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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Nutrient Exposure Alters Microbial Composition, Structure, and Mercury Methylating Activity in Periphyton in a Contaminated Watershed

The conversion of mercury (Hg) to monomethylmercury (MMHg) is a critical area of concern in global Hg cycling. Periphyton biofilms may harbor significant amounts of MMHg but little is known about the Hg-methylating potential of the periphyton microbiome. Therefore, we used high-throughput amplicon sequencing of the 16S rRNA gene, ITS2 region, and Hg methylation gene pair (hgcAB) to characterize the archaea/bacteria, fungi, and Hg-methylating microorganisms in periphyton communities grown in a contaminated watershed in East Tennes(see United States). Furthermore, we examined how nutrient amendments (nitrate and/or phosphate) altered periphyton community structure and function. We found that bacterial/archaeal richness in experimental conditions decreased in summer and increased in autumn relative to control treatments, while fungal diversity generally increased in summer and decreased in autumn relative to control treatments. Interestingly, the Hg-methylating communities were dominated by Proteobacteria followed by Candidatus Atribacteria across both seasons. Surprisingly, Hg methylation potential correlated with numerous bacterial families that do not contain hgcAB, suggesting that the overall microbiome structure of periphyton communities influences rates of Hg transformation within these microbial mats. To further explore these complex community interactions, we performed a microbial network analysis and found that the nitrate-amended treatment resulted in the highest number of hub taxa that also corresponded with enhanced Hg methylation potential. This work provides insight into community interactions within the periphyton microbiome that may contribute to Hg cycling and will inform future research that will focus on establishing mixed microbial consortia to uncover mechanisms driving shifts in Hg cycling within periphyton habitats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Advancing quantitative models of soil microbiology, ecology, and biochemistry

Rapid advances in soil biology are increasingly reflected in mathematical models. From molecular to global scales, models contribute to fundamental understanding and prediction of critical soil processes, such as carbon sequestration. Recent models explicitly incorporate microbial control over soil carbon and nutrient cycling, an approach that offers greater biological realism but raises new challenges in model parameterization and stability. Although they have proliferated at community and ecosystem scales, microbial-explicit models have yet to be incorporated into most Earth system models. Applying soil models across scales requires integration with data to support parameterization and validation using approaches such as Bayesian data assimilation. In conclusion, to catalyze future progress, we recommend breaking down barriers between modeling and empirical disciplines while broadening access to the computational infrastructure that supports soil biological modeling.

Xie, Wally↗
DOE OSTI2020

The skin microbiome of elasmobranchs follows phylosymbiosis, but in teleost fishes, the microbiomes converge

Background: The vertebrate clade diverged into Chondrichthyes (sharks, rays, and chimeras) and Osteichthyes fishes (bony fishes) approximately 420 mya, with each group accumulating vast anatomical and physiological differences, including skin properties. The skin of Chondrichthyes fishes is covered in dermal denticles, whereas Osteichthyes fishes are covered in scales and are mucous rich. The divergence time among these two fish groups is hypothesized to result in predictable variation among symbionts. Here, using shotgun metagenomics, we test if patterns of diversity in the skin surface microbiome across the two fish clades match predictions made by phylosymbiosis theory. In this work, we hypothesize (1) the skin microbiome will be host and clade-specific, (2) evolutionary difference in elasmobranch and teleost will correspond with a concomitant increase in host-microbiome dissimilarity, and (3) the skin structure of the two groups will affect the taxonomic and functional composition of the microbiomes. Results: We show that the taxonomic and functional composition of the microbiomes is host-specific. Teleost fish had lower average microbiome within clade similarity compared to among clade comparison, but their composition is not different among clade in a null based model. Elasmobranch’s average similarity within clade was not different than across clade and not different in a null based model of comparison. In the comparison of host distance with microbiome distance, we found that the taxonomic composition of the microbiome was related to host distance for the elasmobranchs, but not the teleost fishes. In comparison, the gene function composition was not related to the host-organism distance for elasmobranchs but was negatively correlated with host distance for teleost fishes. Conclusion: Our results show the patterns of phylosymbiosis are not consistent across both fish clades, with the elasmobranchs showing phylosymbiosis, while the teleost fish are not. The discrepancy may be linked to alternative processes underpinning microbiome assemblage, including possible historical host-microbiome evolution of the elasmobranchs and convergent evolution in the teleost which filter specific microbial groups. Our comparison of the microbiomes among fishes represents an investigation into the microbial relationships of the oldest divergence of extant vertebrate hosts and reveals that microbial relationships are not consistent across evolutionary timescales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Drought increases microbial allocation to stress tolerance but with few tradeoffs among community-level traits

We leverage the Kellogg Biological Station Long Term Ecological Research Main Cropping System Experiment (KBS MCSE) field experiment to test trade-offs among high growth yield (Y), resource acquisition (A), and stress tolerance (S) traits using metagenomic data from plots with different land use, drought manipulation, and C addition.

