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

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Intestinal microbiome profile of the brown rock sea cucumber ( Holothuria glaberrima ) using ITS and 16S rDNA amplicons from direct mechanical, enzymatic, and chemical metagenomic extraction

Using direct mechanical, enzymatic, and chemical extraction methods, the intestinal microbiome of the marine invertebrate Holothuria glaberrima was obtained. ITS and 16S rDNA regions were sequenced to enrich and investigate the prokaryotic and fungal diversity profiles from different anatomical regions within the sea cucumber’s intestinal biology.

16S↗
DOE OSTI2023

Inferring microbial interactions with their environment from genomic and metagenomic data

Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized , and so name our tool MetConSIN: Met abolically Con textualized S pecies I nteraction N etworks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Combined analysis of microbial metagenomic and metatranscriptomic sequencing data to assess in situ physiological conditions in the premature infant gut

Microbes alter their transcriptomic profiles in response to the environment. The physiological conditions experienced by a microbial community can thus be inferred using meta-transcriptomic sequencing by comparing transcription levels of specifically chosen genes. However, this analysis requires accurate reference genomes to identify the specific genes from which RNA reads originate. In addition, such an analysis should avoid biases in transcript counts related to differences in organism abundance. In this study we describe an approach to address these difficulties. Sample-specific meta-genomic assembled genomes (MAGs) were used as reference genomes to accurately identify the origin of RNA reads, and transcript ratios of genes with opposite transcription responses were compared to eliminate biases related to differences in organismal abundance, an approach hereafter named the “diametric ratio” method. We used this approach to probe the environmental conditions experienced by Escherichia spp. in the gut of 4 premature infants, 2 of whom developed necrotizing enterocolitis (NEC), a severe inflammatory intestinal disease. We analyzed twenty fecal samples taken from four premature infants (4–6 time points from each infant), and found significantly higher diametric ratios of genes associated with low oxygen levels in samples of infants later diagnosed with NEC than in samples without NEC. We also show this method can be used for examining other physiological conditions, such as exposure to nitric oxide and osmotic pressure. These study results should be treated with caution, due to the presence of confounding factors that might also distinguish between NEC and control infants. Nevertheless, together with benchmarking analyses, we show here that the diametric ratio approach can be applied for evaluating the physiological conditions experienced by microbes in situ. Results from similar studies can be further applied for designing diagnostic methods to detect NEC in its early developmental stages.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

KBase Narrative - Atacama Trench & Sn Clemente 2018 cruise Metagenomes -Sequence quality filtration & Assembly-

Deep-sea microbiomes sampled with traditional or pressure-retaining instrumentation A fundamental property of life in the sea is that with increasing depth must come increased adaptation to elevated hydrostatic pressures. This project aims to assess technology for the accurate characterization of microbial biodiversity and biochemistry at greater depths, and validate its use in some of the deepest and least-explored waters in the Ocean: Hadal Trench. Compared to the 0.1 megapascal (MPa) of surface waters, organisms inhabiting at the average depth of the Ocean (3800 m) have to deal with pressures of 38 MPa (5,500 psi). The effects of these high pressure have been most thoroughly investigated in microorganisms, where it has been found to affect membrane integrity and transport, cytoskeletal assembly and both DNA and protein synthesis. We will explore the diversity and activity of microorganisms from 2000 m to >8,000 m depths within the highly productive water column of hadal trenches, comparing seawater samples obtained with and without substantial pressure changes during recovery. A pressure retaining device was used to sample the microbial communities of the watercolumn with reduced decompression during retreival (Peoples et. al., 2019). References: Peoples LM, Norenberg M, Price D, McGoldrick M, Novotny M, Bochdansky A, Bartlett DH. A full-ocean-depth rated modular lander and pressure-retaining sampler capable of collecting hadal-endemic microbes under in situ conditions. Deep Sea Research Part I: Oceanographic Research Papers. 2019 Jan 1;143:50-7.

Plominsky, Alvaro M.↗
DOE OSTI2021

bioreactor sludge metagenome

Anaerobic fermentation bioreactor microbial communities from Madison, Wisconsin, United States. The goal of this study is to characterize a central set of genes relevant to medium chain fatty acid (MCFA) production to guide screens of putative MCFA-producing microorganisms from additional environments or grown on different substrates.

butyric↗
DOE OSTI2022

phyllosphere metagenome

Genome-centric analyses of seasonal phyllosphere microbiome activities in perennial crops

bacteria/genetics↗
DOE OSTI2021

A k-mer based approach for classifying viruses without taxonomy identifies viral associations in human autism and plant microbiomes

