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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Modeling of nitrogen fixation and polymer production in the heterotrophic diazotroph Azotobacter vinelandii DJ

Nitrogen fixation is an important metabolic process carried out by microorganisms, which converts molecular nitrogen into inorganic nitrogenous compounds such as ammonia (NH 3 ). These nitrogenous compounds are crucial for biogeochemical cycles and for the synthesis of essential biomolecules, i.e. nucleic acids, amino acids and proteins. Azotobacter vinelandii is a bacterial non-photosynthetic model organism to study aerobic nitrogen fixation (diazotrophy) and hydrogen production. Moreover, the diazotroph can produce biopolymers like alginate and polyhydroxybutyrate (PHB) that have important industrial applications. However, many metabolic processes such as partitioning of carbon and nitrogen metabolism in A. vinelandii remain unknown to date. Genome-scale metabolic models (M-models) represent reliable tools to unravel and optimize metabolic functions at genome-scale. M-models are mathematical representations that contain information about genes, reactions, metabolites and their associations. M-models can simulate optimal reaction fluxes under a wide variety of conditions using experimentally determined constraints. Here we report on the development of a M-model of the wild type bacterium A. vinelandii DJ (iDT1278) which consists of 2,003 metabolites, 2,469 reactions, and 1,278 genes. We validated the model using high-throughput phenotypic and physiological data, testing 180 carbon sources and 95 nitrogen sources. iDT1278 was able to achieve an accuracy of 89% and 91% for growth with carbon sources and nitrogen source, respectively. This comprehensive M-model will help to comprehend metabolic processes associated with nitrogen fixation, ammonium assimilation, and production of organic nitrogen in an environmentally important microorganism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Microbiome engineering for sustainable agriculture: using synthetic biology to enhance nitrogen metabolism in plant-associated microbes

Plants benefit from symbiotic relationships with their microbiomes. Modifying these microbiomes to further promote plant growth and improve stress tolerance in crops is a promising strategy. However, such efforts have had limited success, perhaps because the original microbiomes quickly re-establish. Since the complex biological networks involved are little understood, progress through conventional means is time-consuming. Synthetic biology, with its practical successes in multiple industries, could speed up this research considerably. Some fascinating candidates for production by synthetic microbiomes are organic nitrogen metabolites and related pyridoxal-5'-phosphate-dependent enzymes, which have pivotal roles in microbe-microbe and plant-microbe interactions. This review summarizes recent studies of these metabolites and enzymes and discusses prospective synthetic biology platforms for sustainable agriculture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Computational prediction of plant metabolic pathways

Uncovering genes encoding enzymes responsible for the biosynthesis of diverse plant metabolites is essential for metabolic engineering and production of plant metabolite-derived medicine. With the availability of multi-omics data for an ever-increasing number of plant species and the development of computational approaches, the metabolic pathways of many important plant compounds can be predicted, complementing a more traditional genetic and/or biochemical approach. In this work, we summarize recent progress in predicting plant metabolic pathways using genome, transcriptome, proteome, interactome, and/or metabolome data, and the utility of integrating these data with machine learning to further improve metabolic pathway predictions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Introducing furanocoumarin biosynthetic genes in tomato results in coumarins accumulation and impacts growth

Over the past three decades, efforts to decipher plant metabolism have shed light on key enzymes driving specialized metabolite biosynthesis. Although only few pathways have been completely investigated to date, their characterization paves the way for exploring the potential effects of specialized metabolites on plant physiology. Among them is the linear furanocoumarin pathway, which was recently completed to produce up to psoralen. In this study, we report the first metabolic engineering of the linear furanocoumarin pathway to enable artificial psoralen production in tomato, through the integration of four genes coding for the enzymes: Umbelliferone Synthase (PsDiox), Demethylsuberosin Synthase (PsPT1), Marmesin Synthase (FcCYP76F112) and Psoralen Synthase (PsCYP71AJ3). Metabolic analyses confirmed the detection of small quantities of psoralen in the transgenic tomato line, but also highlighted a larger accumulation of coumarins and particularly scopoletin. Using morphophysiological and multi-omics analyses, we explorate how such metabolic modifications, could impact growth and affect plant physiology.

coumarins↗
DOE OSTI2025

Insight into industrial hemp ( Cannabis sativa L.) root exudation composition in a simulated soil environment: a rhizosphere-on-a-chip study

