scholarly journals A systematic simulation of the effect of salicylic acid on sphingolipid metabolism

2014 ◽  
Author(s):  
Chao Shi ◽  
Jian Yin ◽  
Zhe Liu ◽  
Jian-Xin Wu ◽  
Qi Zhao ◽  
...  
Keyword(s):  
Salicylic Acid ◽  
Turnover Rate ◽  
Metabolic Flux ◽  
Defense Responses ◽  
Sphingolipid Metabolism ◽  
Short Time Scale ◽  
Flux Analysis ◽  
Cell Simulation ◽  
Balance Analysis ◽  
Short Time

The phytohormone salicylic acid (SA) affects plant development and defense responses. Recent studies revealed that SA is also involved in the regulation of sphingolipid metabolism, but the details of this regulation remain to be explored. Here, we use in silico Flux Balance Analysis (FBA) with published microarray data to construct a whole-cell simulation model, including 23 pathways, 259 reactions and 172 metabolites, to predict the alterations in flux of major sphingolipid species after treatment with exogenous SA. This model predicts significant changes in fluxes of certain sphingolipid species after SA treatment, changes that likely trigger downstream physiological and phenotypic effects. To validate the simulation, we used isotopic non-stationary metabolic flux analysis to measure sphingolipid contents and turnover rate in Arabidopsis thaliana seedlings treated with SA or the SA analog benzothiadiazole (BTH). The results show that both SA and BTH affect sphingolipid metabolism by not only concentration of certain species, but also the optimal flux distribution and turnover rate of sphingolipid contents. Our strategy allows us to formally estimate sphingolipid fluxes on a short time scale and gives us a systemic view of the effect of SA on sphingolipid homeostasis.

Metabolic flux analysis at ultra short time scale: Isotopically non-stationary 13C labeling experiments

2007 ◽  
Vol 129 (2) ◽  
pp. 249-267 ◽  
Author(s):  
Katharina Nöh ◽  
Karsten Grönke ◽  
Bing Luo ◽  
Ralf Takors ◽  
Marco Oldiges ◽  
...  
Keyword(s):  
Metabolic Flux Analysis ◽  
Time Scale ◽  
Metabolic Flux ◽  
Short Time Scale ◽  
Flux Analysis ◽  
13C Labeling ◽  
Short Time

scholarly journals Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions

2020 ◽  
Author(s):  
Claudio Tomi-Andrino ◽  
Rupert Norman ◽  
Thomas Millat ◽  
Philippe Soucaille ◽  
Klaus Winzer ◽  
...  
Keyword(s):  
Experimental Data ◽  
Metabolic Flux Analysis ◽  
In Silico ◽  
Physicochemical Parameters ◽  
Metabolic Flux ◽  
Living Systems ◽  
Flux Analysis ◽  
Flux Balance ◽  
Metabolic Fluxes ◽  
Balance Analysis

AbstractMetabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and 13C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between 13C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed.Author summaryBiotechnology has benefitted from the development of high throughput methods characterising living systems at different levels (e.g. concerning genes or proteins), allowing the industrial production of chemical commodities. Recently, focus has been placed on determining reaction rates (or metabolic fluxes) in the metabolic network of certain microorganisms, in order to identify bottlenecks hindering their exploitation. Two main approaches are commonly used, termed metabolic flux analysis (MFA) and flux balance analysis (FBA), based on measuring and estimating fluxes, respectively. While the influence of thermodynamics in living systems was accepted several decades ago, its application to study biochemical networks has only recently been enabled. In this sense, a multitude of different approaches constraining well-established modelling methods with thermodynamics has been suggested. However, physicochemical parameters are generally not properly adjusted to the experimental conditions, which might affect their predictive capabilities. In this study, we have explored the reliability of currently available tools by investigating the impact of varying said parameters in the simulation of metabolic fluxes and metabolite concentration values. Additionally, our in-depth analysis allowed us to highlight limitations and potential solutions that should be considered in future studies.

