Search NASA⌕ Search

SEARCH · Search NASA

Results for “Metagenomic Samples”

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

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

At least 19 records
All sourcesNASA NTRSDOE OSTI
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

Reference-free structural variant detection in microbiomes via long-read co-assembly graphs

Motivation: The study of bacterial genome dynamics is vital for understanding the mechanisms underlying microbial adaptation, growth, and their impact on host phenotype. Structural variants (SVs), genomic alterations of 50 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations. While SV detection in isolate genomes is relatively straightforward, metagenomes present broader challenges due to the absence of clear reference genomes and the presence of mixed strains. In response, our proposed method rhea, forgoes reference genomes and metagenome-assembled genomes (MAGs) by encompassing all metagenomic samples in a series (time or other metric) into a single co-assembly graph. The log fold change in graph coverage between successive samples is then calculated to call SVs that are thriving or declining. Results: We show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes, particularly as the simulated reads diverge from reference genomes and an increase in strain diversity is incorporated. We additionally demonstrate use cases for rhea on series metagenomic data of environmental and fermented food microbiomes to detect specific sequence alterations between successive time and temperature samples, suggesting host advantage. Our approach leverages previous work in assembly graph structural and coverage patterns to provide versatility in studying SVs across diverse and poorly characterized microbial communities for more comprehensive insights into microbial gene flux.

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 OSTI2024

Time-series metagenomics reveals changing protistan ecology of a temperate dimictic lake

Abstract Background Protists, single-celled eukaryotic organisms, are critical to food web ecology, contributing to primary productivity and connecting small bacteria and archaea to higher trophic levels. Lake Mendota is a large, eutrophic natural lake that is a Long-Term Ecological Research site and among the world’s best-studied freshwater systems. Metagenomic samples have been collected and shotgun sequenced from Lake Mendota for the last 20 years. Here, we analyze this comprehensive time series to infer changes to the structure and function of the protistan community and to hypothesize about their interactions with bacteria. Results Based on small subunit rRNA genes extracted from the metagenomes and metagenome-assembled genomes of microeukaryotes, we identify shifts in the eukaryotic phytoplankton community over time, which we predict to be a consequence of reduced zooplankton grazing pressures after the invasion of a invasive predator (the spiny water flea) to the lake. The metagenomic data also reveal the presence of the spiny water flea and the zebra mussel, a second invasive species to Lake Mendota, prior to their visual identification during routine monitoring. Furthermore, we use species co-occurrence and co-abundance analysis to connect the protistan community with bacterial taxa. Correlation analysis suggests that protists and bacteria may interact or respond similarly to environmental conditions. Cryptophytes declined in the second decade of the timeseries, while many alveolate groups (e.g., ciliates and dinoflagellates) and diatoms increased in abundance, changes that have implications for food web efficiency in Lake Mendota. Conclusions We demonstrate that metagenomic sequence-based community analysis can complement existing efforts to monitor protists in Lake Mendota based on microscopy-based count surveys. We observed patterns of seasonal abundance in microeukaryotes in Lake Mendota that corroborated expectations from other systems, including high abundance of cryptophytes in winter and diatoms in fall and spring, but with much higher resolution than previous surveys. Our study identified long-term changes in the abundance of eukaryotic microbes and provided context for the known establishment of an invasive species that catalyzes a trophic cascade involving protists. Our findings are important for decoding potential long-term consequences of human interventions, including invasive species introduction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

CoverM: read alignment statistics for metagenomics

SUMMARY: Genome-centric analysis of metagenomic samples is a powerful method for understanding the function of microbial communities. Calculating read coverage is a central part of analysis, enabling differential coverage binning for recovery of genomes and estimation of microbial community composition. Coverage is determined by processing read alignments to reference sequences of either contigs or genomes. Per-reference coverage is typically calculated in an ad-hoc manner, with each software package providing its own implementation and specific definition of coverage. Here we present a unified software package CoverM which calculates several coverage statistics for contigs and genomes in an ergonomic and flexible manner. It uses "Mosdepth arrays" for computational efficiency and avoids unnecessary I/O overhead by calculating coverage statistics from streamed read alignment results. AVAILABILITY AND IMPLEMENTATION: CoverM is free software available at https://github.com/wwood/coverm. CoverM is implemented in Rust, with Python (https://github.com/apcamargo/pycoverm) and Julia (https://github.com/JuliaBinaryWrappers/CoverM_jll.jl) interfaces.