Jones, Jennifer M. [WK Kellogg Biological Station,↗
DOE OSTI2021

Fire alters plant microbiome assembly patterns: integrating the plant and soil microbial response to disturbance

Summary It is increasingly evident that the plant microbiome is a strong determinant of plant health. While the ability to manipulate the microbiome in plants and ecosystems recovering from disturbance may be useful, our understanding of the plant microbiome in regenerating plant communities is currently limited. Using 16S ribosomal RNA (rRNA) gene and internal transcribed spacer (ITS) region amplicon sequencing, we characterized the leaf, stem, fine root, rhizome, and rhizosphere microbiome of < 1‐yr‐old aspen saplings and the associated bulk soil after a recent high‐intensity prescribed fire across a burn severity gradient. Consistent with previous studies, we found that soil microbiomes are responsive to fire. We extend these findings by showing that certain plant tissue microbiomes also change in response to fire. Differences in soil microbiome compositions could be attributed to soil chemical characteristics, but, generally, plant tissue microbiomes were not related to plant tissue elemental concentrations. Using source tracking modeling, we also show that fire influences the relative dominance of microbial inoculum and the vertical inheritance of the sapling microbiome from the parent tree. Overall, our results demonstrate how fire impacts plant microbiome assembly, diversity, and composition and highlights potential for further research towards increasing plant fitness and ecosystem recovery after fire events.

16rRNA↗
DOE OSTI2022

In silico evaluation of a targeted metaproteomics strategy for broad screening of cellulolytic enzyme capacities in anaerobic microbiome bioreactors

Microbial-driven solubilization of lignocellulosic material is a natural mechanism that is exploited in anaerobic digesters (ADs) to produce biogas and other valuable bioproducts. Glycoside hydrolases (GHs) are the main enzymes that bacterial and archaeal populations use to break down complex polysaccharides in these reactors. Methodologies for rapidly screening the physical presence and types of GHs can provide information about their functional activities as well as the taxonomical diversity within AD systems but are largely unavailable. Targeted proteomic methods could potentially be used to provide snapshots of the GHs expressed by microbial consortia in ADs, giving valuable insights into the functional lignocellulolytic degradation diversity of a community. Such observations would be essential to evaluate the hydrolytic performance of a reactor or potential issues with it.

09 BIOMASS FUELS↗
NASA NTRS1989

Ecology and life history of an amoebomastigote, Paratetramitus jugosus, from a microbial mat: new evidence for multiple fission

Five microbial habitats (gypsum crust, gypsum photosynthetic community, Microcoleus mat, Thiocapsa scum, and black mud) were sampled for the presence of the euryhaline, rapidly growing amoebomastigote, Paratetramitus jugosus. Field investigations of microbial mats from Baja California Norte, Mexico, and Salina Bido near Matanzas, Cuba, reveal that P. jugosus is most frequently found in the Thiocapsa layer of microbial mats. Various stages of the life history were studied using phase-contrast, differential-interference, and transmission electron microscopy. Mastigote stages were induced and studied by electron microscopy; mastigotes that actively feed on bacteria bear two or more undulipodia. A three-dimensional drawing of the kinetid ("basal apparatus") based on electron micrographs is presented. Although promitoses were occasionally observed, it is unlikely that they can account for the rapid growth of P. jugosus populations on culture media. Dense, refractile, spherical, and irregular-shaped bodies were seen at all times in all cultures along with small mononucleate (approximately 2-7 micrometers diameter) amoebae. Cytochemical studies employing two different fluorescent stains for DNA (DAPI, mithramycin) verified the presence of DNA in these small bodies. Chromatin-like material seen in electron micrographs within the cytoplasm and blebbing off nuclei were interpreted to the chromatin bodies. Our interpretation, consistent with the data but not proven, is that propagation by multiple fission of released chromatin bodies that become small amoebae may occur in Paratetramitus jugosus. These observations are consistent with descriptions of amoeba propagules in the early literature (Hogue, 1914).

