarchaeal system was characterized using proteomics and it helped us better understand the metabolic capabilities of these organisms as well as how co-evolution shapes the genomes of interacting species. It was also one of the very few cases in which prior genomic data was used to successfully design an approach to culture an organism, which remains the gold standard in microbiology research. As a better understanding of interspecies interaction requires multiple model systems, we have pursued identification and genomic characterization or isolation of additional nanoarchaeal systems from geographically and geochemically distinct environments. Two additional nanoarchaeal systems are presently being characterized from hot springs in Yellowstone and Iceland and will be the subject to future publications.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRSDate not supplied

RNASeq and Fluorescence Analysis of the Response of ERF2 and ERF104 in Arabidopsis thaliana under Simulated Altered Gravity

As NASA moves closer to long-term human space exploration, the need to understand how to sustain life in space is increasingly pressing. Plants are essential to human sustenance, making it important to understand how spaceflight affects plant health. We used differential gene expression analysis to examine GLDS-251 (RNAseq analysis of the response of Arabidopsis thaliana to fractional gravity under blue-light stimulation during spaceflight) from NASA’s GeneLab data repository and found downregulation of ERF2 and ERF104, transcription factors of the ethylene response factor families, that integrate hormonal pathways involved in abiotic stress responses. Downregulation of ERF2 and ERF104 during spaceflight may indicate a dysregulation of the ethylene signaling pathway. Our hypothesis is that altered gravity downregulates the expression of ERF2 and ERF104 in Arabidopsis thaliana, altering the ethylene signaling pathway and affecting the electron transport chain and light-dependent reactions in chloroplast thylakoids. To test this hypothesis, we propose to grow A. thaliana seedlings (wild-type and mutant/knockout of ERF2 and ERF104) in altered gravity conditions to determine the effects on the expression of ERF2, ERF104, and photosynthesis. We anticipate that ERF2 and ERF104 will be underexpressed in altered gravity conditions and result in decreased regulation of the ethylene signaling pathway.

Arabidopsis↗

NASA NTRS2015

Software Development: 3D Animations and Creating User Interfaces for Realistic Simulations

My fall 2015 semester was spent at the Lyndon B. Johnson Space Center working in the Integrated Graphics, Operations, and Analysis Laboratory (IGOAL). My first project was to create a video animation that could tell the story of OMICS. OMICS is a term being used in the field of biomedical science to describe the collective technologies that study biological systems, such as what makes up a cell and how it functions with other systems. In the IGOAL I used a large 23 inch Wacom monitor to draw storyboards, graphics, and line art animations. I used Blender as the 3D environment to sculpt, shape, cut or modify the several scenes and models for the video. A challenge creating this video was to take a term used in biomedical science and describe it in such a way that an 8th grade student can understand. I used a line art style because it would visually set the tone for what we thought was an educational style. In order to get a handle on the perspective and overall feel for the animation without overloading my workspace, I split up the 2 minute animation into several scenes. I used Blender's python scripting capabilities which allowed for the addition of plugins to add or modify tools. The scripts can also directly interact with the objects to create naturalistic patterns or movements. After collecting the rendered scenes, I used Blender's built-in video editing workspace to output the animation. My second project was to write software that emulates a physical system's interface. The interface was to simulate a boat, ROV, and winch system. Simulations are a time and cost effective way to test complicated data and provide training for operators without having to use expensive hardware. We created the virtual controls with 3-D Blender models and 2-D graphics, and then add functionality in C# using the Unity game engine. The Unity engine provides several essential behaviors of a simulator, such as the start and update functions. A framework for Unity, which was developed in the lab, provided a way to place the different widgets on the virtual console dock and have them resize correctly based on the window dimensions.. My task in this project was to create the controls and visualizations for the data coming in from the simulator for the boat portion of the project. I wrote a class for each control window to handle the functionality of that widget. I implemented 11 widgets that make up the ship portion of the simulator. The members of the lab were each masters of their craft and I'm glad I had the opportunity to learn from them. I learned to plan strategically so I could finish this project on time. I allotted time for storyboarding, development, and refinement. In regards to animating I learned to use modifiers like lattice, boolean and build deformers. I also learned how to animate with drivers, how to use the dope sheet, and how to use the graph editor. In coding I learned to limit the chances for bugs by privatizing functions that should be exclusive to their class. I learned how to use the GIT repository to commit, stash and pull the latest build. I learned a bit of everything because I had the chance to see the entire application development process from the artwork, to the implementation.