scholarly journals iMTBGO: An Algorithm for Integrating Metabolic Networks with Transcriptomes Based on Gene Ontology Analysis

2019 ◽  
Vol 20 (4) ◽  
pp. 252-259
Author(s):  
Zhitao Mao ◽  
Hongwu Ma
Keyword(s):  
Gene Expression ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Network Models ◽  
Reaction Rates ◽  
Marker Genes ◽  
Published Data ◽  
Flux Analysis ◽  
Prediction Errors ◽  
Balance Analysis

Background:Constraint-based metabolic network models have been widely used in phenotypic prediction and metabolic engineering design. In recent years, researchers have attempted to improve prediction accuracy by integrating regulatory information and multiple types of “omics” data into this constraint-based model. The transcriptome is the most commonly used data type in integration, and a large number of FBA (flux balance analysis)-based integrated algorithms have been developed.Methods and Results:We mapped the Kcat values to the tree structure of GO terms and found that the Kcat values under the same GO term have a higher similarity. Based on this observation, we developed a new method, called iMTBGO, to predict metabolic flux distributions by constraining reaction boundaries based on gene expression ratios normalized by marker genes under the same GO term. We applied this method to previously published data and compared the prediction results with other metabolic flux analysis methods which also utilize gene expression data. The prediction errors of iMTBGO for both growth rates and fluxes in the central metabolic pathways were smaller than those of earlier published methods.Conclusion:Considering the fact that reaction rates are not only determined by genes/expression levels, but also by the specific activities of enzymes, the iMTBGO method allows us to make more precise predictions of metabolic fluxes by using expression values normalized based on GO.

scholarly journals The Entner-Doudoroff and Nonoxidative Pentose Phosphate Pathways Bypass Glycolysis and the Oxidative Pentose Phosphate Pathway in Ralstonia solanacearum

mSystems ◽  
2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Poonam Jyoti ◽  
Manu Shree ◽  
Chandrakant Joshi ◽  
Tulika Prakash ◽  
Suvendra Kumar Ray ◽  
...  
Keyword(s):  
Network Analysis ◽  
Metabolic Network ◽  
Pentose Phosphate Pathway ◽  
Ralstonia Solanacearum ◽  
Metabolic Flux ◽  
Pentose Phosphate ◽  
Flux Analysis ◽  
Content Type ◽  
Metabolic Network Analysis ◽  
Balance Analysis

ABSTRACT In Ralstonia solanacearum, a devastating phytopathogen whose metabolism is poorly understood, we observed that the Entner-Doudoroff (ED) pathway and nonoxidative pentose phosphate pathway (non-OxPPP) bypass glycolysis and OxPPP under glucose oxidation. Evidence derived from 13C stable isotope feeding and genome annotation-based comparative metabolic network analysis supported the observations. Comparative metabolic network analysis derived from the currently available 53 annotated R. solanacearum strains, including a recently reported strain (F1C1), representing the four phylotypes, confirmed the lack of key genes coding for phosphofructokinase (pfk-1) and phosphogluconate dehydrogenase (gnd) enzymes that are relevant for glycolysis and OxPPP, respectively. R. solanacearum F1C1 cells fed with [13C]glucose (99% [1-13C]glucose or 99% [1,2-13C]glucose or 40% [13C6]glucose) followed by gas chromatography-mass spectrometry (GC-MS)-based labeling analysis of fragments from amino acids, glycerol, and ribose provided clear evidence that rather than glycolysis and the OxPPP, the ED pathway and non-OxPPP are the main routes sustaining metabolism in R. solanacearum. The 13C incorporation in the mass ions of alanine (m/z 260 and m/z 232), valine (m/z 288 and m/z 260), glycine (m/z 218), serine (m/z 390 and m/z 362), histidine (m/z 440 and m/z 412), tyrosine (m/z 466 and m/z 438), phenylalanine (m/z 336 and m/z 308), glycerol (m/z 377), and ribose (m/z 160) mapped the pathways supporting the observations. The outcomes help better define the central carbon metabolic network of R. solanacearum that can be integrated with 13C metabolic flux analysis as well as flux balance analysis studies for defining the metabolic phenotypes. IMPORTANCE Understanding the metabolic versatility of Ralstonia solanacearum is important, as it regulates the trade-off between virulence and metabolism (1, 2) in a wide range of plant hosts. Due to a lack of clear evidence until this work, several published research papers reported on the potential roles of glycolysis and the oxidative pentose phosphate pathway (OxPPP) in R. solanacearum (3, 4). This work provided evidence from 13C stable isotope feeding and genome annotation-based comparative metabolic network analysis that the Entner-Doudoroff pathway and non-OxPPP bypass glycolysis and OxPPP during the oxidation of glucose, a component of the host xylem pool that serves as a potential carbon source (5). The outcomes help better define the central carbon metabolic network of R. solanacearum that can be integrated with 13C metabolic flux analysis as well as flux balance analysis studies for defining the metabolic phenotypes. The study highlights the need to critically examine phytopathogens whose metabolism is poorly understood.