Non-NASA Center↗
DOE OSTI2026

Data and scripts associated with a manuscript modeling microbial regulation of priming effects

This data package is associated with the publication “Modeling Microbial Regulatory Feedback in Organic Matter Decomposition Identifies Copiotrophic Traits as Key Drivers of Positive Priming” published as a preprint on BioRXiv by Ahamed et al. (2026); https://doi.org/10.1101/2024.08.11.607483. The package contains MATLAB scripts and saved simulation outputs used to implement a cybernetic model of microbial regulation during complex organic matter (OM) decomposition governing priming effects. It includes models of (i) single microbial functional groups (copiotrophic or oligotrophic degraders) and (ii) binary consortia composed of degraders and non-degraders with contrasting or common growth traits. Simulation results were generated using Monte Carlo analyses, with randomized key model parameters across a range of environmental mixing fractions of complex and labile OM. The dataset was created to provide a transparent and reusable computational framework for systematically exploring how microbial growth traits, metabolic regulation, and community composition influence OM decomposition dynamics and priming effects. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes the variable definitions. This package includes: (1) annotated MATLAB code implementing the system of ordinary differential equations and cybernetic control laws; (2) saved output files containing data (e.g., biomass, substrates, enzyme levels, priming metrics); and (3) scripts for processing saved outputs and regenerating figures. Specifically, the data package contains three main MATLAB scripts: runPrimingModel.m, runPlotData.m, and runPlotSuppFigS1.m, along with this readme and supporting documentation. Users should begin with runPrimingModel.m, which contains the annotated code implementing the system of ordinary differential equations and cybernetic control laws. This script runs the Monte Carlo simulations of microbial OM decomposition and allows users to modify microbial trait definitions, adjust parameter distributions, or define new community configurations. Simulation outputs are automatically saved as .mat files in the folder named SavedData, which stores all pre-generated results included in this package. The second script, runPlotData.m, reads files from the SavedData folder and processes them to regenerate the figures presented in the manuscript. The third script, runPlotSuppFigS1.m, specifically generates Figure S1 in the Supplementary Material of the manuscript. The package also includes the aforementioned files in non-proprietary .txt format. If users intend to use them, they should first save the files in their respective .m or .mat formats prior to execution in MATLAB.

Biomass concentration↗
DOE OSTI2021

SPRUCE Data

This data set provides 16S Bacterial and ITS fungal communities via DNA or RNA, microbial biomass carbon and nitrogen, and extracellular enzyme activity at the time of fresh peat, ingrowth peat or sand sampling. These samples were collected as part of the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment.

Smith, Montana L↗
DOE OSTI2021

Nutrient Inputs Stimulate Mercury Methylation by Syntrophs in a Subarctic Peatland

Climate change dramatically impacts Arctic and subarctic regions, inducing shifts in wetland nutrient regimes as a consequence of thawing permafrost. Altered hydrological regimes may drive changes in the dynamics of microbial mercury (Hg) methylation and bioavailability. Important knowledge gaps remain on the contribution of specific microbial groups to methylmercury (MeHg) production in wetlands of various trophic status. Here, we measured aqueous chemistry, potential methylation rates (k meth ), volatile fatty acid (VFA) dynamics in peat-soil incubations, and genetic potential for Hg methylation across a groundwater-driven nutrient gradient in an interior Alaskan fen. We tested the hypotheses that (1) nutrient inputs will result in increased methylation potentials, and (2) syntrophic interactions contribute to methylation in subarctic wetlands. We observed that concentrations of nutrients, total Hg, and MeHg, abundance of hgcA genes, and rates of methylation in peat incubations (k meth ) were highest near the groundwater input and declined downgradient. hgcA sequences near the input were closely related to those from sulfate-reducing bacteria (SRB), methanogens, and syntrophs. Hg methylation in peat incubations collected near the input source (FPF2) were impacted by the addition of sulfate and some metabolic inhibitors while those down-gradient (FPF5) were not. Sulfate amendment to FPF2 incubations had higher k meth relative to unamended controls despite no effect on k meth from addition of the sulfate reduction inhibitor molybdate. The addition of the methanogenic inhibitor BES (25 mM) led to the accumulation of VFAs, but unlike molybdate, it did not affect Hg methylation rates. Rather, the concurrent additions of BES and molybdate significantly decreased k meth , suggesting a role for interactions between SRB and methanogens in Hg methylation. The reduction in k meth with combined addition of BES and molybdate, and accumulation of VFA in peat incubations containing BES, and a high abundance of syntroph-related hgcAsequences in peat metagenomes provide evidence for MeHg production by microorganisms growing in syntrophy. Collectively the results suggest that wetland nutrient regimes influence the activity of Hg methylating microorganisms and, consequently, Hg methylation rates. Our results provide key information about microbial Hg methylation and methylating communities under nutrient conditions that are expected to become more common as permafrost soils thaw.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Prediction of Distributed River Sediment Respiration Rates Using Community-Generated Data and Machine Learning

