scholarly journals Metabolic control of nitrogen fixation in rhizobium-legume symbioses

2021 ◽  
Vol 7 (31) ◽  
pp. eabh2433
Author(s):  
Carolin C. M. Schulte ◽  
Khushboo Borah ◽  
Rachel M. Wheatley ◽  
Jason J. Terpolilli ◽  
Gerhard Saalbach ◽  
...  
Keyword(s):  
Metabolic Flux ◽  
Tricarboxylic Acid Cycle ◽  
Oxygen Demand ◽  
Metabolic Model ◽  
Redox Balance ◽  
Ammonia Assimilation ◽  
Nodule Formation ◽  
Flux Analysis ◽  
Carbon And Nitrogen ◽  
Genome Scale

Rhizobia induce nodule formation on legume roots and differentiate into bacteroids, which catabolize plant-derived dicarboxylates to reduce atmospheric N2 into ammonia. Despite the agricultural importance of this symbiosis, the mechanisms that govern carbon and nitrogen allocation in bacteroids and promote ammonia secretion to the plant are largely unknown. Using a metabolic model derived from genome-scale datasets, we show that carbon polymer synthesis and alanine secretion by bacteroids facilitate redox balance in microaerobic nodules. Catabolism of dicarboxylates induces not only a higher oxygen demand but also a higher NADH/NAD+ ratio than sugars. Modeling and 13C metabolic flux analysis indicate that oxygen limitation restricts the decarboxylating arm of the tricarboxylic acid cycle, which limits ammonia assimilation into glutamate. By tightly controlling oxygen supply and providing dicarboxylates as the energy and electron source donors for N2 fixation, legumes promote ammonia secretion by bacteroids. This is a defining feature of rhizobium-legume symbioses.

scholarly journals Metabolic constraints on nitrogen fixation by rhizobia in legume nodules

2021 ◽  
Author(s):  
Carolin C. M. Schulte ◽  
Khushboo Borah ◽  
Rachel M. Wheatley ◽  
Jason J. Terpolilli ◽  
Gerhard Saalbach ◽  
...  
Keyword(s):  
Nitrogen Fixation ◽  
Metabolic Flux ◽  
Oxygen Demand ◽  
Tca Cycle ◽  
Redox Balance ◽  
Ammonia Assimilation ◽  
Nodule Formation ◽  
Flux Analysis ◽  
Electron Sources ◽  
Genome Scale

AbstractRhizobia induce nodule formation on legume roots and differentiate into bacteroids, which use plant-derived dicarboxylates as energy and electron sources for reduction of atmospheric N2 into ammonia for secretion to plants. Using heterogeneous genome-scale datasets, we reconstructed a model of bacteroid metabolism to investigate the effects of varying dicarboxylate and oxygen supply on carbon and nitrogen allocation. Modelling and 13C metabolic flux analysis in bacteroids indicate that microaerobiosis restricts the decarboxylating arm of the TCA cycle and limits ammonia assimilation into glutamate. Catabolism of dicarboxylates induces a higher oxygen demand but also a higher NADH/NAD+ ratio compared to sugars. Carbon polymer synthesis and alanine secretion by bacteroids facilitate redox balance in microaerobic nodules with alanine secretion increasing as oxygen tension decreases. Our results provide a framework for understanding fundamental constraints on rhizobial metabolism during symbiotic nitrogen fixation.

scholarly journals Reconstruction and analysis of genome-scale metabolic model of weak Crabtree positive yeast Lachancea kluyveri

2020 ◽  
Author(s):  
Piyush Nanda ◽  
Pradipta Patra ◽  
Manali Das ◽  
Amit Ghosh
Keyword(s):  
Ethyl Acetate ◽  
Metabolic Flux ◽  
Carbon Sources ◽  
Growth Dynamics ◽  
Dry Weight ◽  
Metabolic Model ◽  
Flux Analysis ◽  
Specific Rate ◽  
A Genome ◽  
Genome Scale