Gordillo, Orlando Enrique↗

DOE OSTI2021

The F-box protein gene exo-1 is a target for reverse engineering enzyme hypersecretion in filamentous fungi

Carbohydrate active enzymes (CAZymes) are vital for the lignocellulose-based biorefinery. The development of hypersecreting fungal protein production hosts is therefore a major aim for both academia and industry. However, despite advances in our understanding of their regulation, the number of promising candidate genes for targeted strain engineering remains limited. Here, we resequenced the genome of the classical hypersecreting Neurospora crassa mutant exo-1 and identified the causative point of mutation to reside in the F-box protein–encoding gene, NCU09899. The corresponding deletion strain displayed amylase and invertase activities exceeding those of the carbon catabolite derepressed strain ?cre-1, while glucose repression was still mostly functional in ?exo-1. Surprisingly, RNA sequencing revealed that while plant cell wall degradation genes are broadly misexpressed in ?exo-1, only a small fraction of CAZyme genes and sugar transporters are up-regulated, indicating that EXO-1 affects specific regulatory factors. Aiming to elucidate the underlying mechanism of enzyme hypersecretion, we found the high secretion of amylases and invertase in ?exo-1 to be completely dependent on the transcriptional regulator COL-26. Furthermore, misregulation of COL-26, CRE-1, and cellular carbon and nitrogen metabolism was confirmed by proteomics. Finally, we successfully transferred the hypersecretion trait of the exo-1 disruption by reverse engineering into the industrially deployed fungus Myceliophthora thermophila using CRISPR-Cas9. Our identification of an important F-box protein demonstrates the strength of classical mutants combined with next-generation sequencing to uncover unanticipated candidates for engineering. These data contribute to a more complete understanding of CAZyme regulation and will facilitate targeted engineering of hypersecretion in further organisms of interest.

Gabriel, Raphael↗

DOE OSTI2025

Time‐series multi‐omics analysis of micronutrient stress in Sorghum bicolor reveals iron and zinc crosstalk and regulatory network conservation

Micronutrient stress impacts growth, biomass production, and grain yield in crops. Multi-omics studies are valuable resources in identifying genes for functional studies and trait improvement, such as accumulation of Fe or Zn under deficient or excess conditions for bioenergy or grain agriculture. We conducted transcriptomics and ionomics analyses on Sorghum bicolor BTx623, grown under Fe and Zn limited and excess conditions over a 21-day period. To identify early and late transcriptional response in roots and leaves, 180 RNAseq libraries were sequenced for differential expression and co-expression network analyses. Fe and Zn accumulation was measured using ICP-MS at each time point, and a fluorometer was used to estimate chlorophyll content in leaves. Among the four treatments, Fe limitation and Zn excess resulted in the largest phenotypic effects and transcriptional response in roots and leaves. Several of the reduction (Strategy I) and chelation (Strategy II) strategy genes that improve bioavailability of Fe and Zn in plant roots often used by non-grass and grass species, respectively, were differentially expressed. Gene regulatory network (GRN) analysis of roots revealed enrichment of genes from Fe limiting and Zn excess which strongly connect to homologues of SbFIT, SbPYE, and SbBTS as hub genes. The GRN for leaf responses showed homologues of SbPYE and SbBTS as hubs connecting genes for chloroplast biosynthesis, Fe-S cluster assembly, photosynthesis, and ROS scavenging. Expression analyses suggest sorghum uses Strategy II genes for Fe and Zn uptake, as expected, but can also utilize Strategy I genes, which may be advantageous in variable moisture environments. We found strong overlap between Fe and Zn responsive GRNs, indicative of micronutrient crosstalk. We also found conservation of root and leaf GRNs, and known homologous genes suggest strong constraints on homeostasis networks in plants. These data will provide a resource for functional genetics to enhance micronutrient transport in sorghum, and opportunities to conduct further comparative GRN analysis across diverse crops species.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Predicting chronic postsurgical pain: current evidence and a novel program to develop predictive biomarker signatures