Aroney, Samuel T N↗
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 OSTI2025

A metagenomic perspective on the microbial prokaryotic genome census

Following 30 years of sequencing, we assessed the phylogenetic diversity (PD) of >1.5 million microbial genomes in public databases, including metagenome-assembled genomes (MAGs) of uncultivated microbes. As compared to the vast diversity uncovered by metagenomic sequences, cultivated taxa account for a modest portion of the overall diversity, 9.73% in bacteria and 6.55% in archaea, while MAGs contribute 48.54% and 57.05%, respectively. Therefore, a substantial fraction of bacterial (41.73%) and archaeal PD (36.39%) still lacks any genomic representation. This unrepresented diversity manifests primarily at lower taxonomic ranks, exemplified by 134,966 species identified in 18,087 metagenomic samples. Our study exposes diversity hotspots in freshwater, marine subsurface, sediment, soil, and other environments, whereas human samples yielded minimal novelty within the context of existing datasets. These results offer a roadmap for future genome recovery efforts, delineating uncaptured taxa in underexplored environments and underscoring the necessity for renewed isolation and sequencing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Mining Thermophile Photosynthesis Genes: A Synthetic Operon Expressing Chloroflexota Species Reaction Center Genes in Rhodobacter sphaeroides

Photosynthesis is the foundation of the vast majority of life systems, and is therefore the most important bioenergetic process on earth. The greatest diversity of photosynthetic systems is found in microorganisms. However, our understanding of the biophysical and biochemical processes that transduce light into chemical energy is derived from a relatively small subset of proteins from microbes that are amenable to cultivation, in contrast to the huge number of predicted proteins that catalyze the initial photochemical reactions deposited in databases, such as from metagenomics. We describe the use of a Rhodobacter sphaeroides laboratory strain for the expression of heterologous photosynthesis genes to demonstrate the feasibility of mining this resource, focusing on hot spring Chloroflexota gene sequences. Using a synthetic operon of genes, we produced a photochemically active complex of reaction center proteins in our biological system. We also present bioinformatic analyses of anoxygenic type II reaction center sequences from metagenomic samples collected from hot (42–90 °C) springs available through the JGI IMG database, to generate a resource of diverse sequences that are potentially adapted to photosynthesis at such temperatures. These data provide a view into the natural diversity of anoxygenic photosynthesis, through a lens focused on high-temperature environments. The approach we took to express such genes can be applied for potential biotechnology purposes as well as for studies of fundamental catalytic properties of these heretofore inaccessible protein complexes.

Chloroflexota↗
DOE OSTI2024

Coassembly and binning of a twenty-year metagenomic time-series from Lake Mendota

Abstract The North Temperate Lakes Long-Term Ecological Research (NTL-LTER) program has been extensively used to improve understanding of how aquatic ecosystems respond to environmental stressors, climate fluctuations, and human activities. Here, we report on the metagenomes of samples collected between 2000 and 2019 from Lake Mendota, a freshwater eutrophic lake within the NTL-LTER site. We utilized the distributed metagenome assembler MetaHipMer to coassemble over 10 terabases (Tbp) of data from 471 individual Illumina-sequenced metagenomes. A total of 95,523,664 contigs were assembled and binned to generate 1,894 non-redundant metagenome-assembled genomes (MAGs) with ≥50% completeness and ≤10% contamination. Phylogenomic analysis revealed that the MAGs were nearly exclusively bacterial, dominated by Pseudomonadota (Proteobacteria, N = 623) and Bacteroidota (N = 321). Nine eukaryotic MAGs were identified by eukCC with six assigned to the phylum Chlorophyta. Additionally, 6,350 high-quality viral sequences were identified by geNomad with the majority classified in the phylum Uroviricota. This expansive coassembled metagenomic dataset provides an unprecedented foundation to advance understanding of microbial communities in freshwater ecosystems and explore temporal ecosystem dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Metagenome-assembled genomes from soil samples in control and warming plots in Blodgett Forest, CA (2014-2021)