Viruses are an underrepresented taxa in the study and identification of microbiome constituents; however, they play an essential role in health, microbiome regulation, and transfer of genetic material. Only a few thousand viruses have been isolated, sequenced, and assigned a taxonomy, which limits the ability to identify and quantify viruses in the microbiome. Additionally, the vast diversity of viruses represents a challenge for classification, not only in constructing a viral taxonomy, but also in identifying similarities between a virus’ genotype and its phenotype. However, the diversity of viral sequences can be leveraged to classify their sequences in metagenomic and metatranscriptomic samples, even if they do not have a taxonomy. To identify and quantify viruses in transcriptomic and genomic samples, we developed a dynamic programming algorithm for creating a classification tree out of 715,672 metagenome viruses. To create the classification tree, we clustered proportional similarity scores generated from the k-mer profiles of each of the metagenome viruses to create a database of metagenomic viruses. The resulting Kraken2 database of the metagenomic viruses can be found here: https://www.osti.gov/biblio/1615774 and is compatible with Kraken2. We then integrated the viral classification database with databases created with genomes from NCBI for use with ParaKraken (a parallelized version of Kraken provided in Supplemental Zip 1), a metagenomic/transcriptomic classifier. To illustrate the breadth of our utility for classifying metagenome viruses, we analyzed data from a plant metagenome study identifying genotypic and compartment specific differences between two Populus genotypes in three different compartments. We also identified a significant increase in abundance of eight viral sequences in post mortem brains in a human metatranscriptome study comparing Autism Spectrum Disorder patients and controls. We also show the potential accuracy for classifying viruses by utilizing both the JGI and NCBI viral databases to identify the uniqueness of viral sequences. Finally, we validate the accuracy of viral classification with NCBI databases containing viruses with taxonomy to identify pathogenic viruses in known COVID-19 and cassava brown streak virus infection samples. Our method represents the compulsory first step in better understanding the role of viruses in the microbiome by allowing for a more complete identification of sequences without taxonomy. Better classification of viruses will improve identifying associations between viruses and their hosts as well as viruses and other microbiome members. Despite the lack of taxonomy, this database of metagenomic viruses can be used with any tool that utilizes a taxonomy, such as Kraken, for accurate classification of viruses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Tetranucleotide frequencies differentiate genomic boundaries and metabolic strategies across environmental microbiomes

Microbiomes are constrained by physicochemical conditions, nutrient regimes, and community interactions across diverse environments, yet genomic signatures of this adaptation remain unclear. Metagenome sequencing is a powerful technique to analyze genomic content in the context of natural environments, establishing concepts of microbial ecological trends. Here, we developed a data discovery tool-a tetranucleotide-informed metagenome stability diagram-that is publicly available in the integrated microbial genomes and microbiomes (IMG/M) platform for metagenome ecosystem analyses. We analyzed the tetranucleotide frequencies from quality-filtered and unassembled sequence data of over 12,000 metagenomes to assess ecosystem-specific microbial community composition and function. We found that tetranucleotide frequencies can differentiate communities across various natural environments and that specific functional and metabolic trends can be observed in this structuring. Our tool places metagenomes sampled from diverse environments into clusters and along gradients of tetranucleotide frequency similarity, suggesting microbiome community compositions specific to gradient conditions. Within the resulting metagenome clusters, we identify protein-coding gene identifiers that are most differentiated between ecosystem classifications. We plan for annual updates to the metagenome stability diagram in IMG/M with new data, allowing for refinement of the ecosystem classifications delineated here. This framework has the potential to inform future studies on microbiome engineering, bioremediation, and the prediction of microbial community responses to environmental change. IMPORTANCE: Microbes adapt to diverse environments influenced by factors like temperature, acidity, and nutrient availability. We developed a new tool to analyze and visualize the genetic makeup of over 12,000 microbial communities, revealing patterns linked to specific functions and metabolic processes. This tool groups similar microbial communities and identifies characteristic genes within environments. By continually updating this tool, we aim to advance our understanding of microbial ecology, enabling applications like microbial engineering, bioremediation, and predicting responses to environmental change.

