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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Grassland ecosystem type drives AM fungal diversity and functional guild distribution in North American grasslands

Nutrient exchange forms the basis of the ancient symbiotic relationship that occurs between most land plants and arbuscular mycorrhizal (AM) fungi. Plants provide carbon (C) to AM fungi and fungi provide the plant with nutrients such as nitrogen (N) and phosphorous (P). Nutrient addition can alter this symbiotic coupling in key ways, such as reducing AM fungal root colonization and changing the AM fungal community composition. However, environmental parameters that differentiate ecosystems and drive plant distribution patterns (e.g., pH, moisture), are also known to impact AM fungal communities. Identifying the relative contribution of environmental factors impacting AM fungal distribution patterns is important for predicting biogeochemical cycling patterns and plant-microbe relationships across ecosystems. Here, to evaluate the relative impacts of local environmental conditions and long-term nutrient addition on AM fungal abundance and composition across grasslands, we studied experimental plots amended for 10 years with N, P, or N and P fertilizer in different grassland ecosystem types, including tallgrass prairie, montane, shortgrass prairie, and desert grasslands. Contrary to our hypothesis, we found ecosystem type, not nutrient treatment, was the main driver of AM fungal root colonization, diversity, and community composition, even when accounting for site-specific nutrient limitations. We identified several important environmental drivers of grassland ecosystem AM fungal distribution patterns, including aridity, mean annual temperature, root moisture, and soil pH. This work provides empirical evidence for niche partitioning strategies of AM fungal functional guilds and emphasizes the importance of long-term, large scale research projects to provide ecologically relevant context to nutrient addition studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Motion Dynamics of Motile Microbes in Pore-Networks and its Implications for Reactive Transport Processes

This report outlines new methods to improve simulations of microbial transport and microbially mediated reactions in porous media. A range of experimental, modeling, and machine learning tools are introduced to make these simulations faster, more reliable, and useful for real-world applications. At the microscopic level, the study investigates how different types of bacteria move through confined spaces. A new artificial intelligence tool called DeepTrackStat, is introduced to track motions dynamics as observed in videos of particles migrating through pore networks. This tool is especially helpful for studying fast-moving microbes and requires less computing power than traditional tracking methods. At larger scales, the research looks at how microbes and chemicals interact in zones where surface water and groundwater meet. To connect the small- and large-scale findings, the study presents a neural network model called STAMNet. This tool helps scale up detailed small-scale microbial motion behaviors to predict large-scale environmental changes more efficiently. By combining lab experiments, computer models, and artificial intelligence, the research presented supports smarter environmental decision-making, especially in bioremediation of contaminated groundwater and protection of water quality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Inferring the Contribution of Microbial Taxa and Organic Matter Molecular Formulas to Ecological Assembly

Understanding the mechanisms underlying the assembly of communities has long been the goal of many ecological studies. While several studies have evaluated community wide ecological assembly, fewer have focused on investigating the impacts of individual members within a community or assemblage on ecological assembly. Here, we adapted a previous null model β-nearest taxon index (βNTI) to measure the contribution of individual features within an ecological community to overall assembly. This new metric, called feature-level βNTI (βNTI feat ), enables researchers to determine whether ecological features (e.g., individual microbial taxa) contribute to divergence, convergence, or have insignificant impacts across spatiotemporally resolved metacommunities or meta-assemblages. Using βNTI feat , we revealed that unclassified microbial lineages often contributed to community divergence while diverse groups (e.g., Crenarchaeota, Alphaproteobacteria, and Gammaproteobacteria) contributed to convergence. We also demonstrate that βNTI feat can be extended to other ecological assemblages such as organic molecules comprising organic matter (OM) pools. OM had more inconsistent trends compared to the microbial community though CHO-containing molecular formulas often contributed to convergence, while nitrogen and phosphorus-containing formulas contributed to both convergence and divergence. A network analysis was used to relate βNTI feat values from the putatively active microbial community and the OM assemblage and examine potentially common contributions to ecological assembly across different communities/assemblages. This analysis revealed that P-containing formulas often contributed to convergence/divergence separately from other ecological features and N-containing formulas often contributed to assembly in coordination with microorganisms. Additionally, members of Family Geobacteraceae were often observed to contribute to convergence/divergence in conjunction with both N- and P-containing formulas, suggesting a coordinated ecological role for family members and the nitrogen/phosphorus cycle. Overall, we show that βNTI feat offers opportunities to investigate the community or assemblage members, which shape the phylogenetic or functional landscape, and demonstrate the potential to evaluate potential points of coordination across various community types.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