scholarly journals The Entner-Doudoroff Pathway is an Essential Metabolic Route for Methylotuvimicrobium buryatense 5GB1C

2020 ◽  
Author(s):  
Lian He ◽  
Joseph D. Groom ◽  
Mary E. Lidstrom
Keyword(s):  
Metabolic Flux ◽  
Target Genes ◽  
Genetic Manipulation ◽  
Carbon Sources ◽  
Normal Growth ◽  
Flux Analysis ◽  
Enzymatic Reactions ◽  
High Concentration ◽  
Genes Encoding ◽  
Balance Analysis

Methylotuvimicrobium buryatense 5GB1C, a fast-growing gammaproteobacterial methanotroph, is equipped with two glycolytic pathways: the Entner-Doudoroff (ED) pathway and the Embden-Meyerhof-Parnas (EMP) pathway. Metabolic flux analysis and 13C labeling experiments have shown the EMP pathway is the principle glycolytic route in M. buryatense 5GB1C, while the ED pathway appears to be metabolically and energetically insignificant. However, it has not been possible to obtain null mutant in the edd-eda genes encoding the two unique enzymatic reactions in the ED pathway, suggesting the ED pathway may be essential for M. buryatense 5GB1C growth. In this study, the inducible PBAD promoter was used to manipulate gene expression of edd-eda, and in addition, the expression of these two genes was separated from that of a downstream gltA gene. The resulting strain shows arabinose-dependent growth that correlates with ED pathway activity, with normal growth achieved in the presence of ∼0.1 g/liter arabinose. Flux balance analysis shows that M. buryatense 5GB1C with a strong ED pathway has a reduced energy budget, thereby limiting the mutant growth at a high concentration of arabinose. Collectively, our study demonstrates that the ED pathway is essential for M. buryatense 5GB1C. However, no known mechanism can directly explain the essentiality of the ED pathway, and thus it may have a yet unknown regulatory role required for sustaining a healthy and functional metabolism in this bacterium. IMPORTANCE The gammaproteobacterial methanotrophs possess a unique central metabolic architecture, where methane and other reduced C1 carbon sources are assimilated through the ribulose monophosphate cycle. Although efforts have been made to better understand methanotrophic metabolism in these bacteria via experimental and computational approaches, many questions remain unanswered. One of these is the essentiality of the ED pathway for M. buryatense 5GB1C, as current results appear contradictory. By creating a construct with edd-eda and gltA genes controlled by PBAD and PJ23101, respectively, we demonstrated the essentiality of the ED pathway for this obligate methanotroph. It is also demonstrated that these genetic tools are applicable to M. buryatense 5GB1C and that expression of the target genes can be tightly controlled across an extensive range. Our study adds to the expanding knowledge of methanotrophic metabolism and practical approaches to genetic manipulation for obligate methanotrophs.