River sediment microbial respiration is a key indicator of ecosystem functioning and the biogeochemical fluxes across this critical zone link surface and subsurface waters. As such, there is tremendous interest in measuring and mapping these respiration rates. Respiration observations are expensive and labor intensive; there is limited data available to the community. An open science, collaborative initiative is collecting samples for respiration rate analysis and multi-scale metadata; this evolving data set is being used for making machine learning (ML) predictions at unsampled sites to help inform continued community engagement. However, it is a challenge to find an optimum configuration for ML models to work with this feature-rich (i.e., 100+ possible input variables) data set. Here, we present results from a two-tiered approach to managing the analysis of this complex data set: (a) a stacked ensemble of models that automatically optimizes hyperparameters and manages the training of many models and (b) feature permutation importance to detect the most important features in the models. The major elements of this workflow are modular, portable, open, and cloud-based thus making this implementation a potential template for other applications. The models developed here predict that sediment organic matter chemistry is one of the most important features for predicting sediment respiration rate. Other larger-scale, important features fall into the categories of climatic, ecological, geological, and fluvial settings. Leveraging these larger-scale features to generate data-driven estimates of river sediment respiration rates reveals spatially consistent but heterogeneous patterns across the river network of the Columbia River Basin.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Diel partitioning in microbial phosphorus acquisition in the Sargasso Sea

The daily cycle of photosynthetic primary production at the base of marine food webs is often limited by the availability of scarce nutrients. Microbial competition for these scarce resources can be alleviated insofar as the intensity of nutrient uptake and assimilation activities are distributed heterogeneously across organisms over periodic input cycles. Recent analysis of community transcriptional dynamics in the nitrogen-limited subtropical North Pacific gyre revealed evidence of temporal partitioning of nitrogen uptake and assimilation between eukaryotic phytoplankton, cyanobacteria, and heterotrophic bacteria over day-night cycles. Here, we present results from a Lagrangian metatranscriptomic time series survey in the Sargasso Sea and demonstrate temporally partitioned phosphorus uptake in this phosphorus-limited environment. In the Sargasso, heterotrophic bacteria, eukaryotic phytoplankton, and cyanobacteria express genes for phosphorus assimilation during the morning, day, and dusk, respectively. In conclusion, these results support the generality of temporal niche partitioning as an emergent mechanism that can structure uptake of limiting nutrients and facilitate coexistence of diverse microbes in open ocean ecosystems.

biological oceanography↗
DOE OSTI2024

Community standards and future opportunities for synthetic communities in plant–microbiota research

Harnessing beneficial microorganisms is seen as a promising approach to enhance sustainable agriculture production. Synthetic communities (SynComs) are increasingly being used to study relevant microbial activities and interactions with the plant host. Yet, the lack of community standards limits the efficiency and progress in this important area of research. Here, to address this gap, we recommend three actions: (1) defining reference SynComs; (2) establishing community standards, protocols and benchmark data for constructing and using SynComs; and (3) creating an infrastructure for sharing strains and data. We also outline opportunities to develop SynCom research through technical advances, linking to field studies, and filling taxonomic blind spots to move towards fully representative SynComs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Modeling microbial carbon fluxes and stocks in global soils from 1901 to 2016