How Does Escherichia coli Allocate Proteome?

Microorganisms are shown to actively partition their intracellular resources, such as pro- teins, for growth optimization. Recent experiments have begun to reveal molecular com- ponents unpinning the partition; however, it remains unclear quantitatively how individual parts orchestrate to yield precise resource allocation that is both robust and dynamic. Here we developed a coarse-grained mathematical framework that centers on guanosine pentaphosphate (ppGpp)-mediated regulation, and used it to systematically uncover the design principles of proteome allocation in Escherichia coli. Our results showed that cellular ability of resource partition lies in an ultrasensitive, negative feedback control- ling topology with the ultrasensitivity arising from zero-order amino acid kinetics and the negative feedback from ppGpp-controlled ribosome synthesis. In addition, together with the time-scale separation between slow ribosome kinetics and fast turnovers of ppGpp and amino acids, the network topology confers the organism an optimization mechanism which mimics sliding mode control, a nonlinear optimization strategy that is widely used in man-made systems. We further showed that such a controlling mechanism is robust against parameter variations and molecular fluctuations, and is also efficient for biomass production over time. Furthermore, this work elucidates the fundamental controlling mechanism of E. coli proteome allocation, thereby providing insights into quantitative microbial physiology as well as the design of synthetic gene networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS)

Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are emerging as a valuable avenue for predicting, understanding and designing microbial communities. Computation of microbial ecosystems in time and space (COMETS) extends dynamic flux balance analysis to generate simulations of multiple microbial species in molecularly complex and spatially structured environments. Here we describe how to best use and apply the most recent version of COMETS, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, evolutionary dynamics and extracellular enzyme activity modules. In addition to a command-line option, COMETS includes user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, as well as comprehensive documentation and tutorials. Overall, this protocol provides a detailed guideline for installing, testing and applying COMETS to different scenarios, generating simulations that take from a few minutes to several days to run, with broad applicability to microbial communities across biomes and scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Chemotaxonomic patterns in intracellular metabolites of marine microbial plankton

Most biological diversity on Earth is contained within microbial communities. In the ocean, these communities dominate processes related to carbon fixation and nutrient recycling. Yet, specific factors that determine community composition and metabolic activity are difficult to resolve in complex microbial populations, complicating predictions of microbial processes in a changing ocean. Microbial metabolism generates small organic molecules that reflect both the biochemical and physiological diversity as well as the taxonomic specificity of these biological processes. These small molecules serve as the conduit for taxon-specific signaling and exchange. Here, we use liquid chromatography-mass spectrometry (LC-MS)-based metabolomics to taxonomically categorize 111 metabolites that include small molecules in central and secondary metabolism across 42 taxa representing numerically dominant and metabolically important lineages of microbial autotrophs and heterotrophs. Patterns in metabolite presence-absence broadly reflected taxonomic lineages. A subset of metabolites that includes osmolytes, sulfur-containing metabolites, sugars, and amino acid derivatives provided chemotaxonomic information among phytoplankton taxa. A variety of phytohormones and signaling molecules were predominantly found in the heterotrophic bacteria and archaea, expanding knowledge of metabolites implicated in modulating interactions between microbes. This chemotaxonomic inventory of marine microbial metabolites is a key step in deciphering metabolic networks that influence ocean biogeochemical cycles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Non-mercury methylating microbial taxa are integral to understanding links between mercury methylation and elemental cycles in marine and freshwater sediments