Microfluidic technologies provide a reduced complexity and soil-free environment to study plant-soil interactions at the microscale. Traditionally used for model plants such as Arabidopsis thaliana, this study represents the first application of a rhizosphere-on-a-chip (RhizoChip) to investigate root exudation in industrial hemp (Cannabis sativa L.), an agronomic crop with growing economic importance. By incorporating soil-like minerals (kaolinite, potassium feldspar, and biotite), the RhizoChip addresses limitations of previous research. Hemp seedlings grown in mineral-containing chips exhibited significant root growth, emphasizing the critical role of minerals in root development. Using untargeted metabolomics, 170 compounds were identified, including organic acids, amino acids, and secondary metabolites, with distinct profiles across conditions. Metabolic pathway analysis revealed activity in amino acid metabolism, the citric acid cycle, and secondary metabolite biosynthesis. In conclusion, this study highlights the RhizoChip's potential for long-term studies of root exudates in non-model crops and offers insights into rhizosphere processes with implications for sustainable agriculture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Associations between SARS-CoV-2 Infection or COVID-19 Vaccination and Human Milk Composition: A Multi-Omics Approach

Background: The risk of contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via human milk-feeding is virtually nonexistent. Adverse effects of coronavirus disease 2019 (COVID-19) vaccination for lactating individuals are not different from the general population, and no evidence has been found that their infants exhibit adverse effects. Yet, there remains substantial hesitation among this population globally regarding the safety of these vaccines. Objectives: Herein, we aimed to determine if compositional changes in milk occur following SARS-CoV-2 infection or COVID-19 vaccination, including any evidence of vaccine components. Methods: An extensive multiomics approach was taken using a subset of milk samples obtained as part of our broad studies examining the effects on milk of SARS-CoV-2 infection and COVID-19 vaccination. Results: We found that compared with unvaccinated individuals, SARS-CoV-2 infection was associated with significant compositional differences in 67 proteins, 385 lipids, and 13 metabolites. In contrast, COVID-19 vaccination was not associated with any changes in lipids or metabolites, although it was associated with changes in 13 or fewer proteins. Compositional changes in milk differed by vaccine. Changes following vaccination were greatest after 1–6 h for the mRNA-based Moderna vaccine (8 changed proteins), 3 d for the mRNA-based Pfizer (4 changed proteins), and adenovirus-based Johnson and Johnson (13 changed proteins) vaccines. Proteins that changed after both natural infection and Johnson and Johnson vaccine were associated mainly with systemic inflammatory responses. In addition, no vaccine components were detected in any milk sample. Conclusions: Together, our data provide evidence of only minimal changes in milk composition because of COVID-19 vaccination, with much greater changes after natural SARS-CoV-2 infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Dynamic control over feedback regulatory mechanisms improves NADPH flux and xylitol biosynthesis in engineered E. coli

We report improved NADPH flux and xylitol biosynthesis in engineered E. coli. Xylitol is produced from xylose via an NADPH dependent reductase. We utilize 2-stage dynamic metabolic control to compare two approaches to optimize xylitol biosynthesis, a stoichiometric approach, wherein competitive fluxes are decreased, and a regulatory approach wherein the levels of key regulatory metabolites are reduced. The stoichiometric and regulatory approaches lead to a 20-fold and 90-fold improvement in xylitol production, respectively. Strains with reduced levels of enoyl-ACP reductase and glucose-6-phosphate dehydrogenase, led to altered metabolite pools resulting in the activation of the membrane bound transhydrogenase and an NADPH generation pathway, consisting of pyruvate ferredoxin oxidoreductase coupled with NADPH dependent ferredoxin reductase, leading to increased NADPH fluxes, despite a reduction in NADPH pools. Furthermore, these strains produced titers of 200 g/L of xylitol from xylose at 86% of theoretical yield in instrumented bioreactors. We expect dynamic control over the regulation of the membrane bound transhydrogenase as well as NADPH production through pyruvate ferredoxin oxidoreductase to broadly enable improved NADPH dependent bioconversions or production via NADPH dependent metabolic pathways.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Quantifying the propagation of parametric uncertainty on flux balance analysis