The pathways of carbon transport and loss through and from soils—soil organic matter (SOM) depolymerization to dissolved organic carbon and mineralization to carbon dioxide (CO2)—are fundamentally driven by microbial activity, which is strongly regulated by environmental conditions. As part of LBNL (Lawrence Berkeley National Laboratory) TES (Terrestrial Ecosystem Science) Belowground Biogeochemistry Science Focus Area (SFA), we have established a novel whole-soil long-term warming experiment at the University of California (UC) Blodgett Forest Research Station (Sierra Nevada) in 2014, where we study the role of biogeochemical, microbial and geochemical process interactions in SOM decomposition and stabilization.Here, we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community from soil depth profiles collected from 2014 to 2021 from three paired control and warming plots. We collected soil samples across a range of depth profiles (spanning surface to 90 cm deep) from three paired control and warming plots from a temperate mixed forest in Northern California. Each paired plot had been subjected to experimental warming since June 2014 to simulate a predicted climate change scenario for northern California. 101 soil metagenomes were sequenced at JGI (Joint Genome Institute) and UCSF (University of California San Francisco) Center for Advanced Technology and can be found under the JGI (Joint Genome Institute) GOLD (Genomes Online Database) Sequencing project Gs0151586 and NCBI (National Center for Biotechnology Information) Projects PRJNA1225762 and PRJEB39497. Metagenomes were assembled using JGI (Joint Genome Institute) Metagenome Workflow (10.1128/mSystems.00804-20). For each metagenome, the assembled contigs were binned into genomes using 3 binning algorithms (cocacola, metabat, and maxbin) and the resulting bins were consolidated using dastool. The consolidated bins from all metagenomes were pooled, filtered by completeness (>50%) and contamination (<25%), and dereplicated at 99% ANI (average nucleotide identity) using dRep (https://github.com/MrOlm/drep).The dataset includes a zip file of 2321 MAG (Metagenome Assembled Genome) fasta files, the accession numbers for the underlying metagenomes, and a csv file with MAG (Metagenome Assembled Genome) quality metrics and taxonomic classification (GTDB -Genome Taxonomy Database-RS220). 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. A sample metadata file (samples.csv) that contains site information has also been included.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Metagenome-assembled genomes from topsoils along a hillslope water gradient across early snowmelt to late summer in East River, CO

Drought is changing the American Mountain West at unprecedented rates with unknown consequences to soil microbiome composition and function. As a part of LBNL Watershed Science Focus Area (SFA), we investigated shifts in microbial community and transcriptional activity on a subalpine conifer-meadow transition zone throughout the summer of 2023 as soil dried down. This work took place in Crested Butte, CO on Snodgrass mountain, using a proxy for drought conditions.Here we present metagenome assembled genomes (MAGs) for the bacterial and archaeal community at 0-10cm from three sites along a hillslope water gradient across five timepoints from early snowmelt to late summer. 42 metagenomes were sequenced at Joint Genome Institute (JGI) and can be found under the JGI GOLD (Genomes Online Database) sequencing project Gs0166660. Metagenomes were assembled through an inhouse pipeline (see methods), binned using four autobinners (concoct, maxbin2, metabat2, and vamb) and consolidated using dastool. The consolidated bins from all metagenomes were pooled, filtered by completeness (>70%) and contamination (<10%), and dereplicated at 95% ANI using drep. This dataset (1) a zip file of 157 MAGs (as fasta files, Gs0166660_bins_tar.gz), (2) sample metadata file with sample IGSNs (International Generic Sample Numbers) (samples.csv), (3) bounding box coordinates for the sampled locations (Gs0166660.kml), (4) metagenome assembly and coassembly metadata file listing IMG/M (Integrated Microbial Genomes/Metagenomes) metagenome accessions linking samples to metagenomes (EastRiver_Drought_ESSDive_Metadata.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 OSTI2025

Metagenome-assembled-genomes recovered from the Arctic drift expedition MOSAiC

The Multidisciplinary Observatory for Study of the Arctic Climate (MOSAiC) expedition consisted of a year-long drifting survey of the Central Arctic Ocean. The ecosystems component of MOSAiC included the sampling of molecular data, with metagenomes collected from a diverse range of environments. The generation of metagenome-assembled-genomes (MAGs) from metagenomes are a starting point for genome-resolved analyses. This dataset presents a catalogue of MAGs recovered from a set of 73 samples from MOSAiC, including 2407 prokaryotic and 56 eukaryotic MAGs, as well as annotations of a near complete eukaryotic MAG using the Joint Genome Institute (JGI) annotation pipeline. The metagenomic samples are from the surface ocean, chlorophyll maximum, mesopelagic and bathypelagic, within leads and under-ice ocean, as well as melt ponds, ice ridges, and first- and second-year sea ice. This set of MAGs can be used to benchmark microbial biodiversity in the Central Arctic Ocean, compare individual strains across space and time, and to study changes in Arctic microbial communities from the winter to summer, at a genomic level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Neurospora intermedia from a traditional fermented food enables waste-to-food conversion

AbstractFungal fermentation of food and agricultural by-products holds promise for improving food sustainability and security. However, the molecular basis of fungal waste-to-food upcycling remains poorly understood. Here we use a multi-omics approach to characterize oncom, a fermented food traditionally produced from soymilk by-products in Java, Indonesia. Metagenomic sequencing of samples from small-scale producers in Western Java indicated that the fungus Neurospora intermedia dominates oncom. Further transcriptomic, metabolomic and phylogenomic analysis revealed that oncom-derived N. intermedia utilizes pectin and cellulose degradation during fermentation and belongs to a genetically distinct subpopulation associated with human-generated by-products. Finally, we found that N. intermedia grew on diverse by-products such as fruit and vegetable pomace and plant-based milk waste, did not encode mycotoxins, and could create foods that were positively perceived by consumers outside Indonesia. These results showcase the traditional significance and future potential of fungal fermentation for creating delicious and nutritious foods from readily available by-products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Sub-daily virus sampling at the Bermuda Atlantic Time Series reveals diel and depth-structured population dynamics without community-level shifts