The goal of this study was to explore the role of non-mercury (Hg) methylating taxa in mercury methylation and to identify potential links between elemental cycles and Hg methylation. Statistical approaches were utilized to investigate the microbial community and biochemical functions in relation to methylmercury (MeHg) concentrations in marine and freshwater sediments. Sediments were collected from the methylation zone (top 15 cm) in four Hg-contaminated sites. Both abiotic (e.g., sulfate, sulfide, iron, salinity, total organic matter, etc.) and biotic factors (e.g., hgcA, abundances of methylating and non-methylating taxa) were quantified. Random forest and stepwise regression were performed to assess whether non-methylating taxa were significantly associated with MeHg concentration. Co-occurrence and functional network analyses were constructed to explore associations between taxa by examining microbial community structure, composition, and biochemical functions across sites. Regression analysis showed that approximately 80% of the variability in sediment MeHg concentration was predicted by total mercury concentration, the abundances of Hg methylating taxa, and the abundances of the non-Hg methylating taxa. The co-occurrence networks identified Paludibacteraceae and Syntrophorhabdaceae as keystone non Hg methylating taxa in multiple sites, indicating the potential for syntrophic interactions with Hg methylators. Strong associations were also observed between methanogens and sulfate-reducing bacteria, which were likely symbiotic associations. The functional network results suggested that non-Hg methylating taxa play important roles in sulfur respiration, nitrogen respiration, and the carbon metabolism-related functions methylotrophy, methanotrophy, and chemoheterotrophy. Interestingly, keystone functions varied by site and did not involve carbon- and sulfur-related functions only. In conclusion, our findings highlight associations between methylating and non-methylating taxa and sulfur, carbon, and nitrogen cycles in sediment methylation zones, with implications for predicting and understanding the impact of climate and land/sea use changes on Hg methylation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study

ABSTRACT Context-specific genome-scale model (CS-GSM) reconstruction is becoming an efficient strategy for integrating and cross-comparing experimental multi-scale data to explore the relationship between cellular genotypes, facilitating fundamental or applied research discoveries. However, the application of CS modeling for non-conventional microbes is still challenging. Here, we present a graphical user interface that integrates COBRApy, EscherPy, and RIPTiDe, Python-based tools within the BioUML platform, and streamlines the reconstruction and interrogation of the CS genome-scale metabolic frameworks via Jupyter Notebook. The approach was tested using -omics data collected for Methylotuvimicrobium alcaliphilum 20Z R , a prominent microbial chassis for methane capturing and valorization. We optimized the previously reconstructed whole genome-scale metabolic network by adjusting the flux distribution using gene expression data. The outputs of the automatically reconstructed CS metabolic network were comparable to manually optimized i IA409 models for Ca-growth conditions. However, the CS model questions the reversibility of the phosphoketolase pathway and suggests higher flux via primary oxidation pathways. The model also highlighted unresolved carbon partitioning between assimilatory and catabolic pathways at the formaldehyde-formate node. Only a very few genes and only one enzyme with a predicted function in C1 metabolism, a homolog of the formaldehyde oxidation enzyme ( fae1-2 ), showed a significant change in expression in La-growth conditions. The CS-GSM predictions agreed with the experimental measurements under the assumption that the Fae1-2 is a part of the tetrahydrofolate-linked pathway. The cellular roles of the tungsten (W)-dependent formate dehydrogenase ( fdhAB ) and fae homologs ( fae1-2 and fae3 ) were investigated via mutagenesis. The phenotype of the f dhAB mutant followed the model prediction. Furthermore, a more significant reduction of the biomass yield was observed during growth in La-supplemented media, confirming a higher flux through formate. M. alcaliphilum 20Z R mutants lacking fae1-2 did not display any significant defects in methane or methanol-dependent growth. However, contrary to fae1, the fae1-2 homolog failed to restore the formaldehyde-activating enzyme function in complementation tests. Overall, the presented data suggest that the developed computational workflow supports the reconstruction and validation of CS-GSM networks of non-model microbes. IMPORTANCE The interrogation of various types of data is a routine strategy to explore the relationship between genotype and phenotype. An efficient approach for integrating and cross-comparing experimental multi-scale data in the context of whole-genome-based metabolic network reconstruction becomes a powerful tool that facilitates fundamental and applied research discoveries. The present study describes the reconstruction of a context-specific (CS) model for the methane-utilizing bacterium, Methylotuvimicrobium alcaliphilum 20Z R . M. alcaliphilum 20Z R is becoming an attractive microbial platform for the production of biofuels, chemicals, pharmaceuticals, and bio-sorbents for capturing atmospheric methane. We demonstrate that this pipeline can help reconstruct metabolic models that are similar to manually curated networks. Furthermore, the model is able to highlight previously overlooked pathways, thus advancing fundamental knowledge of non-model microbial systems or promoting their development toward biotechnological or environmental implementations.