Chronic pain affects more than 50 million Americans. Treatments remain inadequate, in large part, because the pathophysiological mechanisms underlying the development of chronic pain remain poorly understood. Pain biomarkers could potentially identify and measure biological pathways and phenotypical expressions that are altered by pain, provide insight into biological treatment targets, and help identify at-risk patients who might benefit from early intervention. Biomarkers are used to diagnose, track, and treat other diseases, but no validated clinical biomarkers exist yet for chronic pain. To address this problem, the National Institutes of Health Common Fund launched the Acute to Chronic Pain Signatures (A2CPS) program to evaluate candidate biomarkers, develop them into biosignatures, and discover novel biomarkers for chronification of pain after surgery. This article discusses candidate biomarkers identified by A2CPS for evaluation, including genomic, proteomic, metabolomic, lipidomic, neuroimaging, psychophysical, psychological, and behavioral measures. Acute to Chronic Pain Signatures will provide the most comprehensive investigation of biomarkers for the transition to chronic postsurgical pain undertaken to date. Data and analytic resources generatedby A2CPS will be shared with the scientific community in hopes that other investigators will extract valuable insights beyond A2CPS’s initial findings. This article will review the identified biomarkers and rationale for including them, the current state of the science on biomarkers of the transition from acute to chronic pain, gaps in the literature, and how A2CPS will address these gaps.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

19-LW-045 Full Length Final Report. Molecular Mechanisms of Bacterial Pathogenesis: Waging the Arms Race with Superbugs

As the current global pandemic makes abundantly clear, we need a better understanding of infectious disease to safeguard human health, the economy and global security. Modern omics techniques hold the promise of providing a comprehensive understanding of the molecular mechanisms of life, including causes of pathogenesis from infectious disease at the molecular level, but we there is a serious gap in annotation of gene function. For as much as half of the genes and gene products encoded in genomes the molecular and/or cellular function is unknown or only partially understood. Recent innovations in fluorescence microscopy for live cell imaging and genetic engineering make it possible to determine the temporal correlation between molecular events, such as a gene being expressed due to host-pathogen interaction, and cellular events, such as bacterial invasion of immune cells. This is turn allows us to gain new insight as to the molecular and cellular role of individual genes and will enable the discovery and validation of new molecular mechanisms essential for infectious disease. Knowing the molecular mechanisms of disease processes will provide new therapeutic targets or novel countermeasure strategies. We aimed to develop a lattice light sheet fluorescence microscope as a unique resource at LLNL for long time course live cell imaging experiments; to develop the reagents and cell lines needed to monitor molecular events during the course pathogenic bacteria infecting mammalian immune cells; and to demonstrate that we could capture molecular events during an infection. We fully commissioned the LLNL lattice light sheet microscope and conducted initial proof of principle imaging experiments on mammalian immune cells and pathogenic bacteria. It is clear from the experience gained that long time course live cell imaging has tremendous potential to help elucidate molecular mechanisms of host-pathogen interactions and to help annotate gene function, which would establish a basis for new countermeasures. It is also clear that if live cell imaging is to realize its full potential new data processing and analysis tools will need to be developed to facilitate analysis of molecular events within cells; new sample chambers and stages could facilitate studies with a wider range of cell and tissue types; and alternative molecular tagging methods need to be explored to enable more facile engineering of cells labeled with molecular specificity.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Metagenome-assembled genomes measured at 3 depths during snowmelt period in East River, CO (March, May, and June, September 2017)