Flux balance analysis (FBA) and associated techniques operating on stoichiometric genome-scale metabolic models play a central role in quantifying metabolic flows and constraining feasible phenotypes. At the heart of these methods lie two important assumptions: (i) the biomass precursors and energy requirements neither change in response to growth conditions nor environmental/genetic perturbations, and (ii) metabolite production and consumption rates are equal at all times (i.e., steady-state). Despite the stringency of these two assumptions, FBA has been shown to be surprisingly robust at predicting cellular phenotypes. In this paper, we formally assess the impact of these two assumptions on FBA results by quantifying how uncertainty in biomass reaction coefficients, and departures from steady-state due to temporal fluctuations could propagate to FBA results. In the first case, conditional sampling of parameter space is required to re-weigh the biomass reaction so as the molecular weight remains equal to 1 g mmol –1 , and in the second case, metabolite (and elemental) pool conservation must be imposed under temporally varying conditions. Results confirm the importance of enforcing the aforementioned constraints and explain the robustness of FBA biomass yield predictions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale

The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by 13 C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A dynamic kinetic model captures cell-free metabolism for improved butanol production

Cell-free systems are useful tools for prototyping metabolic pathways and optimizing the production of various bioproducts. Mechanistically-based kinetic models are uniquely suited to analyze dynamic experimental data collected from cell-free systems and provide vital qualitative insight. However, to date, dynamic kinetic models have not been applied with rigorous biological constraints or trained on adequate experimental data to the degree that they would give high confidence in predictions and broadly demonstrate the potential for widespread use of such kinetic models. In this work, we construct a large-scale dynamic model of cell-free metabolism with the goal of understanding and optimizing butanol production in a cell-free system. Using a combination of parameterization methods, the resultant model captures experimental metabolite measurements across two experimental conditions for nine metabolites at timepoints between 0 and 24 hours. Here, we present analysis of the model predictions, provide recommendations for butanol optimization, and identify the aldehyde/alcohol dehydrogenase as the primary bottleneck in butanol production. Sensitivity analysis further reveals the extent to which various parameters are constrained, and our approach for probing valid parameter ranges can be applied to other modeling efforts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states

Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. Finally, this versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Untargeted Spatial Metabolomics and Spatial Proteomics on the Same Tissue Section

An increasing number of spatial multiomic workflows have been recently developed. Some of these approaches have leveraged initial mass spectrometry imaging (MSI)-based spatial metabolomics to inform region of interest (ROI) selection for downstream spatial proteomics. However, these workflows have been limited by varied substrate requirements between modalities or have required analyzing serial sections (i.e., one section per modality). To mitigate these issues, we present a novel multiomic workflow that uses desorption electrospray ionization (DESI)-MSI to identify representative spatial metabolite patterns on-tissue prior to spatial proteomic analyses on the same tissue section. Further, this workflow is demonstrated here with a model mammalian tissue (coronal rat brain section) mounted on a polyethylene naphthalate-membrane slide. Initial DESI-MSI resulted in 160 annotations (SwissLipids) within to the METASPACE platform (≤20% false discovery rate). A segmentation map from the annotated ion images informed downstream ROI selection for spatial proteomics characterization from the same sample. The unspecific substrate requirements and minimal sample disruption inherent to DESI-MSI allowed for an optimized, downstream spatial proteomics assay, resulting in 3888 ± 240 to 4717 ± 48 proteins being confidently directed per ROI (200 µm x 200 µm). Finally, we demonstrate the integration of multiomic information, where we found ceramide localization to be correlated with SMPD3 abundance (ceramide synthesis protein), and we also utilized protein abundance to resolve metabolite isomeric ambiguity. Overall, the integration of DESI-MSI into the multiomic workflow allows for complementary spatial and molecular-level information to be achieved from optimized implementations of each MS assay inherent to the workflow itself.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Computational Framework for Machine-Learning-Enabled 13 C Fluxomics

13 C metabolic flux analysis (MFA) has emerged as a powerful tool for synthetic biology. This optimization-based approach suffers long computation time and unstable solutions depending on the initial guess. Here, we develop a machine-learning-based framework for 13 C fluxomics. Specifically, training and test data sets are generated by metabolic network decomposition and flux sampling, in which flux ratios at metabolic nodes and simulated labeling patterns of metabolites are used as training targets and features, respectively. To improve prediction accuracy and simplify the model, automated processes are developed for flux ratio selection based on solvability and feature screening based on importance. We found that predictive performance can be significantly improved using both amino acids and central carbon metabolites in comparison with amino acids alone. Together with measured external fluxes, the predicted flux ratios determine the mass balance system, yielding global flux distributions. This approach is validated by flux estimation using both simulated and experimental data in comparison with canonical 13 C MFA. The approach represents a reliable fluxomics method readily applicable to high-throughput metabolic phenotyping, which highlights the advances of intelligent learning algorithms in synthetic biology, specifically in the Test and Learn stage of the Design-Build-Test-Learn cycle.