Kellom, Matthew↗
DOE OSTI2023

ShadeLab/PAPER_Howe_2023_switchgrass_MetaT

The raw data (metagenomes and metatranscriptomes) for this study are available in the Joint Genomes Institute Genome Portal (https://genome.jgi.doe.gov/portal/ Project ID 503249) with projects designated by year and product type. The MAG genomes analyzed in this paper are available on NCBI, as bioproject PRJNA800073. Plants and microorganisms form beneficial associations. Understanding plant-microbe interactions will inform microbiome management to enhance crop productivity and resilience to stress. Here, we apply a genome-centric approach to identify ecologically important leaf microbiome members on field-grown switchgrass and miscanthus and to quantify their activities for switchgrass over two growing seasons. We integrate metagenome and metatranscriptome sequencing from 192 leaf samples collected over representative time points in crop phenology. We curated 40 medium- and high-quality metagenome-assembled-genomes (MAGs) and focused analysis on seasonal transcript recruitment to them. Classes represented by these focal MAGs (Actinomycetia, Alpha- and Gamma- Proteobacteria, and Bacteroidota) were active and had increases in transcripts for short-chain dehydrogenase, molybdopterin oxidoreductase, and polyketide cyclase in the late season. The majority of MAGs had activated stress-associated pathways, including trehalose metabolism, indole acetic acid degradation, betaine biosynthesis, and reactive oxygen species degradation, suggesting direct engagement with the host environment. We also detected seasonally activated biosynthetic pathways for terpenes (carotenoids and isoprenoids) and for various non-ribosomal peptide pathways that were poorly annotated. Overall, this study overcame laboratory and bioinformatic challenges associated with field-based leaf metatranscriptome analysis to inform both general and likely specialized activities of these phyllosphere populations. These activities collectively support that leaf-associated bacterial populations are seasonally dynamic, responsive to host cues, and interactively engage in feedback with the plant. This analysis represented quality filtering of metagenomes and metatranscriptomes (data-preparation folder), metagenome assemblies (metagenome-assembly folder) and metagenome-assembled genome binning, curation, refinement, annotation (mag-evaluation folder). Abundances of sequencing libraries were calculated based on reads mapped (mapping folder). Additionallly, analysis of our annotated results are also included (analysis folder).

Howe, Adina↗
DOE OSTI2024

Exabiome: Advancing Microbial Science through Exascale Computing

The Exabiome project seeks to improve the understanding of microbiomes through the development of methods for accelerating metagenomic science using exascale computing. This article gives an overview of scientific impact of the three components of the project: metagenome assembly, protein family detection, and comparative analysis of metagenomes. Exabiome developed MetaHipMer, the only metagenome assembler capable of scaling to full exascale systems. MetaHipMer has enabled ground-breaking assemblies on the Frontier supercomputer, with many scientific benefits, such as the discovery of rare species and viral genomes. To investigate protein families, Exabiome developed two exascale tools, PASTIS and HipMCL. Together, these can utilize exascale resources to understand the functional diversity of billions of dark matter proteins and novel protein families. For comparative analysis, Exabiome developed kmerprof, a tool that can be used to compare huge metagenomes for many different scientific purposes, for example, grouping human microbiomes according to body location.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

JGI QC impact on assembly, binning, phylogenomics, and functional analysis

Background Investigators using metagenomic sequencing to study their microbiomes are often provided data that has been trimmed and decontaminated or do it themselves without knowing the effect these procedures can have on their downstream analyses. Here we evaluated the impact that JGI trimming and decontamination procedures had on assembly and binning metrics, placement of metagenome assembled genomes into species trees, and functional profiles of metagenome-assembled genomes (MAGs) extracted from twenty three complex rhizosphere metagenomes. We also investigated how more aggressive trimming impacts these binning metrics. Results We found that JGI trimmed and decontamination of input reads had some significant impacts in assembly and binning metrics compared to raw reads, and that differences in placement of MAGs in species trees increased with decreasing completeness and contamination thresholds. More aggressive trimming beyond those used by JGI were found to reduce MAG counts. Conclusions Mild trimming and decontamination of metagenomics reads prior to assembly can change an investigator’s answer to the questions, “Who is there and what are they doing? However, mild trimming and decontamination of metagenomic reads with high quality scores is recommended for those who elect to do so.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Narrative for Bioproject PRJNA623111

This Narrative is part of a collection used in publications focused on the microbial ecology of fjord sediment in the high Arctic, Svalbard. This paper took a genome-centric approach to uncover novel metabolic capabilities of the Acidobacteriota clade. Metagenomic data was coupled with amplicon sequencing of 16S rRNA and dissimilatory sulfite reductase (dsrB) genes, and transcripts (available under NCBI-Genbank Bioproject PRJNA623111), and gene expression analyses of tetrathionate-amended microcosms. This Narrative contains the metagenomic assemblies that were constructed within KBase for reproducibility. These include: stnF metagenome assembly stnAB metagenome assembly stnAC metagenome assembly These metagenome assemblies served as the launching point for an additional publication focused on a different clade of bacteria (Buongiorno et al., 2020). Link to that Narrative landing page here. Please cite: Flieder, Mathias, Joy Buongiorno, Craig W. Herbold, Bela Hausmann, Thomas Rattei, Karen G. Lloyd, Alexander Loy, and Kenneth Wasmund. "Novel taxa of Acidobacteriota implicated in seafloor sulfur cycling." The ISME Journal (2021): 1-22.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Dispersal, habitat filtering, and eco-evolutionary dynamics as drivers of local and global wetland viral biogeography