Kulyashov, M. A.↗
DOE OSTI2025

Three-dimensional modeling of hyphal fusion, branching, and nutrient transport in filamentous fungi

Fungi exhibit behaviors distinct from other microbes. Filamentous fungi grow by extending complex networks of branched filaments collectively referred to as the mycelium. These networks can expand over large distances and traverse low-nutrient areas by translocating nutrients through the filament network. This spatial characteristic makes filamentous fungi crucial for soil ecosystems, supporting stable microbial communities and promoting plant growth. However, simulating these behaviors is complex. The elongated nature of fungal compartments results in different mechanical interactions compared to the commonly modeled spherical bacteria. These detailed hyphal mechanics require specialized consideration and are often excluded from conventional fungal simulation packages. Additionally, the extensive fungal networks in nature demand computationally intensive simulations, necessitating high-performance algorithms. Therefore, realistic fungi simulations require specialized software. Here, we introduce a fungal modeling expansion to the high-performance biological modelling and interface exchange (bmx) software suite. bmx leverages adaptive mesh refinement in AMReX for chemical diffusion and incorporates a full mechanical model for bacterial cells, accelerated by GPUs. By extending bmx to model filamentous particles, we demonstrate the formation of complex filament networks through interactions like hyphal branching and fusion (anastomosis). We show that the networks produced match real-world fungal structures through various metrics. This work supports computational studies of fungal growth dynamics and can be adapted to investigate the growth of other filamentous structures in biology or materials science. The expanded-BMX package is open-sourced and is available online.

Cell mechanics↗
DOE OSTI2025

Challenges in integrating dissolved organic matter chemodiversity into kinetic models of soil respiration

The chemodiversity of dissolved organic matter (DOM) in soil has been proposed to influence the microbial metabolism and fate of belowground organic carbon (C). However, integrating DOM chemistry into soil C cycle models to improve predictions of C stocks and fluxes—beyond simply considering DOM pool size—remains a challenge. While recent research suggests that incorporating DOM chemodiversity into models can improve predictions of microbial respiration, there is still a lack of mechanistic understanding describing how DOM chemodiversity affects microbial metabolism and soil respiration. Here, we evaluated whether DOM chemodiversity was a determinant of soil respiration using paired measurements of high-resolution DOM chemistry, obtained from Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), and potential soil respiration rates from across the United States (U.S.), all data provided by the Molecular Observation Network. Our objectives were to (1) assess statistical relationships between DOM chemodiversity and microbial respiration, and (2) evaluate the ability of kinetic models to leverage DOM chemistry to explain empirical relationships found in statistical models. Statistical regressions revealed that DOM chemodiversity (alpha diversity) was nonlinearly related to potential soil respiration rates, both independently and through its interactions with DOM and total C concentrations. In soils with relatively high DOM but low total C concentrations, potential soil respiration rates were negatively correlated with DOM alpha diversity, whereas in soils with relatively low DOM and high total C concentrations showed the opposite trend. However, when metabolic transition theory kinetic models were modified to include chemodiversity, their performance was comparable to traditional Monod kinetics approaches, which simulate respiration rates as a function of DOM concentration. The inability to account for nonlinearities in DOM chemodiversity–respiration relationships highlight an opportunity to advance substrate uptake kinetics by establishing causal links between DOM chemodiversity, microbial metabolism trade-offs, and potential interactions under varied environmental conditions.