13C metabolic flux analysis↗
DOE OSTI2023

Mechanistic Insights into Cell-Free Gene Expression through an Integrated -Omics Analysis of Extract Processing Methods

Cell-free systems derived from crude cell extracts have developed into tools for gene expression, with applications in prototyping, biosensing, and protein production. Key to the development of these systems is optimization of cell extract preparation methods. However, the applied nature of these optimizations often limits investigation into the complex nature of the extracts themselves, which contain thousands of proteins and reaction networks with hundreds of metabolites. In this report we sought to uncover the black box of proteins and metabolites in Escherichia coli cell-free reactions based on different extract preparation methods. We assess changes in transcription and translation activity from σ70 promoters in extracts prepared with acetate or glutamate buffer and the common post-lysis processing steps of a runoff incubation and dialysis. We then utilize proteomic and metabolomic analyses to uncover potential mechanisms behind these changes in gene expression, highlighting the impact of cold shock-like proteins and the role of buffer composition.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Cell-Free-Based Thermophilic Biocatalyst for the Synthesis of Amino Acids from One-Carbon Feedstocks

Bioproduction from one-carbon compounds, such as formate, is an attractive prospect due to reduced energy requirements and the possibility for using CO 2 as a sustainable feedstock. Formate-fixing pathways engineered using Escherichia coli lysate-based cell-free expression (CFE) biocatalysts have the potential to route 100% of feedstock carbon toward chemical synthesis but are undermined by siphoning of in-pathway metabolites and cofactors by the CFE background metabolism. To address this limitation, we engineer a CFE-based thermophilic multienzyme biocatalyst for the synthesis of serine and glycine from formate, bicarbonate, and ammonia. After expression of the thermophilic formate-to-serine pathway in a one-pot reaction, the mesophilic E. coli CFE background machinery is removed by simple heat denaturation, eliminating the siphoning of cofactors, inpathway metabolites, and products. After bioprocess optimization, including pathway gene expression duration and chemical synthesis temperature, we achieve near stoichiometric conversion of formate and bicarbonate to serine and glycine, reaching 97% of stoichiometric yield. The use of a moderately thermophilic biocatalyst allowed chemical synthesis to take place at mesophilic temperatures, enabling the balance of optimal enzyme activity with minimal metabolite/cofactor thermal degradation. In a fed-batch experiment, the biocatalyst shows sustained chemical synthesis rates for 8 h, paving the way toward a continuous bioprocess. Finally, a sensitivity analysis of cofactor usage revealed that the most expensive cofactors, THF and NADPH, can be reduced by 5-fold without significantly lowering product yields. To the best of our knowledge, this is the first instance of expressing a thermophilic pathway in an E. coli lysate-based CFE system to generate a thermophilic biocatalyst for use at mesophilic temperatures. The CFEbased thermophilic formate-to-serine biocatalyst triples the combined serine and glycine yield previously obtained by a CFE-based mesophilic formate-to-serine biocatalyst (30%), and quadruple the yield obtained by a purified enzyme system (22%). Ultimately, this work opens the door to using E. coli lysate-based CFE for thermophilic biocatalyst generation to achieve high chemical synthesis yields.

bacteria↗
DOE OSTI2022

The Single Residue K12 Governs the Exceptional Voltage Sensitivity of Mitochondrial Voltage-Dependent Anion Channel Gating