Snowmelt is a critical biogeochemical period that accounts for large nitrogen (N) export events from high-elevation watersheds. Soil microbial populations bloom and immobilize N during snowmelt, yet the population size crashes in spring, which releases a pulse of soil N. We sought to discover the N sources fueling this microbial bloom and determine the fate of N following microbial die-off. Here, focusing on the snowmelt period within a headwater catchment of the Upper Colorado River Basin (East River, CO), we deployed strain-resolved metagenomics to identify the metabolic pathways and processes that mobilize soil N during and after snowmelt. Soil metagenome samples were taken from 6 snowpits from 3 depths (0-5cm, 5-15cm, >15cm) at 4 time points during snowmelt period (March 2017, May 2017, and June 2017, September 2017) generating 48 metagenomes. We reconstructed 474 metagenome-assembled genomes (MAGs) across all metagenomes.All 48 metagenomes were sequenced at JGI and raw data can be found under JGI (Joint Genome Institute) GOLD Study Gs0135149. Metagenome assemblies from IMG under the same study were used for genome binning. This dataset (1) a zip file of 474 MAGs (as fasta files, Gs0135149_bins_tar.gz), (2) sample metadata file with sample IGSNs (International Generic Sample Numbers) (samples.csv), (3) bounding box coordinates for the sampled locations (Gs0135149.kml), (4) metagenome metadata file listing IMG/M (Integrated Microbial Genomes/Metagenomes) metagenome accessions linking samples to metagenomes (metagenomes.csv), (5) location metadata file (locations.csv), (6) file-level metadata file (flmd.csv) and (7) data dictionary (dd.csv) file.This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

A shift between mineral and nonmineral sources of iron and sulfur causes proteome-wide changes in Methanosarcina barkeri

ABSTRACT Iron (Fe) and sulfur (S) are required elements for life, and changes in their availability can limit the ecological distribution and function of microorganisms. In anoxic environments, soluble Fe typically exists as ferrous iron [Fe(II)] and S as sulfide (HS − ). These species exhibit a strong affinity that ultimately drives the formation of sedimentary pyrite (FeS 2 ). Recently, paradigm-shifting studies indicate that Fe and S in FeS 2 can be made bioavailable by methanogens through a reductive dissolution process. However, the impact of the utilization of FeS 2 , as opposed to canonical Fe and S sources, on the phenotype of cells is not fully understood. Here, shotgun proteomics was utilized to measure changes in the phenotype of Methanosarcina barkeri MS grown with FeS 2 , Fe(II)/HS − , or Fe(II)/cysteine. Shotgun proteomics tracked 1,019 proteins overall, with 307 observed to change between growth conditions. Functional characterization and pathway analyses revealed these changes to be systemic and largely tangential to Fe/S metabolism. As a final step, the proteomics data were viewed with respect to previously collected transcriptomics data to deepen the analysis. Presented here is evidence that M. barkeri adopts distinct phenotypes to exploit specific sources of Fe and S in its environment. This is supported by observed protein abundance changes across broad categories of cellular biology. DNA adjacent metabolism, central carbon metabolism methanogenesis, metal trafficking, quorum sensing, and porphyrin biosynthesis pathways are all features in the phenotypic differentiation. Differences in trace metal availability attributed to complexation with HS − , either as a component of the growth medium [Fe(II)/HS − ] or generated through reduction of FeS 2 , were likely a major factor underpinning these phenotypic differences. IMPORTANCE The methanogenic archaeon Methanosarcina barkeri holds great potential for industrial bio-mining and energy generation technologies. Much of the biochemistry of this microbe is poorly understood, and its characterization will provide a glimpse into biological processes that evolved close to life’s origin. The discovery of its ability to extract iron and sulfur from bulk, solid-phase minerals shifted a longstanding paradigm that these elements were inaccessible to biological systems. The full elucidation of this process has the potential to help scientists and engineers extract valuable metals from low-grade ore and mine waste generating energy in the form of methane while doing so.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Extending a Tandem Mass Spectral Library to Include MS{sup 2} Spectra of Fragment Ions Produced In-Source and MS{sup n} Spectra