Abstract. Soil microbes play a crucial role in the carbon (C) cycle; however, they have been overlooked in predicting the terrestrial C cycle. We applied a microbial-explicit Earth system model – the Community Land Model-Microbe (CLM-Microbe) – to investigate the dynamics of soil microbes during 1901 to 2016. The CLM-Microbe model was able to reproduce the variations of gross (GPP) and net (NPP) primary productivity, heterotrophic (HR) and soil (SR) respiration, microbial (MBC) biomass C in fungi (FBC) and bacteria (BBC) in the top 30 cm and 1 m, and dissolved (DOC) and soil organic C (SOC) in the top 30 cm and 1 m during 1901–2016. During the study period, simulated C variables increased by approximately 12 PgC yr−1 for HR, 25 PgC yr−1 for SR, 1.0 PgC for FBC and 0.4 PgC for BBC in 0–30 cm, and 1.2 PgC for FBC and 0.7 PgC for BBC in 0–1 m. Increases in microbial C fluxes and pools were widely found, particularly at high latitudes and in equatorial regions, but we also observed their decreases in some grids. Overall, the area-weighted averages of HR, SR, FBC, and BBC in the top 1 m were significantly correlated with those of soil moisture and soil temperature in the top 1 m. These results suggested that microbial C fluxes and pools were jointly governed by vegetation C input and soil temperature and moisture. Our simulations revealed the spatial and temporal patterns of microbial C fluxes and pools in response to environmental change, laying the foundation for an improved understanding of soil microbial roles in the global terrestrial C cycle.

He, Liyuan↗
DOE OSTI2023

Cooperative microbial interactions drive spatial segregation in porous environments

The role of microbial interactions and the underlying mechanisms that shape complex biofilm communities are poorly understood. Here we employ a microfluidic chip to represent porous subsurface environments and show that cooperative microbial interactions between free-living and biofilm-forming bacteria trigger active spatial segregation to promote their respective dominance in segregated microhabitats. During initial colonization, free-living and biofilm-forming microbes are segregated from the mixed planktonic inoculum to occupy the ambient fluid and grain surface. Contrary to spatial exclusion through competition, the active spatial segregation is induced by cooperative interactions which improves the fitness of both biofilm and planktonic populations. We further show that free-living Arthrobacter induces the surface colonization by scavenging the biofilm inhibitor, D-amino acids and receives benefits from the public goods secreted by the biofilm-forming strains. Collectively, our results reveal how cooperative microbial interactions may contribute to microbial coexistence in segregated microhabitats and drive subsurface biofilm community succession.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Increasing accessibility to deep learning-based analytics for space biology: pretrained models, transfer learning, and analytics platform development

Biological systems react in complex ways to the stressors of spaceflight, and the data capturing these relationships is concomitantly high-dimensional and complex. Deep learning and machine learning approaches are increasingly popular as an analytical approach for space biosciences, due to their ability to model complex relationships in complex data. However, such approaches often require large datasets and extensive computational resources. New approaches that minimize data sizes and computational power needed to leverage machine learning, and resources that make these approaches accessible, are needed to increase accessibility and adoption of machine learning in the space biosciences. Transfer learning, in which a pretrained model of broad utility is trained on a large dataset, and subsequently reused on downstream applications for which data is more limited, is one approach to minimizing data and computational intensity of deep learning applications. This transfer learning approach results in more performant models in high-dimensional, low-sample-size settings such as space biology, as compared to training models on limited data from scratch. This presentation will outline efforts to generate pretrained models for the space biology community, and highlight transfer learning applications modeling microbial antibiotic resistance during spaceflight. Finally, in order to increase accessibility of these models and tools, as well as others, for the broader space biology community, we present a modeling and analysis platform facilitating machine learning applications in space biology. This platform streamlines machine learning training and analysis in a notebook format, facilitates download and use of space biology data from the NASA GeneLab database, and can be utilized on NASA-hosted servers or downloaded and hosted locally. This effort, as part of the AI4LS (Artificial Intelligence for Life in Space) working group, will increase accessibility, feasibility, and performance of machine learning approaches for the space biology community.

Adrienne Hoarfrost↗
DOE OSTI2020

Microbial diversity drives carbon use efficiency in a model soil

Abstract Empirical evidence for the response of soil carbon cycling to the combined effects of warming, drought and diversity loss is scarce. Microbial carbon use efficiency (CUE) plays a central role in regulating the flow of carbon through soil, yet how biotic and abiotic factors interact to drive it remains unclear. Here, we combine distinct community inocula (a biotic factor) with different temperature and moisture conditions (abiotic factors) to manipulate microbial diversity and community structure within a model soil. While community composition and diversity are the strongest predictors of CUE, abiotic factors modulated the relationship between diversity and CUE, with CUE being positively correlated with bacterial diversity only under high moisture. Altogether these results indicate that the diversity × ecosystem-function relationship can be impaired under non-favorable conditions in soils, and that to understand changes in soil C cycling we need to account for the multiple facets of global changes.

54 ENVIRONMENTAL SCIENCES↗