Abstract Background Lachancea kluyveri, a weak Crabtree positive yeast, has been extensively studied for its unique URC pyrimidine catabolism pathway. It produces more biomass than Saccharomyces cerevisiae due to the underlying weak Crabtree effect and resorts to optimal fermentation only in oxygen limiting conditions that render it a suitable host for industrial-scale protein production. Ethyl acetate, an important industrial chemical, has been demonstrated to be a major overflow metabolite during aerobic batch cultivation with a specific rate of 0.12 g per g dry weight per hour. Here, we reconstruct a genome-scale metabolic model of the yeast to better explain the observed phenotypes and aid further hypothesis generation. Results We report the first genome-scale metabolic model, iPN730, using Build Fungal Model in KBase workspace. The inconsistencies in the draft model were semi-automatically corrected using literature and published datasets. The curated model comprises of 1235 reactions, 1179 metabolites, and 730 genes distributed in 8 compartments (organelles). The in silico viability in different media conditions and the growth characteristics in various carbon sources show good agreement with experimental data. Dynamic flux balance analysis describes the growth dynamics, substrate utilization and product formation kinetics in various oxygen-limited conditions. The URC pyrimidine degradation pathway incorporated into the model enables it to grow on uracil or urea as the sole nitrogen source. Conclusion The genome-scale metabolic construction of L. kluyveri will provide a better understanding of metabolism, particularly that of pyrimidine metabolism and ethyl acetate production. Metabolic flux analysis using the model will enable hypotheses generation to gain a deeper understanding of metabolism in weakly Crabtree positive yeast and in fungal biodiversity in general.

scholarly journals Optimized sampling protocol for mass spectrometry-based metabolomics in Streptomyces

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Xiaoyun Liu ◽  
Tong Wang ◽  
Xiaojuan Sun ◽  
Zejian Wang ◽  
Xiwei Tian ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Metabolic Flux ◽  
Transition Energy ◽  
Isotope Dilution Mass Spectrometry ◽  
Metabolic Model ◽  
Flux Analysis ◽  
Sampling Protocol ◽  
Quantitative Metabolomics ◽  
Base Solution ◽  
Genome Scale

Abstract In quantitative metabolomics studies, the most crucial step was arresting snapshots of all interesting metabolites. However, the procedure customized for Streptomyces was so rare that most studies consulted the procedure from other bacteria even yeast, leading to inaccurate and unreliable metabolomics analysis. In this study, a base solution (acetone: ethanol = 1:1, mol/mol) was added to a quenching solution to keep the integrity of the cell membrane. Based on the molar transition energy (ET) of the organic solvents, five solutions were used to carry out the quenching procedures. These were acetone, isoamylol, propanol, methanol, and 60% (v/v) methanol. To the best of our knowledge, this is the first report which has utilized a quenching solution with ET values. Three procedures were also adopted for extraction. These were boiling, freezing–thawing, and grinding ethanol. Following the analysis of the mass balance, amino acids, organic acids, phosphate sugars, and sugar alcohols were measured using gas chromatography with an isotope dilution mass spectrometry. It was found that using isoamylol with a base solution (5:1, v/v) as a quenching solution and that freezing–thawing in liquid nitrogen within 50% (v/v) methanol as an extracting procedure were the best pairing for the quantitative metabolomics of Streptomyces ZYJ-6, and resulted in average recoveries of close to 100%. The concentration of intracellular metabolites obtained from this new quenching solution was between two and ten times higher than that from 60% (v/v) methanol, which until now has been the most commonly used solution. Our findings are the first systematic quantitative metabolomics tools for Streptomyces ZYJ-6 and, therefore, will be important references for research in fields such as 13C based metabolic flux analysis, multi-omic research and genome-scale metabolic model establishment, as well as for other Streptomyces.

scholarly journals Reconstruction and analysis of genome-scale metabolic model of weak Crabtree positive yeast Lachancea kluyveri

2019 ◽  
Author(s):  
Piyush Nanda ◽  
Pradipta Patra ◽  
Manali Das ◽  
Amit Ghosh
Keyword(s):  
Ethyl Acetate ◽  
Metabolic Flux ◽  
Carbon Sources ◽  
Growth Dynamics ◽  
Dry Weight ◽  
Metabolic Model ◽  
Product Formation ◽  
Flux Analysis ◽  
Specific Rate ◽  
Genome Scale

Abstract Background Lachancea kluyveri , a weak Crabtree positive yeast, has been extensively studied for the unique URC pyrimidine catabolism it harbours. It produces more biomass than Saccharomyces cerevisiae due to the underlying Crabtree effect and resorts to fermentation only in oxygen limiting conditions that makes it suitable host for industrial scale protein production. Ethyl acetate, an important industrial chemical, has been demonstrated to be a major overflow metabolite during aerobic batch cultivation with a specific rate of 0.12 g per g dry weight per hour. Here, we attempted to reconstruct the metabolism of the yeast from the genome to better explain the observed phenotypes and aid further hypothesis generation. Results We report the first genome-scale metabolic model, iPN730, using Build Fungal Model in KBase workspace. The inconsistencies in the model were manually corrected using literature and published datasets. The model comprises of 1235 reactions, 1179 metabolites and 730 genes distributed in 8 compartments. The in silico viability and the growth rates in various carbon sources show good agreement. The gene essentiality of the metabolic model also performs well in comparison to experimental data confirmed by statistical analysis. Dynamic flux balance analysis describes the growth dynamics, substrate utilization and product formation kinetics in various oxygen limited conditions. The URC pyrimidine degradation pathway incorporated into the model enables it to grow on uracil or urea as the sole nitrogen source. Conclusion The genome-scale metabolic construction of L. kluyveri provides better understanding of metabolism, particularly that of pyrimidine metabolism and ethyl acetate production. Metabolic flux analysis using the model will enable hypotheses generation to gain deeper understanding of metabolism in weakly Crabtree positive yeast.