Bioenergetic model↗
DOE OSTI2022

Single-cell stable isotope probing in microbial ecology

Abstract Environmental and host-associated microbiomes are typically diverse assemblages of organisms performing myriad activities and engaging in a network of interactions that play out in spatially structured contexts. As the sum of these activities and interactions give rise to overall microbiome function, with important consequences for environmental processes and human health, elucidating specific microbial activities within complex communities is a pressing challenge. Single-cell stable isotope probing (SC-SIP) encompasses multiple techniques that typically utilize Raman microspectroscopy or nanoscale secondary ion mass spectrometry (NanoSIMS) to enable spatially resolved tracking of isotope tracers in cells, cellular components, and metabolites. SC-SIP techniques are uniquely suited for illuminating single-cell activities in microbial communities and for testing hypotheses about cellular functions generated for example from meta-omics datasets. Here, we illustrate the insights enabled by SC-SIP techniques by reviewing selected applications in microbiology and offer a perspective on their potential for future research.

Alcolombri, Uria (ORCID:0000000335615091)↗
DOE OSTI2021

Microcoleus (Cyanobacteria) form watershed-wide populations without strong gradients in population structure

The relative importance of separation by distance and by environment to population genetic diversity can be conveniently tested in river networks, where these two drivers are often independently distributed over space. To evaluate the importance of dispersal and environmental conditions in shaping microbial population structures, we performed genome-resolved metagenomic analyses of benthic Microcoleus -dominated cyanobacterial mats collected in the Eel and Russian River networks (California, USA). The 64 Microcoleus genomes were clustered into three species that shared >96.5% average nucleotide identity (ANI). Most mats were dominated by one strain, but minor alleles within mats were often shared, even over large spatial distances (>300 km). Within the most common Microcoleus species, the ANI between the dominant strains within mats decreased with increasing spatial separation. However, over shorter spatial distances (tens of kilometres), mats from different subwatersheds had lower ANI than mats from the same subwatershed, suggesting that at shorter spatial distances environmental differences between subwatersheds in factors like canopy cover, conductivity, and mean annual temperature decreases ANI. Since mats in smaller creeks had similar levels of nucleotide diversity (π) as mats in larger downstream subwatersheds, within-mat genetic diversity does not appear to depend on the downstream accumulation of upstream-derived strains. The four-gamete test and sequence length bias suggest recombination occurs between almost all strains within each species, even between populations separated by large distances or living in different habitats. Overall, our results show that, despite some isolation by distance and environmental conditions, sufficient gene-flow occurs among cyanobacterial strains to prevent either driver from producing distinctive population structures across the watershed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Graphene oxide exposure alters gut microbial community composition and metabolism in an in vitro human model