Tandem mass spectral library searching is finding increased use as an effective means of determining chemical identity in mass spectrometry-based omics studies. We previously reported on constructing a tandem mass spectral library that includes spectra for multiple precursor ions for each analyte. Here we report our method for expanding this library to include MS{sup 2} spectra of fragment ions generated during the ionization process (in-source fragment ions) as well as MS{sup 3} and MS{sup 4} spectra. These can assist the chemical identification process. A simple density-based clustering algorithm was used to cluster all significant precursor ions from MS{sup 1} scans for an analyte acquired during an infusion experiment. The MS{sup 2} spectra associated with these precursor ions were grouped into the same precursor clusters. Subsequently, a new top-down hierarchical divisive clustering algorithm was developed for clustering the spectra from fragmentation of ions in each precursor cluster, including the MS{sup 2} spectra of the original precursors and of the in-source fragments as well as the MS{sup n} spectra. This algorithm starts with all the spectra of one precursor in one cluster and then separates them into sub-clusters of similar spectra based on the fragment patterns. Herein, we describe the algorithms and spectral evaluation methods for extending the library. The new library features were demonstrated by searching the high resolution spectra of E. coli extracts against the extended library, allowing identification of compounds and their in-source fragment ions in a manner that was not possible before. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA) (Final Scientific/Technical Report, Subcontract Award No. 6953691)

The overarching objective the Baliga lab is to develop a mechanistic understanding of the field relevant metabolic processes underlying community partitioning of respiration pathways and physiological state shifts. In order to dissect how environmental perturbations influence microbial regulatory networks, we have constructed data-driven models that capture the dynamic interplay between abiotic and biotic factors during laboratory simulations. Specifically, we have developed multiple computational models (i.e., cMonkey2, EGRIN 2.0, and multiple genome-scale metabolic models) connecting environmental influence to genome-encoded regulatory programs. In addition, we have generated new tools such as Live Anaerobic Cell Sorting (LAnCS) and Fluidized Bed Reactors (FBRs) to investigate the dynamics of community partitioning of metabolic processes supporting sulfate or nitrate respiration, which are two critical activities observed from the field site at Oak Ridge National Labs (ORNL). This framework of connecting field phenomena with laboratory simulations and computational modeling has created the Environmental Simulations and Modelling (EnvSim) campaign within ENIGMA. The campaign, which spans multiple laboratories across ENIGMA and is led by the Baliga group, has established synthetic communities (SynComs) to discover, characterize, and dissect key microbial processes relevant to field observations, such as emissions of the greenhouse gas nitrous oxide (N 2 O). For example, the EnvSim campaign so far has deduced four potential mechanisms that may account for the N 2 O emissions at the FRC and are currently being investigated by labs across ENIGMA. Denitrification may be driven by complete denitrifiers, however, their NosZ enzymes, which catalyze the final step in denitrification by converting N 2 O to N 2 , may be sensitive at a lower pH. Additionally, the denitrification process could be partitioned among organisms and some may have pH-sensitive NosZ genes. Another factor contributing to variable N 2 O emissions relates to the metal co-factors involved in the denitrification pathway. For instance, excess Cu, Al, Mn, U, Ni, Co, Cu, and/or Cd may have inhibitory effects on multiple enzymatic steps during denitrification, while the enzymatic production of nitrite, the precursor of N 2 O, can be limited by the essential metal Mo. Lastly, abiotic production of N 2 O may occur as a result of chemodenitrification, in which metals like Fe, Mn, and some organic compounds can drive redox reactions that convert nitrogen cycle intermediates to N 2 O under the right conditions. Current investigations and analyses have generated multiple transcriptomic profiles aiding the refinement of new metabolic and gene regulatory network model for our denitrifying SynCom. In sum, the previous funding cycle has generated 33 peer-reviewed publications spanning predictive network biology to co-operativity of mutualistic interspecies interactions of evolved communities. Our integration of multiple omics datasets and the development of new network modelling tools has revealed important insights that can explain field observed phenomenon and generated new hypotheses to be investigated in the field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