scholarly journals In vivo 13C MRS in the mouse brain at 14.1 Tesla and metabolic flux quantification under infusion of [1,6-13C2]glucose

2017 ◽  
Vol 38 (10) ◽  
pp. 1701-1714 ◽  
Author(s):  
Marta Lai ◽  
Bernard Lanz ◽  
Carole Poitry-Yamate ◽  
Jackeline F Romero ◽  
Corina M Berset ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Metabolic Flux ◽  
Direct Detection ◽  
Tricarboxylic Acid Cycle ◽  
Quantitative Information ◽  
Glutamine Metabolism ◽  
Resonance Spectroscopy ◽  
Flux Analysis ◽  
Carboxylase Activity

In vivo 13C magnetic resonance spectroscopy (MRS) enables the investigation of cerebral metabolic compartmentation while, e.g. infusing 13C-labeled glucose. Metabolic flux analysis of 13C turnover previously yielded quantitative information of glutamate and glutamine metabolism in humans and rats, while the application to in vivo mouse brain remains exceedingly challenging. In the present study, 13C direct detection at 14.1 T provided highly resolved in vivo spectra of the mouse brain while infusing [1,6-13C2]glucose for up to 5 h. 13C incorporation to glutamate and glutamine C4, C3, and C2 and aspartate C3 were detected dynamically and fitted to a two-compartment model: flux estimation of neuron-glial metabolism included tricarboxylic acid cycle (TCA) flux in astrocytes (Vg = 0.16 ± 0.03 µmol/g/min) and neurons (VTCAn = 0.56 ± 0.03 µmol/g/min), pyruvate carboxylase activity (VPC = 0.041 ± 0.003 µmol/g/min) and neurotransmission rate (VNT = 0.084 ± 0.008 µmol/g/min), resulting in a cerebral metabolic rate of glucose (CMRglc) of 0.38 ± 0.02 µmol/g/min, in excellent agreement with that determined with concomitant 18F-fluorodeoxyglucose positron emission tomography (18FDG PET).We conclude that modeling of neuron-glial metabolism in vivo is accessible in the mouse brain from 13C direct detection with an unprecedented spatial resolution under [1,6-13C2]glucose infusion.

Metabolic Flux Analysis and Optimal Control in the Process of Ectoine Fermentation

2011 ◽  
Vol 393-395 ◽  
pp. 851-854
Author(s):  
Lin Hua Zhang ◽  
Xin Zheng ◽  
Ya Jun Lang
Keyword(s):  
Optimal Control ◽  
Batch Fermentation ◽  
Metabolic Flux ◽  
Monosodium Glutamate ◽  
Nitrogen Sources ◽  
Flux Analysis ◽  
Key Factors ◽  
Carbon And Nitrogen Sources ◽  
Carbon And Nitrogen ◽  
Key Nodes

In this study, the metabolic network of ectoine by Halomonas venusta DSM 4743 was established. The key nodes to influence the ectoine fermentation in metabolic flux and the basis during optimal control of fermentation process were investigated. The results showed that G6P, α-KG and OAA nodes were the key factors to influence the synthesis of ectoine. The metabolic flux distributions at the key nodes were significantly improved and ectoine concentration was enhanced in ectoine fermentation by adopting monosodium glutamate as the sole carbon and nitrogen sources, feeding monosodium glutamate and supplying oxygen limitedly. The batch fermentation was carried out in 10 L fermentor , the concentration and yield of ectoine was 8.4 g/L and 0.1 g/g, respectively, which were increased by 2.8 and 2 times, by comparison with batch fermentation using glucose as carbon source.

scholarly journals Bayesian metabolic flux analysis reveals intracellular flux couplings

2019 ◽  
Vol 35 (14) ◽  
pp. i548-i557 ◽  
Author(s):  
Markus Heinonen ◽  
Maria Osmala ◽  
Henrik Mannerström ◽  
Janne Wallenius ◽  
Samuel Kaski ◽  
...  
Keyword(s):  
Steady State ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Reaction Network ◽  
Reaction Rates ◽  
Supplementary Information ◽  
Flux Analysis ◽  
Metabolic Reaction ◽  
Metabolic Systems ◽  
Genome Scale

AbstractMotivationMetabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates.ResultsWe introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis.Availability and implementationThe COBRA compatible software is available at github.com/markusheinonen/bamfa.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals Manually curated genome-scale reconstruction of the metabolic network of Bacillus megaterium DSM319

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Javad Aminian-Dehkordi ◽  
Seyyed Mohammad Mousavi ◽  
Arezou Jafari ◽  
Ivan Mijakovic ◽  
Sayed-Amir Marashi
Keyword(s):  
Experimental Data ◽  
Metabolic Network ◽  
Bacillus Megaterium ◽  
In Silico ◽  
Metabolic Model ◽  
Growth Behavior ◽  
Industrial Biotechnology ◽  
Flux Analysis ◽  
A Genome ◽  
Genome Scale

AbstractBacillus megaterium is a microorganism widely used in industrial biotechnology for production of enzymes and recombinant proteins, as well as in bioleaching processes. Precise understanding of its metabolism is essential for designing engineering strategies to further optimize B. megaterium for biotechnology applications. Here, we present a genome-scale metabolic model for B. megaterium DSM319, iJA1121, which is a result of a metabolic network reconciliation process. The model includes 1709 reactions, 1349 metabolites, and 1121 genes. Based on multiple-genome alignments and available genome-scale metabolic models for other Bacillus species, we constructed a draft network using an automated approach followed by manual curation. The refinements were performed using a gap-filling process. Constraint-based modeling was used to scrutinize network features. Phenotyping assays were performed in order to validate the growth behavior of the model using different substrates. To verify the model accuracy, experimental data reported in the literature (growth behavior patterns, metabolite production capabilities, metabolic flux analysis using 13C glucose and formaldehyde inhibitory effect) were confronted with model predictions. This indicated a very good agreement between in silico results and experimental data. For example, our in silico study of fatty acid biosynthesis and lipid accumulation in B. megaterium highlighted the importance of adopting appropriate carbon sources for fermentation purposes. We conclude that the genome-scale metabolic model iJA1121 represents a useful tool for systems analysis and furthers our understanding of the metabolism of B. megaterium.

scholarly journals Advances in metabolic flux analysis toward genome-scale profiling of higher organisms

2018 ◽  
Vol 38 (6) ◽  
Author(s):  
Georg Basler ◽  
Alisdair R. Fernie ◽  
Zoran Nikoloski
Keyword(s):  
Large Scale ◽  
Metabolic Flux ◽  
Cell Types ◽  
Flux Analysis ◽  
Metabolic Labeling ◽  
Modeling Framework ◽  
Metabolomics Data ◽  
Technological Advances ◽  
A Genome ◽  
Genome Scale

Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.

scholarly journals State-of-the-Art Genetic Modalities to Engineer Cyanobacteria for Sustainable Biosynthesis of Biofuel and Fine-Chemicals to Meet Bio–Economy Challenges

Life ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 54 ◽  
Author(s):  
Aqib Zafar Khan ◽  
Muhammad Bilal ◽  
Shahid Mehmood ◽  
Ashutosh Sharma ◽  
Hafiz M. N. Iqbal
Keyword(s):  
High Throughput ◽  
Metabolic Flux Analysis ◽  
Large Scale ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
Fine Chemicals ◽  
Carbon Regulation ◽  
Regulation Mechanisms ◽  
Large Scale Cultivation ◽  
Genome Scale

In recent years, metabolic engineering of microorganisms has attained much research interest to produce biofuels and industrially pertinent chemicals. Owing to the relatively fast growth rate, genetic malleability, and carbon neutral production process, cyanobacteria has been recognized as a specialized microorganism with a significant biotechnological perspective. Metabolically engineering cyanobacterial strains have shown great potential for the photosynthetic production of an array of valuable native or non-native chemicals and metabolites with profound agricultural and pharmaceutical significance using CO2 as a building block. In recent years, substantial improvements in developing and introducing novel and efficient genetic tools such as genome-scale modeling, high throughput omics analyses, synthetic/system biology tools, metabolic flux analysis and clustered regularly interspaced short palindromic repeats (CRISPR)-associated nuclease (CRISPR/cas) systems have been made for engineering cyanobacterial strains. Use of these tools and technologies has led to a greater understanding of the host metabolism, as well as endogenous and heterologous carbon regulation mechanisms which consequently results in the expansion of maximum productive ability and biochemical diversity. This review summarizes recent advances in engineering cyanobacteria to produce biofuel and industrially relevant fine chemicals of high interest. Moreover, the development and applications of cutting-edge toolboxes such as the CRISPR-cas9 system, synthetic biology, high-throughput “omics”, and metabolic flux analysis to engineer cyanobacteria for large-scale cultivation are also discussed.

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