Graphene oxide (GO) nanomaterials have unique physicochemical properties that make them highly promising for biomedical, environmental, and agricultural applications. Despite the increasing interest and the use of GO, assessments of its nanotoxicity have largely not interrogated its potential impact on the gut microbiome. This study addresses an important knowledge gap by investigating the impact of GO exposure- both at low (25 ppm) and high (250ppm) doses and fed (nutrient rich) and fasted (nutrient deplete) conditions- on the gut microbial community structure and function, using an in vitro human colon bioreactor model. 16S rRNA amplicon sequencing revealed that GO exposure resulted in a restructuring of community composition. 25 ppm GO induced a marked decrease in the Bacteroidota phylum and increased the ratio of Firmicutes to Bacteroidota (F/B). Untargeted metabolomics on the supernatants indicated that 25 ppm GO impaired microbial utilization and metabolism of substrates (amino acids, carbohydrate metabolites) and reduced production of beneficial microbial metabolites such as 5-hydroxyindole-3-acetic acid and GABA. Exposure to 250 ppm GO resulted in community composition and metabolome profiles that were very similar to the controls that lacked both GO and digestive enzymes, suggesting that high concentrations of GO may interact with digestive enzymes to form protein coronas, causing their depletion in the gut environment. Differential abundance analyses revealed that 3 genera from the phylum Bacteroidota (Bacteroides, Dysgonomonas, and Parabacteroides) were more abundant after 250 ppm GO exposure, irrespective of feed state. Integrative correlation network analysis indicated that the phylum Bacteroidota showed strong positive correlations to multiple microbial metabolites including GABA and 3-indoleacetic acid, are much larger number of correlations compared to other phyla. These results show that GO exposure has a significant impact on gut microbial community composition and metabolism and different mechanisms are at play for low and high GO concentrations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Carbon availability, soil pH, and microbial allocation to nitrogen acquisition shape grassland heterotrophic respiration in response to a decade of nitrogen addition

Previous work has found that anthropogenic inputs of nitrogen (N) and phosphorus (P) impact heterotrophic respiration during soil organic matter decomposition in grasslands, a critical pathway through which carbon (C) is lost from soil to the atmosphere. While N addition typically reduces heterotrophic respiration, why the strength and direction of this N effect varies among sites is unclear. Here, to address this, we conducted a 339-day laboratory incubation to measure heterotrophic respiration from nine grasslands across North America that have received 10 years of factorial N and P fertilization. N addition reduced cumulative respiration most at sites with low pH, low microbial allocation towards N acquisition, and high soil C concentration and availability. However, N addition had neutral rather than positive effects on heterotrophic respiration in sites with high pH and decomposer allocation towards N acquisition. Across sites, a decade of N addition reduced heterotrophic respiration by ∼24 %, driven by reductions in microbial biomass. Heterotrophic respiration was less sensitive to P addition, despite its increasing microbial biomass. However, simultaneous N and P addition did ameliorate negative N effects. These results show that previously observed variation in the response of heterotrophic respiration to N addition can be explained by soil C availability and pH status, widely measured factors which can be used to predict how grassland C fluxes may change under continuing nutrient deposition.

N addition↗
DOE OSTI2021

Unnatural biosynthesis by an engineered microorganism with heterologously expressed natural enzymes and an artificial metalloenzyme

Synthetic biology enables microbial hosts to produce complex molecules from organisms that are rare or difficult to cultivate, but the structures of these molecules are limited to those formed by reactions of natural enzymes. The integration of artificial metalloenzymes (ArMs) that catalyse unnatural reactions into metabolic networks could broaden the cache of molecules produced biosynthetically. Here, in this work, we report an engineered microbial cell expressing a heterologous biosynthetic pathway, containing both natural enzymes and ArMs, that produces an unnatural product with high diastereoselectivity. We engineered Escherichia coli with a heterologous terpene biosynthetic pathway and an ArM containing an iridium–porphyrin complex that was transported into the cell with a heterologous transport system. We improved the diastereoselectivity and product titre of the unnatural product by evolving the ArM and selecting the appropriate gene induction and cultivation conditions. This work shows that synthetic biology and synthetic chemistry can produce, by combining natural and artificial enzymes in whole cells, molecules that were previously inaccessible to nature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Molecular Transformation and Metabolic Insights of Microbial Electrolysis Treatment and Valorization of Post-hydrothermal Liquefaction Wastewater