scholarly journals Time Integrated Flux Analysis: Exploiting the Concentration Measurements Directly for Cost-Effective Metabolic Network Flux Analysis

2019 ◽  
Vol 7 (12) ◽  
pp. 620 ◽  
Author(s):  
Portela ◽  
Richelle ◽  
Dumas ◽  
von Stosch
Keyword(s):  
Metabolic Flux ◽  
Cost Effective ◽  
Flux Analysis ◽  
Concentration Data ◽  
Practical Applications ◽  
Variability Analysis ◽  
Flux Balance ◽  
Balance Analysis ◽  
The Impact

Background: Flux analyses, such as Metabolic Flux Analysis (MFA), Flux Balance Analysis (FBA), Flux Variability Analysis (FVA) or similar methods, can provide insights into the cellular metabolism, especially in combination with experimental data. The most common integration of extracellular concentration data requires the estimation of the specific fluxes (/rates) from the measured concentrations. This is a time-consuming, mathematically ill-conditioned inverse problem, raising high requirements for the quality and quantity of data. Method: In this contribution, a time integrated flux analysis approach is proposed which avoids the error-prone estimation of specific flux values. The approach is adopted for a Metabolic time integrated Flux Analysis and (sparse) time integrated Flux Balance/Variability Analysis. The proposed approach is applied to three case studies: (1) a simulated bioprocess case studying the impact of the number of samples (experimental points) and measurements’ noise on the performance; (2) a simulation case to understand the impact of network redundancies and reaction irreversibility; and (3) an experimental bioprocess case study, showing its relevance for practical applications. Results: It is observed that this method can successfully estimate the time integrated flux values, even with relatively low numbers of samples and significant noise levels. In addition, the method allows the integration of additional constraints (e.g., bounds on the estimated concentrations) and since it eliminates the need for estimating fluxes from measured concentrations, it significantly reduces the workload while providing about the same level of insight into the metabolism as classic flux analysis methods.

Fluxes through plant metabolic networks: measurements, predictions, insights and challenges

2014 ◽  
Vol 465 (1) ◽  
pp. 27-38 ◽  
Author(s):  
Nicholas J. Kruger ◽  
R. George Ratcliffe
Keyword(s):  
Metabolic Networks ◽  
Metabolic Flux ◽  
Cell Function ◽  
Kinetic Modelling ◽  
Reducing Power ◽  
Central Carbon Metabolism ◽  
Flux Analysis ◽  
Stable Isotope Labelling ◽  
Balance Analysis ◽  
Power And Energy

Although the flows of material through metabolic networks are central to cell function, they are not easy to measure other than at the level of inputs and outputs. This is particularly true in plant cells, where the network spans multiple subcellular compartments and where the network may function either heterotrophically or photoautotrophically. For many years, kinetic modelling of pathways provided the only method for describing the operation of fragments of the network. However, more recently, it has become possible to map the fluxes in central carbon metabolism using the stable isotope labelling techniques of metabolic flux analysis (MFA), and to predict intracellular fluxes using constraints-based modelling procedures such as flux balance analysis (FBA). These approaches were originally developed for the analysis of microbial metabolism, but over the last decade, they have been adapted for the more demanding analysis of plant metabolic networks. Here, the principal features of MFA and FBA as applied to plants are outlined, followed by a discussion of the insights that have been gained into plant metabolic networks through the application of these time-consuming and non-trivial methods. The discussion focuses on how a system-wide view of plant metabolism has increased our understanding of network structure, metabolic perturbations and the provision of reducing power and energy for cell function. Current methodological challenges that limit the scope of plant MFA are discussed and particular emphasis is placed on the importance of developing methods for cell-specific MFA.

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