Abstract Wetlands store 20–30% of the world’s soil carbon, and identifying the microbial controls on these carbon reserves is essential to predicting feedbacks to climate change. Although viral infections likely play important roles in wetland ecosystem dynamics, we lack a basic understanding of wetland viral ecology. Here 63 viral size-fraction metagenomes (viromes) and paired total metagenomes were generated from three time points in 2021 at seven fresh- and saltwater wetlands in the California Bodega Marine Reserve. We recovered 12,826 viral population genomic sequences (vOTUs), only 4.4% of which were detected at the same field site two years prior, indicating a small degree of population stability or recurrence. Viral communities differed most significantly among the seven wetland sites and were also structured by habitat (plant community composition and salinity). Read mapping to a new version of our reference database, PIGEONv2.0 (515,763 vOTUs), revealed 196 vOTUs present over large geographic distances, often reflecting shared habitat characteristics. Wetland vOTU microdiversity was significantly lower locally than globally and lower within than between time points, indicating greater divergence with increasing spatiotemporal distance. Viruses tended to have broad predicted host ranges via CRISPR spacer linkages to metagenome-assembled genomes, and increased SNP frequencies in CRISPR-targeted major tail protein genes suggest potential viral eco-evolutionary dynamics in response to both immune targeting and changes in host cell receptors involved in viral attachment. Together, these results highlight the importance of dispersal, environmental selection, and eco-evolutionary dynamics as drivers of local and global wetland viral biogeography.

Environmental Sciences & Ecology↗
DOE OSTI2023

DL-TODA: A Deep Learning Tool for Omics Data Analysis

Metagenomics is a technique for genome-wide profiling of microbiomes; this technique generates billions of DNA sequences called reads. Given the multiplication of metagenomic projects, computational tools are necessary to enable the efficient and accurate classification of metagenomic reads without needing to construct a reference database. The program DL-TODA presented here aims to classify metagenomic reads using a deep learning model trained on over 3000 bacterial species. A convolutional neural network architecture originally designed for computer vision was applied for the modeling of species-specific features. Using synthetic testing data simulated with 2454 genomes from 639 species, DL-TODA was shown to classify nearly 75% of the reads with high confidence. The classification accuracy of DL-TODA was over 0.98 at taxonomic ranks above the genus level, making it comparable with Kraken2 and Centrifuge, two state-of-the-art taxonomic classification tools. DL-TODA also achieved an accuracy of 0.97 at the species level, which is higher than 0.93 by Kraken2 and 0.85 by Centrifuge on the same test set. Application of DL-TODA to the human oral and cropland soil metagenomes further demonstrated its use in analyzing microbiomes from diverse environments. Compared to Centrifuge and Kraken2, DL-TODA predicted distinct relative abundance rankings and is less biased toward a single taxon.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Seasonal Patterns in Mobile Colistin Resistance Gene Variants in Wastewater Bioaerosols and Liquid Sludge

To address the growing threat of global antimicrobial resistance, a one-health approach is needed to understand the complex socioecological cycling of antibiotic resistance genes. In this study, a metagenomics approach using DNA shotgun sequencing, metagenome assembly, and antibiotic resistance gene (ARG) annotation was used to examine seasonal patterns in the abundance of mobile colistin resistance (mcr) gene variants in bioaerosols and liquid sludge in three wastewater treatment plants (WWTPs). ARGs represented 0.2–0.8 and 0.1–0.2% of the bioaerosol and liquid sludge metagenomes, respectively, while mcr genes represented 0–0.3 and 0–0.5% of the identified ARGs in bioaerosol and liquid sludge metagenomes. Seven of the ten known mcr variants were detected in wastewater bioaerosol and liquid samples, with mcr-5 and mcr-8 being the most prevalent across all seasons and sites. Furthermore, additional functional and taxonomic annotation of mcr-containing metagenomic contigs showed that mcr genes were often located on contigs with other co-occurring ARGs and mobile genetic elements and may be harbored by opportunistic human pathogens and other bacterial taxa not previously associated with mcr genes. Atmospheric dispersion modeling showed that mcr-containing bioaerosols can be transported kilometers away from the WWTPs, resulting in the possible dissemination of these ARGs into surrounding environments and communities.

Antimicrobial agents↗