Hydrothermal liquefaction (HTL) presents a promising approach for the conversion of wet waste into biocrude and biofuels. However, the post-hydrothermal liquefaction wastewater (PHW) poses significant challenges for treatment and valorization due to its high concentration and complex nature. In this study, we investigated the conversion pathways of major organic contaminants within the microbial electrolytic treatment of PHW from food waste HTL. To achieve this, we employed high-performance liquid chromatography (HPLC) and 2D nuclear magnetic resonance (NMR). Our findings demonstrate volatile fatty acids (VFAs) and monohydric and polyhydric alcohols were effectively transformed through the synergistic metabolism of fermentative and electroactive bacteria, which led to over 70% COD removal of the recalcitrant compounds and a record high H2 production rate (1.62 L L -1 d -1 ). We also employed the liquid-state 15N NMR on wastewater samples for the first time and revealed that the nitrogen-containing heteroaromatics were persistent to microbial electrochemical treatment. By integrating the chemical profiles with bioanode community profiles, we constructed a metabolic network that provides insights for enhancing treatment efficiency and facilitating resource recovery.

08 HYDROGEN↗
DOE OSTI2022

The nitrogen gap in soil health concepts and fertility measurements

Soil nitrogen (N) often limits productivity in agroecosystems, prompting fertilizer applications that increase crop yields but can degrade the environment. Nitrogen's dual role in both productivity and environmental quality should center it in soil health frameworks. We use recent evidence to argue that N availability is an emergent property of the integrated soil biogeochemical system and is strongly influenced by plant traits and their interactions with microbes and minerals. Building upon this, we theorize that the sources of plant and microbial N shift across soil health gradients, from inorganic N dependence in ecologically simple systems with poor soil health to a highly networked supply of organic N in healthy soils; ergo, investments in soil health should increase ecological complexity and the pathways by which plants can access N, leading to more resilient nutrient supplies and yields in a variable climate. However, current N assessment methods derive from a historical emphasis on inorganic N pool sizes and are unable to capture the shifting drivers of N availability across soil health gradients. We highlight the need to better understand the plant-microbial-mineral interactions that regulate bioavailable N as a first step to improving our ability to measure it. We conclude it will be necessary to harness agroecosystem complexity, account for plant and microbial drivers, and carefully integrate external N inputs into soils' internal N network to expand the routes by which N from organic pools can be made bioavailable. Finally, by emphasizing N in soil health concepts, we argue that researchers can accelerate advances in N use efficiency and resiliency.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Soil microbial ecology and microbiome-metabolite linkages improve understanding of ecosystem states along terrestrial-aquatic interfaces

Coastal soils are dynamic systems where unique microbial niches are shaped by the intensity and duration of flooding between the terrestrial and aquatic boundaries of the terrestrial-aquatic interface (TAI). We aimed to understand the soil microbial community (16S rRNA gene) along the TAIs of a freshwater versus estuarine region and how it relates to organic matter (OM, via Fourier Transform Ion Cyclotron Resonance Mass Spectrometry). We studied the TAI gradients along a transect from upland (forested), transition (stressed forest), to wetland at three sites in each of the Lake Erie (freshwater) and Chesapeake Bay (estuarine) regions. Microbial communities differed significantly by region, transect position, and site. Contrary to expectations, given their dynamic hydrologies, transitions represented midpoints in microbial richness and diversity. We identified a core microbiome conserved across all transect positions within a region, highlighting potential microbial functions most resilient to environmental change. Indicator taxa unique to each transect position defined specific niches shaped by soil biogeochemistry. Co-expression networks of feature-level β-nearest-taxon indices revealed positive relationships in bacterial and OM feature contributions to community assembly. Our study provides critical insights into microbial communities at the forefront of hydrological changes in coastal areas that connect the land to lakes and oceans and remain vulnerable to changing weather patterns.

coastal ecosystems↗