The voltage-dependent anion channel (VDAC) is a β-barrel channel of the mitochondrial outer membrane (MOM) that passively transports ions, metabolites, polypeptides, and single-stranded DNA. VDAC responds to a transmembrane potential by “gating,” i.e. transitioning to one of a variety of low-conducting states of unknown structure. The gated state results in nearly complete suppression of multivalent mitochondrial metabolite (such as ATP and ADP) transport, while enhancing calcium transport. Voltage gating is a universal property of β-barrel channels, but VDAC gating is anomalously sensitive to transmembrane potential. Here, we show that a single residue in the pore interior, K12, is responsible for most of VDAC’s voltage sensitivity. Using the analysis of over 40 μs of atomistic molecular dynamics (MD) simulations, we explore correlations between motions of charged residues inside the VDAC pore and geometric deformations of the β-barrel. Residue K12 is bistable; its motions between two widely separated positions along the pore axis enhance the fluctuations of the β-barrel and augment the likelihood of gating. Single channel electrophysiology of various K12 mutants reveals a dramatic reduction of the voltage-induced gating transitions. The crystal structure of the K12E mutant at a resolution of 2.6 Å indicates a similar architecture of the K12E mutant to the wild type; however, 60 μs of atomistic MD simulations using the K12E mutant show restricted motion of residue 12, due to enhanced connectivity with neighboring residues, and diminished amplitude of barrel motions. We conclude that β-barrel fluctuations, governed particularly by residue K12, drive VDAC gating transitions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Proteome specialization of anaerobic fungi during ruminal degradation of recalcitrant plant fiber

The rumen harbors a complex microbial mixture of archaea, bacteria, protozoa, and fungi that efficiently breakdown plant biomass and its complex dietary carbohydrates into soluble sugars that can be fermented and subsequently converted into metabolites and nutrients utilized by the host animal. While rumen bacterial populations have been well documented, only a fraction of the rumen eukarya are taxonomically and functionally characterized, despite the recognition that they contribute to the cellulolytic phenotype of the rumen microbiota. To investigate how anaerobic fungi actively engage in digestion of recalcitrant fiber that is resistant to degradation, we resolved genome-centric metaproteome and metatranscriptome datasets generated from switchgrass samples incubated for 48 h in nylon bags within the rumen of cannulated dairy cows. Across a gene catalog covering anaerobic rumen bacteria, fungi and viruses, a significant portion of the detected proteins originated from fungal populations. Intriguingly, the carbohydrate-active enzyme (CAZyme) profile suggested a domain-specific functional specialization, with bacterial populations primarily engaged in the degradation of hemicelluloses, whereas fungi were inferred to target recalcitrant cellulose structures via the detection of a number of endo- and exo-acting enzymes belonging to the glycoside hydrolase (GH) family 5, 6, 8, and 48. Notably, members of the GH48 family were amongst the highest abundant CAZymes and detected representatives from this family also included dockerin domains that are associated with fungal cellulosomes. A eukaryote-selected metatranscriptome further reinforced the contribution of uncultured fungi in the ruminal degradation of recalcitrant fibers. These findings elucidate the intricate networks of in situ recalcitrant fiber deconstruction, and importantly, suggest that the anaerobic rumen fungi contribute a specific set of CAZymes that complement the enzyme repertoire provided by the specialized plant cell wall degrading rumen bacteria.

Hagen, Live↗
DOE OSTI2021

Novel metabolic interactions and environmental conditions mediate the boreal peatmoss-cyanobacteria mutualism

Abstract Interactions between Sphagnum (peat moss) and cyanobacteria play critical roles in terrestrial carbon and nitrogen cycling processes. Knowledge of the metabolites exchanged, the physiological processes involved, and the environmental conditions allowing the formation of symbiosis is important for a better understanding of the mechanisms underlying these interactions. In this study, we used a cross-feeding approach with spatially resolved metabolite profiling and metatranscriptomics to characterize the symbiosis between Sphagnum and Nostoc cyanobacteria. A pH gradient study revealed that the Sphagnum–Nostoc symbiosis was driven by pH, with mutualism occurring only at low pH. Metabolic cross-feeding studies along with spatially resolved matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) identified trehalose as the main carbohydrate source released by Sphagnum, which were depleted by Nostoc along with sulfur-containing choline-O-sulfate, taurine and sulfoacetate. In exchange, Nostoc increased exudation of purines and amino acids. Metatranscriptome analysis indicated that Sphagnum host defense was downregulated when in direct contact with the Nostoc symbiont, but not as a result of chemical contact alone. The observations in this study elucidated environmental, metabolic, and physiological underpinnings of the widespread plant–cyanobacterial symbioses with important implications for predicting carbon and nitrogen cycling in peatland ecosystems as well as the basis of general host-microbe interactions.

59 BASIC BIOLOGICAL SCIENCES↗