Ocean microbes contribute to biogeochemical cycles and ecosystem function, but they do so under top-down pressure imposed by viruses. While viruses are increasingly understood spatially and beginning to be incorporated into predictive modeling, high-frequency ocean virus dynamics remain understudied due to methodological challenges. Here we sampled stratified Bermuda Atlantic Time Series (BATS) waters for 112 hours at sub-daily 4- (surface) or 12- (deep chlorophyll maximum) hour intervals, purified viral particles from these samples, sequenced their metagenomes, and used the resulting data to characterize high-frequency virus community dynamics. Aggregated community diversity metrics changed with depth, but were not statistically significant temporally at a fixed location. However, finer-scale population-level analyses revealed both depth and temporal change, including physicochemical depth-driven differences and, in surface waters, thousands of viral populations that exhibited statistically significant diel rhythms. Statistical analyses revealed three main archetypes of temporal dynamics that themselves differed in abundance patterns, host predictions, viral taxonomy, and gene functions. Among these, highlights include viruses resembling an archetype with a night peaking pattern in activity that include an over-representation of viruses that putatively infect Prochlorococcus, a phototrophic cyanobacteria. Together, these efforts provide baseline community- and population-scale short-time-frame observations relevant to future climate state modeling.

Carrillo, Alfonso [The Ohio State University, Colu↗
DOE OSTI2024

Multi-scale Simulation, Calibration, and Optimization of Calcium Carbonate Precipitation in Microbial Communities

Ensuring the efficient engineering of microbially induced calcium carbonate precipitation (MICP) is crucial for a variety of environmental and civil engineering applications, such as soil stabilization and carbon sequestration. Addressing this need, we present a comprehensive multi-scale workflow that begins with the isolation of calcium carbonate-producing microbes from soil samples, followed by metagenomic sequencing and metabolic reconstruction. We then characterize microbial growth phenotypes under diverse nutrient conditions, compare observed growth with metabolic model predictions, and apply the Consistent Reproduction of Phenotype (CROP) algorithm to refine these models. Furthermore, we analyze metabolite consumption and production, and develop a consumer-resource model that is calibrated using time-series measurements of growth rates, pH levels, and calcium carbonate precipitation. The primary benefit of our approach lies in its ability to predict and control MICP outcomes, facilitated by a Bayesian methodology that incorporates priors on initial conditions and parameters. This allows us to compute posteriors by integrating experimental data, and to solve a risk optimization problem under uncertainty to identify nutrient conditions that maximize calcium carbonate production. In contrast to non-Bayesian methods, which fail to quantify uncertainty accurately, our approach provides a more reliable pathway to optimizing nutrient conditions, enhancing the likelihood of achieving desired MICP outcomes. This positions our method as a superior alternative in the quest to improve MICP through engineered microbial consortia.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

UFMP and CAW Bioreactor Metagenome

CSTR anaerobic bioreactors fed UltraFiltered Milk Permeate (UFMP) and Cottage cheese Acid Whey (CAW) were inoculated with acid sludge microbial communities and monitored overtime for lactic acid production. Samples were taken for metagenomics analyses.

bioreactor metagenome↗
DOE OSTI2024

Microbial Metagenomes Across a Complete Phytoplankton Bloom Cycle: High-Resolution Sampling Every 4 Hours Over 22 Days

In May and June of 2021, marine microbial samples were collected for DNA sequencing in East Sound, WA, USA every 4 hours for 22 days. This high temporal resolution sampling effort captured the last 3 days of a Rhizosolenia sp. bloom, the initiation and complete bloom cycle of Chaetoceros socialis (8 days), and the following bacterial bloom (2 days). Metagenomes were completed on the time series, and the dataset includes 128 size-fractionated microbial samples (0.22–1.2 µm), providing gene abundances for the dominant members of bacteria, archaea, and viruses. This dataset also has time-matched nutrient analyses, flow cytometry data, and physical parameters of the environment at a single point of sampling within a coastal ecosystem that experiences regular bloom events, facilitating a range of modeling efforts that can be leveraged to understand microbial community structure and their influences on the growth, maintenance, and senescence of phytoplankton blooms.

59 BASIC BIOLOGICAL SCIENCES↗